system

Abstract

Provide a system. 【Solution means】Means for obtaining the user's profile information, means for obtaining the profile information of the other party, means for analyzing the obtained profile information of the user and the other party and extracting important keywords, means for generating an appropriate message based on the analysis result using generative AI, means for generating candidates for dating destinations based on common interests and concerns, means for presenting the generated message and proposal to the user and obtaining the user's approval, means for sending the message to the other party after the user's approval, means for analyzing the reply from the other party and generating a more appropriate reply message and the next proposal, A system including.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In modern society, matching apps have become a major means for finding an ideal partner. However, due to the complexity of message exchanges and the busy schedule of users, the exchanges often break off. As a result, it is difficult to build a sense of trust and it is difficult to arrange a first date. To solve this problem, there is a need for a system that automates the creation of user messages and the generation of proposals and supports exchanges efficiently and effectively.

Means for Solving the Problems

[0005] The present invention provides a system that includes means for acquiring user profile information, means for acquiring other party profile information, means for analyzing the acquired user and other party profile information and extracting important keywords, means for generating appropriate messages based on the analysis results using generative AI, means for generating date location candidates based on common interests, means for presenting the generated messages and suggestions to the user and obtaining user approval, means for sending the message to the other party after user approval, and means for analyzing the other party's reply and generating even more appropriate reply messages and next date suggestions. This makes it possible for users to exchange messages efficiently, build trust more easily, and lead to a first date.

[0006] A "user" refers to an individual who communicates with others using a matching application.

[0007] "Profile information" refers to personal information about the user and the other party, including, for example, name, occupation, hobbies, interests, and place of residence.

[0008] "Generative AI" refers to artificial intelligence that can automatically generate appropriate messages and suggestions in a natural context based on given data.

[0009] "Analysis" refers to the process of processing acquired data and extracting important keywords and information.

[0010] "Message generation" refers to the means of creating an appropriate message based on the analysis results.

[0011] "Shared interests" refers to the hobbies and areas of interest that a user and the other person share.

[0012] "Suggested date locations" refer to places that are suitable for a user and their date to visit on their first date.

[0013] A "proposal" refers to a specific suggestion made to the user or their partner, based on the generated date location options and common topics of conversation.

[0014] "Message sending" refers to the process of sending a message that a user has approved to a recipient.

[0015] "Reply analysis" refers to the process of analyzing the response message received from the other party and generating the next appropriate message.

[0016] "Continuous interaction" refers to the ongoing communication between a user and another party that follows the initial message. [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] This shows an emotion map where multiple emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Embodiment 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 Embodiment 2 when the 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 the 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 terms used in the following description will be explained.

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

[0021] ​​​​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] The embodiments for carrying out this invention are described below.

[0039] Overall structure

[0040] This system consists of a user terminal, a server, and a generative AI. The server acquires and analyzes user and recipient profile information, and generates messages and suggestions. The terminal receives the generated content from the server, presents it to the user for confirmation, and performs operations such as modification and approval as needed.

[0041] Retrieving profile information

[0042] server

[0043] When a user logs into the matching app, the server requests profile information of their current match. Specifically, it accesses the database to retrieve personal information such as the user's and their match's names, occupations, hobbies, interests, and place of residence. The retrieved profile information is analyzed using natural language processing (NLP) techniques to extract important keywords (e.g., hobbies, interests, place of residence).

[0044] Message generation

[0045] terminal

[0046] The terminal invokes a generative AI based on profile information received from the server. The generative AI then generates an initial message that takes into account tone and length, based on the given profile information.

[0047] Specific example

[0048] For example, if the recipient is an "engineer," a generative AI will generate the following initial message:

[0049] "Hello, I see you work as an engineer. I'm also interested in the technical field. I understand you enjoy watching movies; what movies have you seen recently?"

[0050] Proposal generation

[0051] server

[0052] The server compares the user's and the other person's profile information and analyzes their common interests. Based on this, it generates suggested date locations that match their shared hobbies and interests. For example, if both live in Tokyo and enjoy watching movies, the server will suggest nearby movie theaters.

[0053] Specific example

[0054] "How about watching a movie at a cinema in Odaiba, Tokyo? Afterwards, it would be fun to share our thoughts about the movie at a nearby cafe."

[0055] Confirm and send the message.

[0056] User

[0057] Users review the generated messages and suggestions through their devices. They can edit the message content as needed. Finally, once the user approves the message and presses the send button, the message is sent to the recipient through the matching app.

[0058] Support for ongoing communication

[0059] terminal

[0060] The device receives a response message from the other party and analyzes its content. A generative AI then generates an appropriate reply message based on the response content.

[0061] Specific example

[0062] If the other person replies, "I recently watched a thriller movie," the generative AI will generate a response such as, "Thriller movies, that's great! I also had the chance to watch one recently. What scene was the most memorable for you?" It can also suggest future date plans or new topics of conversation.

[0063] Plan execution

[0064] User

[0065] If the user accepts the proposal and an agreement is reached with the other party, a concrete date plan is created. Finally, the details of the date (date, time, place, meeting place, etc.) are confirmed and notified to the other party.

[0066] Therefore, by using this system, users can efficiently exchange messages, build trust more easily, and ultimately lead to a first date.

[0067] The following describes the processing flow.

[0068] Program processing steps

[0069] Step 1: Obtain profile information

[0070] server

[0071] 1. Profile Request: When a user logs into the dating app, the server requests the profile information of their current match.

[0072] 2. Database Access: The server connects to the matching app's database and retrieves the user's profile information (name, occupation, hobbies, interests, and place of residence) of their potential matches.

[0073] 3. Information Analysis: The acquired profile information is analyzed using natural language processing (NLP) techniques to extract important keywords.

[0074] Step 2: Generating the initial message

[0075] terminal

[0076] 1. Profile reception: The device calls a generative AI based on the profile information it receives from the server.

[0077] 2. Message Generation: The generative AI generates an initial message, taking into account tone and length, based on the given profile information.

[0078] Specific example: Initial message generated by the device: "Hello, Mr. Tanaka. Your work as an engineer is wonderful! I'm also interested in the technical field. I read that you enjoy watching movies; what movies have you seen recently?"

[0079] Step 3: Proposal Generation

[0080] server

[0081] 1. Commonality Analysis: The server compares the user's and the other party's profile information and analyzes common interests.

[0082] 2. Selecting a date location: Based on shared interests and the other person's place of residence (e.g., Tokyo), generate suitable date location options.

[0083] 3. Suggestion Generation: The server generates a suggestion. "Mr. Tanaka, since you seem to enjoy movies, how about going to a movie theater in Odaiba, Tokyo? Afterwards, it would be fun to share our thoughts on the movie at a nearby cafe."

[0084] Step 4: Confirm and send the message.

[0085] User

[0086] 1. Message confirmation: The user reviews the generated messages and suggestions through their device.

[0087] 2. Content Adjustment: Users can also adjust the content of their messages as needed.

[0088] 3. Message Sending: Once the user approves the message and presses the send button, the message is sent to the other party through the matching app.

[0089] Step 5: Support for ongoing communication

[0090] terminal

[0091] 1. Reply received: The device receives a reply message from the other party.

[0092] 2. Reply Analysis: Analyze the received response message and extract important keywords.

[0093] 3. Reply message generation: Generative AI is used to generate appropriate reply messages based on the analysis results.

[0094] For example, if the other person replies, "I recently watched a thriller movie," the generative AI will generate a response like, "Thriller movies, that's great! I also had the chance to watch one recently. What scene left the biggest impression on you?"

[0095] Step 6: Next Proposal

[0096] server

[0097] 1. Next Date Suggestion Generation: The server generates the next date plan and new topic suggestions based on the ongoing interaction.

[0098] Specific example: "How about going to a small film festival being held in Jiyugaoka this weekend?"

[0099] Step 7: Execute the plan

[0100] User

[0101] 1. Proposal Confirmation: The user reviews the proposed plan, and if they agree with the other party, they proceed to plan the actual date.

[0102] 2. Final confirmation: Confirm the date details (date, time, location, meeting place, etc.) and notify the other person.

[0103] This allows users to exchange messages efficiently, build trust more easily, and potentially lead to a first date.

[0104] (Example 1)

[0105] 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."

[0106] In traditional dating apps, users often spend time and effort crafting their initial messages and date proposals, which can make effective communication difficult. Similar problems arise when considering appropriate replies and future date suggestions. This makes it difficult for users to build trust and hinders smooth communication.

[0107] 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.

[0108] In this invention, the server includes means for authenticating user identification information, means for acquiring user profile information, means for acquiring the other party's profile information, means for analyzing the acquired user and other party profile information and extracting important keywords, means for generating appropriate messages based on the analysis results using a generative AI model, means for generating date destination candidates based on common interests, means for presenting the generated messages and suggestions to the user and obtaining user approval, means for sending the message to the other party after user approval, and means for analyzing the other party's reply and generating even more appropriate reply messages and next suggestions using a generative AI model. This makes it possible for users to efficiently exchange impressive messages and even propose dates.

[0109] "User identification information" refers to information used to identify a user, and is used for login authentication, etc.

[0110] "Profile information" refers to personal information about the user and the other party, including name, occupation, hobbies, interests, and place of residence.

[0111] A "generative AI model" is an algorithm that uses artificial intelligence technology to generate text, and includes natural language processing technology.

[0112] "Natural language processing" is a technology for analyzing, understanding, and generating data from text, including keyword extraction and sentiment analysis.

[0113] "Key keywords" are major words and phrases extracted from profile information that are related to the user's and the other person's hobbies and interests.

[0114] A "message" is text generated by a generative AI model and exchanged between a user and another party.

[0115] "Date destination suggestions" are places to go on a date that are proposed based on shared interests and preferences.

[0116] "Approval" refers to the act of a user reviewing and agreeing to the generated message or proposal.

[0117] A "reply message" is text generated in response to a message sent by the other party.

[0118] "Next suggestion" refers to a suggestion regarding the next interaction or date, and is related information generated along with the reply message.

[0119] The embodiments for carrying out this invention are described below.

[0120] Overall structure

[0121] This system consists of a user terminal, a server, and a generative AI. The server acquires and analyzes user and recipient profile information, and generates messages and suggestions. The terminal receives the generated content from the server, presents it to the user for confirmation, and performs operations such as modification and approval as needed.

[0122] Hardware and software to be used

[0123] Device: Use a smartphone or personal computer.

[0124] Server: A server computer with high-speed processing capabilities is used to perform tasks such as acquiring and analyzing profile information and generating dating suggestions.

[0125] Generative AI: Uses a generative AI model (e.g., ChatGPT® from OpenAI®) and natural language processing technology (e.g., spaCy, NLTK).

[0126] Retrieving profile information

[0127] When a user logs into the matching app, the server performs the following actions: It authenticates the user's identification information and accesses the database to retrieve the user's and their potential matches' profile information. This information includes name, occupation, hobbies, interests, and place of residence. The retrieved information is temporarily stored.

[0128] example:

[0129] When the server receives a login event, it executes a database query to retrieve profile information corresponding to the user's ID.

[0130] Analysis of profile information

[0131] The server analyzes the acquired profile information using natural language processing (NLP) techniques. It extracts important keywords (hobbies, interests, place of residence, etc.) and prepares them to be passed to a generative AI.

[0132] example:

[0133] We will use an NLP library (e.g., spaCy, NLTK) to extract important keywords from text data.

[0134] Message generation

[0135] The terminal calls a generative AI based on keywords received from the server to generate the initial message. The generated message is then sent to the server.

[0136] example:

[0137] The device receives keywords such as "engineer" and "watching movies" and generates a message like, "Hello, I see you work as an engineer. I'm also interested in technology. You mentioned you enjoy watching movies; what movies have you seen recently?"

[0138] Confirm and send the message.

[0139] The user reviews the message generated through their device and makes any necessary corrections. After approving the message and pressing the send button, the message is sent to the recipient.

[0140] example:

[0141] The user reads the message displayed on their device screen, adds or modifies it with something like "I like movies too," and finally presses the send button.

[0142] Generating a reply message

[0143] The device receives the reply message from the other party and analyzes its content. Based on the analysis results, it calls a generative AI to generate an appropriate reply message. After that, it presents the new message to the user.

[0144] example:

[0145] If the other person replies, "I recently watched a thriller movie," the system will generate a reply such as, "Thriller movies are great! I also had the chance to watch one recently. What scene left the biggest impression on you?"

[0146] Generating date proposals

[0147] The server compares the user's and the other person's profile information and generates suggested date locations based on shared interests. The generated suggestions are then sent to the user.

[0148] example:

[0149] The system generates a suggestion: "How about watching a movie at a cinema in Odaiba, Tokyo? Afterwards, it would be fun to share our thoughts about the movie at a nearby cafe."

[0150] Plan execution

[0151] The user accepts the proposal, and once an agreement is reached with the other party, they create a detailed date plan. Finally, they notify the other party of the details such as the date, time, place, and meeting place.

[0152] example:

[0153] The message reads, "Let's meet at the movie theater in Odaiba at 2 PM on March 20th."

[0154] Examples of prompts for generative AI models

[0155] "Generate the initial message based on the user's profile information."

[0156] "Please create a date proposal based on the other person's profile."

[0157] This system allows users to exchange messages efficiently, build trust more easily, and ultimately lead to a first date.

[0158] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0159] Step 1: User login and retrieval of profile information

[0160] When a user logs into the matching app, the server authenticates the user's identity. If authentication is successful, the following processes are performed.

[0161] Input: User ID, Password

[0162] Data processing: Verify the user ID and password on the authentication server.

[0163] Output: If the user ID is valid, retrieve the user and the other party's profile information from the database.

[0164] Specific operation: The server accesses the database and retrieves and temporarily stores information such as the user's and the other party's names, occupations, hobbies, interests, and place of residence.

[0165] Step 2: Analyze profile information

[0166] The server analyzes the acquired user and other party profile information using natural language processing (NLP) technology.

[0167] Input: User and other party's profile information

[0168] Data processing: Use NLP techniques to extract important keywords from text data.

[0169] Output: Extracted key keywords (e.g., hobbies, interests, place of residence)

[0170] Specific operation: The server uses an NLP library (e.g., spaCy, NLTK) to extract keywords from the text data and prepare them to be passed to the generative AI.

[0171] Step 3: Generating the initial message

[0172] The terminal calls a generative AI based on keywords received from the server and generates the initial message.

[0173] Input: Important keywords

[0174] Data processing: Input prompts and keywords into a generative AI model to generate appropriate messages.

[0175] Output: First message

[0176] Specific operation: The terminal inputs the following prompt to the generative AI: "Please generate an initial message based on keywords such as 'engineer' and 'movie watching'." The generative AI model then generates a message such as, "Hello, I see you work as an engineer. I'm also interested in technology. You mentioned you enjoy watching movies; what movies have you seen recently?"

[0177] Step 4: Confirm and send the message.

[0178] The user reviews the message generated through their device and makes any necessary corrections. They then approve the message and press the send button.

[0179] Input: Initial message generated

[0180] Data processing: The user reads the message, modifies it as needed, and finally approves it.

[0181] Output: Approved message

[0182] Specific actions: The user reads the message displayed on the device screen, adds or modifies content such as "I like movies too," and finally presses the send button.

[0183] Step 5: Generating a reply message

[0184] The device receives the reply message from the other party and analyzes its content. Based on the analysis results, it calls a generative AI to generate an appropriate reply message.

[0185] Input: Reply message from the other party

[0186] Data processing: The response content is analyzed by a generative AI to generate an appropriate response.

[0187] Output: Reply message

[0188] Specific operation: The device analyzes the message from the other party, "I recently watched a thriller movie," and generates a reply saying, "Thriller movies are great! I also had the chance to watch one recently. What scene was the most memorable for you?"

[0189] Step 6: Generating Date Proposals

[0190] The server compares the user's and the other person's profile information and generates suggested date locations based on shared interests.

[0191] Input: User and other party's profile information, common keywords

[0192] Data processing: Generate date suggestions based on shared interests and preferences.

[0193] Output: Dating proposals

[0194] Specific operation: The server generates a suggestion such as, "How about watching a movie at a movie theater in Odaiba, Tokyo? Afterwards, it would be fun to share our thoughts about the movie at a nearby cafe." based on common points such as "movie" and "Tokyo."

[0195] Step 7: Execute the plan

[0196] The user accepts the proposal, and once they reach an agreement with the other party, they create a concrete plan for the date.

[0197] Input: Date suggestion

[0198] Data processing: Based on the proposal, finalize the details of the date.

[0199] Output: Confirmed date details (date, time, location, meeting place, etc.)

[0200] Specific action: The user agrees to the proposal, decides on a specific date and time with the other party, and notifies them, "Let's meet at the movie theater in Odaiba at 2 PM on March 20th."

[0201] (Application Example 1)

[0202] 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."

[0203] In modern streaming services, a challenge exists in that users spend time and effort searching for content that matches their interests and preferences. The lack of personalized recommendation systems makes it difficult for users to select appropriate content from a large selection. Furthermore, the insufficient personalized communication and messaging to users hinders the improvement of the user experience.

[0204] 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.

[0205] In this invention, the server includes means for acquiring user profile information, extracting important keywords, and generating content recommendations for the user; means for generating personalized messages based on viewing history and profile information using generative AI and presenting them to the user; and means for further generating appropriate reply messages and next-time suggestions. This enables users to efficiently find content that matches their interests and preferences, thereby improving the user experience.

[0206] "User profile information" refers to personal information including the user's occupation, hobbies, interests, place of residence, and viewing history.

[0207] "Key keywords" are words that indicate the user's characteristics and interests, extracted from profile information using natural language processing technology.

[0208] "Generative AI" refers to artificial intelligence technology that generates appropriate messages or content based on given input data.

[0209] A "personalized message" is a message tailored to a specific user based on their profile information and viewing history.

[0210] "Viewing history" refers to a list of movies, TV shows, and other content that a user has watched so far.

[0211] "Content recommendation" refers to suggesting video content and programs that users are likely to be interested in, based on their profile information and viewing history.

[0212] A "message with adjusted tone and length" is a message whose tone and length have been appropriately adjusted to accommodate the different communication styles of various users.

[0213] A description of embodiments for carrying out this invention will be given.

[0214] Overall structure

[0215] This system consists of a user terminal, a server, and a generative AI. The terminal is responsible for presenting the user with personalized messages and content recommendations based on the user's profile information and viewing history.

[0216] Retrieving user profile information

[0217] server

[0218] When a user logs into a streaming service, the server retrieves the user's profile information and viewing history from the database. Specifically, it accesses the database and collects information such as the user's occupation, hobbies, interests, and place of residence, along with a history of the content they have watched.

[0219] Profile information analysis and keyword extraction

[0220] server

[0221] The acquired profile information is analyzed using natural language processing (NLP) techniques. This analysis extracts important keywords that indicate the user's hobbies and interests. NLP libraries such as SpaCy and NLTK are used for this process.

[0222] Content recommendation and personalized message generation

[0223] server

[0224] The server uses generative AI models (e.g., OpenAI GPT-4®) to generate personalized messages and content recommendations based on the user's profile information and viewing history. The generated messages are optimized in tone and length and presented to the user in an appropriately tailored format.

[0225] Specific examples of recommendations

[0226] For example, if a user's occupation is an engineer and their hobby is watching suspense movies, the following recommendation message will be generated:

[0227] "Based on your profile information and viewing history, we'll suggest movies and TV shows that you might enjoy. Recent suspense films you've seen include 'Inception' and 'The Matrix,' and you've also shown interest in dystopian novels, right? Based on that, we highly recommend checking out the new suspense film, 'Tenet.'"

[0228] Presentation of messages and recommendations

[0229] terminal

[0230] The user's device receives personalized messages and content recommendations sent from the server and presents them to the user. The user can review these and select content that interests them.

[0231] Examples of specific prompt statements to use

[0232] "User profile information: An engineer who has always loved suspense movies, and recently enjoys reading dystopian novels. Movies he has seen include 'Inception,' 'The Matrix,' and 'Blade Runner.' Based on this, please suggest some movies and TV shows."

[0233] By using this system, users can efficiently find content that matches their interests and preferences, improving their experience using streaming services. The above describes the embodiment of this invention.

[0234] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0235] Step 1: Obtain user profile information and viewing history.

[0236] When a user logs into a streaming service, the server retrieves the user's occupation, hobbies, interests, place of residence, and viewing history from the database. The server issues queries to the database and collects the user's personal information and viewing history based on these queries. The input data is the user ID, and the output data is the user's profile information and viewing history.

[0237] Step 2: Profile information analysis and keyword extraction

[0238] The server analyzes the acquired profile information using natural language processing (NLP) techniques. Specifically, it uses an NLP library (e.g., SpaCy or NLTK) to extract important keywords that indicate the user's hobbies and interests. The input data is profile information, and the output data is important keywords.

[0239] Step 3: Content Recommendation Generation

[0240] The server uses a generative AI model (e.g., OpenAI GPT-4) to generate content recommendations based on extracted keywords and viewing history. The server generates prompts and provides them as input to the generative AI model. Based on these prompts, the AI ​​generates a message recommending content suitable for the user. The input data consists of keywords and viewing history, and the output data is a message recommending content.

[0241] Step 4: Generating Personalized Messages

[0242] The server uses generative AI to generate personalized messages based on the user's profile information and viewing history. The generated messages are optimized in terms of tone and length to match the user's communication style. The input data is profile information and viewing history, and the output data is the personalized message.

[0243] Step 5: Presenting messages and recommendations

[0244] The device receives personalized messages and content recommendations sent from the server and presents them to the user. The user can then select content of interest based on the presented messages and recommendations. The input data consists of messages and recommendations from the server, while the output data is the content presented to the user.

[0245] Step 6: Analyze the user's response

[0246] The server analyzes the content and response messages selected by the user. This analysis provides feedback data for future recommendations and message generation. The input data consists of the user's selections and response messages, while the output data is feedback for generating future suggestions.

[0247] Step 7: Generating the next proposal

[0248] The server generates content recommendations and personalized messages based on the user's previous responses and selections. The server uses generative AI to generate these suggestions and messages and sends them to the device. Input data is feedback data, and output data is the next suggestions and messages.

[0249] This allows users to efficiently find content that matches their interests and preferences, improving their streaming service experience.

[0250] 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.

[0251] The embodiments for carrying out this invention will be described in detail, divided into each processing step.

