system

Abstract

We provide the system. [Solution] Means for receiving user identification information, Means for acquiring first profile data based on the aforementioned identification information, Means for obtaining second profile data based on the aforementioned identification information, Means for integrating the first profile data and the second profile data to generate integrated profile data, A means for inputting the aforementioned integrated profile data into a generative artificial intelligence system to generate question and answer data, means for outputting the aforementioned question and answer data, A system that includes this.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance as a response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] There is a demand for integrating the usage history and profile of users who use online services and mobile communication services, respectively, and providing individually optimized question-and-answer responses. However, in conventional systems, it has been difficult to effectively integrate two different profile data and generate question-and-answer responses based on it. Therefore, in order to improve user convenience, there is a demand for providing a system that can integrate these profile data and use a generation-based artificial intelligence to provide individually optimized question-and-answer responses.

Means for Solving the Problems

[0005] To solve this problem, the present invention provides the following means: a system including means for receiving user identification information, means for acquiring first profile data based on the identification information, means for acquiring second profile data based on the identification information, means for integrating the first profile data and the second profile data to generate integrated profile data, means for inputting the integrated profile data into a generative artificial intelligence system to generate question and answer data, and means for outputting the question and answer data. This makes it possible to effectively integrate multiple user profile data and generate and provide individually optimized question and answer responses, thereby improving user convenience.

[0006] "User identification information" refers to information used to uniquely identify users of online services and mobile communication services.

[0007] "Primary profile data" refers to data that includes online service usage history and user attribute information.

[0008] "Second profile data" refers to data that includes the usage history of mobile communication services and the user's contract information.

[0009] "Integrated profile data" refers to the comprehensive user profile data generated by integrating the first profile data and the second profile data.

[0010] "Generative artificial intelligence" refers to artificial intelligence that has the ability to automatically generate natural language sentences based on given input data.

[0011] "Question and answer data" refers to response data to user questions generated by a generative artificial intelligence system based on integrated profile data.

[0012] "Means of receiving" refers to means that have the function of incorporating user identification information into the system.

[0013] "Means of acquisition" refers to means that have the function of identifying and collecting relevant profile data based on identification information.

[0014] "Means of integration" refers to means that have the function of combining multiple profile data into a single integrated profile data.

[0015] "Input method" refers to a means that has the function of passing integrated profile data to a generative artificial intelligence.

[0016] "Outputting means" refers to means that have the function of providing the generated question and answer data to the user. [Brief explanation of the drawing]

[0017] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10]Shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when an emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when an emotion engine is combined.

Mode for Carrying Out the Invention

[0018] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0019] First, the language used in the following description will be explained.

[0020] In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be one arithmetic unit or a combination of a plurality of arithmetic units. Also, the processor may be one type of arithmetic unit or a combination of a plurality of types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0021] In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0022] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0023] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0024] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0025] [First Embodiment]

[0026] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0027] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0028] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0029] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0030] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0031] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0032] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0033] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0034] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0035] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0036] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0037] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0038] This invention relates to a system that receives user identification information, integrates different profile data, and generates and provides individually optimized question and answer responses using generative artificial intelligence.

[0039] 1. Receiving user identification information

[0040] The server first receives user identification information. This identification information is used to uniquely identify the user in both online and mobile communication services.

[0041] 2. Obtaining profile data

[0042] The server uses the received identification information to obtain the following two profile data:

[0043] Primary profile data: Data including usage history and user attribute information related to online services.

[0044] Second profile data: Data including usage history and contract information related to mobile communication services.

[0045] 3. Integration of profile data

[0046] The server integrates the first and second profile data to generate new integrated profile data. This provides a comprehensive profile of the user.

[0047] 4. Generation of questions and answers using generative artificial intelligence.

[0048] The server inputs integrated profile data into a generative artificial intelligence (AI) system, which then generates question-and-answer data based on the user's profile. The generative AI system has the ability to automatically generate question-and-answer responses in natural language based on the given integrated profile data.

[0049] 5. Output of Q&A data

[0050] The server returns the generated Q&A data to the user's terminal, allowing the user to review it. This enables the user to receive personalized Q&A based on their profile.

[0051] Specific example

[0052] For example, suppose a user has the identification information "Y12345". This user's first profile data is their online service usage history, which includes "10 logins in the past year," and their second profile data is their mobile communication service contract information, which includes "contract period of 12 months, monthly fee of 5000 yen."

[0053] The server receives the user's identification information and retrieves various profile data. It then integrates this data to generate integrated profile data, which is passed to a generative artificial intelligence system. This generates question-and-answer data similar to the following:

[0054] Question: "I've logged in 10 times in the past year. Do you know of any ways to further improve my convenience?"

[0055] Question: "Your contract has reached its 12-month mark. What plan would you prefer for your next renewal?"

[0056] Thus, the system of the present invention allows users to receive questions and answers based on their specific usage situation, thereby improving convenience.

[0057] The embodiment of the present invention is realized through the cooperation between a main server and a user terminal, and is characterized by its efficient integration of specific profile information and the provision of answer data by generative artificial intelligence. This system aims to respond quickly and accurately to the diverse needs of users.

[0058] The following describes the processing flow.

[0059] Step 1:

[0060] The user accesses the system using a terminal, enters and transmits identification information. This identification information is necessary to uniquely identify the user.

[0061] Step 2:

[0062] The server receives identification information sent by the user. Using this identification information, it prepares to retrieve profile data related to the user's online and mobile communication services.

[0063] Step 3:

[0064] The server retrieves primary profile data based on identification information. Specifically, it searches the database for and retrieves usage history and attribute information related to online services.

[0065] Step 4:

[0066] The server then retrieves second profile data based on the identification information. Here, it searches the database for and retrieves contract information and usage history related to mobile communication services.

[0067] Step 5:

[0068] The server integrates the acquired first and second profile data. It creates an instance of the UserProfile class and uses its methods to generate the integrated profile data.

[0069] Step 6:

[0070] The server inputs the generated integrated profile data into the generative artificial intelligence. The generative artificial intelligence then generates question-and-answer data based on this integrated profile data.

[0071] Step 7:

[0072] The server receives question-and-answer data generated by a generative artificial intelligence. This data is optimized for the user's individual profile.

[0073] Step 8:

[0074] The server sends the generated question-and-answer data to the user's terminal. This allows the user to receive questions and answers tailored to their profile.

[0075] Step 9:

[0076] Users review the Q&A data provided through their device and take the necessary actions. This Q&A data is used to improve the user experience.

[0077] The specific actions at each step describe a series of processes in which the system collects user information, integrates it, and generates and provides optimized responses based on generative artificial intelligence. This processing flow makes it possible to improve user convenience and satisfaction.

[0078] (Example 1)

[0079] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0080] Conventional systems lacked a means to provide unified Q&A based on users' online service usage history and mobile communication service usage history. As a result, users were unable to receive appropriate feedback and suggestions based on their overall usage. Furthermore, the difficulty in integrating information across different services led to a degraded user experience.

[0081] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0082] In this invention, the server includes means for receiving user identification information, means for acquiring first profile data based on the identification information, means for acquiring second profile data based on the identification information, means for integrating the first profile data and the second profile data to generate integrated profile data, means for inputting the integrated profile data into a generative artificial intelligence system to generate question and answer data based on the integrated profile, and means for outputting the question and answer data. This makes it possible for the user to receive question and answer based on their overall usage status.

[0083] "User identification information" refers to information that uniquely identifies a user within a specific online service and mobile communication service.

[0084] "Primary profile data" refers to data that includes the user's online service usage history and attribute information.

[0085] "Second profile data" refers to data that includes the user's mobile communication service usage history and contract information.

[0086] "Integrated profile data" refers to comprehensive user profile data generated by integrating the first profile data and the second profile data.

[0087] "Generative artificial intelligence" refers to an artificial intelligence model that has the ability to generate natural language questions and answers based on input data.

[0088] "Question and answer data" refers to question and answer data generated by generative artificial intelligence, based on the user's integrated profile data.

[0089] This invention relates to a system that receives user identification information, integrates different profile data, and generates and provides individually optimized question and answer responses using generative artificial intelligence.

[0090] First, the server receives user identification information. This identification information is used to uniquely identify the user within specific online and mobile communication services. Specifically, the server receives this identification information in real time using WebSockets.

[0091] Next, the server retrieves the first profile data and the second profile data based on the received identification information. The first profile data includes online service usage history and attribute information, while the second profile data includes mobile communication service usage history and contract information. The server sends a query to an SQL database (e.g., MySQL®) to retrieve the first profile data. Then, it sends a request to a NoSQL database (e.g., MongoDB) to retrieve the second profile data.

[0092] Subsequently, the server integrates the first and second profile data to generate new integrated profile data. Data integration tools and ETL (Extract, Transform, Load) tools can be used for this. For example, the server might use Apache® NiFi to transform and integrate data obtained from different data sources. Specifically, it might combine online usage history and contract information into a single JSON object.

[0093] After the integrated profile data is generated, the server inputs this data into a generative artificial intelligence (AI) to generate question-and-answer data. The generative AI automatically generates questions and answers in natural language based on the input data. Specifically, the server sends a request to the API of a generative AI model (e.g., GPT-3®) using a prompt such as: "Please generate questions based on the online service usage history of user ID Y12345 (10 logins in the past year) and mobile communication service contract information (contract period 12 months, monthly fee 5000 yen)."

[0094] Finally, the server returns the generated Q&A data to the user's terminal. This can be done using a RESTful API or WebSocket. Specifically, the server uses Django to build a RESTful API and returns the generated questions to the user's terminal in JSON format.

[0095] This invention allows users to receive Q&A based on their overall usage, improving the convenience of the service. Furthermore, it facilitates information integration between different services on different servers, enhancing the user experience.

[0096] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0097] Step 1:

[0098] The server receives user identification information. The server receives user identification information in real time using WebSockets. Specifically, when a user accesses the service with a web browser, that identification information (for example, user ID "Y12345") is sent. The received identification information is used as input for the next step.

[0099] Step 2:

[0100] The server retrieves primary profile data based on the received identification information. The server sends a SELECT query to an SQL database (e.g., MySQL) to retrieve online service usage history and attribute information corresponding to user ID "Y12345". The input is the user's identification information, and the output is primary profile data (e.g., "Logged in 10 times in the past year").

[0101] Step 3:

[0102] The server retrieves secondary profile data based on the received identification information. The server queries a NoSQL database (e.g., MongoDB) to retrieve the usage history and contract information for the mobile communication service corresponding to user ID "Y12345". The input is the user's identification information, and the output is secondary profile data (e.g., "Contract period 12 months, monthly fee 5000 yen").

[0103] Step 4:

[0104] The server integrates the first and second profile data to generate new integrated profile data. The server uses a data integration tool (e.g., Apache NiFi) to transform and integrate data obtained from different data sources. For example, it might combine the first and second profile data into a single JSON object. The input is the first and second profile data, and the output is the integrated profile data (e.g., "{Login history: '10 times in the past year', Contract information: 'Contract period 12 months, monthly fee 5000 yen'}").

[0105] Step 5:

[0106] The server inputs integrated profile data into a generative artificial intelligence (AI) system to generate question-and-answer data. The server sends a request to the API of the generative AI model (e.g., GPT-3) using a prompt statement. The input is integrated profile data, and the output is question-and-answer data. For example, the prompt statement might be: "Generate a question based on user ID Y12345's online service usage history (10 logins in the past year) and mobile communication service contract information (contract period 12 months, monthly fee 5000 yen)."

[0107] Step 6:

[0108] The server returns the generated Q&A data to the user's device. The server builds a RESTful API (e.g., Django) and returns the generated questions in JSON format to the user's device. The input is the Q&A data, and the output is the Q&A displayed on the user's device. For example, the user's browser will display: "Question: 'You have logged in 10 times in the past year. Do you know of any ways to further improve your experience?'" "Question: 'Your contract has reached 12 months. What plan would you prefer for your next renewal?'"

[0109] (Application Example 1)

[0110] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0111] Modern content delivery services require personalized content recommendations based on users' viewing history and preferences. However, traditional systems struggle to manage and comprehensively analyze various profile data individually. Furthermore, there is a lack of means to implement dynamic content recommendations through question-and-answer sessions utilizing generative artificial intelligence. As a result, it has been difficult to sufficiently improve user convenience. New systems are needed to address these challenges in future content delivery services.

[0112] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0113] In this invention, the server includes means for receiving user identification information, means for acquiring first profile data based on the identification information, means for acquiring second profile data based on the identification information, means for integrating the first profile data and the second profile data to generate integrated profile data, means for inputting the integrated profile data into a generative artificial intelligence system to generate question and answer data, and means for outputting the question and answer data to the user's terminal to recommend content. This makes it possible to centrally manage a wide variety of user profile data, generate personalized question and answer responses using generative artificial intelligence, and recommend content optimized for each user.

[0114] "User identification information" refers to information used to uniquely identify a user.

[0115] "Primary profile data" refers to data that includes the user's online service usage history and attribute information.

[0116] "Second profile data" refers to data that includes the user's mobile communication service usage history and contract information.

[0117] "Integrated profile data" refers to the user's comprehensive profile data, generated by integrating the first profile data and the second profile data.

[0118] "Generative artificial intelligence" refers to artificial intelligence that has the ability to automatically generate questions and answers in natural language based on profile data.

[0119] "Question and answer data" refers to question and answer information generated by generative artificial intelligence based on the user's profile.

[0120] "Content recommendation" refers to the act of recommending content that is individually optimized based on the user's profile data.

[0121] "Terminal" refers to an electronic device used by a user, and includes, for example, smartphones and personal computers.

[0122] This invention relates to a system that receives user identification information, integrates multiple profile data, and generates and provides individually optimized question-and-answer responses using generative artificial intelligence. This system consists of a terminal used by the user and a server.

[0123] The server first receives user identification information. This received identification information is used to uniquely identify the user and is based on their online service and mobile communication service usage history and contract information. Based on this identification information, the server obtains the first profile data and the second profile data.

[0124] The first profile data includes online service usage history and attribute information. For example, this includes the types and frequency of content the user has viewed in the past. The second profile data includes mobile communication service usage history and contract information, such as contract period and monthly usage fees.

[0125] The server integrates the acquired first and second profile data to generate new integrated profile data. This integrated profile data represents the user's overall profile and serves as the foundational data for personalized content recommendations.

[0126] Next, this integrated profile data is input into a generative artificial intelligence (for example, a model like GPT-3) to generate question-and-answer data. The generative artificial intelligence can generate question-and-answer responses in natural language based on the input profile data.

[0127] The generated question-and-answer data is then sent back from the server to the user's device. Through this device, the user can receive content recommendations based on these questions and answers. In this way, the most suitable content is personalized and recommended to each user.

[0128] Hardware and software to be used

[0129] Hardware: Smartphones, servers

[0130] Software: Gensim (Python library for natural language processing), Requests (Python library for sending HTTP requests), generative artificial intelligence models (e.g., GPT-3)

[0131] Specific example of processing

[0132] For example, suppose a user A has the identification information "Y12345". Based on this identification information, the server obtains user A's first profile data (online service usage history: "watched 10 science fiction movies in the past year") and second profile data (mobile communication service contract information: "contract period 12 months, monthly fee 5000 yen"). These data are integrated to generate integrated profile data, and the prompt message "User profile: {online service profile data}, {mobile communication service profile data}\nGenerate question:" is input to the generative artificial intelligence.

[0133] The following question-and-answer data is generated by a generative artificial intelligence system and sent back to user A's terminal.

[0134] Question: "I've watched 10 sci-fi movies in the past year, but I'd recommend a sci-fi drama next. Are you interested?"

[0135] Question: "Your contract has reached its 12-month mark. Would you like a new plan for your next renewal?"

[0136] This allows user A to receive optimal content recommendations based on their specific usage history.

[0137] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0138] Step 1:

[0139] The server receives user identification information. When a user logs into the application via their device, this identification information is sent to the server. This inputs unique identification information, such as a user ID. This input identification information is then used to retrieve various profile data.

[0140] Step 2:

[0141] The server retrieves primary profile data based on the identification information. Using the identification information as a key, it retrieves the user's usage history and attribute information from the online service database. The input for this step is the user's identification information, and the output is the primary profile data for the online service. It sends queries to the database and extracts the corresponding usage history and attribute information.

[0142] Step 3:

[0143] The server retrieves secondary profile data based on the identification information. Using this identification information, it retrieves usage history and contract information from the mobile communication service database. The input for this step is the user's identification information, and the output is secondary profile data for the mobile communication service. It then queries the database to extract the relevant contract information and usage history.

[0144] Step 4:

[0145] The server integrates the acquired first and second profile data to generate new integrated profile data. In this step, the server combines the first and second profile data to create a single integrated profile data. The input is the two profile data obtained in the previous step, and the output is the integrated profile data. Specifically, the data fields are combined to form a single integrated profile data.

[0146] Step 5:

[0147] Integrated profile data is input to a generative artificial intelligence (AI) to generate question-and-answer data. Integrated profile data is input as a prompt and passed to the model of the generative AI (e.g., GPT-3). The input is integrated profile data, and the output is the generated question-and-answer data. The AI ​​model performs the operation of generating a question-and-answer in natural language based on the prompt.

[0148] Step 6:

[0149] The server sends the generated question-and-answer data to the user's terminal. The generated question-and-answer data, created by generative artificial intelligence, is sent to and displayed on the user's terminal. The input for this step is the question-and-answer data, and the output is the question-and-answer displayed on the user's terminal. The server uses a communication protocol such as HTTP or WebSocket to send data back as a response to the user's terminal.

[0150] Step 7:

[0151] Users review question-and-answer data through their devices and receive appropriate content recommendations. The final output is personalized question-and-answer sessions and content recommendations for the user, thereby improving user engagement. Users interact with the application interface to ask specific questions and review recommended content.

