system

Abstract

A system is provided.SOLUTION: The system includes a means for collecting data of a user, a means for analyzing the collected data, a means for automatically generating SNS contribution contents on the basis of an analysis result, a means for displaying the generated contribution contents to the user for confirmation and correction, and a means for finally confirming the corrected contribution contents and automatically posting them to the SNS.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] In modern society, opportunities for individuals and businesses to use social networking services (SNS) are increasing. However, manually creating content to post on SNS requires time and effort, which is a burden, especially for busy users. Furthermore, selecting the appropriate timing and content to post in a timely manner can be difficult. Therefore, there is a need for a system that automatically generates SNS post content using users' schedules, emails, and photo data, and allows users to easily check and edit the content. [Means for solving the problem]

[0005] The present invention provides a system that includes a means for collecting user data, a means for analyzing the collected data, a means for automatically generating social networking site (SNS) posting content based on the analysis results, a means for displaying the generated posting content to the user so that the user can confirm and correct it, and a means for final confirmation of the corrected posting content and automatically posting it to the SNS. This system allows users to easily and quickly generate, confirm, and correct appropriate social networking site (SNS) posting content based on schedule data, email data, and photo data, thereby solving conventional problems. Specifically, the system includes a means for collecting schedule data, email data, and photo data, and further includes an analysis means for extracting important keywords and topics from the email data using natural language processing technology. This automatically generates social networking site (SNS) posting content, reducing the burden on the user.

[0006] The "means for collecting user data" refers to software or hardware for acquiring the user's schedule data, email data, and photo data.

[0007] "Means for analyzing collected data" refers to algorithms and tools used to decipher the collected data and extract and classify the necessary information.

[0008] "Means for generating content to post on SNS" refers to a system or software that automatically generates text and images to be posted on SNS based on the analysis results.

[0009] "Means for displaying to users so that they can check and correct" refers to a tool or system that visually presents the generated post content to users and provides an interface that allows users to easily edit and correct it.

[0010] "Means for final confirmation and automatic posting to SNS" refers to a system or software that rechecks the corrected content and automatically posts it to the SNS account set by the user.

[0011] "Natural language processing" is a field of computer science that refers to algorithms and methods for understanding, interpreting, and generating human language.

[0012] "Means for extracting important keywords and topics from email data" refers to technologies and algorithms that analyze the content of emails and identify and extract specific keywords and topics from them. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

[0021] [First embodiment]

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

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

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

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

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

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

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

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

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

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

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

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

[0034] This invention relates to a system that collects and analyzes users' schedule data, email data, and photo data, and automatically generates content to post to SNS. The generated content is displayed to the user, who can then check and correct it before it is automatically posted to the SNS.

[0035] System Implementation

[0036] This system consists of a terminal that collects data, a server that performs analysis, and a terminal that users use to make corrections. Users use devices that store various types of data, and the system has a means of collecting the necessary information from these devices.

[0037] Program processing flow

[0038] The terminal collects the user's schedule data, email data, and photo data, and sends the collected data to the server. The following describes how each data is processed.

[0039] 1. Collecting schedule data

[0040] The device retrieves the user's event data from the calendar app and sends it to the server via an API.

[0041] Example: Use the Google (registered trademark) Calendar API to obtain event information such as "December 25th: Christmas Party."

[0042] 2. Email Data Collection

[0043] The device retrieves the user's email data from the email app and filters it for important keywords.

[0044] The device uses NLP technology to extract keywords and send them to the server.

[0045] Example: Parse an email that says "A new project has been started" and extract related topics.

[0046] 3. Photo data collection

[0047] The device retrieves the most recently taken photos from the photo gallery and sends the data to the server.

[0048] The device also collects photo metadata (e.g., date taken, location information).

[0049] Example: Getting a photo of a Christmas tree and parsing its metadata.

[0050] The server analyzes the data it receives, extracting and synthesizing key information, including:

[0051] 1. Data Analysis

[0052] The server analyzes the schedule data, email data, and photo data.

[0053] Analyze schedule event information to extract specific dates or events.

[0054] Natural language processing techniques are used to process email data and extract important topics and keywords.

[0055] Image analysis technology is used to analyze photo data and extract the content and related information of the photo.

[0056] 2. Generating social media posts

[0057] The server generates a draft of the SNS post based on the analysis results.

[0058] The generated draft will be written in natural language and will combine information from schedules, emails, and photos.

[0059] Example: Integrate events based on the user's schedule, the launch of a new project via email, and recently taken photos to generate a draft titled "Today is December 25th, Christmas party day! A new project has also started. I'll post a photo of the Christmas tree. Christmas New Project."

[0060] The terminal displays the generated draft to the user, who can then check the contents and make corrections as necessary.

[0061] 1. Viewing and correcting drafts

[0062] The terminal displays the draft to the user and provides an interface that allows revisions to be made.

[0063] Users can view the draft and make edits or corrections.

[0064] Example: User reviews draft and amends it to "Today is December 25th, Christmas party day! I've started a new project and am having a lot of fun. I'm going to post a picture of my Christmas tree. Christmas New project Fun times."

[0065] Finally, the device sends the corrected post content to the server, which then performs a final check and automatically posts it to the social networking site.

[0066] 1. Final confirmation and automatic posting

[0067] The terminal transmits the corrected content to the server.

[0068] The server performs a final check of the content and automatically posts it using the SNS API.

[0069] Example: The confirmed post content is actually posted to social media via the Twitter API or Instagram API.

[0070] In this way, this system reduces the burden on users and enables them to post to social media in a timely and effective manner.

[0071] The processing flow will be explained below.

[0072] Step 1:

[0073] The device collects the user's schedule data. The device calls the calendar app's API to obtain the user's event information. This information includes events for the next week and specific dates.

[0074] Step 2:

[0075] The device collects email data. The device accesses a mail server (for example, by connecting using the IMAP protocol) and retrieves new emails from the user's mailbox. The retrieved email data is then analyzed using natural language processing technology to extract important keywords and topics.

[0076] Step 3:

[0077] The device accesses the user's photo gallery and collects recently taken photos, along with the photo's metadata (e.g., the date and time of the photo, location information), and uses image analysis technology to recognize and tag the content of each photo.

[0078] Step 4:

[0079] The schedule data, email data, and photo data collected by the device are sent to the server in a single packet using a secure protocol.

[0080] Step 5:

[0081] The server analyzes the schedule data it receives. It extracts detailed information such as the date, time, name, and location of the event, and evaluates the importance of the event. For example, it extracts event information such as "December 25th: Christmas Party."

[0082] Step 6:

[0083] The server analyzes the received email data using natural language processing technology to extract important keywords and topics, evaluates the importance of the email based on the extracted keywords and topics, and determines the content to include in the social media post.

[0084] Step 7:

[0085] The server uses image analysis technology to analyze the received photo data and recognize the content of the photo. Based on the recognized content, tags and captions are generated for each photo. For example, a photo of a Christmas tree is tagged as "Christmas tree."

[0086] Step 8:

[0087] The server generates a draft of a social media post based on the analysis results. It integrates the collected and analyzed schedule, email, and photo data to generate an automatically generated text. The generated text is structured based on a template.

[0088] Step 9:

[0089] The server sends the generated draft to the terminal, which displays it to the user so that the user can check and correct it. The UI (user interface) includes a text editor for editing, allowing the user to freely edit the text.

[0090] Step 10:

[0091] The user reviews the draft and makes corrections as necessary, such as changing expressions or adding new hashtags. After reviewing, the user confirms the corrected text.

[0092] Step 11:

[0093] The terminal sends the corrected content to the server using a secure protocol.

[0094] Step 12:

[0095] The server performs a final check of the corrected post content received and automatically posts it to social media. The post is actually made using a social media API (e.g., Twitter API, Instagram API). For example, the following content might be automatically posted: "Today is December 25th, Christmas party day! We've started a new project and are having a lot of fun. I'll post a photo of the Christmas tree. Christmas, new project, fun times."

[0096] These are the specific processing steps of this system. By using this system, users can post to SNS at the appropriate time without any burden.

[0097] Example 1

[0098] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0099] In modern society, posting to social networking sites has become a widespread means of disseminating information, but busy daily schedules make it difficult to post regularly and effectively. In particular, as users' schedules and activities become more and more varied, creating content to post to social networking sites requires a great deal of time and effort. Furthermore, gathering necessary information from multiple data sources (schedules, emails, photos, etc.) and integrating it to create content to post places a significant burden on users. Therefore, there is a need for a method that reduces users' effort and allows them to post to social networking sites efficiently and effectively.

[0100] 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.

[0101] In this invention, the server includes a means for collecting user information, a means for analyzing the collected information, and a means for automatically generating SNS post content based on the analysis results. This enables the automatic generation of integrated SNS post content based on the user's schedule information, email information, and image information. Furthermore, by using a generative AI model, the analysis results are converted into natural and attractive post content, which can then be automatically posted to the SNS after the user has confirmed and corrected it. This allows users to effectively use SNS regardless of their daily busy schedules.

[0102] "User" refers to any individual or entity that uses the System.

[0103] "Information" refers to data related to a user, and specifically includes schedule information, e-mail information, and image information.

[0104] "Collection methods" refers to methods for securing and obtaining user information, including calendar apps, email apps, photo galleries, etc.

[0105] "Analysis methods" refer to methods used to analyze collected information and extract important keywords and topics, including natural language processing and image analysis techniques.

[0106] "Generation means" refers to a method for automatically generating a draft of the content of a SNS post from the analyzed data.

[0107] "SNS post content" refers to a post that includes text and images generated based on user information.

[0108] "Correctable interface" refers to an interface that allows users to check and edit the content of SNS posts they have created.

[0109] "SNS API" refers to an application programming interface that allows a program to access a specific SNS platform and automatically post to it.

[0110] "Natural language processing technology" is a technology for analyzing text data such as email information and extracting important keywords and topics.

[0111] "Image analysis technology" is a technology for analyzing image data and extracting its contents and related information.

[0112] A "generative AI model" refers to an artificial intelligence model that automatically generates sentences written in natural language based on given data.

[0113] MODE FOR CARRYING OUT THE INVENTION

[0114] This invention is a system that collects and analyzes users' schedule information, email information, and image information, and automatically generates content to post on social media based on this information. The system consists of a terminal that collects user information, a server that analyzes the collected information, and a terminal on which the user can make corrections.

[0115] Hardware and software used

[0116] Device:

[0117] Hardware: devices such as smartphones, tablets, and computers

[0118] Software: Calendar apps (e.g., Google Calendar), email apps (e.g., general email clients), photo gallery apps, natural language processing libraries (e.g., spaCy), image analysis libraries (e.g., OpenCV)

[0119] server:

[0120] Hardware: Cloud server (e.g., AWS (registered trademark), Google Cloud Platform)

[0121] Software: Data analysis engine, generative AI model (e.g., GPT-3 (registered trademark)), social media API (e.g., Twitter API, Instagram API)

[0122] System processing flow

[0123] Data collection:

[0124] The device retrieves schedule information from the user's calendar app via API. For example, if a user adds an event called "December 25th: Christmas Party" to Google Calendar, the device retrieves this information.

[0125] The device retrieves email data from the email app and uses natural language processing technology to extract important keywords and topics, such as an email that says, "A new project has been started."

[0126] The device retrieves recently taken photos from the photo gallery along with their metadata (date taken, location, etc.), including a photo of a Christmas tree, for example.

[0127] Data transmission and analysis:

[0128] The devices send the collected data over the Internet to a server, which analyzes the data and consolidates the information.

[0129] The server extracts specific dates and events from the schedule data, analyzes email data using natural language processing techniques, and uses image analysis techniques on photo data.

[0130] Generate social media posts:

[0131] The server uses a generative AI model (e.g., GPT-3) based on the analysis results to generate a draft of the content to post on social media. For example, it generates a sentence such as, "Today is December 25th, Christmas party day! A new project has also started. I'll post a photo of the Christmas tree. Christmas New Project."

[0132] View and correct drafts:

[0133] The terminal displays the generated draft to the user and provides an interface that allows the user to check and correct it.