[0252] Overall structure

[0253] This system consists of a user terminal, a server, a generative AI, and an emotion engine. The server acquires and analyzes user and recipient profile information and generates messages and suggestions. The terminal receives the generated content from the server, presents it to the user for confirmation, and allows for modification and approval as needed. The emotion engine recognizes the user's emotions in real time and adjusts appropriate message generation and suggestions based on that information.

[0254] Retrieving profile information

[0255] server

[0256] When a user logs into the matching app, the server requests profile information of their current match. Specifically, it accesses the database to retrieve personal information such as the user's and the other person's names, occupations, hobbies, interests, and place of residence. The retrieved profile information is analyzed using natural language processing (NLP) techniques to extract important keywords (e.g., hobbies, interests, place of residence).

[0257] Message generation

[0258] terminal

[0259] The device invokes a generative AI based on profile information received from the server. The generative AI generates an initial message that takes into account tone and length, based on the given profile information and the user's emotional information obtained from the emotion engine.

[0260] Specific example

[0261] For example, if the other party is an "engineer" and the user's emotion engine detects a relaxed emotion, the generative AI will generate an initial message like this:

[0262] "Hello, I see you work as an engineer. I'm also interested in technology. I read that you enjoy watching movies; what movies have you seen recently?"

[0263] Proposal generation

[0264] server

[0265] The server compares the user's and the other person's profile information and analyzes their common interests. It also considers the user's current emotional state, as determined by the emotion engine, to generate suggested date locations that align with shared hobbies and interests. For example, if both users live in Tokyo, enjoy watching movies, and are in a relaxed state, the server might suggest a nearby movie theater.

[0266] Specific example

[0267] "How about watching a movie at a cinema in Odaiba, Tokyo? Afterwards, it would be fun to share our thoughts about the movie at a nearby cafe."

[0268] Confirm and send the message.

[0269] User

[0270] Users review the generated messages and suggestions through their devices. They can adjust the message content themselves as needed. Finally, once the user approves the message and presses the send button, the message is sent to the recipient through the matching app.

[0271] Support for ongoing communication

[0272] terminal

[0273] The device receives the response message from the other party and analyzes its content. Furthermore, the emotion engine analyzes the user's emotional state, and based on that, the generative AI generates the next appropriate reply message.

[0274] Specific example

[0275] If the other party replies with "I watched a thriller movie recently", the emotion engine detects the user's excited emotion, and the generative AI generates "Thriller movies are wonderful! I also had a chance to watch one recently. Which scene impressed you the most?"

[0276] Generation of the next proposal

[0277] Server

[0278] Based on continuous conversations, the server generates the next date plan and new topic proposals. The emotion engine evaluates the user's stress level and satisfaction, and adjusts the next proposal based on the evaluation results.

[0279] Specific example

[0280] If the emotion engine evaluates that the user is in a relaxed state, "How about going to a small film festival held in Jiyugaoka this weekend?"

[0281] Implementation of the plan

[0282] User

[0283] If the user accepts the proposal and reaches an agreement with the other party, a specific date plan is made. Finally, the details of the date (date, time, meeting place, etc.) are confirmed and notified to the other party.

[0284] By using this system as described above, the user can efficiently conduct message exchanges, and appropriate proposals according to the user's emotional state are made, making it easier to build a sense of trust and leading to a first date.

[0285] The processing flow will be described below.

[0286] Processing steps of the program

[0287] Step 1: Obtain profile information

[0288] server

[0289] 1. Profile Request: When a user logs into the dating app, the server requests the profile information of their current match.

[0290] 2. Database Access: The server connects to the matching app's database and retrieves the user's profile information (name, occupation, hobbies, interests, and place of residence) of their potential matches.

[0291] 3. Information Analysis: The acquired profile information is analyzed using natural language processing (NLP) techniques to extract important keywords.

[0292] Step 2: Recognizing the user's emotions

[0293] terminal

[0294] 1. Acquisition of emotional data: The device uses an emotion engine to recognize the user's emotional state. For example, it analyzes facial expressions and voice tone using the camera and microphone.

[0295] 2. Emotional Data Transmission: The acquired emotional data is sent to the server.

[0296] Step 3: Generating the initial message

[0297] server

[0298] 1. Profile reception: The server integrates profile information and sentiment data received from the terminal and database.

[0299] 2. Message Generation: The generative AI generates an initial message that takes into account tone and length, based on profile information and sentiment data.

[0300] Specific example

[0301] For example, when the other party is an "engineer" and the user's emotion engine detects a relaxed emotion, the generative AI generates the following initial message:

[0302] "Hello, you're working as an engineer. I'm also interested in the technical field. It says you like watching movies. What movies have you watched recently?"

[0303] Step 4: Proposal generation

[0304] Server

[0305] 1. Common point analysis: The server compares the profile information of the user and the other party and analyzes the common interests and concerns.

[0306] 2. Date location selection: Based on the common interests and the other party's place of residence (e.g., Tokyo), generate candidates for appropriate date locations.

[0307] 3. Proposal text generation: The server generates a proposal text. "Mr. Tanaka, since you seem to like movies, how about going to the movie theater in Odaiba, Tokyo? After that, it might be fun to share our thoughts on the movie at a nearby café."

[0308] Step 5: Message confirmation and sending

[0309] User

[0310] 1. Message confirmation: The user checks the generated message and proposal through the terminal.

[0311] 2. Content adjustment: If necessary, the user can also adjust the content of the message by themselves.

[0312] 3. Message sending: When the user approves the message and presses the send button, the message is sent to the other party through the matching app.

[0313] Step 6: Support for ongoing communication

[0314] terminal

[0315] 1. Reply received: The device receives a reply message from the other party.

[0316] 2. Reply Analysis: Analyze the received response message and extract important keywords.

[0317] 3. Emotion Analysis: The emotion engine analyzes the user's emotional state, and based on that, the generative AI generates the next appropriate reply message.

[0318] Specific example

[0319] If the other person replies, "I recently watched a thriller movie," the emotion engine detects the user's excited emotions, and the generative AI generates, "Thriller movies, that's great! I also had the chance to watch one recently. What scene left the biggest impression on you?"

[0320] Step 7: Next Proposal

[0321] server

[0322] 1. Next Date Suggestion Generation: The server generates the next date plan and new topic suggestions based on the ongoing interaction.

[0323] 2. Emotional Evaluation: The emotional engine evaluates the user's stress level and satisfaction level, and adjusts the next suggestion based on the evaluation results.

[0324] Specific example

[0325] If the emotion engine assesses that the user is in a relaxed state, it might suggest, "How about going to a small film festival being held in Jiyugaoka this weekend?"

[0326] Step 8: Execute the plan

[0327] User

[0328] 1. Proposal Confirmation: The user reviews the proposed plan, and if they agree with the other party, they proceed to plan the actual date.

[0329] 2. Final confirmation: Confirm the date details (date, time, location, meeting place, etc.) and notify the other person.

[0330] This allows users to exchange messages efficiently, receive appropriate suggestions tailored to their emotional state, build trust more easily, and ultimately lead to a first date.

[0331] (Example 2)

[0332] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0333] Current online dating apps and communication systems require users to spend a significant amount of time and effort to generate appropriate messages. Furthermore, it's difficult to create optimal suggestions and messages tailored to the user's emotions and situation, often resulting in one-sided communication. Additionally, the lack of features to support continuous interaction makes efficient communication challenging.

[0334] The identification processing by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for acquiring user identification information, means for acquiring the other party's identification information, means for analyzing the acquired user and other party identification information and extracting key keywords, means for generating an appropriate communication message based on the analysis results using generative artificial intelligence, means for generating proposals based on common interests of both parties, means for presenting the generated communication message and proposal to the user and obtaining the user's approval, means for sending the communication message to the other party after the user's approval, and means for analyzing the response from the other party and generating an appropriate reply message and next proposal. As a result, the user can exchange messages efficiently, and appropriate proposals are made according to the user's emotional state, making it easier to build trust and enabling smooth communication.

[0335] "User identification information" refers to information used to identify a user, including login ID, name, and contact information.

[0336] "Other party identification information" refers to information used to identify the person a user has been matched with, and includes the other party's login ID, name, contact information, etc.

[0337] "Key keywords" are important pieces of information extracted from the user's and the other party's profile information, such as hobbies, interests, and place of residence.

[0338] "Generative artificial intelligence" refers to artificial intelligence models that have the ability to generate text messages in natural language based on input data.

[0339] A "communication message" is a text-based message shared between a user and another user, including the initial message and replies.

[0340] "Shared interests" refer to the hobbies and interests that both users share, which are analyzed from their respective profile information.

[0341] A "suggestion" is a suggestion of activities or date plans that can be shared between the user and the other person based on their common interests.

[0342] "Emotional state" refers to information that indicates the current emotional state of the user or the other party, and includes states such as relaxed, excited, and stressed.

[0343] "Approval" is the act of confirming that a user agrees to the content of a generated message or proposal and confirms that they will submit it.

[0344] A "response" is a reply message sent by the other party.

[0345] "Continuous interaction" refers to the act of a user and another party exchanging messages multiple times.

[0346] This invention relates to a system that analyzes user identification information and recipient identification information, and provides appropriate message generation and suggestions using generative artificial intelligence. This system consists of a user terminal, a server, generative artificial intelligence, and an emotion engine.

[0347] Overall system configuration

[0348] This system consists of a user terminal, a server, a generative artificial intelligence system, and an emotion engine. It fully automates the generation of user messages and suggestions, and supports communication tailored to the user's emotional state.

[0349] server

[0350] The server acquires and analyzes user and other party identification information, extracts key keywords, and generates suggestions based on common interests. The acquired information and analysis results are then input into a generative artificial intelligence system.

[0351] terminal

[0352] The terminal presents the user with generated messages and suggestions sent from the server. The user reviews these using the terminal, makes any necessary modifications, and then approves them.

[0353] Generative artificial intelligence

[0354] Generative artificial intelligence generates messages of appropriate tone and length based on given identification and emotional information. Specifically, it uses natural language processing techniques to analyze prompt sentences and generate appropriate responses.

[0355] Emotional Engine

[0356] The emotion engine analyzes the user's emotional state in real time. This information is reflected in the generative artificial intelligence, influencing the tone and content of the generated messages.

[0357] Acquisition and analysis of profile information

[0358] server

[0359] When a user logs into the matching app, the server immediately retrieves the user's and the other person's identification information from the database. Then, using natural language processing (NLP) techniques, this information is analyzed to extract key keywords. An NLP library (e.g., spaCy) is used for this analysis.

[0360] Message generation

[0361] terminal

[0362] The terminal receives the analyzed profile information sent from the server and invokes the generative artificial intelligence. The generative AI then creates a prompt statement based on this information and generates the initial message.

[0363] Specific example

[0364] For example, if the other party is an engineer and the user's emotion engine detects a relaxed emotion, the generative artificial intelligence will generate the following initial message:

[0365] "Hello, I see you work as an engineer. I'm also interested in technology. I read that you enjoy watching movies; what movies have you seen recently?"

[0366] Proposal generation

[0367] server

[0368] The server generates suggestions based on the shared interests of the user and the other party. When generating suggestions, it also considers the user's emotional state, which is obtained from the emotion engine.

[0369] Specific example

[0370] "How about watching a movie at a cinema in Odaiba, Tokyo? Afterwards, it would be fun to share our thoughts about the movie at a nearby cafe."

[0371] Review and send messages and proposals.

[0372] User

[0373] The user reviews the messages and suggestions generated through their device. They modify them as needed, and after final approval, send them to the recipient via the server.

[0374] This system allows users to exchange messages efficiently through automated processes, enabling smoother and more emotionally-driven communication.

[0375] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0376] Step 1: User Login

[0377] User

[0378] The user logs into the matching app. The input is the user's identification information (e.g., user ID and password). The device sends this to the server, and the user is authenticated. The output is the session information provided after the user has been authenticated.

[0379] Step 2: Obtain profile information

[0380] server

[0381] When a user logs in, the server retrieves user and other user identification information from the database. The input is the user's identification information (e.g., User ID). The server issues a query to the database like the following:

[0382] sql

[0383] SELECT Name, Occupation, Hobbies, Interests, Location FROM Profile WHERE User ID = 'USER_ID';

[0384] The output consists of the retrieved user and other party profile information.

[0385] Step 3: Analyze profile information

[0386] server

[0387] The server analyzes the acquired profile information using natural language processing technology (e.g., spaCy). The input is the user's and the other party's profile information. Through analysis, key keywords (e.g., hobbies, interests, place of residence) are extracted. The output is a list of the extracted keywords.

[0388] Step 4: Create a prompt for message generation

[0389] terminal

[0390] The terminal generates prompts for the AI ​​model based on the parsed profile information received from the server. The input consists of the parsed profile information and the user's sentiment information. The prompts are constructed, for example, as follows:

[0391] "The recipient is an engineer, and the user is relaxed. Please generate the initial message."

[0392] The output is a prompt message for the generative AI model.

[0393] Step 5: Generating the initial message

[0394] terminal

[0395] The terminal invokes a generative AI model (e.g., GPT-3®) and generates the initial message using the prompt text as input. The input is the prompt text. The generative AI model generates the initial message based on the given prompt text using natural language processing techniques. The output is the generated initial message.

[0396] Step 6: Confirm the message and proposal

[0397] User

[0398] The user reviews the generated message and suggestions via the terminal. The input consists of the generated message and suggestions presented by the terminal. The user reviews these and makes modifications as needed. The output is the final message approved by the user.

[0399] Step 7: Sending a message

[0400] User

[0401] When the user presses the approve button, the device sends a message to the server. The input is the final message approved by the user. The server sends this message to the recipient using the matching app's messaging API. The output is confirmation that the message was sent to the recipient.

[0402] Step 8: Support for ongoing communication

[0403] terminal

[0404] The device receives the reply message from the other party and analyzes its content. The emotion engine analyzes the user's real-time emotional state, and the generation AI generates the next appropriate reply message. The input is the other party's reply message and the user's emotional information. The output is the generated next reply message.

[0405] Step 9: Generating the next proposal

[0406] server

[0407] The server generates the next date plan and topic suggestions based on the content of the ongoing interaction and feedback from the emotion engine. The input is the content of the interaction and emotion information. The server generates new suggestions based on this. The output is the suggestions for the next date.

[0408] Step 10: Execute the plan

[0409] User

[0410] If the user accepts the proposal and reaches an agreement with the other party, they will then plan a specific date. The input is the agreement between the user and the other party. The user confirms details such as the date, time, place, and meeting place, and then sends a final confirmation to the other party via the device. The output is the finalized date details.

[0411] (Application Example 2)

[0412] 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".

[0413] Traditional workplace communication has not adequately supported improvements in work efficiency and safety. In particular, there is a need for a system that can grasp workers' emotions and fatigue levels in real time and propose appropriate breaks and tasks accordingly. Therefore, balancing worker health and productivity is a key challenge.

[0414] In Application Example 2, the specific processing performed by the specific processing unit 290 of the data processing device 12 is realized by the following means. In this invention, the server includes means for acquiring user profile information, means for acquiring the other party's profile information, means for analyzing the acquired user and other party profile information and extracting important keywords, means for generating an appropriate message based on the analysis results using a generative AI, means for generating dating destination candidates based on common interests, means for presenting the generated message or suggestion to the user and obtaining user approval, means for sending the message to the other party after user approval, means for analyzing the other party's reply and generating an even more appropriate reply message or next suggestion, means for recognizing the emotional state from voice input using an emotion engine and adjusting the content of the dialogue accordingly, and means for generating work suggestions and break suggestions based on the worker's profile information and emotional state in a factory environment. This makes it possible to make appropriate work suggestions and break suggestions in real time based on the worker's profile information and emotional state.

[0415] "Means for obtaining user profile information" refers to devices or methods for collecting personal information such as a user's name, occupation, hobbies, interests, and place of residence.

[0416] "Means of obtaining the other party's profile information" refers to devices or methods for collecting personal information such as the name, occupation, hobbies, interests, and place of residence of a user through matching apps or similar means.

[0417] "Means for extracting important keywords" refers to a device or method that uses natural language processing technology to extract highly relevant keywords such as hobbies and interests from collected profile information.

[0418] "Means for generating appropriate messages using generative AI" refers to a device or method that automatically creates a message that takes into account the tone and length of the conversation, based on given information and using artificial intelligence technology.

[0419] "Means for generating date destination candidates" refers to a device or method that suggests date destinations based on the shared hobbies and interests of the user and their partner.

[0420] "Means of presenting to the user and obtaining user approval" refers to a device or method for displaying generated messages or suggestions to the user and obtaining their approval to review and modify their content.

[0421] "Means of sending a message to another party" refers to a device or method for sending an approved message to another party through a matching app or similar means.

[0422] "Means for analyzing replies from the other party and generating more appropriate reply messages and suggestions for the next step" refers to a device or method that analyzes the content of a reply from the other party and automatically creates appropriate reply messages and suggestions for the next step according to the content and the user's emotional state.

[0423] "Means for recognizing emotional states from voice input using an emotion engine" refers to a device or method that analyzes voice input to recognize the user's emotional state in real time.

[0424] "Means for generating work and rest suggestions based on worker profile information and emotional state in a factory environment" refers to a device or method that makes appropriate work and rest suggestions based on the profile information and emotional state of workers in a factory.

[0425] This invention is a system designed to streamline worker communication in a factory environment and achieve both worker health and productivity. The system consists of a user terminal, a server, a generative AI, and an emotion engine.

[0426] Overall structure

[0427] This system acquires user and recipient profile information, analyzes this information, and extracts important keywords. The generative AI generates and suggests appropriate messages based on this analysis. Furthermore, the emotion engine recognizes the emotional state in real time from voice input and adjusts the message content and tone based on that information.

[0428] Acquisition and analysis of user profile information

[0429] server

[0430] When a user logs into the system, the server retrieves their profile information (name, occupation, hobbies, interests, place of residence, etc.) from the database. The retrieved profile information is then analyzed using natural language processing (NLP) techniques to extract important keywords.

[0431] Recognition of emotional states

[0432] Emotional Engine

[0433] The emotion engine analyzes the worker's voice input to recognize their emotional state in real time. This is done using EmotionRecognizer software. For example, if a worker says "I'm tired," the emotion engine detects the emotion "fatigue."

[0434] Message generation

[0435] Generative AI

[0436] The generative AI generates dialogue messages based on analysis results and emotional states. The GPT-2 model is used here to generate appropriate responses based on prompt sentences.

[0437] For example, if a worker says, "Today's work is tough," the emotion engine detects "fatigue." Based on this information, the generative AI generates a message with the following prompt:

[0438] Prompt message:

[0439] Worker: Engineer, in his 30s, hobby is watching movies.

[0440] Emotional state: Fatigue

[0441] Message: Today's work is tough.

[0442] Robot: I think you should take a short break today. How about resting at a nearby rest area?

[0443] Proposal generation

[0444] server

[0445] The server generates work and break suggestions based on the worker's profile information and emotional state, aligning with their common hobbies and interests. For example, if a worker is an engineer and their hobby is watching movies, the server might suggest, "How about watching a movie at a nearby rest area?"

[0446] Confirm and send the message.

[0447] User

[0448] Users can review the messages and suggestions presented by the system and make modifications as needed. Finally, once the user approves the message and presses the send button, it is sent to the worker.

[0449] Support for continuous communication

[0450] terminal

[0451] The terminal receives a response message from the worker and analyzes its content. The emotion engine analyzes the emotion again, and the generative AI generates the next appropriate message. For example, if the worker says, "I recently watched a movie," the emotion engine detects "excitement," and the generative AI generates a message like this:

[0452] Prompt message:

[0453] Worker: I recently watched a movie.

[0454] Emotional state: Excitement

[0455] Message: That's wonderful! Which scene left the biggest impression on you?

[0456] Through the steps outlined above, this system allows workers to communicate efficiently and receive appropriate breaks and work suggestions. Furthermore, real-time emotional analysis can improve work efficiency and overall well-being.

[0457] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0458] Step 1:

[0459] Retrieve the user's profile information.

[0460] When a worker logs into the system, the server retrieves personal information from the database, such as the worker's name, occupation, hobbies, interests, and place of residence. It then creates a query for the database search and extracts the relevant records. It receives the worker ID as input and obtains profile information as output.

[0461] Step 2:

[0462] Retrieve the other person's profile information.

[0463] The server retrieves the profile information of the worker's counterpart from the database. Here too, database access technology is used to retrieve the counterpart's information. It receives the counterpart's worker ID as input and obtains the counterpart's profile information as output.

[0464] Step 3:

[0465] The acquired information is analyzed, and important keywords are extracted.

[0466] The server analyzes the acquired profile information of the worker and the other party using natural language processing (NLP) techniques to extract important keywords such as hobbies, interests, and occupation. It receives profile information as input and obtains important keywords as output.

[0467] Step 4:

[0468] To recognize emotional states.

[0469] The terminal analyzes the worker's voice input using an EmotionRecognizer and recognizes their emotional state in real time. For example, if the voice input is "Today's work is tough," the emotion engine detects the emotion "fatigue." It receives voice data as input and obtains an emotional state as output.

[0470] Step 5:

[0471] Generate an appropriate message based on the analysis results.

[0472] The generative AI (GPT-2 model) generates messages with adjusted tone and length based on profile information and emotional state. It receives important keywords, emotional state, and an initial message as input, and outputs a generated message.

[0473] Step 6:

[0474] The generated message and proposal are presented to the user, and their approval is obtained.

[0475] The terminal displays the generated messages and suggestions to the worker for review. The worker can then modify or approve the messages. It receives the generated messages as input and the approved messages as output.

[0476] Step 7:

[0477] Send a message to the recipient.

[0478] The terminal sends the approved message to the worker. Here, a communication protocol is used to send the message data. It receives the approved message as input and obtains the message sent to the other party as output.

[0479] Step 8:

[0480] Analyze the response from the other party and generate the next appropriate response.

[0481] The server receives the reply message from the other party and analyzes the emotional state using an emotion engine. Then, it uses a generative AI to generate the next appropriate reply message. It receives the reply message as input and obtains the generated reply message as output.

[0482] Step 9:

[0483] We support continuous dialogue and suggestions.

[0484] The terminal presents the worker with generated reply messages and suggestions for the next steps, supporting continuous dialogue. It receives new messages as input and the presented messages as output.