[0152] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0153] This invention relates to a system that receives user identification information, integrates different profile data, and generates and provides individually optimized question and answer responses using an emotion engine and generative artificial intelligence.

[0154] 1. Receiving user identification information

[0155] The server first receives user identification information. This identification information is necessary to uniquely identify the user and is used for both online and mobile communication services.

[0156] 2. Obtaining profile data

[0157] The server uses the received identification information to obtain the following two profile data:

[0158] Primary profile data: Data including usage history and user attribute information related to online services.

[0159] Second profile data: Data including usage history and contract information related to mobile communication services.

[0160] 3. Integration of profile data

[0161] The server integrates the first and second profile data to generate new integrated profile data. This provides a comprehensive profile of the user.

[0162] 4. Emotion recognition by an emotion engine

[0163] The server uses an emotion engine to analyze the user's emotional state from their voice input or text messages. The emotion engine recognizes the user's emotional state (e.g., joy, sadness, surprise) and generates an analysis result.

[0164] 5. Generation of Q&A using generative artificial intelligence

[0165] The server inputs integrated profile data and the results of the emotion engine's analysis into the generative artificial intelligence system, generating question-and-answer data based on the user's profile and emotional state. This ensures that the answers are appropriate to the user's emotional state.

[0166] 6. Output of Q&A data

[0167] The server returns the generated question-and-answer data to the user's terminal, allowing the user to review it. This enables the user to receive questions and answers based on their profile and emotional state.

[0168] Specific example

[0169] For example, suppose a user has the identification information "Y12345". This user's first profile data is their online service usage history, which includes "10 logins in the past year," and their second profile data is their mobile communication service contract information, which includes "contract period of 12 months, monthly fee of 5000 yen."

[0170] The server receives the user's identification information and retrieves various profile data. It then integrates this data to generate unified profile data.

[0171] Furthermore, the sentiment engine analyzes the text message entered by the user (e.g., "I'm disappointed with the recent service") and recognizes that the user is dissatisfied. Based on this, the following Q&A data is generated:

[0172] Question: "You seem dissatisfied with the service. What specific improvements would you like to see?"

[0173] Question: "We can suggest a review of your contract terms. What do you think?"

[0174] Thus, with the system of the present invention, users can receive question-and-answer sessions based on their specific usage situation and emotional state, resulting in a more personalized experience and improved satisfaction.

[0175] Embodiments of the present invention are realized through the cooperation of a main server, a user terminal, and an emotion engine, and are characterized by their efficient integration of specific profile and emotion information, and the provision of answer data by generative artificial intelligence. This system aims to respond quickly and accurately to the diverse needs and emotional states of users.

[0176] The following describes the processing flow.

[0177] Step 1:

[0178] The user accesses the system using a terminal, enters and transmits identification information. This identification information is necessary to uniquely identify the user.

[0179] Step 2:

[0180] The server receives identification information sent by the user. Using this identification information, it prepares to retrieve profile data related to the user's online and mobile communication services.

[0181] Step 3:

[0182] The server retrieves primary profile data based on identification information. Specifically, it searches the database for and retrieves usage history and attribute information related to online services.

[0183] Step 4:

[0184] The server then retrieves second profile data based on the identification information. Here, it searches the database for and retrieves contract information and usage history related to mobile communication services.

[0185] Step 5:

[0186] The server integrates the acquired first and second profile data. It creates an instance of the UserProfile class and uses its methods to generate the integrated profile data.

[0187] Step 6:

[0188] The server receives voice and text messages entered by the user into the system. This input data is necessary for analysis by the emotion engine.

[0189] Step 7:

[0190] The server inputs received voice and text messages into the emotion engine, which analyzes the user's emotional state. The emotion engine identifies emotions such as joy, sadness, and anger, and generates a result.

[0191] Step 8:

[0192] The server inputs integrated profile data and the results of the emotion engine's analysis into the generative artificial intelligence. Based on this input data, the generative artificial intelligence generates question and answer data optimized for the user's profile and emotional state.

[0193] Step 9:

[0194] The server receives question-and-answer data generated by a generative artificial intelligence. This data is personalized, taking into account the user's emotional state.

[0195] Step 10:

[0196] The server sends the generated question-and-answer data to the user's terminal. This allows the user to receive questions and answers based on their profile and emotional state.

[0197] Step 11:

[0198] Users review the question-and-answer data provided through their device and take the necessary actions. This question-and-answer data is used to improve the user experience.

[0199] The specific actions at each step describe a series of processes in which the system collects user information, integrates it, and generates and provides optimized responses based on generative artificial intelligence through analysis by the emotion engine. This processing flow makes it possible to improve user convenience and satisfaction.

[0200] (Example 2)

[0201] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0202] Conventional systems faced the challenge of comprehensively analyzing users' usage patterns and emotional states across different services to provide personalized Q&A. Specifically, online service and mobile communication service profile data were handled separately, and each set of data was analyzed individually without integration. As a result, it was impossible to provide answers that were appropriate to the user's overall needs and emotional state. Consequently, the quality of the user experience deteriorated, and satisfaction levels did not improve.

[0203] In Example 2, the identification processing by the identification processing unit 290 of the data processing device 12 is realized by the following means. In this invention, the server includes means for receiving user identification information, means for acquiring first profile data based on the identification information, means for acquiring second profile data based on the identification information, means for integrating the first profile data and the second profile data to generate integrated profile data, means for inputting the integrated profile data and the results of an emotion engine for analyzing the user's emotional state into a generative artificial intelligence system to generate question and answer data, and means for outputting the question and answer data. This makes it possible to provide personalized question and answer based on the user's overall profile and emotional state.

[0204] "User identification information" refers to information used to uniquely identify a user.

[0205] "Primary profile data" refers to data that includes user usage history and attribute information related to online services.

[0206] "Second profile data" refers to data that includes user usage history and contract information related to mobile communication services.

[0207] "Integrated profile data" refers to data containing comprehensive user profile information, generated by integrating the first profile data and the second profile data.

[0208] An "emotion engine" is an engine that analyzes and identifies the emotional state of a user from their voice input or text messages.

[0209] "Generative artificial intelligence" refers to artificial intelligence models that automatically generate questions and answers or text based on input data.

[0210] "Question and answer data" refers to data of questions and answers generated by generative artificial intelligence.

[0211] This invention relates to a system that receives user identification information, integrates different profile data, and generates and provides individually optimized question-and-answer responses using an emotion engine and generative artificial intelligence. This system is realized through the cooperation of a server, a terminal, and an emotion engine.

[0212] Overall system configuration

[0213] The system consists of the following hardware and software.

[0214] Server: The server is responsible for receiving user identification information, retrieving profile data from the database, and integrating it. Furthermore, it performs processing to run the emotion engine and generative artificial intelligence. Specifically, it uses MySQL or MongoDB for the database and Google® Cloud Natural Language API or IBM Watson® for the emotion engine.

[0215] Terminal: This is the device operated by the user, which transmits identification information and receives and displays the generated question-and-answer data. Examples include PCs, smartphones, and tablets.

[0216] Emotion Engine: This engine analyzes the emotional state of a user from their voice input or text messages. It utilizes Google Cloud Natural Language API or IBM Watson.

[0217] Generative artificial intelligence: Artificial intelligence that generates question-and-answer responses using integrated profile data and the results of emotion engine analysis as input. For example, it utilizes the ChatGPT® model from OpenAI®.

[0218] System operation

[0219] Receive user identification information

[0220] The server receives identification information sent by the user from the terminal. This identification information is used in each processing step within the system.

[0221] Acquisition of profile data

[0222] Based on the received identification information, the server retrieves primary profile data (usage history and attribute information related to online services) and secondary profile data (usage history and contract information related to mobile communication services) from the database.

[0223] Integration of profile data

[0224] The server integrates the acquired profile data and generates new integrated profile data. ETL tools or dedicated integration algorithms are used for this integration.

[0225] Emotion recognition by an emotion engine

[0226] The emotion engine analyzes text and voice messages sent by the user to recognize their emotional state. The analysis results are sent to the server and used in the next step.

[0227] Generation of Q&A using generative artificial intelligence

[0228] The server takes the integrated profile data and the results of the emotion engine as input and requests processing from a generative artificial intelligence model (such as ChatGPT). For example, the prompt statement can be created as follows:

[0229] "Generate a Q&A based on the user's profile data and emotional state. The user's profile data is '10 logins in the past year,' and the contract information is '12-month contract, monthly fee of 5000 yen.' The user's emotional state is dissatisfied, and the text message says 'I'm disappointed with the recent service.'"

[0230] Output of Q&A data

[0231] The server sends the generated Q&A data to the user's terminal for the user to review. For example, Q&A data such as "You seem dissatisfied with the service; what specific improvements would you like to see?" is generated and displayed on the user's terminal.

[0232] Specific example

[0233] For example, suppose a user has the identifier "Y12345". This user's first profile data is "logged in 10 times in the past year", and the second profile data is "contract period 12 months, monthly fee 5000 yen". If the user enters "I'm disappointed with the recent service", the emotion engine analyzes the dissatisfaction and generates a Q&A based on that. In this way, the system of the present invention can provide a Q&A based on the user's overall profile and emotional state.

[0234] This personalizes the user experience and improves satisfaction.

[0235] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0236] Step 1: Receive user identification information

[0237] The server receives identification information sent by the user from their terminal. This identification information is sent via REST API or socket communication.

[0238] Input: User identification information (e.g., User ID)

[0239] Output: User identification information stored in memory

[0240] Specific operation: The server receives an HTTP request from the terminal, extracts identification information including the user ID, and stores it in memory.

[0241] Step 2: Obtain profile data

[0242] Based on the received identification information, the server retrieves primary profile data (usage history and attribute information related to online services) and secondary profile data (usage history and contract information related to mobile communication services) from the database.

[0243] Input: User identification information stored in memory

[0244] Output: First profile data and second profile data (in JSON format) retrieved from the database.

[0245] Specific operation: The server executes queries against MySQL or MongoDB and retrieves the necessary profile data based on the user ID. The retrieved data is stored in memory in JSON format.

[0246] Step 3: Integrating profile data

[0247] The server integrates the acquired profile data and generates new integrated profile data. ETL tools or dedicated integration algorithms are used for this integration.

[0248] Input: First profile data and second profile data (JSON format)

[0249] Output: Integrated profile data (JSON format containing integrated profile information)

[0250] Specific operation: The server merges the first and second profile data while checking data integrity, and generates new integrated profile data. The generated data is held in memory.

[0251] Step 4: Emotion recognition by the emotion engine

[0252] The server receives text and voice messages sent by users, sends them to the emotion engine for analysis, and so on.

[0253] Input: User's text messages or voice messages

[0254] Output: Emotion engine analysis results (data including the user's emotional state)

[0255] Specific operation: The server sends input data to speech recognition APIs and text analysis APIs, analyzes the emotional state, receives the results, and stores them in memory.

[0256] Step 5: Generation of Q&A using generative artificial intelligence

[0257] The server inputs integrated profile data and emotion engine results into a generative artificial intelligence model (such as ChatGPT) to generate question and answer responses.

[0258] Input: Integrated profile data and sentiment engine results

[0259] Output: Question and answer data (generated questions and answers)

[0260] Specific operation: The server generates appropriate prompt statements and sends them to a generative artificial intelligence model. For example, it might use a prompt statement like this: "Generate a question and answer based on profile data and emotional state. The user's profile data is 'logged in 10 times in the past year,' and the contract information is 'contract period 12 months, monthly fee 5000 yen.' The user's emotional state is dissatisfied, and the text message says 'I'm disappointed with the recent service.'" The generated question and answer data is stored in memory.

[0261] Step 6: Output of Q&A data

[0262] The server sends the generated question-and-answer data to the user's terminal. HTTP protocol or WebSocket is used for transmission.

[0263] Input: Question and answer data

[0264] Output: Question and answer data displayed on the user's device.

[0265] Specific operation: The server sends question and answer data to the terminal for the user to review. On the user's terminal, the received data is displayed, the user reviews it, and sends further questions or feedback as needed.

[0266] (Application Example 2)

[0267] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0268] Traditional content delivery services recommend content based on users' usage history and basic attribute information, but this alone makes it difficult to provide personalized recommendations that appropriately reflect users' emotional states and instantaneous needs. Furthermore, there is a need for a method to generate more detailed user profiles by integrating usage history from multiple different services, and to achieve highly accurate content recommendations based on those profiles.

[0269] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0270] In this invention, the server includes means for receiving user identification information, means for acquiring first profile data, means for acquiring second profile data, means for integrating the first and second profile data to generate integrated profile data, means for inputting the integrated profile data and emotion recognition results into a generative artificial intelligence system to generate question-and-answer data, means for outputting the question-and-answer data, and means for recommending individually optimized content based on the integrated profile data and emotional state. This enables more personalized content recommendations based on the user's overall profile and emotional state.

[0271] "User identification information" refers to information used to uniquely identify a user.

[0272] "Primary profile data" refers to data that includes usage history and user attribute information related to online services.

[0273] "Second profile data" refers to data that includes usage history and contract information related to mobile communication services.

[0274] "Integrated profile data" refers to a comprehensive user profile generated by integrating the first profile data and the second profile data.

[0275] "Emotion recognition results" refer to data indicating the user's emotional state, analyzed from the user's input voice or text message.

[0276] "Generative artificial intelligence" refers to an artificial intelligence system that generates question-and-answer data based on integrated profile data and emotion recognition results.

[0277] "Question and answer data" refers to data generated by generative artificial intelligence that includes answers to user questions and additional questions.

[0278] "Personalized optimization" refers to providing the most appropriate responses and recommendations to each user based on their specific profile and emotional state.

[0279] "Content" is a general term for information or entertainment provided to users, such as movies, dramas, music, and articles.

[0280] "Recommendation methods" refer to methods or systems for selecting and presenting content that is appropriate for a user based on integrated profile data and emotional state.

[0281] This invention relates to a system that receives user identification information, integrates different profile data, and generates and provides individually optimized question-and-answer sessions and content recommendations using an emotion engine and generative artificial intelligence. This system integrates the usage history of online services and mobile communication services based on the user's identification information and recommends optimal content based on the user's emotional state.

[0282] First, the server receives the user's identification information. This identification information is necessary to uniquely identify the user and is used to obtain profile data including the usage history of the user's online services and mobile communication services. Based on the identification information, the server obtains usage history data and attribute information (first profile data) related to the online services. Similarly, based on the identification information, the server also obtains usage history data and contract information (second profile data) related to the mobile communication services.

[0283] Next, the server integrates the obtained first profile data and second profile data to generate integrated profile data. As a result, the comprehensive attributes and usage history of the user can be integrated into one profile data.

[0284] Furthermore, the server uses an emotion engine for emotion recognition. The emotion engine analyzes the input voice and text messages from the user to recognize the user's emotional state (e.g., joy, sadness, surprise). The result of this emotion recognition is input into the generative artificial intelligence together with the user's integrated profile data.

[0285] Based on the integrated profile data and the emotion recognition result, the generative artificial intelligence generates question-and-answer data suitable for the user. This question-and-answer data is individually optimized based on the specific usage situation and emotional state of the user. As a result, the user can receive appropriate answers and additional questions according to the emotional state.

[0286] Furthermore, this system recommends suitable content based on the user's integrated profile data and emotional state. For example, when the user inputs "I'm feeling sad recently", the emotion engine recognizes "sadness" as the emotional state, and based on this, the generative artificial intelligence recommends "moving movies" or "inspiring articles" to the user.

[0287] Specific example:

[0288] For example, suppose a user has the identification information "Y12345". This user's first profile data is their online service usage history, which includes "10 logins in the past year," and their second profile data is their mobile communication service usage history, which includes "contract period of 12 months, monthly fee of 5000 yen." The server receives this user's identification information, retrieves the first and second profile data, and integrates them.

[0289] The emotion engine analyzes a text message entered by the user, for example, "I've been feeling sad lately," and recognizes that the user is experiencing the emotion of "sadness." Based on this, the generative artificial intelligence recommends optimized content such as the following:

[0290] A moving film

[0291] Encouraging articles

[0292] Here are some examples of prompts to input into a generative AI model:

[0293] "Based on integrated user profile data, recommend content that is most suitable for users experiencing sadness. Simultaneously, recommend content that helps shift their emotions to a more positive state."

[0294] Thus, the system of the present invention can provide highly accurate, personalized question-and-answer sessions and content recommendations based on the user's overall profile and emotional state.

[0295] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0296] Step 1:

[0297] The server receives user identification information. A user ID (identification information) is provided as input and sent to the server. The server uses this identification information to retrieve subsequent profile data.

[0298] Step 2:

[0299] The server retrieves online service usage history data and user attribute information (first profile data) based on the aforementioned identification information. Specifically, it uses the identification information to search the database for relevant usage history and attribute information and retrieves it. The output is the first profile data.

[0300] Step 3:

[0301] The server retrieves mobile communication service usage history data and contract information (second profile data) based on the aforementioned identification information. Specifically, it uses the identification information to search the mobile communication service database for relevant usage history and contract information and retrieves it. The output is the second profile data.

[0302] Step 4:

[0303] The server integrates the first and second profile data to generate integrated profile data. Specifically, it merges this data in dictionary or key-value format, and integrates duplicate data according to unified rules. The output is the integrated profile data.

[0304] Step 5:

[0305] The server inputs the user's voice or text message into the emotion engine and obtains an emotion recognition result. Specifically, it sends the user's voice or text data to the emotion recognition engine, which analyzes the emotional state and obtains a classification result (e.g., joy, sadness, etc.). The output is the emotion recognition result.

[0306] Step 6:

[0307] The server inputs the integrated profile data and the emotion recognition results into the generative artificial intelligence to generate question-and-answer data. Specifically, it passes the integrated profile data and the emotion recognition results to the generative artificial intelligence, and based on this information, generates the optimal answer or additional questions in response to the user's questions. The output is the question-and-answer data.

