System

Abstract

To provide a system.SOLUTION: A system includes: means for acquiring information on interactions in a group including a user; means for generating a prompt text to be input to a generative AI model on the basis of the acquired interaction information; means for inputting the generated prompt text to the generative AI model to obtain a summary sentence of the interaction from the generative AI model; and means for providing the acquired sentence.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] There is no system in place that allows users to look back on past interactions and evoke emotional feelings by compiling the interactions between users or within groups of users on a messenger app into a single story and randomly providing it to them, regardless of whether it is recent or past, or with whom. [Means for solving the problem]

[0005] This invention provides a means for a generative AI to automatically summarize interactions between users or within a group on a messenger app, presenting the chats as a single story, and a means for randomly presenting these interactions regardless of whether they are recent, past, or with whom. It also provides a means for users to review past interactions and evoke emotional feelings. The free version also provides a means for users to display randomly, while the paid version provides a means for users to specify summaries by user, by date range, etc. This allows users to review past interactions and evoke emotional feelings. [Brief explanation of the drawings]

[0006] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 2 is a sequence diagram showing a flow of processing in the data processing system according to the first embodiment of the first form example. [Figure 12]FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1 of Embodiment 1. [Figure 13] FIG. 10 is a sequence diagram showing a processing flow of a data processing system in a second embodiment of the second form example. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 of Embodiment Example 2. [Figure 15] FIG. 10 is a sequence diagram showing the flow of processing in a data processing system according to a third embodiment of the third embodiment. [Figure 16] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 3 of Embodiment 3. [Figure 17] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in the first embodiment of the first form example when an emotion engine is combined. [Figure 18] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1 of Form Example 1 when an emotion engine is combined. [Figure 19] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in the second embodiment of the second form example when an emotion engine is combined. [Figure 20] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 of Form Example 2 when an emotion engine is combined. [Figure 21] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in the third embodiment of the third form example when an emotion engine is combined. [Figure 22] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 3 of Form Example 3 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0007] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0008] First, the terms used in the following description will be explained.

[0009] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, the processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), or a TPU (TENSOR PROCESSING UNIT (registered trademark)).

[0010] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0011] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0012] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0013] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0014] [First embodiment]

[0015] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0016] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0017] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0018] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0019] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0020] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0021] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0022] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0023] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0024] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0025] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0026] Next, the specific processing by the specific processing unit 290 of the data processing device 12 will be described.

[0027] "Example 1"

[0028] In a first embodiment of the present invention, a generative AI implemented in a messenger app automatically summarizes interactions between users or within a group. This generative AI uses natural language processing technology to analyze users' messages and summarize them as a story. For example, if user A and user B are interacting on the topic of "movies," the generative AI summarizes the conversation into a single sentence: "User A and user B had a passionate discussion about movies."

[0029] "Example 2"

[0030] In a second embodiment of the present invention, a function is implemented that randomly provides summarized stories, regardless of whether they are recent or past, or with whom. This function displays a randomly selected story that summarizes a past conversation every time the user opens the app. For example, when a user opens the app, the following sentence is displayed: "Last night, User A and User B had a passionate discussion about movies."

[0031] "Example 3"

[0032] As a third embodiment of the present invention, a free version and a paid version are provided. In the free version, summarized stories are displayed randomly. On the other hand, in the paid version, the user can specify the person and the period to display a story summarizing a specific conversation. For example, if the user specifies "yesterday's conversation with user B," the following sentence is displayed: "Last night, user A and user B had a passionate discussion about movies."

[0033] The processing flow of each embodiment will be described below.

[0034] "Example 1"

[0035] Step 1: A user starts a conversation in a messenger app.

[0036] Step 2: Generative AI analyzes the user's message in real time and summarizes it as a story, using natural language processing techniques.

[0037] Step 3: The generative AI saves the summarized story in a database.

[0038] "Example 2"

[0039] Step 1: User opens the app.

[0040] Step 2: The system randomly selects a summarized story from the database.

[0041] Step 3: Display the selected story to the user.

[0042] "Example 3"

[0043] Step 1: User chooses free or paid version.

[0044] Step 2: If you choose the paid version, the user will specify the recipient and the period.

[0045] Step 3: Based on the specified criteria, the system selects a summarized story from the database and displays it to the user.

[0046] Example 1

[0047] Next, a description will be given of Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0048] In modern messaging apps, the volume of interactions between users and groups is enormous, making it difficult to review important conversations and emotional exchanges from the past. There is also a lack of a way to concisely summarize these interactions and provide them in an easy-to-understand format. Furthermore, users cannot obtain summaries based on a specific period or person, making it difficult to organize and search for information.

[0049] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0050] In this invention, the server includes means for automatically summarizing exchanges between users of the messenger app or within a group using generative artificial intelligence and providing the chats as a single story (text), means for receiving messages sent by users, means for analyzing the received messages using natural language processing technology, means for generating summaries based on the analysis results, means for sending the generated summaries to users, means for randomly providing them regardless of whether they are recent or past or to whom, means for reviewing past exchanges and evoking emotional feelings in the user, and means for displaying summaries randomly in the free version or by specifying the person, period, etc. in the paid version. This allows users to easily review important conversations and emotional exchanges from the past, making it easier to organize and search for information.

[0051] A "messenger app" is a software application that allows users to send and receive text messages and multimedia content in real time.

[0052] "User" means an individual or group that sends or receives messages using the Messenger App.

[0053] "Generative AI" is an AI system that uses natural language processing technology to analyze text data and generate summaries and new sentences.

[0054] A "summary" is a short sentence that succinctly summarizes the content of the original message.

[0055] "Natural language processing technology" is a technology that allows computers to understand, analyze, and generate human language.

[0056] "Analysis" is the process of structurally breaking down a received message and extracting meaning and important information.

[0057] A "story" is a collection of multiple messages organized into one continuous sentence.

[0058] "Randomly presented" means presenting summaries selected at random, without following any particular order or criteria.

[0059] "Emotional evocation" means allowing users to relive emotions from past interactions.

[0060] The "free version" is a version that provides basic services free of charge, but with certain limited functionality.

[0061] "Paid Version" means a version that requires a fee to provide additional features or services.

[0062] "Other party" refers to other users with whom you exchange messages on the messenger app.

[0063] A "period" refers to a specific time range and covers messages and interactions within that range.

[0064] MODE FOR CARRYING OUT THE INVENTION

[0065] The present invention provides a system for automatically summarizing and providing a concise summary of interactions between users or within a group in a messenger app. Specific embodiments for implementing this system are described below.

[0066] System configuration

[0067] Hardware and Software Configuration

[0068] The server automatically summarizes messages between users and within groups using generative artificial intelligence (AI). This generative AI uses the Python natural language processing libraries "spaCy" and "Transformers." The server processes messages using the following steps:

[0069] 1. Sending a message

[0070] A user sends a message to another user using a messenger app. For example, user A sends a message saying, "The movie I saw yesterday was really good."

[0071] 2. Receiving a message

[0072] The server receives messages sent by users, stores them in a database, and then passes the message data to the generative AI.

[0073] 3. Message Analysis

[0074] The server calls the generative AI and analyzes the received message. The generative AI uses spaCy to perform morphological analysis of the message and extract important keywords. For example, from the message "The movie I saw yesterday was really good," the generative AI extracts the keywords "yesterday," "saw," "movie," "really," and "good."

[0075] 4. Summary Generation

[0076] The server then uses the generative AI to generate a summary based on the analysis results. The generative AI uses the Transformers library to generate a summary based on the extracted keywords and sentence structure. For example, it generates a summary such as "User A said that the movie was good."

[0077] 5. Submit your summary

[0078] The server sends the generated summary to User A and User B. User A and User B can check the summary on the messenger app.

[0079] Specific examples

[0080] For example, if user A and user B are talking about movies, the following conversation will take place:

[0081] User A: The movie I saw yesterday was really good.

[0082] User B: What movie was it?

[0083] User A: It was an action movie and the story was interesting.

[0084] User B: That's interesting. What's the title?

[0085] If we feed this conversation into a generative AI, it will summarize it as follows:

[0086] User A and User B had a lively discussion about movies.

[0087] In this way, generative AI automatically summarizes interactions between users and provides them in a concise format, allowing users to easily revisit important conversations and emotional exchanges from the past, making it easier to organize and search for information.

[0088] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0089] Program processing flow

[0090] Step 1:

[0091] A user sends a message to another user using a messenger app. For example, user A types a message such as "The movie I saw yesterday was really good" and presses the send button. The input is the text message sent by the user, and the output is the message data sent to the server.

[0092] Step 2:

[0093] The server receives messages sent by users. The received messages are stored in a database. The input is the message data sent by the user, and the output is the message data stored in the database.

[0094] Step 3:

[0095] The server calls the generative AI and analyzes the received message. The generative AI performs morphological analysis of the message using the Python natural language processing library "spaCy." For example, from the message "The movie I saw yesterday was really good," it extracts the keywords "yesterday," "saw," "movie," "really," and "good." The input is the message data stored in the database, and the output is the extracted keywords.

[0096] Step 4:

[0097] The server then uses the generative AI to generate a summary based on the analysis results. The generative AI uses the Transformers library to generate a summary based on the extracted keywords and sentence structure. For example, it generates a summary such as "User A said that the movie was good." The input is the extracted keywords, and the output is the generated summary.

[0098] Step 5:

[0099] The server sends the generated summary to user A and user B. User A and user B can check the summary on their messenger apps. The input is the generated summary, and the output is the summary sent to the user.

[0100] Specific actions

[0101] Step 1:

[0102] User A opens the messenger app, types a message to User B saying, "The movie I saw yesterday was really good," and presses the send button. This sends the message data to the server.

[0103] Step 2:

[0104] The server receives the message sent by user A and stores it in a database. The stored message data is used in subsequent processing.

[0105] Step 3:

[0106] The server calls the generative AI and analyzes the received message. The generative AI uses spaCy to perform morphological analysis of the message and extract important keywords. For example, from the message "The movie I saw yesterday was really good," the generative AI extracts the keywords "yesterday," "saw," "movie," "really," and "good."

[0107] Step 4:

[0108] The server then uses the generative AI to generate a summary based on the analysis results. The generative AI uses the Transformers library to generate a summary based on the extracted keywords and sentence structure. For example, it generates a summary such as "User A said that the movie was good."

[0109] Step 5:

[0110] The server sends the generated summary to User A and User B. User A and User B can then check the summary on their messenger app, allowing users to easily review important conversations and emotional interactions from the past.

[0111] (Application example 1)

[0112] Next, a description will be given of Application Example 1 of Embodiment Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0113] In conventional messaging apps, the volume of interactions between users and within groups became enormous, making it difficult to review important conversations and emotional exchanges. Furthermore, the lack of a function to summarize discussions about content viewed by users and recommend new content reduced user convenience.

[0114] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0115] In this invention, the server includes a means for automatically summarizing interactions between users of the messenger app or within a group using a generative AI and providing the chat as a single story (text), a means for displaying the summarized content in the user's feed, and a means for recommending new content based on the summary. This allows users to easily review important conversations and emotional interactions from the past, and also allows them to receive recommendations for new content.

[0116] A "messenger app" is application software that allows users to send and receive text messages, images, videos, etc. between users or within groups.

[0117] "Generative AI" is artificial intelligence that uses natural language processing technology to analyze input text data and generate summaries or new sentences.

[0118] A "summary" is a short, concise summary of the important parts of the original text data.

[0119] "Feed" refers to the stream or list of information that a user views within an application.

[0120] "Content" is a general term for information and entertainment that users view or use, such as movies, dramas, music, and articles.

[0121] "Recommendation" is the act of suggesting new information or entertainment based on a user's past behavior and interests.

[0122] "Random" means chosen at random without following any particular rule or order.

[0123] A "system" is a collection of devices and programs that are composed of a combination of multiple elements and functions.

[0124] The system for implementing this invention uses generative AI to automatically summarize interactions between users of a messenger app or within a group, and presents the chats as a single story (text). Specific embodiments of this system are described below.

[0125] Hardware and software used

[0126] Hardware:

[0127] Smartphone (iOS or ANDROID (registered trademark))

[0128] Server (cloud-based, e.g., AWS® or Google® Cloud)

[0129] software:

[0130] Natural language processing libraries (e.g., spaCy, NLTK)

[0131] Generative AI models (e.g., OpenAI's GPT-3)

[0132] Messenger app APIs (e.g., WhatsApp API, Facebook Messenger API)

[0133] Data processing and calculation

[0134] Data collection

[0135] The server uses the API of the messenger app to collect user message data. For example, it calls the API as follows to retrieve messages.

[0136] messages = get_messages_from_api(user_id)

[0137] Data analysis

[0138] The server uses a natural language processing library to analyze the collected message data, which is then input into a generative AI model.

[0139] parsed_messages = nlp(messages)

[0140] Summary Generation

[0141] The server uses a generative AI model to summarize the parsed messages, which are then displayed in the user's feed.

[0142] summary = gpt3_generate_summary(parsed_messages)

[0143] Feed display

[0144] The server then displays the summaries in the user's feed, allowing them to easily revisit important conversations and emotional interactions from the past.

[0145] display_summary_in_feed(user_id, summary)

[0146] Content Recommendation

[0147] The server recommends new content based on the summaries, allowing users to receive new information and entertainment.

[0148] recommended_content = recommended_content_based_on_summary(summary)

[0149] Specific examples

[0150] If user A and user B are discussing the movie "ABCDEF," the following happens:

[0151] Original message:

[0152] User A: "ABCDEF is so funny!"

[0153] User B: "Yeah, the dream scene was amazing!"

[0154] summary:

[0155] "User A and User B had a passionate discussion about the movie 'ABCDEF'."

[0156] Prompt Sentence Examples

[0157] Below is an example of a prompt sentence to input to a generative AI model.

[0158] User A and User B are discussing the movie "ABCDEF." Summarize the following message:

[0159] User A: "ABCDEF is so funny!"

[0160] User B: "Yeah, the dream scene was amazing!"

[0161] In this way, user discussions are automatically summarized and displayed in the feed, allowing users to easily review past discussions and also receive recommendations for new content.

[0162] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0163] Step 1:

[0164] The server collects user message data using the messenger app's API. Specifically, it calls the API to retrieve messages related to the user ID. The input is the user ID, and the output is the message data.

[0165] messages = get_messages_from_api(user_id)

[0166] Step 2:

[0167] The server uses a natural language processing library (e.g., spaCy or NLTK) to analyze the collected message data. Specifically, it tokenizes the message data and performs grammatical analysis. The input is the message data, and the output is the analyzed message data.

[0168] parsed_messages = nlp(messages)

[0169] Step 3:

[0170] The server uses a generative AI model (e.g., OpenAI's GPT-3) to summarize the parsed message data. Specifically, the parsed data is input as prompts to the generative AI model, which then generates a summary. The input is the parsed message data, and the output is a summarized sentence.

[0171] summary = gpt3_generate_summary(parsed_messages)

[0172] Step 4:

[0173] The server displays the summarized content in the user's feed. Specifically, it adds the summarized sentence to the user's feed and displays it. The input is the summarized sentence, and the output is the summary displayed in the feed.

[0174] display_summary_in_feed(user_id, summary)

[0175] Step 5:

[0176] The server recommends new content based on the summary. Specifically, it analyzes the summarized content and runs an algorithm to recommend related new content. The input is the summarized text, and the output is the recommended content.

[0177] recommended_content = recommended_content_based_on_summary(summary)

[0178] In this way, user discussions are automatically summarized and displayed in the feed, allowing users to easily review past discussions and also receive recommendations for new content.

[0179] Example 2

[0180] Next, a description will be given of Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0181] Conventional messenger apps lacked the ability to summarize interactions between users, making it time-consuming to look back on past conversations. They also lacked the ability to randomly provide past conversations, allowing users to discover new things or evoke emotional feelings. Furthermore, there was insufficient differentiation between the free and paid versions of the app, and improvements were needed to increase user satisfaction.

[0182] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0183] In this invention, the server includes: a means for a user to launch an application; a means for a terminal to send a request to the server for a random summary of past conversations; a means for the server to randomly acquire past conversation data from a database; a means for the server to send the acquired conversation data to a generative AI model as a prompt; a means for the generative AI model to generate a conversation summary based on the prompt; a means for the server to send the generated summary to the terminal; and a means for the terminal to display the received summary to the user. This allows users to randomly summarize and display past conversations simply by launching the application, eliminating the need to revisit past conversations and evoking new discoveries and emotional feelings. It also enables differentiated functionality between the free and paid versions, increasing user satisfaction.

[0184] "User" means any individual or entity using the Application.

[0185] An "application" refers to a software program that users use on devices such as smartphones and PCs.

[0186] "Terminal" refers to the hardware device through which a user runs applications.

[0187] "Server" refers to a computer system that processes data and manages databases to provide application functions.

[0188] "Database" refers to an information system for storing and managing past conversation data.

[0189] "Generative AI model" refers to an artificial intelligence model that generates a summary of a conversation based on an input prompt.

[0190] "Prompt sentence" refers to an instruction sentence input to a generative AI model to generate a summary.

[0191] A "summary" refers to a sentence that concisely summarizes past conversation data.

[0192] A "request" refers to a communication message in which a terminal requests a server to perform a specific process.

[0193] A "response" refers to a communication message in which a server returns a result in response to a request to a terminal.

[0194] "Random" means that something is chosen at random without following any particular rules or order.

[0195] "Free Version" means a version of the Application that is available to Users without payment of a fee.

[0196] "Paid Version" means a version of an Application that a User pays to use.

[0197] The present invention relates to a system that randomly summarizes and displays past conversations when a user launches an application. Specific embodiments of this system are described below.

[0198] Hardware and software used

[0199] Servers: Database servers, application servers

[0200] Device: User's smartphone or PC

[0201] Software: Generative AI models (e.g., GPT-4®), database management systems (e.g., MySQL®), application frameworks (e.g., Django)

[0202] System configuration

[0203] 1. The user launches the application

[0204] A user taps or clicks to launch an application on their smartphone or PC.

[0205] 2. The device sends a request to the server

[0206] The terminal sends an HTTP request to the application server requesting a summary of a random past conversation.

[0207] 3. The server retrieves the data from the database

[0208] The server connects to the database server and executes an SQL query to randomly retrieve past conversation data.

[0209] 4. The server sends a prompt to the generative AI model

[0210] The server sends prompts to the generative AI model to summarize the conversation data it has acquired.

[0211] 5. Generative AI model generates summary

[0212] A generative AI model generates a summary of the conversation based on the prompts.

[0213] 6. The server sends the summary to the device.

[0214] The server returns the generated summary to the terminal as an HTTP response.

[0215] 7. The device displays a summary

[0216] The device displays the received summary to the user.

[0217] Specific examples

[0218] The user launches the application

[0219] The user taps the application icon on the smartphone's home screen.

[0220] The device sends a request to the server

[0221] The device sends an HTTP request to the endpoint "GET / api / random-summary".

[0222] The server retrieves the data from the database

[0223] The server executes the SQL query "SELECT FROM conversations ORDER BY RAND() LIMIT 1;" to retrieve random conversation data.

[0224] The server sends prompts to the generative AI model

[0225] The server sends a prompt to the generative AI model: "Summarize the following conversation: Last night, User A and User B had a lively discussion about movies."

[0226] Generative AI model generates summary

[0227] The generative AI model generates a summary such as, "Last night, User A and User B had a passionate discussion about movies."

[0228] The server sends the summary to the terminal.

[0229] The server returns the generated summary to the terminal as an HTTP response.

[0230] The terminal displays a summary

[0231] The device displays the text "Last night, User A and User B had a lively discussion about movies" in the application UI.

[0232] Prompt Sentence Examples

[0233] "Summarize the following conversation: User A and User B had a lively discussion about movies last night."

[0234] "Summarize the following conversation: Last week, User C and User D were talking about a new restaurant."

[0235] In this way, we create a system that displays a randomly selected summary of past conversations every time a user launches the application.

[0236] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0237] Step 1:

[0238] The user launches an application.

[0239] Specifically, a user taps an application icon on the smartphone's home screen. This launches the application and displays its initial screen. The input is the user's tap operation, and the output is the application's initial screen.

[0240] Step 2:

[0241] The device sends a request to the server.

[0242] Specifically, the terminal sends an HTTP request to the endpoint "GET / api / random-summary." The input is a request from the initial screen of the application, and the output is a request sent to the server.

[0243] Step 3:

[0244] The server retrieves the data from the database.

[0245] Specifically, the server connects to the database server and executes the SQL query "SELECT FROM conversations ORDER BY RAND() LIMIT 1;" The input is the request to the server, and the output is randomly selected past conversation data.

[0246] Step 4:

[0247] The server sends prompts to the generative AI model.

[0248] Specifically, the server converts the acquired conversation data into a prompt sentence such as "Please summarize the following conversation: [conversation data]" and sends it to the generative AI model. The input is randomly selected past conversation data, and the output is the generation and transmission of a prompt sentence.

[0249] Step 5:

[0250] A generative AI model generates the summary.

[0251] Specifically, the generative AI model generates a summary of the conversation based on the prompt sentence, with the input being the prompt sentence and the output being the generated summary.

[0252] Step 6:

[0253] The server sends the summary to the terminal.

[0254] Specifically, the server sends the generated summary back to the terminal as an HTTP response. The input is the generated summary, and the output is the response sent to the terminal.

[0255] Step 7:

[0256] The terminal displays a summary.

[0257] Specifically, the device displays the summary it receives on the application's UI. The input is the response from the server, and the output is the summary displayed to the user.

[0258] In this way, a system is realized in which a randomly selected summary of past conversations is displayed each time the user launches the application.

[0259] (Application example 2)

[0260] Next, a description will be given of Application Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0261] Traditional messaging applications make it difficult for users to review past conversations and viewing history, and lack a way to easily recall past memories. They also lack the ability to provide a summary of past conversations and viewing history, which can lead to users missing important conversations or viewing content. Furthermore, they lack the ability to display a new summary every time a user opens the application, making it difficult to keep users engaged.

[0262] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for automatically summarizing interactions between users of a messaging application or within a group using a generative artificial intelligence and providing the communications as a single story (text), means for randomly providing such summaries regardless of whether they are recent or past or the recipient, means for randomly displaying summaries of past viewing history and chats in which the user has participated, and means for displaying a different summary each time the user opens the application. This allows the user to easily look back on past interactions and viewing history, which can evoke emotional feelings. Furthermore, a new summary is displayed each time the application is opened, thereby maintaining the user's interest.

[0263] A "messaging application" is software that allows users to send and receive text messages, images, audio, video, etc.

[0264] "User" means an individual or group of people who communicate using a messaging application.

[0265] "Generative artificial intelligence" refers to algorithms or models that automatically generate text or information based on input data.

[0266] A "summary" is a short sentence that succinctly summarizes the contents of a long piece of text or conversation.

[0267] "Communication" refers to the exchange of information between users through messaging applications.

[0268] A "story" is a collection of events or conversations organized into a single continuous sentence.

[0269] "Viewing history" is a record of content that a user has viewed in the past.

[0270] "Chat" is a real-time text-based conversation conducted through a messaging application.

[0271] "Randomly presented" means presenting content selected at random without following any particular order or rules.

[0272] "Emotional evocation" means eliciting an emotional response in the user by recalling past events or conversations.

[0273] "Free Version" means a version of the Software that provides basic functionality free of charge.