[0485] 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.

[0486] 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.

[0487] 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.

[0488] [Second Embodiment]

[0489] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0490] 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.

[0491] 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).

[0492] 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.

[0493] 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.

[0494] 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).

[0495] 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.

[0496] 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.

[0497] 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.

[0498] 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.

[0499] 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.

[0500] 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".

[0501] The embodiments for carrying out this invention are described below.

[0502] Overall structure

[0503] This system consists of a user terminal, a server, and a generative AI. The server acquires and analyzes user and recipient profile information, and generates messages and suggestions. The terminal receives the generated content from the server, presents it to the user for confirmation, and performs operations such as modification and approval as needed.

[0504] Retrieving profile information

[0505] server

[0506] When a user logs into the matching app, the server requests profile information of their current match. Specifically, it accesses the database to retrieve personal information such as the user's and their match's names, occupations, hobbies, interests, and place of residence. The retrieved profile information is analyzed using natural language processing (NLP) techniques to extract important keywords (e.g., hobbies, interests, place of residence).

[0507] Message generation

[0508] terminal

[0509] The terminal invokes a generative AI based on profile information received from the server. The generative AI then generates an initial message that takes into account tone and length, based on the given profile information.

[0510] Specific example

[0511] For example, if the recipient is an "engineer," a generative AI will generate the following initial message:

[0512] "Hello, I see you work as an engineer. I'm also interested in the technical field. I understand you enjoy watching movies; what movies have you seen recently?"

[0513] Proposal generation

[0514] server

[0515] The server compares the user's and the other person's profile information and analyzes their common interests. Based on this, it generates suggested date locations that match their shared hobbies and interests. For example, if both live in Tokyo and enjoy watching movies, the server will suggest nearby movie theaters.

[0516] Specific example

[0517] "How about watching a movie at a cinema in Odaiba, Tokyo? Afterwards, it would be fun to share our thoughts about the movie at a nearby cafe."

[0518] Confirm and send the message.

[0519] User

[0520] Users review the generated messages and suggestions through their devices. They can adjust the message content themselves as needed. Finally, once the user approves the message and presses the send button, the message is sent to the recipient through the matching app.

[0521] Support for ongoing communication

[0522] terminal

[0523] The device receives a response message from the other party and analyzes its content. A generative AI then generates an appropriate reply message based on the response content.

[0524] Specific example

[0525] If the other person replies, "I recently watched a thriller movie," the generative AI will generate a response such as, "Thriller movies, that's great! I also had the chance to watch one recently. What scene was the most memorable for you?" It can also suggest future date plans or new topics of conversation.

[0526] Plan execution

[0527] User

[0528] If the user accepts the proposal and an agreement is reached with the other party, a concrete date plan is created. Finally, the details of the date (date, time, place, meeting place, etc.) are confirmed and notified to the other party.

[0529] Therefore, by using this system, users can efficiently exchange messages, build trust more easily, and ultimately lead to a first date.

[0530] The following describes the processing flow.

[0531] Program processing steps

[0532] Step 1: Obtain profile information

[0533] server

[0534] 1. Profile Request: When a user logs into the dating app, the server requests the profile information of their current match.

[0535] 2. Database Access: The server connects to the matching app's database and retrieves the user's profile information (name, occupation, hobbies, interests, and place of residence) of their potential matches.

[0536] 3. Information Analysis: The acquired profile information is analyzed using natural language processing (NLP) techniques to extract important keywords.

[0537] Step 2: Generating the initial message

[0538] terminal

[0539] 1. Profile reception: The device calls a generative AI based on the profile information it receives from the server.

[0540] 2. Message Generation: The generative AI generates an initial message, taking into account tone and length, based on the given profile information.

[0541] Specific example: Initial message generated by the device: "Hello, Mr. Tanaka. Your work as an engineer is wonderful! I'm also interested in the technical field. I read that you enjoy watching movies; what movies have you seen recently?"

[0542] Step 3: Proposal Generation

[0543] server

[0544] 1. Commonality Analysis: The server compares the user's and the other party's profile information and analyzes common interests.

[0545] 2. Selecting a date location: Based on shared interests and the other person's place of residence (e.g., Tokyo), generate suitable date location options.

[0546] 3. Suggestion Generation: The server generates a suggestion. "Mr. Tanaka, since you seem to enjoy movies, how about going to a movie theater in Odaiba, Tokyo? Afterwards, it would be fun to share our thoughts on the movie at a nearby cafe."

[0547] Step 4: Confirm and send the message.

[0548] User

[0549] 1. Message confirmation: The user reviews the generated messages and suggestions through their device.

[0550] 2. Content Adjustment: Users can also adjust the content of their messages as needed.

[0551] 3. Message Sending: Once the user approves the message and presses the send button, the message is sent to the other party through the matching app.

[0552] Step 5: Support for ongoing communication

[0553] terminal

[0554] 1. Reply received: The device receives a reply message from the other party.

[0555] 2. Reply Analysis: Analyze the received response message and extract important keywords.

[0556] 3. Reply message generation: Generative AI is used to generate appropriate reply messages based on the analysis results.

[0557] For example, if the other person replies, "I recently watched a thriller movie," the generative AI will generate a response like, "Thriller movies, that's great! I also had the chance to watch one recently. What scene left the biggest impression on you?"

[0558] Step 6: Next Proposal

[0559] server

[0560] 1. Next Date Suggestion Generation: The server generates the next date plan and new topic suggestions based on the ongoing interaction.

[0561] Specific example: "How about going to a small film festival being held in Jiyugaoka this weekend?"

[0562] Step 7: Execute the plan

[0563] User

[0564] 1. Proposal Confirmation: The user reviews the proposed plan, and if they agree with the other party, they proceed to plan the actual date.

[0565] 2. Final confirmation: Confirm the date details (date, time, location, meeting place, etc.) and notify the other person.

[0566] This allows users to exchange messages efficiently, build trust more easily, and potentially lead to a first date.

[0567] (Example 1)

[0568] 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."

[0569] In traditional dating apps, users often spend time and effort crafting their initial messages and date proposals, which can make effective communication difficult. Similar problems arise when considering appropriate replies and future date suggestions. This makes it difficult for users to build trust and hinders smooth communication.

[0570] 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.

[0571] In this invention, the server includes means for authenticating user identification information, means for acquiring user profile information, means for acquiring the other party's profile information, means for analyzing the acquired user and other party profile information and extracting important keywords, means for generating appropriate messages based on the analysis results using a generative AI model, means for generating date destination candidates based on common interests, means for presenting the generated messages and suggestions to the user and obtaining user approval, means for sending the message to the other party after user approval, and means for analyzing the other party's reply and generating even more appropriate reply messages and next suggestions using a generative AI model. This makes it possible for users to efficiently exchange impressive messages and even propose dates.

[0572] "User identification information" refers to information used to identify a user, and is used for login authentication, etc.

[0573] "Profile information" refers to personal information about the user and the other party, including name, occupation, hobbies, interests, and place of residence.

[0574] A "generative AI model" is an algorithm that uses artificial intelligence technology to generate text, and includes natural language processing technology.

[0575] "Natural language processing" is a technology for analyzing, understanding, and generating data from text, including keyword extraction and sentiment analysis.

[0576] "Key keywords" are major words and phrases extracted from profile information that are related to the user's and the other person's hobbies and interests.

[0577] A "message" is text generated by a generative AI model and exchanged between a user and another party.

[0578] "Date destination suggestions" are places to go on a date that are proposed based on shared interests and preferences.

[0579] "Approval" refers to the act of a user reviewing and agreeing to the generated message or proposal.

[0580] A "reply message" is text generated in response to a message sent by the other party.

[0581] "Next suggestion" refers to a suggestion regarding the next interaction or date, and is related information generated along with the reply message.

[0582] The embodiments for carrying out this invention are described below.

[0583] Overall structure

[0584] This system consists of a user terminal, a server, and a generative AI. The server acquires and analyzes user and recipient profile information, and generates messages and suggestions. The terminal receives the generated content from the server, presents it to the user for confirmation, and performs operations such as modification and approval as needed.

[0585] Hardware and software to be used

[0586] Device: Use a smartphone or personal computer.

[0587] Server: A server computer with high-speed processing capabilities is used to perform tasks such as acquiring and analyzing profile information and generating dating suggestions.

[0588] Generative AI: Uses a generative AI model (e.g., OpenAI's ChatGPT) and natural language processing techniques (e.g., spaCy, NLTK).

[0589] Retrieving profile information

[0590] When a user logs into the matching app, the server performs the following actions: It authenticates the user's identification information and accesses the database to retrieve the user's and their potential matches' profile information. This information includes name, occupation, hobbies, interests, and place of residence. The retrieved information is temporarily stored.

[0591] example:

[0592] When the server receives a login event, it executes a database query to retrieve profile information corresponding to the user's ID.

[0593] Analysis of profile information

[0594] The server analyzes the acquired profile information using natural language processing (NLP) techniques. It extracts important keywords (hobbies, interests, place of residence, etc.) and prepares them to be passed to a generative AI.

[0595] example:

[0596] We will use an NLP library (e.g., spaCy, NLTK) to extract important keywords from text data.

[0597] Message generation

[0598] The terminal calls a generative AI based on keywords received from the server to generate the initial message. The generated message is then sent to the server.

[0599] example:

[0600] The device receives keywords such as "engineer" and "watching movies" and generates a message like, "Hello, I see you work as an engineer. I'm also interested in technology. You mentioned you enjoy watching movies; what movies have you seen recently?"

[0601] Confirm and send the message.

[0602] The user reviews the message generated through their device and makes any necessary corrections. After approving the message and pressing the send button, the message is sent to the recipient.

[0603] example:

[0604] The user reads the message displayed on their device screen, adds or modifies it with something like "I like movies too," and finally presses the send button.

[0605] Generating a reply message

[0606] The device receives the reply message from the other party and analyzes its content. Based on the analysis results, it calls a generative AI to generate an appropriate reply message. After that, it presents the new message to the user.

[0607] example:

[0608] If the other person replies, "I recently watched a thriller movie," the system will generate a reply such as, "Thriller movies are great! I also had the chance to watch one recently. What scene left the biggest impression on you?"

[0609] Generating date proposals

[0610] The server compares the user's and the other person's profile information and generates suggested date locations based on shared interests. The generated suggestions are then sent to the user.

[0611] example:

[0612] The system generates a suggestion: "How about watching a movie at a cinema in Odaiba, Tokyo? Afterwards, it would be fun to share our thoughts about the movie at a nearby cafe."

[0613] Plan execution

[0614] The user accepts the proposal, and once an agreement is reached with the other party, they create a detailed date plan. Finally, they notify the other party of the details such as the date, time, place, and meeting place.

[0615] example:

[0616] The message reads, "Let's meet at the movie theater in Odaiba at 2 PM on March 20th."

[0617] Examples of prompts for generative AI models

[0618] "Generate the initial message based on the user's profile information."

[0619] "Please create a date proposal based on the other person's profile."

[0620] This system allows users to exchange messages efficiently, build trust more easily, and ultimately lead to a first date.

[0621] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0622] Step 1: User login and retrieval of profile information

[0623] When a user logs into the matching app, the server authenticates the user's identity. If authentication is successful, the following processes are performed.

[0624] Input: User ID, Password

[0625] Data processing: Verify the user ID and password on the authentication server.

[0626] Output: If the user ID is valid, retrieve the user and the other party's profile information from the database.

[0627] Specific operation: The server accesses the database and retrieves and temporarily stores information such as the user's and the other party's names, occupations, hobbies, interests, and place of residence.

[0628] Step 2: Analyze profile information

[0629] The server analyzes the acquired user and other party profile information using natural language processing (NLP) technology.

[0630] Input: User and other party's profile information

[0631] Data processing: Use NLP techniques to extract important keywords from text data.

[0632] Output: Extracted key keywords (e.g., hobbies, interests, place of residence)

[0633] Specific operation: The server uses an NLP library (e.g., spaCy, NLTK) to extract keywords from the text data and prepare them to be passed to the generative AI.

[0634] Step 3: Generating the initial message

[0635] The terminal calls a generative AI based on keywords received from the server and generates the initial message.

[0636] Input: Important keywords

[0637] Data processing: Input prompts and keywords into a generative AI model to generate appropriate messages.

[0638] Output: First message

[0639] Specific operation: The terminal inputs the following prompt to the generative AI: "Please generate an initial message based on keywords such as 'engineer' and 'movie watching'." The generative AI model then generates a message such as, "Hello, I see you work as an engineer. I'm also interested in technology. You mentioned you enjoy watching movies; what movies have you seen recently?"

[0640] Step 4: Confirm and send the message.

[0641] The user reviews the message generated through their device and makes any necessary corrections. They then approve the message and press the send button.

[0642] Input: Initial message generated

[0643] Data processing: The user reads the message, modifies it as needed, and finally approves it.

[0644] Output: Approved message

[0645] Specific actions: The user reads the message displayed on the device screen, adds or modifies content such as "I like movies too," and finally presses the send button.

[0646] Step 5: Generating a reply message

[0647] The device receives the reply message from the other party and analyzes its content. Based on the analysis results, it calls a generative AI to generate an appropriate reply message.

[0648] Input: Reply message from the other party

[0649] Data processing: The response content is analyzed by a generative AI to generate an appropriate response.

[0650] Output: Reply message

[0651] Specific operation: The device analyzes the message from the other party, "I recently watched a thriller movie," and generates a reply saying, "Thriller movies are great! I also had the chance to watch one recently. What scene was the most memorable for you?"

[0652] Step 6: Generating Date Proposals

[0653] The server compares the user's and the other person's profile information and generates suggested date locations based on shared interests.

[0654] Input: User and other party's profile information, common keywords

[0655] Data processing: Generate date suggestions based on shared interests and preferences.

[0656] Output: Dating proposals

[0657] Specific operation: The server generates a suggestion such as, "How about watching a movie at a movie theater in Odaiba, Tokyo? Afterwards, it would be fun to share our thoughts about the movie at a nearby cafe." based on common points such as "movie" and "Tokyo."

[0658] Step 7: Execute the plan

[0659] The user accepts the proposal, and once they reach an agreement with the other party, they create a concrete plan for the date.

[0660] Input: Date suggestion

[0661] Data processing: Based on the proposal, finalize the details of the date.

[0662] Output: Confirmed date details (date, time, location, meeting place, etc.)

[0663] Specific action: The user agrees to the proposal, decides on a specific date and time with the other party, and notifies them, "Let's meet at the movie theater in Odaiba at 2 PM on March 20th."

[0664] (Application Example 1)

[0665] 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."

[0666] In modern streaming services, a challenge exists in that users spend time and effort searching for content that matches their interests and preferences. The lack of personalized recommendation systems makes it difficult for users to select appropriate content from a large selection. Furthermore, the insufficient personalized communication and messaging to users hinders the improvement of the user experience.

[0667] 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.

[0668] In this invention, the server includes means for acquiring user profile information, extracting important keywords, and generating content recommendations for the user; means for generating personalized messages based on viewing history and profile information using generative AI and presenting them to the user; and means for further generating appropriate reply messages and next-time suggestions. This enables users to efficiently find content that matches their interests and preferences, thereby improving the user experience.

[0669] "User profile information" refers to personal information including the user's occupation, hobbies, interests, place of residence, and viewing history.

[0670] "Key keywords" are words that indicate the user's characteristics and interests, extracted from profile information using natural language processing technology.

[0671] "Generative AI" refers to artificial intelligence technology that generates appropriate messages or content based on given input data.

[0672] A "personalized message" is a message tailored to a specific user based on their profile information and viewing history.

[0673] "Viewing history" refers to a list of movies, TV shows, and other content that a user has watched so far.

[0674] "Content recommendation" refers to suggesting video content and programs that users are likely to be interested in, based on their profile information and viewing history.

[0675] A "message with adjusted tone and length" is a message whose tone and length have been appropriately adjusted to accommodate the different communication styles of various users.

[0676] A description of embodiments for carrying out this invention will be given.

[0677] Overall structure

[0678] This system consists of a user terminal, a server, and a generative AI. The terminal is responsible for presenting the user with personalized messages and content recommendations based on the user's profile information and viewing history.

[0679] Retrieving user profile information

[0680] server

[0681] When a user logs into a streaming service, the server retrieves the user's profile information and viewing history from the database. Specifically, it accesses the database and collects information such as the user's occupation, hobbies, interests, and place of residence, along with a history of the content they have watched.

[0682] Profile information analysis and keyword extraction

[0683] server

[0684] The acquired profile information is analyzed using natural language processing (NLP) techniques. This analysis extracts important keywords that indicate the user's hobbies and interests. NLP libraries such as SpaCy and NLTK are used for this process.

[0685] Content recommendation and personalized message generation

[0686] server

[0687] The server uses generative AI models (e.g., OpenAI GPT-4) to generate personalized messages and content recommendations based on the user's profile information and viewing history. The generated messages are optimized in tone and length and presented to the user in an appropriately tailored format.

[0688] Specific examples of recommendations

[0689] For example, if a user's occupation is an engineer and their hobby is watching suspense movies, the following recommendation message will be generated:

[0690] "Based on your profile information and viewing history, we'll suggest movies and TV shows that you might enjoy. Recent suspense films you've seen include 'Inception' and 'The Matrix,' and you've also shown interest in dystopian novels, right? Based on that, we highly recommend checking out the new suspense film, 'Tenet.'"

[0691] Presentation of messages and recommendations

[0692] terminal

[0693] The user's device receives personalized messages and content recommendations sent from the server and presents them to the user. The user can review these and select content that interests them.

[0694] Examples of specific prompt statements to use

[0695] "User profile information: An engineer who has always loved suspense movies, and recently enjoys reading dystopian novels. Movies he has seen include 'Inception,' 'The Matrix,' and 'Blade Runner.' Based on this, please suggest some movies and TV shows."

[0696] By using this system, users can efficiently find content that matches their interests and preferences, improving their experience using streaming services. The above describes the embodiment of this invention.

[0697] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0698] Step 1: Obtain user profile information and viewing history.

[0699] When a user logs into a streaming service, the server retrieves the user's occupation, hobbies, interests, place of residence, and viewing history from the database. The server issues queries to the database and collects the user's personal information and viewing history based on these queries. The input data is the user ID, and the output data is the user's profile information and viewing history.

[0700] Step 2: Profile information analysis and keyword extraction

[0701] The server analyzes the acquired profile information using natural language processing (NLP) techniques. Specifically, it uses an NLP library (e.g., SpaCy or NLTK) to extract important keywords that indicate the user's hobbies and interests. The input data is profile information, and the output data is important keywords.

[0702] Step 3: Content Recommendation Generation

[0703] The server uses a generative AI model (e.g., OpenAI GPT-4) to generate content recommendations based on extracted keywords and viewing history. The server generates prompts and provides them as input to the generative AI model. Based on these prompts, the AI ​​generates a message recommending content suitable for the user. The input data consists of keywords and viewing history, and the output data is a message recommending content.

[0704] Step 4: Generating Personalized Messages

[0705] The server uses generative AI to generate personalized messages based on the user's profile information and viewing history. The generated messages are optimized in terms of tone and length to match the user's communication style. The input data is profile information and viewing history, and the output data is the personalized message.

[0706] Step 5: Presenting messages and recommendations

[0707] The device receives personalized messages and content recommendations sent from the server and presents them to the user. The user can then select content of interest based on the presented messages and recommendations. The input data consists of messages and recommendations from the server, while the output data is the content presented to the user.

[0708] Step 6: Analyze the user's response

[0709] The server analyzes the content and response messages selected by the user. This analysis provides feedback data for future recommendations and message generation. The input data consists of the user's selections and response messages, while the output data is feedback for generating future suggestions.

[0710] Step 7: Generating the next proposal

[0711] The server generates content recommendations and personalized messages based on the user's previous responses and selections. The server uses generative AI to generate these suggestions and messages and sends them to the device. Input data is feedback data, and output data is the next suggestions and messages.

[0712] This allows users to efficiently find content that matches their interests and preferences, improving their streaming service experience.

[0713] 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.

[0714] The embodiments for carrying out this invention will be described in detail, divided into each processing step.

[0715] Overall structure

[0716] This system consists of a user terminal, a server, a generative AI, and an emotion engine. The server acquires and analyzes user and recipient profile information and generates messages and suggestions. The terminal receives the generated content from the server, presents it to the user for confirmation, and allows for modification and approval as needed. The emotion engine recognizes the user's emotions in real time and adjusts appropriate message generation and suggestions based on that information.

[0717] Retrieving profile information

[0718] server

[0719] When a user logs into the matching app, the server requests profile information of their current match. Specifically, it accesses the database to retrieve personal information such as the user's and the other person's names, occupations, hobbies, interests, and place of residence. The retrieved profile information is analyzed using natural language processing (NLP) techniques to extract important keywords (e.g., hobbies, interests, place of residence).

[0720] Message generation

[0721] terminal

[0722] The device invokes a generative AI based on profile information received from the server. The generative AI generates an initial message that takes into account tone and length, based on the given profile information and the user's emotional information obtained from the emotion engine.

[0723] Specific example

[0724] For example, if the other party is an "engineer" and the user's emotion engine detects a relaxed emotion, the generative AI will generate an initial message like this:

[0725] "Hello, I see you work as an engineer. I'm also interested in technology. I read that you enjoy watching movies; what movies have you seen recently?"

[0726] Proposal generation

[0727] server

[0728] The server compares the user's and the other person's profile information and analyzes their common interests. It also considers the user's current emotional state, as determined by the emotion engine, to generate suggested date locations that align with shared hobbies and interests. For example, if both users live in Tokyo, enjoy watching movies, and are in a relaxed state, the server might suggest a nearby movie theater.

[0729] Specific example

[0730] "How about watching a movie at a cinema in Odaiba, Tokyo? Afterwards, it would be fun to share our thoughts about the movie at a nearby cafe."

[0731] Confirm and send the message.

[0732] User

[0733] Users review the generated messages and suggestions through their devices. They can adjust the message content themselves as needed. Finally, once the user approves the message and presses the send button, the message is sent to the recipient through the matching app.

[0734] Support for ongoing communication

[0735] terminal

[0736] The device receives the response message from the other party and analyzes its content. Furthermore, the emotion engine analyzes the user's emotional state, and based on that, the generative AI generates the next appropriate reply message.