[0134] The user can review the generated draft and make corrections as necessary. For example, they could correct it to "Today is December 25th, Christmas party day! We've started a new project and are having a lot of fun. I'll post a picture of the Christmas tree. Christmas, new project, fun time."

[0135] Final check and auto-post:

[0136] The device sends the corrected post content to the server.

[0137] The server performs a final check of the content and automatically posts it to SNS using the SNS API.

[0138] Examples of concrete examples and prompts

[0139] Examples:

[0140] "December 25th: Christmas Party" has been added to the user's schedule.

[0141] The user has received an email with the topic "A new project has been started."

[0142] The user's recent photos include a "photo of a Christmas tree."

[0143] Example prompt for a generative AI model:

[0144] "Based on the user's schedule, email, and photo data, please generate social media post content in the following format.

[0145] Schedule: December 25th, Christmas Party

[0146] Email Topic: New project started

[0147] Photo: Christmas tree photo

[0148] Example of generated result: Today is December 25th, Christmas party day! A new project has also started. I will post a photo of the Christmas tree. Christmas New Project"

[0149] In this way, the present invention significantly reduces the burden on users and enables efficient and effective posting to SNS.

[0150] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0151] Program processing flow

[0152] Step 1:

[0153] Data collection

[0154] 1.1 Schedule data collection:

[0155] The device collects schedule information and obtains event information through a calendar app (e.g., Google Calendar) installed on the user's device. Specifically, the device calls the Google Calendar API to obtain event data stored in the user's calendar. The input for this operation is the API key and user ID, and the output is the schedule information registered in the calendar.

[0156] Input: API key, user ID

[0157] Output: Event information such as "December 25th: Christmas Party"

[0158] 1.2 Email Data Collection:

[0159] The device collects email data from the email app. The device accesses the user's mailbox and retrieves new or relevant emails. NLP techniques are used to extract important keywords and topics from the email body. The input for this operation is email account information and a rule set (e.g., "new project"), and the output is the analyzed keywords and topics.

[0160] Input: Email account information, rule set

[0161] Output: Important keywords and topics, such as "A new project has been launched."

[0162] 1.3 Photographic Data Collection:

[0163] The device collects recently taken photos and their metadata (such as the date of taking the photos, location information, etc.) from the photo gallery. The input of this operation is the path to the photo folder and access permissions, and the output is the photo data and its metadata.

[0164] Input: Path to photo folder, access permissions

[0165] Output: A photo of a Christmas tree and metadata such as "Date taken: December 25th, Location: Home"

[0166] Step 2:

[0167] Sending data

[0168] 2.1 Data transmission to the server:

[0169] The terminal transmits the collected schedule data, email data, and photo data to the server via the Internet. The input of this operation is the collected data, and the output is the transmission completion status.

[0170] Input: Schedule data, email data, photo data

[0171] Output: Data transmission completion status

[0172] Step 3:

[0173] Data analysis

[0174] 3.1 Schedule Data Analysis:

[0175] The server analyzes the received schedule data and extracts specific date and event information. Specifically, the server parses the schedule data and identifies important dates and event names. The input of this operation is the schedule data, and the output is the analyzed event information.

[0176] Input: Schedule data

[0177] Output: Event information such as "December 25th: Christmas Party"

[0178] 3.2 Email Data Analysis:

[0179] The server uses natural language processing techniques (e.g., spaCy) to analyze the email data and extract important keywords and topics. The input of this operation is the email data, and the output is the extracted keywords and topics.

[0180] Input: Email data

[0181] Output: Important keywords, such as "new project"

[0182] 3.3 Image analysis:

[0183] The server analyzes the photo data using image analysis techniques (e.g., OpenCV) to extract its content and metadata. The input to this operation is the photo data and metadata, and the output is the extracted image content information.

[0184] Input: Photo data, metadata

[0185] Output: Information such as "Christmas tree", "Photo date: December 25th, Location: Home", etc.

[0186] Step 4:

[0187] Generating social media posts

[0188] 4.1 Social Media Post Generation:

[0189] The server integrates all the analyzed data and generates a draft of the social media post using a generative AI model (e.g., GPT-3). The input of this operation is the analyzed events, keywords, and image information, and the output is the social media post draft.

[0190] Input: Analyzed event information, keywords, image information

[0191] Output: A draft social media post that reads, "Today is December 25th, Christmas party day! A new project has started. I'll post a picture of my Christmas tree. Christmas New Project."

[0192] Step 5:

[0193] Viewing and correcting drafts

[0194] 5.1 Viewing and correcting drafts:

[0195] The terminal displays the generated SNS post draft to the user and provides an interface for correction. The user uses this interface to check and correct the draft. The input of the operation is the generated SNS post draft, and the output is the draft corrected by the user.

[0196] Input: Generated social media post draft

[0197] Output: Revised social media post draft (e.g., "Today is December 25th, Christmas party day! I've started a new project and am having a lot of fun. I'm posting a picture of my Christmas tree. Christmas. New project. Fun time.")

[0198] Step 6:

[0199] Final confirmation and automatic posting

[0200] 6.1 Final Review and Submission:

[0201] The device sends the corrected post content to the server, which then performs a final check using the SNS API and automatically posts it to the SNS. The input for the operation is the corrected SNS post draft, and the output is the status of the post completion to the SNS.

[0202] Input: Revised social media post draft

[0203] Output: SNS post completion status

[0204] (Application example 1)

[0205] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0206] In today's world, posting to social media accounts for an important marketing strategy, but manually posting requires time and effort. Furthermore, in certain fields, such as the food delivery industry, continuous and timely information transmission is required, placing a significant burden on users. This invention aims to solve these problems by providing a system that automatically generates social media posting content and allows users to easily review and correct the content.

[0207] 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.

[0208] In this invention, the server includes means for collecting user data, means for analyzing the collected data, means for automatically generating SNS post content based on the analysis results, means for displaying the generated post content to the user so that the user can confirm and correct it, means for final confirmation of the corrected post content and automatically posting it to the SNS, means for integrating various electronic data and using natural language processing and image analysis techniques to generate SNS post content, and means for providing a correction interface on the user management terminal, thereby reducing the burden on the user and enabling timely and effective SNS posting.

[0209] "User data" refers to various information related to a user, such as the user's schedule data, email data, and photo data, which are stored on an electronic device.

[0210] "Collection means" refers to the software and hardware components used to obtain the required data from a user's device.

[0211] "Means of analysis" refers to algorithms and analytical techniques used to integrate and filter matching data based on collected data.

[0212] "Means for generating content to post on SNS" refers to a program that automatically creates text and media content to post on SNS on behalf of the user based on the analysis results.

[0213] "Means for enabling review and correction" refers to a software configuration that provides an interface for users to review the generated posted content and edit or correct it as necessary.

[0214] "Means for final confirmation and automatic posting to SNS" refers to the API and program for final confirmation of the corrected data and automatically posting it through the user's SNS account.

[0215] "Natural language processing technology" refers to artificial intelligence technology for analyzing text data and extracting meaningful information.

[0216] "Image analysis technology" refers to technology for analyzing photographs and image data and extracting their content and metadata.

[0217] "User management terminal" refers to a device owned by a user and used for configuration and operation.

[0218] "Correction interface" refers to a software configuration that provides a user-friendly screen for users to correct and edit the content of their generated SNS posts.

[0219] "Means for automatically obtaining new menu and promotional information" refers to APIs and data mining technologies for automatically collecting information on new products and campaigns offered by food delivery services and other services.

[0220] This invention is a system that collects and analyzes user data and automatically generates content to post on social media. The system of this invention is composed of a user terminal for collecting data, a server for analyzing the data and generating content to post on social media, and a terminal for providing a user correction interface.

[0221] 1. Overall system configuration

[0222] The system relies on the following components:

[0223] User terminal: A device for collecting user schedule data, email data, photo data, new menus and promotional information.

[0224] Server: Has the core function of analyzing collected data and automatically generating content to post on social media.

[0225] Terminal that provides the correction interface: A device that displays the generated SNS post content to the user and allows them to check and correct it.

[0226] 2. Data Collection

[0227] First, the user's device retrieves the user's schedule data using the Google Calendar API. Email data is collected via an email client API, and then important keywords and topics are extracted using NLP technology. For photo data, the Google Photos API is used to collect recently taken photos and their metadata. Additionally, new menu items and promotional information from food delivery services are automatically retrieved.

[0228] 3. Data Analysis

[0229] The collected data is sent to a server for analysis. The server uses NLP technology to extract important keywords and topics from the email data, and image analysis technology to analyze the content of the photo data. This generates integrated information from the user's schedule, email content, photo data, new menus, and promotional information.

[0230] 4.Generate social media posting content

[0231] The server automatically generates the content to post on social media based on the analysis results. For example, the generated post might look something like, "Today is December 25th, Christmas party day! I've started a new project and am having a lot of fun. I'll post a photo of my Christmas tree. Christmas, new project, fun time." Natural language generation technology is used to generate this content.

[0232] 5. Viewing and correcting drafts

[0233] An interface for displaying the generated draft is provided on the user's device. The user can check the draft content through this interface and make corrections as necessary. For example, a user can change the content "Today is December 25th, Christmas party day! A new project has started and I'm having a lot of fun. I'll post a picture of the Christmas tree. Christmas New project Fun time" to "Today is December 25th, Christmas party day! A new project has started and I'm having a lot of fun. I'll post a picture of the Christmas tree. Christmas New project Fun time."

[0234] 6. Final confirmation and automatic posting

[0235] The corrected content is sent back to the server, where it is checked for final approval and then automatically posted to the social networking site using the social networking site API, such as Twitter or Instagram.

[0236] Example prompt sentence:

[0237] "Use calendar, email, and photo data to generate posts about the user's schedule. Analyze using the following information."

[0238] Schedule data: (event details, date and time)

[0239] Email data: (Email content)

[0240] Photo data: (date, time, location)

[0241] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0242] Step 1:

[0243] The user's device collects user data. Specifically, it uses the Google Calendar API to obtain schedule data and the email client API to obtain email data. It also uses the Google Photos API to obtain photo data and metadata, and automatically collects new menu items and promotional information for food delivery services. This input data is temporarily stored on the user's device and later sent to the server.

[0244] Step 2:

[0245] The user device sends the collected data to the server. The data sent includes schedule data, email data, photo data, and menu / promotion information. The server receives this data and prepares it for analysis.

[0246] Step 3:

[0247] The schedule data received by the server is analyzed. Specifically, event content and date information are analyzed to extract important events. For example, event information such as "December 25th: Christmas Party" is the subject of analysis. The analysis results are passed on to the next processing step.

[0248] Step 4:

[0249] The server analyzes the email data. Natural language processing (NLP) technology is used to extract important keywords and topics from the email content. For example, the keyword "new project" is extracted from an email that reads "A new project has been started." The analysis results are applied to the next processing step.

[0250] Step 5:

[0251] The server analyzes the photo data. It uses image analysis technology to determine the content of the photo and extract relevant information, including metadata (date and time of the photo, location information). For example, a photo of a Christmas tree is analyzed to determine its metadata. The analysis results are passed on to the next processing step.

[0252] Step 6:

[0253] The server integrates the collected and analyzed results and generates the content to post on social media. The generated post content is written in natural language and combines schedule, email, and photo information. For example, the generated content might read, "Today is December 25th, Christmas party day! A new project has also started. I'll post a photo of the Christmas tree. Christmas New Project." This generative AI model generates the content to post on social media based on the specified prompt.

[0254] Step 7:

[0255] The server sends the generated draft of the SNS post to the user's device. The user's device displays the draft and provides an interface that allows the user to check and correct the content. The user uses this interface to edit and modify the generated post content.

[0256] Step 8:

[0257] The user resubmits the corrected post to the server. The server receives the corrections and performs a final check. Once this check is complete, the post is automatically posted to the social networking site using the social networking site API (e.g., Twitter API or Instagram API).

[0258] 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.

[0259] This invention relates to a system that collects and analyzes a user's schedule data, email data, and photo data, understands the user's emotions using an emotion engine, and automatically generates content to post to an SNS. The generated content is displayed to the user, who can then check and correct it, before it is automatically posted to the SNS.