[0274] "Paid Version" means a version of the Software that provides additional features or services and requires a fee.

[0275] "Specify" means to set specific conditions or criteria.

[0276] A system for implementing the present invention includes a messaging application, a generative artificial intelligence, a database, and a user interface. Specific embodiments are described below.

[0277] System Configuration

[0278] 1. Messaging applications:

[0279] A messaging application is software that allows users to send and receive text messages, images, audio, and video. The application is installed on mobile devices such as smartphones and tablets.

[0280] 2. Generative AI:

[0281] Generative AI is an algorithm or model that automatically summarizes a user's past interactions and viewing history and presents it as a narrative. Specifically, it uses natural language processing technology to summarize long conversations and viewing histories into concise sentences.

[0282] 3. Database:

[0283] The database is intended to store users' past interactions and viewing history, including text messages, images, audio, video, and other data.

[0284] 4. User Interface:

[0285] The user interface is the screen through which the user interacts with the application, displaying random summaries of past interactions and viewing history.

[0286] Processing flow

[0287] 1. Data Collection:

[0288] Messaging applications store user interactions and viewing history in a database.

[0289] 2. Data Summary:

[0290] Generative AI retrieves past interactions and viewing history from a database and summarizes them using natural language processing techniques.

[0291] 3. Random display:

[0292] Each time a user opens the application, the generative artificial intelligence displays a randomly selected summary in the user interface.

[0293] Hardware and software used

[0294] Hardware:

[0295] Smartphones, tablets, servers

[0296] software:

[0297] Messaging applications, generative artificial intelligence (natural language processing algorithms), database management systems

[0298] Specific examples

[0299] Here's an example of the summary that appears when a user opens the application:

[0300] Example prompt sentence:

[0301] "Show me random summaries of past conversations and viewing history."

[0302] An example of the summary that will be displayed:

[0303] "On 2023-10-01, User A and User B had a passionate discussion about movies."

[0304] In this way, each time the user opens the application, a different summary is displayed, allowing them to reminisce about their past experiences, which can evoke emotional feelings and keep them interested in the application.

[0305] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0306] Step 1:

[0307] The server collects user interactions and viewing history through messaging applications and stores them in a database. The input is data such as text messages, images, audio, and video sent by users, and the output is that this data is stored in the database. Specifically, the messaging application captures user interactions in real time and sends them to the server.

[0308] Step 2:

[0309] The server uses generative artificial intelligence to retrieve past interactions and viewing history from a database and summarize them. The input is the past interactions and viewing history stored in the database, and the output is a summarized short sentence. Specifically, the generative artificial intelligence uses natural language processing technology to summarize long conversations and viewing history into concise sentences.

[0310] Step 3:

[0311] The server randomly selects one of the generated summaries and displays it each time the user opens the application. The input is a set of sentences summarized by the generative AI, and the output is a randomly selected summary that is displayed in the user interface. Specifically, the server uses a random algorithm to select one of the summaries and send it to the user interface.

[0312] Step 4:

[0313] The user sees a new summary every time they open the application. The input is a randomly selected summary sent from the server, and the output is the summary the user sees on their screen. Specifically, when the user launches the application, the summary sent from the server is displayed in the user interface.

[0314] Step 5:

[0315] The user looks at the displayed summary and recalls past interactions and viewing history, evoking emotional feelings. The input is the summary displayed in the user interface, and the output is the user's emotional response. The specific action is for the user to read the displayed summary and recall past events.

[0316] Example 3

[0317] Next, a description will be given of a third embodiment of the third embodiment. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0318] With conventional messenger apps, when users want to review past conversations, they have to manually search for the necessary information from a huge amount of data, which is extremely time-consuming. Furthermore, since there is no function to display a summary of a specific conversation, it is difficult for users to quickly grasp important information. Furthermore, since the distinction between the free and paid versions is unclear, it is unclear which features users can use. These issues need to be resolved.

[0319] The specific processing by the specific processing unit 290 of the data processing device 12 in the third embodiment is realized by the following means.

[0320] In this invention, the server includes a means for a user to log in to the system, a means for the user to select either the free version or the paid version, and a means for the server to randomly select conversation data in the free version. This allows the user to efficiently summarize and view past conversations. The paid version also includes a means for the user to specify a person and a period, a means for the server to filter the conversation data based on the specified conditions, a means for the server to input a prompt sentence to the generative AI model, a means for the generative AI model to generate a summary, and a means for the server to display the generated summary to the user. This allows the user to quickly summarize and view a specific conversation.

[0321] The "means for a user to log in to the system" refers to a function that allows a user to access the system using a terminal and log in by entering authentication information.

[0322] "Means for users to select the free version or the paid version" refers to the interface and functionality that allows users to select either the free version or the paid version within the system.

[0323] The "means by which the server randomly selects conversation data" refers to the algorithm and function by which the server randomly extracts conversation data from the database.

[0324] The "means for the user to specify a partner and a period" refers to an interface and function that allows the user to input a specific partner and a period.

[0325] "Means for the server to filter conversation data based on specified conditions" refers to the algorithms and functions that allow the server to search the database based on conditions specified by the user and extract relevant conversation data.

[0326] "Means for the server to input prompt sentences into the generative AI model" refers to a function that enables the server to generate prompt sentences based on filtered conversation data and input them into the generative AI model.

[0327] The "means by which the generative AI model generates a summary" refers to the algorithms and functions by which the generative AI model generates a summary based on the input prompt sentence.

[0328] "Means for the server to display the generated summary to the user" refers to the function that allows the server to receive the summary returned from the generative AI model and display it on the user's device.

[0329] The present invention provides a system that enables users to efficiently summarize and view past conversations. A specific embodiment of this system will be described below.

[0330] System program generation

[0331] The server generates a program that allows users to log in to the system and select either the free or paid version. This program collects the user's conversation data and generates summaries using a generative AI model. The hardware used is a high-performance cloud server (e.g., AWS EC2 instance), and the generative AI model used is OpenAI's GPT-3.

[0332] Explanation of program processing

[0333] When a user logs in to the system, the server verifies the user's authentication information and allows the user to log in. After logging in, the user is taken to a screen where they can choose between the free version or the paid version. If the user selects the free version, the server randomly selects conversation data from the database and inputs prompt sentences into the generative AI model. The generative AI model generates a summary, which the server displays to the user.

[0334] If the paid version is selected, the user enters information into an input form to specify a specific person and time period. The server uses this information to search the database and filter the relevant conversation data. A prompt is generated based on the filtered conversation data and input into the generative AI model. The generative AI model then generates a summary, which the server displays to the user.

[0335] Specific examples

[0336] For example, if a user is using the free version, the server inputs randomly selected conversation data into the generative AI model. For example, the prompt might say, "Please summarize the conversation between user A and user B." The generative AI model generates a summary that says, "User A and user B discussed movies," and the server displays it to the user.

[0337] As a specific example of the paid version, if a user specifies "yesterday's conversation with user B," the server filters the conversation data that matches that condition and inputs the prompt sentence "Please summarize the conversation between user A and user B about movies last night" into the generative AI model. The generative AI model generates a summary that reads "Last night, user A and user B had a passionate discussion about movies," and the server displays it to the user.

[0338] Prompt Sentence Examples

[0339] Free version: "Please summarize the conversation between User A and User B"

[0340] Paid version: "Please summarize the conversation between User A and User B about movies last night."

[0341] In this way, the user can efficiently summarize and view past conversations. This system allows the user to quickly grasp important information. The flow of the identification process in the third embodiment will be described with reference to FIG. 15.

[0342] Step 1:

[0343] A user logs in to the system.

[0344] A user accesses the system using a terminal and enters a user ID and password on the login screen. The server checks the entered authentication information against the database and allows login if authentication is successful. The input is the user ID and password, and the output is the authentication result. If authentication is successful, the user can proceed to the next screen.

[0345] Step 2:

[0346] The user chooses the free or paid version.

[0347] After logging in, the user selects either the free version or the paid version. The selection information is sent to the server. The input is the user's selection information, and the output is the selection result. The server determines the next process based on the user's selection.

[0348] Step 3:

[0349] In the free version, the server randomly selects conversation data.

[0350] The server runs an algorithm to randomly extract conversation data from the database. The input is all the conversation data in the database, and the output is a randomly selected conversation data. The server uses this randomly selected conversation data in the next step.

[0351] Step 4:

[0352] In the paid version, the user specifies the recipient and the period.

[0353] The user enters information into an input form to specify a specific person and time period. The input is the person and time period information, and the output is the specified conditions. The server prepares to search the database based on these conditions.

[0354] Step 5:

[0355] The server filters the conversation data based on the specified criteria.

[0356] The server searches the database based on the conditions specified by the user and extracts the relevant conversation data. The input is the specified conditions and all the conversation data in the database, and the output is the filtered conversation data. The server uses this filtered conversation data in the next step.

[0357] Step 6:

[0358] The server inputs a prompt sentence into the generative AI model.

[0359] The server generates a prompt sentence based on the filtered conversation data and inputs it to the generative AI model. The input is the filtered conversation data, and the output is the generated prompt sentence. The server sends this prompt sentence to the generative AI model.

[0360] Step 7:

[0361] A generative AI model generates the summary.

[0362] The generative AI model generates a summary based on the input prompt. The input is the prompt, and the output is the generated summary. The generative AI model returns this summary to the server.

[0363] Step 8:

[0364] The server displays the generated summary to the user.

[0365] The server receives the summary returned by the generative AI model and displays it on the user's device. The input is the generated summary, and the output is the summary displayed on the user's device. The user can view the displayed summary.

[0366] (Application example 3)

[0367] Next, a description will be given of Application Example 3 of Form Example 3. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0368] In modern messaging applications, the volume of conversations between users is so large that it is difficult to review past exchanges. Furthermore, there is a lack of a way to easily obtain summaries of conversations focused on specific people or periods. Furthermore, there is a need for a system that can evoke emotional feelings in users by providing conversation summaries in a narrative format.

[0369] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 3 is realized by the following means.

[0370] In this invention, the server includes a means for automatically summarizing conversations between users of the messaging application or within a group using generative artificial intelligence, providing the conversation as a single story (text), a means for randomly providing the summary regardless of whether it is recent or past, or by whom, and, in the paid version, a means for displaying summaries by specifying a specific person or period. This allows users to easily look back on past conversations and easily obtain summaries focused on a specific person or period. Furthermore, the narrative-style summaries can evoke emotional feelings in users.

[0371] A "messaging application" is software that allows users to send and receive messages, including text, audio, images, and videos, between each other.

[0372] "User" means any individual or group of people who use the Message Application.

[0373] "Generative artificial intelligence" refers to algorithms or systems that automatically generate text or information based on input data.

[0374] A "summary" is a sentence that briefly summarizes the content of the original conversation.

[0375] A "narrative" is a piece of writing that presents multiple events or conversations as a series of stories.

[0376] "Most recent" refers to the period in the past that is closest to the present.

[0377] "Past" refers to a time before the present.

[0378] "Random" means that something is chosen at random without following any particular rules or order.

[0379] "Paid Version" refers to a version that requires a fee to use certain features or services.

[0380] "Specific person" refers to a specific user designated by the user.

[0381] A "period" refers to a range of time between a specific start date and an end date.

[0382] "Conversation history" refers to a record of past message exchanges.

[0383] A "smartphone" is a mobile device that has the functionality of a mobile phone and is also capable of connecting to the Internet and running applications.

[0384] Systems for implementing the present invention include messaging applications, generative artificial intelligence, and smartphones. Specific embodiments are described below.

[0385] First, the server collects interactions between users and groups through messaging applications. These interactions are saved in the form of text, audio, images, and video. Next, generative AI automatically analyzes these interactions and generates summaries. These summaries express the conversation as a single narrative (text).

[0386] The server has a means to randomly provide generated summaries, regardless of whether they were recent or past, or with whom. This allows users to look back on past conversations without specifying specific conditions. The paid version also provides a means to display summaries by specifying a specific person or period. This allows users to easily obtain summaries focused on a specific conversation or period.

[0387] As an application installed on a smartphone, this system generates and displays a narrative summary based on the user's conversation history. Specifically, when a user opens the application and specifies a specific person and time period, the generative AI generates a summary based on those conditions and displays it on the smartphone screen.

[0388] For example, if a user requests, "Please summarize the conversation with User B on October 1, 2023," the generative AI will analyze the conversation based on that condition and generate a summary such as, "On October 1, 2023, User A and User B had a passionate discussion about movies." This summary is displayed on the smartphone screen, allowing users to easily look back on past conversations.

[0389] An example of a prompt is as follows:

[0390] "Please summarize the conversation between User A and User B on 2023-10-01."

[0391] This system allows users to easily review past conversations and easily obtain summaries of specific people or periods. Furthermore, narrative summaries can evoke emotional feelings in users.

[0392] The flow of the specific processing in Application Example 3 will be described with reference to FIG.

[0393] Step 1:

[0394] The server collects messages between users and groups through messaging applications. The input includes message data such as text, audio, images, and videos. The server stores this data in a database. The output is the stored message data.

[0395] Step 2:

[0396] The server provides the stored message data as input to the generative AI. The generative AI analyzes this data and generates a summary of the conversation. Natural language processing techniques are used to analyze the text data and extract important information. The output is summarized conversation data.

[0397] Step 3:

[0398] The server has a means to randomly provide generated summaries, regardless of whether they are recent or past, or for any other party. The input includes the summarized conversation data. The server uses a random algorithm to randomly select a summary and provide it to the user. The output is a randomly selected summary.

[0399] Step 4:

[0400] A user can specify a specific contact and time period through an application installed on their smartphone. The input includes the contact and time period information specified by the user. The device sends this information to the server. The output is a request based on the specified conditions.

[0401] Step 5:

[0402] The server receives a request from a user and filters conversation data based on the specified contact and time period. The input includes the information on the contact and time period specified by the user, as well as the stored message data. The data is processed by using a filtering algorithm to extract the relevant conversation data. The output is the filtered conversation data.

[0403] Step 6:

[0404] The server provides the filtered conversation data to a generative AI to generate a summary. The input includes the filtered conversation data. The generative AI again uses natural language processing techniques to generate a summary. The output is a summary based on the specified criteria.

[0405] Step 7:

[0406] The server sends the generated summary to the user's smartphone. The input includes a summary based on the specified conditions. The server sends the summary to the device using a communication protocol. The output is a summary that is displayed on the user's smartphone.

[0407] Step 8:

[0408] The user checks the summary displayed on the smartphone screen. The input includes the summary sent from the server. The device displays the summary on the screen. The output is a summary that the user can visually check.

[0409] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0410] "Example 1"

[0411] One embodiment of the present invention is a messenger app system incorporating an emotion engine. In this system, when a user converses on the messenger app, a generative AI analyzes the conversation in real time. Natural language processing technology is used for the analysis, and the emotion engine recognizes the user's emotion from the content of the conversation. For example, if a user sends a message saying "I had fun today," the emotion engine recognizes the emotion of "joy" from the message.

[0412] "Example 2"

[0413] Furthermore, the way the summarized story is presented is adjusted based on the emotion recognized by the emotion engine. For example, if the emotion engine recognizes the emotion of "joy," the user will be presented with a summarized story that enhances the emotion of joy. This allows the user to better understand their own emotions and recreate emotional experiences.

[0414] "Example 3"

[0415] Furthermore, the emotions recognized by the emotion engine are fed back to the user. For example, after a user sends a message saying, "Today was fun," the system will provide feedback such as, "You seem to be feeling joy right now." This allows users to objectively understand their own emotions and deepen their self-understanding.

[0416] The processing flow of each embodiment will be described below.

[0417] "Example 1"

[0418] Step 1: A user starts a conversation in a messenger app.

[0419] Step 2: The generative AI analyzes the conversation in real time, using natural language processing technology.

[0420] Step 3: The emotion engine is activated to recognize the user's emotions from the content of the conversation.

[0421] "Example 2"

[0422] Step 1: The emotion engine recognizes the user's emotion.

[0423] Step 2: Based on the perceived emotions, the delivery of the summarized story is adjusted.

[0424] Step 3: The user is provided with an emotionally charged, summarized story.

[0425] Step 1: The emotion engine recognizes the user's emotion.

[0426] Step 2: The recognized emotion is fed back to the user.

[0427] Step 3: Users objectively understand their own feelings through feedback.

[0428] Example 1

[0429] Next, a description will be given of Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0430] With traditional messaging apps, the volume of conversations between users was enormous, making it difficult to review past interactions. Furthermore, since there was no function to summarize the contents of conversations, there was a risk of missing important information. Furthermore, since there was no ability to recognize emotions, it was difficult to understand the user's feelings.

[0431] The identification process by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means. In this invention, the server includes: means for automatically summarizing interactions between users or within a group of users of a messenger app using a generative AI and providing the chat as a single story (text); means for a terminal to transmit message data to the server when a user sends a message using the messenger app; means for the server to analyze the received message data using natural language processing technology and summarize the conversation; means for the server to recognize emotions from the message using an emotion engine; means for the terminal to transmit the summary result and the emotion recognition result and display them to the user; and means for randomly providing them regardless of whether they are recent, past, or with whom. This allows the user to easily look back on past interactions, avoid missing important information, and further understand the emotions in the conversation.

[0432] A "messenger app" is software that allows users to send and receive text messages.

[0433] "Generative AI" is artificial intelligence that uses natural language processing technology to analyze text data and perform summarization and generation.

[0434] "Natural language processing technology" is a technology that allows computers to understand, interpret, and generate human language.

[0435] An "emotion engine" is software that recognizes user emotions from text data.

[0436] A "server" is a computer system for processing and storing data.

[0437] "Device" refers to a device that is directly operated by a user and on which a messenger app is installed.

[0438] "Summarization" is the process of summarizing long text data and extracting only the important information.

[0439] "Emotion recognition" refers to extracting and understanding a user's emotions from text data.

[0440] A "story (text)" is a collection of multiple messages organized into a single continuous text.

[0441] "Randomly providing" means providing data randomly without relying on any particular order or conditions.

[0442] This invention is a system that uses generative AI implemented in a messenger app to automatically summarize interactions between users or within a group, and further recognizes emotions from messages using an emotion engine. When a user sends a message via the messenger app, the device sends the message data to a server, which analyzes the data, summarizes it, and recognizes emotions, and then provides the results to the user.

[0443] Hardware and software used

[0444] Hardware:

[0445] Server: A computer system for processing and storing data.

[0446] Terminal: Devices that users directly operate (smartphones, tablets, PCs, etc.)

[0447] software:

[0448] Messenger app: Software that allows users to send and receive text messages.

[0449] Generative AI: Artificial intelligence that uses natural language processing technology to analyze text data and generate summaries and information (e.g., BERT, GPT-3)

[0450] Emotion engine: Software for recognizing user emotions from text data (e.g., Azure® Cognitive Services Text Analytics API)

[0451] Data processing and calculation

[0452] The server receives message data sent from the device and analyzes it using natural language processing technology. Specifically, it uses generative AI models such as BERT and GPT-3 to understand the content of the message and generate a summary. It also uses an emotion engine to extract emotions from the message. This allows it to summarize the user's conversation as a story and recognize the emotions contained in the conversation.

[0453] Specific examples

[0454] For example, if user A and user B are interacting on the topic of "movies," the process will proceed as follows:

[0455] User A: The movie yesterday was great!

[0456] User B: Yeah, the final scene was especially moving.

[0457] The generative AI summarizes this conversation as, "User A and User B talked about a touching scene in a movie." The emotion engine also recognizes the emotions of "joy" and "emotion."

[0458] In this way, users can easily look back at past interactions, avoid missing important information, and understand the sentiment of the conversation.

[0459] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0460] Step 1:

[0461] A user sends a message using a messenger app. A user uses a messenger app to converse with other users. For example, user A sends a message saying, "The movie yesterday was great!" This message becomes input data.

[0462] Step 2:

[0463] The terminal sends message data to the server. The terminal sends message data sent by the user to the server. This data includes the message content and sender information. The input is the user's message data, and the output is data sent to the server.

[0464] Step 3:

[0465] The server receives the message data and analyzes it using natural language processing technology. The server then analyzes the received message data using natural language processing technology (e.g., BERT or GPT-3). The input is the message data sent from the device, and the output is the analysis result. Specifically, the server understands the content of the message and extracts important information.

[0466] Step 4:

[0467] The server summarizes the conversation based on the analysis results. For example, if the conversation between user A and user B is about "movies," it will be summarized into a single sentence: "User A and user B had a passionate discussion about movies." The input is the analysis results, and the output is the summarized sentence.

[0468] Step 5:

[0469] The server uses an emotion engine to recognize emotions from messages. The server uses an emotion engine (e.g., Azure Cognitive Services' Text Analytics API) to recognize emotions from messages. For example, it extracts the emotion "joy" from the message "Yesterday's movie was great!" The input is the message data, and the output is the recognized emotion.

[0470] Step 6:

[0471] The server sends the summary results and emotion recognition results to the terminal. The server sends the summary results and emotion recognition results to the terminal. This data includes the summarized text and the recognized emotion. The input is the summary results and emotion recognition results, and the output is data transmission to the terminal.

[0472] Step 7:

[0473] The terminal displays the summary results and emotion recognition results to the user. The terminal displays the summary results and emotion recognition results received from the server to the user. The user can check the conversation summary and emotion recognition results. The input is data from the server, and the output is what is displayed to the user.

[0474] (Application example 1)

[0475] Next, a description will be given of Application Example 1 of Embodiment Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0476] Previous messaging apps lacked a way to efficiently summarize user interactions, making it time-consuming to review past conversations. Furthermore, they lacked a way to concisely grasp the content of news articles and blog posts when reading them, and lacked the ability to analyze the sentiment of articles and provide this information to users. This led to users becoming overwhelmed by information overload and potentially missing important information.

[0477] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0478] In this invention, the server includes a means for automatically summarizing interactions between users of a messenger app or within a group by a generative AI and providing the chat as a single story (text), a means for randomly providing such summaries regardless of whether they are recent or past or who the recipient is, a means for automatically summarizing news articles and blog articles, and a means for analyzing the sentiment of articles and providing them to users. This enables users to efficiently summarize and understand past conversations and news articles, and by understanding the sentiment of the articles, they can quickly determine the importance and tone of the information.

[0479] A "messenger app" is software that allows users to send and receive text messages, images, videos, etc.

[0480] "Generative AI" is artificial intelligence that uses natural language processing technology to analyze text data and generate summaries and new sentences.

[0481] A "summary" is a short summary of a long piece of text or conversation, extracting only the important information.

[0482] A "story" is a piece of writing that compiles a series of events or conversations.

[0483] A "random presentation method" is a method of presenting information without following any particular order or rule.

[0484] A "news article" is a piece of text that describes events or information reported in newspapers, websites, etc.

[0485] A "blog article" is a diary-style piece of writing that is published on the web by an individual or organization.

[0486] "Means for analyzing emotions" refers to a method for identifying emotions from text data and evaluating the type and intensity of those emotions.

[0487] "User" refers to any individual or organization that uses the messenger app, news articles, or blog posts.

[0488] A "server" is a computer system that stores and processes data on a network.

[0489] The system for implementing this invention includes a messenger app, generative AI, a sentiment analysis engine, and a function for summarizing news articles and blog articles. Specific embodiments are described below.

[0490] System Configuration

[0491] The system uses the following hardware and software:

[0492] Hardware: Smartphones, PCs, servers

[0493] software:

[0494] Messenger app: an application that allows users to send and receive text messages.