[0308] Step 7:

[0309] The server transmits the question-and-answer data to the user terminal and displays it to the user. Specifically, it converts the question-and-answer data into an appropriate format (text message or voice) and transmits it to the user terminal. The output is the question-and-answer content displayed on the user terminal.

[0310] Step 8:

[0311] The server recommends the optimal content to the generative artificial intelligence based on the integrated profile data and the emotional state. Specifically, it passes the integrated profile data and the emotion recognition results to the generative artificial intelligence, and based on this, selects and recommends the optimal content (movies, music, articles, etc.) for the user. The output is the recommended content.

[0312] Step 9:

[0313] The server transmits the recommended content to the user terminal and displays it to the user. Specifically, it converts the recommended content information into an appropriate format (link, thumbnail information, etc.) and transmits it to the user terminal. The output is the recommended content displayed on the user terminal. <​​​​​The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0316] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0317] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0318] [Second Embodiment]

[0319] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0320] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0321] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0322] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0323] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0324] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0325] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0326] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0327] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0328] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0329] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0330] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0331] This invention relates to a system that receives user identification information, integrates different profile data, and generates and provides individually optimized question and answer responses using generative artificial intelligence.

[0332] 1. Receiving user identification information

[0333] The server first receives user identification information. This identification information is used to uniquely identify the user in both online and mobile communication services.

[0334] 2. Obtaining profile data

[0335] The server uses the received identification information to obtain the following two profile data:

[0336] Primary profile data: Data including usage history and user attribute information related to online services.

[0337] Second profile data: Data including usage history and contract information related to mobile communication services.

[0338] 3. Integration of profile data

[0339] The server integrates the first and second profile data to generate new integrated profile data. This provides a comprehensive profile of the user.

[0340] 4. Generation of questions and answers using generative artificial intelligence.

[0341] The server inputs integrated profile data into a generative artificial intelligence (AI) system, which then generates question-and-answer data based on the user's profile. The generative AI system has the ability to automatically generate question-and-answer responses in natural language based on the given integrated profile data.

[0342] 5. Output of Q&A data

[0343] The server returns the generated Q&A data to the user's terminal, allowing the user to review it. This enables the user to receive personalized Q&A based on their profile.

[0344] Specific example

[0345] For example, suppose a user has the identification information "Y12345". This user's first profile data is their online service usage history, which includes "10 logins in the past year," and their second profile data is their mobile communication service contract information, which includes "contract period of 12 months, monthly fee of 5000 yen."

[0346] The server receives the user's identification information and retrieves various profile data. It then integrates this data to generate integrated profile data, which is passed to a generative artificial intelligence system. This generates question-and-answer data similar to the following:

[0347] Question: "I've logged in 10 times in the past year. Do you know of any ways to further improve my convenience?"

[0348] Question: "Your contract has reached its 12-month mark. What plan would you prefer for your next renewal?"

[0349] Thus, the system of the present invention allows users to receive questions and answers based on their specific usage situation, thereby improving convenience.

[0350] The embodiment of the present invention is realized through the cooperation between a main server and a user terminal, and is characterized by its efficient integration of specific profile information and the provision of answer data by generative artificial intelligence. This system aims to respond quickly and accurately to the diverse needs of users.

[0351] The following describes the processing flow.

[0352] Step 1:

[0353] The user accesses the system using a terminal, enters and transmits identification information. This identification information is necessary to uniquely identify the user.

[0354] Step 2:

[0355] The server receives identification information sent by the user. Using this identification information, it prepares to retrieve profile data related to the user's online and mobile communication services.

[0356] Step 3:

[0357] The server retrieves primary profile data based on identification information. Specifically, it searches the database for and retrieves usage history and attribute information related to online services.

[0358] Step 4:

[0359] The server then retrieves second profile data based on the identification information. Here, it searches the database for and retrieves contract information and usage history related to mobile communication services.

[0360] Step 5:

[0361] The server integrates the acquired first and second profile data. It creates an instance of the UserProfile class and uses its methods to generate the integrated profile data.

[0362] Step 6:

[0363] The server inputs the generated integrated profile data into the generative artificial intelligence. The generative artificial intelligence then generates question-and-answer data based on this integrated profile data.

[0364] Step 7:

[0365] The server receives question-and-answer data generated by a generative artificial intelligence. This data is optimized for the user's individual profile.

[0366] Step 8:

[0367] The server sends the generated question-and-answer data to the user's terminal. This allows the user to receive questions and answers tailored to their profile.

[0368] Step 9:

[0369] Users review the Q&A data provided through their device and take the necessary actions. This Q&A data is used to improve the user experience.

[0370] The specific actions at each step describe a series of processes in which the system collects user information, integrates it, and generates and provides optimized responses based on generative artificial intelligence. This processing flow makes it possible to improve user convenience and satisfaction.

[0371] (Example 1)

[0372] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0373] Conventional systems lacked a means to provide unified Q&A based on users' online service usage history and mobile communication service usage history. As a result, users were unable to receive appropriate feedback and suggestions based on their overall usage. Furthermore, the difficulty in integrating information across different services led to a degraded user experience.

[0374] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0375] In this invention, the server includes means for receiving user identification information, means for acquiring first profile data based on the identification information, means for acquiring second profile data based on the identification information, means for integrating the first profile data and the second profile data to generate integrated profile data, means for inputting the integrated profile data into a generative artificial intelligence system to generate question and answer data based on the integrated profile, and means for outputting the question and answer data. This makes it possible for the user to receive question and answer based on their overall usage status.

[0376] "User identification information" refers to information that uniquely identifies a user within a specific online service and mobile communication service.

[0377] "Primary profile data" refers to data that includes the user's online service usage history and attribute information.

[0378] "Second profile data" refers to data that includes the user's mobile communication service usage history and contract information.

[0379] "Integrated profile data" refers to comprehensive user profile data generated by integrating the first profile data and the second profile data.

[0380] "Generative artificial intelligence" refers to an artificial intelligence model that has the ability to generate natural language questions and answers based on input data.

[0381] "Question and answer data" refers to question and answer data generated by generative artificial intelligence, based on the user's integrated profile data.

[0382] This invention relates to a system that receives user identification information, integrates different profile data, and generates and provides individually optimized question and answer responses using generative artificial intelligence.

[0383] First, the server receives user identification information. This identification information is used to uniquely identify the user within specific online and mobile communication services. Specifically, the server receives this identification information in real time using WebSockets.

[0384] Next, the server retrieves the first profile data and the second profile data based on the received identification information. The first profile data includes online service usage history and attribute information, while the second profile data includes mobile communication service usage history and contract information. The server sends a query to an SQL database (e.g., MySQL) to retrieve the first profile data. Then, it sends a request to a NoSQL database (e.g., MongoDB) to retrieve the second profile data.

[0385] Subsequently, the server integrates the first and second profile data to generate new integrated profile data. Data integration tools and ETL (Extract, Transform, Load) tools can be used for this. For example, the server might use Apache NiFi to transform and integrate data obtained from different data sources. Specifically, it might combine online usage history and contract information into a single JSON object.

[0386] After the integrated profile data is generated, the server inputs this data into a generative artificial intelligence (AI) to generate question-and-answer data. The generative AI automatically generates questions and answers in natural language based on the input data. Specifically, the server sends a request to the API of the generative AI model (e.g., GPT-3) using a prompt such as: "Please generate questions based on the online service usage history of user ID Y12345 (10 logins in the past year) and mobile communication service contract information (contract period 12 months, monthly fee 5000 yen)."

[0387] Finally, the server returns the generated Q&A data to the user's terminal. This can be done using a RESTful API or WebSocket. Specifically, the server uses Django to build a RESTful API and returns the generated questions to the user's terminal in JSON format.

[0388] This invention allows users to receive Q&A based on their overall usage, improving the convenience of the service. Furthermore, it facilitates information integration between different services on different servers, enhancing the user experience.

[0389] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0390] Step 1:

[0391] The server receives user identification information. The server receives user identification information in real time using WebSockets. Specifically, when a user accesses the service with a web browser, that identification information (for example, user ID "Y12345") is sent. The received identification information is used as input for the next step.

[0392] Step 2:

[0393] The server retrieves primary profile data based on the received identification information. The server sends a SELECT query to an SQL database (e.g., MySQL) to retrieve online service usage history and attribute information corresponding to user ID "Y12345". The input is the user's identification information, and the output is primary profile data (e.g., "Logged in 10 times in the past year").

[0394] Step 3:

[0395] The server retrieves secondary profile data based on the received identification information. The server queries a NoSQL database (e.g., MongoDB) to retrieve the usage history and contract information for the mobile communication service corresponding to user ID "Y12345". The input is the user's identification information, and the output is secondary profile data (e.g., "Contract period 12 months, monthly fee 5000 yen").

[0396] Step 4:

[0397] The server integrates the first and second profile data to generate new integrated profile data. The server uses a data integration tool (e.g., Apache NiFi) to transform and integrate data obtained from different data sources. For example, it might combine the first and second profile data into a single JSON object. The input is the first and second profile data, and the output is the integrated profile data (e.g., "{Login history: '10 times in the past year', Contract information: 'Contract period 12 months, monthly fee 5000 yen'}").

[0398] Step 5:

[0399] The server inputs integrated profile data into a generative artificial intelligence (AI) system to generate question-and-answer data. The server sends a request to the API of the generative AI model (e.g., GPT-3) using a prompt statement. The input is integrated profile data, and the output is question-and-answer data. For example, the prompt statement might be: "Generate a question based on user ID Y12345's online service usage history (10 logins in the past year) and mobile communication service contract information (contract period 12 months, monthly fee 5000 yen)."

[0400] Step 6:

[0401] The server returns the generated Q&A data to the user's device. The server builds a RESTful API (e.g., Django) and returns the generated questions in JSON format to the user's device. The input is the Q&A data, and the output is the Q&A displayed on the user's device. For example, the user's browser will display: "Question: 'You have logged in 10 times in the past year. Do you know of any ways to further improve your experience?'" "Question: 'Your contract has reached 12 months. What plan would you prefer for your next renewal?'"

[0402] (Application Example 1)

[0403] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0404] Modern content delivery services require personalized content recommendations based on users' viewing history and preferences. However, traditional systems struggle to manage and comprehensively analyze various profile data individually. Furthermore, there is a lack of means to implement dynamic content recommendations through question-and-answer sessions utilizing generative artificial intelligence. As a result, it has been difficult to sufficiently improve user convenience. New systems are needed to address these challenges in future content delivery services.

[0405] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0406] In this invention, the server includes means for receiving user identification information, means for acquiring first profile data based on the identification information, means for acquiring second profile data based on the identification information, means for integrating the first profile data and the second profile data to generate integrated profile data, means for inputting the integrated profile data into a generative artificial intelligence system to generate question and answer data, and means for outputting the question and answer data to the user's terminal to recommend content. This makes it possible to centrally manage a wide variety of user profile data, generate personalized question and answer responses using generative artificial intelligence, and recommend content optimized for each user.

[0407] "User identification information" refers to information used to uniquely identify a user.

[0408] "Primary profile data" refers to data that includes the user's online service usage history and attribute information.

[0409] "Second profile data" refers to data that includes the user's mobile communication service usage history and contract information.

[0410] "Integrated profile data" refers to the user's comprehensive profile data, generated by integrating the first profile data and the second profile data.

[0411] "Generative artificial intelligence" refers to artificial intelligence that has the ability to automatically generate questions and answers in natural language based on profile data.

[0412] "Question and answer data" refers to question and answer information generated by generative artificial intelligence based on the user's profile.

[0413] "Content recommendation" refers to the act of recommending content that is individually optimized based on the user's profile data.

[0414] "Terminal" refers to an electronic device used by a user, and includes, for example, smartphones and personal computers.

[0415] This invention relates to a system that receives user identification information, integrates multiple profile data, and generates and provides individually optimized question-and-answer responses using generative artificial intelligence. This system consists of a terminal used by the user and a server.

[0416] The server first receives user identification information. This received identification information is used to uniquely identify the user and is based on their online service and mobile communication service usage history and contract information. Based on this identification information, the server obtains the first profile data and the second profile data.

[0417] The first profile data includes online service usage history and attribute information. For example, this includes the types and frequency of content the user has viewed in the past. The second profile data includes mobile communication service usage history and contract information, such as contract period and monthly usage fees.

[0418] The server integrates the acquired first and second profile data to generate new integrated profile data. This integrated profile data represents the user's overall profile and serves as the foundational data for personalized content recommendations.

[0419] Next, this integrated profile data is input into a generative artificial intelligence (for example, a model like GPT-3) to generate question-and-answer data. The generative artificial intelligence can generate question-and-answer responses in natural language based on the input profile data.

[0420] The generated question-and-answer data is then sent back from the server to the user's device. Through this device, the user can receive content recommendations based on these questions and answers. In this way, the most suitable content is personalized and recommended to each user.

[0421] Hardware and software to be used

[0422] Hardware: Smartphones, servers

[0423] Software: Gensim (Python library for natural language processing), Requests (Python library for sending HTTP requests), generative artificial intelligence models (e.g., GPT-3)

[0424] Specific example of processing

[0425] For example, suppose a user A has the identification information "Y12345". Based on this identification information, the server obtains user A's first profile data (online service usage history: "watched 10 science fiction movies in the past year") and second profile data (mobile communication service contract information: "contract period 12 months, monthly fee 5000 yen"). These data are integrated to generate integrated profile data, and the prompt message "User profile: {online service profile data}, {mobile communication service profile data}\nGenerate question:" is input to the generative artificial intelligence.

[0426] The following question-and-answer data is generated by a generative artificial intelligence system and sent back to user A's terminal.

[0427] Question: "I've watched 10 sci-fi movies in the past year, but I'd recommend a sci-fi drama next. Are you interested?"

[0428] Question: "Your contract has reached its 12-month mark. Would you like a new plan for your next renewal?"

[0429] This allows user A to receive optimal content recommendations based on their specific usage history.

[0430] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0431] Step 1:

[0432] The server receives user identification information. When a user logs into the application via their device, this identification information is sent to the server. This inputs unique identification information, such as a user ID. This input identification information is then used to retrieve various profile data.

[0433] Step 2:

[0434] The server retrieves primary profile data based on the identification information. Using the identification information as a key, it retrieves the user's usage history and attribute information from the online service database. The input for this step is the user's identification information, and the output is the primary profile data for the online service. It sends queries to the database and extracts the corresponding usage history and attribute information.

[0435] Step 3:

[0436] The server retrieves secondary profile data based on the identification information. Using this identification information, it retrieves usage history and contract information from the mobile communication service database. The input for this step is the user's identification information, and the output is secondary profile data for the mobile communication service. It then queries the database to extract the relevant contract information and usage history.

[0437] Step 4:

[0438] The server integrates the acquired first and second profile data to generate new integrated profile data. In this step, the server combines the first and second profile data to create a single integrated profile data. The input is the two profile data obtained in the previous step, and the output is the integrated profile data. Specifically, the data fields are combined to form a single integrated profile data.

[0439] Step 5:

[0440] Integrated profile data is input to a generative artificial intelligence (AI) to generate question-and-answer data. Integrated profile data is input as a prompt and passed to the model of the generative AI (e.g., GPT-3). The input is integrated profile data, and the output is the generated question-and-answer data. The AI ​​model performs the operation of generating a question-and-answer in natural language based on the prompt.

[0441] Step 6:

[0442] The server sends the generated question-and-answer data to the user's terminal. The generated question-and-answer data, created by generative artificial intelligence, is sent to and displayed on the user's terminal. The input for this step is the question-and-answer data, and the output is the question-and-answer displayed on the user's terminal. The server uses a communication protocol such as HTTP or WebSocket to send data back as a response to the user's terminal.

[0443] Step 7:

[0444] Users review question-and-answer data through their devices and receive appropriate content recommendations. The final output is personalized question-and-answer sessions and content recommendations for the user, thereby improving user engagement. Users interact with the application interface to ask specific questions and review recommended content.

[0445] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0446] This invention relates to a system that receives user identification information, integrates different profile data, and generates and provides individually optimized question and answer responses using an emotion engine and generative artificial intelligence.

[0447] 1. Receiving user identification information

[0448] The server first receives user identification information. This identification information is necessary to uniquely identify the user and is used for both online and mobile communication services.

[0449] 2. Obtaining profile data

[0450] The server uses the received identification information to obtain the following two profile data:

[0451] Primary profile data: Data including usage history and user attribute information related to online services.

[0452] Second profile data: Data including usage history and contract information related to mobile communication services.

[0453] 3. Integration of profile data

[0454] The server integrates the first and second profile data to generate new integrated profile data. This provides a comprehensive profile of the user.

[0455] 4. Emotion recognition by an emotion engine

[0456] The server uses an emotion engine to analyze the user's emotional state from their voice input or text messages. The emotion engine recognizes the user's emotional state (e.g., joy, sadness, surprise) and generates an analysis result.

[0457] 5. Generation of Q&A using generative artificial intelligence

[0458] The server inputs integrated profile data and the results of the emotion engine's analysis into the generative artificial intelligence system, generating question-and-answer data based on the user's profile and emotional state. This ensures that the answers are appropriate to the user's emotional state.

[0459] 6. Output of Q&A data

[0460] The server returns the generated question-and-answer data to the user's terminal, allowing the user to review it. This enables the user to receive questions and answers based on their profile and emotional state.

[0461] Specific example

[0462] For example, suppose a user has the identification information "Y12345". This user's first profile data is their online service usage history, which includes "10 logins in the past year," and their second profile data is their mobile communication service contract information, which includes "contract period of 12 months, monthly fee of 5000 yen."

[0463] The server receives the user's identification information and retrieves various profile data. It then integrates this data to generate unified profile data.

[0464] Furthermore, the sentiment engine analyzes the text message entered by the user (e.g., "I'm disappointed with the recent service") and recognizes that the user is dissatisfied. Based on this, the following Q&A data is generated:

[0465] Question: "You seem dissatisfied with the service. What specific improvements would you like to see?"