[0737] Specific example

[0738] If the other person replies, "I recently watched a thriller movie," the emotion engine detects the user's excited emotions, and the generative AI generates, "Thriller movies, that's great! I also had the chance to watch one recently. What scene left the biggest impression on you?"

[0739] Next proposal generation

[0740] server

[0741] The server generates the next date plan and new topic suggestions based on ongoing interactions. The emotion engine evaluates the user's stress level and satisfaction, and adjusts the next suggestions based on the evaluation results.

[0742] Specific example

[0743] If the emotion engine assesses that the user is in a relaxed state, it might suggest, "How about going to a small film festival being held in Jiyugaoka this weekend?"

[0744] Plan execution

[0745] User

[0746] If the user accepts the proposal and an agreement is reached with the other party, a detailed date plan will be created. Finally, the details of the date (date, time, place, meeting place, etc.) will be confirmed and notified to the other party.

[0747] In summary, by using this system, users can efficiently exchange messages, receive appropriate suggestions tailored to their emotional state, build trust more easily, and ultimately lead to a first date.

[0748] The following describes the processing flow.

[0749] Program processing steps

[0750] Step 1: Obtain profile information

[0751] server

[0752] 1. Profile Request: When a user logs into the dating app, the server requests the profile information of their current match.

[0753] 2. Database Access: The server connects to the matching app's database and retrieves the user's profile information (name, occupation, hobbies, interests, and place of residence) of their potential matches.

[0754] 3. Information Analysis: The acquired profile information is analyzed using natural language processing (NLP) techniques to extract important keywords.

[0755] Step 2: Recognizing the user's emotions

[0756] terminal

[0757] 1. Acquisition of emotional data: The device uses an emotion engine to recognize the user's emotional state. For example, it analyzes facial expressions and voice tone using the camera and microphone.

[0758] 2. Emotional Data Transmission: The acquired emotional data is sent to the server.

[0759] Step 3: Generating the initial message

[0760] server

[0761] 1. Profile reception: The server integrates profile information and sentiment data received from the terminal and database.

[0762] 2. Message Generation: The generative AI generates an initial message that takes into account tone and length, based on profile information and sentiment data.

[0763] Specific example

[0764] For example, if the other party is an "engineer" and the user's emotion engine detects a relaxed emotion, the generative AI will generate an initial message like this:

[0765] "Hello, I see you work as an engineer. I'm also interested in technology. I read that you enjoy watching movies; what movies have you seen recently?"

[0766] Step 4: Proposal Generation

[0767] server

[0768] 1. Commonality Analysis: The server compares the user's and the other party's profile information and analyzes common interests.

[0769] 2. Selecting a date location: Based on shared interests and the other person's place of residence (e.g., Tokyo), generate suitable date location options.

[0770] 3. Suggestion Generation: The server generates a suggestion. "Mr. Tanaka, since you seem to enjoy movies, how about going to a movie theater in Odaiba, Tokyo? Afterwards, it would be fun to share our thoughts on the movie at a nearby cafe."

[0771] Step 5: Confirm and send the message.

[0772] User

[0773] 1. Message confirmation: The user reviews the generated messages and suggestions through their device.

[0774] 2. Content Adjustment: Users can also adjust the content of their messages as needed.

[0775] 3. Message Sending: Once the user approves the message and presses the send button, the message is sent to the other party through the matching app.

[0776] Step 6: Support for ongoing communication

[0777] terminal

[0778] 1. Reply received: The device receives a reply message from the other party.

[0779] 2. Reply Analysis: Analyze the received response message and extract important keywords.

[0780] 3. Emotion Analysis: The emotion engine analyzes the user's emotional state, and based on that, the generative AI generates the next appropriate reply message.

[0781] Specific example

[0782] If the other person replies, "I recently watched a thriller movie," the emotion engine detects the user's excited emotions, and the generative AI generates, "Thriller movies, that's great! I also had the chance to watch one recently. What scene left the biggest impression on you?"

[0783] Step 7: Next Proposal

[0784] server

[0785] 1. Next Date Suggestion Generation: The server generates the next date plan and new topic suggestions based on the ongoing interaction.

[0786] 2. Emotional Evaluation: The emotional engine evaluates the user's stress level and satisfaction level, and adjusts the next suggestion based on the evaluation results.

[0787] Specific example

[0788] If the emotion engine assesses that the user is in a relaxed state, it might suggest, "How about going to a small film festival being held in Jiyugaoka this weekend?"

[0789] Step 8: Execute the plan

[0790] User

[0791] 1. Proposal Confirmation: The user reviews the proposed plan, and if they agree with the other party, they proceed to plan the actual date.

[0792] 2. Final confirmation: Confirm the date details (date, time, location, meeting place, etc.) and notify the other person.

[0793] This allows users to exchange messages efficiently, receive appropriate suggestions tailored to their emotional state, build trust more easily, and ultimately lead to a first date.

[0794] (Example 2)

[0795] 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".

[0796] Current online dating apps and communication systems require users to spend a significant amount of time and effort to generate appropriate messages. Furthermore, it's difficult to create optimal suggestions and messages tailored to the user's emotions and situation, often resulting in one-sided communication. Additionally, the lack of features to support continuous interaction makes efficient communication challenging.

[0797] The identification processing by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for acquiring user identification information, means for acquiring the other party's identification information, means for analyzing the acquired user and other party identification information and extracting key keywords, means for generating an appropriate communication message based on the analysis results using generative artificial intelligence, means for generating proposals based on common interests of both parties, means for presenting the generated communication message and proposal to the user and obtaining the user's approval, means for sending the communication message to the other party after the user's approval, and means for analyzing the response from the other party and generating an appropriate reply message and next proposal. As a result, the user can exchange messages efficiently, and appropriate proposals are made according to the user's emotional state, making it easier to build trust and enabling smooth communication.

[0798] "User identification information" refers to information used to identify a user, including login ID, name, and contact information.

[0799] "Other party identification information" refers to information used to identify the person a user has been matched with, and includes the other party's login ID, name, contact information, etc.

[0800] "Key keywords" are important pieces of information extracted from the user's and the other party's profile information, such as hobbies, interests, and place of residence.

[0801] "Generative artificial intelligence" refers to artificial intelligence models that have the ability to generate text messages in natural language based on input data.

[0802] A "communication message" is a text-based message shared between a user and another user, including the initial message and replies.

[0803] "Shared interests" refer to the hobbies and interests that both users share, which are analyzed from their respective profile information.

[0804] A "suggestion" is a suggestion of activities or date plans that can be shared between the user and the other person based on their common interests.

[0805] "Emotional state" refers to information that indicates the current emotional state of the user or the other party, and includes states such as relaxed, excited, and stressed.

[0806] "Approval" is the act of confirming that a user agrees to the content of a generated message or proposal and confirms that they will submit it.

[0807] A "response" is a reply message sent by the other party.

[0808] "Continuous interaction" refers to the act of a user and another party exchanging messages multiple times.

[0809] This invention relates to a system that analyzes user identification information and recipient identification information, and provides appropriate message generation and suggestions using generative artificial intelligence. This system consists of a user terminal, a server, generative artificial intelligence, and an emotion engine.

[0810] Overall system configuration

[0811] This system consists of a user terminal, a server, a generative artificial intelligence system, and an emotion engine. It fully automates the generation of user messages and suggestions, and supports communication tailored to the user's emotional state.

[0812] server

[0813] The server acquires and analyzes user and other party identification information, extracts key keywords, and generates suggestions based on common interests. The acquired information and analysis results are then input into a generative artificial intelligence system.

[0814] terminal

[0815] The terminal presents the user with generated messages and suggestions sent from the server. The user reviews these using the terminal, makes any necessary modifications, and then approves them.

[0816] Generative artificial intelligence

[0817] Generative artificial intelligence generates messages of appropriate tone and length based on given identification and emotional information. Specifically, it uses natural language processing techniques to analyze prompt sentences and generate appropriate responses.

[0818] Emotional Engine

[0819] The emotion engine analyzes the user's emotional state in real time. This information is reflected in the generative artificial intelligence, influencing the tone and content of the generated messages.

[0820] Acquisition and analysis of profile information

[0821] server

[0822] When a user logs into the matching app, the server immediately retrieves the user's and the other person's identification information from the database. Then, using natural language processing (NLP) techniques, this information is analyzed to extract key keywords. An NLP library (e.g., spaCy) is used for this analysis.

[0823] Message generation

[0824] terminal

[0825] The terminal receives the analyzed profile information sent from the server and invokes the generative artificial intelligence. The generative AI then creates a prompt statement based on this information and generates the initial message.

[0826] Specific example

[0827] For example, if the other party is an engineer and the user's emotion engine detects a relaxed emotion, the generative artificial intelligence will generate the following initial message:

[0828] "Hello, I see you work as an engineer. I'm also interested in technology. I read that you enjoy watching movies; what movies have you seen recently?"

[0829] Proposal generation

[0830] server

[0831] The server generates suggestions based on the shared interests of the user and the other party. When generating suggestions, it also considers the user's emotional state, which is obtained from the emotion engine.

[0832] Specific example

[0833] "How about watching a movie at a cinema in Odaiba, Tokyo? Afterwards, it would be fun to share our thoughts about the movie at a nearby cafe."

[0834] Review and send messages and proposals.

[0835] User

[0836] The user reviews the messages and suggestions generated through their device. They modify them as needed, and after final approval, send them to the recipient via the server.

[0837] This system allows users to exchange messages efficiently through automated processes, enabling smoother and more emotionally-driven communication.

[0838] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0839] Step 1: User Login

[0840] User

[0841] The user logs into the matching app. The input is the user's identification information (e.g., user ID and password). The device sends this to the server, and the user is authenticated. The output is the session information provided after the user has been authenticated.

[0842] Step 2: Obtain profile information

[0843] server

[0844] When a user logs in, the server retrieves user and other user identification information from the database. The input is the user's identification information (e.g., User ID). The server issues a query to the database like the following:

[0845] sql

[0846] SELECT Name, Occupation, Hobbies, Interests, Location FROM Profile WHERE User ID = 'USER_ID';

[0847] The output consists of the retrieved user and other party profile information.

[0848] Step 3: Analyze profile information

[0849] server

[0850] The server analyzes the acquired profile information using natural language processing technology (e.g., spaCy). The input is the user's and the other party's profile information. Through analysis, key keywords (e.g., hobbies, interests, place of residence) are extracted. The output is a list of the extracted keywords.

[0851] Step 4: Create a prompt for message generation

[0852] terminal

[0853] The terminal generates prompts for the AI ​​model based on the parsed profile information received from the server. The input consists of the parsed profile information and the user's sentiment information. The prompts are constructed, for example, as follows:

[0854] "The recipient is an engineer, and the user is relaxed. Please generate the initial message."

[0855] The output is a prompt message for the generative AI model.

[0856] Step 5: Generating the initial message

[0857] terminal

[0858] The terminal invokes a generative AI model (e.g., GPT-3) and generates the initial message using the prompt text as input. The input is the prompt text. The generative AI model uses natural language processing techniques to generate the initial message based on the given prompt text. The output is the generated initial message.

[0859] Step 6: Confirm the message and proposal

[0860] User

[0861] The user reviews the generated message and suggestions via the terminal. The input consists of the generated message and suggestions presented by the terminal. The user reviews these and makes modifications as needed. The output is the final message approved by the user.

[0862] Step 7: Sending a message

[0863] User

[0864] When the user presses the approve button, the device sends a message to the server. The input is the final message approved by the user. The server sends this message to the recipient using the matching app's messaging API. The output is confirmation that the message was sent to the recipient.

[0865] Step 8: Support for ongoing communication

[0866] terminal

[0867] The device receives the reply message from the other party and analyzes its content. The emotion engine analyzes the user's real-time emotional state, and the generation AI generates the next appropriate reply message. The input is the other party's reply message and the user's emotional information. The output is the generated next reply message.

[0868] Step 9: Generating the next proposal

[0869] server

[0870] The server generates the next date plan and topic suggestions based on the content of the ongoing interaction and feedback from the emotion engine. The input is the content of the interaction and emotion information. The server generates new suggestions based on this. The output is the suggestions for the next date.

[0871] Step 10: Execute the plan

[0872] User

[0873] If the user accepts the proposal and reaches an agreement with the other party, they will then plan a specific date. The input is the agreement between the user and the other party. The user confirms details such as the date, time, place, and meeting place, and then sends a final confirmation to the other party via the device. The output is the finalized date details.

[0874] (Application Example 2)

[0875] 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."

[0876] Traditional workplace communication has not adequately supported improvements in work efficiency and safety. In particular, there is a need for a system that can grasp workers' emotions and fatigue levels in real time and propose appropriate breaks and tasks accordingly. Therefore, balancing worker health and productivity is a key challenge.

[0877] In Application Example 2, the specific processing performed by the specific processing unit 290 of the data processing device 12 is realized by the following means. In this invention, the server includes means for acquiring user profile information, means for acquiring the other party's profile information, means for analyzing the acquired user and other party profile information and extracting important keywords, means for generating an appropriate message based on the analysis results using a generative AI, means for generating dating destination candidates based on common interests, means for presenting the generated message or suggestion to the user and obtaining user approval, means for sending the message to the other party after user approval, means for analyzing the other party's reply and generating an even more appropriate reply message or next suggestion, means for recognizing the emotional state from voice input using an emotion engine and adjusting the content of the dialogue accordingly, and means for generating work suggestions and break suggestions based on the worker's profile information and emotional state in a factory environment. This makes it possible to make appropriate work suggestions and break suggestions in real time based on the worker's profile information and emotional state.

[0878] "Means for obtaining user profile information" refers to devices or methods for collecting personal information such as a user's name, occupation, hobbies, interests, and place of residence.

[0879] "Means of obtaining the other party's profile information" refers to devices or methods for collecting personal information such as the name, occupation, hobbies, interests, and place of residence of a user through matching apps or similar means.

[0880] "Means for extracting important keywords" refers to a device or method that uses natural language processing technology to extract highly relevant keywords such as hobbies and interests from collected profile information.

[0881] "Means for generating appropriate messages using generative AI" refers to a device or method that automatically creates a message that takes into account the tone and length of the conversation, based on given information and using artificial intelligence technology.

[0882] "Means for generating date destination candidates" refers to a device or method that suggests date destinations based on the shared hobbies and interests of the user and their partner.

[0883] "Means of presenting to the user and obtaining user approval" refers to a device or method for displaying generated messages or suggestions to the user and obtaining their approval to review and modify their content.

[0884] "Means of sending a message to another party" refers to a device or method for sending an approved message to another party through a matching app or similar means.

[0885] "Means for analyzing replies from the other party and generating more appropriate reply messages and suggestions for the next step" refers to a device or method that analyzes the content of a reply from the other party and automatically creates appropriate reply messages and suggestions for the next step according to the content and the user's emotional state.

[0886] "Means for recognizing emotional states from voice input using an emotion engine" refers to a device or method that analyzes voice input to recognize the user's emotional state in real time.

[0887] "Means for generating work and rest suggestions based on worker profile information and emotional state in a factory environment" refers to a device or method that makes appropriate work and rest suggestions based on the profile information and emotional state of workers in a factory.

[0888] This invention is a system designed to streamline worker communication in a factory environment and achieve both worker health and productivity. The system consists of a user terminal, a server, a generative AI, and an emotion engine.

[0889] Overall structure

[0890] This system acquires user and recipient profile information, analyzes this information, and extracts important keywords. The generative AI generates and suggests appropriate messages based on this analysis. Furthermore, the emotion engine recognizes the emotional state in real time from voice input and adjusts the message content and tone based on that information.

[0891] Acquisition and analysis of user profile information

[0892] server

[0893] When a user logs into the system, the server retrieves their profile information (name, occupation, hobbies, interests, place of residence, etc.) from the database. The retrieved profile information is then analyzed using natural language processing (NLP) techniques to extract important keywords.

[0894] Recognition of emotional states

[0895] Emotional Engine

[0896] The emotion engine analyzes the worker's voice input to recognize their emotional state in real time. This is done using EmotionRecognizer software. For example, if a worker says "I'm tired," the emotion engine detects the emotion "fatigue."

[0897] Message generation

[0898] Generative AI

[0899] The generative AI generates dialogue messages based on analysis results and emotional states. The GPT-2 model is used here to generate appropriate responses based on prompt sentences.

[0900] For example, if a worker says, "Today's work is tough," the emotion engine detects "fatigue." Based on this information, the generative AI generates a message with the following prompt:

[0901] Prompt message:

[0902] Worker: Engineer, in his 30s, hobby is watching movies.

[0903] Emotional state: Fatigue

[0904] Message: Today's work is tough.

[0905] Robot: I think you should take a short break today. How about resting at a nearby rest area?

[0906] Proposal generation

[0907] server

[0908] The server generates work and break suggestions based on the worker's profile information and emotional state, aligning with their common hobbies and interests. For example, if a worker is an engineer and their hobby is watching movies, the server might suggest, "How about watching a movie at a nearby rest area?"

[0909] Confirm and send the message.

[0910] User

[0911] Users can review the messages and suggestions presented by the system and make modifications as needed. Finally, once the user approves the message and presses the send button, it is sent to the worker.

[0912] Support for continuous communication

[0913] terminal

[0914] The terminal receives a response message from the worker and analyzes its content. The emotion engine analyzes the emotion again, and the generative AI generates the next appropriate message. For example, if the worker says, "I recently watched a movie," the emotion engine detects "excitement," and the generative AI generates a message like this:

[0915] Prompt message:

[0916] Worker: I recently watched a movie.

[0917] Emotional state: Excitement

[0918] Message: That's wonderful! Which scene left the biggest impression on you?

[0919] Through the steps outlined above, this system allows workers to communicate efficiently and receive appropriate breaks and work suggestions. Furthermore, real-time emotional analysis can improve work efficiency and overall well-being.

[0920] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0921] Step 1:

[0922] Retrieve the user's profile information.

[0923] When a worker logs into the system, the server retrieves personal information from the database, such as the worker's name, occupation, hobbies, interests, and place of residence. It then creates a query for the database search and extracts the relevant records. It receives the worker ID as input and obtains profile information as output.

[0924] Step 2:

[0925] Retrieve the other person's profile information.

[0926] The server retrieves the profile information of the worker's counterpart from the database. Here too, database access technology is used to retrieve the counterpart's information. It receives the counterpart's worker ID as input and obtains the counterpart's profile information as output.

[0927] Step 3:

[0928] The acquired information is analyzed, and important keywords are extracted.

[0929] The server analyzes the acquired profile information of the worker and the other party using natural language processing (NLP) techniques to extract important keywords such as hobbies, interests, and occupation. It receives profile information as input and obtains important keywords as output.

[0930] Step 4:

[0931] To recognize emotional states.

[0932] The terminal analyzes the worker's voice input using an EmotionRecognizer and recognizes their emotional state in real time. For example, if the voice input is "Today's work is tough," the emotion engine detects the emotion "fatigue." It receives voice data as input and obtains an emotional state as output.

[0933] Step 5:

[0934] Generate an appropriate message based on the analysis results.

[0935] The generative AI (GPT-2 model) generates messages with adjusted tone and length based on profile information and emotional state. It receives important keywords, emotional state, and an initial message as input, and outputs a generated message.

[0936] Step 6:

[0937] The generated message and proposal are presented to the user, and their approval is obtained.

[0938] The terminal displays the generated messages and suggestions to the worker for review. The worker can then modify or approve the messages. It receives the generated messages as input and the approved messages as output.

[0939] Step 7:

[0940] Send a message to the recipient.

[0941] The terminal sends the approved message to the worker. Here, a communication protocol is used to send the message data. It receives the approved message as input and obtains the message sent to the other party as output.

[0942] Step 8:

[0943] Analyze the response from the other party and generate the next appropriate response.

[0944] The server receives the reply message from the other party and analyzes the emotional state using an emotion engine. Then, it uses a generative AI to generate the next appropriate reply message. It receives the reply message as input and obtains the generated reply message as output.

[0945] Step 9:

[0946] We support continuous dialogue and suggestions.

[0947] The terminal presents the worker with generated reply messages and suggestions for the next steps, supporting continuous dialogue. It receives new messages as input and the presented messages as output.

[0948] 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.

[0949] 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.

[0950] 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.

[0951] [Third Embodiment]

[0952] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0953] 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.

[0954] 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).

[0955] 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.

[0956] 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.

[0957] 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).

[0958] 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.

[0959] 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.

[0960] 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.

[0961] 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.

[0962] 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.

[0963] 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".

[0964] The embodiments for carrying out this invention are described below.

[0965] Overall structure

[0966] This system consists of a user terminal, a server, and a generative AI. The server acquires and analyzes user and recipient profile information, and generates messages and suggestions. The terminal receives the generated content from the server, presents it to the user for confirmation, and performs operations such as modification and approval as needed.

[0967] Retrieving profile information

[0968] server

[0969] When a user logs into the matching app, the server requests profile information of their current match. Specifically, it accesses the database to retrieve personal information such as the user's and their match's names, occupations, hobbies, interests, and place of residence. The retrieved profile information is analyzed using natural language processing (NLP) techniques to extract important keywords (e.g., hobbies, interests, place of residence).

[0970] Message generation

[0971] terminal

[0972] The terminal invokes a generative AI based on profile information received from the server. The generative AI then generates an initial message that takes into account tone and length, based on the given profile information.

[0973] Specific example

[0974] For example, if the recipient is an "engineer," a generative AI will generate the following initial message:

[0975] "Hello, I see you work as an engineer. I'm also interested in the technical field. I understand you enjoy watching movies; what movies have you seen recently?"

[0976] Proposal generation

[0977] server

[0978] The server compares the user's and the other person's profile information and analyzes their common interests. Based on this, it generates suggested date locations that match their shared hobbies and interests. For example, if both live in Tokyo and enjoy watching movies, the server will suggest nearby movie theaters.

[0979] Specific example

[0980] "How about watching a movie at a cinema in Odaiba, Tokyo? Afterwards, it would be fun to share our thoughts about the movie at a nearby cafe."

[0981] Confirm and send the message.

[0982] User

[0983] Users review the generated messages and suggestions through their devices. They can adjust the message content themselves as needed. Finally, once the user approves the message and presses the send button, the message is sent to the recipient through the matching app.