[0495] Generative AI: Analyze text data using OpenAI APIs to generate summaries and new sentences

[0496] Sentiment Analysis Engine: Analyzing sentiment from text data using TextBlob

[0497] Program processing

[0498] The server includes the following means:

[0499] 1. A way to summarize conversations on messenger apps: Using generative AI, messages between users are automatically summarized and presented as a single story.

[0500] 2. A way to summarize news articles and blog posts: Use generative AI to automatically summarize news articles and blog posts.

[0501] 3. Means of sentiment analysis: Using a sentiment analysis engine, the sentiment of the summarized article or message is analyzed and provided to the user.

[0502] Processing flow

[0503] 1. Summary of Messenger app conversation:

[0504] A user has a conversation on a messenger app.

[0505] The server uses generative AI to analyze the conversation in real time and generate a summary.

[0506] The summarized conversation is presented to the user as a single story.

[0507] 2. Summarizing news articles and blog posts:

[0508] A user reads a news article or blog post.

[0509] The server uses generative AI to summarize the article.

[0510] The summarized article is provided to the user.

[0511] 3. Emotion Analysis:

[0512] Text data from summarized conversations and articles is fed into a sentiment analysis engine.

[0513] The sentiment analysis engine analyzes emotions from text data and provides the results to the user.

[0514] Specific examples

[0515] For example, if a user is reading political news, they might input the following prompt into the generative AI:

[0516] Summarize the following passage:

[0517] Today marks the start of an important conference on climate change that is attracting worldwide attention.

[0518] Many countries participated and there was lively discussion about reducing greenhouse gas emissions.

[0519] The generative AI summarizes the news article based on this prompt, and the sentiment analysis engine analyzes the article's tone (e.g., optimistic, pessimistic, angry, etc.), allowing users to quickly understand the article's content and sentiment.

[0520] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0521] Step 1:

[0522] A user has a conversation in a messenger app. The user types and sends a text message. The input is the user's text message, and the output is the message being sent.

[0523] Step 2:

[0524] The server sends the received message to the generative AI. The input is the user's text message, and the output is the transmission of the message to the generative AI.

[0525] Step 3:

[0526] The generative AI analyzes the received message and generates a summary. The input is the user's text message, and the output is the summarized text. The generative AI uses natural language processing techniques to extract the key parts of the message and summarize it as a story.

[0527] Step 4:

[0528] The server provides the generated summary to the user. The input is the summary text from the generative AI, and the output is the transmission of the summary text to the user.

[0529] Step 5:

[0530] A user reads a news article or blog post. The user enters the text of the article and submits it to a server. The input is the text of the user's news article or blog post, and the output is the text submitted to the server.

[0531] Step 6:

[0532] The server sends the received articles to the generative AI. The input is the text of the user's news article or blog post, and the output is the article sent to the generative AI.

[0533] Step 7:

[0534] Generative AI analyzes incoming articles and generates summaries. The input is the text of a user's news article or blog post, and the output is the summarized text. Generative AI uses natural language processing techniques to extract and summarize the important parts of the article.

[0535] Step 8:

[0536] The server provides the generated summary to the user. The input is the summary text from the generative AI, and the output is the transmission of the summary text to the user.

[0537] Step 9:

[0538] The server sends the summarized text to a sentiment analysis engine. The input is the summarized text and the output is the text sent to the sentiment analysis engine.

[0539] Step 10:

[0540] The Sentiment Analysis Engine analyzes incoming text and identifies sentiment. The input is the summarized text and the output is the sentiment analysis result. The Sentiment Analysis Engine uses TextBlob to analyze the sentiment of the text and evaluate its type and intensity.

[0541] Step 11:

[0542] The server provides the emotion analysis results to the user. The input is the emotion analysis results from the emotion analysis engine, and the output is the transmission of the emotion analysis results to the user.

[0543] Step 12:

[0544] The user can quickly understand the information by viewing the provided summary and sentiment analysis results. The input is the summary and sentiment analysis results from the server, and the output is the user's understanding.

[0545] Example 2

[0546] Next, a description will be given of Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0547] In conventional messaging apps, when reviewing past conversations, it was difficult to find a specific conversation from a huge chat log, making it difficult for users to relive past emotional experiences. Furthermore, there was a lack of functionality to provide a summary of past conversations, meaning users had no easy way to review past interactions. Furthermore, there was also a lack of functionality to adjust the summarized narrative based on the user's emotions, making it impossible to deepen the user's emotional experience.

[0548] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0549] In this invention, the server includes: means for automatically summarizing interactions between users of a messaging app or within a group and providing the chat as a single story (text); means for randomly providing such a story regardless of whether it is recent or past, or with whom; means for summarizing a randomly selected conversation from a past conversation database each time a user opens the app; means for inputting prompt text into a generative AI model to generate a summarized story; means for analyzing the user's emotions using an emotion engine and adjusting the summarized story based on the recognized emotions; means for sending the adjusted summarized story to the user's device; and means for displaying the summarized story received by the device on a screen. This allows users to easily look back on past conversations and relive emotional experiences.

[0550] A "messaging app" is software that allows users to send and receive text messages and multimedia.

[0551] "User" refers to an individual or group who uses a messaging app.

[0552] "Generative AI" is a system that uses artificial intelligence technology to automatically generate or summarize text data.

[0553] "Summarization" is the process of extracting important information from original text data and reconstructing it in a concise form.

[0554] "Narrative (Text)" is a format that provides summarized text data as a single continuous sentence.

[0555] "Randomly provided" refers to providing data selected at random without following any particular order or rules.

[0556] A "conversation database" is a database for storing past interactions and chat logs between users.

[0557] A "generative AI model" is an algorithm or model that uses natural language processing techniques to generate or summarize text data.

[0558] A "prompt" is an instruction given to a generative AI model that provides guidelines for performing a specific task.

[0559] An "emotion engine" is software that analyzes a user's text data and behavioral data to recognize emotions.

[0560] "Adjusting" refers to changing the content or presentation of the summarized story based on perceived emotions.

[0561] A "terminal" is a device on which a user uses a messaging app, including a smartphone, tablet, or PC.

[0562] "Displaying on the screen" refers to the visual presentation of text data or other information on a device display.

[0563] The present invention provides a system that allows users of messaging apps to easily look back on past conversations and relive emotional experiences. Specific embodiments of the system are described below.

[0564] Server Processing

[0565] When the server detects that a user has opened the app, it accesses a conversation database and randomly selects a past conversation log. This conversation database stores text messages and chat history between users. The server then uses a generative AI model (e.g., GPT-3) to summarize the selected conversation log. Examples of prompts that can be input to the generative AI model include:

[0566] "Please summarize the following conversation log: Conversation log: 'On October 1, 2023, User A and User B were engaged in a lively discussion about movies. User A was excited to see the trailer for a new movie. User B spoke at length about the director of the movie.'"

[0567] The generative AI model generates a summary of the story based on this prompt, for example, "User A and User B had a passionate discussion about movies."

[0568] Next, the server uses an emotion engine (e.g., Azure Cognitive Services) to analyze the user's emotions. The emotion engine receives the user's recent messages and behavioral data as input and recognizes emotions. For example, it analyzes the user's recent message, "I'm happy about my new job," and recognizes the emotion as "joy."

[0569] The server adjusts the summarized story based on the recognized emotion. For example, if the emotion is "joy," the summary sentence is adjusted to "User A and User B had a passionate discussion about movies. User A was especially excited about the new movie trailer." The adjusted summary story is sent to the user's device.

[0570] Terminal handling

[0571] The device receives the summary story sent from the server. Then, it activates its built-in emotion engine to analyze the user's real-time emotion. The emotion engine can use, for example, Google Cloud's Natural Language API. If the emotion engine analyzes the user's emotion as "joy," the device adjusts the way the summary story is displayed. For example, it displays it using bright colors and positive icons.

[0572] The device displays a tailored summary story on the screen. When a user opens the app, it might say something like, "User A and User B had a lively discussion about movies. User A was particularly excited about the new movie trailer."

[0573] User Experience

[0574] When users open the app, they can read the summary story displayed on their device. This allows users to easily look back on past conversations and relive emotional experiences. For example, users can relive past joyful emotions by reading the displayed summary.

[0575] In this way, the present invention provides a system that allows users to easily revisit past conversations and deepen their emotional experience.

[0576] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0577] Step 1:

[0578] The server detects when a user opens the app. As input, it receives the user's app launch event. Based on this, the server accesses the conversation database and randomly selects a past conversation log. As output, it obtains the selected conversation log.

[0579] Step 2:

[0580] The server uses a generative AI model (e.g., GPT-3) to summarize the selected conversation log. As input, the selected conversation log and a prompt sentence are input to the generative AI model. An example of a specific prompt sentence is, "Please summarize the following conversation log: Conversation log: 'On October 1, 2023, User A and User B had a passionate discussion about movies. User A was excited to see the trailer for a new movie. User B talked in detail about the director of the movie.'" As output, the generative AI model provides a summarized story.

[0581] Step 3:

[0582] The server analyzes the user's emotions using an emotion engine to adjust the summarized story. As input, it inputs the user's recent messages and behavioral data into the emotion engine. Specifically, it analyzes the user's recent message, "I'm happy about my new job." As output, it obtains the recognized emotion (e.g., "joy") from the emotion engine.

[0583] Step 4:

[0584] The server adjusts the summarized narrative based on the recognized emotion. It uses the summarized narrative and the recognized emotion as input. Specifically, if the emotion is "joy," it adjusts the summary sentence to "User A and User B had a passionate discussion about movies. User A, in particular, was excited about the new movie trailer." As output, it obtains the adjusted summarized narrative.

[0585] Step 5:

[0586] The server sends the adjusted summary story to the user's terminal. As input, it uses the adjusted summary story. As output, it obtains the summary story sent to the user's terminal.

[0587] Step 6:

[0588] The terminal receives the summary story sent from the server, receives the summary story sent from the server as input, and obtains the received summary story as output.

[0589] Step 7:

[0590] The device activates the built-in emotion engine to analyze the user's real-time emotion. The user's real-time data is input to the emotion engine as input, and the analyzed emotion is obtained as output.

[0591] Step 8:

[0592] The device adjusts the display method of the summary story based on the analysis results of the emotion engine. It uses the received summary story and the analyzed emotions as input. Specifically, if the user expresses the emotion of "joy," it displays it using bright colors and positive icons. The adjusted display method is obtained as output.

[0593] Step 9:

[0594] The terminal displays the adjusted summary story on the screen, using the adjusted display method as input, and obtaining the summary story displayed to the user as output.

[0595] Step 10:

[0596] The user opens the app and reads the summary narrative displayed on the device. As input, the user receives the summary narrative displayed on the device. As output, the user can revisit past conversations and relive emotional experiences.

[0597] (Application example 2)

[0598] Next, a description will be given of Application Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0599] Conventional messaging apps have the drawback of requiring users to manually search for past conversations when reviewing them, which takes time and effort. Furthermore, they lack the functionality to summarize past conversations and recreate emotional experiences, making it difficult to deeply understand the user's emotions. Furthermore, as an application installed on a smartphone, there was a demand for a function that provides summarized stories based on emotions.

[0600] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0601] In this invention, the server includes a means for automatically summarizing interactions between users of a messenger app or within a group by a generative AI and providing the chat as a single story (text), a means for randomly providing the summary regardless of whether it is recent or past or the person to whom it is provided, a means for adjusting the method for providing the summarized story based on emotions recognized by an emotion engine, and a means for operating as an application to be installed on a smartphone. This allows users to easily look back on past conversations and relive emotional experiences through emotion-based summarized stories.

[0602] A "messenger app" is software that allows users to exchange text messages, images, videos, etc. in real time.

[0603] "Generative AI" is a system that uses artificial intelligence technology to analyze data and automatically generate new information and content.

[0604] A "summary" is a short sentence that concisely summarizes the original information and extracts only the main points.

[0605] A "narrative" is a sequential depiction of events or conversations, expressed as a meaningful series of sentences.

[0606] An "emotion engine" is a system that analyzes and recognizes user emotions from data such as text and voice.

[0607] A "smartphone" is a portable information terminal that can access the Internet and run applications in addition to making calls.

[0608] An "application" is a software program designed to provide a particular function or service.

[0609] "Random" means chosen at random, without following any particular rule or order.

[0610] "Past conversations" are records of previous message exchanges between users.

[0611] "Emotional experience" refers to the emotions and feelings a user has in response to a particular event or piece of information.

[0612] The system for implementing this invention consists of a messenger app, generative AI, an emotion engine, and an application installed on a smartphone. The details of each component and their operation are explained below.

[0613] System Configuration

[0614] 1. Messenger App

[0615] Messenger apps are software that allow users to exchange text messages, images, videos, etc. in real time. The apps also store users' past conversation data and make it accessible when needed.

[0616] 2. Generative AI

[0617] Generative AI is a system that analyzes past conversation data and automatically generates summaries. This AI uses natural language processing techniques to extract the main points of a conversation and summarize them as a concise narrative.

[0618] 3. Emotion Engine

[0619] The emotion engine is a system that analyzes and recognizes user emotions from the generated summaries. The engine extracts emotions from text data and adjusts the way the summarized story is presented based on those emotions.

[0620] 4. Applications installed on smartphones

[0621] The app allows users to view stories that summarize past conversations through their smartphones. Every time a user opens the app, it summarizes a randomly selected past conversation and displays a story tailored based on their emotions.

[0622] How it works

[0623] The server retrieves past conversation data from the messenger app and summarizes it using generative AI. The summarized data is then analyzed by an emotion engine and adjusted based on the user's emotions. The adjusted summary is then provided to the user through an application installed on their smartphone.

[0624] Specific examples

[0625] For example, when a user opens the app, a summary is generated from past conversation data: "Last night, User A and User B had a passionate discussion about movies." If the emotion engine recognizes the emotion "joy" from this summary, the summary is refined to "We had a great time: Last night, User A and User B had a passionate discussion about movies."

[0626] Prompt Sentence Examples

[0627] An example of a prompt sentence to input to the generative AI model is as follows:

[0628] Summarize past conversations and generate sentiment-tuned narratives. Use the following conversation data:

[0629] Conversation data: User A and User B had a lively discussion about movies last night.

[0630] Emotion: Joy

[0631] Using this prompt, the generative AI model can provide appropriate summarization and emotional adjustments.

[0632] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0633] Step 1:

[0634] The server retrieves past conversation data from the messenger app. The input is past conversation data between users, and the output is the retrieved conversation data. Specifically, the server searches the database for a specified period or user's conversation history and extracts the necessary data.

[0635] Step 2:

[0636] The server inputs the acquired conversation data into a generative AI to generate a summary. The input is past conversation data, and the output is a summarized story. Specifically, the generative AI uses natural language processing technology to extract the main points of the conversation and summarize them into concise sentences.

[0637] Step 3:

[0638] The server inputs the generated summary into the emotion engine for emotion analysis. The input is the summarized story, and the output is the recognized emotion. Specifically, the emotion engine extracts emotions from the text data and classifies them.

[0639] Step 4:

[0640] The server adjusts the summarized story based on the recognized emotion. The input is the summarized story and the recognized emotion, and the output is the adjusted story. Specifically, the server changes the expression of the summary according to the emotion, adjusting it to enhance the user's emotion.

[0641] Step 5:

[0642] The server sends the adjusted story to an application installed on the smartphone. The input is the adjusted story, and the output is the story displayed on the user's smartphone. In specific operations, the server sends the adjusted story to the user's smartphone over the network, and the application displays it.

[0643] Step 6:

[0644] The user opens the smartphone application and views the tailored story. The input is the tailored story sent from the server, and the output is the user's viewing experience. In concrete terms, the user launches the application, reads the displayed story, and recreates the emotional experience.

[0645] Example 3

[0646] Next, a description will be given of a third embodiment of the third embodiment. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0647] Conventional messenger apps lacked the ability to summarize user interactions and provide them as a narrative, and users could not obtain summaries by specifying a specific person or period. Furthermore, they lacked the ability to recognize users' emotions and provide feedback, making it difficult for users to objectively understand their own emotions. To address these issues, the present invention aims to provide a system that summarizes users' conversations, recognizes their emotions, and provides feedback.

[0648] The specific processing by the specific processing unit 290 of the data processing device 12 in the third embodiment is realized by the following means.

[0649] In this invention, the server includes means for automatically summarizing interactions between users of a messenger app or within a group using a generative artificial intelligence and providing the chat as a single story (text), means for randomly providing such summaries regardless of whether they are recent or past or for any particular person, means for users to acquire summaries by specifying a specific person or period, and means for recognizing emotions from users' messages using an emotion engine and providing feedback. This not only enables users to easily check their own conversations as summaries, but also enables them to deepen their self-understanding through emotional feedback.

[0650] A "messenger app" is software that allows users to send and receive text messages, images, audio, video, etc.

[0651] "Generative AI" is AI that has the ability to analyze text data using natural language processing technology and generate summaries and new sentences.

[0652] A "summary" is a sentence that extracts important information from the original text data and expresses it in a shortened form.

[0653] A "story" is a textual expression that sequentially summarizes multiple events or pieces of information.

[0654] A "random provision method" is a method of providing users with randomly selected data without following any specific rules or order.

[0655] "Means for obtaining summaries by specifying a specific person or time period" refers to a method in which a user specifies a specific person or a specific time range, and a summary based on those conditions is generated and provided.

[0656] An "emotion engine" is software or algorithms for analyzing and recognizing emotions from text data.

[0657] "Means for recognizing emotions and providing feedback" refers to a method for analyzing emotions from a user's message and notifying the user of the results.

[0658] MODE FOR CARRYING OUT THE INVENTION

[0659] The present invention is a system that uses a messenger app to summarize interactions between users, recognize emotions, and provide feedback. Specific embodiments of this system are described below.

[0660] 1. Program Generation

[0661] The server uses generative artificial intelligence to automatically summarize interactions between users and groups on the messenger app, generating a program that presents the chats as a single narrative (text). This program includes a function that allows users to obtain summaries by specifying a specific person or period, and a function that uses an emotion engine to recognize emotions from user messages and provide feedback.

[0662] 2. Program Processing

[0663] The server collects user conversation data and generates summaries using natural language processing (NLP) technology. Specifically, it uses a natural language processing API to analyze the content of the conversation and create summaries. In the free version, summarized stories are displayed randomly, but in the paid version, users can obtain summaries for specific people and periods.

[0664] The system also uses emotion analysis software as an emotion engine to recognize emotions from users' messages. The recognized emotions are then fed back to the user. For example, if a user sends a message saying, "I had a great time today," the server will provide feedback such as, "You seem to be feeling joyful right now."

[0665] 3. Examples of concrete examples and prompts

[0666] For example, if the user specifies "yesterday's conversation with user B," the server will generate the following summary:

[0667] "User A and User B had a lively discussion about movies last night."

[0668] As a concrete example of an emotion engine, if a user sends a message saying "I had fun today," the server will provide the following feedback:

[0669] "You're feeling joy right now."

[0670] Example prompts to input to a generative AI model:

[0671] "Please summarize the conversation between User A and User B yesterday."

[0672] "Recognize the sentiment in the user's message and provide feedback."

[0673] In this way, the server realizes a system that summarizes the user's conversation, recognizes emotions, and provides feedback. The flow of the identification process in the third embodiment will be described with reference to FIG.

[0674] Step 1:

[0675] A user logs in to the system.

[0676] A user logs into the system using a terminal. A username and password are required for login. After a successful login, the user can access the main screen of the system. The input is the username and password, and the output is the main screen after a successful login.

[0677] Step 2:

[0678] The user enters or selects conversation data.

[0679] A user uses a terminal to input conversation data or select past conversation data. For example, a user can select "yesterday's conversation with user B." The input is conversation data or selected past conversation data, and the output is the selected conversation data.

[0680] Step 3:

[0681] The server collects conversation data and performs natural language processing (NLP).

[0682] The server collects conversation data entered or selected by the user. The collected data is analyzed using a natural language processing API. The server understands the content of the conversation and extracts important points. The input is the collected conversation data, and the output is the analysis results.

[0683] Step 4:

[0684] The server generates the summary.

[0685] The server generates a summary of the conversation based on the analysis results. For example, a summary such as "Last night, User A and User B had a lively discussion about movies" is generated. The input is the analysis results, and the output is the generated summary.

[0686] Step 5:

[0687] The server recognizes emotions using an emotion engine.

[0688] The server uses emotion analysis software to recognize emotions from the conversation data. For example, if a user sends a message saying "I had fun today," the server recognizes the emotion "joy." The input is the conversation data, and the output is the recognized emotion.

[0689] Step 6:

[0690] The server provides the summary and emotional feedback to the user.

[0691] The server provides the generated summary and emotional feedback to the user. For example, if a user selects "yesterday's conversation with user B," the server provides the summary "user A and user B had a lively discussion about movies last night" and the feedback "you seem to be feeling joyful right now." The input is the generated summary and the recognized emotion, and the output is the summary and emotional feedback provided to the user.

[0692] (Application example 3)

[0693] Next, a description will be given of Application Example 3 of Form Example 3. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0694] Modern messaging applications accumulate vast amounts of conversational data, but lack a means to efficiently summarize it and provide it in a useful format for users. They also lack a feedback function that allows users to objectively understand their own emotions. Furthermore, the lack of clear differentiation between the free and paid versions makes it difficult to increase user satisfaction. A system that can solve these issues is needed.

[0695] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 3 is realized by the following means.

[0696] In this invention, the server includes a means for automatically summarizing interactions between users of a messaging application or within a group, using a generative artificial intelligence to present the conversation as a single narrative (text), a means for randomly presenting such interactions regardless of whether they are recent or past, or by whom, a means for an emotion recognition engine to analyze the user's emotions and provide feedback, and a means for displaying randomly in the free version or summarizing by specifying the person, period, etc. in the paid version. This allows users to efficiently review past conversations and deepen self-understanding through emotional feedback. Furthermore, clear differentiation between the free and paid versions increases user satisfaction.

[0697] A "messaging application" is software that enables users to send and receive text messages, images, audio, video, etc.

[0698] "User" means any individual or group of people who use the Message Application.

[0699] "Generative AI" is an AI system that uses natural language processing technology to analyze text data and automatically summarize and generate information.

[0700] "Summarization" refers to extracting important information from the original text data and providing it in a shortened form.

[0701] A "story (text)" is a collection of multiple messages organized into a single continuous text.

[0702] An "emotion recognition engine" is software that analyzes a user's emotions from text data and identifies specific emotional states.

[0703] "Feedback" refers to the response or information a system provides to a user.

[0704] "Free Version" means a version of the Software that has certain functionality limitations but is available to Users free of charge.

[0705] "Paid Version" means a version of the Software that provides additional features or services but requires a fee from the User.

[0706] "Random display" refers to displaying randomly selected data without following any specific rules.

[0707] "Partner" means another user with whom you communicate within a messaging application.

[0708] A "period" refers to a specific time range that can be used as a basis for searching past interactions.

[0709] A "smartphone" is a mobile phone that can connect to the Internet and install applications.

[0710] A system for implementing the present invention includes a messaging application, a generative artificial intelligence, an emotion recognition engine, and a smartphone. A specific embodiment of this system will be described below.

[0711] System configuration

[0712] 1. Messaging Applications

[0713] A messaging application is software that allows users to send and receive text messages, images, audio, video, etc. The application stores the user's chat history and provides the data when needed.