[0466] Question: "We can suggest a review of your contract terms. What do you think?"

[0467] Thus, with the system of the present invention, users can receive question-and-answer sessions based on their specific usage situation and emotional state, resulting in a more personalized experience and improved satisfaction.

[0468] Embodiments of the present invention are realized through the cooperation of a main server, a user terminal, and an emotion engine, and are characterized by their efficient integration of specific profile and emotion information, and the provision of answer data by generative artificial intelligence. This system aims to respond quickly and accurately to the diverse needs and emotional states of users.

[0469] The following describes the processing flow.

[0470] Step 1:

[0471] The user accesses the system using a terminal, enters and transmits identification information. This identification information is necessary to uniquely identify the user.

[0472] Step 2:

[0473] The server receives identification information sent by the user. Using this identification information, it prepares to retrieve profile data related to the user's online and mobile communication services.

[0474] Step 3:

[0475] The server retrieves primary profile data based on identification information. Specifically, it searches the database for and retrieves usage history and attribute information related to online services.

[0476] Step 4:

[0477] The server then retrieves second profile data based on the identification information. Here, it searches the database for and retrieves contract information and usage history related to mobile communication services.

[0478] Step 5:

[0479] The server integrates the acquired first and second profile data. It creates an instance of the UserProfile class and uses its methods to generate the integrated profile data.

[0480] Step 6:

[0481] The server receives voice and text messages entered by the user into the system. This input data is necessary for analysis by the emotion engine.

[0482] Step 7:

[0483] The server inputs received voice and text messages into the emotion engine, which analyzes the user's emotional state. The emotion engine identifies emotions such as joy, sadness, and anger, and generates a result.

[0484] Step 8:

[0485] The server inputs integrated profile data and the results of the emotion engine's analysis into the generative artificial intelligence. Based on this input data, the generative artificial intelligence generates question and answer data optimized for the user's profile and emotional state.

[0486] Step 9:

[0487] The server receives question-and-answer data generated by a generative artificial intelligence. This data is personalized, taking into account the user's emotional state.

[0488] Step 10:

[0489] The server sends the generated question-and-answer data to the user's terminal. This allows the user to receive questions and answers based on their profile and emotional state.

[0490] Step 11:

[0491] Users review the question-and-answer data provided through their device and take the necessary actions. This question-and-answer data is used to improve the user experience.

[0492] The specific actions at each step describe a series of processes in which the system collects user information, integrates it, and generates and provides optimized responses based on generative artificial intelligence through analysis by the emotion engine. This processing flow makes it possible to improve user convenience and satisfaction.

[0493] (Example 2)

[0494] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0495] Conventional systems faced the challenge of comprehensively analyzing users' usage patterns and emotional states across different services to provide personalized Q&A. Specifically, online service and mobile communication service profile data were handled separately, and each set of data was analyzed individually without integration. As a result, it was impossible to provide answers that were appropriate to the user's overall needs and emotional state. Consequently, the quality of the user experience deteriorated, and satisfaction levels did not improve.

[0496] In Example 2, the identification processing by the identification processing unit 290 of the data processing device 12 is realized by the following means. In this invention, the server includes means for receiving user identification information, means for acquiring first profile data based on the identification information, means for acquiring second profile data based on the identification information, means for integrating the first profile data and the second profile data to generate integrated profile data, means for inputting the integrated profile data and the results of an emotion engine for analyzing the user's emotional state into a generative artificial intelligence system to generate question and answer data, and means for outputting the question and answer data. This makes it possible to provide personalized question and answer based on the user's overall profile and emotional state.

[0497] "User identification information" refers to information used to uniquely identify a user.

[0498] "Primary profile data" refers to data that includes user usage history and attribute information related to online services.

[0499] "Second profile data" refers to data that includes user usage history and contract information related to mobile communication services.

[0500] "Integrated profile data" refers to data containing comprehensive user profile information, generated by integrating the first profile data and the second profile data.

[0501] An "emotion engine" is an engine that analyzes and identifies the emotional state of a user from their voice input or text messages.

[0502] "Generative artificial intelligence" refers to artificial intelligence models that automatically generate questions and answers or text based on input data.

[0503] "Question and answer data" refers to data of questions and answers generated by generative artificial intelligence.

[0504] This invention relates to a system that receives user identification information, integrates different profile data, and generates and provides individually optimized question-and-answer responses using an emotion engine and generative artificial intelligence. This system is realized through the cooperation of a server, a terminal, and an emotion engine.

[0505] Overall system configuration

[0506] The system consists of the following hardware and software.

[0507] Server: The server is responsible for receiving user identification information, retrieving profile data from the database, and integrating it. Furthermore, it performs processing to run the emotion engine and generative artificial intelligence. Specifically, it uses MySQL or MongoDB for the database and Google Cloud Natural Language API or IBM Watson for the emotion engine.

[0508] Terminal: This is the device operated by the user, which transmits identification information and receives and displays the generated question-and-answer data. Examples include PCs, smartphones, and tablets.

[0509] Emotion Engine: This engine analyzes the emotional state of a user from their voice input or text messages. It utilizes Google Cloud Natural Language API or IBM Watson.

[0510] Generative artificial intelligence: Artificial intelligence that generates question-and-answer responses using integrated profile data and the results of emotion engine analysis as input. For example, it utilizes OpenAI's ChatGPT model.

[0511] System operation

[0512] Receive user identification information

[0513] The server receives identification information sent by the user from the terminal. This identification information is used in each processing step within the system.

[0514] Acquisition of profile data

[0515] Based on the received identification information, the server retrieves primary profile data (usage history and attribute information related to online services) and secondary profile data (usage history and contract information related to mobile communication services) from the database.

[0516] Integration of profile data

[0517] The server integrates the acquired profile data and generates new integrated profile data. ETL tools or dedicated integration algorithms are used for this integration.

[0518] Emotion recognition by an emotion engine

[0519] The emotion engine analyzes text and voice messages sent by the user to recognize their emotional state. The analysis results are sent to the server and used in the next step.

[0520] Generation of Q&A using generative artificial intelligence

[0521] The server takes the integrated profile data and the results of the emotion engine as input and requests processing from a generative artificial intelligence model (such as ChatGPT). For example, the prompt statement can be created as follows:

[0522] "Generate a Q&A based on the user's profile data and emotional state. The user's profile data is '10 logins in the past year,' and the contract information is '12-month contract, monthly fee of 5000 yen.' The user's emotional state is dissatisfied, and the text message says 'I'm disappointed with the recent service.'"

[0523] Output of Q&A data

[0524] The server sends the generated Q&A data to the user's terminal for the user to review. For example, Q&A data such as "You seem dissatisfied with the service; what specific improvements would you like to see?" is generated and displayed on the user's terminal.

[0525] Specific example

[0526] For example, suppose a user has the identifier "Y12345". This user's first profile data is "logged in 10 times in the past year", and the second profile data is "contract period 12 months, monthly fee 5000 yen". If the user enters "I'm disappointed with the recent service", the emotion engine analyzes the dissatisfaction and generates a Q&A based on that. In this way, the system of the present invention can provide a Q&A based on the user's overall profile and emotional state.

[0527] This personalizes the user experience and improves satisfaction.

[0528] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0529] Step 1: Receive user identification information

[0530] The server receives identification information sent by the user from their terminal. This identification information is sent via REST API or socket communication.

[0531] Input: User identification information (e.g., User ID)

[0532] Output: User identification information stored in memory

[0533] Specific operation: The server receives an HTTP request from the terminal, extracts identification information including the user ID, and stores it in memory.

[0534] Step 2: Obtain profile data

[0535] Based on the received identification information, the server retrieves primary profile data (usage history and attribute information related to online services) and secondary profile data (usage history and contract information related to mobile communication services) from the database.

[0536] Input: User identification information stored in memory

[0537] Output: First profile data and second profile data (in JSON format) retrieved from the database.

[0538] Specific operation: The server executes queries against MySQL or MongoDB and retrieves the necessary profile data based on the user ID. The retrieved data is stored in memory in JSON format.

[0539] Step 3: Integrating profile data

[0540] The server integrates the acquired profile data and generates new integrated profile data. ETL tools or dedicated integration algorithms are used for this integration.

[0541] Input: First profile data and second profile data (JSON format)

[0542] Output: Integrated profile data (JSON format containing integrated profile information)

[0543] Specific operation: The server merges the first and second profile data while checking data integrity, and generates new integrated profile data. The generated data is held in memory.

[0544] Step 4: Emotion recognition by the emotion engine

[0545] The server receives text and voice messages sent by users, sends them to the emotion engine for analysis, and so on.

[0546] Input: User's text messages or voice messages

[0547] Output: Emotion engine analysis results (data including the user's emotional state)

[0548] Specific operation: The server sends input data to speech recognition APIs and text analysis APIs, analyzes the emotional state, receives the results, and stores them in memory.

[0549] Step 5: Generation of Q&A using generative artificial intelligence

[0550] The server inputs integrated profile data and emotion engine results into a generative artificial intelligence model (such as ChatGPT) to generate question and answer responses.

[0551] Input: Integrated profile data and sentiment engine results

[0552] Output: Question and answer data (generated questions and answers)

[0553] Specific operation: The server generates appropriate prompt statements and sends them to a generative artificial intelligence model. For example, it might use a prompt statement like this: "Generate a question and answer based on profile data and emotional state. The user's profile data is 'logged in 10 times in the past year,' and the contract information is 'contract period 12 months, monthly fee 5000 yen.' The user's emotional state is dissatisfied, and the text message says 'I'm disappointed with the recent service.'" The generated question and answer data is stored in memory.

[0554] Step 6: Output of Q&A data

[0555] The server sends the generated question-and-answer data to the user's terminal. HTTP protocol or WebSocket is used for transmission.

[0556] Input: Question and answer data

[0557] Output: Question and answer data displayed on the user's device.

[0558] Specific operation: The server sends question and answer data to the terminal for the user to review. On the user's terminal, the received data is displayed, the user reviews it, and sends further questions or feedback as needed.

[0559] (Application Example 2)

[0560] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0561] Traditional content delivery services recommend content based on users' usage history and basic attribute information, but this alone makes it difficult to provide personalized recommendations that appropriately reflect users' emotional states and instantaneous needs. Furthermore, there is a need for a method to generate more detailed user profiles by integrating usage history from multiple different services, and to achieve highly accurate content recommendations based on those profiles.

[0562] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0563] In this invention, the server includes means for receiving user identification information, means for acquiring first profile data, means for acquiring second profile data, means for integrating the first and second profile data to generate integrated profile data, means for inputting the integrated profile data and emotion recognition results into a generative artificial intelligence system to generate question-and-answer data, means for outputting the question-and-answer data, and means for recommending individually optimized content based on the integrated profile data and emotional state. This enables more personalized content recommendations based on the user's overall profile and emotional state.

[0564] "User identification information" refers to information used to uniquely identify a user.

[0565] "Primary profile data" refers to data that includes usage history and user attribute information related to online services.

[0566] "Second profile data" refers to data that includes usage history and contract information related to mobile communication services.

[0567] "Integrated profile data" refers to a comprehensive user profile generated by integrating the first profile data and the second profile data.

[0568] "Emotion recognition results" refer to data indicating the user's emotional state, analyzed from the user's input voice or text message.

[0569] "Generative artificial intelligence" refers to an artificial intelligence system that generates question-and-answer data based on integrated profile data and emotion recognition results.

[0570] "Question and answer data" refers to data generated by generative artificial intelligence that includes answers to user questions and additional questions.

[0571] "Personalized optimization" refers to providing the most appropriate responses and recommendations to each user based on their specific profile and emotional state.

[0572] "Content" is a general term for information or entertainment provided to users, such as movies, dramas, music, and articles.

[0573] "Recommendation methods" refer to methods or systems for selecting and presenting content that is appropriate for a user based on integrated profile data and emotional state.

[0574] This invention relates to a system that receives user identification information, integrates different profile data, and generates and provides individually optimized question-and-answer sessions and content recommendations using an emotion engine and generative artificial intelligence. This system integrates the usage history of online services and mobile communication services based on the user's identification information and recommends optimal content based on the user's emotional state.

[0575] First, the server receives user identification information. This identification information is necessary to uniquely identify the user and is used to obtain profile data, including the user's online service and mobile communication service usage history. Based on the identification information, the server obtains usage history data and attribute information (first profile data) related to online services. Similarly, based on the identification information, it also obtains usage history data and contract information (second profile data) related to mobile communication services.

[0576] Next, the server integrates the acquired first and second profile data to generate integrated profile data. This allows the user's overall attributes and usage history to be consolidated into a single profile data.

[0577] Furthermore, the server uses an emotion engine for emotion recognition. The emotion engine analyzes the user's input voice and text messages to recognize the user's emotional state (e.g., joy, sadness, surprise). This emotion recognition result, along with the user's integrated profile data, is then input into the generative artificial intelligence.

[0578] Generative artificial intelligence generates question-and-answer data tailored to the user based on integrated profile data and emotion recognition results. This question-and-answer data is individually optimized based on the user's specific usage situation and emotional state. As a result, users can receive appropriate answers and additional questions that match their emotional state.

[0579] Furthermore, this system recommends suitable content based on the user's integrated profile data and emotional state. For example, if a user enters "I've been feeling sad lately," the emotion engine recognizes "sadness" as an emotional state, and based on that, the generative artificial intelligence recommends "inspirational movies" or "encouraging articles" to the user.

[0580] Specific example:

[0581] For example, suppose a user has the identification information "Y12345". This user's first profile data is their online service usage history, which includes "10 logins in the past year," and their second profile data is their mobile communication service usage history, which includes "contract period of 12 months, monthly fee of 5000 yen." The server receives this user's identification information, retrieves the first and second profile data, and integrates them.

[0582] The emotion engine analyzes a text message entered by the user, for example, "I've been feeling sad lately," and recognizes that the user is experiencing the emotion of "sadness." Based on this, the generative artificial intelligence recommends optimized content such as the following:

[0583] A moving film

[0584] Encouraging articles

[0585] Here are some examples of prompts to input into a generative AI model:

[0586] "Based on integrated user profile data, recommend content that is most suitable for users experiencing sadness. Simultaneously, recommend content that helps shift their emotions to a more positive state."

[0587] Thus, the system of the present invention can provide highly accurate, personalized question-and-answer sessions and content recommendations based on the user's overall profile and emotional state.

[0588] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0589] Step 1:

[0590] The server receives user identification information. A user ID (identification information) is provided as input and sent to the server. The server uses this identification information to retrieve subsequent profile data.

[0591] Step 2:

[0592] The server retrieves online service usage history data and user attribute information (first profile data) based on the aforementioned identification information. Specifically, it uses the identification information to search the database for relevant usage history and attribute information and retrieves it. The output is the first profile data.

[0593] Step 3:

[0594] The server retrieves mobile communication service usage history data and contract information (second profile data) based on the aforementioned identification information. Specifically, it uses the identification information to search the mobile communication service database for relevant usage history and contract information and retrieves it. The output is the second profile data.

[0595] Step 4:

[0596] The server integrates the first and second profile data to generate integrated profile data. Specifically, it merges this data in dictionary or key-value format, and integrates duplicate data according to unified rules. The output is the integrated profile data.

[0597] Step 5:

[0598] The server inputs the user's voice or text message into the emotion engine and obtains an emotion recognition result. Specifically, it sends the user's voice or text data to the emotion recognition engine, which analyzes the emotional state and obtains a classification result (e.g., joy, sadness, etc.). The output is the emotion recognition result.

[0599] Step 6:

[0600] The server inputs integrated profile data and emotion recognition results into a generative artificial intelligence (AI) system to generate question-and-answer data. Specifically, it passes integrated profile data and emotion recognition results to the AI ​​system, which then uses this information to generate the most appropriate answer or additional questions in response to the user's questions. The output is question-and-answer data.

[0601] Step 7:

[0602] The server sends question-and-answer data to the user's terminal and displays it to the user. Specifically, it converts the question-and-answer data into an appropriate format (text message or audio) and sends it to the user's terminal. The output is the question-and-answer content displayed on the user's terminal.

[0603] Step 8:

[0604] The server uses generative artificial intelligence to recommend optimal content based on integrated profile data and emotional states. Specifically, it provides integrated profile data and emotion recognition results to the generative AI, which then selects and recommends the most suitable content (movies, music, articles, etc.) for the user. The output is the recommended content.

[0605] Step 9:

[0606] The server sends the recommended content to the user's terminal and displays it to the user. Specifically, it converts the recommended content information into an appropriate format (such as links and thumbnail information) and sends it to the user's terminal. The output is the recommended content displayed on the user's terminal.

[0607] Through this series of steps, users can receive personalized inquiry responses and content recommendations based on their emotional state and profile.

[0608] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0609] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0610] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0611] [Third Embodiment]

[0612] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0613] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0614] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0615] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0616] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0617] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0618] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0619] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0620] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0621] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0622] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0623] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0624] This invention relates to a system that receives user identification information, integrates different profile data, and generates and provides individually optimized question and answer responses using generative artificial intelligence.

[0625] 1. Receiving user identification information

[0626] The server first receives user identification information. This identification information is used to uniquely identify the user in both online and mobile communication services.

[0627] 2. Obtaining profile data

[0628] The server uses the received identification information to obtain the following two profile data:

[0629] Primary profile data: Data including usage history and user attribute information related to online services.

[0630] Second profile data: Data including usage history and contract information related to mobile communication services.

[0631] 3. Integration of profile data

[0632] The server integrates the first and second profile data to generate new integrated profile data. This provides a comprehensive profile of the user.

[0633] 4. Generation of questions and answers using generative artificial intelligence.

[0634] The server inputs integrated profile data into a generative artificial intelligence (AI) system, which then generates question-and-answer data based on the user's profile. The generative AI system has the ability to automatically generate question-and-answer responses in natural language based on the given integrated profile data.