[0984] Support for ongoing communication

[0985] terminal

[0986] The device receives a response message from the other party and analyzes its content. A generative AI then generates an appropriate reply message based on the response content.

[0987] Specific example

[0988] If the other person replies, "I recently watched a thriller movie," the generative AI will generate a response such as, "Thriller movies, that's great! I also had the chance to watch one recently. What scene was the most memorable for you?" It can also suggest future date plans or new topics of conversation.

[0989] Plan execution

[0990] User

[0991] If the user accepts the proposal and an agreement is reached with the other party, a concrete date plan is created. Finally, the details of the date (date, time, place, meeting place, etc.) are confirmed and notified to the other party.

[0992] Therefore, by using this system, users can efficiently exchange messages, build trust more easily, and ultimately lead to a first date.

[0993] The following describes the processing flow.

[0994] Program processing steps

[0995] Step 1: Obtain profile information

[0996] server

[0997] 1. Profile Request: When a user logs into the dating app, the server requests the profile information of their current match.

[0998] 2. Database Access: The server connects to the matching app's database and retrieves the user's profile information (name, occupation, hobbies, interests, and place of residence) of their potential matches.

[0999] 3. Information Analysis: The acquired profile information is analyzed using natural language processing (NLP) techniques to extract important keywords.

[1000] Step 2: Generating the initial message

[1001] terminal

[1002] 1. Profile reception: The device calls a generative AI based on the profile information it receives from the server.

[1003] 2. Message Generation: The generative AI generates an initial message, taking into account tone and length, based on the given profile information.

[1004] Specific example: Initial message generated by the device: "Hello, Mr. Tanaka. Your work as an engineer is wonderful! I'm also interested in the technical field. I read that you enjoy watching movies; what movies have you seen recently?"

[1005] Step 3: Proposal Generation

[1006] server

[1007] 1. Commonality Analysis: The server compares the user's and the other party's profile information and analyzes common interests.

[1008] 2. Selecting a date location: Based on shared interests and the other person's place of residence (e.g., Tokyo), generate suitable date location options.

[1009] 3. Suggestion Generation: The server generates a suggestion. "Mr. Tanaka, since you seem to enjoy movies, how about going to a movie theater in Odaiba, Tokyo? Afterwards, it would be fun to share our thoughts on the movie at a nearby cafe."

[1010] Step 4: Confirm and send the message.

[1011] User

[1012] 1. Message confirmation: The user reviews the generated messages and suggestions through their device.

[1013] 2. Content Adjustment: Users can also adjust the content of their messages as needed.

[1014] 3. Message Sending: Once the user approves the message and presses the send button, the message is sent to the other party through the matching app.

[1015] Step 5: Support for ongoing communication

[1016] terminal

[1017] 1. Reply received: The device receives a reply message from the other party.

[1018] 2. Reply Analysis: Analyze the received response message and extract important keywords.

[1019] 3. Reply message generation: Generative AI is used to generate appropriate reply messages based on the analysis results.

[1020] For example, if the other person replies, "I recently watched a thriller movie," the generative AI will generate a response like, "Thriller movies, that's great! I also had the chance to watch one recently. What scene left the biggest impression on you?"

[1021] Step 6: Next Proposal

[1022] server

[1023] 1. Next Date Suggestion Generation: The server generates the next date plan and new topic suggestions based on the ongoing interaction.

[1024] Specific example: "How about going to a small film festival being held in Jiyugaoka this weekend?"

[1025] Step 7: Execute the plan

[1026] User

[1027] 1. Proposal Confirmation: The user reviews the proposed plan, and if they agree with the other party, they proceed to plan the actual date.

[1028] 2. Final confirmation: Confirm the date details (date, time, location, meeting place, etc.) and notify the other person.

[1029] This allows users to exchange messages efficiently, build trust more easily, and potentially lead to a first date.

[1030] (Example 1)

[1031] 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."

[1032] In traditional dating apps, users often spend time and effort crafting their initial messages and date proposals, which can make effective communication difficult. Similar problems arise when considering appropriate replies and future date suggestions. This makes it difficult for users to build trust and hinders smooth communication.

[1033] 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.

[1034] In this invention, the server includes means for authenticating user identification information, means for acquiring user profile information, means for acquiring the other party's profile information, means for analyzing the acquired user and other party profile information and extracting important keywords, means for generating appropriate messages based on the analysis results using a generative AI model, means for generating date destination candidates based on common interests, means for presenting the generated messages and suggestions to the user and obtaining user approval, means for sending the message to the other party after user approval, and means for analyzing the other party's reply and generating even more appropriate reply messages and next suggestions using a generative AI model. This makes it possible for users to efficiently exchange impressive messages and even propose dates.

[1035] "User identification information" refers to information used to identify a user, and is used for login authentication, etc.

[1036] "Profile information" refers to personal information about the user and the other party, including name, occupation, hobbies, interests, and place of residence.

[1037] A "generative AI model" is an algorithm that uses artificial intelligence technology to generate text, and includes natural language processing technology.

[1038] "Natural language processing" is a technology for analyzing, understanding, and generating data from text, including keyword extraction and sentiment analysis.

[1039] "Key keywords" are major words and phrases extracted from profile information that are related to the user's and the other person's hobbies and interests.

[1040] A "message" is text generated by a generative AI model and exchanged between a user and another party.

[1041] "Date destination suggestions" are places to go on a date that are proposed based on shared interests and preferences.

[1042] "Approval" refers to the act of a user reviewing and agreeing to the generated message or proposal.

[1043] A "reply message" is text generated in response to a message sent by the other party.

[1044] "Next suggestion" refers to a suggestion regarding the next interaction or date, and is related information generated along with the reply message.

[1045] The embodiments for carrying out this invention are described below.

[1046] Overall structure

[1047] This system consists of a user terminal, a server, and a generative AI. The server acquires and analyzes user and recipient profile information, and generates messages and suggestions. The terminal receives the generated content from the server, presents it to the user for confirmation, and performs operations such as modification and approval as needed.

[1048] Hardware and software to be used

[1049] Device: Use a smartphone or personal computer.

[1050] Server: A server computer with high-speed processing capabilities is used to perform tasks such as acquiring and analyzing profile information and generating dating suggestions.

[1051] Generative AI: Uses a generative AI model (e.g., OpenAI's ChatGPT) and natural language processing techniques (e.g., spaCy, NLTK).

[1052] Retrieving profile information

[1053] When a user logs into the matching app, the server performs the following actions: It authenticates the user's identification information and accesses the database to retrieve the user's and their potential matches' profile information. This information includes name, occupation, hobbies, interests, and place of residence. The retrieved information is temporarily stored.

[1054] example:

[1055] When the server receives a login event, it executes a database query to retrieve profile information corresponding to the user's ID.

[1056] Analysis of profile information

[1057] The server analyzes the acquired profile information using natural language processing (NLP) techniques. It extracts important keywords (hobbies, interests, place of residence, etc.) and prepares them to be passed to a generative AI.

[1058] example:

[1059] We will use an NLP library (e.g., spaCy, NLTK) to extract important keywords from text data.

[1060] Message generation

[1061] The terminal calls a generative AI based on keywords received from the server to generate the initial message. The generated message is then sent to the server.

[1062] example:

[1063] The device receives keywords such as "engineer" and "watching movies" and generates a message like, "Hello, I see you work as an engineer. I'm also interested in technology. You mentioned you enjoy watching movies; what movies have you seen recently?"

[1064] Confirm and send the message.

[1065] The user reviews the message generated through their device and makes any necessary corrections. After approving the message and pressing the send button, the message is sent to the recipient.

[1066] example:

[1067] The user reads the message displayed on their device screen, adds or modifies it with something like "I like movies too," and finally presses the send button.

[1068] Generating a reply message

[1069] The device receives the reply message from the other party and analyzes its content. Based on the analysis results, it calls a generative AI to generate an appropriate reply message. After that, it presents the new message to the user.

[1070] example:

[1071] If the other person replies, "I recently watched a thriller movie," the system will generate a reply such as, "Thriller movies are great! I also had the chance to watch one recently. What scene left the biggest impression on you?"

[1072] Generating date proposals

[1073] The server compares the user's and the other person's profile information and generates suggested date locations based on shared interests. The generated suggestions are then sent to the user.

[1074] example:

[1075] The system generates a suggestion: "How about watching a movie at a cinema in Odaiba, Tokyo? Afterwards, it would be fun to share our thoughts about the movie at a nearby cafe."

[1076] Plan execution

[1077] The user accepts the proposal, and once an agreement is reached with the other party, they create a detailed date plan. Finally, they notify the other party of the details such as the date, time, place, and meeting place.

[1078] example:

[1079] The message reads, "Let's meet at the movie theater in Odaiba at 2 PM on March 20th."

[1080] Examples of prompts for generative AI models

[1081] "Generate the initial message based on the user's profile information."

[1082] "Please create a date proposal based on the other person's profile."

[1083] This system allows users to exchange messages efficiently, build trust more easily, and ultimately lead to a first date.

[1084] The flow of the specific processing in Example 1 will be explained using Figure 11.

[1085] Step 1: User login and retrieval of profile information

[1086] When a user logs into the matching app, the server authenticates the user's identity. If authentication is successful, the following processes are performed.

[1087] Input: User ID, Password

[1088] Data processing: Verify the user ID and password on the authentication server.

[1089] Output: If the user ID is valid, retrieve the user and the other party's profile information from the database.

[1090] Specific operation: The server accesses the database and retrieves and temporarily stores information such as the user's and the other party's names, occupations, hobbies, interests, and place of residence.

[1091] Step 2: Analyze profile information

[1092] The server analyzes the acquired user and other party profile information using natural language processing (NLP) technology.

[1093] Input: User and other party's profile information

[1094] Data processing: Use NLP techniques to extract important keywords from text data.

[1095] Output: Extracted key keywords (e.g., hobbies, interests, place of residence)

[1096] Specific operation: The server uses an NLP library (e.g., spaCy, NLTK) to extract keywords from the text data and prepare them to be passed to the generative AI.

[1097] Step 3: Generating the initial message

[1098] The terminal calls a generative AI based on keywords received from the server and generates the initial message.

[1099] Input: Important keywords

[1100] Data processing: Input prompts and keywords into a generative AI model to generate appropriate messages.

[1101] Output: First message

[1102] Specific operation: The terminal inputs the following prompt to the generative AI: "Please generate an initial message based on keywords such as 'engineer' and 'movie watching'." The generative AI model then generates a message such as, "Hello, I see you work as an engineer. I'm also interested in technology. You mentioned you enjoy watching movies; what movies have you seen recently?"

[1103] Step 4: Confirm and send the message.

[1104] The user reviews the message generated through their device and makes any necessary corrections. They then approve the message and press the send button.

[1105] Input: Initial message generated

[1106] Data processing: The user reads the message, modifies it as needed, and finally approves it.

[1107] Output: Approved message

[1108] Specific actions: The user reads the message displayed on the device screen, adds or modifies content such as "I like movies too," and finally presses the send button.

[1109] Step 5: Generating a reply message

[1110] The device receives the reply message from the other party and analyzes its content. Based on the analysis results, it calls a generative AI to generate an appropriate reply message.

[1111] Input: Reply message from the other party

[1112] Data processing: The response content is analyzed by a generative AI to generate an appropriate response.

[1113] Output: Reply message

[1114] Specific operation: The device analyzes the message from the other party, "I recently watched a thriller movie," and generates a reply saying, "Thriller movies are great! I also had the chance to watch one recently. What scene was the most memorable for you?"

[1115] Step 6: Generating Date Proposals

[1116] The server compares the user's and the other person's profile information and generates suggested date locations based on shared interests.

[1117] Input: User and other party's profile information, common keywords

[1118] Data processing: Generate date suggestions based on shared interests and preferences.

[1119] Output: Dating proposals

[1120] Specific operation: The server generates a suggestion such as, "How about watching a movie at a movie theater in Odaiba, Tokyo? Afterwards, it would be fun to share our thoughts about the movie at a nearby cafe." based on common points such as "movie" and "Tokyo."

[1121] Step 7: Execute the plan

[1122] The user accepts the proposal, and once they reach an agreement with the other party, they create a concrete plan for the date.

[1123] Input: Date suggestion

[1124] Data processing: Based on the proposal, finalize the details of the date.

[1125] Output: Confirmed date details (date, time, location, meeting place, etc.)

[1126] Specific action: The user agrees to the proposal, decides on a specific date and time with the other party, and notifies them, "Let's meet at the movie theater in Odaiba at 2 PM on March 20th."

[1127] (Application Example 1)

[1128] 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."

[1129] In modern streaming services, a challenge exists in that users spend time and effort searching for content that matches their interests and preferences. The lack of personalized recommendation systems makes it difficult for users to select appropriate content from a large selection. Furthermore, the insufficient personalized communication and messaging to users hinders the improvement of the user experience.

[1130] 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.

[1131] In this invention, the server includes means for acquiring user profile information, extracting important keywords, and generating content recommendations for the user; means for generating personalized messages based on viewing history and profile information using generative AI and presenting them to the user; and means for further generating appropriate reply messages and next-time suggestions. This enables users to efficiently find content that matches their interests and preferences, thereby improving the user experience.

[1132] "User profile information" refers to personal information including the user's occupation, hobbies, interests, place of residence, and viewing history.

[1133] "Key keywords" are words that indicate the user's characteristics and interests, extracted from profile information using natural language processing technology.

[1134] "Generative AI" refers to artificial intelligence technology that generates appropriate messages or content based on given input data.

[1135] A "personalized message" is a message tailored to a specific user based on their profile information and viewing history.

[1136] "Viewing history" refers to a list of movies, TV shows, and other content that a user has watched so far.

[1137] "Content recommendation" refers to suggesting video content and programs that users are likely to be interested in, based on their profile information and viewing history.

[1138] A "message with adjusted tone and length" is a message whose tone and length have been appropriately adjusted to accommodate the different communication styles of various users.

[1139] A description of embodiments for carrying out this invention will be given.

[1140] Overall structure

[1141] This system consists of a user terminal, a server, and a generative AI. The terminal is responsible for presenting the user with personalized messages and content recommendations based on the user's profile information and viewing history.

[1142] Retrieving user profile information

[1143] server

[1144] When a user logs into a streaming service, the server retrieves the user's profile information and viewing history from the database. Specifically, it accesses the database and collects information such as the user's occupation, hobbies, interests, and place of residence, along with a history of the content they have watched.

[1145] Profile information analysis and keyword extraction

[1146] server

[1147] The acquired profile information is analyzed using natural language processing (NLP) techniques. This analysis extracts important keywords that indicate the user's hobbies and interests. NLP libraries such as SpaCy and NLTK are used for this process.

[1148] Content recommendation and personalized message generation

[1149] server

[1150] The server uses generative AI models (e.g., OpenAI GPT-4) to generate personalized messages and content recommendations based on the user's profile information and viewing history. The generated messages are optimized in tone and length and presented to the user in an appropriately tailored format.

[1151] Specific examples of recommendations

[1152] For example, if a user's occupation is an engineer and their hobby is watching suspense movies, the following recommendation message will be generated:

[1153] "Based on your profile information and viewing history, we'll suggest movies and TV shows that you might enjoy. Recent suspense films you've seen include 'Inception' and 'The Matrix,' and you've also shown interest in dystopian novels, right? Based on that, we highly recommend checking out the new suspense film, 'Tenet.'"

[1154] Presentation of messages and recommendations

[1155] terminal

[1156] The user's device receives personalized messages and content recommendations sent from the server and presents them to the user. The user can review these and select content that interests them.

[1157] Examples of specific prompt statements to use

[1158] "User profile information: An engineer who has always loved suspense movies, and recently enjoys reading dystopian novels. Movies he has seen include 'Inception,' 'The Matrix,' and 'Blade Runner.' Based on this, please suggest some movies and TV shows."

[1159] By using this system, users can efficiently find content that matches their interests and preferences, improving their experience using streaming services. The above describes the embodiment of this invention.

[1160] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[1161] Step 1: Obtain user profile information and viewing history.

[1162] When a user logs into a streaming service, the server retrieves the user's occupation, hobbies, interests, place of residence, and viewing history from the database. The server issues queries to the database and collects the user's personal information and viewing history based on these queries. The input data is the user ID, and the output data is the user's profile information and viewing history.

[1163] Step 2: Profile information analysis and keyword extraction

[1164] The server analyzes the acquired profile information using natural language processing (NLP) techniques. Specifically, it uses an NLP library (e.g., SpaCy or NLTK) to extract important keywords that indicate the user's hobbies and interests. The input data is profile information, and the output data is important keywords.

[1165] Step 3: Content Recommendation Generation

[1166] The server uses a generative AI model (e.g., OpenAI GPT-4) to generate content recommendations based on extracted keywords and viewing history. The server generates prompts and provides them as input to the generative AI model. Based on these prompts, the AI ​​generates a message recommending content suitable for the user. The input data consists of keywords and viewing history, and the output data is a message recommending content.

[1167] Step 4: Generating Personalized Messages

[1168] The server uses generative AI to generate personalized messages based on the user's profile information and viewing history. The generated messages are optimized in terms of tone and length to match the user's communication style. The input data is profile information and viewing history, and the output data is the personalized message.

[1169] Step 5: Presenting messages and recommendations

[1170] The device receives personalized messages and content recommendations sent from the server and presents them to the user. The user can then select content of interest based on the presented messages and recommendations. The input data consists of messages and recommendations from the server, while the output data is the content presented to the user.

[1171] Step 6: Analyze the user's response

[1172] The server analyzes the content and response messages selected by the user. This analysis provides feedback data for future recommendations and message generation. The input data consists of the user's selections and response messages, while the output data is feedback for generating future suggestions.

[1173] Step 7: Generating the next proposal

[1174] The server generates content recommendations and personalized messages based on the user's previous responses and selections. The server uses generative AI to generate these suggestions and messages and sends them to the device. Input data is feedback data, and output data is the next suggestions and messages.

[1175] This allows users to efficiently find content that matches their interests and preferences, improving their streaming service experience.

[1176] 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.

[1177] The embodiments for carrying out this invention will be described in detail, divided into each processing step.

[1178] Overall structure

[1179] This system consists of a user terminal, a server, a generative AI, and an emotion engine. The server acquires and analyzes user and recipient profile information and generates messages and suggestions. The terminal receives the generated content from the server, presents it to the user for confirmation, and allows for modification and approval as needed. The emotion engine recognizes the user's emotions in real time and adjusts appropriate message generation and suggestions based on that information.

[1180] Retrieving profile information

[1181] server

[1182] When a user logs into the matching app, the server requests profile information of their current match. Specifically, it accesses the database to retrieve personal information such as the user's and the other person's names, occupations, hobbies, interests, and place of residence. The retrieved profile information is analyzed using natural language processing (NLP) techniques to extract important keywords (e.g., hobbies, interests, place of residence).

[1183] Message generation

[1184] terminal

[1185] The device invokes a generative AI based on profile information received from the server. The generative AI generates an initial message that takes into account tone and length, based on the given profile information and the user's emotional information obtained from the emotion engine.

[1186] Specific example

[1187] For example, if the other party is an "engineer" and the user's emotion engine detects a relaxed emotion, the generative AI will generate an initial message like this:

[1188] "Hello, I see you work as an engineer. I'm also interested in technology. I read that you enjoy watching movies; what movies have you seen recently?"

[1189] Proposal generation

[1190] server

[1191] The server compares the user's and the other person's profile information and analyzes their common interests. It also considers the user's current emotional state, as determined by the emotion engine, to generate suggested date locations that align with shared hobbies and interests. For example, if both users live in Tokyo, enjoy watching movies, and are in a relaxed state, the server might suggest a nearby movie theater.

[1192] Specific example

[1193] "How about watching a movie at a cinema in Odaiba, Tokyo? Afterwards, it would be fun to share our thoughts about the movie at a nearby cafe."

[1194] Confirm and send the message.

[1195] User

[1196] Users review the generated messages and suggestions through their devices. They can adjust the message content themselves as needed. Finally, once the user approves the message and presses the send button, the message is sent to the recipient through the matching app.

[1197] Support for ongoing communication

[1198] terminal

[1199] The device receives the response message from the other party and analyzes its content. Furthermore, the emotion engine analyzes the user's emotional state, and based on that, the generative AI generates the next appropriate reply message.

[1200] Specific example

[1201] If the other person replies, "I recently watched a thriller movie," the emotion engine detects the user's excited emotions, and the generative AI generates, "Thriller movies, that's great! I also had the chance to watch one recently. What scene left the biggest impression on you?"

[1202] Next proposal generation

[1203] server

[1204] The server generates the next date plan and new topic suggestions based on ongoing interactions. The emotion engine evaluates the user's stress level and satisfaction, and adjusts the next suggestions based on the evaluation results.

[1205] Specific example

[1206] If the emotion engine assesses that the user is in a relaxed state, it might suggest, "How about going to a small film festival being held in Jiyugaoka this weekend?"

[1207] Plan execution

[1208] User

[1209] If the user accepts the proposal and an agreement is reached with the other party, a detailed date plan will be created. Finally, the details of the date (date, time, place, meeting place, etc.) will be confirmed and notified to the other party.

[1210] In summary, by using this system, users can efficiently exchange messages, receive appropriate suggestions tailored to their emotional state, build trust more easily, and ultimately lead to a first date.

[1211] The following describes the processing flow.

[1212] Program processing steps

[1213] Step 1: Obtain profile information

[1214] server

[1215] 1. Profile Request: When a user logs into the dating app, the server requests the profile information of their current match.

[1216] 2. Database Access: The server connects to the matching app's database and retrieves the user's profile information (name, occupation, hobbies, interests, and place of residence) of their potential matches.

[1217] 3. Information Analysis: The acquired profile information is analyzed using natural language processing (NLP) techniques to extract important keywords.

[1218] Step 2: Recognizing the user's emotions

[1219] terminal

[1220] 1. Acquisition of emotional data: The device uses an emotion engine to recognize the user's emotional state. For example, it analyzes facial expressions and voice tone using the camera and microphone.

[1221] 2. Emotional Data Transmission: The acquired emotional data is sent to the server.

[1222] Step 3: Generating the initial message

[1223] server

[1224] 1. Profile reception: The server integrates profile information and sentiment data received from the terminal and database.

[1225] 2. Message Generation: The generative AI generates an initial message that takes into account tone and length, based on profile information and sentiment data.