[0714] 2. Generative Artificial Intelligence

[0715] Generative AI is a system that uses natural language processing technology to analyze text data and automatically summarize and generate it. This AI extracts important information from a user's chat history and provides it as a shortened narrative (text).

[0716] 3. Emotion Recognition Engine

[0717] An emotion recognition engine is software that analyzes user emotions from text data and identifies specific emotional states. The engine analyzes the user's messages and provides emotional feedback.

[0718] 4. Smartphone

[0719] A smartphone is a mobile phone that can connect to the Internet and install applications. This system is implemented as an application installed on the smartphone.

[0720] Data processing and calculation

[0721] 1. Loading chat history

[0722] The server loads the user's chat history, which is stored within the messaging application.

[0723] 2. Random Summary Generation

[0724] The generative AI randomly selects one conversation from the chat history and generates a summary of it, allowing users to efficiently review past conversations.

[0725] 3. Generating specific conversation summaries

[0726] Generative AI searches for conversations based on a specified user and date, generating summaries that allow users to revisit specific conversations in detail.

[0727] 4. Providing emotional feedback

[0728] The emotion recognition engine analyzes the user's message and recognizes the emotion. Based on the analysis results, it provides emotional feedback to the user, allowing the user to objectively understand their own emotions.

[0729] Specific examples

[0730] Free version random summary

[0731] "I want to talk about movies with User B on 2023-10-01"

[0732] Specific conversation summaries in the paid version

[0733] "The movie I saw with User B yesterday on 2023-10-01 was amazing."

[0734] Emotional Feedback

[0735] "You're feeling joy right now."

[0736] Prompt Sentence Examples

[0737] "Summarize yesterday's conversation with User B."

[0738] "Provide emotional feedback for the message 'I had fun today'"

[0739] In this way, analyzing users' chat history and providing summaries and emotional feedback can help users deepen their self-understanding. Also, clear differentiation between the free and paid versions can increase user satisfaction.

[0740] The flow of the specific processing in Application Example 3 will be described with reference to FIG.

[0741] Step 1:

[0742] The server loads the user's chat history. As input, a user ID is given, and as output, the chat history data for that user is obtained. This data includes text messages, images, audio, video, etc. stored in the messaging application.

[0743] Step 2:

[0744] The server analyzes the chat history using generative AI. The chat history data acquired in step 1 is given as input, and summarized text data is generated as output. The generative AI extracts important information using natural language processing technology and provides it as a shortened narrative (text).

[0745] Step 3:

[0746] The server generates a random summary. As input, it receives the summary text data generated in step 2, and as output, it provides a randomly selected summary, allowing users to efficiently review past conversations.

[0747] Step 4:

[0748] The server generates a specific conversation summary. As input, it receives information about the person and time period specified by the user, and as output, it generates a summary of the corresponding conversation. The generative AI searches for conversations based on the specified user and date, and generates the summary.

[0749] Step 5:

[0750] The server analyzes the user's emotions using an emotion recognition engine. The user's message is given as input, and emotional feedback is provided as output. The emotion recognition engine analyzes the user's emotions from the text data and identifies a specific emotional state.

[0751] Step 6:

[0752] The device displays emotional feedback to the user. As input, the emotional feedback generated in step 5 is given, and as output, a feedback message is displayed on the user's screen. This allows the user to objectively understand their own emotions.

[0753] Step 7:

[0754] The terminal switches between the free and paid versions. The user's account information is input, and the output is a random display in the free version, and a summary display with the specified contact, period, etc. in the paid version. This increases user satisfaction.

[0755] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0756] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0757] Another example of generative AI is Gemini (registered trademark) (Internet search engine). <url: https: gemini.google.com ?hl="ja">) are mentioned.

[0758] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0759] [Second embodiment]

[0760] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0761] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0762] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0763] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0764] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0765] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0766] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0767] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0768] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0769] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0770] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0771] Next, the specific processing by the specific processing unit 290 of the data processing device 12 will be described.

[0772] "Example 1"

[0773] In a first embodiment of the present invention, a generative AI implemented in a messenger app automatically summarizes interactions between users or within a group. This generative AI uses natural language processing technology to analyze users' messages and summarize them as a story. For example, if user A and user B are interacting on the topic of "movies," the generative AI summarizes the conversation into a single sentence: "User A and user B had a passionate discussion about movies."

[0774] "Example 2"

[0775] In a second embodiment of the present invention, a function is implemented that randomly provides summarized stories, regardless of whether they are recent or past, or with whom. This function displays a randomly selected story that summarizes a past conversation every time the user opens the app. For example, when a user opens the app, the following sentence is displayed: "Last night, User A and User B had a passionate discussion about movies."

[0776] "Example 3"

[0777] As a third embodiment of the present invention, a free version and a paid version are provided. In the free version, summarized stories are displayed randomly. On the other hand, in the paid version, the user can specify the person and the period to display a story summarizing a specific conversation. For example, if the user specifies "yesterday's conversation with user B," the following sentence is displayed: "Last night, user A and user B had a passionate discussion about movies."

[0778] The processing flow of each embodiment will be described below.

[0779] "Example 1"

[0780] Step 1: A user starts a conversation in a messenger app.

[0781] Step 2: Generative AI analyzes the user's message in real time and summarizes it as a story, using natural language processing techniques.

[0782] Step 3: The generative AI saves the summarized story in a database.

[0783] "Example 2"

[0784] Step 1: User opens the app.

[0785] Step 2: The system randomly selects a summarized story from the database.

[0786] Step 3: Display the selected story to the user.

[0787] "Example 3"

[0788] Step 1: User chooses free or paid version.

[0789] Step 2: If you choose the paid version, the user will specify the recipient and the period.

[0790] Step 3: Based on the specified criteria, the system selects a summarized story from the database and displays it to the user.

[0791] Example 1

[0792] Next, a description will be given of Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0793] In modern messaging apps, the volume of interactions between users and groups is enormous, making it difficult to review important conversations and emotional exchanges from the past. There is also a lack of a way to concisely summarize these interactions and provide them in an easy-to-understand format. Furthermore, users cannot obtain summaries based on a specific period or person, making it difficult to organize and search for information.

[0794] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0795] In this invention, the server includes means for automatically summarizing exchanges between users of the messenger app or within a group using generative artificial intelligence and providing the chats as a single story (text), means for receiving messages sent by users, means for analyzing the received messages using natural language processing technology, means for generating summaries based on the analysis results, means for sending the generated summaries to users, means for randomly providing them regardless of whether they are recent or past or to whom, means for reviewing past exchanges and evoking emotional feelings in the user, and means for displaying summaries randomly in the free version or by specifying the person, period, etc. in the paid version. This allows users to easily review important conversations and emotional exchanges from the past, making it easier to organize and search for information.

[0796] A "messenger app" is a software application that allows users to send and receive text messages and multimedia content in real time.

[0797] "User" means an individual or group that sends or receives messages using the Messenger App.

[0798] "Generative AI" is an AI system that uses natural language processing technology to analyze text data and generate summaries and new sentences.

[0799] A "summary" is a short sentence that succinctly summarizes the content of the original message.

[0800] "Natural language processing technology" is a technology that allows computers to understand, analyze, and generate human language.

[0801] "Analysis" is the process of structurally breaking down a received message and extracting meaning and important information.

[0802] A "story" is a collection of multiple messages organized into one continuous sentence.

[0803] "Randomly presented" means presenting summaries selected at random, without following any particular order or criteria.

[0804] "Emotional evocation" means allowing users to relive emotions from past interactions.

[0805] The "free version" is a version that provides basic services free of charge, but with certain limited functionality.

[0806] "Paid Version" means a version that requires a fee to provide additional features or services.

[0807] "Other party" refers to other users with whom you exchange messages on the messenger app.

[0808] A "period" refers to a specific time range and covers messages and interactions within that range.

[0809] MODE FOR CARRYING OUT THE INVENTION

[0810] The present invention provides a system for automatically summarizing and providing a concise summary of interactions between users or within a group in a messenger app. Specific embodiments for implementing this system are described below.

[0811] System configuration

[0812] Hardware and Software Configuration

[0813] The server automatically summarizes messages between users and within groups using generative artificial intelligence (AI). This generative AI uses the Python natural language processing libraries "spaCy" and "Transformers." The server processes messages using the following steps:

[0814] 1. Sending a message

[0815] A user sends a message to another user using a messenger app. For example, user A sends a message saying, "The movie I saw yesterday was really good."

[0816] 2. Receiving a message

[0817] The server receives messages sent by users, stores them in a database, and then passes the message data to the generative AI.

[0818] 3. Message Analysis

[0819] The server calls the generative AI and analyzes the received message. The generative AI uses spaCy to perform morphological analysis of the message and extract important keywords. For example, from the message "The movie I saw yesterday was really good," the generative AI extracts the keywords "yesterday," "saw," "movie," "really," and "good."

[0820] 4. Summary Generation

[0821] The server then uses the generative AI to generate a summary based on the analysis results. The generative AI uses the Transformers library to generate a summary based on the extracted keywords and sentence structure. For example, it generates a summary such as "User A said that the movie was good."

[0822] 5. Submit your summary

[0823] The server sends the generated summary to User A and User B. User A and User B can check the summary on the messenger app.

[0824] Specific examples

[0825] For example, if user A and user B are talking about movies, the following conversation will take place:

[0826] User A: The movie I saw yesterday was really good.

[0827] User B: What movie was it?

[0828] User A: It was an action movie and the story was interesting.

[0829] User B: That's interesting. What's the title?

[0830] If we feed this conversation into a generative AI, it will summarize it as follows:

[0831] User A and User B had a lively discussion about movies.

[0832] In this way, generative AI automatically summarizes interactions between users and provides them in a concise format, allowing users to easily revisit important conversations and emotional exchanges from the past, making it easier to organize and search for information.

[0833] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0834] Program processing flow

[0835] Step 1:

[0836] A user sends a message to another user using a messenger app. For example, user A types a message such as "The movie I saw yesterday was really good" and presses the send button. The input is the text message sent by the user, and the output is the message data sent to the server.

[0837] Step 2:

[0838] The server receives messages sent by users. The received messages are stored in a database. The input is the message data sent by the user, and the output is the message data stored in the database.

[0839] Step 3:

[0840] The server calls the generative AI and analyzes the received message. The generative AI performs morphological analysis of the message using the Python natural language processing library "spaCy." For example, from the message "The movie I saw yesterday was really good," it extracts the keywords "yesterday," "saw," "movie," "really," and "good." The input is the message data stored in the database, and the output is the extracted keywords.

[0841] Step 4:

[0842] The server then uses the generative AI to generate a summary based on the analysis results. The generative AI uses the Transformers library to generate a summary based on the extracted keywords and sentence structure. For example, it generates a summary such as "User A said that the movie was good." The input is the extracted keywords, and the output is the generated summary.

[0843] Step 5:

[0844] The server sends the generated summary to user A and user B. User A and user B can check the summary on their messenger apps. The input is the generated summary, and the output is the summary sent to the user.

[0845] Specific actions

[0846] Step 1:

[0847] User A opens the messenger app, types a message to User B saying, "The movie I saw yesterday was really good," and presses the send button. This sends the message data to the server.

[0848] Step 2:

[0849] The server receives the message sent by user A and stores it in a database. The stored message data is used in subsequent processing.

[0850] Step 3:

[0851] The server calls the generative AI and analyzes the received message. The generative AI uses spaCy to perform morphological analysis of the message and extract important keywords. For example, from the message "The movie I saw yesterday was really good," the generative AI extracts the keywords "yesterday," "saw," "movie," "really," and "good."

[0852] Step 4:

[0853] The server then uses the generative AI to generate a summary based on the analysis results. The generative AI uses the Transformers library to generate a summary based on the extracted keywords and sentence structure. For example, it generates a summary such as "User A said that the movie was good."

[0854] Step 5:

[0855] The server sends the generated summary to User A and User B. User A and User B can then check the summary on their messenger app, allowing users to easily review important conversations and emotional interactions from the past.

[0856] (Application example 1)

[0857] Next, a description will be given of Application Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0858] In conventional messaging apps, the volume of interactions between users and within groups became enormous, making it difficult to review important conversations and emotional exchanges. Furthermore, the lack of a function to summarize discussions about content viewed by users and recommend new content reduced user convenience.

[0859] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0860] In this invention, the server includes a means for automatically summarizing interactions between users of the messenger app or within a group using a generative AI and providing the chat as a single story (text), a means for displaying the summarized content in the user's feed, and a means for recommending new content based on the summary. This allows users to easily review important conversations and emotional interactions from the past, and also allows them to receive recommendations for new content.

[0861] A "messenger app" is application software that allows users to send and receive text messages, images, videos, etc. between users or within groups.

[0862] "Generative AI" is artificial intelligence that uses natural language processing technology to analyze input text data and generate summaries or new sentences.

[0863] A "summary" is a short, concise summary of the important parts of the original text data.

[0864] "Feed" refers to the stream or list of information that a user views within an application.

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

[0866] "Recommendation" is the act of suggesting new information or entertainment based on a user's past behavior and interests.

[0867] "Random" means chosen at random without following any particular rule or order.

[0868] A "system" is a collection of devices and programs that are composed of a combination of multiple elements and functions.

[0869] The system for implementing this invention uses generative AI to automatically summarize interactions between users of a messenger app or within a group, and presents the chats as a single story (text). Specific embodiments of this system are described below.

[0870] Hardware and software used

[0871] Hardware:

[0872] Smartphone (iOS or Android)

[0873] Server (cloud-based, e.g. AWS or Google Cloud)

[0874] software:

[0875] Natural language processing libraries (e.g., spaCy, NLTK)

[0876] Generative AI models (e.g., OpenAI's GPT-3)

[0877] Messenger app APIs (e.g., WhatsApp API, Facebook Messenger API)

[0878] Data processing and calculation

[0879] Data collection

[0880] The server uses the API of the messenger app to collect user message data. For example, it calls the API as follows to retrieve messages.

[0881] messages = get_messages_from_api(user_id)

[0882] Data analysis

[0883] The server uses a natural language processing library to analyze the collected message data, which is then input into a generative AI model.

[0884] parsed_messages = nlp(messages)

[0885] Summary Generation

[0886] The server uses a generative AI model to summarize the parsed messages, which are then displayed in the user's feed.

[0887] summary = gpt3_generate_summary(parsed_messages)

[0888] Feed display

[0889] The server then displays the summaries in the user's feed, allowing them to easily revisit important conversations and emotional interactions from the past.

[0890] display_summary_in_feed(user_id, summary)

[0891] Content Recommendation

[0892] The server recommends new content based on the summaries, allowing users to receive new information and entertainment.

[0893] recommended_content = recommended_content_based_on_summary(summary)

[0894] Specific examples

[0895] If user A and user B are discussing the movie "ABCDEF," the following happens:

[0896] Original message:

[0897] User A: "ABCDEF is so funny!"

[0898] User B: "Yeah, the dream scene was amazing!"

[0899] summary:

[0900] "User A and User B had a passionate discussion about the movie 'ABCDEF'."

[0901] Prompt Sentence Examples

[0902] Below is an example of a prompt sentence to input to a generative AI model.

[0903] User A and User B are discussing the movie "ABCDEF." Summarize the following message:

[0904] User A: "ABCDEF is so funny!"

[0905] User B: "Yeah, the dream scene was amazing!"

[0906] In this way, user discussions are automatically summarized and displayed in the feed, allowing users to easily review past discussions and also receive recommendations for new content.

[0907] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0908] Step 1:

[0909] The server collects user message data using the messenger app's API. Specifically, it calls the API to retrieve messages related to the user ID. The input is the user ID, and the output is the message data.

[0910] messages = get_messages_from_api(user_id)

[0911] Step 2:

[0912] The server uses a natural language processing library (e.g., spaCy or NLTK) to analyze the collected message data. Specifically, it tokenizes the message data and performs grammatical analysis. The input is the message data, and the output is the analyzed message data.

[0913] parsed_messages = nlp(messages)

[0914] Step 3:

[0915] The server uses a generative AI model (e.g., OpenAI's GPT-3) to summarize the parsed message data. Specifically, the parsed data is input as prompts to the generative AI model, which then generates a summary. The input is the parsed message data, and the output is a summarized sentence.

[0916] summary = gpt3_generate_summary(parsed_messages)

[0917] Step 4:

[0918] The server displays the summarized content in the user's feed. Specifically, it adds the summarized sentence to the user's feed and displays it. The input is the summarized sentence, and the output is the summary displayed in the feed.

[0919] display_summary_in_feed(user_id, summary)

[0920] Step 5:

[0921] The server recommends new content based on the summary. Specifically, it analyzes the summarized content and runs an algorithm to recommend related new content. The input is the summarized text, and the output is the recommended content.

[0922] recommended_content = recommended_content_based_on_summary(summary)

[0923] In this way, user discussions are automatically summarized and displayed in the feed, allowing users to easily review past discussions and also receive recommendations for new content.

[0924] Example 2

[0925] Next, a description will be given of Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0926] Conventional messenger apps lacked the ability to summarize interactions between users, making it time-consuming to look back on past conversations. They also lacked the ability to randomly provide past conversations, allowing users to discover new things or evoke emotional feelings. Furthermore, there was insufficient differentiation between the free and paid versions of the app, and improvements were needed to increase user satisfaction.

[0927] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0928] In this invention, the server includes: a means for a user to launch an application; a means for a terminal to send a request to the server for a random summary of past conversations; a means for the server to randomly acquire past conversation data from a database; a means for the server to send the acquired conversation data to a generative AI model as a prompt; a means for the generative AI model to generate a conversation summary based on the prompt; a means for the server to send the generated summary to the terminal; and a means for the terminal to display the received summary to the user. This allows users to randomly summarize and display past conversations simply by launching the application, eliminating the need to revisit past conversations and evoking new discoveries and emotional feelings. It also enables differentiated functionality between the free and paid versions, increasing user satisfaction.

[0929] "User" means any individual or entity using the Application.

[0930] An "application" refers to a software program that users use on devices such as smartphones and PCs.

[0931] "Terminal" refers to the hardware device through which a user runs applications.

[0932] "Server" refers to a computer system that processes data and manages databases to provide application functions.

[0933] "Database" refers to an information system for storing and managing past conversation data.

[0934] "Generative AI model" refers to an artificial intelligence model that generates a summary of a conversation based on an input prompt.

[0935] "Prompt sentence" refers to an instruction sentence input to a generative AI model to generate a summary.

[0936] A "summary" refers to a sentence that concisely summarizes past conversation data.

[0937] A "request" refers to a communication message in which a terminal requests a server to perform a specific process.

[0938] A "response" refers to a communication message in which a server returns a result in response to a request to a terminal.

[0939] "Random" means that something is chosen at random without following any particular rules or order.

[0940] "Free Version" means a version of the Application that is available to Users without payment of a fee.

[0941] "Paid Version" means a version of an Application that a User pays to use.

[0942] The present invention relates to a system that randomly summarizes and displays past conversations when a user launches an application. Specific embodiments of this system are described below.

[0943] Hardware and software used

[0944] Servers: Database servers, application servers

[0945] Device: User's smartphone or PC

[0946] Software: Generative AI models (e.g., GPT-4), database management systems (e.g., MySQL), application frameworks (e.g., Django)

[0947] System configuration

[0948] 1. The user launches the application

[0949] A user taps or clicks to launch an application on their smartphone or PC.

[0950] 2. The device sends a request to the server

[0951] The terminal sends an HTTP request to the application server requesting a summary of a random past conversation.

[0952] 3. The server retrieves the data from the database

[0953] The server connects to the database server and executes an SQL query to randomly retrieve past conversation data.

[0954] 4. The server sends a prompt to the generative AI model

[0955] The server sends prompts to the generative AI model to summarize the conversation data it has acquired.

[0956] 5. Generative AI model generates summary

[0957] A generative AI model generates a summary of the conversation based on the prompts.

[0958] 6. The server sends the summary to the device.

[0959] The server returns the generated summary to the terminal as an HTTP response.

[0960] 7. The device displays a summary

[0961] The device displays the received summary to the user.

[0962] Specific examples

[0963] The user launches the application

[0964] The user taps the application icon on the smartphone's home screen.

[0965] The device sends a request to the server

[0966] The device sends an HTTP request to the endpoint "GET / api / random-summary".

[0967] The server retrieves the data from the database

[0968] The server executes the SQL query "SELECT FROM conversations ORDER BY RAND() LIMIT 1;" to retrieve random conversation data.

[0969] The server sends prompts to the generative AI model

[0970] The server sends a prompt to the generative AI model: "Summarize the following conversation: Last night, User A and User B had a lively discussion about movies."

[0971] Generative AI model generates summary

[0972] The generative AI model generates a summary such as, "Last night, User A and User B had a passionate discussion about movies."

[0973] The server sends the summary to the terminal.

[0974] The server returns the generated summary to the terminal as an HTTP response.

[0975] The terminal displays a summary

[0976] The device displays the text "Last night, User A and User B had a lively discussion about movies" in the application UI.

[0977] Prompt Sentence Examples

[0978] "Summarize the following conversation: User A and User B had a lively discussion about movies last night."

[0979] "Summarize the following conversation: Last week, User C and User D were talking about a new restaurant."

[0980] In this way, we create a system that displays a randomly selected summary of past conversations every time a user launches the application.

[0981] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0982] Step 1:

[0983] The user launches an application.

[0984] Specifically, a user taps an application icon on the smartphone's home screen. This launches the application and displays its initial screen. The input is the user's tap operation, and the output is the application's initial screen.

[0985] Step 2:

[0986] The device sends a request to the server.

[0987] Specifically, the terminal sends an HTTP request to the endpoint "GET / api / random-summary." The input is a request from the initial screen of the application, and the output is a request sent to the server.

[0988] Step 3:

[0989] The server retrieves the data from the database.

[0990] Specifically, the server connects to the database server and executes the SQL query "SELECT FROM conversations ORDER BY RAND() LIMIT 1;" The input is the request to the server, and the output is randomly selected past conversation data.

[0991] Step 4:

[0992] The server sends prompts to the generative AI model.

[0993] Specifically, the server converts the acquired conversation data into a prompt sentence such as "Please summarize the following conversation: [conversation data]" and sends it to the generative AI model. The input is randomly selected past conversation data, and the output is the generation and transmission of a prompt sentence.

[0994] Step 5:

[0995] A generative AI model generates the summary.

[0996] Specifically, the generative AI model generates a summary of the conversation based on the prompt sentence, with the input being the prompt sentence and the output being the generated summary.

[0997] Step 6:

[0998] The server sends the summary to the terminal.

[0999] Specifically, the server sends the generated summary back to the terminal as an HTTP response. The input is the generated summary, and the output is the response sent to the terminal.

[1000] Step 7:

[1001] The terminal displays a summary.

[1002] Specifically, the device displays the received summary on the application's UI. The input is the response from the server, and the output is the summary displayed to the user.

[1003] In this way, a system is realized in which a randomly selected summary of past conversations is displayed each time the user launches the application.

[1004] (Application example 2)

[1005] Next, a description will be given of Application Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1006] Traditional messaging applications make it difficult for users to review past conversations and viewing history, and lack a way to easily recall past memories. They also lack the ability to provide a summary of past conversations and viewing history, which can lead to users missing important conversations or viewing content. Furthermore, they lack the ability to display a new summary every time a user opens the application, making it difficult to keep users engaged.