[0260] System Implementation

[0261] This system consists of a device that collects data, a server that analyzes the data and recognizes emotions, and a device that the user can use to make corrections. The functions of each component and specific examples of their operation are shown below.

[0262] Program processing flow

[0263] The device collects the user's schedule data, email data, and photo data, and sends the collected data to the server. The following describes how each data is processed.

[0264] 1. Collecting schedule data

[0265] The device retrieves event data from the user's calendar app and sends it to the server via an API.

[0266] Example: Use the Google Calendar API to retrieve event information such as "December 25th: Christmas Party."

[0267] 2. Email Data Collection

[0268] The device retrieves the user's email data from the email app and extracts important keywords.

[0269] The device uses NLP (natural language processing) technology to extract keywords and topics, and then uses an emotion analysis engine to recognize the user's emotions and send them to the server.

[0270] Example: Parsing an email that says "A new project has been launched" and extracting relevant topics and emotions (e.g., excitement or joy).

[0271] 3. Photo data collection

[0272] The device accesses the user's photo gallery to collect recently taken photos and obtains the photo's metadata (date and time of taking, location information).

[0273] The device uses image analysis technology and an emotion analysis engine to recognize the content and emotions of the photo and send them to the server.

[0274] Example: Analyzing a photo of a Christmas tree and recognizing associated emotions (e.g., joy and celebration).

[0275] The server analyzes the received data, extracts and synthesizes important information and user sentiment. The analysis includes:

[0276] 1. Data Analysis

[0277] The server analyzes the schedule data, email data, and photo data to extract important events and topics.

[0278] Using natural language processing technology and a sentiment analysis engine, information including user emotions is extracted and content is generated.

[0279] 2. Generating social media posts

[0280] The server generates a draft of a social media post based on the analysis results and emotional information. For example, it creates a post by combining schedule events, email topics, photo content, and associated emotions.

[0281] Example: By combining events based on the user's schedule, excitement based on sentiment analysis, and recently taken photos, we can generate a draft that reads, "Today is December 25th, Christmas party day! We've started a new project and are having a lot of fun. I'll post a photo of the Christmas tree. Christmas, new project, fun times."

[0282] The terminal displays the generated draft to the user, who can then check the contents and make corrections as necessary.

[0283] 1. Viewing and correcting drafts

[0284] The terminal displays the draft to the user and provides an interface for review and correction, allowing the user to edit the draft and make any necessary corrections.

[0285] Example: User reviews draft and amends it to "Today is December 25th, Christmas party day! I've started a new project and am having so much fun. I'm going to post a picture of my Christmas tree. Christmas New project Fun times."

[0286] Finally, the device sends the corrected post content to the server, which performs a final check and then automatically posts it to the social networking site.

[0287] 1. Final confirmation and automatic posting

[0288] The terminal transmits the corrected content to the server.

[0289] The server performs a final check of the content and automatically posts it using the SNS API.

[0290] Example: Post the confirmed content to a social networking site via a social networking site API (e.g., Twitter API, Instagram API).

[0291] In this way, the system takes into consideration the user's feelings, reduces the burden on the user, and enables timely and effective posting on social media.

[0292] The processing flow will be explained below.

[0293] Step 1:

[0294] The device collects the user's schedule data. The device calls the calendar app's API to obtain the user's event information. For example, this data might be "December 25th: Christmas Party."

[0295] Step 2:

[0296] The device collects the user's email data. It then connects to a mail server to retrieve new emails. The retrieved email data is analyzed using natural language processing technology to extract important keywords and topics. Furthermore, a sentiment analysis engine is used to recognize the sentiment of the email.

[0297] Step 3:

[0298] The device accesses the user's photo gallery and collects recently taken photos. It also obtains the photo's metadata (e.g., date and time of the photo, location information). It then uses image analysis technology and an emotion analysis engine to recognize the content of the photo and its associated emotions. For example, it can recognize emotions such as "blessing" and "happiness" from a photo of a Christmas tree.

[0299] Step 4:

[0300] The schedule data, email data, and photo data collected by the device are sent to the server in bulk as data packets using secure protocols such as SSL / TLS.

[0301] Step 5:

[0302] The server analyzes the schedule data it receives. It analyzes the event date, time, name, location, etc. in detail and evaluates their importance. For example, it extracts information such as "December 25th: Christmas Party" and marks it as an important date.

[0303] Step 6:

[0304] The server uses natural language processing technology to analyze the email data it receives and extracts important keywords and topics. It also uses a sentiment analysis engine to extract the emotion of the email (e.g., joy, sadness, excitement). For example, the emotion of an email that says, "A new project starts today," is recognized as "excitement."

[0305] Step 7:

[0306] The server uses image analysis technology to analyze the received photo data and recognize the content of the photo. It then uses an emotion analysis engine to extract the emotion associated with the photo and tag the photo. For example, a photo of a Christmas tree can be tagged with "Christmas tree" and the emotion "blessing."

[0307] Step 8:

[0308] The server generates a draft of a social media post based on the analysis results. It integrates the collected and analyzed schedule, email, and photo data to generate an automatically generated sentence. For example, it generates a draft that reads, "Today is December 25th, Christmas party day! I've started a new project and am having a lot of fun. I'll post a photo of my Christmas tree. Christmas, new project, fun time."

[0309] Step 9:

[0310] The server sends the generated draft to the terminal, which displays the draft to the user and provides an interface for the user to review and correct it. The UI includes a text editor for editing.

[0311] Step 10:

[0312] Users can review the draft and make corrections as needed. They can change the wording or add new hashtags. For example, they could correct it to "Today is December 25th, Christmas party day! I've started a new project and I'm having a lot of fun. I'm posting a picture of my Christmas tree. Christmas. New project. Fun time."

[0313] Step 11:

[0314] The terminal sends the corrected content to the server using a secure protocol.

[0315] Step 12:

[0316] The server performs a final check of the corrected post content received and automatically posts it to social media. The post is actually made using a social media API (e.g., Twitter API, Instagram API). For example, the following content might be automatically posted: "Today is December 25th, Christmas party day! We've started a new project and are having a lot of fun. I'll post a photo of the Christmas tree. Christmas, new project, fun times."

[0317] These are the specific processing steps of this system. By using this system, users can easily and timely post appropriate, emotional posts to social media.

[0318] Example 2

[0319] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0320] In today's social media, timely posting and high-quality content are important. However, it is a significant burden for users to think about appropriate content and post content that reflects their emotions every time. There is a need for a system that can reduce this burden and automatically post social media content that accurately reflects users' emotions. However, existing systems lack sufficient integration of user data and emotion analysis, which leads to issues with the quality of posted content.

[0321] 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.

[0322] In this invention, the server includes means for collecting user data, means for analyzing the collected data and recognizing emotions, and means for automatically generating content to post to an SNS based on the analysis results, thereby reducing the burden on users and enabling high-quality SNS posts that accurately reflect their emotions.

[0323] "User" refers to an individual or organization that uses the system to post on social media.

[0324] "Data" includes information such as the user's schedule data, email data, and image data.

[0325] "Means for collecting" refers to the methods and devices used to obtain the user's schedule data, email data, and image data.

[0326] "Means for analyzing" refers to the methods and devices used to analyze the acquired data, extract important keywords and topics, and further recognize emotions.

[0327] "Means for recognizing emotions" refers to technologies and systems for identifying a user's emotions based on collected data.

[0328] "Generating means" refers to a method or device for automatically generating social media posts based on the analysis results and the recognized emotions.

[0329] "Means for displaying, reviewing, and correcting" refers to a method or device that provides an interface for showing the generated post content to the user, allowing the user to review the content and edit or correct it as necessary.

[0330] "Means for final confirmation and automatic posting to SNS" refers to a method or device for final checking of the posted content confirmed and corrected by the user and automatically posting it to the SNS platform.

[0331] "Natural language processing technology" refers to technology that enables computers to understand and process human language.

[0332] "Schedule data" refers to information about appointments and events recorded on a user's calendar.

[0333] "Email data" refers to the content, subject, sender information, etc. contained in a user's email.

[0334] "Image data" refers to the contents of photos and image files taken by users, as well as their metadata.

[0335] The present invention relates to a system that collects and analyzes a user's schedule data, email data, and image data, understands the user's emotions using an emotion engine, and automatically generates content to post on an SNS. An embodiment of the program for this system will be described in detail below.

[0336] System configuration

[0337] This system consists of a terminal that collects data, a server that analyzes the data and recognizes emotions, and a terminal that allows users to make corrections.

[0338] Hardware and software used

[0339] Hardware:

[0340] Device: The user's smartphone, tablet, or personal computer

[0341] Server: Cloud server or local server

[0342] software:

[0343] Calendar API (e.g. Google Calendar API)

[0344] Email API (e.g. Gmail API)

[0345] Natural language processing technology (e.g., Python NLP libraries)

[0346] Image analysis technology

[0347] Sentiment Analysis Engine

[0348] SNS API (e.g. Twitter API, Instagram API)

[0349] Program processing

[0350] The terminal first collects the user's schedule data, email data, and photo data, and then sends them to the server. This process involves the following specific steps:

[0351] Schedule data collection:

[0352] The device uses the Google Calendar API to retrieve event information from the user's calendar. For example, event information such as "December 25th: Christmas Party" is sent to the server.

[0353] Email Data Collection:

[0354] The device uses the Gmail API to retrieve the user's email data. From the retrieved email data, it uses natural language processing (NLP) technology to extract important keywords and topics, uses a sentiment analysis engine to recognize the user's emotions, and sends the results to the server. For example, it analyzes an email that says "A new project has been launched" and extracts related topics and emotions (e.g., excitement or joy).

[0355] Photo data collection:

[0356] The device accesses the photo gallery to collect recently taken photos, obtains the metadata of the collected photos (date and time of taking, location information), and uses image analysis technology to recognize the content of the photos. The recognized content and associated emotion data are then sent to the server. For example, a photo of a Christmas tree is analyzed to recognize associated emotions (e.g., joy or celebration).

[0357] Analysis and post generation

[0358] The server analyzes and integrates the received data, specifically following the steps below.

[0359] Data Analysis:

[0360] The server uses natural language processing technology and a sentiment analysis engine to extract important events and topics from schedule data, email data, and photo data and recognize user emotions.

[0361] Generate social media posts:

[0362] The server generates a draft social media post based on the analysis results and the recognized emotions, such as "Today is December 25th, Christmas party day! I've started a new project and I'm having a lot of fun. I'll post a photo of my Christmas tree. Christmas, new project, fun time."

[0363] User corrections and final posts

[0364] 1. The device displays the generated draft to the user and provides an interface for confirmation and correction. The user checks the displayed content and makes corrections as necessary. For example, the user could correct it to "Today is December 25th, Christmas party day! We've started a new project and are having a lot of fun. I'll post a photo of the Christmas tree. Christmas, new project, fun time."

[0365] 2. The device sends the corrected content to the server, which then performs a final check and automatically posts the content using a social media API. For example, the confirmed content is posted to a social media platform via the Twitter API or Instagram API.

[0366] Prompt Sentence Examples

[0367] Below is a specific example of a prompt sentence to input to a generative AI model.

[0368] Prompt statement:

[0369] The user has a schedule for "December 25th: Christmas Party." He also received an email saying "A new project has started," which makes him excited. A recent photo he took is of a Christmas tree. Based on this information, generate content to post on social media.

[0370] As a result, this system reduces the burden on users while enabling high-quality, timely SNS posts that accurately reflect their emotions.

[0371] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0372] Step 1:

[0373] The terminal collects the user's schedule data.

[0374] Specific operation: The device uses the Google Calendar API to retrieve event information stored in the user's calendar.

[0375] Input: Calendar API access key and user ID

[0376] Data processing: Converts event information obtained from the calendar API into JSON format.

[0377] Output: Schedule data in JSON format (e.g. "December 25th: Christmas Party")

[0378] Step 2:

[0379] The terminal collects the user's email data.

[0380] What happens: The device uses the Gmail API to access the user's inbox and retrieve the most recent emails.

[0381] Input: Gmail API access key and user ID

[0382] Data processing: The acquired email data is converted into text format and important keywords and topics are extracted using natural language processing (NLP) techniques.