[0635] 5. Output of Q&A data

[0636] The server returns the generated Q&A data to the user's terminal, allowing the user to review it. This enables the user to receive personalized Q&A based on their profile.

[0637] Specific example

[0638] For example, suppose a user has the identification information "Y12345". This user's first profile data is their online service usage history, which includes "10 logins in the past year," and their second profile data is their mobile communication service contract information, which includes "contract period of 12 months, monthly fee of 5000 yen."

[0639] The server receives the user's identification information and retrieves various profile data. It then integrates this data to generate integrated profile data, which is passed to a generative artificial intelligence system. This generates question-and-answer data similar to the following:

[0640] Question: "I've logged in 10 times in the past year. Do you know of any ways to further improve my convenience?"

[0641] Question: "Your contract has reached its 12-month mark. What plan would you prefer for your next renewal?"

[0642] Thus, the system of the present invention allows users to receive questions and answers based on their specific usage situation, thereby improving convenience.

[0643] The embodiment of the present invention is realized through the cooperation between a main server and a user terminal, and is characterized by its efficient integration of specific profile information and the provision of answer data by generative artificial intelligence. This system aims to respond quickly and accurately to the diverse needs of users.

[0644] The following describes the processing flow.

[0645] Step 1:

[0646] The user accesses the system using a terminal, enters and transmits identification information. This identification information is necessary to uniquely identify the user.

[0647] Step 2:

[0648] The server receives identification information sent by the user. Using this identification information, it prepares to retrieve profile data related to the user's online and mobile communication services.

[0649] Step 3:

[0650] The server retrieves primary profile data based on identification information. Specifically, it searches the database for and retrieves usage history and attribute information related to online services.

[0651] Step 4:

[0652] The server then retrieves second profile data based on the identification information. Here, it searches the database for and retrieves contract information and usage history related to mobile communication services.

[0653] Step 5:

[0654] The server integrates the acquired first and second profile data. It creates an instance of the UserProfile class and uses its methods to generate the integrated profile data.

[0655] Step 6:

[0656] The server inputs the generated integrated profile data into the generative artificial intelligence. The generative artificial intelligence then generates question-and-answer data based on this integrated profile data.

[0657] Step 7:

[0658] The server receives question-and-answer data generated by a generative artificial intelligence. This data is optimized for the user's individual profile.

[0659] Step 8:

[0660] The server sends the generated question-and-answer data to the user's terminal. This allows the user to receive questions and answers tailored to their profile.

[0661] Step 9:

[0662] Users review the Q&A data provided through their device and take the necessary actions. This Q&A data is used to improve the user experience.

[0663] The specific actions at each step describe a series of processes in which the system collects user information, integrates it, and generates and provides optimized responses based on generative artificial intelligence. This processing flow makes it possible to improve user convenience and satisfaction.

[0664] (Example 1)

[0665] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0666] Conventional systems lacked a means to provide unified Q&A based on users' online service usage history and mobile communication service usage history. As a result, users were unable to receive appropriate feedback and suggestions based on their overall usage. Furthermore, the difficulty in integrating information across different services led to a degraded user experience.

[0667] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0668] In this invention, the server includes means for receiving user identification information, means for acquiring first profile data based on the identification information, means for acquiring second profile data based on the identification information, means for integrating the first profile data and the second profile data to generate integrated profile data, means for inputting the integrated profile data into a generative artificial intelligence system to generate question and answer data based on the integrated profile, and means for outputting the question and answer data. This makes it possible for the user to receive question and answer based on their overall usage status.

[0669] "User identification information" refers to information that uniquely identifies a user within a specific online service and mobile communication service.

[0670] "Primary profile data" refers to data that includes the user's online service usage history and attribute information.

[0671] "Second profile data" refers to data that includes the user's mobile communication service usage history and contract information.

[0672] "Integrated profile data" refers to comprehensive user profile data generated by integrating the first profile data and the second profile data.

[0673] "Generative artificial intelligence" refers to an artificial intelligence model that has the ability to generate natural language questions and answers based on input data.

[0674] "Question and answer data" refers to question and answer data generated by generative artificial intelligence, based on the user's integrated profile data.

[0675] This invention relates to a system that receives user identification information, integrates different profile data, and generates and provides individually optimized question and answer responses using generative artificial intelligence.

[0676] First, the server receives user identification information. This identification information is used to uniquely identify the user within specific online and mobile communication services. Specifically, the server receives this identification information in real time using WebSockets.

[0677] Next, the server retrieves the first profile data and the second profile data based on the received identification information. The first profile data includes online service usage history and attribute information, while the second profile data includes mobile communication service usage history and contract information. The server sends a query to an SQL database (e.g., MySQL) to retrieve the first profile data. Then, it sends a request to a NoSQL database (e.g., MongoDB) to retrieve the second profile data.

[0678] Subsequently, the server integrates the first and second profile data to generate new integrated profile data. Data integration tools and ETL (Extract, Transform, Load) tools can be used for this. For example, the server might use Apache NiFi to transform and integrate data obtained from different data sources. Specifically, it might combine online usage history and contract information into a single JSON object.

[0679] After the integrated profile data is generated, the server inputs this data into a generative artificial intelligence (AI) to generate question-and-answer data. The generative AI automatically generates questions and answers in natural language based on the input data. Specifically, the server sends a request to the API of the generative AI model (e.g., GPT-3) using a prompt such as: "Please generate questions based on the online service usage history of user ID Y12345 (10 logins in the past year) and mobile communication service contract information (contract period 12 months, monthly fee 5000 yen)."

[0680] Finally, the server returns the generated Q&A data to the user's terminal. This can be done using a RESTful API or WebSocket. Specifically, the server uses Django to build a RESTful API and returns the generated questions to the user's terminal in JSON format.

[0681] This invention allows users to receive Q&A based on their overall usage, improving the convenience of the service. Furthermore, it facilitates information integration between different services on different servers, enhancing the user experience.

[0682] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0683] Step 1:

[0684] The server receives user identification information. The server receives user identification information in real time using WebSockets. Specifically, when a user accesses the service with a web browser, that identification information (for example, user ID "Y12345") is sent. The received identification information is used as input for the next step.

[0685] Step 2:

[0686] The server retrieves primary profile data based on the received identification information. The server sends a SELECT query to an SQL database (e.g., MySQL) to retrieve online service usage history and attribute information corresponding to user ID "Y12345". The input is the user's identification information, and the output is primary profile data (e.g., "Logged in 10 times in the past year").

[0687] Step 3:

[0688] The server retrieves secondary profile data based on the received identification information. The server queries a NoSQL database (e.g., MongoDB) to retrieve the usage history and contract information for the mobile communication service corresponding to user ID "Y12345". The input is the user's identification information, and the output is secondary profile data (e.g., "Contract period 12 months, monthly fee 5000 yen").

[0689] Step 4:

[0690] The server integrates the first and second profile data to generate new integrated profile data. The server uses a data integration tool (e.g., Apache NiFi) to transform and integrate data obtained from different data sources. For example, it might combine the first and second profile data into a single JSON object. The input is the first and second profile data, and the output is the integrated profile data (e.g., "{Login history: '10 times in the past year', Contract information: 'Contract period 12 months, monthly fee 5000 yen'}").

[0691] Step 5:

[0692] The server inputs integrated profile data into a generative artificial intelligence (AI) system to generate question-and-answer data. The server sends a request to the API of the generative AI model (e.g., GPT-3) using a prompt statement. The input is integrated profile data, and the output is question-and-answer data. For example, the prompt statement might be: "Generate a question based on user ID Y12345's online service usage history (10 logins in the past year) and mobile communication service contract information (contract period 12 months, monthly fee 5000 yen)."

[0693] Step 6:

[0694] The server returns the generated Q&A data to the user's device. The server builds a RESTful API (e.g., Django) and returns the generated questions in JSON format to the user's device. The input is the Q&A data, and the output is the Q&A displayed on the user's device. For example, the user's browser will display: "Question: 'You have logged in 10 times in the past year. Do you know of any ways to further improve your experience?'" "Question: 'Your contract has reached 12 months. What plan would you prefer for your next renewal?'"

[0695] (Application Example 1)

[0696] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0697] Modern content delivery services require personalized content recommendations based on users' viewing history and preferences. However, traditional systems struggle to manage and comprehensively analyze various profile data individually. Furthermore, there is a lack of means to implement dynamic content recommendations through question-and-answer sessions utilizing generative artificial intelligence. As a result, it has been difficult to sufficiently improve user convenience. New systems are needed to address these challenges in future content delivery services.

[0698] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0699] In this invention, the server includes means for receiving user identification information, means for acquiring first profile data based on the identification information, means for acquiring second profile data based on the identification information, means for integrating the first profile data and the second profile data to generate integrated profile data, means for inputting the integrated profile data into a generative artificial intelligence system to generate question and answer data, and means for outputting the question and answer data to the user's terminal to recommend content. This makes it possible to centrally manage a wide variety of user profile data, generate personalized question and answer responses using generative artificial intelligence, and recommend content optimized for each user.

[0700] "User identification information" refers to information used to uniquely identify a user.

[0701] "Primary profile data" refers to data that includes the user's online service usage history and attribute information.

[0702] "Second profile data" refers to data that includes the user's mobile communication service usage history and contract information.

[0703] "Integrated profile data" refers to the user's comprehensive profile data, generated by integrating the first profile data and the second profile data.

[0704] "Generative artificial intelligence" refers to artificial intelligence that has the ability to automatically generate questions and answers in natural language based on profile data.

[0705] "Question and answer data" refers to question and answer information generated by generative artificial intelligence based on the user's profile.

[0706] "Content recommendation" refers to the act of recommending content that is individually optimized based on the user's profile data.

[0707] "Terminal" refers to an electronic device used by a user, and includes, for example, smartphones and personal computers.

[0708] This invention relates to a system that receives user identification information, integrates multiple profile data, and generates and provides individually optimized question-and-answer responses using generative artificial intelligence. This system consists of a terminal used by the user and a server.

[0709] The server first receives user identification information. This received identification information is used to uniquely identify the user and is based on their online service and mobile communication service usage history and contract information. Based on this identification information, the server obtains the first profile data and the second profile data.

[0710] The first profile data includes online service usage history and attribute information. For example, this includes the types and frequency of content the user has viewed in the past. The second profile data includes mobile communication service usage history and contract information, such as contract period and monthly usage fees.

[0711] The server integrates the acquired first and second profile data to generate new integrated profile data. This integrated profile data represents the user's overall profile and serves as the foundational data for personalized content recommendations.

[0712] Next, this integrated profile data is input into a generative artificial intelligence (for example, a model like GPT-3) to generate question-and-answer data. The generative artificial intelligence can generate question-and-answer responses in natural language based on the input profile data.

[0713] The generated question-and-answer data is then sent back from the server to the user's device. Through this device, the user can receive content recommendations based on these questions and answers. In this way, the most suitable content is personalized and recommended to each user.

[0714] Hardware and software to be used

[0715] Hardware: Smartphones, servers

[0716] Software: Gensim (Python library for natural language processing), Requests (Python library for sending HTTP requests), generative artificial intelligence models (e.g., GPT-3)

[0717] Specific example of processing

[0718] For example, suppose a user A has the identification information "Y12345". Based on this identification information, the server obtains user A's first profile data (online service usage history: "watched 10 science fiction movies in the past year") and second profile data (mobile communication service contract information: "contract period 12 months, monthly fee 5000 yen"). These data are integrated to generate integrated profile data, and the prompt message "User profile: {online service profile data}, {mobile communication service profile data}\nGenerate question:" is input to the generative artificial intelligence.

[0719] The following question-and-answer data is generated by a generative artificial intelligence system and sent back to user A's terminal.

[0720] Question: "I've watched 10 sci-fi movies in the past year, but I'd recommend a sci-fi drama next. Are you interested?"

[0721] Question: "Your contract has reached its 12-month mark. Would you like a new plan for your next renewal?"

[0722] This allows user A to receive optimal content recommendations based on their specific usage history.

[0723] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0724] Step 1:

[0725] The server receives user identification information. When a user logs into the application via their device, this identification information is sent to the server. This inputs unique identification information, such as a user ID. This input identification information is then used to retrieve various profile data.

[0726] Step 2:

[0727] The server retrieves primary profile data based on the identification information. Using the identification information as a key, it retrieves the user's usage history and attribute information from the online service database. The input for this step is the user's identification information, and the output is the primary profile data for the online service. It sends queries to the database and extracts the corresponding usage history and attribute information.

[0728] Step 3:

[0729] The server retrieves secondary profile data based on the identification information. Using this identification information, it retrieves usage history and contract information from the mobile communication service database. The input for this step is the user's identification information, and the output is secondary profile data for the mobile communication service. It then queries the database to extract the relevant contract information and usage history.

[0730] Step 4:

[0731] The server integrates the acquired first and second profile data to generate new integrated profile data. In this step, the server combines the first and second profile data to create a single integrated profile data. The input is the two profile data obtained in the previous step, and the output is the integrated profile data. Specifically, the data fields are combined to form a single integrated profile data.

[0732] Step 5:

[0733] Integrated profile data is input to a generative artificial intelligence (AI) to generate question-and-answer data. Integrated profile data is input as a prompt and passed to the model of the generative AI (e.g., GPT-3). The input is integrated profile data, and the output is the generated question-and-answer data. The AI ​​model performs the operation of generating a question-and-answer in natural language based on the prompt.

[0734] Step 6:

[0735] The server sends the generated question-and-answer data to the user's terminal. The generated question-and-answer data, created by generative artificial intelligence, is sent to and displayed on the user's terminal. The input for this step is the question-and-answer data, and the output is the question-and-answer displayed on the user's terminal. The server uses a communication protocol such as HTTP or WebSocket to send data back as a response to the user's terminal.

[0736] Step 7:

[0737] Users review question-and-answer data through their devices and receive appropriate content recommendations. The final output is personalized question-and-answer sessions and content recommendations for the user, thereby improving user engagement. Users interact with the application interface to ask specific questions and review recommended content.

[0738] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0739] This invention relates to a system that receives user identification information, integrates different profile data, and generates and provides individually optimized question and answer responses using an emotion engine and generative artificial intelligence.

[0740] 1. Receiving user identification information

[0741] The server first receives user identification information. This identification information is necessary to uniquely identify the user and is used for both online and mobile communication services.

[0742] 2. Obtaining profile data

[0743] The server uses the received identification information to obtain the following two profile data:

[0744] Primary profile data: Data including usage history and user attribute information related to online services.

[0745] Second profile data: Data including usage history and contract information related to mobile communication services.

[0746] 3. Integration of profile data

[0747] The server integrates the first and second profile data to generate new integrated profile data. This provides a comprehensive profile of the user.

[0748] 4. Emotion recognition by an emotion engine

[0749] The server uses an emotion engine to analyze the user's emotional state from their voice input or text messages. The emotion engine recognizes the user's emotional state (e.g., joy, sadness, surprise) and generates an analysis result.

[0750] 5. Generation of Q&A using generative artificial intelligence

[0751] The server inputs integrated profile data and the results of the emotion engine's analysis into the generative artificial intelligence system, generating question-and-answer data based on the user's profile and emotional state. This ensures that the answers are appropriate to the user's emotional state.

[0752] 6. Output of Q&A data

[0753] The server returns the generated question-and-answer data to the user's terminal, allowing the user to review it. This enables the user to receive questions and answers based on their profile and emotional state.

[0754] Specific example

[0755] For example, suppose a user has the identification information "Y12345". This user's first profile data is their online service usage history, which includes "10 logins in the past year," and their second profile data is their mobile communication service contract information, which includes "contract period of 12 months, monthly fee of 5000 yen."

[0756] The server receives the user's identification information and retrieves various profile data. It then integrates this data to generate unified profile data.

[0757] Furthermore, the sentiment engine analyzes the text message entered by the user (e.g., "I'm disappointed with the recent service") and recognizes that the user is dissatisfied. Based on this, the following Q&A data is generated:

[0758] Question: "You seem dissatisfied with the service. What specific improvements would you like to see?"

[0759] Question: "We can suggest a review of your contract terms. What do you think?"

[0760] Thus, with the system of the present invention, users can receive question-and-answer sessions based on their specific usage situation and emotional state, resulting in a more personalized experience and improved satisfaction.

[0761] Embodiments of the present invention are realized through the cooperation of a main server, a user terminal, and an emotion engine, and are characterized by their efficient integration of specific profile and emotion information, and the provision of answer data by generative artificial intelligence. This system aims to respond quickly and accurately to the diverse needs and emotional states of users.

[0762] The following describes the processing flow.

[0763] Step 1:

[0764] The user accesses the system using a terminal, enters and transmits identification information. This identification information is necessary to uniquely identify the user.

[0765] Step 2:

[0766] The server receives identification information sent by the user. Using this identification information, it prepares to retrieve profile data related to the user's online and mobile communication services.

[0767] Step 3:

[0768] The server retrieves primary profile data based on identification information. Specifically, it searches the database for and retrieves usage history and attribute information related to online services.

[0769] Step 4:

[0770] The server then retrieves second profile data based on the identification information. Here, it searches the database for and retrieves contract information and usage history related to mobile communication services.

[0771] Step 5:

[0772] The server integrates the acquired first and second profile data. It creates an instance of the UserProfile class and uses its methods to generate the integrated profile data.

[0773] Step 6:

[0774] The server receives voice and text messages entered by the user into the system. This input data is necessary for analysis by the emotion engine.

[0775] Step 7:

[0776] The server inputs received voice and text messages into the emotion engine, which analyzes the user's emotional state. The emotion engine identifies emotions such as joy, sadness, and anger, and generates a result.

[0777] Step 8:

[0778] The server inputs integrated profile data and the results of the emotion engine's analysis into the generative artificial intelligence. Based on this input data, the generative artificial intelligence generates question and answer data optimized for the user's profile and emotional state.