[1007] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for automatically summarizing interactions between users of a messaging application or within a group using a generative artificial intelligence and providing the communications as a single story (text), means for randomly providing such summaries regardless of whether they are recent or past or the recipient, means for randomly displaying summaries of past viewing history and chats in which the user has participated, and means for displaying a different summary each time the user opens the application. This allows the user to easily look back on past interactions and viewing history, which can evoke emotional feelings. Furthermore, a new summary is displayed each time the application is opened, thereby maintaining the user's interest.

[1008] A "messaging application" is software that allows users to send and receive text messages, images, audio, video, etc.

[1009] "User" means an individual or group of people who communicate using a messaging application.

[1010] "Generative artificial intelligence" refers to algorithms or models that automatically generate text or information based on input data.

[1011] A "summary" is a short sentence that succinctly summarizes the contents of a long piece of text or conversation.

[1012] "Communication" refers to the exchange of information between users through messaging applications.

[1013] A "story" is a collection of events or conversations organized into a single continuous sentence.

[1014] "Viewing history" is a record of content that a user has viewed in the past.

[1015] "Chat" is a real-time text-based conversation conducted through a messaging application.

[1016] "Randomly presented" means presenting content selected at random without following any particular order or rules.

[1017] "Emotional evocation" means eliciting an emotional response in the user by recalling past events or conversations.

[1018] "Free Version" means a version of the Software that provides basic functionality free of charge.

[1019] "Paid Version" means a version of the Software that provides additional features or services and requires a fee.

[1020] "Specify" means to set specific conditions or criteria.

[1021] A system for implementing the present invention includes a messaging application, a generative artificial intelligence, a database, and a user interface. Specific embodiments are described below.

[1022] System Configuration

[1023] 1. Messaging applications:

[1024] A messaging application is software that allows users to send and receive text messages, images, audio, and video. The application is installed on mobile devices such as smartphones and tablets.

[1025] 2. Generative AI:

[1026] Generative AI is an algorithm or model that automatically summarizes a user's past interactions and viewing history and presents it as a narrative. Specifically, it uses natural language processing technology to summarize long conversations and viewing histories into concise sentences.

[1027] 3. Database:

[1028] The database is intended to store users' past interactions and viewing history, including text messages, images, audio, video, and other data.

[1029] 4. User Interface:

[1030] The user interface is the screen through which the user interacts with the application, displaying random summaries of past interactions and viewing history.

[1031] Processing flow

[1032] 1. Data Collection:

[1033] Messaging applications store user interactions and viewing history in a database.

[1034] 2. Data Summary:

[1035] Generative AI retrieves past interactions and viewing history from a database and summarizes them using natural language processing techniques.

[1036] 3. Random display:

[1037] Each time a user opens the application, the generative artificial intelligence displays a randomly selected summary in the user interface.

[1038] Hardware and software used

[1039] Hardware:

[1040] Smartphones, tablets, servers

[1041] software:

[1042] Messaging applications, generative artificial intelligence (natural language processing algorithms), database management systems

[1043] Specific examples

[1044] Here's an example of the summary that appears when a user opens the application:

[1045] Example prompt sentence:

[1046] "Show me random summaries of past conversations and viewing history."

[1047] An example of the summary that will be displayed:

[1048] "On 2023-10-01, User A and User B had a passionate discussion about movies."

[1049] In this way, each time the user opens the application, a different summary is displayed, allowing them to reminisce about their past experiences, which can evoke emotional feelings and keep them interested in the application.

[1050] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1051] Step 1:

[1052] The server collects user interactions and viewing history through messaging applications and stores them in a database. The input is data such as text messages, images, audio, and video sent by users, and the output is that this data is stored in the database. Specifically, the messaging application captures user interactions in real time and sends them to the server.

[1053] Step 2:

[1054] The server uses generative artificial intelligence to retrieve past interactions and viewing history from a database and summarize them. The input is the past interactions and viewing history stored in the database, and the output is a summarized short sentence. Specifically, the generative artificial intelligence uses natural language processing technology to summarize long conversations and viewing history into concise sentences.

[1055] Step 3:

[1056] The server randomly selects one of the generated summaries and displays it each time the user opens the application. The input is a set of sentences summarized by the generative AI, and the output is a randomly selected summary that is displayed in the user interface. Specifically, the server uses a random algorithm to select one of the summaries and send it to the user interface.

[1057] Step 4:

[1058] The user sees a new summary every time they open the application. The input is a randomly selected summary sent from the server, and the output is the summary the user sees on their screen. Specifically, when the user launches the application, the summary sent from the server is displayed in the user interface.

[1059] Step 5:

[1060] The user looks at the displayed summary and recalls past interactions and viewing history, evoking emotional feelings. The input is the summary displayed in the user interface, and the output is the user's emotional response. The specific action is for the user to read the displayed summary and recall past events.

[1061] Example 3

[1062] Next, a description will be given of Example 3 of Form Example 3. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1063] With conventional messenger apps, when users want to review past conversations, they have to manually search for the necessary information from a huge amount of data, which is extremely time-consuming. Furthermore, since there is no function to display a summary of a specific conversation, it is difficult for users to quickly grasp important information. Furthermore, since the distinction between the free and paid versions is unclear, it is unclear which features users can use. These issues need to be resolved.

[1064] The specific processing by the specific processing unit 290 of the data processing device 12 in the third embodiment is realized by the following means.

[1065] In this invention, the server includes a means for a user to log in to the system, a means for the user to select either the free version or the paid version, and a means for the server to randomly select conversation data in the free version. This allows the user to efficiently summarize and view past conversations. The paid version also includes a means for the user to specify a person and a period, a means for the server to filter the conversation data based on the specified conditions, a means for the server to input a prompt sentence to the generative AI model, a means for the generative AI model to generate a summary, and a means for the server to display the generated summary to the user. This allows the user to quickly summarize and view a specific conversation.

[1066] The "means for a user to log in to the system" refers to a function that allows a user to access the system using a terminal and log in by entering authentication information.

[1067] "Means for users to select the free version or the paid version" refers to the interface and functionality that allows users to select either the free version or the paid version within the system.

[1068] The "means by which the server randomly selects conversation data" refers to the algorithm and function by which the server randomly extracts conversation data from the database.

[1069] The "means for the user to specify a partner and a period" refers to an interface and function that allows the user to input a specific partner and a period.

[1070] "Means for the server to filter conversation data based on specified conditions" refers to the algorithms and functions that allow the server to search the database based on conditions specified by the user and extract relevant conversation data.

[1071] "Means for the server to input prompt sentences into the generative AI model" refers to a function that enables the server to generate prompt sentences based on filtered conversation data and input them into the generative AI model.

[1072] The "means by which the generative AI model generates a summary" refers to the algorithms and functions by which the generative AI model generates a summary based on the input prompt sentence.

[1073] "Means for the server to display the generated summary to the user" refers to the function that allows the server to receive the summary returned from the generative AI model and display it on the user's device.

[1074] The present invention provides a system that enables users to efficiently summarize and view past conversations. A specific embodiment of this system will be described below.

[1075] System program generation

[1076] The server generates a program that allows users to log in to the system and select either the free or paid version. This program collects the user's conversation data and generates summaries using a generative AI model. The hardware used is a high-performance cloud server (e.g., AWS EC2 instance), and the generative AI model used is OpenAI's GPT-3.

[1077] Explanation of program processing

[1078] When a user logs in to the system, the server verifies the user's authentication information and allows the user to log in. After logging in, the user is taken to a screen where they can choose between the free version or the paid version. If the user selects the free version, the server randomly selects conversation data from the database and inputs prompt sentences into the generative AI model. The generative AI model generates a summary, which the server displays to the user.

[1079] If the paid version is selected, the user enters information into an input form to specify a specific person and time period. The server uses this information to search the database and filter the relevant conversation data. A prompt is generated based on the filtered conversation data and input into the generative AI model. The generative AI model then generates a summary, which the server displays to the user.

[1080] Specific examples

[1081] For example, if a user is using the free version, the server inputs randomly selected conversation data into the generative AI model. For example, the prompt might say, "Please summarize the conversation between user A and user B." The generative AI model generates a summary that says, "User A and user B discussed movies," and the server displays it to the user.

[1082] As a specific example of the paid version, if a user specifies "yesterday's conversation with user B," the server filters the conversation data that matches that condition and inputs the prompt sentence "Please summarize the conversation between user A and user B about movies last night" into the generative AI model. The generative AI model generates a summary that reads "Last night, user A and user B had a passionate discussion about movies," and the server displays it to the user.

[1083] Prompt Sentence Examples

[1084] Free version: "Please summarize the conversation between User A and User B"

[1085] Paid version: "Please summarize the conversation between User A and User B about movies last night."

[1086] In this way, the user can efficiently summarize and view past conversations. This system allows the user to quickly grasp important information. The flow of the identification process in the third embodiment will be described with reference to FIG. 15.

[1087] Step 1:

[1088] A user logs in to the system.

[1089] A user accesses the system using a terminal and enters a user ID and password on the login screen. The server checks the entered authentication information against the database and allows login if authentication is successful. The input is the user ID and password, and the output is the authentication result. If authentication is successful, the user can proceed to the next screen.

[1090] Step 2:

[1091] The user chooses the free or paid version.

[1092] After logging in, the user selects either the free version or the paid version. The selection information is sent to the server. The input is the user's selection information, and the output is the selection result. The server determines the next process based on the user's selection.

[1093] Step 3:

[1094] In the free version, the server randomly selects conversation data.

[1095] The server runs an algorithm to randomly extract conversation data from the database. The input is all the conversation data in the database, and the output is a randomly selected conversation data. The server uses this randomly selected conversation data in the next step.

[1096] Step 4:

[1097] In the paid version, the user specifies the recipient and the period.

[1098] The user enters information into an input form to specify a specific person and time period. The input is the person and time period information, and the output is the specified conditions. The server prepares to search the database based on these conditions.

[1099] Step 5:

[1100] The server filters the conversation data based on the specified criteria.

[1101] The server searches the database based on the conditions specified by the user and extracts the relevant conversation data. The input is the specified conditions and all the conversation data in the database, and the output is the filtered conversation data. The server uses this filtered conversation data in the next step.

[1102] Step 6:

[1103] The server inputs a prompt sentence into the generative AI model.

[1104] The server generates a prompt sentence based on the filtered conversation data and inputs it to the generative AI model. The input is the filtered conversation data, and the output is the generated prompt sentence. The server sends this prompt sentence to the generative AI model.

[1105] Step 7:

[1106] A generative AI model generates the summary.

[1107] The generative AI model generates a summary based on the input prompt. The input is the prompt, and the output is the generated summary. The generative AI model returns this summary to the server.

[1108] Step 8:

[1109] The server displays the generated summary to the user.

[1110] The server receives the summary returned by the generative AI model and displays it on the user's device. The input is the generated summary, and the output is the summary displayed on the user's device. The user can view the displayed summary.

[1111] (Application example 3)

[1112] Next, a description will be given of Application Example 3 of Form Example 3. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1113] In modern messaging applications, the volume of conversations between users is so large that it is difficult to review past exchanges. Furthermore, there is a lack of a way to easily obtain summaries of conversations focused on specific people or periods. Furthermore, there is a need for a system that can evoke emotional feelings in users by providing conversation summaries in a narrative format.

[1114] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 3 is realized by the following means.

[1115] In this invention, the server includes a means for automatically summarizing conversations between users of the messaging application or within a group using generative artificial intelligence, providing the conversation as a single story (text), a means for randomly providing the summary regardless of whether it is recent or past, or by whom, and, in the paid version, a means for displaying summaries by specifying a specific person or period. This allows users to easily look back on past conversations and easily obtain summaries focused on a specific person or period. Furthermore, the narrative-style summaries can evoke emotional feelings in users.

[1116] A "messaging application" is software that allows users to send and receive messages, including text, audio, images, and videos, between each other.

[1117] "User" means any individual or group of people who use the Message Application.

[1118] "Generative artificial intelligence" refers to algorithms or systems that automatically generate text or information based on input data.

[1119] A "summary" is a sentence that briefly summarizes the content of the original conversation.

[1120] A "narrative" is a piece of writing that presents multiple events or conversations as a series of stories.

[1121] "Most recent" refers to the period in the past that is closest to the present.

[1122] "Past" refers to a time before the present.

[1123] "Random" means that something is chosen at random without following any particular rules or order.

[1124] "Paid Version" refers to a version that requires a fee to use certain features or services.

[1125] "Specific person" refers to a specific user designated by the user.

[1126] A "period" refers to a range of time between a specific start date and an end date.

[1127] "Conversation history" refers to a record of past message exchanges.

[1128] A "smartphone" is a mobile device that has the functionality of a mobile phone and is also capable of connecting to the Internet and running applications.

[1129] Systems for implementing the present invention include messaging applications, generative artificial intelligence, and smartphones. Specific embodiments are described below.

[1130] First, the server collects interactions between users and groups through messaging applications. These interactions are saved in the form of text, audio, images, and video. Next, generative AI automatically analyzes these interactions and generates summaries. These summaries express the conversation as a single narrative (text).

[1131] The server has a means to randomly provide generated summaries, regardless of whether they were recent or past, or with whom. This allows users to look back on past conversations without specifying specific conditions. The paid version also provides a means to display summaries by specifying a specific person or period. This allows users to easily obtain summaries focused on a specific conversation or period.

[1132] As an application installed on a smartphone, this system generates and displays a narrative summary based on the user's conversation history. Specifically, when a user opens the application and specifies a specific person and time period, the generative AI generates a summary based on those conditions and displays it on the smartphone screen.

[1133] For example, if a user requests, "Please summarize the conversation with User B on October 1, 2023," the generative AI will analyze the conversation based on that condition and generate a summary such as, "On October 1, 2023, User A and User B had a passionate discussion about movies." This summary is displayed on the smartphone screen, allowing users to easily look back on past conversations.

[1134] An example of a prompt is as follows:

[1135] "Please summarize the conversation between User A and User B on 2023-10-01."

[1136] This system allows users to easily review past conversations and easily obtain summaries of specific people or periods. Furthermore, narrative summaries can evoke emotional feelings in users.

[1137] The flow of the specific processing in Application Example 3 will be described with reference to FIG.

[1138] Step 1:

[1139] The server collects messages between users and groups through messaging applications. The input includes message data such as text, audio, images, and videos. The server stores this data in a database. The output is the stored message data.

[1140] Step 2:

[1141] The server provides the stored message data as input to the generative AI. The generative AI analyzes this data and generates a summary of the conversation. Natural language processing techniques are used to analyze the text data and extract important information. The output is summarized conversation data.

[1142] Step 3:

[1143] The server has a means to randomly provide generated summaries, regardless of whether they are recent or past, or for any other party. The input includes the summarized conversation data. The server uses a random algorithm to randomly select a summary and provide it to the user. The output is a randomly selected summary.

[1144] Step 4:

[1145] A user can specify a specific contact and time period through an application installed on their smartphone. The input includes the contact and time period information specified by the user. The device sends this information to the server. The output is a request based on the specified conditions.

[1146] Step 5:

[1147] The server receives a request from a user and filters conversation data based on the specified contact and time period. The input includes the information on the contact and time period specified by the user, as well as the stored message data. The data is processed by using a filtering algorithm to extract the relevant conversation data. The output is the filtered conversation data.

[1148] Step 6:

[1149] The server provides the filtered conversation data to a generative AI to generate a summary. The input includes the filtered conversation data. The generative AI again uses natural language processing techniques to generate a summary. The output is a summary based on the specified criteria.

[1150] Step 7:

[1151] The server sends the generated summary to the user's smartphone. The input includes a summary based on the specified conditions. The server sends the summary to the device using a communication protocol. The output is a summary that is displayed on the user's smartphone.

[1152] Step 8:

[1153] The user checks the summary displayed on the smartphone screen. The input includes the summary sent from the server. The device displays the summary on the screen. The output is a summary that the user can visually check.

[1154] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1155] "Example 1"

[1156] One embodiment of the present invention is a messenger app system incorporating an emotion engine. In this system, when a user converses on the messenger app, a generative AI analyzes the conversation in real time. Natural language processing technology is used for the analysis, and the emotion engine recognizes the user's emotion from the content of the conversation. For example, if a user sends a message saying "I had fun today," the emotion engine recognizes the emotion of "joy" from the message.

[1157] "Example 2"

[1158] Furthermore, the way the summarized story is presented is adjusted based on the emotion recognized by the emotion engine. For example, if the emotion engine recognizes the emotion of "joy," the user will be presented with a summarized story that enhances the emotion of joy. This allows the user to better understand their own emotions and recreate emotional experiences.

[1159] "Example 3"

[1160] Furthermore, the emotions recognized by the emotion engine are fed back to the user. For example, after a user sends a message saying, "Today was fun," the system will provide feedback such as, "You seem to be feeling joy right now." This allows users to objectively understand their own emotions and deepen their self-understanding.

[1161] The processing flow of each embodiment will be described below.

[1162] "Example 1"

[1163] Step 1: A user starts a conversation in a messenger app.

[1164] Step 2: The generative AI analyzes the conversation in real time, using natural language processing technology.

[1165] Step 3: The emotion engine is activated to recognize the user's emotions from the content of the conversation.

[1166] "Example 2"

[1167] Step 1: The emotion engine recognizes the user's emotion.

[1168] Step 2: Based on the perceived emotions, the delivery of the summarized story is adjusted.

[1169] Step 3: The user is provided with an emotionally charged, summarized story.

[1170] Step 1: The emotion engine recognizes the user's emotion.

[1171] Step 2: The recognized emotion is fed back to the user.

[1172] Step 3: Users objectively understand their own feelings through feedback.

[1173] Example 1

[1174] Next, a description will be given of Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1175] With traditional messaging apps, the volume of conversations between users was enormous, making it difficult to review past interactions. Furthermore, since there was no function to summarize the contents of conversations, there was a risk of missing important information. Furthermore, since there was no ability to recognize emotions, it was difficult to understand the user's feelings.

[1176] The identification process by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means. In this invention, the server includes: means for automatically summarizing interactions between users or within a group of users of a messenger app using a generative AI and providing the chat as a single story (text); means for a terminal to transmit message data to the server when a user sends a message using the messenger app; means for the server to analyze the received message data using natural language processing technology and summarize the conversation; means for the server to recognize emotions from the message using an emotion engine; means for the terminal to transmit the summary result and the emotion recognition result and display them to the user; and means for randomly providing them regardless of whether they are recent, past, or with whom. This allows the user to easily look back on past interactions, avoid missing important information, and further understand the emotions in the conversation.

[1177] A "messenger app" is software that allows users to send and receive text messages.

[1178] "Generative AI" is artificial intelligence that uses natural language processing technology to analyze text data and perform summarization and generation.

[1179] "Natural language processing technology" is a technology that allows computers to understand, interpret, and generate human language.

[1180] An "emotion engine" is software that recognizes user emotions from text data.

[1181] A "server" is a computer system for processing and storing data.

[1182] "Device" refers to a device that is directly operated by a user and on which a messenger app is installed.

[1183] "Summarization" is the process of summarizing long text data and extracting only the important information.

[1184] "Emotion recognition" refers to extracting and understanding a user's emotions from text data.

[1185] A "story (text)" is a collection of multiple messages organized into a single continuous text.

[1186] "Randomly providing" means providing data randomly without relying on any particular order or conditions.

[1187] This invention is a system that uses generative AI implemented in a messenger app to automatically summarize interactions between users or within a group, and further recognizes emotions from messages using an emotion engine. When a user sends a message via the messenger app, the device sends the message data to a server, which analyzes the data, summarizes it, and recognizes emotions, and then provides the results to the user.

[1188] Hardware and software used

[1189] Hardware:

[1190] Server: A computer system for processing and storing data.

[1191] Terminal: Devices that users directly operate (smartphones, tablets, PCs, etc.)

[1192] software:

[1193] Messenger app: Software that allows users to send and receive text messages.

[1194] Generative AI: Artificial intelligence that uses natural language processing technology to analyze text data and generate summaries and information (e.g., BERT, GPT-3)

[1195] Emotion engine: Software for recognizing user emotions from text data (e.g., Azure Cognitive Services' Text Analytics API)

[1196] Data processing and calculation

[1197] The server receives message data sent from the device and analyzes it using natural language processing technology. Specifically, it uses generative AI models such as BERT and GPT-3 to understand the content of the message and generate a summary. It also uses an emotion engine to extract emotions from the message. This allows it to summarize the user's conversation as a story and recognize the emotions contained in the conversation.

[1198] Specific examples

[1199] For example, if user A and user B are interacting on the topic of "movies," the process will proceed as follows:

[1200] User A: The movie yesterday was great!

[1201] User B: Yeah, the final scene was especially moving.

[1202] The generative AI summarizes this conversation as, "User A and User B talked about a touching scene in a movie." The emotion engine also recognizes the emotions of "joy" and "emotion."

[1203] In this way, users can easily look back at past interactions, avoid missing important information, and understand the sentiment of the conversation.

[1204] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1205] Step 1:

[1206] A user sends a message using a messenger app. A user uses a messenger app to converse with other users. For example, user A sends a message saying, "The movie yesterday was great!" This message becomes input data.

[1207] Step 2:

[1208] The terminal sends message data to the server. The terminal sends message data sent by the user to the server. This data includes the message content and sender information. The input is the user's message data, and the output is data sent to the server.

[1209] Step 3:

[1210] The server receives the message data and analyzes it using natural language processing technology. The server then analyzes the received message data using natural language processing technology (e.g., BERT or GPT-3). The input is the message data sent from the device, and the output is the analysis result. Specifically, the server understands the content of the message and extracts important information.

[1211] Step 4:

[1212] The server summarizes the conversation based on the analysis results. For example, if the conversation between user A and user B is about "movies," it will be summarized into a single sentence: "User A and user B had a passionate discussion about movies." The input is the analysis results, and the output is the summarized sentence.

[1213] Step 5:

[1214] The server uses an emotion engine to recognize emotions from messages. The server uses an emotion engine (e.g., Azure Cognitive Services' Text Analytics API) to recognize emotions from messages. For example, it extracts the emotion "joy" from the message "Yesterday's movie was great!" The input is the message data, and the output is the recognized emotion.

[1215] Step 6:

[1216] The server sends the summary results and emotion recognition results to the terminal. The server sends the summary results and emotion recognition results to the terminal. This data includes the summarized text and the recognized emotion. The input is the summary results and emotion recognition results, and the output is data transmission to the terminal.

[1217] Step 7:

[1218] The terminal displays the summary results and emotion recognition results to the user. The terminal displays the summary results and emotion recognition results received from the server to the user. The user can check the conversation summary and emotion recognition results. The input is data from the server, and the output is what is displayed to the user.

[1219] (Application example 1)

[1220] Next, a description will be given of Application Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1221] Previous messaging apps lacked a way to efficiently summarize user interactions, making it time-consuming to review past conversations. Furthermore, they lacked a way to concisely grasp the content of news articles and blog posts when reading them, and lacked the ability to analyze the sentiment of articles and provide this information to users. This led to users becoming overwhelmed by information overload and potentially missing important information.

[1222] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1223] In this invention, the server includes a means for automatically summarizing interactions between users of a messenger app or within a group by a generative AI and providing the chat as a single story (text), a means for randomly providing such summaries regardless of whether they are recent or past or who the recipient is, a means for automatically summarizing news articles and blog articles, and a means for analyzing the sentiment of articles and providing them to users. This enables users to efficiently summarize and understand past conversations and news articles, and by understanding the sentiment of the articles, they can quickly determine the importance and tone of the information.