[1226] Specific example

[1227] For example, if the other party is an "engineer" and the user's emotion engine detects a relaxed emotion, the generative AI will generate an initial message like this:

[1228] "Hello, I see you work as an engineer. I'm also interested in technology. I read that you enjoy watching movies; what movies have you seen recently?"

[1229] Step 4: Proposal Generation

[1230] server

[1231] 1. Commonality Analysis: The server compares the user's and the other party's profile information and analyzes common interests.

[1232] 2. Selecting a date location: Based on shared interests and the other person's place of residence (e.g., Tokyo), generate suitable date location options.

[1233] 3. Suggestion Generation: The server generates a suggestion. "Mr. Tanaka, since you seem to enjoy movies, how about going to a movie theater in Odaiba, Tokyo? Afterwards, it would be fun to share our thoughts on the movie at a nearby cafe."

[1234] Step 5: Confirm and send the message.

[1235] User

[1236] 1. Message confirmation: The user reviews the generated messages and suggestions through their device.

[1237] 2. Content Adjustment: Users can also adjust the content of their messages as needed.

[1238] 3. Message Sending: Once the user approves the message and presses the send button, the message is sent to the other party through the matching app.

[1239] Step 6: Support for ongoing communication

[1240] terminal

[1241] 1. Reply received: The device receives a reply message from the other party.

[1242] 2. Reply Analysis: Analyze the received response message and extract important keywords.

[1243] 3. Emotion Analysis: The emotion engine analyzes the user's emotional state, and based on that, the generative AI generates the next appropriate reply message.

[1244] Specific example

[1245] If the other person replies, "I recently watched a thriller movie," the emotion engine detects the user's excited emotions, and the generative AI generates, "Thriller movies, that's great! I also had the chance to watch one recently. What scene left the biggest impression on you?"

[1246] Step 7: Next Proposal

[1247] server

[1248] 1. Next Date Suggestion Generation: The server generates the next date plan and new topic suggestions based on the ongoing interaction.

[1249] 2. Emotional Evaluation: The emotional engine evaluates the user's stress level and satisfaction level, and adjusts the next suggestion based on the evaluation results.

[1250] Specific example

[1251] If the emotion engine assesses that the user is in a relaxed state, it might suggest, "How about going to a small film festival being held in Jiyugaoka this weekend?"

[1252] Step 8: Execute the plan

[1253] User

[1254] 1. Proposal Confirmation: The user reviews the proposed plan, and if they agree with the other party, they proceed to plan the actual date.

[1255] 2. Final confirmation: Confirm the date details (date, time, location, meeting place, etc.) and notify the other person.

[1256] This allows users to exchange messages efficiently, receive appropriate suggestions tailored to their emotional state, build trust more easily, and ultimately lead to a first date.

[1257] (Example 2)

[1258] 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."

[1259] Current online dating apps and communication systems require users to spend a significant amount of time and effort to generate appropriate messages. Furthermore, it's difficult to create optimal suggestions and messages tailored to the user's emotions and situation, often resulting in one-sided communication. Additionally, the lack of features to support continuous interaction makes efficient communication challenging.

[1260] The identification processing by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for acquiring user identification information, means for acquiring the other party's identification information, means for analyzing the acquired user and other party identification information and extracting key keywords, means for generating an appropriate communication message based on the analysis results using generative artificial intelligence, means for generating proposals based on common interests of both parties, means for presenting the generated communication message and proposal to the user and obtaining the user's approval, means for sending the communication message to the other party after the user's approval, and means for analyzing the response from the other party and generating an appropriate reply message and next proposal. As a result, the user can exchange messages efficiently, and appropriate proposals are made according to the user's emotional state, making it easier to build trust and enabling smooth communication.

[1261] "User identification information" refers to information used to identify a user, including login ID, name, and contact information.

[1262] "Other party identification information" refers to information used to identify the person a user has been matched with, and includes the other party's login ID, name, contact information, etc.

[1263] "Key keywords" are important pieces of information extracted from the user's and the other party's profile information, such as hobbies, interests, and place of residence.

[1264] "Generative artificial intelligence" refers to artificial intelligence models that have the ability to generate text messages in natural language based on input data.

[1265] A "communication message" is a text-based message shared between a user and another user, including the initial message and replies.

[1266] "Shared interests" refer to the hobbies and interests that both users share, which are analyzed from their respective profile information.

[1267] A "suggestion" is a suggestion of activities or date plans that can be shared between the user and the other person based on their common interests.

[1268] "Emotional state" refers to information that indicates the current emotional state of the user or the other party, and includes states such as relaxed, excited, and stressed.

[1269] "Approval" is the act of confirming that a user agrees to the content of a generated message or proposal and confirms that they will submit it.

[1270] A "response" is a reply message sent by the other party.

[1271] "Continuous interaction" refers to the act of a user and another party exchanging messages multiple times.

[1272] This invention relates to a system that analyzes user identification information and recipient identification information, and provides appropriate message generation and suggestions using generative artificial intelligence. This system consists of a user terminal, a server, generative artificial intelligence, and an emotion engine.

[1273] Overall system configuration

[1274] This system consists of a user terminal, a server, a generative artificial intelligence system, and an emotion engine. It fully automates the generation of user messages and suggestions, and supports communication tailored to the user's emotional state.

[1275] server

[1276] The server acquires and analyzes user and other party identification information, extracts key keywords, and generates suggestions based on common interests. The acquired information and analysis results are then input into a generative artificial intelligence system.

[1277] terminal

[1278] The terminal presents the user with generated messages and suggestions sent from the server. The user reviews these using the terminal, makes any necessary modifications, and then approves them.

[1279] Generative artificial intelligence

[1280] Generative artificial intelligence generates messages of appropriate tone and length based on given identification and emotional information. Specifically, it uses natural language processing techniques to analyze prompt sentences and generate appropriate responses.

[1281] Emotional Engine

[1282] The emotion engine analyzes the user's emotional state in real time. This information is reflected in the generative artificial intelligence, influencing the tone and content of the generated messages.

[1283] Acquisition and analysis of profile information

[1284] server

[1285] When a user logs into the matching app, the server immediately retrieves the user's and the other person's identification information from the database. Then, using natural language processing (NLP) techniques, this information is analyzed to extract key keywords. An NLP library (e.g., spaCy) is used for this analysis.

[1286] Message generation

[1287] terminal

[1288] The terminal receives the analyzed profile information sent from the server and invokes the generative artificial intelligence. The generative AI then creates a prompt statement based on this information and generates the initial message.

[1289] Specific example

[1290] For example, if the other party is an engineer and the user's emotion engine detects a relaxed emotion, the generative artificial intelligence will generate the following initial message:

[1291] "Hello, I see you work as an engineer. I'm also interested in technology. I read that you enjoy watching movies; what movies have you seen recently?"

[1292] Proposal generation

[1293] server

[1294] The server generates suggestions based on the shared interests of the user and the other party. When generating suggestions, it also considers the user's emotional state, which is obtained from the emotion engine.

[1295] Specific example

[1296] "How about watching a movie at a cinema in Odaiba, Tokyo? Afterwards, it would be fun to share our thoughts about the movie at a nearby cafe."

[1297] Review and send messages and proposals.

[1298] User

[1299] The user reviews the messages and suggestions generated through their device. They modify them as needed, and after final approval, send them to the recipient via the server.

[1300] This system allows users to exchange messages efficiently through automated processes, enabling smoother and more emotionally-driven communication.

[1301] The flow of the specific processing in Example 2 will be explained using Figure 13.

[1302] Step 1: User Login

[1303] User

[1304] The user logs into the matching app. The input is the user's identification information (e.g., user ID and password). The device sends this to the server, and the user is authenticated. The output is the session information provided after the user has been authenticated.

[1305] Step 2: Obtain profile information

[1306] server

[1307] When a user logs in, the server retrieves user and other user identification information from the database. The input is the user's identification information (e.g., User ID). The server issues a query to the database like the following:

[1308] sql

[1309] SELECT Name, Occupation, Hobbies, Interests, Location FROM Profile WHERE User ID = 'USER_ID';

[1310] The output consists of the retrieved user and other party profile information.

[1311] Step 3: Analyze profile information

[1312] server

[1313] The server analyzes the acquired profile information using natural language processing technology (e.g., spaCy). The input is the user's and the other party's profile information. Through analysis, key keywords (e.g., hobbies, interests, place of residence) are extracted. The output is a list of the extracted keywords.

[1314] Step 4: Create a prompt for message generation

[1315] terminal

[1316] The terminal generates prompts for the AI ​​model based on the parsed profile information received from the server. The input consists of the parsed profile information and the user's sentiment information. The prompts are constructed, for example, as follows:

[1317] "The recipient is an engineer, and the user is relaxed. Please generate the initial message."

[1318] The output is a prompt message for the generative AI model.

[1319] Step 5: Generating the initial message

[1320] terminal

[1321] The terminal invokes a generative AI model (e.g., GPT-3) and generates the initial message using the prompt text as input. The input is the prompt text. The generative AI model uses natural language processing techniques to generate the initial message based on the given prompt text. The output is the generated initial message.

[1322] Step 6: Confirm the message and proposal

[1323] User

[1324] The user reviews the generated message and suggestions via the terminal. The input consists of the generated message and suggestions presented by the terminal. The user reviews these and makes modifications as needed. The output is the final message approved by the user.

[1325] Step 7: Sending a message

[1326] User

[1327] When the user presses the approve button, the device sends a message to the server. The input is the final message approved by the user. The server sends this message to the recipient using the matching app's messaging API. The output is confirmation that the message was sent to the recipient.

[1328] Step 8: Support for ongoing communication

[1329] terminal

[1330] The device receives the reply message from the other party and analyzes its content. The emotion engine analyzes the user's real-time emotional state, and the generation AI generates the next appropriate reply message. The input is the other party's reply message and the user's emotional information. The output is the generated next reply message.

[1331] Step 9: Generating the next proposal

[1332] server

[1333] The server generates the next date plan and topic suggestions based on the content of the ongoing interaction and feedback from the emotion engine. The input is the content of the interaction and emotion information. The server generates new suggestions based on this. The output is the suggestions for the next date.

[1334] Step 10: Execute the plan

[1335] User

[1336] If the user accepts the proposal and reaches an agreement with the other party, they will then plan a specific date. The input is the agreement between the user and the other party. The user confirms details such as the date, time, place, and meeting place, and then sends a final confirmation to the other party via the device. The output is the finalized date details.

[1337] (Application Example 2)

[1338] 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."

[1339] Traditional workplace communication has not adequately supported improvements in work efficiency and safety. In particular, there is a need for a system that can grasp workers' emotions and fatigue levels in real time and propose appropriate breaks and tasks accordingly. Therefore, balancing worker health and productivity is a key challenge.

[1340] In Application Example 2, the specific processing performed by the specific processing unit 290 of the data processing device 12 is realized by the following means. In this invention, the server includes means for acquiring user profile information, means for acquiring the other party's profile information, means for analyzing the acquired user and other party profile information and extracting important keywords, means for generating an appropriate message based on the analysis results using a generative AI, means for generating dating destination candidates based on common interests, means for presenting the generated message or suggestion to the user and obtaining user approval, means for sending the message to the other party after user approval, means for analyzing the other party's reply and generating an even more appropriate reply message or next suggestion, means for recognizing the emotional state from voice input using an emotion engine and adjusting the content of the dialogue accordingly, and means for generating work suggestions and break suggestions based on the worker's profile information and emotional state in a factory environment. This makes it possible to make appropriate work suggestions and break suggestions in real time based on the worker's profile information and emotional state.

[1341] "Means for obtaining user profile information" refers to devices or methods for collecting personal information such as a user's name, occupation, hobbies, interests, and place of residence.

[1342] "Means of obtaining the other party's profile information" refers to devices or methods for collecting personal information such as the name, occupation, hobbies, interests, and place of residence of a user through matching apps or similar means.

[1343] "Means for extracting important keywords" refers to a device or method that uses natural language processing technology to extract highly relevant keywords such as hobbies and interests from collected profile information.

[1344] "Means for generating appropriate messages using generative AI" refers to a device or method that automatically creates a message that takes into account the tone and length of the conversation, based on given information and using artificial intelligence technology.

[1345] "Means for generating date destination candidates" refers to a device or method that suggests date destinations based on the shared hobbies and interests of the user and their partner.

[1346] "Means of presenting to the user and obtaining user approval" refers to a device or method for displaying generated messages or suggestions to the user and obtaining their approval to review and modify their content.

[1347] "Means of sending a message to another party" refers to a device or method for sending an approved message to another party through a matching app or similar means.

[1348] "Means for analyzing replies from the other party and generating more appropriate reply messages and suggestions for the next step" refers to a device or method that analyzes the content of a reply from the other party and automatically creates appropriate reply messages and suggestions for the next step according to the content and the user's emotional state.

[1349] "Means for recognizing emotional states from voice input using an emotion engine" refers to a device or method that analyzes voice input to recognize the user's emotional state in real time.

[1350] "Means for generating work and rest suggestions based on worker profile information and emotional state in a factory environment" refers to a device or method that makes appropriate work and rest suggestions based on the profile information and emotional state of workers in a factory.

[1351] This invention is a system designed to streamline worker communication in a factory environment and achieve both worker health and productivity. The system consists of a user terminal, a server, a generative AI, and an emotion engine.

[1352] Overall structure

[1353] This system acquires user and recipient profile information, analyzes this information, and extracts important keywords. The generative AI generates and suggests appropriate messages based on this analysis. Furthermore, the emotion engine recognizes the emotional state in real time from voice input and adjusts the message content and tone based on that information.

[1354] Acquisition and analysis of user profile information

[1355] server

[1356] When a user logs into the system, the server retrieves their profile information (name, occupation, hobbies, interests, place of residence, etc.) from the database. The retrieved profile information is then analyzed using natural language processing (NLP) techniques to extract important keywords.

[1357] Recognition of emotional states

[1358] Emotional Engine

[1359] The emotion engine analyzes the worker's voice input to recognize their emotional state in real time. This is done using EmotionRecognizer software. For example, if a worker says "I'm tired," the emotion engine detects the emotion "fatigue."

[1360] Message generation

[1361] Generative AI

[1362] The generative AI generates dialogue messages based on analysis results and emotional states. The GPT-2 model is used here to generate appropriate responses based on prompt sentences.

[1363] For example, if a worker says, "Today's work is tough," the emotion engine detects "fatigue." Based on this information, the generative AI generates a message with the following prompt:

[1364] Prompt message:

[1365] Worker: Engineer, in his 30s, hobby is watching movies.

[1366] Emotional state: Fatigue

[1367] Message: Today's work is tough.

[1368] Robot: I think you should take a short break today. How about resting at a nearby rest area?

[1369] Proposal generation

[1370] server

[1371] The server generates work and break suggestions based on the worker's profile information and emotional state, aligning with their common hobbies and interests. For example, if a worker is an engineer and their hobby is watching movies, the server might suggest, "How about watching a movie at a nearby rest area?"

[1372] Confirm and send the message.

[1373] User

[1374] Users can review the messages and suggestions presented by the system and make modifications as needed. Finally, once the user approves the message and presses the send button, it is sent to the worker.

[1375] Support for continuous communication

[1376] terminal

[1377] The terminal receives a response message from the worker and analyzes its content. The emotion engine analyzes the emotion again, and the generative AI generates the next appropriate message. For example, if the worker says, "I recently watched a movie," the emotion engine detects "excitement," and the generative AI generates a message like this:

[1378] Prompt message:

[1379] Worker: I recently watched a movie.

[1380] Emotional state: Excitement

[1381] Message: That's wonderful! Which scene left the biggest impression on you?

[1382] Through the steps outlined above, this system allows workers to communicate efficiently and receive appropriate breaks and work suggestions. Furthermore, real-time emotional analysis can improve work efficiency and overall well-being.

[1383] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[1384] Step 1:

[1385] Retrieve the user's profile information.

[1386] When a worker logs into the system, the server retrieves personal information from the database, such as the worker's name, occupation, hobbies, interests, and place of residence. It then creates a query for the database search and extracts the relevant records. It receives the worker ID as input and obtains profile information as output.

[1387] Step 2:

[1388] Retrieve the other person's profile information.

[1389] The server retrieves the profile information of the worker's counterpart from the database. Here too, database access technology is used to retrieve the counterpart's information. It receives the counterpart's worker ID as input and obtains the counterpart's profile information as output.

[1390] Step 3:

[1391] The acquired information is analyzed, and important keywords are extracted.

[1392] The server analyzes the acquired profile information of the worker and the other party using natural language processing (NLP) techniques to extract important keywords such as hobbies, interests, and occupation. It receives profile information as input and obtains important keywords as output.

[1393] Step 4:

[1394] To recognize emotional states.

[1395] The terminal analyzes the worker's voice input using an EmotionRecognizer and recognizes their emotional state in real time. For example, if the voice input is "Today's work is tough," the emotion engine detects the emotion "fatigue." It receives voice data as input and obtains an emotional state as output.

[1396] Step 5:

[1397] Generate an appropriate message based on the analysis results.

[1398] The generative AI (GPT-2 model) generates messages with adjusted tone and length based on profile information and emotional state. It receives important keywords, emotional state, and an initial message as input, and outputs a generated message.

[1399] Step 6:

[1400] The generated message and proposal are presented to the user, and their approval is obtained.

[1401] The terminal displays the generated messages and suggestions to the worker for review. The worker can then modify or approve the messages. It receives the generated messages as input and the approved messages as output.

[1402] Step 7:

[1403] Send a message to the recipient.

[1404] The terminal sends the approved message to the worker. Here, a communication protocol is used to send the message data. It receives the approved message as input and obtains the message sent to the other party as output.

[1405] Step 8:

[1406] Analyze the response from the other party and generate the next appropriate response.

[1407] The server receives the reply message from the other party and analyzes the emotional state using an emotion engine. Then, it uses a generative AI to generate the next appropriate reply message. It receives the reply message as input and obtains the generated reply message as output.

[1408] Step 9:

[1409] We support continuous dialogue and suggestions.

[1410] The terminal presents the worker with generated reply messages and suggestions for the next steps, supporting continuous dialogue. It receives new messages as input and the presented messages as output.

[1411] 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.

[1412] 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.

[1413] 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.

[1414] [Fourth Embodiment]

[1415] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[1416] 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.

[1417] 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).

[1418] 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.

[1419] 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.

[1420] 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).

[1421] 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.

[1422] 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.

[1423] 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.

[1424] 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.

[1425] 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.

[1426] 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.

[1427] 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".

[1428] The embodiments for carrying out this invention are described below.

[1429] Overall structure

[1430] This system consists of a user terminal, a server, and a generative AI. The server acquires and analyzes user and recipient profile information, and generates messages and suggestions. The terminal receives the generated content from the server, presents it to the user for confirmation, and performs operations such as modification and approval as needed.

[1431] Retrieving profile information

[1432] server

[1433] When a user logs into the matching app, the server requests profile information of their current match. Specifically, it accesses the database to retrieve personal information such as the user's and their match's names, occupations, hobbies, interests, and place of residence. The retrieved profile information is analyzed using natural language processing (NLP) techniques to extract important keywords (e.g., hobbies, interests, place of residence).

[1434] Message generation

[1435] terminal

[1436] The terminal invokes a generative AI based on profile information received from the server. The generative AI then generates an initial message that takes into account tone and length, based on the given profile information.

[1437] Specific example

[1438] For example, if the recipient is an "engineer," a generative AI will generate the following initial message:

[1439] "Hello, I see you work as an engineer. I'm also interested in the technical field. I understand you enjoy watching movies; what movies have you seen recently?"

[1440] Proposal generation

[1441] server

[1442] The server compares the user's and the other person's profile information and analyzes their common interests. Based on this, it generates suggested date locations that match their shared hobbies and interests. For example, if both live in Tokyo and enjoy watching movies, the server will suggest nearby movie theaters.

[1443] Specific example

[1444] "How about watching a movie at a cinema in Odaiba, Tokyo? Afterwards, it would be fun to share our thoughts about the movie at a nearby cafe."

[1445] Confirm and send the message.

[1446] User

[1447] Users review the generated messages and suggestions through their devices. They can adjust the message content themselves as needed. Finally, once the user approves the message and presses the send button, the message is sent to the recipient through the matching app.

[1448] Support for ongoing communication

[1449] terminal

[1450] The device receives a response message from the other party and analyzes its content. A generative AI then generates an appropriate reply message based on the response content.

[1451] Specific example

[1452] If the other person replies, "I recently watched a thriller movie," the generative AI will generate a response such as, "Thriller movies, that's great! I also had the chance to watch one recently. What scene was the most memorable for you?" It can also suggest future date plans or new topics of conversation.

[1453] Plan execution

[1454] User

[1455] If the user accepts the proposal and an agreement is reached with the other party, a concrete date plan is created. Finally, the details of the date (date, time, place, meeting place, etc.) are confirmed and notified to the other party.

[1456] Therefore, by using this system, users can efficiently exchange messages, build trust more easily, and ultimately lead to a first date.

[1457] The following describes the processing flow.

[1458] Program processing steps

[1459] Step 1: Obtain profile information

[1460] server

[1461] 1. Profile Request: When a user logs into the dating app, the server requests the profile information of their current match.

[1462] 2. Database Access: The server connects to the matching app's database and retrieves the user's profile information (name, occupation, hobbies, interests, and place of residence) of their potential matches.

[1463] 3. Information Analysis: The acquired profile information is analyzed using natural language processing (NLP) techniques to extract important keywords.

[1464] Step 2: Generating the initial message

[1465] terminal

[1466] 1. Profile reception: The device calls a generative AI based on the profile information it receives from the server.

[1467] 2. Message Generation: The generative AI generates an initial message, taking into account tone and length, based on the given profile information.

[1468] Specific example: Initial message generated by the device: "Hello, Mr. Tanaka. Your work as an engineer is wonderful! I'm also interested in the technical field. I read that you enjoy watching movies; what movies have you seen recently?"

[1469] Step 3: Proposal Generation

[1470] server

[1471] 1. Commonality Analysis: The server compares the user's and the other party's profile information and analyzes common interests.

[1472] 2. Selecting a date location: Based on shared interests and the other person's place of residence (e.g., Tokyo), generate suitable date location options.