[0779] Step 9:

[0780] The server receives question-and-answer data generated by a generative artificial intelligence. This data is personalized, taking into account the user's emotional state.

[0781] Step 10:

[0782] The server sends the generated question-and-answer data to the user's terminal. This allows the user to receive questions and answers based on their profile and emotional state.

[0783] Step 11:

[0784] Users review the question-and-answer data provided through their device and take the necessary actions. This question-and-answer data is used to improve the user experience.

[0785] The specific actions at each step describe a series of processes in which the system collects user information, integrates it, and generates and provides optimized responses based on generative artificial intelligence through analysis by the emotion engine. This processing flow makes it possible to improve user convenience and satisfaction.

[0786] (Example 2)

[0787] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0788] Conventional systems faced the challenge of comprehensively analyzing users' usage patterns and emotional states across different services to provide personalized Q&A. Specifically, online service and mobile communication service profile data were handled separately, and each set of data was analyzed individually without integration. As a result, it was impossible to provide answers that were appropriate to the user's overall needs and emotional state. Consequently, the quality of the user experience deteriorated, and satisfaction levels did not improve.

[0789] In Example 2, the identification processing by the identification processing unit 290 of the data processing device 12 is realized by the following means. In this invention, the server includes means for receiving user identification information, means for acquiring first profile data based on the identification information, means for acquiring second profile data based on the identification information, means for integrating the first profile data and the second profile data to generate integrated profile data, means for inputting the integrated profile data and the results of an emotion engine for analyzing the user's emotional state into a generative artificial intelligence system to generate question and answer data, and means for outputting the question and answer data. This makes it possible to provide personalized question and answer based on the user's overall profile and emotional state.

[0790] "User identification information" refers to information used to uniquely identify a user.

[0791] "Primary profile data" refers to data that includes user usage history and attribute information related to online services.

[0792] "Second profile data" refers to data that includes user usage history and contract information related to mobile communication services.

[0793] "Integrated profile data" refers to data containing comprehensive user profile information, generated by integrating the first profile data and the second profile data.

[0794] An "emotion engine" is an engine that analyzes and identifies the emotional state of a user from their voice input or text messages.

[0795] "Generative artificial intelligence" refers to artificial intelligence models that automatically generate questions and answers or text based on input data.

[0796] "Question and answer data" refers to data of questions and answers generated by generative artificial intelligence.

[0797] This invention relates to a system that receives user identification information, integrates different profile data, and generates and provides individually optimized question-and-answer responses using an emotion engine and generative artificial intelligence. This system is realized through the cooperation of a server, a terminal, and an emotion engine.

[0798] Overall system configuration

[0799] The system consists of the following hardware and software.

[0800] Server: The server is responsible for receiving user identification information, retrieving profile data from the database, and integrating it. Furthermore, it performs processing to run the emotion engine and generative artificial intelligence. Specifically, it uses MySQL or MongoDB for the database and Google Cloud Natural Language API or IBM Watson for the emotion engine.

[0801] Terminal: This is the device operated by the user, which transmits identification information and receives and displays the generated question-and-answer data. Examples include PCs, smartphones, and tablets.

[0802] Emotion Engine: This engine analyzes the emotional state of a user from their voice input or text messages. It utilizes Google Cloud Natural Language API or IBM Watson.

[0803] Generative artificial intelligence: Artificial intelligence that generates question-and-answer responses using integrated profile data and the results of emotion engine analysis as input. For example, it utilizes OpenAI's ChatGPT model.

[0804] System operation

[0805] Receive user identification information

[0806] The server receives identification information sent by the user from the terminal. This identification information is used in each processing step within the system.

[0807] Acquisition of profile data

[0808] Based on the received identification information, the server retrieves primary profile data (usage history and attribute information related to online services) and secondary profile data (usage history and contract information related to mobile communication services) from the database.

[0809] Integration of profile data

[0810] The server integrates the acquired profile data and generates new integrated profile data. ETL tools or dedicated integration algorithms are used for this integration.

[0811] Emotion recognition by an emotion engine

[0812] The emotion engine analyzes text and voice messages sent by the user to recognize their emotional state. The analysis results are sent to the server and used in the next step.

[0813] Generation of Q&A using generative artificial intelligence

[0814] The server takes the integrated profile data and the results of the emotion engine as input and requests processing from a generative artificial intelligence model (such as ChatGPT). For example, the prompt statement can be created as follows:

[0815] "Generate a Q&A based on the user's profile data and emotional state. The user's profile data is '10 logins in the past year,' and the contract information is '12-month contract, monthly fee of 5000 yen.' The user's emotional state is dissatisfied, and the text message says 'I'm disappointed with the recent service.'"

[0816] Output of Q&A data

[0817] The server sends the generated Q&A data to the user's terminal for the user to review. For example, Q&A data such as "You seem dissatisfied with the service; what specific improvements would you like to see?" is generated and displayed on the user's terminal.

[0818] Specific example

[0819] For example, suppose a user has the identifier "Y12345". This user's first profile data is "logged in 10 times in the past year", and the second profile data is "contract period 12 months, monthly fee 5000 yen". If the user enters "I'm disappointed with the recent service", the emotion engine analyzes the dissatisfaction and generates a Q&A based on that. In this way, the system of the present invention can provide a Q&A based on the user's overall profile and emotional state.

[0820] This personalizes the user experience and improves satisfaction.

[0821] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0822] Step 1: Receive user identification information

[0823] The server receives identification information sent by the user from their terminal. This identification information is sent via REST API or socket communication.

[0824] Input: User identification information (e.g., User ID)

[0825] Output: User identification information stored in memory

[0826] Specific operation: The server receives an HTTP request from the terminal, extracts identification information including the user ID, and stores it in memory.

[0827] Step 2: Obtain profile data

[0828] Based on the received identification information, the server retrieves primary profile data (usage history and attribute information related to online services) and secondary profile data (usage history and contract information related to mobile communication services) from the database.

[0829] Input: User identification information stored in memory

[0830] Output: First profile data and second profile data (in JSON format) retrieved from the database.

[0831] Specific operation: The server executes queries against MySQL or MongoDB and retrieves the necessary profile data based on the user ID. The retrieved data is stored in memory in JSON format.

[0832] Step 3: Integrating profile data

[0833] The server integrates the acquired profile data and generates new integrated profile data. ETL tools or dedicated integration algorithms are used for this integration.

[0834] Input: First profile data and second profile data (JSON format)

[0835] Output: Integrated profile data (JSON format containing integrated profile information)

[0836] Specific operation: The server merges the first and second profile data while checking data integrity, and generates new integrated profile data. The generated data is held in memory.

[0837] Step 4: Emotion recognition by the emotion engine

[0838] The server receives text and voice messages sent by users, sends them to the emotion engine for analysis, and so on.

[0839] Input: User's text messages or voice messages

[0840] Output: Emotion engine analysis results (data including the user's emotional state)

[0841] Specific operation: The server sends input data to speech recognition APIs and text analysis APIs, analyzes the emotional state, receives the results, and stores them in memory.

[0842] Step 5: Generation of Q&A using generative artificial intelligence

[0843] The server inputs integrated profile data and emotion engine results into a generative artificial intelligence model (such as ChatGPT) to generate question and answer responses.

[0844] Input: Integrated profile data and sentiment engine results

[0845] Output: Question and answer data (generated questions and answers)

[0846] Specific operation: The server generates appropriate prompt statements and sends them to a generative artificial intelligence model. For example, it might use a prompt statement like this: "Generate a question and answer based on profile data and emotional state. The user's profile data is 'logged in 10 times in the past year,' and the contract information is 'contract period 12 months, monthly fee 5000 yen.' The user's emotional state is dissatisfied, and the text message says 'I'm disappointed with the recent service.'" The generated question and answer data is stored in memory.

[0847] Step 6: Output of Q&A data

[0848] The server sends the generated question-and-answer data to the user's terminal. HTTP protocol or WebSocket is used for transmission.

[0849] Input: Question and answer data

[0850] Output: Question and answer data displayed on the user's device.

[0851] Specific operation: The server sends question and answer data to the terminal for the user to review. On the user's terminal, the received data is displayed, the user reviews it, and sends further questions or feedback as needed.

[0852] (Application Example 2)

[0853] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0854] Traditional content delivery services recommend content based on users' usage history and basic attribute information, but this alone makes it difficult to provide personalized recommendations that appropriately reflect users' emotional states and instantaneous needs. Furthermore, there is a need for a method to generate more detailed user profiles by integrating usage history from multiple different services, and to achieve highly accurate content recommendations based on those profiles.

[0855] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0856] In this invention, the server includes means for receiving user identification information, means for acquiring first profile data, means for acquiring second profile data, means for integrating the first and second profile data to generate integrated profile data, means for inputting the integrated profile data and emotion recognition results into a generative artificial intelligence system to generate question-and-answer data, means for outputting the question-and-answer data, and means for recommending individually optimized content based on the integrated profile data and emotional state. This enables more personalized content recommendations based on the user's overall profile and emotional state.

[0857] "User identification information" refers to information used to uniquely identify a user.

[0858] "Primary profile data" refers to data that includes usage history and user attribute information related to online services.

[0859] "Second profile data" refers to data that includes usage history and contract information related to mobile communication services.

[0860] "Integrated profile data" refers to a comprehensive user profile generated by integrating the first profile data and the second profile data.

[0861] "Emotion recognition results" refer to data indicating the user's emotional state, analyzed from the user's input voice or text message.

[0862] "Generative artificial intelligence" refers to an artificial intelligence system that generates question-and-answer data based on integrated profile data and emotion recognition results.

[0863] "Question and answer data" refers to data generated by generative artificial intelligence that includes answers to user questions and additional questions.

[0864] "Personalized optimization" refers to providing the most appropriate responses and recommendations to each user based on their specific profile and emotional state.

[0865] "Content" is a general term for information or entertainment provided to users, such as movies, dramas, music, and articles.

[0866] "Recommendation methods" refer to methods or systems for selecting and presenting content that is appropriate for a user based on integrated profile data and emotional state.

[0867] This invention relates to a system that receives user identification information, integrates different profile data, and generates and provides individually optimized question-and-answer sessions and content recommendations using an emotion engine and generative artificial intelligence. This system integrates the usage history of online services and mobile communication services based on the user's identification information and recommends optimal content based on the user's emotional state.

[0868] First, the server receives user identification information. This identification information is necessary to uniquely identify the user and is used to obtain profile data, including the user's online service and mobile communication service usage history. Based on the identification information, the server obtains usage history data and attribute information (first profile data) related to online services. Similarly, based on the identification information, it also obtains usage history data and contract information (second profile data) related to mobile communication services.

[0869] Next, the server integrates the acquired first and second profile data to generate integrated profile data. This allows the user's overall attributes and usage history to be consolidated into a single profile data.

[0870] Furthermore, the server uses an emotion engine for emotion recognition. The emotion engine analyzes the user's input voice and text messages to recognize the user's emotional state (e.g., joy, sadness, surprise). This emotion recognition result, along with the user's integrated profile data, is then input into the generative artificial intelligence.

[0871] Generative artificial intelligence generates question-and-answer data tailored to the user based on integrated profile data and emotion recognition results. This question-and-answer data is individually optimized based on the user's specific usage situation and emotional state. As a result, users can receive appropriate answers and additional questions that match their emotional state.

[0872] Furthermore, this system recommends suitable content based on the user's integrated profile data and emotional state. For example, if a user enters "I've been feeling sad lately," the emotion engine recognizes "sadness" as an emotional state, and based on that, the generative artificial intelligence recommends "inspirational movies" or "encouraging articles" to the user.

[0873] Specific example:

[0874] For example, suppose a user has the identification information "Y12345". This user's first profile data is their online service usage history, which includes "10 logins in the past year," and their second profile data is their mobile communication service usage history, which includes "contract period of 12 months, monthly fee of 5000 yen." The server receives this user's identification information, retrieves the first and second profile data, and integrates them.

[0875] The emotion engine analyzes a text message entered by the user, for example, "I've been feeling sad lately," and recognizes that the user is experiencing the emotion of "sadness." Based on this, the generative artificial intelligence recommends optimized content such as the following:

[0876] A moving film

[0877] Encouraging articles

[0878] Here are some examples of prompts to input into a generative AI model:

[0879] "Based on integrated user profile data, recommend content that is most suitable for users experiencing sadness. Simultaneously, recommend content that helps shift their emotions to a more positive state."

[0880] Thus, the system of the present invention can provide highly accurate, personalized question-and-answer sessions and content recommendations based on the user's overall profile and emotional state.

[0881] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0882] Step 1:

[0883] The server receives user identification information. A user ID (identification information) is provided as input and sent to the server. The server uses this identification information to retrieve subsequent profile data.

[0884] Step 2:

[0885] The server retrieves online service usage history data and user attribute information (first profile data) based on the aforementioned identification information. Specifically, it uses the identification information to search the database for relevant usage history and attribute information and retrieves it. The output is the first profile data.

[0886] Step 3:

[0887] The server retrieves mobile communication service usage history data and contract information (second profile data) based on the aforementioned identification information. Specifically, it uses the identification information to search the mobile communication service database for relevant usage history and contract information and retrieves it. The output is the second profile data.

[0888] Step 4:

[0889] The server integrates the first and second profile data to generate integrated profile data. Specifically, it merges this data in dictionary or key-value format, and integrates duplicate data according to unified rules. The output is the integrated profile data.

[0890] Step 5:

[0891] The server inputs the user's voice or text message into the emotion engine and obtains an emotion recognition result. Specifically, it sends the user's voice or text data to the emotion recognition engine, which analyzes the emotional state and obtains a classification result (e.g., joy, sadness, etc.). The output is the emotion recognition result.

[0892] Step 6:

[0893] The server inputs integrated profile data and emotion recognition results into a generative artificial intelligence (AI) system to generate question-and-answer data. Specifically, it passes integrated profile data and emotion recognition results to the AI ​​system, which then uses this information to generate the most appropriate answer or additional questions in response to the user's questions. The output is question-and-answer data.

[0894] Step 7:

[0895] The server sends question-and-answer data to the user's terminal and displays it to the user. Specifically, it converts the question-and-answer data into an appropriate format (text message or audio) and sends it to the user's terminal. The output is the question-and-answer content displayed on the user's terminal.

[0896] Step 8:

[0897] The server uses generative artificial intelligence to recommend optimal content based on integrated profile data and emotional states. Specifically, it provides integrated profile data and emotion recognition results to the generative AI, which then selects and recommends the most suitable content (movies, music, articles, etc.) for the user. The output is the recommended content.

[0898] Step 9:

[0899] The server sends the recommended content to the user's terminal and displays it to the user. Specifically, it converts the recommended content information into an appropriate format (such as links and thumbnail information) and sends it to the user's terminal. The output is the recommended content displayed on the user's terminal.

[0900] Through this series of steps, users can receive personalized inquiry responses and content recommendations based on their emotional state and profile.

[0901] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0902] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0903] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0904] [Fourth Embodiment]

[0905] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0906] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0907] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0908] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0909] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0910] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0911] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0912] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0913] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0914] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0915] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0916] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0917] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0918] This invention relates to a system that receives user identification information, integrates different profile data, and generates and provides individually optimized question and answer responses using generative artificial intelligence.

[0919] 1. Receiving user identification information

[0920] The server first receives user identification information. This identification information is used to uniquely identify the user in both online and mobile communication services.

[0921] 2. Obtaining profile data

[0922] The server uses the received identification information to obtain the following two profile data:

[0923] Primary profile data: Data including usage history and user attribute information related to online services.

[0924] Second profile data: Data including usage history and contract information related to mobile communication services.

[0925] 3. Integration of profile data

[0926] The server integrates the first and second profile data to generate new integrated profile data. This provides a comprehensive profile of the user.

[0927] 4. Generation of questions and answers using generative artificial intelligence.

[0928] The server inputs integrated profile data into a generative artificial intelligence (AI) system, which then generates question-and-answer data based on the user's profile. The generative AI system has the ability to automatically generate question-and-answer responses in natural language based on the given integrated profile data.

[0929] 5. Output of Q&A data

[0930] The server returns the generated Q&A data to the user's terminal, allowing the user to review it. This enables the user to receive personalized Q&A based on their profile.

[0931] Specific example

[0932] For example, suppose a user has the identification information "Y12345". This user's first profile data is their online service usage history, which includes "10 logins in the past year," and their second profile data is their mobile communication service contract information, which includes "contract period of 12 months, monthly fee of 5000 yen."

[0933] The server receives the user's identification information and retrieves various profile data. It then integrates this data to generate integrated profile data, which is passed to a generative artificial intelligence system. This generates question-and-answer data similar to the following:

[0934] Question: "I've logged in 10 times in the past year. Do you know of any ways to further improve my convenience?"

[0935] Question: "Your contract has reached its 12-month mark. What plan would you prefer for your next renewal?"

[0936] Thus, the system of the present invention allows users to receive questions and answers based on their specific usage situation, thereby improving convenience.

[0937] The embodiment of the present invention is realized through the cooperation between a main server and a user terminal, and is characterized by its efficient integration of specific profile information and the provision of answer data by generative artificial intelligence. This system aims to respond quickly and accurately to the diverse needs of users.

[0938] The following describes the processing flow.

[0939] Step 1:

[0940] The user accesses the system using a terminal, enters and transmits identification information. This identification information is necessary to uniquely identify the user.

[0941] Step 2:

[0942] The server receives identification information sent by the user. Using this identification information, it prepares to retrieve profile data related to the user's online and mobile communication services.

[0943] Step 3:

[0944] The server retrieves primary profile data based on identification information. Specifically, it searches the database for and retrieves usage history and attribute information related to online services.

[0945] Step 4:

[0946] The server then retrieves second profile data based on the identification information. Here, it searches the database for and retrieves contract information and usage history related to mobile communication services.