[1224] A "messenger app" is software that allows users to send and receive text messages, images, videos, etc.

[1225] "Generative AI" is artificial intelligence that uses natural language processing technology to analyze text data and generate summaries and new sentences.

[1226] A "summary" is a short summary of a long piece of text or conversation, extracting only the important information.

[1227] A "story" is a piece of writing that compiles a series of events or conversations.

[1228] A "random presentation method" is a method of presenting information without following any particular order or rule.

[1229] A "news article" is a piece of text that describes events or information reported in newspapers, websites, etc.

[1230] A "blog article" is a diary-style piece of writing that is published on the web by an individual or organization.

[1231] "Means for analyzing emotions" refers to a method for identifying emotions from text data and evaluating the type and intensity of those emotions.

[1232] "User" refers to any individual or organization that uses the messenger app, news articles, or blog posts.

[1233] A "server" is a computer system that stores and processes data on a network.

[1234] The system for implementing this invention includes a messenger app, generative AI, a sentiment analysis engine, and a function for summarizing news articles and blog articles. Specific embodiments are described below.

[1235] System Configuration

[1236] The system uses the following hardware and software:

[1237] Hardware: Smartphones, PCs, servers

[1238] software:

[1239] Messenger app: an application that allows users to send and receive text messages.

[1240] Generative AI: Analyze text data using OpenAI APIs to generate summaries and new sentences

[1241] Sentiment Analysis Engine: Analyzing sentiment from text data using TextBlob

[1242] Program processing

[1243] The server includes the following means:

[1244] 1. A way to summarize conversations on messenger apps: Using generative AI, messages between users are automatically summarized and presented as a single story.

[1245] 2. A way to summarize news articles and blog posts: Use generative AI to automatically summarize news articles and blog posts.

[1246] 3. Means of sentiment analysis: Using a sentiment analysis engine, the sentiment of the summarized article or message is analyzed and provided to the user.

[1247] Processing flow

[1248] 1. Summary of Messenger app conversation:

[1249] A user has a conversation on a messenger app.

[1250] The server uses generative AI to analyze the conversation in real time and generate a summary.

[1251] The summarized conversation is presented to the user as a single story.

[1252] 2. Summarizing news articles and blog posts:

[1253] A user reads a news article or blog post.

[1254] The server uses generative AI to summarize the article.

[1255] The summarized article is provided to the user.

[1256] 3. Emotion Analysis:

[1257] Text data from summarized conversations and articles is fed into a sentiment analysis engine.

[1258] The sentiment analysis engine analyzes emotions from text data and provides the results to the user.

[1259] Specific examples

[1260] For example, if a user is reading political news, they might input the following prompt into the generative AI:

[1261] Summarize the following passage:

[1262] Today marks the start of an important conference on climate change that is attracting worldwide attention.

[1263] Many countries participated and there was lively discussion about reducing greenhouse gas emissions.

[1264] The generative AI summarizes the news article based on this prompt, and the sentiment analysis engine analyzes the article's tone (e.g., optimistic, pessimistic, angry, etc.), allowing users to quickly understand the article's content and sentiment.

[1265] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1266] Step 1:

[1267] A user has a conversation in a messenger app. The user types and sends a text message. The input is the user's text message, and the output is the message being sent.

[1268] Step 2:

[1269] The server sends the received message to the generative AI. The input is the user's text message, and the output is the transmission of the message to the generative AI.

[1270] Step 3:

[1271] The generative AI analyzes the received message and generates a summary. The input is the user's text message, and the output is the summarized text. The generative AI uses natural language processing techniques to extract the key parts of the message and summarize it as a story.

[1272] Step 4:

[1273] The server provides the generated summary to the user. The input is the summary text from the generative AI, and the output is the transmission of the summary text to the user.

[1274] Step 5:

[1275] A user reads a news article or blog post. The user enters the text of the article and submits it to a server. The input is the text of the user's news article or blog post, and the output is the text submitted to the server.

[1276] Step 6:

[1277] The server sends the received articles to the generative AI. The input is the text of the user's news article or blog post, and the output is the article sent to the generative AI.

[1278] Step 7:

[1279] Generative AI analyzes incoming articles and generates summaries. The input is the text of a user's news article or blog post, and the output is the summarized text. Generative AI uses natural language processing techniques to extract and summarize the important parts of the article.

[1280] Step 8:

[1281] The server provides the generated summary to the user. The input is the summary text from the generative AI, and the output is the transmission of the summary text to the user.

[1282] Step 9:

[1283] The server sends the summarized text to a sentiment analysis engine. The input is the summarized text and the output is the text sent to the sentiment analysis engine.

[1284] Step 10:

[1285] The Sentiment Analysis Engine analyzes incoming text and identifies sentiment. The input is the summarized text and the output is the sentiment analysis result. The Sentiment Analysis Engine uses TextBlob to analyze the sentiment of the text and evaluate its type and intensity.

[1286] Step 11:

[1287] The server provides the emotion analysis results to the user. The input is the emotion analysis results from the emotion analysis engine, and the output is the transmission of the emotion analysis results to the user.

[1288] Step 12:

[1289] The user can quickly understand the information by viewing the provided summary and sentiment analysis results. The input is the summary and sentiment analysis results from the server, and the output is the user's understanding.

[1290] Example 2

[1291] Next, a description will be given of Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1292] In conventional messaging apps, when reviewing past conversations, it was difficult to find a specific conversation from a huge chat log, making it difficult for users to relive past emotional experiences. Furthermore, there was a lack of functionality to provide a summary of past conversations, meaning users had no easy way to review past interactions. Furthermore, there was also a lack of functionality to adjust the summarized narrative based on the user's emotions, making it impossible to deepen the user's emotional experience.

[1293] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1294] In this invention, the server includes: means for automatically summarizing interactions between users of a messaging app or within a group and providing the chat as a single story (text); means for randomly providing such a story regardless of whether it is recent or past, or with whom; means for summarizing a randomly selected conversation from a past conversation database each time a user opens the app; means for inputting prompt text into a generative AI model to generate a summarized story; means for analyzing the user's emotions using an emotion engine and adjusting the summarized story based on the recognized emotions; means for sending the adjusted summarized story to the user's device; and means for displaying the summarized story received by the device on a screen. This allows users to easily look back on past conversations and relive emotional experiences.

[1295] A "messaging app" is software that allows users to send and receive text messages and multimedia.

[1296] "User" refers to an individual or group who uses a messaging app.

[1297] "Generative AI" is a system that uses artificial intelligence technology to automatically generate or summarize text data.

[1298] "Summarization" is the process of extracting important information from original text data and reconstructing it in a concise form.

[1299] "Narrative (Text)" is a format that provides summarized text data as a single continuous sentence.

[1300] "Randomly provided" refers to providing data selected at random without following any particular order or rules.

[1301] A "conversation database" is a database for storing past interactions and chat logs between users.

[1302] A "generative AI model" is an algorithm or model that uses natural language processing techniques to generate or summarize text data.

[1303] A "prompt" is an instruction given to a generative AI model that provides guidelines for performing a specific task.

[1304] An "emotion engine" is software that analyzes a user's text data and behavioral data to recognize emotions.

[1305] "Adjusting" refers to changing the content or presentation of the summarized story based on perceived emotions.

[1306] A "terminal" is a device on which a user uses a messaging app, including a smartphone, tablet, or PC.

[1307] "Displaying on the screen" refers to the visual presentation of text data or other information on a device display.

[1308] The present invention provides a system that allows users of messaging apps to easily look back on past conversations and relive emotional experiences. Specific embodiments of the system are described below.

[1309] Server Processing

[1310] When the server detects that a user has opened the app, it accesses a conversation database and randomly selects a past conversation log. This conversation database stores text messages and chat history between users. The server then uses a generative AI model (e.g., GPT-3) to summarize the selected conversation log. Examples of prompts that can be input to the generative AI model include:

[1311] "Please summarize the following conversation log: Conversation log: 'On October 1, 2023, User A and User B were engaged in a lively discussion about movies. User A was excited to see the trailer for a new movie. User B spoke at length about the director of the movie.'"

[1312] The generative AI model generates a summary of the story based on this prompt, for example, "User A and User B had a passionate discussion about movies."

[1313] Next, the server uses an emotion engine (e.g., Azure Cognitive Services) to analyze the user's emotions. The emotion engine receives the user's recent messages and behavioral data as input and recognizes emotions. For example, it analyzes the user's recent message, "I'm happy about my new job," and recognizes the emotion as "joy."

[1314] The server adjusts the summarized story based on the recognized emotion. For example, if the emotion is "joy," the summary sentence is adjusted to "User A and User B had a passionate discussion about movies. User A was especially excited about the new movie trailer." The adjusted summary story is sent to the user's device.

[1315] Terminal handling

[1316] The device receives the summary story sent from the server. Then, it activates its built-in emotion engine to analyze the user's real-time emotion. The emotion engine can use, for example, Google Cloud's Natural Language API. If the emotion engine analyzes the user's emotion as "joy," the device adjusts the way the summary story is displayed. For example, it displays it using bright colors and positive icons.

[1317] The device displays a tailored summary story on the screen. When a user opens the app, it might say something like, "User A and User B had a lively discussion about movies. User A was particularly excited about the new movie trailer."

[1318] User Experience

[1319] When users open the app, they can read the summary story displayed on their device. This allows users to easily look back on past conversations and relive emotional experiences. For example, users can relive past joyful emotions by reading the displayed summary.

[1320] In this way, the present invention provides a system that allows users to easily revisit past conversations and deepen their emotional experience.

[1321] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1322] Step 1:

[1323] The server detects when a user opens the app. As input, it receives the user's app launch event. Based on this, the server accesses the conversation database and randomly selects a past conversation log. As output, it obtains the selected conversation log.

[1324] Step 2:

[1325] The server uses a generative AI model (e.g., GPT-3) to summarize the selected conversation log. As input, the selected conversation log and a prompt sentence are input to the generative AI model. An example of a specific prompt sentence is, "Please summarize the following conversation log: Conversation log: 'On October 1, 2023, User A and User B had a passionate discussion about movies. User A was excited to see the trailer for a new movie. User B talked in detail about the director of the movie.'" As output, the generative AI model provides a summarized story.

[1326] Step 3:

[1327] The server analyzes the user's emotions using an emotion engine to adjust the summarized story. As input, it inputs the user's recent messages and behavioral data into the emotion engine. Specifically, it analyzes the user's recent message, "I'm happy about my new job." As output, it obtains the recognized emotion (e.g., "joy") from the emotion engine.

[1328] Step 4:

[1329] The server adjusts the summarized narrative based on the recognized emotion. It uses the summarized narrative and the recognized emotion as input. Specifically, if the emotion is "joy," it adjusts the summary sentence to "User A and User B had a passionate discussion about movies. User A, in particular, was excited about the new movie trailer." As output, it obtains the adjusted summarized narrative.

[1330] Step 5:

[1331] The server sends the adjusted summary story to the user's terminal. As input, it uses the adjusted summary story. As output, it obtains the summary story sent to the user's terminal.

[1332] Step 6:

[1333] The terminal receives the summary story sent from the server, receives the summary story sent from the server as input, and obtains the received summary story as output.

[1334] Step 7:

[1335] The device activates the built-in emotion engine to analyze the user's real-time emotion. The user's real-time data is input to the emotion engine as input, and the analyzed emotion is obtained as output.

[1336] Step 8:

[1337] The device adjusts the display method of the summary story based on the analysis results of the emotion engine. It uses the received summary story and the analyzed emotions as input. Specifically, if the user expresses the emotion of "joy," it displays it using bright colors and positive icons. The adjusted display method is obtained as output.

[1338] Step 9:

[1339] The terminal displays the adjusted summary story on the screen, using the adjusted display method as input, and obtaining the summary story displayed to the user as output.

[1340] Step 10:

[1341] The user opens the app and reads the summary narrative displayed on the device. As input, the user receives the summary narrative displayed on the device. As output, the user can revisit past conversations and relive emotional experiences.

[1342] (Application example 2)

[1343] Next, a description will be given of Application Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1344] Conventional messaging apps have the drawback of requiring users to manually search for past conversations when reviewing them, which takes time and effort. Furthermore, they lack the functionality to summarize past conversations and recreate emotional experiences, making it difficult to deeply understand the user's emotions. Furthermore, as an application installed on a smartphone, there was a demand for a function that provides summarized stories based on emotions.

[1345] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1346] In this invention, the server includes a means for automatically summarizing interactions between users of a messenger app or within a group by a generative AI and providing the chat as a single story (text), a means for randomly providing the summary regardless of whether it is recent or past or the person to whom it is provided, a means for adjusting the method for providing the summarized story based on emotions recognized by an emotion engine, and a means for operating as an application to be installed on a smartphone. This allows users to easily look back on past conversations and relive emotional experiences through emotion-based summarized stories.

[1347] A "messenger app" is software that allows users to exchange text messages, images, videos, etc. in real time.

[1348] "Generative AI" is a system that uses artificial intelligence technology to analyze data and automatically generate new information and content.

[1349] A "summary" is a short sentence that concisely summarizes the original information and extracts only the main points.

[1350] A "narrative" is a sequential depiction of events or conversations, expressed as a meaningful series of sentences.

[1351] An "emotion engine" is a system that analyzes and recognizes user emotions from data such as text and voice.

[1352] A "smartphone" is a portable information terminal that can access the Internet and run applications in addition to making calls.

[1353] An "application" is a software program designed to provide a particular function or service.

[1354] "Random" means chosen at random, without following any particular rule or order.

[1355] "Past conversations" are records of previous message exchanges between users.

[1356] "Emotional experience" refers to the emotions and feelings a user has in response to a particular event or piece of information.

[1357] The system for implementing this invention consists of a messenger app, generative AI, an emotion engine, and an application installed on a smartphone. The details of each component and their operation are explained below.

[1358] System Configuration

[1359] 1. Messenger App

[1360] Messenger apps are software that allow users to exchange text messages, images, videos, etc. in real time. The apps store users' past conversation data and make it accessible when needed.

[1361] 2. Generative AI

[1362] Generative AI is a system that analyzes past conversation data and automatically generates summaries. This AI uses natural language processing techniques to extract the main points of a conversation and summarize them as a concise narrative.

[1363] 3. Emotion Engine

[1364] The emotion engine is a system that analyzes and recognizes user emotions from the generated summaries. The engine extracts emotions from text data and adjusts the way the summarized story is presented based on those emotions.

[1365] 4. Applications installed on smartphones

[1366] The app allows users to view stories that summarize past conversations through their smartphones. Every time a user opens the app, it summarizes a randomly selected past conversation and displays a story tailored based on their emotions.

[1367] How it works

[1368] The server retrieves past conversation data from the messenger app and summarizes it using generative AI. The summarized data is then analyzed by an emotion engine and adjusted based on the user's emotions. The adjusted summary is then provided to the user through an application installed on their smartphone.

[1369] Specific examples

[1370] For example, when a user opens the app, a summary is generated from past conversation data: "Last night, User A and User B had a passionate discussion about movies." If the emotion engine recognizes the emotion "joy" from this summary, the summary is refined to "We had a great time: Last night, User A and User B had a passionate discussion about movies."

[1371] Prompt Sentence Examples

[1372] An example of a prompt sentence to input to the generative AI model is as follows:

[1373] Summarize past conversations and generate sentiment-tuned narratives. Use the following conversation data:

[1374] Conversation data: User A and User B had a lively discussion about movies last night.

[1375] Emotion: Joy

[1376] Using this prompt, the generative AI model can provide appropriate summarization and emotional adjustments.

[1377] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1378] Step 1:

[1379] The server retrieves past conversation data from the messenger app. The input is past conversation data between users, and the output is the retrieved conversation data. Specifically, the server searches the database for a specified period or user's conversation history and extracts the necessary data.

[1380] Step 2:

[1381] The server inputs the acquired conversation data into a generative AI to generate a summary. The input is past conversation data, and the output is a summarized story. Specifically, the generative AI uses natural language processing technology to extract the main points of the conversation and summarize them into concise sentences.

[1382] Step 3:

[1383] The server inputs the generated summary into the emotion engine for emotion analysis. The input is the summarized story, and the output is the recognized emotion. Specifically, the emotion engine extracts emotions from the text data and classifies them.

[1384] Step 4:

[1385] The server adjusts the summarized story based on the recognized emotion. The input is the summarized story and the recognized emotion, and the output is the adjusted story. Specifically, the server changes the expression of the summary according to the emotion, adjusting it to enhance the user's emotion.

[1386] Step 5:

[1387] The server sends the adjusted story to an application installed on the smartphone. The input is the adjusted story, and the output is the story displayed on the user's smartphone. In specific operations, the server sends the adjusted story to the user's smartphone over the network, and the application displays it.

[1388] Step 6:

[1389] The user opens the smartphone application and views the tailored story. The input is the tailored story sent from the server, and the output is the user's viewing experience. In concrete terms, the user launches the application, reads the displayed story, and recreates the emotional experience.

[1390] Example 3

[1391] Next, a description will be given of Example 3 of Form Example 3. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1392] Conventional messenger apps lacked the ability to summarize user interactions and provide them as a narrative, and users could not obtain summaries by specifying a specific person or period. Furthermore, they lacked the ability to recognize users' emotions and provide feedback, making it difficult for users to objectively understand their own emotions. To address these issues, the present invention aims to provide a system that summarizes users' conversations, recognizes their emotions, and provides feedback.

[1393] The specific processing by the specific processing unit 290 of the data processing device 12 in the third embodiment is realized by the following means.

[1394] In this invention, the server includes means for automatically summarizing interactions between users of a messenger app or within a group using a generative artificial intelligence and providing the chat as a single story (text), means for randomly providing such summaries regardless of whether they are recent or past or for any particular person, means for users to acquire summaries by specifying a specific person or period, and means for recognizing emotions from users' messages using an emotion engine and providing feedback. This not only enables users to easily check their own conversations as summaries, but also enables them to deepen their self-understanding through emotional feedback.

[1395] A "messenger app" is software that allows users to send and receive text messages, images, audio, video, etc.

[1396] "Generative AI" is AI that has the ability to analyze text data using natural language processing technology and generate summaries and new sentences.

[1397] A "summary" is a sentence that extracts important information from the original text data and expresses it in a shortened form.

[1398] A "story" is a textual expression that sequentially summarizes multiple events or pieces of information.

[1399] A "random provision method" is a method of providing users with randomly selected data without following any specific rules or order.

[1400] "Means for obtaining summaries by specifying a specific person or time period" refers to a method in which a user specifies a specific person or a specific time range, and a summary based on those conditions is generated and provided.

[1401] An "emotion engine" is software or algorithms for analyzing and recognizing emotions from text data.

[1402] "Means for recognizing emotions and providing feedback" refers to a method for analyzing emotions from a user's message and notifying the user of the results.

[1403] MODE FOR CARRYING OUT THE INVENTION

[1404] The present invention is a system that uses a messenger app to summarize interactions between users, recognize emotions, and provide feedback. Specific embodiments of this system are described below.

[1405] 1. Program Generation

[1406] The server uses generative artificial intelligence to automatically summarize interactions between users and groups on the messenger app, generating a program that presents the chats as a single narrative (text). This program includes a function that allows users to obtain summaries by specifying a specific person or period, and a function that uses an emotion engine to recognize emotions from user messages and provide feedback.

[1407] 2. Program Processing

[1408] The server collects user conversation data and generates summaries using natural language processing (NLP) technology. Specifically, it uses a natural language processing API to analyze the content of the conversation and create summaries. In the free version, summarized stories are displayed randomly, but in the paid version, users can obtain summaries for specific people and periods.

[1409] The system also uses emotion analysis software as an emotion engine to recognize emotions from users' messages. The recognized emotions are then fed back to the user. For example, if a user sends a message saying, "I had a great time today," the server will provide feedback such as, "You seem to be feeling joyful right now."

[1410] 3. Examples of concrete examples and prompts

[1411] For example, if the user specifies "yesterday's conversation with user B," the server will generate the following summary:

[1412] "User A and User B had a lively discussion about movies last night."

[1413] As a concrete example of an emotion engine, if a user sends a message saying "I had fun today," the server will provide the following feedback:

[1414] "You're feeling joy right now."

[1415] Example prompts to input to a generative AI model:

[1416] "Please summarize the conversation between User A and User B yesterday."

[1417] "Recognize the sentiment in the user's message and provide feedback."

[1418] In this way, the server realizes a system that summarizes the user's conversation, recognizes emotions, and provides feedback. The flow of the identification process in the third embodiment will be described with reference to FIG.

[1419] Step 1:

[1420] A user logs in to the system.

[1421] A user logs into the system using a terminal. A username and password are required for login. After a successful login, the user can access the main screen of the system. The input is the username and password, and the output is the main screen after a successful login.

[1422] Step 2:

[1423] The user enters or selects conversation data.

[1424] A user uses a terminal to input conversation data or select past conversation data. For example, a user can select "yesterday's conversation with user B." The input is conversation data or selected past conversation data, and the output is the selected conversation data.

[1425] Step 3:

[1426] The server collects conversation data and performs natural language processing (NLP).

[1427] The server collects conversation data entered or selected by the user. The collected data is analyzed using a natural language processing API. The server understands the content of the conversation and extracts important points. The input is the collected conversation data, and the output is the analysis results.

[1428] Step 4:

[1429] The server generates the summary.

[1430] The server generates a summary of the conversation based on the analysis results. For example, a summary such as "Last night, User A and User B had a lively discussion about movies" is generated. The input is the analysis results, and the output is the generated summary.

[1431] Step 5:

[1432] The server recognizes emotions using an emotion engine.

[1433] The server uses emotion analysis software to recognize emotions from the conversation data. For example, if a user sends a message saying "I had fun today," the server recognizes the emotion "joy." The input is the conversation data, and the output is the recognized emotion.

[1434] Step 6:

[1435] The server provides the summary and emotional feedback to the user.

[1436] The server provides the generated summary and emotional feedback to the user. For example, if a user selects "yesterday's conversation with user B," the server provides the summary "user A and user B had a lively discussion about movies last night" and the feedback "you seem to be feeling joyful right now." The input is the generated summary and the recognized emotion, and the output is the summary and emotional feedback provided to the user.

[1437] (Application example 3)

[1438] Next, a description will be given of Application Example 3 of Form Example 3. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1439] Modern messaging applications accumulate vast amounts of conversational data, but lack a means to efficiently summarize it and provide it in a useful format for users. They also lack a feedback function that allows users to objectively understand their own emotions. Furthermore, the lack of clear differentiation between the free and paid versions makes it difficult to increase user satisfaction. A system that can solve these issues is needed.

[1440] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 3 is realized by the following means.

[1441] In this invention, the server includes a means for automatically summarizing interactions between users of a messaging application or within a group, using a generative artificial intelligence to present the conversation as a single narrative (text), a means for randomly presenting such interactions regardless of whether they are recent or past, or by whom, a means for an emotion recognition engine to analyze the user's emotions and provide feedback, and a means for displaying randomly in the free version or summarizing by specifying the person, period, etc. in the paid version. This allows users to efficiently review past conversations and deepen self-understanding through emotional feedback. Furthermore, clear differentiation between the free and paid versions increases user satisfaction.

[1442] A "messaging application" is software that enables users to send and receive text messages, images, audio, video, etc.

[1443] "User" means any individual or group of people who use the Message Application.