[0383] Output: Analyzed keywords and topics, along with associated sentiment data (e.g., "Project started" and "Excited" from an email saying "A new project has started")

[0384] Step 3:

[0385] The terminal collects the user's photo data.

[0386] What happens: The device accesses the photo gallery to retrieve the most recently taken photos.

[0387] Input: Photo Gallery Access Permission

[0388] Data processing: Analyze the metadata of the acquired photos (date and time of shooting, location information) and use image analysis technology to recognize the content of the photos.

[0389] Output: Emotional data related to the image content (e.g., "joy" or "blessing" from a photo of a Christmas tree)

[0390] Step 4:

[0391] The terminal transmits the collected schedule data, email data, and photo data to the server.

[0392] Specific operation: The device packages the data it previously collected into JSON format and sends it to the server using an HTTP request.

[0393] Input: Schedule data, email data, photo data

[0394] Data processing: Consolidate the data into a single JSON file.

[0395] Output: Consolidated data sent to the server

[0396] Step 5:

[0397] The server analyzes the received data.

[0398] How it works: The server uses natural language processing technology and a sentiment analysis engine to extract important events and topics from schedule data, email data, and photo data, and recognizes the user's emotions.

[0399] Input: Integrated data sent from the device

[0400] Data processing: Analyze the data and integrate the events, keywords, and sentiment extracted from each data source.

[0401] Output: Analysis results and emotion information

[0402] Step 6:

[0403] The server generates a draft of the SNS post based on the analysis results.

[0404] How it works: The server uses the analysis results to generate a draft of the content for the social media post. Using a generative AI model, the draft is created in a natural-sounding sentence format.

[0405] Input: Analysis results and emotion information

[0406] Data processing: Combining the analysis results with emotional information, we generate content for social media posts based on templates.

[0407] Output: Draft social media post (e.g., "Today is December 25th, Christmas party day! I've started a new project and am having a lot of fun. I'm posting a picture of my Christmas tree. Christmas. New project. Fun time.")

[0408] Step 7:

[0409] The terminal displays the generated draft to the user.

[0410] Specific behavior: The device provides an interface for the user to view the generated draft of the social media post.

[0411] Input: Draft content sent from the server

[0412] Data processing: None

[0413] Output: The draft content displayed on the user interface

[0414] Step 8:

[0415] The user checks the displayed draft and makes corrections as necessary.

[0416] What happens: The user reviews the draft and, if necessary, edits the text and makes corrections.

[0417] Input: Displayed draft content

[0418] Data manipulation: User-edited text

[0419] Output: Corrected draft content

[0420] Step 9:

[0421] The terminal transmits the corrected content to the server.

[0422] Specific operation: The device sends the corrected SNS post content back to the server.

[0423] Input: Corrected draft content

[0424] Data processing: None

[0425] Output: Corrections sent to the server

[0426] Step 10:

[0427] The server makes a final check of the content and automatically posts it to social media.

[0428] Specific operation: The server performs a final check and automatically posts using the specified SNS API.

[0429] Input: Corrected social media post content

[0430] Data processing: None

[0431] Output: Posted content on social media (e.g., posted via Twitter API or Instagram API)

[0432] (Application example 2)

[0433] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0434] In recent years, as marketing activities using social networking sites (SNS) have become increasingly popular, there is a particular demand for the automatic generation of personalized advertising content based on the emotions of target users. Conventional systems generate posting content without fully considering user emotions, making it difficult to achieve effective advertising. In addition, the burden on users to manually create posting content is significant. There is a demand for a system that can solve these issues and automatically generate effective advertising content that takes user emotions into account and posts it to SNS.

[0435] 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.

[0436] In this invention, the server includes a means for analyzing a user's emotions, generating advertising content based on the analysis results, and posting the advertising content to an SNS, a means for collecting the user's schedule data, email data, and photo data, and a means for extracting important keywords and topics from the email data using natural language processing technology. This enables automatic generation of personalized advertising content based on emotions and timely posting to an SNS.

[0437] "User's data" is all data related to a user, including information such as the user's schedule, emails, photos, etc.

[0438] "Collection means" refers to a method or device for acquiring a user's schedule, email, photo data, and the like.

[0439] "Means for analysis" refers to methods or devices for processing and analyzing collected data and extracting user sentiment and important keywords.

[0440] "Means for automatically generating content to post on SNS" refers to a method or device for automatically generating content to post on SNS based on the analysis results.

[0441] The "means for enabling confirmation and correction" refers to a method or device for displaying the generated posted content to the user, allowing the user to confirm the content and make corrections as necessary.

[0442] The "means for final confirmation and automatic posting to SNS" refers to a method or device for final confirmation of the corrected content to be posted and automatically posting it to SNS.

[0443] "Means for analyzing emotions" refers to a method or device for extracting and analyzing emotional information from user data.

[0444] "Means for generating advertising content and posting it on SNS" refers to a method or device for generating advertising content based on user emotions and posting it on SNS.

[0445] "Schedule data" refers to data that includes a user's calendar and schedule information.

[0446] "Email Data" means data including the content of emails received or sent by a User.

[0447] "Photo data" refers to data including photos taken by a user and their metadata (such as the date and time of the photo and location information).

[0448] "Natural language processing technology" is a technology for analyzing and processing text and language data and understanding their content.

[0449] A "keyword or topic extraction tool" is a method or device for extracting significant words or themes from email data or other documents.

[0450] The present invention relates to a system that collects and analyzes a user's schedule data, email data, and photo data, understands the user's emotions using an emotion engine, and automatically generates social media posting content and advertising content. The system is composed of a terminal, a server, and an social media platform. A specific embodiment of this system is described below.

[0451] Data collection

[0452] The user's device collects the user's schedule data through a calendar application (e.g., calendar API), retrieves email data using an email client, and extracts important keywords and topics using natural language processing techniques. Additionally, photo data is collected from the user's photo gallery, and metadata and image analysis techniques are used to understand the content of the photos.

[0453] Data analysis

[0454] The server receives the collected data and analyzes user sentiment using a sentiment analysis engine (e.g., scikit-learn or a logistic regression model). Based on the analysis results, it generates social media posting content and advertising content. The generated content is displayed on the user's device, where the user can review and correct it.

[0455] Social media posts

[0456] The posting content and advertising content that the user has finally confirmed are sent to the server, which then automatically posts the content to the SNS platform using the SNS API (e.g., Twitter API, Instagram API).

[0457] Hardware and Software Used

[0458] Use a calendar API (e.g., Google Calendar API) to retrieve schedule data.

[0459] Email data is analyzed using email clients and natural language processing techniques.

[0460] Analyze the photo data using image analysis techniques (e.g., PIL library).

[0461] The sentiment analysis engine uses scikit-learn's Logistic Regression model.

[0462] Use social media APIs to post the final content to social media platforms.

[0463] Specific examples

[0464] For example, event information such as "December 25th: Christmas Party" is obtained from the user's calendar app, topics such as "A new project has started" are extracted from received emails, and "Photos of Christmas trees" are collected from the photo gallery, and the user's emotions are analyzed as "excitement" or "joy."

[0465] By combining this data, social media post content and advertising content such as "Today is December 25th, Christmas party day! We've started a new project and are having a lot of fun. I'm posting a photo of the Christmas tree. Christmas, new project, fun times" is generated.

[0466] An example prompt might look like this:

[0467] "Build an application that analyzes the user's emotional state based on current schedule data, email data, and photo data, generates optimal advertising content, and posts it to social media."

[0468] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0469] Step 1:

[0470] The device obtains the user's schedule data from the calendar application. Specifically, it calls the calendar API to collect event data and sends it to the server. The input is the calendar API authentication information and the user ID, and the output is a list of schedule data.

[0471] Step 2:

[0472] The device retrieves the user's email data from the email client. The retrieved email data is analyzed using natural language processing technology to extract important keywords and topics. The input is the email account authentication information and the retrieved email body, and the output is the extracted keywords and topics. The analysis results are sent to the server.

[0473] Step 3:

[0474] The device collects the user's photo data from the photo gallery. It uses image analysis technology to analyze the content of the photo and extract metadata (date and time of photo capture, location information). The input is the photo gallery's authentication information and the photo file, and the output is the image content and metadata. These data are also sent to the server.

[0475] Step 4:

[0476] The server receives all the data sent from the devices and analyzes the user's emotions using a sentiment analysis engine. The sentiment analysis engine uses scikit-learn's Logistic Regression model to determine emotions from input text data and image data. The inputs are schedule data, email keywords and topics, and photo content and metadata, and the output is recognized emotional information.

[0477] Step 5:

[0478] The server automatically generates social media posts and advertising content based on the analysis results. It uses a generative AI model to create appropriate sentences according to the context. The input is emotional information and collected data, and the output is the generated post content and advertising content.

[0479] Step 6:

[0480] The terminal displays the generated post content and advertising content to the user, who can then review it and make corrections as necessary. The input is the post content and advertising content sent from the server, and the output is the final content corrected by the user.

[0481] Step 7:

[0482] The server performs a final check of the post content and advertising content corrected by the user, and automatically posts it to the SNS using the SNS API. The input is the finalized content, and the output is the result of posting to the SNS.

[0483] 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.

[0484] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0485] 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.

[0486] [Second embodiment]

[0487] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0488] 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.

[0489] 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).

[0490] 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.

[0491] 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.

[0492] 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).

[0493] 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.

[0494] 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.

[0495] 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.

[0496] 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.

[0497] 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.

[0498] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0499] This invention relates to a system that collects and analyzes users' schedule data, email data, and photo data, and automatically generates content to post to SNS. The generated content is displayed to the user, who can then check and correct it before it is automatically posted to the SNS.

[0500] System Implementation

[0501] This system consists of a terminal that collects data, a server that performs analysis, and a terminal that users use to make corrections. Users use devices that store various types of data, and the system has a means of collecting the necessary information from these devices.

[0502] Program processing flow

[0503] The terminal collects the user's schedule data, email data, and photo data, and sends the collected data to the server. The following describes how each data is processed.

[0504] 1. Collecting schedule data

[0505] The device retrieves the user's event data from the calendar app and sends it to the server via an API.

[0506] Example: Use the Google Calendar API to retrieve event information such as "December 25th: Christmas Party."

[0507] 2. Email Data Collection

[0508] The device retrieves the user's email data from the email app and filters it for important keywords.

[0509] The device uses NLP technology to extract keywords and send them to the server.

[0510] Example: Parse an email that says "A new project has been started" and extract related topics.

[0511] 3. Photo data collection

[0512] The device retrieves the most recently taken photos from the photo gallery and sends the data to the server.

[0513] The device also collects photo metadata (e.g., date taken, location information).

[0514] Example: Getting a photo of a Christmas tree and parsing its metadata.

[0515] The server analyzes the data it receives, extracting and synthesizing key information, including:

[0516] 1. Data Analysis

[0517] The server analyzes the schedule data, email data, and photo data.

[0518] Analyze schedule event information to extract specific dates or events.

[0519] Natural language processing techniques are used to process email data and extract important topics and keywords.

[0520] Image analysis technology is used to analyze photo data and extract the content and related information of the photo.

[0521] 2. Generating social media posts

[0522] The server generates a draft of the SNS post based on the analysis results.

[0523] The generated draft will be written in natural language and will combine information from schedules, emails, and photos.

[0524] Example: Integrate events based on the user's schedule, the launch of a new project via email, and recently taken photos to generate a draft titled "Today is December 25th, Christmas party day! A new project has also started. I'll post a photo of the Christmas tree. Christmas New Project."

[0525] The terminal displays the generated draft to the user, who can then check the contents and make corrections as necessary.

[0526] 1. Viewing and correcting drafts

[0527] The terminal displays the draft to the user and provides an interface that allows revisions to be made.

[0528] Users can view the draft and make edits or corrections.

[0529] Example: User reviews draft and amends it to "Today is December 25th, Christmas party day! I've started a new project and am having a lot of fun. I'm going to post a picture of my Christmas tree. Christmas New project Fun times."

[0530] Finally, the device sends the corrected post content to the server, which then performs a final check and automatically posts it to the social networking site.

[0531] 1. Final confirmation and automatic posting

[0532] The terminal transmits the corrected content to the server.