[1473] 3. Suggestion Generation: The server generates a suggestion. "Mr. Tanaka, since you seem to enjoy movies, how about going to a movie theater in Odaiba, Tokyo? Afterwards, it would be fun to share our thoughts on the movie at a nearby cafe."

[1474] Step 4: Confirm and send the message.

[1475] User

[1476] 1. Message confirmation: The user reviews the generated messages and suggestions through their device.

[1477] 2. Content Adjustment: Users can also adjust the content of their messages as needed.

[1478] 3. Message Sending: Once the user approves the message and presses the send button, the message is sent to the other party through the matching app.

[1479] Step 5: Support for ongoing communication

[1480] terminal

[1481] 1. Reply received: The device receives a reply message from the other party.

[1482] 2. Reply Analysis: Analyze the received response message and extract important keywords.

[1483] 3. Reply message generation: Generative AI is used to generate appropriate reply messages based on the analysis results.

[1484] For example, if the other person replies, "I recently watched a thriller movie," the generative AI will generate a response like, "Thriller movies, that's great! I also had the chance to watch one recently. What scene left the biggest impression on you?"

[1485] Step 6: Next Proposal

[1486] server

[1487] 1. Next Date Suggestion Generation: The server generates the next date plan and new topic suggestions based on the ongoing interaction.

[1488] Specific example: "How about going to a small film festival being held in Jiyugaoka this weekend?"

[1489] Step 7: Execute the plan

[1490] User

[1491] 1. Proposal Confirmation: The user reviews the proposed plan, and if they agree with the other party, they proceed to plan the actual date.

[1492] 2. Final confirmation: Confirm the date details (date, time, location, meeting place, etc.) and notify the other person.

[1493] This allows users to exchange messages efficiently, build trust more easily, and potentially lead to a first date.

[1494] (Example 1)

[1495] 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".

[1496] In traditional dating apps, users often spend time and effort crafting their initial messages and date proposals, which can make effective communication difficult. Similar problems arise when considering appropriate replies and future date suggestions. This makes it difficult for users to build trust and hinders smooth communication.

[1497] 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.

[1498] In this invention, the server includes means for authenticating user identification information, means for acquiring user profile information, means for acquiring the other party's profile information, means for analyzing the acquired user and other party profile information and extracting important keywords, means for generating appropriate messages based on the analysis results using a generative AI model, means for generating date destination candidates based on common interests, means for presenting the generated messages and suggestions to the user and obtaining user approval, means for sending the message to the other party after user approval, and means for analyzing the other party's reply and generating even more appropriate reply messages and next suggestions using a generative AI model. This makes it possible for users to efficiently exchange impressive messages and even propose dates.

[1499] "User identification information" refers to information used to identify a user, and is used for login authentication, etc.

[1500] "Profile information" refers to personal information about the user and the other party, including name, occupation, hobbies, interests, and place of residence.

[1501] A "generative AI model" is an algorithm that uses artificial intelligence technology to generate text, and includes natural language processing technology.

[1502] "Natural language processing" is a technology for analyzing, understanding, and generating data from text, including keyword extraction and sentiment analysis.

[1503] "Key keywords" are major words and phrases extracted from profile information that are related to the user's and the other person's hobbies and interests.

[1504] A "message" is text generated by a generative AI model and exchanged between a user and another party.

[1505] "Date destination suggestions" are places to go on a date that are proposed based on shared interests and preferences.

[1506] "Approval" refers to the act of a user reviewing and agreeing to the generated message or proposal.

[1507] A "reply message" is text generated in response to a message sent by the other party.

[1508] "Next suggestion" refers to a suggestion regarding the next interaction or date, and is related information generated along with the reply message.

[1509] The embodiments for carrying out this invention are described below.

[1510] Overall structure

[1511] This system consists of a user terminal, a server, and a generative AI. The server acquires and analyzes user and recipient profile information, and generates messages and suggestions. The terminal receives the generated content from the server, presents it to the user for confirmation, and performs operations such as modification and approval as needed.

[1512] Hardware and software to be used

[1513] Device: Use a smartphone or personal computer.

[1514] Server: A server computer with high-speed processing capabilities is used to perform tasks such as acquiring and analyzing profile information and generating dating suggestions.

[1515] Generative AI: Uses a generative AI model (e.g., OpenAI's ChatGPT) and natural language processing techniques (e.g., spaCy, NLTK).

[1516] Retrieving profile information

[1517] When a user logs into the matching app, the server performs the following actions: It authenticates the user's identification information and accesses the database to retrieve the user's and their potential matches' profile information. This information includes name, occupation, hobbies, interests, and place of residence. The retrieved information is temporarily stored.

[1518] example:

[1519] When the server receives a login event, it executes a database query to retrieve profile information corresponding to the user's ID.

[1520] Analysis of profile information

[1521] The server analyzes the acquired profile information using natural language processing (NLP) techniques. It extracts important keywords (hobbies, interests, place of residence, etc.) and prepares them to be passed to a generative AI.

[1522] example:

[1523] We will use an NLP library (e.g., spaCy, NLTK) to extract important keywords from text data.

[1524] Message generation

[1525] The terminal calls a generative AI based on keywords received from the server to generate the initial message. The generated message is then sent to the server.

[1526] example:

[1527] The device receives keywords such as "engineer" and "watching movies" and generates a message like, "Hello, I see you work as an engineer. I'm also interested in technology. You mentioned you enjoy watching movies; what movies have you seen recently?"

[1528] Confirm and send the message.

[1529] The user reviews the message generated through their device and makes any necessary corrections. After approving the message and pressing the send button, the message is sent to the recipient.

[1530] example:

[1531] The user reads the message displayed on their device screen, adds or modifies it with something like "I like movies too," and finally presses the send button.

[1532] Generating a reply message

[1533] The device receives the reply message from the other party and analyzes its content. Based on the analysis results, it calls a generative AI to generate an appropriate reply message. After that, it presents the new message to the user.

[1534] example:

[1535] If the other person replies, "I recently watched a thriller movie," the system will generate a reply such as, "Thriller movies are great! I also had the chance to watch one recently. What scene left the biggest impression on you?"

[1536] Generating date proposals

[1537] The server compares the user's and the other person's profile information and generates suggested date locations based on shared interests. The generated suggestions are then sent to the user.

[1538] example:

[1539] The system generates a suggestion: "How about watching a movie at a cinema in Odaiba, Tokyo? Afterwards, it would be fun to share our thoughts about the movie at a nearby cafe."

[1540] Plan execution

[1541] The user accepts the proposal, and once an agreement is reached with the other party, they create a detailed date plan. Finally, they notify the other party of the details such as the date, time, place, and meeting place.

[1542] example:

[1543] The message reads, "Let's meet at the movie theater in Odaiba at 2 PM on March 20th."

[1544] Examples of prompts for generative AI models

[1545] "Generate the initial message based on the user's profile information."

[1546] "Please create a date proposal based on the other person's profile."

[1547] This system allows users to exchange messages efficiently, build trust more easily, and ultimately lead to a first date.

[1548] The flow of the specific processing in Example 1 will be explained using Figure 11.

[1549] Step 1: User login and retrieval of profile information

[1550] When a user logs into the matching app, the server authenticates the user's identity. If authentication is successful, the following processes are performed.

[1551] Input: User ID, Password

[1552] Data processing: Verify the user ID and password on the authentication server.

[1553] Output: If the user ID is valid, retrieve the user and the other party's profile information from the database.

[1554] Specific operation: The server accesses the database and retrieves and temporarily stores information such as the user's and the other party's names, occupations, hobbies, interests, and place of residence.

[1555] Step 2: Analyze profile information

[1556] The server analyzes the acquired user and other party profile information using natural language processing (NLP) technology.

[1557] Input: User and other party's profile information

[1558] Data processing: Use NLP techniques to extract important keywords from text data.

[1559] Output: Extracted key keywords (e.g., hobbies, interests, place of residence)

[1560] Specific operation: The server uses an NLP library (e.g., spaCy, NLTK) to extract keywords from the text data and prepare them to be passed to the generative AI.

[1561] Step 3: Generating the initial message

[1562] The terminal calls a generative AI based on keywords received from the server and generates the initial message.

[1563] Input: Important keywords

[1564] Data processing: Input prompts and keywords into a generative AI model to generate appropriate messages.

[1565] Output: First message

[1566] Specific operation: The terminal inputs the following prompt to the generative AI: "Please generate an initial message based on keywords such as 'engineer' and 'movie watching'." The generative AI model then generates a message such as, "Hello, I see you work as an engineer. I'm also interested in technology. You mentioned you enjoy watching movies; what movies have you seen recently?"

[1567] Step 4: Confirm and send the message.

[1568] The user reviews the message generated through their device and makes any necessary corrections. They then approve the message and press the send button.

[1569] Input: Initial message generated

[1570] Data processing: The user reads the message, modifies it as needed, and finally approves it.

[1571] Output: Approved message

[1572] Specific actions: The user reads the message displayed on the device screen, adds or modifies content such as "I like movies too," and finally presses the send button.

[1573] Step 5: Generating a reply message

[1574] The device receives the reply message from the other party and analyzes its content. Based on the analysis results, it calls a generative AI to generate an appropriate reply message.

[1575] Input: Reply message from the other party

[1576] Data processing: The response content is analyzed by a generative AI to generate an appropriate response.

[1577] Output: Reply message

[1578] Specific operation: The device analyzes the message from the other party, "I recently watched a thriller movie," and generates a reply saying, "Thriller movies are great! I also had the chance to watch one recently. What scene was the most memorable for you?"

[1579] Step 6: Generating Date Proposals

[1580] The server compares the user's and the other person's profile information and generates suggested date locations based on shared interests.

[1581] Input: User and other party's profile information, common keywords

[1582] Data processing: Generate date suggestions based on shared interests and preferences.

[1583] Output: Dating proposals

[1584] Specific operation: The server generates a suggestion such as, "How about watching a movie at a movie theater in Odaiba, Tokyo? Afterwards, it would be fun to share our thoughts about the movie at a nearby cafe." based on common points such as "movie" and "Tokyo."

[1585] Step 7: Execute the plan

[1586] The user accepts the proposal, and once they reach an agreement with the other party, they create a concrete plan for the date.

[1587] Input: Date suggestion

[1588] Data processing: Based on the proposal, finalize the details of the date.

[1589] Output: Confirmed date details (date, time, location, meeting place, etc.)

[1590] Specific action: The user agrees to the proposal, decides on a specific date and time with the other party, and notifies them, "Let's meet at the movie theater in Odaiba at 2 PM on March 20th."

[1591] (Application Example 1)

[1592] 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".

[1593] In modern streaming services, a challenge exists in that users spend time and effort searching for content that matches their interests and preferences. The lack of personalized recommendation systems makes it difficult for users to select appropriate content from a large selection. Furthermore, the insufficient personalized communication and messaging to users hinders the improvement of the user experience.

[1594] 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.

[1595] In this invention, the server includes means for acquiring user profile information, extracting important keywords, and generating content recommendations for the user; means for generating personalized messages based on viewing history and profile information using generative AI and presenting them to the user; and means for further generating appropriate reply messages and next-time suggestions. This enables users to efficiently find content that matches their interests and preferences, thereby improving the user experience.

[1596] "User profile information" refers to personal information including the user's occupation, hobbies, interests, place of residence, and viewing history.

[1597] "Key keywords" are words that indicate the user's characteristics and interests, extracted from profile information using natural language processing technology.

[1598] "Generative AI" refers to artificial intelligence technology that generates appropriate messages or content based on given input data.

[1599] A "personalized message" is a message tailored to a specific user based on their profile information and viewing history.

[1600] "Viewing history" refers to a list of movies, TV shows, and other content that a user has watched so far.

[1601] "Content recommendation" refers to suggesting video content and programs that users are likely to be interested in, based on their profile information and viewing history.

[1602] A "message with adjusted tone and length" is a message whose tone and length have been appropriately adjusted to accommodate the different communication styles of various users.

[1603] A description of embodiments for carrying out this invention will be given.

[1604] Overall structure

[1605] This system consists of a user terminal, a server, and a generative AI. The terminal is responsible for presenting the user with personalized messages and content recommendations based on the user's profile information and viewing history.

[1606] Retrieving user profile information

[1607] server

[1608] When a user logs into a streaming service, the server retrieves the user's profile information and viewing history from the database. Specifically, it accesses the database and collects information such as the user's occupation, hobbies, interests, and place of residence, along with a history of the content they have watched.

[1609] Profile information analysis and keyword extraction

[1610] server

[1611] The acquired profile information is analyzed using natural language processing (NLP) techniques. This analysis extracts important keywords that indicate the user's hobbies and interests. NLP libraries such as SpaCy and NLTK are used for this process.

[1612] Content recommendation and personalized message generation

[1613] server

[1614] The server uses generative AI models (e.g., OpenAI GPT-4) to generate personalized messages and content recommendations based on the user's profile information and viewing history. The generated messages are optimized in tone and length and presented to the user in an appropriately tailored format.

[1615] Specific examples of recommendations

[1616] For example, if a user's occupation is an engineer and their hobby is watching suspense movies, the following recommendation message will be generated:

[1617] "Based on your profile information and viewing history, we'll suggest movies and TV shows that you might enjoy. Recent suspense films you've seen include 'Inception' and 'The Matrix,' and you've also shown interest in dystopian novels, right? Based on that, we highly recommend checking out the new suspense film, 'Tenet.'"

[1618] Presentation of messages and recommendations

[1619] terminal

[1620] The user's device receives personalized messages and content recommendations sent from the server and presents them to the user. The user can review these and select content that interests them.

[1621] Examples of specific prompt statements to use

[1622] "User profile information: An engineer who has always loved suspense movies, and recently enjoys reading dystopian novels. Movies he has seen include 'Inception,' 'The Matrix,' and 'Blade Runner.' Based on this, please suggest some movies and TV shows."

[1623] By using this system, users can efficiently find content that matches their interests and preferences, improving their experience using streaming services. The above describes the embodiment of this invention.

[1624] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[1625] Step 1: Obtain user profile information and viewing history.

[1626] When a user logs into a streaming service, the server retrieves the user's occupation, hobbies, interests, place of residence, and viewing history from the database. The server issues queries to the database and collects the user's personal information and viewing history based on these queries. The input data is the user ID, and the output data is the user's profile information and viewing history.

[1627] Step 2: Profile information analysis and keyword extraction

[1628] The server analyzes the acquired profile information using natural language processing (NLP) techniques. Specifically, it uses an NLP library (e.g., SpaCy or NLTK) to extract important keywords that indicate the user's hobbies and interests. The input data is profile information, and the output data is important keywords.

[1629] Step 3: Content Recommendation Generation

[1630] The server uses a generative AI model (e.g., OpenAI GPT-4) to generate content recommendations based on extracted keywords and viewing history. The server generates prompts and provides them as input to the generative AI model. Based on these prompts, the AI ​​generates a message recommending content suitable for the user. The input data consists of keywords and viewing history, and the output data is a message recommending content.

[1631] Step 4: Generating Personalized Messages

[1632] The server uses generative AI to generate personalized messages based on the user's profile information and viewing history. The generated messages are optimized in terms of tone and length to match the user's communication style. The input data is profile information and viewing history, and the output data is the personalized message.

[1633] Step 5: Presenting messages and recommendations

[1634] The device receives personalized messages and content recommendations sent from the server and presents them to the user. The user can then select content of interest based on the presented messages and recommendations. The input data consists of messages and recommendations from the server, while the output data is the content presented to the user.

[1635] Step 6: Analyze the user's response

[1636] The server analyzes the content and response messages selected by the user. This analysis provides feedback data for future recommendations and message generation. The input data consists of the user's selections and response messages, while the output data is feedback for generating future suggestions.

[1637] Step 7: Generating the next proposal

[1638] The server generates content recommendations and personalized messages based on the user's previous responses and selections. The server uses generative AI to generate these suggestions and messages and sends them to the device. Input data is feedback data, and output data is the next suggestions and messages.

[1639] This allows users to efficiently find content that matches their interests and preferences, improving their streaming service experience.

[1640] 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.

[1641] The embodiments for carrying out this invention will be described in detail, divided into each processing step.

[1642] Overall structure

[1643] This system consists of a user terminal, a server, a generative AI, and an emotion engine. The server acquires and analyzes user and recipient profile information and generates messages and suggestions. The terminal receives the generated content from the server, presents it to the user for confirmation, and allows for modification and approval as needed. The emotion engine recognizes the user's emotions in real time and adjusts appropriate message generation and suggestions based on that information.

[1644] Retrieving profile information

[1645] server

[1646] When a user logs into the matching app, the server requests profile information of their current match. Specifically, it accesses the database to retrieve personal information such as the user's and the other person's names, occupations, hobbies, interests, and place of residence. The retrieved profile information is analyzed using natural language processing (NLP) techniques to extract important keywords (e.g., hobbies, interests, place of residence).

[1647] Message generation

[1648] terminal

[1649] The device invokes a generative AI based on profile information received from the server. The generative AI generates an initial message that takes into account tone and length, based on the given profile information and the user's emotional information obtained from the emotion engine.

[1650] Specific example

[1651] For example, if the other party is an "engineer" and the user's emotion engine detects a relaxed emotion, the generative AI will generate an initial message like this:

[1652] "Hello, I see you work as an engineer. I'm also interested in technology. I read that you enjoy watching movies; what movies have you seen recently?"

[1653] Proposal generation

[1654] server

[1655] The server compares the user's and the other person's profile information and analyzes their common interests. It also considers the user's current emotional state, as determined by the emotion engine, to generate suggested date locations that align with shared hobbies and interests. For example, if both users live in Tokyo, enjoy watching movies, and are in a relaxed state, the server might suggest a nearby movie theater.

[1656] Specific example

[1657] "How about watching a movie at a cinema in Odaiba, Tokyo? Afterwards, it would be fun to share our thoughts about the movie at a nearby cafe."

[1658] Confirm and send the message.

[1659] User

[1660] Users review the generated messages and suggestions through their devices. They can adjust the message content themselves as needed. Finally, once the user approves the message and presses the send button, the message is sent to the recipient through the matching app.

[1661] Support for ongoing communication

[1662] terminal

[1663] The device receives the response message from the other party and analyzes its content. Furthermore, the emotion engine analyzes the user's emotional state, and based on that, the generative AI generates the next appropriate reply message.

[1664] Specific example

[1665] If the other person replies, "I recently watched a thriller movie," the emotion engine detects the user's excited emotions, and the generative AI generates, "Thriller movies, that's great! I also had the chance to watch one recently. What scene left the biggest impression on you?"

[1666] Next proposal generation

[1667] server

[1668] The server generates the next date plan and new topic suggestions based on ongoing interactions. The emotion engine evaluates the user's stress level and satisfaction, and adjusts the next suggestions based on the evaluation results.

[1669] Specific example

[1670] If the emotion engine assesses that the user is in a relaxed state, it might suggest, "How about going to a small film festival being held in Jiyugaoka this weekend?"

[1671] Plan execution

[1672] User

[1673] If the user accepts the proposal and an agreement is reached with the other party, a detailed date plan will be created. Finally, the details of the date (date, time, place, meeting place, etc.) will be confirmed and notified to the other party.

[1674] In summary, by using this system, users can efficiently exchange messages, receive appropriate suggestions tailored to their emotional state, build trust more easily, and ultimately lead to a first date.

[1675] The following describes the processing flow.

[1676] Program processing steps

[1677] Step 1: Obtain profile information

[1678] server

[1679] 1. Profile Request: When a user logs into the dating app, the server requests the profile information of their current match.

[1680] 2. Database Access: The server connects to the matching app's database and retrieves the user's profile information (name, occupation, hobbies, interests, and place of residence) of their potential matches.

[1681] 3. Information Analysis: The acquired profile information is analyzed using natural language processing (NLP) techniques to extract important keywords.

[1682] Step 2: Recognizing the user's emotions

[1683] terminal

[1684] 1. Acquisition of emotional data: The device uses an emotion engine to recognize the user's emotional state. For example, it analyzes facial expressions and voice tone using the camera and microphone.

[1685] 2. Emotional Data Transmission: The acquired emotional data is sent to the server.

[1686] Step 3: Generating the initial message

[1687] server

[1688] 1. Profile reception: The server integrates profile information and sentiment data received from the terminal and database.

[1689] 2. Message Generation: The generative AI generates an initial message that takes into account tone and length, based on profile information and sentiment data.

[1690] Specific example

[1691] For example, if the other party is an "engineer" and the user's emotion engine detects a relaxed emotion, the generative AI will generate an initial message like this:

[1692] "Hello, I see you work as an engineer. I'm also interested in technology. I read that you enjoy watching movies; what movies have you seen recently?"

[1693] Step 4: Proposal Generation

[1694] server

[1695] 1. Commonality Analysis: The server compares the user's and the other party's profile information and analyzes common interests.

[1696] 2. Selecting a date location: Based on shared interests and the other person's place of residence (e.g., Tokyo), generate suitable date location options.

[1697] 3. Suggestion Generation: The server generates a suggestion. "Mr. Tanaka, since you seem to enjoy movies, how about going to a movie theater in Odaiba, Tokyo? Afterwards, it would be fun to share our thoughts on the movie at a nearby cafe."

[1698] Step 5: Confirm and send the message.

[1699] User

[1700] 1. Message confirmation: The user reviews the generated messages and suggestions through their device.

[1701] 2. Content Adjustment: Users can also adjust the content of their messages as needed.

[1702] 3. Message Sending: Once the user approves the message and presses the send button, the message is sent to the other party through the matching app.

[1703] Step 6: Support for ongoing communication

[1704] terminal

[1705] 1. Reply received: The device receives a reply message from the other party.

[1706] 2. Reply Analysis: Analyze the received response message and extract important keywords.

[1707] 3. Emotion Analysis: The emotion engine analyzes the user's emotional state, and based on that, the generative AI generates the next appropriate reply message.

[1708] Specific example

[1709] If the other person replies, "I recently watched a thriller movie," the emotion engine detects the user's excited emotions, and the generative AI generates, "Thriller movies, that's great! I also had the chance to watch one recently. What scene left the biggest impression on you?"

[1710] Step 7: Next Proposal

[1711] server

[1712] 1. Next Date Suggestion Generation: The server generates the next date plan and new topic suggestions based on the ongoing interaction.