[1444] "Generative AI" is an AI system that uses natural language processing technology to analyze text data and automatically summarize and generate information.

[1445] "Summarization" refers to extracting important information from the original text data and providing it in a shortened form.

[1446] A "story (text)" is a collection of multiple messages organized into a single continuous text.

[1447] An "emotion recognition engine" is software that analyzes a user's emotions from text data and identifies specific emotional states.

[1448] "Feedback" refers to the response or information a system provides to a user.

[1449] "Free Version" means a version of the Software that has certain functionality limitations but is available to Users free of charge.

[1450] "Paid Version" means a version of the Software that provides additional features or services but requires a fee from the User.

[1451] "Random display" refers to displaying randomly selected data without following any specific rules.

[1452] "Partner" means another user with whom you communicate within a messaging application.

[1453] A "period" refers to a specific time range that can be used as a basis for searching past interactions.

[1454] A "smartphone" is a mobile phone that can connect to the Internet and install applications.

[1455] A system for implementing the present invention includes a messaging application, a generative artificial intelligence, an emotion recognition engine, and a smartphone. A specific embodiment of this system will be described below.

[1456] System configuration

[1457] 1. Messaging Applications

[1458] A messaging application is software that allows users to send and receive text messages, images, audio, video, etc. The application stores the user's chat history and provides the data when needed.

[1459] 2. Generative Artificial Intelligence

[1460] Generative AI is a system that uses natural language processing technology to analyze text data and automatically summarize and generate it. This AI extracts important information from a user's chat history and presents it as a shortened narrative (text).

[1461] 3. Emotion Recognition Engine

[1462] An emotion recognition engine is software that analyzes user emotions from text data and identifies specific emotional states. The engine analyzes the user's messages and provides emotional feedback.

[1463] 4. Smartphone

[1464] A smartphone is a mobile phone that can connect to the Internet and install applications. This system is implemented as an application installed on the smartphone.

[1465] Data processing and calculation

[1466] 1. Loading chat history

[1467] The server loads the user's chat history, which is stored within the messaging application.

[1468] 2. Random Summary Generation

[1469] The generative AI randomly selects one conversation from the chat history and generates a summary of it, allowing users to efficiently review past conversations.

[1470] 3. Generating specific conversation summaries

[1471] Generative AI searches for conversations based on a specified user and date, generating summaries that allow users to revisit specific conversations in detail.

[1472] 4. Providing emotional feedback

[1473] The emotion recognition engine analyzes the user's message and recognizes the emotion. Based on the analysis results, it provides emotional feedback to the user, allowing the user to objectively understand their own emotions.

[1474] Specific examples

[1475] Free version random summary

[1476] "I want to talk about movies with User B on 2023-10-01"

[1477] Specific conversation summaries in the paid version

[1478] "The movie I saw with User B yesterday on 2023-10-01 was amazing."

[1479] Emotional Feedback

[1480] "You're feeling joy right now."

[1481] Prompt Sentence Examples

[1482] "Summarize yesterday's conversation with User B."

[1483] "Provide emotional feedback for the message 'I had fun today'"

[1484] In this way, analyzing users' chat history and providing summaries and emotional feedback can help users deepen their self-understanding. Also, clear differentiation between the free and paid versions can increase user satisfaction.

[1485] The flow of the specific processing in Application Example 3 will be described with reference to FIG.

[1486] Step 1:

[1487] The server loads the user's chat history. As input, a user ID is given, and as output, the chat history data for that user is obtained. This data includes text messages, images, audio, video, etc. stored in the messaging application.

[1488] Step 2:

[1489] The server analyzes the chat history using generative AI. The chat history data acquired in step 1 is given as input, and summarized text data is generated as output. The generative AI extracts important information using natural language processing technology and provides it as a shortened narrative (text).

[1490] Step 3:

[1491] The server generates a random summary. As input, it receives the summary text data generated in step 2, and as output, it provides a randomly selected summary, allowing users to efficiently review past conversations.

[1492] Step 4:

[1493] The server generates a specific conversation summary. As input, it receives information about the person and time period specified by the user, and as output, it generates a summary of the corresponding conversation. The generative AI searches for conversations based on the specified user and date, and generates the summary.

[1494] Step 5:

[1495] The server analyzes the user's emotions using an emotion recognition engine. The user's message is given as input, and emotional feedback is provided as output. The emotion recognition engine analyzes the user's emotions from the text data and identifies a specific emotional state.

[1496] Step 6:

[1497] The device displays emotional feedback to the user. As input, the emotional feedback generated in step 5 is given, and as output, a feedback message is displayed on the user's screen. This allows the user to objectively understand their own emotions.

[1498] Step 7:

[1499] The terminal switches between the free and paid versions. The user's account information is input, and the output is a random display in the free version, and a summary display with the specified contact, period, etc. in the paid version. This increases user satisfaction.

[1500] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1501] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1502] Another example of generative AI is Gemini (internet search engine). <url: https: gemini.google.com ?hl="ja">) are mentioned.

[1503] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[1504] [Third embodiment]

[1505] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1506] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[1507] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1508] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[1509] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1510] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1511] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1512] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1513] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1514] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1515] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1516] Next, the specific processing by the specific processing unit 290 of the data processing device 12 will be described.

[1517] "Example 1"

[1518] In a first embodiment of the present invention, a generative AI implemented in a messenger app automatically summarizes interactions between users or within a group. This generative AI uses natural language processing technology to analyze users' messages and summarize them as a story. For example, if user A and user B are interacting on the topic of "movies," the generative AI summarizes the conversation into a single sentence: "User A and user B had a passionate discussion about movies."

[1519] "Example 2"

[1520] In a second embodiment of the present invention, a function is implemented that randomly provides summarized stories, regardless of whether they are recent or past, or with whom. This function displays a randomly selected story that summarizes a past conversation every time the user opens the app. For example, when a user opens the app, the following sentence is displayed: "Last night, User A and User B had a passionate discussion about movies."

[1521] "Example 3"

[1522] As a third embodiment of the present invention, a free version and a paid version are provided. In the free version, summarized stories are displayed randomly. On the other hand, in the paid version, the user can specify the person and the period to display a story summarizing a specific conversation. For example, if the user specifies "yesterday's conversation with user B," the following sentence is displayed: "Last night, user A and user B had a passionate discussion about movies."

[1523] The processing flow of each embodiment will be described below.

[1524] "Example 1"

[1525] Step 1: A user starts a conversation in a messenger app.

[1526] Step 2: Generative AI analyzes the user's message in real time and summarizes it as a story, using natural language processing techniques.

[1527] Step 3: The generative AI saves the summarized story in a database.

[1528] "Example 2"

[1529] Step 1: User opens the app.

[1530] Step 2: The system randomly selects a summarized story from the database.

[1531] Step 3: Display the selected story to the user.

[1532] "Example 3"

[1533] Step 1: User chooses free or paid version.

[1534] Step 2: If you choose the paid version, the user will specify the recipient and the period.

[1535] Step 3: Based on the specified criteria, the system selects a summarized story from the database and displays it to the user.

[1536] Example 1

[1537] Next, a description will be given of Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1538] In modern messaging apps, the volume of interactions between users and groups is enormous, making it difficult to review important conversations and emotional exchanges from the past. There is also a lack of a way to concisely summarize these interactions and provide them in an easy-to-understand format. Furthermore, users cannot obtain summaries based on a specific period or person, making it difficult to organize and search for information.

[1539] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1540] In this invention, the server includes means for automatically summarizing exchanges between users of the messenger app or within a group using generative artificial intelligence and providing the chats as a single story (text), means for receiving messages sent by users, means for analyzing the received messages using natural language processing technology, means for generating summaries based on the analysis results, means for sending the generated summaries to users, means for randomly providing them regardless of whether they are recent or past or to whom, means for reviewing past exchanges and evoking emotional feelings in the user, and means for displaying summaries randomly in the free version or by specifying the person, period, etc. in the paid version. This allows users to easily review important conversations and emotional exchanges from the past, making it easier to organize and search for information.

[1541] A "messenger app" is a software application that allows users to send and receive text messages and multimedia content in real time.

[1542] "User" means an individual or group that sends or receives messages using the Messenger App.

[1543] "Generative AI" is an AI system that uses natural language processing technology to analyze text data and generate summaries and new sentences.

[1544] A "summary" is a short sentence that succinctly summarizes the content of the original message.

[1545] "Natural language processing technology" is a technology that allows computers to understand, analyze, and generate human language.

[1546] "Analysis" is the process of structurally breaking down a received message and extracting meaning and important information.

[1547] A "story" is a collection of multiple messages organized into one continuous sentence.

[1548] "Randomly presented" means presenting summaries selected at random, without following any particular order or criteria.

[1549] "Emotional evocation" means allowing users to relive emotions from past interactions.

[1550] The "free version" is a version that provides basic services free of charge, but with certain limited functionality.

[1551] "Paid Version" means a version that requires a fee to provide additional features or services.

[1552] "Other party" refers to other users with whom you exchange messages on the messenger app.

[1553] A "period" refers to a specific time range and covers messages and interactions within that range.

[1554] MODE FOR CARRYING OUT THE INVENTION

[1555] The present invention provides a system for automatically summarizing and providing a concise summary of interactions between users or within a group in a messenger app. Specific embodiments for implementing this system are described below.

[1556] System configuration

[1557] Hardware and Software Configuration

[1558] The server automatically summarizes messages between users and within groups using generative artificial intelligence (AI). This generative AI uses the Python natural language processing libraries "spaCy" and "Transformers." The server processes messages using the following steps:

[1559] 1. Sending a message

[1560] A user sends a message to another user using a messenger app. For example, user A sends a message saying, "The movie I saw yesterday was really good."

[1561] 2. Receiving a message

[1562] The server receives messages sent by users, stores them in a database, and then passes the message data to the generative AI.

[1563] 3. Message Analysis

[1564] The server calls the generative AI and analyzes the received message. The generative AI uses spaCy to perform morphological analysis of the message and extract important keywords. For example, from the message "The movie I saw yesterday was really good," the generative AI extracts the keywords "yesterday," "saw," "movie," "really," and "good."

[1565] 4. Summary Generation

[1566] The server then uses the generative AI to generate a summary based on the analysis results. The generative AI uses the Transformers library to generate a summary based on the extracted keywords and sentence structure. For example, it generates a summary such as "User A said that the movie was good."

[1567] 5. Submit your summary

[1568] The server sends the generated summary to User A and User B. User A and User B can check the summary on the messenger app.

[1569] Specific examples

[1570] For example, if user A and user B are talking about movies, the following conversation will take place:

[1571] User A: The movie I saw yesterday was really good.

[1572] User B: What movie was it?

[1573] User A: It was an action movie and the story was interesting.

[1574] User B: That's interesting. What's the title?

[1575] If we feed this conversation into a generative AI, it will summarize it as follows:

[1576] User A and User B had a lively discussion about movies.

[1577] In this way, generative AI automatically summarizes interactions between users and provides them in a concise format, allowing users to easily revisit important conversations and emotional exchanges from the past, making it easier to organize and search for information.

[1578] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1579] Program processing flow

[1580] Step 1:

[1581] A user sends a message to another user using a messenger app. For example, user A types a message such as "The movie I saw yesterday was really good" and presses the send button. The input is the text message sent by the user, and the output is the message data sent to the server.

[1582] Step 2:

[1583] The server receives messages sent by users. The received messages are stored in a database. The input is the message data sent by the user, and the output is the message data stored in the database.

[1584] Step 3:

[1585] The server calls the generative AI and analyzes the received message. The generative AI performs morphological analysis of the message using the Python natural language processing library "spaCy." For example, from the message "The movie I saw yesterday was really good," it extracts the keywords "yesterday," "saw," "movie," "really," and "good." The input is the message data stored in the database, and the output is the extracted keywords.

[1586] Step 4:

[1587] The server then uses the generative AI to generate a summary based on the analysis results. The generative AI uses the Transformers library to generate a summary based on the extracted keywords and sentence structure. For example, it generates a summary such as "User A said that the movie was good." The input is the extracted keywords, and the output is the generated summary.

[1588] Step 5:

[1589] The server sends the generated summary to user A and user B. User A and user B can check the summary on their messenger apps. The input is the generated summary, and the output is the summary sent to the user.

[1590] Specific actions

[1591] Step 1:

[1592] User A opens the messenger app, types a message to User B saying, "The movie I saw yesterday was really good," and presses the send button. This sends the message data to the server.

[1593] Step 2:

[1594] The server receives the message sent by user A and stores it in a database. The stored message data is used in subsequent processing.

[1595] Step 3:

[1596] The server calls the generative AI and analyzes the received message. The generative AI uses spaCy to perform morphological analysis of the message and extract important keywords. For example, from the message "The movie I saw yesterday was really good," the generative AI extracts the keywords "yesterday," "saw," "movie," "really," and "good."

[1597] Step 4:

[1598] The server then uses the generative AI to generate a summary based on the analysis results. The generative AI uses the Transformers library to generate a summary based on the extracted keywords and sentence structure. For example, it generates a summary such as "User A said that the movie was good."

[1599] Step 5:

[1600] The server sends the generated summary to User A and User B. User A and User B can then check the summary on their messenger app, allowing users to easily review important conversations and emotional interactions from the past.

[1601] (Application example 1)

[1602] Next, a description will be given of Application Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1603] In conventional messaging apps, the volume of interactions between users and within groups became enormous, making it difficult to review important conversations and emotional exchanges. Furthermore, the lack of a function to summarize discussions about content viewed by users and recommend new content reduced user convenience.

[1604] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1605] In this invention, the server includes a means for automatically summarizing interactions between users of the messenger app or within a group using a generative AI and providing the chat as a single story (text), a means for displaying the summarized content in the user's feed, and a means for recommending new content based on the summary. This allows users to easily review important conversations and emotional interactions from the past, and also allows them to receive recommendations for new content.

[1606] A "messenger app" is application software that allows users to send and receive text messages, images, videos, etc. between users or within groups.

[1607] "Generative AI" is artificial intelligence that uses natural language processing technology to analyze input text data and generate summaries or new sentences.

[1608] A "summary" is a short, concise summary of the important parts of the original text data.

[1609] "Feed" refers to the stream or list of information that a user views within an application.

[1610] "Content" is a general term for information and entertainment that users view or use, such as movies, dramas, music, and articles.

[1611] "Recommendation" is the act of suggesting new information or entertainment based on a user's past behavior and interests.

[1612] "Random" means chosen at random without following any particular rule or order.

[1613] A "system" is a collection of devices and programs that are composed of a combination of multiple elements and functions.

[1614] The system for implementing this invention uses generative AI to automatically summarize interactions between users of a messenger app or within a group, and presents the chats as a single story (text). Specific embodiments of this system are described below.

[1615] Hardware and software used

[1616] Hardware:

[1617] Smartphone (iOS or Android)

[1618] Server (cloud-based, e.g. AWS or Google Cloud)

[1619] software:

[1620] Natural language processing libraries (e.g., spaCy, NLTK)

[1621] Generative AI models (e.g., OpenAI's GPT-3)

[1622] Messenger app APIs (e.g., WhatsApp API, Facebook Messenger API)

[1623] Data processing and calculation

[1624] Data collection

[1625] The server uses the API of the messenger app to collect user message data. For example, it calls the API as follows to retrieve messages.

[1626] messages = get_messages_from_api(user_id)

[1627] Data analysis

[1628] The server uses a natural language processing library to analyze the collected message data, which is then input into a generative AI model.

[1629] parsed_messages = nlp(messages)

[1630] Summary Generation

[1631] The server uses a generative AI model to summarize the parsed messages, which are then displayed in the user's feed.

[1632] summary = gpt3_generate_summary(parsed_messages)

[1633] Feed display

[1634] The server then displays the summaries in the user's feed, allowing them to easily revisit important conversations and emotional interactions from the past.

[1635] display_summary_in_feed(user_id, summary)

[1636] Content Recommendation

[1637] The server recommends new content based on the summaries, allowing users to receive new information and entertainment.

[1638] recommended_content = recommended_content_based_on_summary(summary)

[1639] Specific examples

[1640] If user A and user B are discussing the movie "ABCDEF," the following happens:

[1641] Original message:

[1642] User A: "ABCDEF is so funny!"

[1643] User B: "Yeah, the dream scene was amazing!"

[1644] summary:

[1645] "User A and User B had a passionate discussion about the movie 'ABCDEF'."

[1646] Prompt Sentence Examples

[1647] Below is an example of a prompt sentence to input to a generative AI model.

[1648] User A and User B are discussing the movie "ABCDEF." Summarize the following message:

[1649] User A: "ABCDEF is so funny!"

[1650] User B: "Yeah, the dream scene was amazing!"

[1651] In this way, user discussions are automatically summarized and displayed in the feed, allowing users to easily review past discussions and also receive recommendations for new content.

[1652] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1653] Step 1:

[1654] The server collects user message data using the messenger app's API. Specifically, it calls the API to retrieve messages related to the user ID. The input is the user ID, and the output is the message data.

[1655] messages = get_messages_from_api(user_id)

[1656] Step 2:

[1657] The server uses a natural language processing library (e.g., spaCy or NLTK) to analyze the collected message data. Specifically, it tokenizes the message data and performs grammatical analysis. The input is the message data, and the output is the analyzed message data.

[1658] parsed_messages = nlp(messages)

[1659] Step 3:

[1660] The server uses a generative AI model (e.g., OpenAI's GPT-3) to summarize the parsed message data. Specifically, the parsed data is input as prompts to the generative AI model, which then generates a summary. The input is the parsed message data, and the output is a summarized sentence.

[1661] summary = gpt3_generate_summary(parsed_messages)

[1662] Step 4:

[1663] The server displays the summarized content in the user's feed. Specifically, it adds the summarized sentence to the user's feed and displays it. The input is the summarized sentence, and the output is the summary displayed in the feed.

[1664] display_summary_in_feed(user_id, summary)

[1665] Step 5:

[1666] The server recommends new content based on the summary. Specifically, it analyzes the summarized content and runs an algorithm to recommend related new content. The input is the summarized text, and the output is the recommended content.

[1667] recommended_content = recommended_content_based_on_summary(summary)

[1668] In this way, user discussions are automatically summarized and displayed in the feed, allowing users to easily review past discussions and also receive recommendations for new content.

[1669] Example 2

[1670] Next, a description will be given of Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1671] Conventional messenger apps lacked the ability to summarize interactions between users, making it time-consuming to look back on past conversations. They also lacked the ability to randomly provide past conversations, allowing users to discover new things or evoke emotional feelings. Furthermore, there was insufficient differentiation between the free and paid versions of the app, and improvements were needed to increase user satisfaction.

[1672] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1673] In this invention, the server includes: a means for a user to launch an application; a means for a terminal to send a request to the server for a random summary of past conversations; a means for the server to randomly acquire past conversation data from a database; a means for the server to send the acquired conversation data to a generative AI model as a prompt; a means for the generative AI model to generate a conversation summary based on the prompt; a means for the server to send the generated summary to the terminal; and a means for the terminal to display the received summary to the user. This allows users to randomly summarize and display past conversations simply by launching the application, eliminating the need to revisit past conversations and evoking new discoveries and emotional feelings. It also enables differentiated functionality between the free and paid versions, increasing user satisfaction.

[1674] "User" means any individual or entity using the Application.

[1675] An "application" refers to a software program that users use on devices such as smartphones and PCs.

[1676] "Terminal" refers to the hardware device through which a user runs applications.

[1677] "Server" refers to a computer system that processes data and manages databases to provide application functions.

[1678] "Database" refers to an information system for storing and managing past conversation data.

[1679] "Generative AI model" refers to an artificial intelligence model that generates a summary of a conversation based on an input prompt.

[1680] "Prompt sentence" refers to an instruction sentence input to a generative AI model to generate a summary.

[1681] A "summary" refers to a sentence that concisely summarizes past conversation data.

[1682] A "request" refers to a communication message in which a terminal requests a server to perform a specific process.

[1683] A "response" refers to a communication message in which a server returns a result in response to a request to a terminal.

[1684] "Random" means that something is chosen at random without following any particular rules or order.

[1685] "Free Version" means a version of the Application that is available to Users without payment of a fee.

[1686] "Paid Version" means a version of an Application that a User pays to use.

[1687] The present invention relates to a system that randomly summarizes and displays past conversations when a user launches an application. Specific embodiments of this system are described below.

[1688] Hardware and software used

[1689] Servers: Database servers, application servers

[1690] Device: User's smartphone or PC

[1691] Software: Generative AI models (e.g., GPT-4), database management systems (e.g., MySQL), application frameworks (e.g., Django)

[1692] System configuration

[1693] 1. The user launches the application

[1694] A user taps or clicks to launch an application on their smartphone or PC.

[1695] 2. The device sends a request to the server

[1696] The terminal sends an HTTP request to the application server requesting a summary of a random past conversation.

[1697] 3. The server retrieves the data from the database

[1698] The server connects to the database server and executes an SQL query to randomly retrieve past conversation data.

[1699] 4. The server sends a prompt to the generative AI model

[1700] The server sends prompts to the generative AI model to summarize the conversation data it has acquired.

[1701] 5. Generative AI model generates summary

[1702] A generative AI model generates a summary of the conversation based on the prompts.

[1703] 6. The server sends the summary to the device.

[1704] The server returns the generated summary to the terminal as an HTTP response.

[1705] 7. The device displays a summary

[1706] The device displays the received summary to the user.

[1707] Specific examples

[1708] The user launches the application

[1709] The user taps the application icon on the smartphone's home screen.

[1710] The device sends a request to the server

[1711] The device sends an HTTP request to the endpoint "GET / api / random-summary".

[1712] The server retrieves the data from the database

[1713] The server executes the SQL query "SELECT FROM conversations ORDER BY RAND() LIMIT 1;" to retrieve random conversation data.

[1714] The server sends prompts to the generative AI model

[1715] The server sends a prompt to the generative AI model: "Summarize the following conversation: Last night, User A and User B had a lively discussion about movies."

[1716] Generative AI model generates summary

[1717] The generative AI model generates a summary such as, "Last night, User A and User B had a passionate discussion about movies."

[1718] The server sends the summary to the terminal.

[1719] The server returns the generated summary to the terminal as an HTTP response.

[1720] The terminal displays a summary

[1721] The device displays the text "Last night, User A and User B had a lively discussion about movies" in the application UI.

[1722] Prompt Sentence Examples

[1723] "Summarize the following conversation: User A and User B had a lively discussion about movies last night."

[1724] "Summarize the following conversation: Last week, User C and User D were talking about a new restaurant."

[1725] In this way, we create a system that displays a randomly selected summary of past conversations every time a user launches the application.

[1726] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1727] Step 1:

[1728] The user launches an application.

[1729] Specifically, a user taps an application icon on the smartphone's home screen. This launches the application and displays its initial screen. The input is the user's tap operation, and the output is the application's initial screen.

[1730] Step 2:

[1731] The device sends a request to the server.

[1732] Specifically, the terminal sends an HTTP request to the endpoint "GET / api / random-summary." The input is a request from the initial screen of the application, and the output is a request sent to the server.

[1733] Step 3:

[1734] The server retrieves the data from the database.

[1735] Specifically, the server connects to the database server and executes the SQL query "SELECT FROM conversations ORDER BY RAND() LIMIT 1;" The input is the request to the server, and the output is randomly selected past conversation data.

[1736] Step 4:

[1737] The server sends prompts to the generative AI model.