[0533] The server performs a final check of the content and automatically posts it using the SNS API.

[0534] Example: The confirmed post content is actually posted to social media via the Twitter API or Instagram API.

[0535] In this way, this system reduces the burden on users and enables them to post to social media in a timely and effective manner.

[0536] The processing flow will be explained below.

[0537] Step 1:

[0538] The device collects the user's schedule data. The device calls the calendar app's API to obtain the user's event information. This information includes events for the next week and specific dates.

[0539] Step 2:

[0540] The device collects email data. The device accesses a mail server (for example, by connecting using the IMAP protocol) and retrieves new emails from the user's mailbox. The retrieved email data is then analyzed using natural language processing technology to extract important keywords and topics.

[0541] Step 3:

[0542] The device accesses the user's photo gallery and collects recently taken photos, along with the photo's metadata (e.g., the date and time of the photo, location information), and uses image analysis technology to recognize and tag the content of each photo.

[0543] Step 4:

[0544] The schedule data, email data, and photo data collected by the device are sent to the server in a single packet using a secure protocol.

[0545] Step 5:

[0546] The server analyzes the schedule data it receives. It extracts detailed information such as the date, time, name, and location of the event, and evaluates the importance of the event. For example, it extracts event information such as "December 25th: Christmas Party."

[0547] Step 6:

[0548] The server analyzes the received email data using natural language processing technology to extract important keywords and topics, evaluates the importance of the email based on the extracted keywords and topics, and determines the content to include in the social media post.

[0549] Step 7:

[0550] The server uses image analysis technology to analyze the received photo data and recognize the content of the photo. Based on the recognized content, tags and captions are generated for each photo. For example, a photo of a Christmas tree is tagged as "Christmas tree."

[0551] Step 8:

[0552] The server generates a draft of a social media post based on the analysis results. It integrates the collected and analyzed schedule, email, and photo data to generate an automatically generated text. The generated text is structured based on a template.

[0553] Step 9:

[0554] The server sends the generated draft to the terminal, which displays it to the user so that the user can check and correct it. The UI (user interface) includes a text editor for editing, allowing the user to freely edit the text.

[0555] Step 10:

[0556] The user reviews the draft and makes corrections as necessary, such as changing expressions or adding new hashtags. After reviewing, the user confirms the corrected text.

[0557] Step 11:

[0558] The terminal sends the corrected content to the server using a secure protocol.

[0559] Step 12:

[0560] The server performs a final check of the corrected post content received and automatically posts it to social media. The post is actually made using a social media API (e.g., Twitter API, Instagram API). For example, the following content might be automatically posted: "Today is December 25th, Christmas party day! We've started a new project and are having a lot of fun. I'll post a photo of the Christmas tree. Christmas, new project, fun times."

[0561] These are the specific processing steps of this system. By using this system, users can post to SNS at the appropriate time without any burden.

[0562] Example 1

[0563] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0564] In modern society, posting to social networking sites has become a widespread means of disseminating information, but busy daily schedules make it difficult to post regularly and effectively. In particular, as users' schedules and activities become more and more varied, creating content to post to social networking sites requires a great deal of time and effort. Furthermore, gathering necessary information from multiple data sources (schedules, emails, photos, etc.) and integrating it to create content to post places a significant burden on users. Therefore, there is a need for a method that reduces users' effort and allows them to post to social networking sites efficiently and effectively.

[0565] 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.

[0566] In this invention, the server includes a means for collecting user information, a means for analyzing the collected information, and a means for automatically generating SNS post content based on the analysis results. This enables the automatic generation of integrated SNS post content based on the user's schedule information, email information, and image information. Furthermore, by using a generative AI model, the analysis results are converted into natural and attractive post content, which can then be automatically posted to the SNS after the user has confirmed and corrected it. This allows users to effectively use SNS regardless of their daily busy schedules.

[0567] "User" refers to any individual or entity that uses the System.

[0568] "Information" refers to data related to a user, and specifically includes schedule information, e-mail information, and image information.

[0569] "Collection methods" refers to methods for securing and obtaining user information, including calendar apps, email apps, photo galleries, etc.

[0570] "Analysis methods" refer to methods used to analyze collected information and extract important keywords and topics, including natural language processing and image analysis techniques.

[0571] "Generation means" refers to a method for automatically generating a draft of the content of a SNS post from the analyzed data.

[0572] "SNS post content" refers to a post that includes text and images generated based on user information.

[0573] "Correctable interface" refers to an interface that allows users to check and edit the content of SNS posts they have created.

[0574] "SNS API" refers to an application programming interface that allows a program to access a specific SNS platform and automatically post to it.

[0575] "Natural language processing technology" is a technology for analyzing text data such as email information and extracting important keywords and topics.

[0576] "Image analysis technology" is a technology for analyzing image data and extracting its contents and related information.

[0577] A "generative AI model" refers to an artificial intelligence model that automatically generates sentences written in natural language based on given data.

[0578] MODE FOR CARRYING OUT THE INVENTION

[0579] This invention is a system that collects and analyzes users' schedule information, email information, and image information, and automatically generates content to post on social media based on this information. The system consists of a terminal that collects user information, a server that analyzes the collected information, and a terminal on which the user can make corrections.

[0580] Hardware and software used

[0581] Device:

[0582] Hardware: devices such as smartphones, tablets, and computers

[0583] Software: Calendar apps (e.g., Google Calendar), email apps (e.g., general email clients), photo gallery apps, natural language processing libraries (e.g., spaCy), image analysis libraries (e.g., OpenCV)

[0584] server:

[0585] Hardware: Cloud server (e.g. AWS, Google Cloud Platform)

[0586] Software: Data analysis engines, generative AI models (e.g., GPT-3), social media APIs (e.g., Twitter API, Instagram API)

[0587] System processing flow

[0588] Data collection:

[0589] The device retrieves schedule information from the user's calendar app via API. For example, if a user adds an event called "December 25th: Christmas Party" to Google Calendar, the device retrieves this information.

[0590] The device retrieves email data from the email app and uses natural language processing technology to extract important keywords and topics, such as an email that says, "A new project has been started."

[0591] The device retrieves recently taken photos from the photo gallery along with their metadata (date taken, location, etc.), including a photo of a Christmas tree, for example.

[0592] Data transmission and analysis:

[0593] The devices send the collected data over the Internet to a server, which analyzes the data and consolidates the information.

[0594] The server extracts specific dates and events from the schedule data, analyzes email data using natural language processing techniques, and uses image analysis techniques on photo data.

[0595] Generate social media posts:

[0596] The server uses a generative AI model (e.g., GPT-3) based on the analysis results to generate a draft of the content to post on social media. For example, it generates a sentence such as, "Today is December 25th, Christmas party day! A new project has also started. I'll post a photo of the Christmas tree. Christmas New Project."

[0597] View and correct drafts:

[0598] The terminal displays the generated draft to the user and provides an interface that allows the user to check and correct it.

[0599] The user can review the generated draft and make corrections as necessary. For example, they could correct it to "Today is December 25th, Christmas party day! We've started a new project and are having a lot of fun. I'll post a picture of the Christmas tree. Christmas, new project, fun time."

[0600] Final check and auto-post:

[0601] The device sends the corrected post content to the server.

[0602] The server performs a final check of the content and automatically posts it to SNS using the SNS API.

[0603] Examples of concrete examples and prompts

[0604] Examples:

[0605] "December 25th: Christmas Party" has been added to the user's schedule.

[0606] The user has received an email with the topic "A new project has been started."

[0607] The user's recent photos include a "photo of a Christmas tree."

[0608] Example prompt for a generative AI model:

[0609] "Based on the user's schedule, email, and photo data, please generate social media post content in the following format.

[0610] Schedule: December 25th, Christmas Party

[0611] Email Topic: New project started

[0612] Photo: Christmas tree photo

[0613] Example of generated result: Today is December 25th, Christmas party day! A new project has also started. I will post a photo of the Christmas tree. Christmas New Project"

[0614] In this way, the present invention significantly reduces the burden on users and enables efficient and effective posting to SNS.

[0615] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0616] Program processing flow

[0617] Step 1:

[0618] Data collection

[0619] 1.1 Schedule data collection:

[0620] The device collects schedule information and obtains event information through a calendar app (e.g., Google Calendar) installed on the user's device. Specifically, the device calls the Google Calendar API to obtain event data stored in the user's calendar. The input for this operation is the API key and user ID, and the output is the schedule information registered in the calendar.

[0621] Input: API key, user ID

[0622] Output: Event information such as "December 25th: Christmas Party"

[0623] 1.2 Email Data Collection:

[0624] The device collects email data from the email app. The device accesses the user's mailbox and retrieves new or relevant emails. NLP techniques are used to extract important keywords and topics from the email body. The input for this operation is email account information and a rule set (e.g., "new project"), and the output is the analyzed keywords and topics.

[0625] Input: Email account information, rule set

[0626] Output: Important keywords and topics, such as "A new project has been launched."

[0627] 1.3 Photographic Data Collection:

[0628] The device collects recently taken photos and their metadata (such as the date of taking the photos, location information, etc.) from the photo gallery. The input of this operation is the path to the photo folder and access permissions, and the output is the photo data and its metadata.

[0629] Input: Path to photo folder, access permissions

[0630] Output: A photo of a Christmas tree and metadata such as "Date taken: December 25th, Location: Home"

[0631] Step 2:

[0632] Sending data

[0633] 2.1 Data transmission to the server:

[0634] The terminal transmits the collected schedule data, email data, and photo data to the server via the Internet. The input of this operation is the collected data, and the output is the transmission completion status.

[0635] Input: Schedule data, email data, photo data

[0636] Output: Data transmission completion status

[0637] Step 3:

[0638] Data analysis

[0639] 3.1 Schedule Data Analysis:

[0640] The server analyzes the received schedule data and extracts specific date and event information. Specifically, the server parses the schedule data and identifies important dates and event names. The input of this operation is the schedule data, and the output is the analyzed event information.

[0641] Input: Schedule data

[0642] Output: Event information such as "December 25th: Christmas Party"

[0643] 3.2 Email Data Analysis:

[0644] The server uses natural language processing techniques (e.g., spaCy) to analyze the email data and extract important keywords and topics. The input of this operation is the email data, and the output is the extracted keywords and topics.

[0645] Input: Email data

[0646] Output: Important keywords, such as "new project"

[0647] 3.3 Image analysis:

[0648] The server analyzes the photo data using image analysis techniques (e.g., OpenCV) to extract its content and metadata. The input to this operation is the photo data and metadata, and the output is the extracted image content information.

[0649] Input: Photo data, metadata

[0650] Output: Information such as "Christmas tree", "Photo date: December 25th, Location: Home", etc.

[0651] Step 4:

[0652] Generating social media posts

[0653] 4.1 Social Media Post Generation:

[0654] The server integrates all the analyzed data and generates a draft of the social media post using a generative AI model (e.g., GPT-3). The input of this operation is the analyzed events, keywords, and image information, and the output is the social media post draft.

[0655] Input: Analyzed event information, keywords, image information

[0656] Output: A draft social media post that reads, "Today is December 25th, Christmas party day! A new project has started. I'll post a picture of my Christmas tree. Christmas New Project."

[0657] Step 5:

[0658] Viewing and correcting drafts

[0659] 5.1 Viewing and correcting drafts:

[0660] The terminal displays the generated SNS post draft to the user and provides an interface for correction. The user uses this interface to check and correct the draft. The input of the operation is the generated SNS post draft, and the output is the draft corrected by the user.

[0661] Input: Generated social media post draft

[0662] Output: Revised social media post draft (e.g., "Today is December 25th, Christmas party day! I've started a new project and am having a lot of fun. I'm posting a picture of my Christmas tree. Christmas. New project. Fun time.")

[0663] Step 6:

[0664] Final confirmation and automatic posting

[0665] 6.1 Final Review and Submission:

[0666] The device sends the corrected post content to the server, which then performs a final check using the SNS API and automatically posts it to the SNS. The input for the operation is the corrected SNS post draft, and the output is the status of the post completion to the SNS.