[0947] Step 5:

[0948] The server integrates the acquired first and second profile data. It creates an instance of the UserProfile class and uses its methods to generate the integrated profile data.

[0949] Step 6:

[0950] The server inputs the generated integrated profile data into the generative artificial intelligence. The generative artificial intelligence then generates question-and-answer data based on this integrated profile data.

[0951] Step 7:

[0952] The server receives question-and-answer data generated by a generative artificial intelligence. This data is optimized for the user's individual profile.

[0953] Step 8:

[0954] The server sends the generated question-and-answer data to the user's terminal. This allows the user to receive questions and answers tailored to their profile.

[0955] Step 9:

[0956] Users review the Q&A data provided through their device and take the necessary actions. This Q&A data is used to improve the user experience.

[0957] The specific actions at each step describe a series of processes in which the system collects user information, integrates it, and generates and provides optimized responses based on generative artificial intelligence. This processing flow makes it possible to improve user convenience and satisfaction.

[0958] (Example 1)

[0959] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0960] Conventional systems lacked a means to provide unified Q&A based on users' online service usage history and mobile communication service usage history. As a result, users were unable to receive appropriate feedback and suggestions based on their overall usage. Furthermore, the difficulty in integrating information across different services led to a degraded user experience.

[0961] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0962] In this invention, the server includes means for receiving user identification information, means for acquiring first profile data based on the identification information, means for acquiring second profile data based on the identification information, means for integrating the first profile data and the second profile data to generate integrated profile data, means for inputting the integrated profile data into a generative artificial intelligence system to generate question and answer data based on the integrated profile, and means for outputting the question and answer data. This makes it possible for the user to receive question and answer based on their overall usage status.

[0963] "User identification information" refers to information that uniquely identifies a user within a specific online service and mobile communication service.

[0964] "Primary profile data" refers to data that includes the user's online service usage history and attribute information.

[0965] "Second profile data" refers to data that includes the user's mobile communication service usage history and contract information.

[0966] "Integrated profile data" refers to comprehensive user profile data generated by integrating the first profile data and the second profile data.

[0967] "Generative artificial intelligence" refers to an artificial intelligence model that has the ability to generate natural language questions and answers based on input data.

[0968] "Question and answer data" refers to question and answer data generated by generative artificial intelligence, based on the user's integrated profile data.

[0969] This invention relates to a system that receives user identification information, integrates different profile data, and generates and provides individually optimized question and answer responses using generative artificial intelligence.

[0970] First, the server receives user identification information. This identification information is used to uniquely identify the user within specific online and mobile communication services. Specifically, the server receives this identification information in real time using WebSockets.

[0971] Next, the server retrieves the first profile data and the second profile data based on the received identification information. The first profile data includes online service usage history and attribute information, while the second profile data includes mobile communication service usage history and contract information. The server sends a query to an SQL database (e.g., MySQL) to retrieve the first profile data. Then, it sends a request to a NoSQL database (e.g., MongoDB) to retrieve the second profile data.

[0972] Subsequently, the server integrates the first and second profile data to generate new integrated profile data. Data integration tools and ETL (Extract, Transform, Load) tools can be used for this. For example, the server might use Apache NiFi to transform and integrate data obtained from different data sources. Specifically, it might combine online usage history and contract information into a single JSON object.

[0973] After the integrated profile data is generated, the server inputs this data into a generative artificial intelligence (AI) to generate question-and-answer data. The generative AI automatically generates questions and answers in natural language based on the input data. Specifically, the server sends a request to the API of the generative AI model (e.g., GPT-3) using a prompt such as: "Please generate questions based on the online service usage history of user ID Y12345 (10 logins in the past year) and mobile communication service contract information (contract period 12 months, monthly fee 5000 yen)."

[0974] Finally, the server returns the generated Q&A data to the user's terminal. This can be done using a RESTful API or WebSocket. Specifically, the server uses Django to build a RESTful API and returns the generated questions to the user's terminal in JSON format.

[0975] This invention allows users to receive Q&A based on their overall usage, improving the convenience of the service. Furthermore, it facilitates information integration between different services on different servers, enhancing the user experience.

[0976] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0977] Step 1:

[0978] The server receives user identification information. The server receives user identification information in real time using WebSockets. Specifically, when a user accesses the service with a web browser, that identification information (for example, user ID "Y12345") is sent. The received identification information is used as input for the next step.

[0979] Step 2:

[0980] The server retrieves primary profile data based on the received identification information. The server sends a SELECT query to an SQL database (e.g., MySQL) to retrieve online service usage history and attribute information corresponding to user ID "Y12345". The input is the user's identification information, and the output is primary profile data (e.g., "Logged in 10 times in the past year").

[0981] Step 3:

[0982] The server retrieves secondary profile data based on the received identification information. The server queries a NoSQL database (e.g., MongoDB) to retrieve the usage history and contract information for the mobile communication service corresponding to user ID "Y12345". The input is the user's identification information, and the output is secondary profile data (e.g., "Contract period 12 months, monthly fee 5000 yen").

[0983] Step 4:

[0984] The server integrates the first and second profile data to generate new integrated profile data. The server uses a data integration tool (e.g., Apache NiFi) to transform and integrate data obtained from different data sources. For example, it might combine the first and second profile data into a single JSON object. The input is the first and second profile data, and the output is the integrated profile data (e.g., "{Login history: '10 times in the past year', Contract information: 'Contract period 12 months, monthly fee 5000 yen'}").

[0985] Step 5:

[0986] The server inputs integrated profile data into a generative artificial intelligence (AI) system to generate question-and-answer data. The server sends a request to the API of the generative AI model (e.g., GPT-3) using a prompt statement. The input is integrated profile data, and the output is question-and-answer data. For example, the prompt statement might be: "Generate a question based on user ID Y12345's online service usage history (10 logins in the past year) and mobile communication service contract information (contract period 12 months, monthly fee 5000 yen)."

[0987] Step 6:

[0988] The server returns the generated Q&A data to the user's device. The server builds a RESTful API (e.g., Django) and returns the generated questions in JSON format to the user's device. The input is the Q&A data, and the output is the Q&A displayed on the user's device. For example, the user's browser will display: "Question: 'You have logged in 10 times in the past year. Do you know of any ways to further improve your experience?'" "Question: 'Your contract has reached 12 months. What plan would you prefer for your next renewal?'"

[0989] (Application Example 1)

[0990] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0991] Modern content delivery services require personalized content recommendations based on users' viewing history and preferences. However, traditional systems struggle to manage and comprehensively analyze various profile data individually. Furthermore, there is a lack of means to implement dynamic content recommendations through question-and-answer sessions utilizing generative artificial intelligence. As a result, it has been difficult to sufficiently improve user convenience. New systems are needed to address these challenges in future content delivery services.

[0992] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0993] In this invention, the server includes means for receiving user identification information, means for acquiring first profile data based on the identification information, means for acquiring second profile data based on the identification information, means for integrating the first profile data and the second profile data to generate integrated profile data, means for inputting the integrated profile data into a generative artificial intelligence system to generate question and answer data, and means for outputting the question and answer data to the user's terminal to recommend content. This makes it possible to centrally manage a wide variety of user profile data, generate personalized question and answer responses using generative artificial intelligence, and recommend content optimized for each user.

[0994] "User identification information" refers to information used to uniquely identify a user.

[0995] "Primary profile data" refers to data that includes the user's online service usage history and attribute information.

[0996] "Second profile data" refers to data that includes the user's mobile communication service usage history and contract information.

[0997] "Integrated profile data" refers to the user's comprehensive profile data, generated by integrating the first profile data and the second profile data.

[0998] "Generative artificial intelligence" refers to artificial intelligence that has the ability to automatically generate questions and answers in natural language based on profile data.

[0999] "Question and answer data" refers to question and answer information generated by generative artificial intelligence based on the user's profile.

[1000] "Content recommendation" refers to the act of recommending content that is individually optimized based on the user's profile data.

[1001] "Terminal" refers to an electronic device used by a user, and includes, for example, smartphones and personal computers.

[1002] This invention relates to a system that receives user identification information, integrates multiple profile data, and generates and provides individually optimized question-and-answer responses using generative artificial intelligence. This system consists of a terminal used by the user and a server.

[1003] The server first receives user identification information. This received identification information is used to uniquely identify the user and is based on their online service and mobile communication service usage history and contract information. Based on this identification information, the server obtains the first profile data and the second profile data.

[1004] The first profile data includes online service usage history and attribute information. For example, this includes the types and frequency of content the user has viewed in the past. The second profile data includes mobile communication service usage history and contract information, such as contract period and monthly usage fees.

[1005] The server integrates the acquired first and second profile data to generate new integrated profile data. This integrated profile data represents the user's overall profile and serves as the foundational data for personalized content recommendations.

[1006] Next, this integrated profile data is input into a generative artificial intelligence (for example, a model like GPT-3) to generate question-and-answer data. The generative artificial intelligence can generate question-and-answer responses in natural language based on the input profile data.

[1007] The generated question-and-answer data is then sent back from the server to the user's device. Through this device, the user can receive content recommendations based on these questions and answers. In this way, the most suitable content is personalized and recommended to each user.

[1008] Hardware and software to be used

[1009] Hardware: Smartphones, servers

[1010] Software: Gensim (Python library for natural language processing), Requests (Python library for sending HTTP requests), generative artificial intelligence models (e.g., GPT-3)

[1011] Specific example of processing

[1012] For example, suppose a user A has the identification information "Y12345". Based on this identification information, the server obtains user A's first profile data (online service usage history: "watched 10 science fiction movies in the past year") and second profile data (mobile communication service contract information: "contract period 12 months, monthly fee 5000 yen"). These data are integrated to generate integrated profile data, and the prompt message "User profile: {online service profile data}, {mobile communication service profile data}\nGenerate question:" is input to the generative artificial intelligence.

[1013] The following question-and-answer data is generated by a generative artificial intelligence system and sent back to user A's terminal.

[1014] Question: "I've watched 10 sci-fi movies in the past year, but I'd recommend a sci-fi drama next. Are you interested?"

[1015] Question: "Your contract has reached its 12-month mark. Would you like a new plan for your next renewal?"

[1016] This allows user A to receive optimal content recommendations based on their specific usage history.

[1017] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[1018] Step 1:

[1019] The server receives user identification information. When a user logs into the application via their device, this identification information is sent to the server. This inputs unique identification information, such as a user ID. This input identification information is then used to retrieve various profile data.

[1020] Step 2:

[1021] The server retrieves primary profile data based on the identification information. Using the identification information as a key, it retrieves the user's usage history and attribute information from the online service database. The input for this step is the user's identification information, and the output is the primary profile data for the online service. It sends queries to the database and extracts the corresponding usage history and attribute information.

[1022] Step 3:

[1023] The server retrieves secondary profile data based on the identification information. Using this identification information, it retrieves usage history and contract information from the mobile communication service database. The input for this step is the user's identification information, and the output is secondary profile data for the mobile communication service. It then queries the database to extract the relevant contract information and usage history.

[1024] Step 4:

[1025] The server integrates the acquired first and second profile data to generate new integrated profile data. In this step, the server combines the first and second profile data to create a single integrated profile data. The input is the two profile data obtained in the previous step, and the output is the integrated profile data. Specifically, the data fields are combined to form a single integrated profile data.

[1026] Step 5:

[1027] Integrated profile data is input to a generative artificial intelligence (AI) to generate question-and-answer data. Integrated profile data is input as a prompt and passed to the model of the generative AI (e.g., GPT-3). The input is integrated profile data, and the output is the generated question-and-answer data. The AI ​​model performs the operation of generating a question-and-answer in natural language based on the prompt.

[1028] Step 6:

[1029] The server sends the generated question-and-answer data to the user's terminal. The generated question-and-answer data, created by generative artificial intelligence, is sent to and displayed on the user's terminal. The input for this step is the question-and-answer data, and the output is the question-and-answer displayed on the user's terminal. The server uses a communication protocol such as HTTP or WebSocket to send data back as a response to the user's terminal.

[1030] Step 7:

[1031] Users review question-and-answer data through their devices and receive appropriate content recommendations. The final output is personalized question-and-answer sessions and content recommendations for the user, thereby improving user engagement. Users interact with the application interface to ask specific questions and review recommended content.

[1032] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[1033] This invention relates to a system that receives user identification information, integrates different profile data, and generates and provides individually optimized question and answer responses using an emotion engine and generative artificial intelligence.

[1034] 1. Receiving user identification information

[1035] The server first receives user identification information. This identification information is necessary to uniquely identify the user and is used for both online and mobile communication services.

[1036] 2. Obtaining profile data

[1037] The server uses the received identification information to obtain the following two profile data:

[1038] Primary profile data: Data including usage history and user attribute information related to online services.

[1039] Second profile data: Data including usage history and contract information related to mobile communication services.

[1040] 3. Integration of profile data

[1041] The server integrates the first and second profile data to generate new integrated profile data. This provides a comprehensive profile of the user.

[1042] 4. Emotion recognition by an emotion engine

[1043] The server uses an emotion engine to analyze the user's emotional state from their voice input or text messages. The emotion engine recognizes the user's emotional state (e.g., joy, sadness, surprise) and generates an analysis result.

[1044] 5. Generation of Q&A using generative artificial intelligence

[1045] The server inputs integrated profile data and the results of the emotion engine's analysis into the generative artificial intelligence system, generating question-and-answer data based on the user's profile and emotional state. This ensures that the answers are appropriate to the user's emotional state.

[1046] 6. Output of Q&A data

[1047] The server returns the generated question-and-answer data to the user's terminal, allowing the user to review it. This enables the user to receive questions and answers based on their profile and emotional state.

[1048] Specific example

[1049] For example, suppose a user has the identification information "Y12345". This user's first profile data is their online service usage history, which includes "10 logins in the past year," and their second profile data is their mobile communication service contract information, which includes "contract period of 12 months, monthly fee of 5000 yen."

[1050] The server receives the user's identification information and retrieves various profile data. It then integrates this data to generate unified profile data.

[1051] Furthermore, the sentiment engine analyzes the text message entered by the user (e.g., "I'm disappointed with the recent service") and recognizes that the user is dissatisfied. Based on this, the following Q&A data is generated:

[1052] Question: "You seem dissatisfied with the service. What specific improvements would you like to see?"

[1053] Question: "We can suggest a review of your contract terms. What do you think?"

[1054] Thus, with the system of the present invention, users can receive question-and-answer sessions based on their specific usage situation and emotional state, resulting in a more personalized experience and improved satisfaction.

[1055] Embodiments of the present invention are realized through the cooperation of a main server, a user terminal, and an emotion engine, and are characterized by their efficient integration of specific profile and emotion information, and the provision of answer data by generative artificial intelligence. This system aims to respond quickly and accurately to the diverse needs and emotional states of users.

[1056] The following describes the processing flow.

[1057] Step 1:

[1058] The user accesses the system using a terminal, enters and transmits identification information. This identification information is necessary to uniquely identify the user.

[1059] Step 2:

[1060] The server receives identification information sent by the user. Using this identification information, it prepares to retrieve profile data related to the user's online and mobile communication services.

[1061] Step 3:

[1062] The server retrieves primary profile data based on identification information. Specifically, it searches the database for and retrieves usage history and attribute information related to online services.

[1063] Step 4:

[1064] The server then retrieves second profile data based on the identification information. Here, it searches the database for and retrieves contract information and usage history related to mobile communication services.

[1065] Step 5:

[1066] The server integrates the acquired first and second profile data. It creates an instance of the UserProfile class and uses its methods to generate the integrated profile data.

[1067] Step 6:

[1068] The server receives voice and text messages entered by the user into the system. This input data is necessary for analysis by the emotion engine.

[1069] Step 7:

[1070] The server inputs received voice and text messages into the emotion engine, which analyzes the user's emotional state. The emotion engine identifies emotions such as joy, sadness, and anger, and generates a result.

[1071] Step 8:

[1072] The server inputs integrated profile data and the results of the emotion engine's analysis into the generative artificial intelligence. Based on this input data, the generative artificial intelligence generates question and answer data optimized for the user's profile and emotional state.

[1073] Step 9:

[1074] The server receives question-and-answer data generated by a generative artificial intelligence. This data is personalized, taking into account the user's emotional state.

[1075] Step 10:

[1076] The server sends the generated question-and-answer data to the user's terminal. This allows the user to receive questions and answers based on their profile and emotional state.

[1077] Step 11:

[1078] Users review the question-and-answer data provided through their device and take the necessary actions. This question-and-answer data is used to improve the user experience.

[1079] The specific actions at each step describe a series of processes in which the system collects user information, integrates it, and generates and provides optimized responses based on generative artificial intelligence through analysis by the emotion engine. This processing flow makes it possible to improve user convenience and satisfaction.

[1080] (Example 2)

[1081] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1082] Conventional systems faced the challenge of comprehensively analyzing users' usage patterns and emotional states across different services to provide personalized Q&A. Specifically, online service and mobile communication service profile data were handled separately, and each set of data was analyzed individually without integration. As a result, it was impossible to provide answers that were appropriate to the user's overall needs and emotional state. Consequently, the quality of the user experience deteriorated, and satisfaction levels did not improve.

[1083] In Example 2, the identification processing by the identification processing unit 290 of the data processing device 12 is realized by the following means. In this invention, the server includes means for receiving user identification information, means for acquiring first profile data based on the identification information, means for acquiring second profile data based on the identification information, means for integrating the first profile data and the second profile data to generate integrated profile data, means for inputting the integrated profile data and the results of an emotion engine for analyzing the user's emotional state into a generative artificial intelligence system to generate question and answer data, and means for outputting the question and answer data. This makes it possible to provide personalized question and answer based on the user's overall profile and emotional state.

[1084] "User identification information" refers to information used to uniquely identify a user.

[1085] "Primary profile data" refers to data that includes user usage history and attribute information related to online services.

[1086] "Second profile data" refers to data that includes user usage history and contract information related to mobile communication services.