[1738] Specifically, the server converts the acquired conversation data into a prompt sentence such as "Please summarize the following conversation: [conversation data]" and sends it to the generative AI model. The input is randomly selected past conversation data, and the output is the generation and transmission of a prompt sentence.

[1739] Step 5:

[1740] A generative AI model generates the summary.

[1741] Specifically, the generative AI model generates a summary of the conversation based on the prompt sentence, with the input being the prompt sentence and the output being the generated summary.

[1742] Step 6:

[1743] The server sends the summary to the terminal.

[1744] Specifically, the server sends the generated summary back to the terminal as an HTTP response. The input is the generated summary, and the output is the response sent to the terminal.

[1745] Step 7:

[1746] The terminal displays a summary.

[1747] Specifically, the device displays the received summary on the application's UI. The input is the response from the server, and the output is the summary displayed to the user.

[1748] In this way, a system is realized in which a randomly selected summary of past conversations is displayed each time the user launches the application.

[1749] (Application example 2)

[1750] Next, a description will be given of Application Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1751] Traditional messaging applications make it difficult for users to review past conversations and viewing history, and lack a way to easily recall past memories. They also lack the ability to provide a summary of past conversations and viewing history, which can lead to users missing important conversations or viewing content. Furthermore, they lack the ability to display a new summary every time a user opens the application, making it difficult to keep users engaged.

[1752] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for automatically summarizing interactions between users of a messaging application or within a group using a generative artificial intelligence and providing the communications as a single story (text), means for randomly providing such summaries regardless of whether they are recent or past or the recipient, means for randomly displaying summaries of past viewing history and chats in which the user has participated, and means for displaying a different summary each time the user opens the application. This allows the user to easily look back on past interactions and viewing history, which can evoke emotional feelings. Furthermore, a new summary is displayed each time the application is opened, thereby maintaining the user's interest.

[1753] A "messaging application" is software that allows users to send and receive text messages, images, audio, video, etc.

[1754] "User" means an individual or group of people who communicate using a messaging application.

[1755] "Generative artificial intelligence" refers to algorithms or models that automatically generate text or information based on input data.

[1756] A "summary" is a short sentence that succinctly summarizes the contents of a long piece of text or conversation.

[1757] "Communication" refers to the exchange of information between users through messaging applications.

[1758] A "story" is a collection of events or conversations organized into a single continuous sentence.

[1759] "Viewing history" is a record of content that a user has viewed in the past.

[1760] "Chat" is a real-time text-based conversation conducted through a messaging application.

[1761] "Randomly presented" means presenting content selected at random without following any particular order or rules.

[1762] "Emotional evocation" means eliciting an emotional response in the user by recalling past events or conversations.

[1763] "Free Version" means a version of the Software that provides basic functionality free of charge.

[1764] "Paid Version" means a version of the Software that provides additional features or services and requires a fee.

[1765] "Specify" means to set specific conditions or criteria.

[1766] A system for implementing the present invention includes a messaging application, a generative artificial intelligence, a database, and a user interface. Specific embodiments are described below.

[1767] System Configuration

[1768] 1. Messaging applications:

[1769] A messaging application is software that allows users to send and receive text messages, images, audio, and video. The application is installed on mobile devices such as smartphones and tablets.

[1770] 2. Generative AI:

[1771] Generative AI is an algorithm or model that automatically summarizes a user's past interactions and viewing history and presents it as a narrative. Specifically, it uses natural language processing technology to summarize long conversations and viewing histories into concise sentences.

[1772] 3. Database:

[1773] The database is intended to store users' past interactions and viewing history, including text messages, images, audio, video, and other data.

[1774] 4. User Interface:

[1775] The user interface is the screen through which the user interacts with the application, displaying random summaries of past interactions and viewing history.

[1776] Processing flow

[1777] 1. Data Collection:

[1778] Messaging applications store user interactions and viewing history in a database.

[1779] 2. Data Summary:

[1780] Generative AI retrieves past interactions and viewing history from a database and summarizes them using natural language processing techniques.

[1781] 3. Random display:

[1782] Each time a user opens the application, the generative artificial intelligence displays a randomly selected summary in the user interface.

[1783] Hardware and software used

[1784] Hardware:

[1785] Smartphones, tablets, servers

[1786] software:

[1787] Messaging applications, generative artificial intelligence (natural language processing algorithms), database management systems

[1788] Specific examples

[1789] Here's an example of the summary that appears when a user opens the application:

[1790] Example prompt sentence:

[1791] "Show me random summaries of past conversations and viewing history."

[1792] An example of the summary that will be displayed:

[1793] "On 2023-10-01, User A and User B had a passionate discussion about movies."

[1794] In this way, each time the user opens the application, a different summary is displayed, allowing them to reminisce about their past experiences, which can evoke emotional feelings and keep them interested in the application.

[1795] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1796] Step 1:

[1797] The server collects user interactions and viewing history through messaging applications and stores them in a database. The input is data such as text messages, images, audio, and video sent by users, and the output is that this data is stored in the database. Specifically, the messaging application captures user interactions in real time and sends them to the server.

[1798] Step 2:

[1799] The server uses generative artificial intelligence to retrieve past interactions and viewing history from a database and summarize them. The input is the past interactions and viewing history stored in the database, and the output is a summarized short sentence. Specifically, the generative artificial intelligence uses natural language processing technology to summarize long conversations and viewing history into concise sentences.

[1800] Step 3:

[1801] The server randomly selects one of the generated summaries and displays it each time the user opens the application. The input is a set of sentences summarized by the generative AI, and the output is a randomly selected summary that is displayed in the user interface. Specifically, the server uses a random algorithm to select one of the summaries and send it to the user interface.

[1802] Step 4:

[1803] The user sees a new summary every time they open the application. The input is a randomly selected summary sent from the server, and the output is the summary the user sees on their screen. Specifically, when the user launches the application, the summary sent from the server is displayed in the user interface.

[1804] Step 5:

[1805] The user looks at the displayed summary and recalls past interactions and viewing history, evoking emotional feelings. The input is the summary displayed in the user interface, and the output is the user's emotional response. The specific action is for the user to read the displayed summary and recall past events.

[1806] Example 3

[1807] Next, a third embodiment of the third embodiment will be described. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1808] With conventional messenger apps, when users want to review past conversations, they have to manually search for the necessary information from a huge amount of data, which is extremely time-consuming. Furthermore, since there is no function to display a summary of a specific conversation, it is difficult for users to quickly grasp important information. Furthermore, since the distinction between the free and paid versions is unclear, it is unclear which features users can use. These issues need to be resolved.

[1809] The specific processing by the specific processing unit 290 of the data processing device 12 in the third embodiment is realized by the following means.

[1810] In this invention, the server includes a means for a user to log in to the system, a means for the user to select either the free version or the paid version, and a means for the server to randomly select conversation data in the free version. This allows the user to efficiently summarize and view past conversations. The paid version also includes a means for the user to specify a person and a period, a means for the server to filter the conversation data based on the specified conditions, a means for the server to input a prompt sentence to the generative AI model, a means for the generative AI model to generate a summary, and a means for the server to display the generated summary to the user. This allows the user to quickly summarize and view a specific conversation.

[1811] The "means for a user to log in to the system" refers to a function that allows a user to access the system using a terminal and log in by entering authentication information.

[1812] "Means for users to select the free version or the paid version" refers to the interface and functionality that allows users to select either the free version or the paid version within the system.

[1813] The "means by which the server randomly selects conversation data" refers to the algorithm and function by which the server randomly extracts conversation data from the database.

[1814] The "means for the user to specify a partner and a period" refers to an interface and function that allows the user to input a specific partner and a period.

[1815] "Means for the server to filter conversation data based on specified conditions" refers to the algorithms and functions that allow the server to search the database based on conditions specified by the user and extract relevant conversation data.

[1816] "Means for the server to input prompt sentences into the generative AI model" refers to a function that enables the server to generate prompt sentences based on filtered conversation data and input them into the generative AI model.

[1817] The "means by which the generative AI model generates a summary" refers to the algorithms and functions by which the generative AI model generates a summary based on the input prompt sentence.

[1818] "Means for the server to display the generated summary to the user" refers to the function that allows the server to receive the summary returned from the generative AI model and display it on the user's device.

[1819] The present invention provides a system that enables users to efficiently summarize and view past conversations. A specific embodiment of this system will be described below.

[1820] System program generation

[1821] The server generates a program that allows users to log in to the system and select either the free or paid version. This program collects the user's conversation data and generates summaries using a generative AI model. The hardware used is a high-performance cloud server (e.g., AWS EC2 instance), and the generative AI model used is OpenAI's GPT-3.

[1822] Explanation of program processing

[1823] When a user logs in to the system, the server verifies the user's authentication information and allows the user to log in. After logging in, the user is taken to a screen where they can choose between the free version or the paid version. If the user selects the free version, the server randomly selects conversation data from the database and inputs prompt sentences into the generative AI model. The generative AI model generates a summary, which the server displays to the user.

[1824] If the paid version is selected, the user enters information into an input form to specify a specific person and time period. The server uses this information to search the database and filter the relevant conversation data. A prompt is generated based on the filtered conversation data and input into the generative AI model. The generative AI model then generates a summary, which the server displays to the user.

[1825] Specific examples

[1826] For example, if a user is using the free version, the server inputs randomly selected conversation data into the generative AI model. For example, the prompt might say, "Please summarize the conversation between user A and user B." The generative AI model generates a summary that says, "User A and user B discussed movies," and the server displays it to the user.

[1827] As a specific example of the paid version, if a user specifies "yesterday's conversation with user B," the server filters the conversation data that matches that condition and inputs the prompt sentence "Please summarize the conversation between user A and user B about movies last night" into the generative AI model. The generative AI model generates a summary that reads "Last night, user A and user B had a passionate discussion about movies," and the server displays it to the user.

[1828] Prompt Sentence Examples

[1829] Free version: "Please summarize the conversation between User A and User B"

[1830] Paid version: "Please summarize the conversation between User A and User B about movies last night."

[1831] In this way, the user can efficiently summarize and view past conversations. This system allows the user to quickly grasp important information. The flow of the identification process in the third embodiment will be described with reference to FIG. 15.

[1832] Step 1:

[1833] A user logs in to the system.

[1834] A user accesses the system using a terminal and enters a user ID and password on the login screen. The server checks the entered authentication information against the database and allows login if authentication is successful. The input is the user ID and password, and the output is the authentication result. If authentication is successful, the user can proceed to the next screen.

[1835] Step 2:

[1836] The user chooses the free or paid version.

[1837] After logging in, the user selects either the free version or the paid version. The selection information is sent to the server. The input is the user's selection information, and the output is the selection result. The server determines the next process based on the user's selection.

[1838] Step 3:

[1839] In the free version, the server randomly selects conversation data.

[1840] The server runs an algorithm to randomly extract conversation data from the database. The input is all the conversation data in the database, and the output is a randomly selected conversation data. The server uses this randomly selected conversation data in the next step.

[1841] Step 4:

[1842] In the paid version, the user specifies the recipient and the period.

[1843] The user enters information into an input form to specify a specific person and time period. The input is the person and time period information, and the output is the specified conditions. The server prepares to search the database based on these conditions.

[1844] Step 5:

[1845] The server filters the conversation data based on the specified criteria.

[1846] The server searches the database based on the conditions specified by the user and extracts the relevant conversation data. The input is the specified conditions and all the conversation data in the database, and the output is the filtered conversation data. The server uses this filtered conversation data in the next step.

[1847] Step 6:

[1848] The server inputs a prompt sentence into the generative AI model.

[1849] The server generates a prompt sentence based on the filtered conversation data and inputs it to the generative AI model. The input is the filtered conversation data, and the output is the generated prompt sentence. The server sends this prompt sentence to the generative AI model.

[1850] Step 7:

[1851] A generative AI model generates the summary.

[1852] The generative AI model generates a summary based on the input prompt. The input is the prompt, and the output is the generated summary. The generative AI model returns this summary to the server.

[1853] Step 8:

[1854] The server displays the generated summary to the user.

[1855] The server receives the summary returned by the generative AI model and displays it on the user's device. The input is the generated summary, and the output is the summary displayed on the user's device. The user can view the displayed summary.

[1856] (Application example 3)

[1857] Next, a description will be given of Application Example 3 of Form Example 3. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1858] In modern messaging applications, the volume of conversations between users is so large that it is difficult to review past exchanges. Furthermore, there is a lack of a way to easily obtain summaries of conversations focused on specific people or periods. Furthermore, there is a need for a system that can evoke emotional feelings in users by providing conversation summaries in a narrative format.

[1859] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 3 is realized by the following means.

[1860] In this invention, the server includes a means for automatically summarizing conversations between users of the messaging application or within a group using generative artificial intelligence, providing the conversation as a single story (text), a means for randomly providing the summary regardless of whether it is recent or past, or by whom, and, in the paid version, a means for displaying summaries by specifying a specific person or period. This allows users to easily look back on past conversations and easily obtain summaries focused on a specific person or period. Furthermore, the narrative-style summaries can evoke emotional feelings in users.

[1861] A "messaging application" is software that allows users to send and receive messages, including text, audio, images, and videos, between each other.

[1862] "User" means any individual or group of people who use the Message Application.

[1863] "Generative artificial intelligence" refers to algorithms or systems that automatically generate text or information based on input data.

[1864] A "summary" is a sentence that briefly summarizes the content of the original conversation.

[1865] A "narrative" is a piece of writing that presents multiple events or conversations as a series of stories.

[1866] "Most recent" refers to the period in the past that is closest to the present.

[1867] "Past" refers to a time before the present.

[1868] "Random" means that something is chosen at random without following any particular rules or order.

[1869] "Paid Version" refers to a version that requires a fee to use certain features or services.

[1870] "Specific person" refers to a specific user designated by the user.

[1871] A "period" refers to a range of time between a specific start date and an end date.

[1872] "Conversation history" refers to a record of past message exchanges.

[1873] A "smartphone" is a mobile device that has the functionality of a mobile phone and is also capable of connecting to the Internet and running applications.

[1874] Systems for implementing the present invention include messaging applications, generative artificial intelligence, and smartphones. Specific embodiments are described below.

[1875] First, the server collects interactions between users and groups through messaging applications. These interactions are saved in the form of text, audio, images, and video. Next, generative AI automatically analyzes these interactions and generates summaries. These summaries express the conversation as a single narrative (text).

[1876] The server has a means to randomly provide generated summaries, regardless of whether they were recent or past, or with whom. This allows users to look back on past conversations without specifying specific conditions. The paid version also provides a means to display summaries by specifying a specific person or period. This allows users to easily obtain summaries focused on a specific conversation or period.

[1877] As an application installed on a smartphone, this system generates and displays a narrative summary based on the user's conversation history. Specifically, when a user opens the application and specifies a specific person and time period, the generative AI generates a summary based on those conditions and displays it on the smartphone screen.

[1878] For example, if a user requests, "Please summarize the conversation with User B on October 1, 2023," the generative AI will analyze the conversation based on that condition and generate a summary such as, "On October 1, 2023, User A and User B had a passionate discussion about movies." This summary is displayed on the smartphone screen, allowing users to easily look back on past conversations.

[1879] An example of a prompt is as follows:

[1880] "Please summarize the conversation between User A and User B on 2023-10-01."

[1881] This system allows users to easily review past conversations and easily obtain summaries of specific people or periods. Furthermore, narrative summaries can evoke emotional feelings in users.

[1882] The flow of the specific processing in Application Example 3 will be described with reference to FIG.

[1883] Step 1:

[1884] The server collects messages between users and groups through messaging applications. The input includes message data such as text, audio, images, and videos. The server stores this data in a database. The output is the stored message data.

[1885] Step 2:

[1886] The server provides the stored message data as input to the generative AI. The generative AI analyzes this data and generates a summary of the conversation. Natural language processing techniques are used to analyze the text data and extract important information. The output is summarized conversation data.

[1887] Step 3:

[1888] The server has a means to randomly provide generated summaries, regardless of whether they are recent or past, or for any other party. The input includes the summarized conversation data. The server uses a random algorithm to randomly select a summary and provide it to the user. The output is a randomly selected summary.

[1889] Step 4:

[1890] A user can specify a specific contact and time period through an application installed on their smartphone. The input includes the contact and time period information specified by the user. The device sends this information to the server. The output is a request based on the specified conditions.

[1891] Step 5:

[1892] The server receives a request from a user and filters conversation data based on the specified contact and time period. The input includes the information on the contact and time period specified by the user, as well as the stored message data. The data is processed by using a filtering algorithm to extract the relevant conversation data. The output is the filtered conversation data.

[1893] Step 6:

[1894] The server provides the filtered conversation data to a generative AI to generate a summary. The input includes the filtered conversation data. The generative AI again uses natural language processing techniques to generate a summary. The output is a summary based on the specified criteria.

[1895] Step 7:

[1896] The server sends the generated summary to the user's smartphone. The input includes a summary based on the specified conditions. The server sends the summary to the device using a communication protocol. The output is a summary that is displayed on the user's smartphone.

[1897] Step 8:

[1898] The user checks the summary displayed on the smartphone screen. The input includes the summary sent from the server. The device displays the summary on the screen. The output is a summary that the user can visually check.

[1899] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1900] "Example 1"

[1901] One embodiment of the present invention is a messenger app system incorporating an emotion engine. In this system, when a user converses on the messenger app, a generative AI analyzes the conversation in real time. Natural language processing technology is used for the analysis, and the emotion engine recognizes the user's emotion from the content of the conversation. For example, if a user sends a message saying "I had fun today," the emotion engine recognizes the emotion of "joy" from the message.

[1902] "Example 2"

[1903] Furthermore, the way the summarized story is presented is adjusted based on the emotion recognized by the emotion engine. For example, if the emotion engine recognizes the emotion of "joy," the user will be presented with a summarized story that enhances the emotion of joy. This allows the user to better understand their own emotions and recreate emotional experiences.

[1904] "Example 3"

[1905] Furthermore, the emotions recognized by the emotion engine are fed back to the user. For example, after a user sends a message saying, "Today was fun," the system will provide feedback such as, "You seem to be feeling joy right now." This allows users to objectively understand their own emotions and deepen their self-understanding.

[1906] The processing flow of each embodiment will be described below.

[1907] "Example 1"

[1908] Step 1: A user starts a conversation in a messenger app.

[1909] Step 2: The generative AI analyzes the conversation in real time, using natural language processing technology.

[1910] Step 3: The emotion engine is activated to recognize the user's emotions from the content of the conversation.

[1911] "Example 2"

[1912] Step 1: The emotion engine recognizes the user's emotion.

[1913] Step 2: Based on the perceived emotions, the delivery of the summarized story is adjusted.

[1914] Step 3: The user is provided with an emotionally charged, summarized story.

[1915] Step 1: The emotion engine recognizes the user's emotion.

[1916] Step 2: The recognized emotion is fed back to the user.

[1917] Step 3: Users objectively understand their own feelings through feedback.

[1918] Example 1

[1919] Next, a description will be given of Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1920] With traditional messaging apps, the volume of conversations between users was enormous, making it difficult to review past interactions. Furthermore, since there was no function to summarize the contents of conversations, there was a risk of missing important information. Furthermore, since there was no ability to recognize emotions, it was difficult to understand the user's feelings.

[1921] The identification process by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means. In this invention, the server includes: means for automatically summarizing interactions between users or within a group of users of a messenger app using a generative AI and providing the chat as a single story (text); means for a terminal to transmit message data to the server when a user sends a message using the messenger app; means for the server to analyze the received message data using natural language processing technology and summarize the conversation; means for the server to recognize emotions from the message using an emotion engine; means for the terminal to transmit the summary result and the emotion recognition result and display them to the user; and means for randomly providing them regardless of whether they are recent, past, or with whom. This allows the user to easily look back on past interactions, avoid missing important information, and further understand the emotions in the conversation.

[1922] A "messenger app" is software that allows users to send and receive text messages.

[1923] "Generative AI" is artificial intelligence that uses natural language processing technology to analyze text data and perform summarization and generation.

[1924] "Natural language processing technology" is a technology that allows computers to understand, interpret, and generate human language.

[1925] An "emotion engine" is software that recognizes user emotions from text data.

[1926] A "server" is a computer system for processing and storing data.

[1927] "Device" refers to a device that is directly operated by a user and on which a messenger app is installed.

[1928] "Summarization" is the process of summarizing long text data and extracting only the important information.

[1929] "Emotion recognition" refers to extracting and understanding a user's emotions from text data.

[1930] A "story (text)" is a collection of multiple messages organized into a single continuous text.

[1931] "Randomly providing" means providing data randomly without relying on any particular order or conditions.

[1932] This invention is a system that uses generative AI implemented in a messenger app to automatically summarize interactions between users or within a group, and further recognizes emotions from messages using an emotion engine. When a user sends a message via the messenger app, the device sends the message data to a server, which analyzes the data, summarizes it, and recognizes emotions, and then provides the results to the user.

[1933] Hardware and software used

[1934] Hardware:

[1935] Server: A computer system for processing and storing data.

[1936] Terminal: Devices that users directly operate (smartphones, tablets, PCs, etc.)

[1937] software:

[1938] Messenger app: Software that allows users to send and receive text messages.

[1939] Generative AI: Artificial intelligence that uses natural language processing technology to analyze text data and generate summaries and information (e.g., BERT, GPT-3)

[1940] Emotion engine: Software for recognizing user emotions from text data (e.g., Azure Cognitive Services' Text Analytics API)

[1941] Data processing and calculation

[1942] The server receives message data sent from the device and analyzes it using natural language processing technology. Specifically, it uses generative AI models such as BERT and GPT-3 to understand the content of the message and generate a summary. It also uses an emotion engine to extract emotions from the message. This allows it to summarize the user's conversation as a story and recognize the emotions contained in the conversation.

[1943] Specific examples

[1944] For example, if user A and user B are interacting on the topic of "movies," the process will proceed as follows:

[1945] User A: The movie yesterday was great!

[1946] User B: Yeah, the final scene was especially moving.

[1947] The generative AI summarizes this conversation as, "User A and User B talked about a touching scene in a movie." The emotion engine also recognizes the emotions of "joy" and "emotion."

[1948] In this way, users can easily look back at past interactions, avoid missing important information, and understand the sentiment of the conversation.

[1949] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1950] Step 1:

[1951] A user sends a message using a messenger app. A user uses a messenger app to converse with other users. For example, user A sends a message saying, "The movie yesterday was great!" This message becomes input data.

[1952] Step 2:

[1953] The terminal sends message data to the server. The terminal sends message data sent by the user to the server. This data includes the message content and sender information. The input is the user's message data, and the output is data sent to the server.

[1954] Step 3:

[1955] The server receives the message data and analyzes it using natural language processing technology. The server then analyzes the received message data using natural langu...

Claims

1. A means for acquiring information on interactions of a group including the user; A means for generating a prompt sentence to be input to a generative AI model based on the acquired interaction information; A means for inputting the generated prompt sentence into the generative AI model and obtaining a sentence summarizing the content of the interaction from the generative AI model; A means for providing the retrieved text; A system including:

2. The system of claim 1 , further comprising means for analyzing a user's emotion using an emotion engine and adjusting the summarized text based on the analyzed user's emotion.

3. The system of claim 1 , further comprising means for analyzing a user's emotion using an emotion engine and for adjusting a manner in which the summarized text is presented based on the analyzed user's emotion.

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