[1713] 2. Emotional Evaluation: The emotional engine evaluates the user's stress level and satisfaction level, and adjusts the next suggestion based on the evaluation results.

[1714] Specific example

[1715] If the emotion engine assesses that the user is in a relaxed state, it might suggest, "How about going to a small film festival being held in Jiyugaoka this weekend?"

[1716] Step 8: Execute the plan

[1717] User

[1718] 1. Proposal Confirmation: The user reviews the proposed plan, and if they agree with the other party, they proceed to plan the actual date.

[1719] 2. Final confirmation: Confirm the date details (date, time, location, meeting place, etc.) and notify the other person.

[1720] This allows users to exchange messages efficiently, receive appropriate suggestions tailored to their emotional state, build trust more easily, and ultimately lead to a first date.

[1721] (Example 2)

[1722] 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".

[1723] Current online dating apps and communication systems require users to spend a significant amount of time and effort to generate appropriate messages. Furthermore, it's difficult to create optimal suggestions and messages tailored to the user's emotions and situation, often resulting in one-sided communication. Additionally, the lack of features to support continuous interaction makes efficient communication challenging.

[1724] The identification processing by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for acquiring user identification information, means for acquiring the other party's identification information, means for analyzing the acquired user and other party identification information and extracting key keywords, means for generating an appropriate communication message based on the analysis results using generative artificial intelligence, means for generating proposals based on common interests of both parties, means for presenting the generated communication message and proposal to the user and obtaining the user's approval, means for sending the communication message to the other party after the user's approval, and means for analyzing the response from the other party and generating an appropriate reply message and next proposal. As a result, the user can exchange messages efficiently, and appropriate proposals are made according to the user's emotional state, making it easier to build trust and enabling smooth communication.

[1725] "User identification information" refers to information used to identify a user, including login ID, name, and contact information.

[1726] "Other party identification information" refers to information used to identify the person a user has been matched with, and includes the other party's login ID, name, contact information, etc.

[1727] "Key keywords" are important pieces of information extracted from the user's and the other party's profile information, such as hobbies, interests, and place of residence.

[1728] "Generative artificial intelligence" refers to artificial intelligence models that have the ability to generate text messages in natural language based on input data.

[1729] A "communication message" is a text-based message shared between a user and another user, including the initial message and replies.

[1730] "Shared interests" refer to the hobbies and interests that both users share, which are analyzed from their respective profile information.

[1731] A "suggestion" is a suggestion of activities or date plans that can be shared between the user and the other person based on their common interests.

[1732] "Emotional state" refers to information that indicates the current emotional state of the user or the other party, and includes states such as relaxed, excited, and stressed.

[1733] "Approval" is the act of confirming that a user agrees to the content of a generated message or proposal and confirms that they will submit it.

[1734] A "response" is a reply message sent by the other party.

[1735] "Continuous interaction" refers to the act of a user and another party exchanging messages multiple times.

[1736] This invention relates to a system that analyzes user identification information and recipient identification information, and provides appropriate message generation and suggestions using generative artificial intelligence. This system consists of a user terminal, a server, generative artificial intelligence, and an emotion engine.

[1737] Overall system configuration

[1738] This system consists of a user terminal, a server, a generative artificial intelligence system, and an emotion engine. It fully automates the generation of user messages and suggestions, and supports communication tailored to the user's emotional state.

[1739] server

[1740] The server acquires and analyzes user and other party identification information, extracts key keywords, and generates suggestions based on common interests. The acquired information and analysis results are then input into a generative artificial intelligence system.

[1741] terminal

[1742] The terminal presents the user with generated messages and suggestions sent from the server. The user reviews these using the terminal, makes any necessary modifications, and then approves them.

[1743] Generative artificial intelligence

[1744] Generative artificial intelligence generates messages of appropriate tone and length based on given identification and emotional information. Specifically, it uses natural language processing techniques to analyze prompt sentences and generate appropriate responses.

[1745] Emotional Engine

[1746] The emotion engine analyzes the user's emotional state in real time. This information is reflected in the generative artificial intelligence, influencing the tone and content of the generated messages.

[1747] Acquisition and analysis of profile information

[1748] server

[1749] When a user logs into the matching app, the server immediately retrieves the user's and the other person's identification information from the database. Then, using natural language processing (NLP) techniques, this information is analyzed to extract key keywords. An NLP library (e.g., spaCy) is used for this analysis.

[1750] Message generation

[1751] terminal

[1752] The terminal receives the analyzed profile information sent from the server and invokes the generative artificial intelligence. The generative AI then creates a prompt statement based on this information and generates the initial message.

[1753] Specific example

[1754] For example, if the other party is an engineer and the user's emotion engine detects a relaxed emotion, the generative artificial intelligence will generate the following initial message:

[1755] "Hello, I see you work as an engineer. I'm also interested in technology. I read that you enjoy watching movies; what movies have you seen recently?"

[1756] Proposal generation

[1757] server

[1758] The server generates suggestions based on the shared interests of the user and the other party. When generating suggestions, it also considers the user's emotional state, which is obtained from the emotion engine.

[1759] Specific example

[1760] "How about watching a movie at a cinema in Odaiba, Tokyo? Afterwards, it would be fun to share our thoughts about the movie at a nearby cafe."

[1761] Review and send messages and proposals.

[1762] User

[1763] The user reviews the messages and suggestions generated through their device. They modify them as needed, and after final approval, send them to the recipient via the server.

[1764] This system allows users to exchange messages efficiently through automated processes, enabling smoother and more emotionally-driven communication.

[1765] The flow of the specific processing in Example 2 will be explained using Figure 13.

[1766] Step 1: User Login

[1767] User

[1768] The user logs into the matching app. The input is the user's identification information (e.g., user ID and password). The device sends this to the server, and the user is authenticated. The output is the session information provided after the user has been authenticated.

[1769] Step 2: Obtain profile information

[1770] server

[1771] When a user logs in, the server retrieves user and other user identification information from the database. The input is the user's identification information (e.g., User ID). The server issues a query to the database like the following:

[1772] sql

[1773] SELECT Name, Occupation, Hobbies, Interests, Location FROM Profile WHERE User ID = 'USER_ID';

[1774] The output consists of the retrieved user and other party profile information.

[1775] Step 3: Analyze profile information

[1776] server

[1777] The server analyzes the acquired profile information using natural language processing technology (e.g., spaCy). The input is the user's and the other party's profile information. Through analysis, key keywords (e.g., hobbies, interests, place of residence) are extracted. The output is a list of the extracted keywords.

[1778] Step 4: Create a prompt for message generation

[1779] terminal

[1780] The terminal generates prompts for the AI ​​model based on the parsed profile information received from the server. The input consists of the parsed profile information and the user's sentiment information. The prompts are constructed, for example, as follows:

[1781] "The recipient is an engineer, and the user is relaxed. Please generate the initial message."

[1782] The output is a prompt message for the generative AI model.

[1783] Step 5: Generating the initial message

[1784] terminal

[1785] The terminal invokes a generative AI model (e.g., GPT-3) and generates the initial message using the prompt text as input. The input is the prompt text. The generative AI model uses natural language processing techniques to generate the initial message based on the given prompt text. The output is the generated initial message.

[1786] Step 6: Confirm the message and proposal

[1787] User

[1788] The user reviews the generated message and suggestions via the terminal. The input consists of the generated message and suggestions presented by the terminal. The user reviews these and makes modifications as needed. The output is the final message approved by the user.

[1789] Step 7: Sending a message

[1790] User

[1791] When the user presses the approve button, the device sends a message to the server. The input is the final message approved by the user. The server sends this message to the recipient using the matching app's messaging API. The output is confirmation that the message was sent to the recipient.

[1792] Step 8: Support for ongoing communication

[1793] terminal

[1794] The device receives the reply message from the other party and analyzes its content. The emotion engine analyzes the user's real-time emotional state, and the generation AI generates the next appropriate reply message. The input is the other party's reply message and the user's emotional information. The output is the generated next reply message.

[1795] Step 9: Generating the next proposal

[1796] server

[1797] The server generates the next date plan and topic suggestions based on the content of the ongoing interaction and feedback from the emotion engine. The input is the content of the interaction and emotion information. The server generates new suggestions based on this. The output is the suggestions for the next date.

[1798] Step 10: Execute the plan

[1799] User

[1800] If the user accepts the proposal and reaches an agreement with the other party, they will then plan a specific date. The input is the agreement between the user and the other party. The user confirms details such as the date, time, place, and meeting place, and then sends a final confirmation to the other party via the device. The output is the finalized date details.

[1801] (Application Example 2)

[1802] 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".

[1803] Traditional workplace communication has not adequately supported improvements in work efficiency and safety. In particular, there is a need for a system that can grasp workers' emotions and fatigue levels in real time and propose appropriate breaks and tasks accordingly. Therefore, balancing worker health and productivity is a key challenge.

[1804] In Application Example 2, the specific processing performed by the specific processing unit 290 of the data processing device 12 is realized by the following means. In this invention, the server includes means for acquiring user profile information, means for acquiring the other party's profile information, means for analyzing the acquired user and other party profile information and extracting important keywords, means for generating an appropriate message based on the analysis results using a generative AI, means for generating dating destination candidates based on common interests, means for presenting the generated message or suggestion to the user and obtaining user approval, means for sending the message to the other party after user approval, means for analyzing the other party's reply and generating an even more appropriate reply message or next suggestion, means for recognizing the emotional state from voice input using an emotion engine and adjusting the content of the dialogue accordingly, and means for generating work suggestions and break suggestions based on the worker's profile information and emotional state in a factory environment. This makes it possible to make appropriate work suggestions and break suggestions in real time based on the worker's profile information and emotional state.

[1805] "Means for obtaining user profile information" refers to devices or methods for collecting personal information such as a user's name, occupation, hobbies, interests, and place of residence.

[1806] "Means of obtaining the other party's profile information" refers to devices or methods for collecting personal information such as the name, occupation, hobbies, interests, and place of residence of a user through matching apps or similar means.

[1807] "Means for extracting important keywords" refers to a device or method that uses natural language processing technology to extract highly relevant keywords such as hobbies and interests from collected profile information.

[1808] "Means for generating appropriate messages using generative AI" refers to a device or method that automatically creates a message that takes into account the tone and length of the conversation, based on given information and using artificial intelligence technology.

[1809] "Means for generating date destination candidates" refers to a device or method that suggests date destinations based on the shared hobbies and interests of the user and their partner.

[1810] "Means of presenting to the user and obtaining user approval" refers to a device or method for displaying generated messages or suggestions to the user and obtaining their approval to review and modify their content.

[1811] "Means of sending a message to another party" refers to a device or method for sending an approved message to another party through a matching app or similar means.

[1812] "Means for analyzing replies from the other party and generating more appropriate reply messages and suggestions for the next step" refers to a device or method that analyzes the content of a reply from the other party and automatically creates appropriate reply messages and suggestions for the next step according to the content and the user's emotional state.

[1813] "Means for recognizing emotional states from voice input using an emotion engine" refers to a device or method that analyzes voice input to recognize the user's emotional state in real time.

[1814] "Means for generating work and rest suggestions based on worker profile information and emotional state in a factory environment" refers to a device or method that makes appropriate work and rest suggestions based on the profile information and emotional state of workers in a factory.

[1815] This invention is a system designed to streamline worker communication in a factory environment and achieve both worker health and productivity. The system consists of a user terminal, a server, a generative AI, and an emotion engine.

[1816] Overall structure

[1817] This system acquires user and recipient profile information, analyzes this information, and extracts important keywords. The generative AI generates and suggests appropriate messages based on this analysis. Furthermore, the emotion engine recognizes the emotional state in real time from voice input and adjusts the message content and tone based on that information.

[1818] Acquisition and analysis of user profile information

[1819] server

[1820] When a user logs into the system, the server retrieves their profile information (name, occupation, hobbies, interests, place of residence, etc.) from the database. The retrieved profile information is then analyzed using natural language processing (NLP) techniques to extract important keywords.

[1821] Recognition of emotional states

[1822] Emotional Engine

[1823] The emotion engine analyzes the worker's voice input to recognize their emotional state in real time. This is done using EmotionRecognizer software. For example, if a worker says "I'm tired," the emotion engine detects the emotion "fatigue."

[1824] Message generation

[1825] Generative AI

[1826] The generative AI generates dialogue messages based on analysis results and emotional states. The GPT-2 model is used here to generate appropriate responses based on prompt sentences.

[1827] For example, if a worker says, "Today's work is tough," the emotion engine detects "fatigue." Based on this information, the generative AI generates a message with the following prompt:

[1828] Prompt message:

[1829] Worker: Engineer, in his 30s, hobby is watching movies.

[1830] Emotional state: Fatigue

[1831] Message: Today's work is tough.

[1832] Robot: I think you should take a short break today. How about resting at a nearby rest area?

[1833] Proposal generation

[1834] server

[1835] The server generates work and break suggestions based on the worker's profile information and emotional state, aligning with their common hobbies and interests. For example, if a worker is an engineer and their hobby is watching movies, the server might suggest, "How about watching a movie at a nearby rest area?"

[1836] Confirm and send the message.

[1837] User

[1838] Users can review the messages and suggestions presented by the system and make modifications as needed. Finally, once the user approves the message and presses the send button, it is sent to the worker.

[1839] Support for continuous communication

[1840] terminal

[1841] The terminal receives a response message from the worker and analyzes its content. The emotion engine analyzes the emotion again, and the generative AI generates the next appropriate message. For example, if the worker says, "I recently watched a movie," the emotion engine detects "excitement," and the generative AI generates a message like this:

[1842] Prompt message:

[1843] Worker: I recently watched a movie.

[1844] Emotional state: Excitement

[1845] Message: That's wonderful! Which scene left the biggest impression on you?

[1846] Through the steps outlined above, this system allows workers to communicate efficiently and receive appropriate breaks and work suggestions. Furthermore, real-time emotional analysis can improve work efficiency and overall well-being.

[1847] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[1848] Step 1:

[1849] Retrieve the user's profile information.

[1850] When a worker logs into the system, the server retrieves personal information from the database, such as the worker's name, occupation, hobbies, interests, and place of residence. It then creates a query for the database search and extracts the relevant records. It receives the worker ID as input and obtains profile information as output.

[1851] Step 2:

[1852] Retrieve the other person's profile information.

[1853] The server retrieves the profile information of the worker's counterpart from the database. Here too, database access technology is used to retrieve the counterpart's information. It receives the counterpart's worker ID as input and obtains the counterpart's profile information as output.

[1854] Step 3:

[1855] The acquired information is analyzed, and important keywords are extracted.

[1856] The server analyzes the acquired profile information of the worker and the other party using natural language processing (NLP) techniques to extract important keywords such as hobbies, interests, and occupation. It receives profile information as input and obtains important keywords as output.

[1857] Step 4:

[1858] To recognize emotional states.

[1859] The terminal analyzes the worker's voice input using an EmotionRecognizer and recognizes their emotional state in real time. For example, if the voice input is "Today's work is tough," the emotion engine detects the emotion "fatigue." It receives voice data as input and obtains an emotional state as output.

[1860] Step 5:

[1861] Generate an appropriate message based on the analysis results.

[1862] The generative AI (GPT-2 model) generates messages with adjusted tone and length based on profile information and emotional state. It receives important keywords, emotional state, and an initial message as input, and outputs a generated message.

[1863] Step 6:

[1864] The generated message and proposal are presented to the user, and their approval is obtained.

[1865] The terminal displays the generated messages and suggestions to the worker for review. The worker can then modify or approve the messages. It receives the generated messages as input and the approved messages as output.

[1866] Step 7:

[1867] Send a message to the recipient.

[1868] The terminal sends the approved message to the worker. Here, a communication protocol is used to send the message data. It receives the approved message as input and obtains the message sent to the other party as output.

[1869] Step 8:

[1870] Analyze the response from the other party and generate the next appropriate response.

[1871] The server receives the reply message from the other party and analyzes the emotional state using an emotion engine. Then, it uses a generative AI to generate the next appropriate reply message. It receives the reply message as input and obtains the generated reply message as output.

[1872] Step 9:

[1873] We support continuous dialogue and suggestions.

[1874] The terminal presents the worker with generated reply messages and suggestions for the next steps, supporting continuous dialogue. It receives new messages as input and the presented messages as output.

[1875] 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.

[1876] 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.

[1877] 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.

[1878] 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.

[1879] 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.

[1880] 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.

[1881] 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.

[1882] 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.

[1883] 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."

[1884] 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.

[1885] 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.

[1886] 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.

[1887] 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.

[1888] 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.

[1889] 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.

[1890] 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.

[1891] 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.

[1892] 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.

[1893] 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.

[1894] 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.

[1895] 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.

[1896] The following is further disclosed regarding the embodiments described above.

[1897] (Claim 1)

[1898] Means for obtaining user profile information,

[1899] Means of obtaining the other party's profile information,

[1900] A means for analyzing acquired user and other party profile information and extracting important keywords,

[1901] A means of generating an appropriate message based on the analysis results using a generative AI,

[1902] A method for generating date location candidates based on shared interests,

[1903] A means of presenting generated messages and suggestions to the user and obtaining user approval,

[1904] A means of sending a message to the recipient after user approval,

[1905] A means to analyze the other party's reply and generate more appropriate reply messages and suggestions for the next time,

[1906] A system that includes this.

[1907] (Claim 2)

[1908] The system according to claim 1, comprising means for analyzing common hobbies and interests between a user and another person based on acquired profile information.

[1909] (Claim 3)

[1910] The system according to claim 1, comprising means for generating messages with adjusted tone and length to facilitate relaxed communication and build trust.

[1911] "Example 1"

[1912] (Claim 1)

[1913] A means of authenticating user identification information,

[1914] Means for obtaining user profile information,

[1915] Means of obtaining the other party's profile information,

[1916] A means for analyzing acquired user and other party profile information and extracting important keywords,

[1917] A means for generating an appropriate message based on the analysis results using a generative AI model,

[1918] A means of generating date destinations based on shared interests,

[1919] A means of presenting generated messages and suggestions to the user and obtaining user approval,

[1920] A means of sending a message to the recipient after user approval,

[1921] A means of analyzing the response from the other party and using a generative AI model to generate more appropriate reply messages and suggestions for the next time,

[1922] A system that includes this.

[1923] (Claim 2)

[1924] The system according to claim 1, comprising means for analyzing common hobbies and interests between a user and another person based on acquired profile information.

[1925] (Claim 3)

[1926] The system according to claim 1, comprising means for generating messages with adjusted tone and length to facilitate relaxed communication and build trust.

[1927] "Application Example 1"

[1928] (Claim 1)

[1929] Means for obtaining user profile information,

[1930] Means of obtaining the other party's profile information,

[1931] A means for analyzing acquired user and other party profile information and extracting important keywords,

[1932] A means of generating an appropriate message based on the analysis results using a generative AI,

[1933] A method for generating date location candidates based on shared interests,

[1934] A means of presenting generated messages and suggestions to the user and obtaining user approval,

[1935] A means of sending a message to the recipient after user approval,

[1936] A means to analyze the other party's reply and generate more appropriate reply messages and suggestions for the next time,

[1937] A means for obtaining user profile information of a streaming service, extracting important keywords, and generating content recommendations for the user,

[1938] A means of generating personalized messages based on viewing history and profile information using generative AI and presenting them to the user,

[1939] A system that includes this.

[1940] (Claim 2)

[1941] The system according to claim 1, comprising means for analyzing common hobbies and interests between a user and another person based on acquired profile information.

[1942] (Claim 3)

[1943] The system according to claim 1, comprising means for generating messages with adjusted tone and length to facilitate relaxed communication and build trust.

[1944] "Example 2 of combining an emotion engine"

[1945] (Claim 1)

[1946] Means for obtaining user identification information,

[1947] Means of obtaining the other party's identification information,

[1948] A means for analyzing the acquired user and counterpart identification information and extracting key keywords,

[1949] A means for generating an appropriate communication message based on analysis results using generative artificial intelligence,

[1950] A means of generating proposals based on common interests of both parties,

[1951] A means of presenting generated communication messages and proposals to the user and obtaining user approval,

[1952] A means of sending a communication message to the other party after user approval,

[1953] A means to analyze the response from the other party and generate more appropriate reply messages and suggestions for the next step,

[1954] A system that includes this.

[1955] (Claim 2)

[1956] The system according to claim 1, comprising means for analyzing the common interests of a user and another party based on identification information.

[1957] (Claim 3)

[1958] The system according to claim 1, comprising means for generating messages with adjusted tone and length to facilitate relaxed communication and build trust.

[1959] "Application example 2 when combining with an emotional engine"

[1960] (Claim 1)

[1961] Means for obtaining user profile information,

[1962] Means of obtaining the other party's profile information,

[1963] A means for analyzing acquired user and other party profile information and extracting important keywords,

[1964] A means of generating an appropriate message based on the analysis results using a generative AI,

[1965] A method for generating date location candidates based on shared interests,

[1966] A means of presenting generated messages and suggestions to the user and obtaining user approval,

[1967] A means of sending a message to the recipient after user approval,

[1968] A means to analyze the other party's reply and generate more appropriate reply messages and suggestions for the next time,

[1969] A means of recognizing emotional states from voice input using an emotion engine and adjusting the content of the dialogue accordingly,

[1970] A means for generating work suggestions and break suggestions based on worker profile information and emotional state in a factory environment,

[1971] A system that includes this.

[1972] (Claim 2)

[1973] The system according to claim 1, comprising means for analyzing common hobbies and interests between a user and another person based on acquired profile information.

[1974] (Claim 3)

[1975] The system according to claim 1, comprising means for generating messages with adjusted tone and length to facilitate relaxed communication and build trust. [Explanation of symbols]

[1976] 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 obtaining user profile information, Means of obtaining the other party's profile information, A means for analyzing acquired user and other party profile information and extracting important keywords, A means of generating an appropriate message based on the analysis results using a generative AI, A method for generating date location candidates based on shared interests, A means of presenting generated messages and suggestions to the user and obtaining user approval, A means of sending a message to the recipient after user approval, A means to analyze the other party's reply and generate more appropriate reply messages and suggestions for the next time, A system that includes this.

2. The system according to claim 1, comprising means for analyzing common hobbies and interests between a user and another person based on acquired profile information.

3. The system according to claim 1, comprising means for generating messages with adjusted tone and length to facilitate relaxed communication and build trust.

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