[1087] "Integrated profile data" refers to data containing comprehensive user profile information, generated by integrating the first profile data and the second profile data.

[1088] An "emotion engine" is an engine that analyzes and identifies the emotional state of a user from their voice input or text messages.

[1089] "Generative artificial intelligence" refers to artificial intelligence models that automatically generate questions and answers or text based on input data.

[1090] "Question and answer data" refers to data of questions and answers generated by generative artificial intelligence.

[1091] This invention relates to a system that receives user identification information, integrates different profile data, and generates and provides individually optimized question-and-answer responses using an emotion engine and generative artificial intelligence. This system is realized through the cooperation of a server, a terminal, and an emotion engine.

[1092] Overall system configuration

[1093] The system consists of the following hardware and software.

[1094] Server: The server is responsible for receiving user identification information, retrieving profile data from the database, and integrating it. Furthermore, it performs processing to run the emotion engine and generative artificial intelligence. Specifically, it uses MySQL or MongoDB for the database and Google Cloud Natural Language API or IBM Watson for the emotion engine.

[1095] Terminal: This is the device operated by the user, which transmits identification information and receives and displays the generated question-and-answer data. Examples include PCs, smartphones, and tablets.

[1096] Emotion Engine: This engine analyzes the emotional state of a user from their voice input or text messages. It utilizes Google Cloud Natural Language API or IBM Watson.

[1097] Generative artificial intelligence: Artificial intelligence that generates question-and-answer responses using integrated profile data and the results of emotion engine analysis as input. For example, it utilizes OpenAI's ChatGPT model.

[1098] System operation

[1099] Receive user identification information

[1100] The server receives identification information sent by the user from the terminal. This identification information is used in each processing step within the system.

[1101] Acquisition of profile data

[1102] Based on the received identification information, the server retrieves primary profile data (usage history and attribute information related to online services) and secondary profile data (usage history and contract information related to mobile communication services) from the database.

[1103] Integration of profile data

[1104] The server integrates the acquired profile data and generates new integrated profile data. ETL tools or dedicated integration algorithms are used for this integration.

[1105] Emotion recognition by an emotion engine

[1106] The emotion engine analyzes text and voice messages sent by the user to recognize their emotional state. The analysis results are sent to the server and used in the next step.

[1107] Generation of Q&A using generative artificial intelligence

[1108] The server takes the integrated profile data and the results of the emotion engine as input and requests processing from a generative artificial intelligence model (such as ChatGPT). For example, the prompt statement can be created as follows:

[1109] "Generate a Q&A based on the user's profile data and emotional state. The user's profile data is '10 logins in the past year,' and the contract information is '12-month contract, monthly fee of 5000 yen.' The user's emotional state is dissatisfied, and the text message says 'I'm disappointed with the recent service.'"

[1110] Output of Q&A data

[1111] The server sends the generated Q&A data to the user's terminal for the user to review. For example, Q&A data such as "You seem dissatisfied with the service; what specific improvements would you like to see?" is generated and displayed on the user's terminal.

[1112] Specific example

[1113] For example, suppose a user has the identifier "Y12345". This user's first profile data is "logged in 10 times in the past year", and the second profile data is "contract period 12 months, monthly fee 5000 yen". If the user enters "I'm disappointed with the recent service", the emotion engine analyzes the dissatisfaction and generates a Q&A based on that. In this way, the system of the present invention can provide a Q&A based on the user's overall profile and emotional state.

[1114] This personalizes the user experience and improves satisfaction.

[1115] The flow of the specific processing in Example 2 will be explained using Figure 13.

[1116] Step 1: Receive user identification information

[1117] The server receives identification information sent by the user from their terminal. This identification information is sent via REST API or socket communication.

[1118] Input: User identification information (e.g., User ID)

[1119] Output: User identification information stored in memory

[1120] Specific operation: The server receives an HTTP request from the terminal, extracts identification information including the user ID, and stores it in memory.

[1121] Step 2: Obtain profile data

[1122] Based on the received identification information, the server retrieves primary profile data (usage history and attribute information related to online services) and secondary profile data (usage history and contract information related to mobile communication services) from the database.

[1123] Input: User identification information stored in memory

[1124] Output: First profile data and second profile data (in JSON format) retrieved from the database.

[1125] Specific operation: The server executes queries against MySQL or MongoDB and retrieves the necessary profile data based on the user ID. The retrieved data is stored in memory in JSON format.

[1126] Step 3: Integrating profile data

[1127] The server integrates the acquired profile data and generates new integrated profile data. ETL tools or dedicated integration algorithms are used for this integration.

[1128] Input: First profile data and second profile data (JSON format)

[1129] Output: Integrated profile data (JSON format containing integrated profile information)

[1130] Specific operation: The server merges the first and second profile data while checking data integrity, and generates new integrated profile data. The generated data is held in memory.

[1131] Step 4: Emotion recognition by the emotion engine

[1132] The server receives text and voice messages sent by users, sends them to the emotion engine for analysis, and so on.

[1133] Input: User's text messages or voice messages

[1134] Output: Emotion engine analysis results (data including the user's emotional state)

[1135] Specific operation: The server sends input data to speech recognition APIs and text analysis APIs, analyzes the emotional state, receives the results, and stores them in memory.

[1136] Step 5: Generation of Q&A using generative artificial intelligence

[1137] The server inputs integrated profile data and emotion engine results into a generative artificial intelligence model (such as ChatGPT) to generate question and answer responses.

[1138] Input: Integrated profile data and sentiment engine results

[1139] Output: Question and answer data (generated questions and answers)

[1140] Specific operation: The server generates appropriate prompt statements and sends them to a generative artificial intelligence model. For example, it might use a prompt statement like this: "Generate a question and answer based on profile data and emotional state. The user's profile data is 'logged in 10 times in the past year,' and the contract information is 'contract period 12 months, monthly fee 5000 yen.' The user's emotional state is dissatisfied, and the text message says 'I'm disappointed with the recent service.'" The generated question and answer data is stored in memory.

[1141] Step 6: Output of Q&A data

[1142] The server sends the generated question-and-answer data to the user's terminal. HTTP protocol or WebSocket is used for transmission.

[1143] Input: Question and answer data

[1144] Output: Question and answer data displayed on the user's device.

[1145] Specific operation: The server sends question and answer data to the terminal for the user to review. On the user's terminal, the received data is displayed, the user reviews it, and sends further questions or feedback as needed.

[1146] (Application Example 2)

[1147] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1148] Traditional content delivery services recommend content based on users' usage history and basic attribute information, but this alone makes it difficult to provide personalized recommendations that appropriately reflect users' emotional states and instantaneous needs. Furthermore, there is a need for a method to generate more detailed user profiles by integrating usage history from multiple different services, and to achieve highly accurate content recommendations based on those profiles.

[1149] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[1150] In this invention, the server includes means for receiving user identification information, means for acquiring first profile data, means for acquiring second profile data, means for integrating the first and second profile data to generate integrated profile data, means for inputting the integrated profile data and emotion recognition results into a generative artificial intelligence system to generate question-and-answer data, means for outputting the question-and-answer data, and means for recommending individually optimized content based on the integrated profile data and emotional state. This enables more personalized content recommendations based on the user's overall profile and emotional state.

[1151] "User identification information" refers to information used to uniquely identify a user.

[1152] "Primary profile data" refers to data that includes usage history and user attribute information related to online services.

[1153] "Second profile data" refers to data that includes usage history and contract information related to mobile communication services.

[1154] "Integrated profile data" refers to a comprehensive user profile generated by integrating the first profile data and the second profile data.

[1155] "Emotion recognition results" refer to data indicating the user's emotional state, analyzed from the user's input voice or text message.

[1156] "Generative artificial intelligence" refers to an artificial intelligence system that generates question-and-answer data based on integrated profile data and emotion recognition results.

[1157] "Question and answer data" refers to data generated by generative artificial intelligence that includes answers to user questions and additional questions.

[1158] "Personalized optimization" refers to providing the most appropriate responses and recommendations to each user based on their specific profile and emotional state.

[1159] "Content" is a general term for information or entertainment provided to users, such as movies, dramas, music, and articles.

[1160] "Recommendation methods" refer to methods or systems for selecting and presenting content that is appropriate for a user based on integrated profile data and emotional state.

[1161] This invention relates to a system that receives user identification information, integrates different profile data, and generates and provides individually optimized question-and-answer sessions and content recommendations using an emotion engine and generative artificial intelligence. This system integrates the usage history of online services and mobile communication services based on the user's identification information and recommends optimal content based on the user's emotional state.

[1162] First, the server receives user identification information. This identification information is necessary to uniquely identify the user and is used to obtain profile data, including the user's online service and mobile communication service usage history. Based on the identification information, the server obtains usage history data and attribute information (first profile data) related to online services. Similarly, based on the identification information, it also obtains usage history data and contract information (second profile data) related to mobile communication services.

[1163] Next, the server integrates the acquired first and second profile data to generate integrated profile data. This allows the user's overall attributes and usage history to be consolidated into a single profile data.

[1164] Furthermore, the server uses an emotion engine for emotion recognition. The emotion engine analyzes the user's input voice and text messages to recognize the user's emotional state (e.g., joy, sadness, surprise). This emotion recognition result, along with the user's integrated profile data, is then input into the generative artificial intelligence.

[1165] Generative artificial intelligence generates question-and-answer data tailored to the user based on integrated profile data and emotion recognition results. This question-and-answer data is individually optimized based on the user's specific usage situation and emotional state. As a result, users can receive appropriate answers and additional questions that match their emotional state.

[1166] Furthermore, this system recommends suitable content based on the user's integrated profile data and emotional state. For example, if a user enters "I've been feeling sad lately," the emotion engine recognizes "sadness" as an emotional state, and based on that, the generative artificial intelligence recommends "inspirational movies" or "encouraging articles" to the user.

[1167] Specific example:

[1168] For example, suppose a user has the identification information "Y12345". This user's first profile data is their online service usage history, which includes "10 logins in the past year," and their second profile data is their mobile communication service usage history, which includes "contract period of 12 months, monthly fee of 5000 yen." The server receives this user's identification information, retrieves the first and second profile data, and integrates them.

[1169] The emotion engine analyzes a text message entered by the user, for example, "I've been feeling sad lately," and recognizes that the user is experiencing the emotion of "sadness." Based on this, the generative artificial intelligence recommends optimized content such as the following:

[1170] A moving film

[1171] Encouraging articles

[1172] Here are some examples of prompts to input into a generative AI model:

[1173] "Based on integrated user profile data, recommend content that is most suitable for users experiencing sadness. Simultaneously, recommend content that helps shift their emotions to a more positive state."

[1174] Thus, the system of the present invention can provide highly accurate, personalized question-and-answer sessions and content recommendations based on the user's overall profile and emotional state.

[1175] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[1176] Step 1:

[1177] The server receives user identification information. A user ID (identification information) is provided as input and sent to the server. The server uses this identification information to retrieve subsequent profile data.

[1178] Step 2:

[1179] The server retrieves online service usage history data and user attribute information (first profile data) based on the aforementioned identification information. Specifically, it uses the identification information to search the database for relevant usage history and attribute information and retrieves it. The output is the first profile data.

[1180] Step 3:

[1181] The server retrieves mobile communication service usage history data and contract information (second profile data) based on the aforementioned identification information. Specifically, it uses the identification information to search the mobile communication service database for relevant usage history and contract information and retrieves it. The output is the second profile data.

[1182] Step 4:

[1183] The server integrates the first and second profile data to generate integrated profile data. Specifically, it merges this data in dictionary or key-value format, and integrates duplicate data according to unified rules. The output is the integrated profile data.

[1184] Step 5:

[1185] The server inputs the user's voice or text message into the emotion engine and obtains an emotion recognition result. Specifically, it sends the user's voice or text data to the emotion recognition engine, which analyzes the emotional state and obtains a classification result (e.g., joy, sadness, etc.). The output is the emotion recognition result.

[1186] Step 6:

[1187] The server inputs integrated profile data and emotion recognition results into a generative artificial intelligence (AI) system to generate question-and-answer data. Specifically, it passes integrated profile data and emotion recognition results to the AI ​​system, which then uses this information to generate the most appropriate answer or additional questions in response to the user's questions. The output is question-and-answer data.

[1188] Step 7:

[1189] The server sends question-and-answer data to the user's terminal and displays it to the user. Specifically, it converts the question-and-answer data into an appropriate format (text message or audio) and sends it to the user's terminal. The output is the question-and-answer content displayed on the user's terminal.

[1190] Step 8:

[1191] The server uses generative artificial intelligence to recommend optimal content based on integrated profile data and emotional states. Specifically, it provides integrated profile data and emotion recognition results to the generative AI, which then selects and recommends the most suitable content (movies, music, articles, etc.) for the user. The output is the recommended content.

[1192] Step 9:

[1193] The server sends the recommended content to the user's terminal and displays it to the user. Specifically, it converts the recommended content information into an appropriate format (such as links and thumbnail information) and sends it to the user's terminal. The output is the recommended content displayed on the user's terminal.

[1194] Through this series of steps, users can receive personalized inquiry responses and content recommendations based on their emotional state and profile.

[1195] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[1196] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1197] 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 this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[1198] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1199] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[1200] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[1201] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[1202] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[1203] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[1204] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[1205] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[1206] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[1207] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[1208] 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.

[1209] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[1210] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[1211] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[1212] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[1213] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[1214] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[1215] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[1216] The following is further disclosed regarding the embodiments described above.

[1217] (Claim 1)

[1218] Means for receiving user identification information,

[1219] Means for acquiring first profile data based on the aforementioned identification information,

[1220] Means for obtaining second profile data based on the aforementioned identification information,

[1221] Means for integrating the first profile data and the second profile data to generate integrated profile data,

[1222] A means for inputting the aforementioned integrated profile data into a generative artificial intelligence system to generate question and answer data,

[1223] means for outputting the aforementioned question and answer data,

[1224] A system that includes this.

[1225] (Claim 2)

[1226] The system according to claim 1, wherein the first profile data includes online service usage history data.

[1227] (Claim 3)

[1228] The system according to claim 1, wherein the second profile data includes mobile communication service usage history data.

[1229] "Example 1"

[1230] (Claim 1)

[1231] Means for receiving user identification information,

[1232] Means for acquiring first profile data based on the aforementioned identification information,

[1233] Means for obtaining second profile data based on the aforementioned identification information,

[1234] Means for integrating the first profile data and the second profile data to generate integrated profile data,

[1235] A means for inputting the aforementioned integrated profile data into a generative artificial intelligence system and generating question and answer data based on the integrated profile,

[1236] means for outputting the aforementioned question and answer data,

[1237] A system that includes this.

[1238] (Claim 2)

[1239] The system according to claim 1, wherein the first profile data includes online service usage history data and attribute information.

[1240] (Claim 3)

[1241] The system according to claim 1, wherein the second profile data includes mobile communication service usage history data and contract information.

[1242] "Application Example 1"

[1243] (Claim 1)

[1244] Means for receiving user identification information,

[1245] Means for acquiring first profile data based on the aforementioned identification information,

[1246] Means for obtaining second profile data based on the aforementioned identification information,

[1247] Means for integrating the first profile data and the second profile data to generate integrated profile data,

[1248] A means for inputting the aforementioned integrated profile data into a generative artificial intelligence system to generate question and answer data,

[1249] A means for outputting the aforementioned question and answer data to the user's terminal and recommending content,

[1250] A system that includes this.

[1251] (Claim 2)

[1252] The system according to claim 1, wherein the first profile data includes online service usage history data.

[1253] (Claim 3)

[1254] The system according to claim 1, wherein the second profile data includes mobile communication service usage history data.

[1255] "Example 2 of combining an emotion engine"

[1256] (Claim 1)

[1257] Means for receiving user identification information,

[1258] Means for acquiring first profile data based on the aforementioned identification information,

[1259] Means for obtaining second profile data based on the aforementioned identification information,

[1260] Means for integrating the first profile data and the second profile data to generate integrated profile data,

[1261] A means for inputting the integrated profile data and the results of an emotion engine for analyzing the user's emotional state into a generative artificial intelligence system to generate question-and-answer data,

[1262] means for outputting the aforementioned question and answer data,

[1263] A system that includes this.

[1264] (Claim 2)

[1265] The system according to claim 1, wherein the first profile data includes online service usage history data.

[1266] (Claim 3)

[1267] The system according to claim 1, wherein the second profile data includes mobile communication service usage history data.

[1268] "Application example 2 when combining with an emotional engine"

[1269] (Claim 1)

[1270] Means for receiving user identification information,

[1271] Means for acquiring first profile data based on the aforementioned identification information,

[1272] Means for obtaining second profile data based on the aforementioned identification information,

[1273] Means for integrating the first profile data and the second profile data to generate integrated profile data,

[1274] A means for inputting the aforementioned integrated profile data and emotion recognition results into a generative artificial intelligence system to generate question and answer data,

[1275] means for outputting the aforementioned question and answer data,

[1276] A means for recommending individually optimized content based on the aforementioned integrated profile data and emotional state,

[1277] A system that includes this.

[1278] (Claim 2)

[1279] The system according to claim 1, wherein the first profile data includes online service usage history data.

[1280] (Claim 3)

[1281] The system according to claim 1, wherein the second profile data includes mobile communication service usage history data. [Explanation of symbols]

[1282] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. Means for receiving user identification information, Means for acquiring first profile data based on the aforementioned identification information, Means for obtaining second profile data based on the aforementioned identification information, Means for integrating the first profile data and the second profile data to generate integrated profile data, A means for inputting the aforementioned integrated profile data into a generative artificial intelligence system to generate question and answer data, means for outputting the aforementioned question and answer data, A system that includes this.

2. The system according to claim 1, wherein the first profile data includes online service usage history data.

3. The system according to claim 1, wherein the second profile data includes mobile communication service usage history data.

Images