[0667] Input: Revised social media post draft

[0668] Output: SNS post completion status

[0669] (Application example 1)

[0670] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0671] In today's world, posting to social media accounts for an important marketing strategy, but manually posting requires time and effort. Furthermore, in certain fields, such as the food delivery industry, continuous and timely information transmission is required, placing a significant burden on users. This invention aims to solve these problems by providing a system that automatically generates social media posting content and allows users to easily review and correct the content.

[0672] 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.

[0673] In this invention, the server includes means for collecting user data, means for analyzing the collected data, means for automatically generating SNS post content based on the analysis results, means for displaying the generated post content to the user so that the user can confirm and correct it, means for final confirmation of the corrected post content and automatically posting it to the SNS, means for integrating various electronic data and using natural language processing and image analysis techniques to generate SNS post content, and means for providing a correction interface on the user management terminal, thereby reducing the burden on the user and enabling timely and effective SNS posting.

[0674] "User data" refers to various information related to a user, such as the user's schedule data, email data, and photo data, which are stored on an electronic device.

[0675] "Collection means" refers to the software and hardware components used to obtain the required data from a user's device.

[0676] "Means of analysis" refers to algorithms and analytical techniques used to integrate and filter matching data based on collected data.

[0677] "Means for generating content to post on SNS" refers to a program that automatically creates text and media content to post on SNS on behalf of the user based on the analysis results.

[0678] "Means for enabling review and correction" refers to a software configuration that provides an interface for users to review the generated posted content and edit or correct it as necessary.

[0679] "Means for final confirmation and automatic posting to SNS" refers to the API and program for final confirmation of the corrected data and automatically posting it through the user's SNS account.

[0680] "Natural language processing technology" refers to artificial intelligence technology for analyzing text data and extracting meaningful information.

[0681] "Image analysis technology" refers to technology for analyzing photographs and image data and extracting their content and metadata.

[0682] "User management terminal" refers to a device owned by a user and used for configuration and operation.

[0683] "Correction interface" refers to a software configuration that provides a user-friendly screen for users to correct and edit the content of their generated SNS posts.

[0684] "Means for automatically obtaining new menu and promotional information" refers to APIs and data mining technologies for automatically collecting information on new products and campaigns offered by food delivery services and other services.

[0685] This invention is a system that collects and analyzes user data and automatically generates content to post on social media. The system of this invention is composed of a user terminal for collecting data, a server for analyzing the data and generating content to post on social media, and a terminal for providing a user correction interface.

[0686] 1. Overall system configuration

[0687] The system relies on the following components:

[0688] User terminal: A device for collecting user schedule data, email data, photo data, new menus and promotional information.

[0689] Server: Has the core function of analyzing collected data and automatically generating content to post on social media.

[0690] Terminal that provides the correction interface: A device that displays the generated SNS post content to the user and allows them to check and correct it.

[0691] 2. Data Collection

[0692] First, the user's device retrieves the user's schedule data using the Google Calendar API. Email data is collected via an email client API, and then important keywords and topics are extracted using NLP technology. For photo data, the Google Photos API is used to collect recently taken photos and their metadata. Additionally, new menu items and promotional information from food delivery services are automatically retrieved.

[0693] 3. Data Analysis

[0694] The collected data is sent to a server for analysis. The server uses NLP technology to extract important keywords and topics from the email data, and image analysis technology to analyze the content of the photo data. This generates integrated information from the user's schedule, email content, photo data, new menus, and promotional information.

[0695] 4.Generate social media posting content

[0696] The server automatically generates the content to post on social media based on the analysis results. For example, the generated post might look something like, "Today is December 25th, Christmas party day! I've started a new project and am having a lot of fun. I'll post a photo of my Christmas tree. Christmas, new project, fun time." Natural language generation technology is used to generate this content.

[0697] 5. Viewing and correcting drafts

[0698] An interface for displaying the generated draft is provided on the user's device. The user can check the draft content through this interface and make corrections as necessary. For example, a user can change the content "Today is December 25th, Christmas party day! A new project has started and I'm having a lot of fun. I'll post a picture of the Christmas tree. Christmas New project Fun time" to "Today is December 25th, Christmas party day! A new project has started and I'm having a lot of fun. I'll post a picture of the Christmas tree. Christmas New project Fun time."

[0699] 6. Final confirmation and automatic posting

[0700] The corrected content is sent back to the server, where it is checked for final approval and then automatically posted to the social networking site using the social networking site API, such as Twitter or Instagram.

[0701] Example prompt sentence:

[0702] "Use calendar, email, and photo data to generate posts about the user's schedule. Analyze using the following information."

[0703] Schedule data: (event details, date and time)

[0704] Email data: (Email content)

[0705] Photo data: (date, time, location)

[0706] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0707] Step 1:

[0708] The user's device collects user data. Specifically, it uses the Google Calendar API to obtain schedule data and the email client API to obtain email data. It also uses the Google Photos API to obtain photo data and metadata, and automatically collects new menu items and promotional information for food delivery services. This input data is temporarily stored on the user's device and later sent to the server.

[0709] Step 2:

[0710] The user device sends the collected data to the server. The data sent includes schedule data, email data, photo data, and menu / promotion information. The server receives this data and prepares it for analysis.

[0711] Step 3:

[0712] The schedule data received by the server is analyzed. Specifically, event content and date information are analyzed to extract important events. For example, event information such as "December 25th: Christmas Party" is the subject of analysis. The analysis results are passed on to the next processing step.

[0713] Step 4:

[0714] The server analyzes the email data. Natural language processing (NLP) technology is used to extract important keywords and topics from the email content. For example, the keyword "new project" is extracted from an email that reads "A new project has been started." The analysis results are applied to the next processing step.

[0715] Step 5:

[0716] The server analyzes the photo data. It uses image analysis technology to determine the content of the photo and extract relevant information, including metadata (date and time of the photo, location information). For example, a photo of a Christmas tree is analyzed to determine its metadata. The analysis results are passed on to the next processing step.

[0717] Step 6:

[0718] The server integrates the collected and analyzed results and generates the content to post on social media. The generated post content is written in natural language and combines schedule, email, and photo information. For example, the generated content might read, "Today is December 25th, Christmas party day! A new project has also started. I'll post a photo of the Christmas tree. Christmas New Project." This generative AI model generates the content to post on social media based on the specified prompt.

[0719] Step 7:

[0720] The server sends the generated draft of the SNS post to the user's device. The user's device displays the draft and provides an interface that allows the user to check and correct the content. The user uses this interface to edit and modify the generated post content.

[0721] Step 8:

[0722] The user resubmits the corrected post to the server. The server receives the corrections and performs a final check. Once this check is complete, the post is automatically posted to the social networking site using the social networking site API (e.g., Twitter API or Instagram API).

[0723] 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.

[0724] This invention relates to a system that collects and analyzes a user's schedule data, email data, and photo data, understands the user's emotions using an emotion engine, and automatically generates content to post to an SNS. The generated content is displayed to the user, who can then check and correct it, before it is automatically posted to the SNS.

[0725] System Implementation

[0726] This system consists of a device that collects data, a server that analyzes the data and recognizes emotions, and a device that the user can use to make corrections. The functions of each component and specific examples of their operation are shown below.

[0727] Program processing flow

[0728] The device collects the user's schedule data, email data, and photo data, and sends the collected data to the server. The following describes how each data is processed.

[0729] 1. Collecting schedule data

[0730] The device retrieves event data from the user's calendar app and sends it to the server via an API.

[0731] Example: Use the Google Calendar API to retrieve event information such as "December 25th: Christmas Party."

[0732] 2. Email Data Collection

[0733] The device retrieves the user's email data from the email app and extracts important keywords.

[0734] The device uses NLP (natural language processing) technology to extract keywords and topics, and then uses an emotion analysis engine to recognize the user's emotions and send them to the server.

[0735] Example: Parsing an email that says "A new project has been launched" and extracting relevant topics and emotions (e.g., excitement or joy).

[0736] 3. Photo data collection

[0737] The device accesses the user's photo gallery to collect recently taken photos and obtains the photo's metadata (date and time of taking, location information).

[0738] The device uses image analysis technology and an emotion analysis engine to recognize the content and emotions of the photo and send them to the server.

[0739] Example: Analyzing a photo of a Christmas tree and recognizing associated emotions (e.g., joy and celebration).

[0740] The server analyzes the received data, extracts and synthesizes important information and user sentiment. The analysis includes:

[0741] 1. Data Analysis

[0742] The server analyzes the schedule data, email data, and photo data to extract important events and topics.

[0743] Using natural language processing technology and a sentiment analysis engine, information including user emotions is extracted and content is generated.

[0744] 2. Generating social media posts

[0745] The server generates a draft of a social media post based on the analysis results and emotional information. For example, it creates a post by combining schedule events, email topics, photo content, and associated emotions.

[0746] Example: By combining events based on the user's schedule, excitement based on sentiment analysis, and recently taken photos, we can generate a draft that reads, "Today is December 25th, Christmas party day! We've started a new project and are having a lot of fun. I'll post a photo of the Christmas tree. Christmas, new project, fun times."

[0747] The terminal displays the generated draft to the user, who can then check the contents and make corrections as necessary.

[0748] 1. Viewing and correcting drafts

[0749] The terminal displays the draft to the user and provides an interface for review and correction, allowing the user to edit the draft and make any necessary corrections.

[0750] Example: User reviews draft and amends it to "Today is December 25th, Christmas party day! I've started a new project and am having so much fun. I'm going to post a picture of my Christmas tree. Christmas New project Fun times."

[0751] Finally, the device sends the corrected post content to the server, which performs a final check and then automatically posts it to the social networking site.

[0752] 1. Final confirmation and automatic posting

[0753] The terminal transmits the corrected content to the server.

[0754] The server performs a final check of the content and automatically posts it using the SNS API.

[0755] Example: Post the confirmed content to a social networking site via a social networking site API (e.g., Twitter API, Instagram API).

[0756] In this way, the system takes into consideration the user's feelings, reduces the burden on the user, and enables timely and effective posting on social media.

[0757] The processing flow will be explained below.

[0758] Step 1:

[0759] The device collects the user's schedule data. The device calls the calendar app's API to obtain the user's event information. For example, this data might be "December 25th: Christmas Party."

[0760] Step 2:

[0761] The device collects the user's email data. It then connects to a mail server to retrieve new emails. The retrieved email data is analyzed using natural language processing technology to extract important keywords and topics. Furthermore, a sentiment analysis engine is used to recognize the sentiment of the email.

[0762] Step 3:

[0763] The device accesses the user's photo gallery and collects recently taken photos. It also obtains the photo's metadata (e.g., date and time of the photo, location information). It then uses image analysis technology and an emotion analysis engine to recognize the content of the photo and its associated emotions. For example, it can recognize emotions such as "blessing" and "happiness" from a photo of a Christmas tree.

[0764] Step 4:

[0765] The schedule data, email data, and photo data collected by the device are sent to the server in bulk as data packets using secure protocols such as SSL / TLS.

[0766] Step 5:

[0767] The server analyzes the schedule data it receives. It analyzes the event date, time, name, location, etc. in detail and evaluates their importance. For example, it extracts information such as "December 25th: Christmas Party" and marks it as an important date.

[0768] Step 6:

[0769] The server uses natural language processing technology to analyze the email data it receives and extracts important keywords and topics. It also uses a sentiment analysis engine to extract the emotion of the email (e.g., joy, sadness, excitement). For example, the emotion of an email that says, "A new project starts today," is recognized as "excitement."

[0770] Step 7:

[0771] The server uses image analysis technology to analyze the received photo data and recognize the content of the photo. It then uses an emotion analysis engine to extract the emotion associated with the photo and tag the photo. For example, a photo of a Christmas tree can be tagged with "Christmas tree" and the emotion "blessing."

[0772] Step 8:

[0773] The server generates a draft of a social media post based on the analysis results. It integrates the collected and analyzed schedule, email, and photo data to generate an automatically generated sentence. For example, it generates a draft that reads, "Today is December 25th, Christmas party day! I've started a new project and am having a lot of fun. I'll post a photo of my Christmas tree. Christmas, new project, fun time."

[0774] Step 9:

[0775] The server sends the generated draft to the terminal, which displays the draft to the user and provides an interface for the user to review and correct it. The UI includes a text editor for editing.

[0776] Step 10:

[0777] Users can review the draft and make corrections as needed. They can change the wording or add new hashtags. For example, they could correct it to "Today is December 25th, Christmas party day! I've started a new project and I'm having a lot of fun. I'm posting a picture of my Christmas tree. Christmas. New project. Fun time."

[0778] Step 11:

[0779] The terminal sends the corrected content to the server using a secure protocol.

[0780] Step 12:

[0781] The server performs a final check of the corrected post content received and automatically posts it to social media. The post is actually made using a social media API (e.g., Twitter API, Instagram API). For example, the following content might be automatically posted: "Today is December 25th, Christmas party day! We've started a new project and are having a lot of fun. I'll post a photo of the Christmas tree. Christmas, new project, fun times."

[0782] These are the specific processing steps of this system. By using this system, users can easily and timely post appropriate, emotional posts to social media.

[0783] Example 2

[0784] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0785] In today's social media, timely posting and high-quality content are important. However, it is a significant burden for users to think about appropriate content and post content that reflects their emotions every time. There is a need for a system that can reduce this burden and automatically post social media content that accurately reflects users' emotions. However, existing systems lack sufficient integration of user data and emotion analysis, which leads to issues with the quality of posted content.

[0786] 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.

[0787] In this invention, the server includes means for collecting user data, means for analyzing the collected data and recognizing emotions, and means for automatically generating content to post to an SNS based on the analysis results, thereby reducing the burden on users and enabling high-quality SNS posts that accurately reflect their emotions.

[0788] "User" refers to an individual or organization that uses the system to post on social media.

[0789] "Data" includes information such as the user's schedule data, email data, and image data.

[0790] "Means for collecting" refers to the methods and devices used to obtain the user's schedule data, email data, and image data.

[0791] "Means for analyzing" refers to the methods and devices used to analyze the acquired data, extract important keywords and topics, and further recognize emotions.

[0792] "Means for recognizing emotions" refers to technologies and systems for identifying a user's emotions based on collected data.

[0793] "Generating means" refers to a method or device for automatically generating social media posts based on the analysis results and the recognized emotions.

[0794] "Means for displaying, reviewing, and correcting" refers to a method or device that provides an interface for showing the generated post content to the user, allowing the user to review the content and edit or correct it as necessary.

[0795] "Means for final confirmation and automatic posting to SNS" refers to a method or device for final checking of the posted content confirmed and corrected by the user and automatically posting it to the SNS platform.

[0796] "Natural language processing technology" refers to technology that enables computers to understand and process human language.

[0797] "Schedule data" refers to information about appointments and events recorded on a user's calendar.

[0798] "Email data" refers to the content, subject, sender information, etc. contained in a user's email.

[0799] "Image data" refers to the contents of photos and image files taken by users, as well as their metadata.

[0800] The present invention relates to a system that collects and analyzes a user's schedule data, email data, and image data, understands the user's emotions using an emotion engine, and automatically generates content to post on an SNS. An embodiment of the program for this system will be described in detail below.

[0801] System configuration

[0802] This system consists of a terminal that collects data, a server that analyzes the data and recognizes emotions, and a terminal that allows users to make corrections.

[0803] Hardware and software used

[0804] Hardware:

[0805] Device: The user's smartphone, tablet, or personal computer

[0806] Server: Cloud server or local server

[0807] software:

[0808] Calendar API (e.g. Google Calendar API)

[0809] Email API (e.g. Gmail API)

[0810] Natural language processing technology (e.g., Python NLP libraries)

[0811] Image analysis technology

[0812] Sentiment Analysis Engine

[0813] SNS API (e.g. Twitter API, Instagram API)

[0814] Program processing

[0815] The terminal first collects the user's schedule data, email data, and photo data, and then sends them to the server. This process involves the following specific steps:

[0816] Schedule data collection:

[0817] The device uses the Google Calendar API to retrieve event information from the user's calendar. For example, event information such as "December 25th: Christmas Party" is sent to the server.

[0818] Email Data Collection:

[0819] The device uses the Gmail API to retrieve the user's email data. From the retrieved email data, it uses natural language processing (NLP) technology to extract important keywords and topics, uses a sentiment analysis engine to recognize the user's emotions, and sends the results to the server. For example, it analyzes an email that says "A new project has been launched" and extracts related topics and emotions (e.g., excitement or joy).

[0820] Photo data collection:

[0821] The device accesses the photo gallery to collect recently taken photos, obtains the metadata of the collected photos (date and time of taking, location information), and uses image analysis technology to recognize the content of the photos. The recognized content and associated emotion data are then sent to the server. For example, a photo of a Christmas tree is analyzed to recognize associated emotions (e.g., joy or celebration).

[0822] Analysis and post generation

[0823] The server analyzes and integrates the received data, specifically following the steps below.

[0824] Data Analysis:

[0825] The server uses natural language processing technology and a sentiment analysis engine to extract important events and topics from schedule data, email data, and photo data and recognize user emotions.

[0826] Generate social media posts:

[0827] The server generates a draft social media post based on the analysis results and the recognized emotions, such as "Today is December 25th, Christmas party day! I've started a new project and I'm having a lot of fun. I'll post a photo of my Christmas tree. Christmas, new project, fun time."

[0828] User corrections and final posts

[0829] 1. The device displays the generated draft to the user and provides an interface for confirmation and correction. The user checks the displayed content and makes corrections as necessary. For example, the user could correct it to "Today is December 25th, Christmas party day! We've started a new project and are having a lot of fun. I'll post a photo of the Christmas tree. Christmas, new project, fun time."

[0830] 2. The device sends the corrected content to the server, which then performs a final check and automatically posts the content using a social media API. For example, the confirmed content is posted to a social media platform via the Twitter API or Instagram API.

[0831] Prompt Sentence Examples

[0832] Below is a specific example of a prompt sentence to input to a generative AI model.

[0833] Prompt statement:

[0834] The user has a schedule for "December 25th: Christmas Party." He also received an email saying "A new project has started," which makes him excited. A recent photo he took is of a Christmas tree. Based on this information, generate content to post on social media.

[0835] As a result, this system reduces the burden on users while enabling high-quality, timely SNS posts that accurately reflect their emotions.

[0836] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0837] Step 1:

[0838] The terminal collects the user's schedule data.

[0839] Specific operation: The device uses the Google Calendar API to retrieve event information stored in the user's calendar.

[0840] Input: Calendar API access key and user ID

[0841] Data processing: Converts event information obtained from the calendar API into JSON format.

[0842] Output: Schedule data in JSON format (e.g. "December 25th: Christmas Party")

[0843] Step 2:

[0844] The terminal collects the user's email data.

[0845] What happens: The device uses the Gmail API to access the user's inbox and retrieve the most recent emails.

[0846] Input: Gmail API access key and user ID

[0847] Data processing: The acquired email data is converted into text format and important keywords and topics are extracted using natural language processing (NLP) techniques.

[0848] Output: Analyzed keywords and topics, along with associated sentiment data (e.g., "Project started" and "Excited" from an email saying "A new project has started")

[0849] Step 3:

[0850] The terminal collects the user's photo data.

[0851] What happens: The device accesses the photo gallery to retrieve the most recently taken photos.

[0852] Input: Photo Gallery Access Permission

[0853] Data processing: Analyze the metadata of the acquired photos (date and time of shooting, location information) and use image analysis technology to recognize the content of the photos.

[0854] Output: Emotional data related to the image content (e.g., "joy" or "blessing" from a photo of a Christmas tree)

[0855] Step 4:

[0856] The terminal transmits the collected schedule data, email data, and photo data to the server.

[0857] Specific operation: The device packages the data it previously collected into JSON format and sends it to the server using an HTTP request.

[0858] Input: Schedule data, email data, photo data

[0859] Data processing: Consolidate the data into a single JSON file.

[0860] Output: Consolidated data sent to the server

[0861] Step 5:

[0862] The server analyzes the received data.

[0863] How it works: The server uses natural language processing technology and a sentiment analysis engine to extract important events and topics from schedule data, email data, and photo data, and recognizes the user's emotions.

[0864] Input: Integrated data sent from the device

[0865] Data processing: Analyze the data and integrate the events, keywords, and sentiment extracted from each data source.

[0866] Output: Analysis results and emotion information

[0867] Step 6:

[0868] The server generates a draft of the SNS post based on the analysis results.

[0869] How it works: The server uses the analysis results to generate a draft of the content for the social media post. Using a generative AI model, the draft is created in a natural-sounding sentence format.

[0870] Input: Analysis results and emotion information

[0871] Data processing: Combining the analysis results with emotional information, we generate content for social media posts based on templates.

[0872] Output: Draft social media post (e.g., "Today is December 25th, Christmas party day! I've started a new project and am having a lot of fun. I'm posting a picture of my Christmas tree. Christmas. New project. Fun time.")

[0873] Step 7:

[0874] The terminal displays the generated draft to the user.

[0875] Specific behavior: The device provides an interface for the user to view the generated draft of the social media post.

[0876] Input: Draft content sent from the server

[0877] Data processing: None

[0878] Output: The draft content displayed on the user interface

[0879] Step 8:

[0880] The user checks the displayed draft and makes corrections as necessary.

[0881] What happens: The user reviews the draft and, if necessary, edits the text and makes corrections.

[0882] Input: Displayed draft content

[0883] Data manipulation: User-edited text

[0884] Output: Corrected draft content

[0885] Step 9:

[0886] The terminal transmits the corrected content to the server.

[0887] Specific operation: The device sends the corrected SNS post content back to the server.

[0888] Input: Corrected draft content

[0889] Data processing: None

[0890] Output: Corrections sent to the server

[0891] Step 10:

[0892] The server makes a final check of the content and automatically posts it to social media.

[0893] Specific operation: The server performs a final check and automatically posts using the specified SNS API.

[0894] Input: Corrected social media post content

[0895] Data processing: None

[0896] Output: Posted content on social media (e.g., posted via Twitter API or Instagram API)

[0897] (Application example 2)

[0898] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0899] In recent years, as marketing activities using social networking sites (SNS) have become increasingly popular, there is a particular demand for the automatic generation of personalized advertising content based on the emotions of target users. Conventional systems generate posting content without fully considering user emotions, making it difficult to achieve effective advertising. In addition, the burden on users to manually create posting content is significant. There is a demand for a system that can solve these issues and automatically generate effective advertising content that takes user emotions into account and posts it to SNS.

[0900] 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.

[0901] In this invention, the server includes a means for analyzing a user's emotions, generating advertising content based on the analysis results, and posting the advertising content to an SNS, a means for collecting the user's schedule data, email data, and photo data, and a means for extracting important keywords and topics from the email data using natural language processing technology. This enables automatic generation of personalized advertising content based on emotions and timely posting to an SNS.

[0902] "User's data" is all data related to a user, including information such as the user's schedule, emails, photos, etc.

[0903] "Collection means" refers to a method or device for acquiring a user's schedule, email, photo data, and the like.

[0904] "Means for analysis" refers to methods or devices for processing and analyzing collected data and extracting user sentiment and important keywords.

[0905] "Means for automatically generating content to post on SNS" refers to a method or device for automatically generating content to post on SNS based on the analysis results.

[0906] The "means for enabling confirmation and correction" refers to a method or device for displaying the generated posted content to the user, allowing the user to confirm the content and make corrections as necessary.

[0907] The "means for final confirmation and automatic posting to SNS" refers to a method or device for final confirmation of the corrected content to be posted and automatically posting it to SNS.

[0908] "Means for analyzing emotions" refers to a method or device for extracting and analyzing emotional information from user data.

[0909] "Means for generating advertising content and posting it on SNS" refers to a method or device for generating advertising content based on user emotions and posting it on SNS.

[0910] "Schedule data" refers to data that includes a user's calendar and schedule information.

[0911] "Email Data" means data including the content of emails received or sent by a User.

[0912] "Photo data" refers to data including photos taken by a user and their metadata (such as the date and time of the photo and location information).

[0913] "Natural language processing technology" is a technology for analyzing and processing text and language data and understanding their content.

[0914] A "keyword or topic extraction tool" is a method or device for extracting significant words or themes from email data or other documents.

[0915] The present invention relates to a system that collects and analyzes a user's schedule data, email data, and photo data, understands the user's emotions using an emotion engine, and automatically generates social media posting content and advertising content. The system is composed of a terminal, a server, and an social media platform. A specific embodiment of this system is described below.

[0916] Data collection

[0917] The user's device collects the user's schedule data through a calendar application (e.g., calendar API), retrieves email data using an email client, and extracts important keywords and topics using natural language processing techniques. Additionally, photo data is collected from the user's photo gallery, and metadata and image analysis techniques are used to understand the content of the photos.

[0918] Data analysis

[0919] The server receives the collected data and analyzes user sentiment using a sentiment analysis engine (e.g., scikit-learn or a logistic regression model). Based on the analysis results, it generates social media posting content and advertising content. The generated content is displayed on the user's device, where the user can review and correct it.

[0920] Social media posts

[0921] The posting content and advertising content that the user has finally confirmed are sent to the server, which then automatically posts the content to the SNS platform using the SNS API (e.g., Twitter API, Instagram API).

[0922] Hardware and Software Used

[0923] Use a calendar API (e.g., Google Calendar API) to retrieve schedule data.

[0924] Email data is analyzed using email clients and natural language processing techniques.

[0925] Analyze the photo data using image analysis techniques (e.g., PIL library).

[0926] The sentiment analysis engine uses scikit-learn's Logistic Regression model.

[0927] Use social media APIs to post the final content to social media platforms.

[0928] Specific examples

[0929] For example, event information such as "December 25th: Christmas Party" is obtained from the user's calendar app, topics such as "A new project has started" are extracted from received emails, and "Photos of Christmas trees" are collected from the photo gallery, and the user's emotions are analyzed as "excitement" or "joy."

[0930] By combining this data, social media post content and advertising content such as "Today is December 25th, Christmas party day! We've started a new project and are having a lot of fun. I'm posting a photo of the Christmas tree. Christmas, new project, fun times" is generated.

[0931] An example prompt might look like this:

[0932] "Build an application that analyzes the user's emotional state based on current schedule data, email data, and photo data, generates optimal advertising content, and posts it to social media."

[0933] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0934] Step 1:

[0935] The device obtains the user's schedule data from the calendar application. Specifically, it calls the calendar API to collect event data and sends it to the server. The input is the calendar API authentication information and the user ID, and the output is a list of schedule data.

[0936] Step 2:

[0937] The device retrieves the user's email data from the email client. The retrieved email data is analyzed using natural language processing technology to extract important keywords and topics. The input is the email account authentication information and the retrieved email body, and the output is the extracted keywords and topics. The analysis results are sent to the server.

[0938] Step 3:

[0939] The device collects the user's photo data from the photo gallery. It uses image analysis technology to analyze the content of the photo and extract metadata (date and time of photo capture, location information). The input is the photo gallery's authentication information and the photo file, and the output is the image content and metadata. These data are also sent to the server.

[0940] Step 4:

[0941] The server receives all the data sent from the devices and analyzes the user's emotions using a sentiment analysis engine. The sentiment analysis engine uses scikit-learn's Logistic Regression model to determine emotions from input text data and image data. The inputs are schedule data, email keywords and topics, and photo content and metadata, and the output is recognized emotional information.

[0942] Step 5:

[0943] The server automatically generates social media posts and advertising content based on the analysis results. It uses a generative AI model to create appropriate sentences according to the context. The input is emotional information and collected data, and the output is the generated post content and advertising content.

[0944] Step 6:

[0945] The terminal displays the generated post content and advertising content to the user, who can then review it and make corrections as necessary. The input is the post content and advertising content sent from the server, and the output is the final content corrected by the user.

[0946] Step 7:

[0947] The server performs a final check of the post content and advertising content corrected by the user, and automatically posts it to the SNS using the SNS API. The input is the finalized content, and the output is the result of posting to the SNS.

[0948] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating 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.

[0949] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0950] 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.

[0951] [Third embodiment]

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

[0953] 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.

[0954] 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).

[0955] 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.

[0956] 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.

[0957] 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).

[0958] 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.

[0959] 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.

[0960] 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.

[0961] 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.

[0962] 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.

[0963] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[0964] This invention relates to a system that collects and analyzes users' schedule data, email data, and photo data, and automatically generates content to post to SNS. The generated content is displayed to the user, who can then check and correct it before it is automatically posted to the SNS.

[0965] System Implementation

[0966] This system consists of a terminal that collects data, a server that performs analysis, and a terminal that users use to make corrections. Users use devices that store various types of data, and the system has a means of collecting the necessary information from these devices.

[0967] Program processing flow

[0968] The terminal collects the user's schedule data, email data, and photo data, and sends the collected data to the server. The following describes how each data is processed.

[0969] 1. Collecting schedule data

[0970] The device retrieves the user's event data from the calendar app and sends it to the server via an API.

[0971] Example: Use the Google Calendar API to retrieve event information such as "December 25th: Christmas Party."

[0972] 2. Email Data Collection

[0973] The device retrieves the user's email data from the email app and filters it for important keywords.

[0974] The device uses NLP technology to extract keywords and send them to the server.

[0975] Example: Parse an email that says "A new project has been started" and extract related topics.

[0976] 3. Photo data collection

[0977] The device retrieves the most recently taken photos from the photo gallery and sends the data to the server.

[0978] The device also collects photo metadata (e.g., date taken, location information).

[0979] Example: Getting a photo of a Christmas tree and parsing its metadata.

[0980] The server analyzes the data it receives, extracting and synthesizing key information, including:

[0981] 1. Data Analysis

[0982] The server analyzes the schedule data, email data, and photo data.

[0983] Analyze schedule event information to extract specific dates or events.

[0984] Natural language processing techniques are used to process email data and extract important topics and keywords.

[0985] Image analysis technology is used to analyze photo data and extract the content and related information of the photo.

[0986] 2. Generating social media posts

[0987] The server generates a draft of the SNS post based on the analysis results.

[0988] The generated draft will be written in natural language and will combine information from schedules, emails, and photos.

[0989] Example: Integrate events based on the user's schedule, the launch of a new project via email, and recently taken photos to generate a draft titled "Today is December 25th, Christmas party day! A new project has also started. I'll post a photo of the Christmas tree. Christmas New Project."

[0990] The terminal displays the generated draft to the user, who can then check the contents and make corrections as necessary.

[0991] 1. Viewing and correcting drafts

[0992] The terminal displays the draft to the user and provides an interface that allows revisions to be made.

[0993] Users can view the draft and make edits or corrections.

[0994] Example: User reviews draft and amends it to "Today is December 25th, Christmas party day! I've started a new project and am having a lot of fun. I'm going to post a picture of my Christmas tree. Christmas New project Fun times."

[0995] Finally, the device sends the corrected post content to the server, which then performs a final check and automatically posts it to the social networking site.

[0996] 1. Final confirmation and automatic posting

[0997] The terminal transmits the corrected content to the server.

[0998] The server performs a final check of the content and automatically posts it using the SNS API.

[0999] Example: The confirmed post content is actually posted to social media via the Twitter API or Instagram API.

[1000] In this way, this system reduces the burden on users and enables them to post to social media in a timely and effective manner.

[1001] The processing flow will be explained below.

[1002] Step 1:

[1003] The device collects the user's schedule data. The device calls the calendar app's API to obtain the user's event information. This information includes events for the next week and specific dates.

[1004] Step 2:

[1005] The device collects email data. The device accesses a mail server (for example, by connecting using the IMAP protocol) and retrieves new emails from the user's mailbox. The retrieved email data is then analyzed using natural language processing technology to extract important keywords and topics.

[1006] Step 3:

[1007] The device accesses the user's photo gallery and collects recently taken photos, along with the photo's metadata (e.g., the date and time of the photo, location information), and uses image analysis technology to recognize and tag the content of each photo.

[1008] Step 4:

[1009] The schedule data, email data, and photo data collected by the device are sent to the server in a single packet using a secure protocol.

[1010] Step 5:

[1011] The server analyzes the schedule data it receives. It extracts detailed information such as the date, time, name, and location of the event, and evaluates the importance of the event. For example, it extracts event information such as "December 25th: Christmas Party."

[1012] Step 6:

[1013] The server analyzes the received email data using natural language processing technology to extract important keywords and topics, evaluates the importance of the email based on the extracted keywords and topics, and determines the content to include in the social media post.

[1014] Step 7:

[1015] The server uses image analysis technology to analyze the received photo data and recognize the content of the photo. Based on the recognized content, tags and captions are generated for each photo. For example, a photo of a Christmas tree is tagged as "Christmas tree."

[1016] Step 8:

[1017] The server generates a draft of a social media post based on the analysis results. It integrates the collected and analyzed schedule, email, and photo data to generate an automatically generated text. The generated text is structured based on a template.

[1018] Step 9:

[1019] The server sends the generated draft to the terminal, which displays it to the user so that the user can check and correct it. The UI (user interface) includes a text editor for editing, allowing the user to freely edit the text.

[1020] Step 10:

[1021] The user reviews the draft and makes corrections as necessary, such as changing expressions or adding new hashtags. After reviewing, the user confirms the corrected text.

[1022] Step 11:

[1023] The terminal sends the corrected content to the server using a secure protocol.

[1024] Step 12:

[1025] The server performs a final check of the corrected post content received and automatically posts it to social media. The post is actually made using a social media API (e.g., Twitter API, Instagram API). For example, the following content might be automatically posted: "Today is December 25th, Christmas party day! We've started a new project and are having a lot of fun. I'll post a photo of the Christmas tree. Christmas, new project, fun times."

[1026] These are the specific processing steps of this system. By using this system, users can post to SNS at the appropriate time without any burden.

[1027] Example 1

[1028] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1029] In modern society, posting to social networking sites has become a widespread means of disseminating information, but busy daily schedules make it difficult to post regularly and effectively. In particular, as users' schedules and activities become more and more varied, creating content to post to social networking sites requires a great deal of time and effort. Furthermore, gathering necessary information from multiple data sources (schedules, emails, photos, etc.) and integrating it to create content to post places a significant burden on users. Therefore, there is a need for a method that reduces users' effort and allows them to post to social networking sites efficiently and effectively.

[1030] 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.

[1031] In this invention, the server includes a means for collecting user information, a means for analyzing the collected information, and a means for automatically generating SNS post content based on the analysis results. This enables the automatic generation of integrated SNS post content based on the user's schedule information, email information, and image information. Furthermore, by using a generative AI model, the analysis results are converted into natural and attractive post content, which can then be automatically posted to the SNS after the user has confirmed and corrected it. This allows users to effectively use SNS regardless of their daily busy schedules.

[1032] "User" refers to any individual or entity that uses the System.

[1033] "Information" refers to data related to a user, and specifically includes schedule information, e-mail information, and image information.

[1034] "Collection methods" refers to methods for securing and obtaining user information, including calendar apps, email apps, photo galleries, etc.

[1035] "Analysis methods" refer to methods used to analyze collected information and extract important keywords and topics, including natural language processing and image analysis techniques.

[1036] "Generation means" refers to a method for automatically generating a draft of the content of a SNS post from the analyzed data.

[1037] "SNS post content" refers to a post that includes text and images generated based on user information.

[1038] "Correctable interface" refers to an interface that allows users to check and edit the content of SNS posts they have created.

[1039] "SNS API" refers to an application programming interface that allows a program to access a specific SNS platform and automatically post to it.

[1040] "Natural language processing technology" is a technology for analyzing text data such as email information and extracting important keywords and topics.

[1041] "Image analysis technology" is a technology for analyzing image data and extracting its contents and related information.

[1042] A "generative AI model" refers to an artificial intelligence model that automatically generates sentences written in natural language based on given data.

[1043] MODE FOR CARRYING OUT THE INVENTION

[1044] This invention is a system that collects and analyzes users' schedule information, email information, and image information, and automatically generates content to post on social media based on this information. The system consists of a terminal that collects user information, a server that analyzes the collected information, and a terminal on which the user can make corrections.

[1045] Hardware and software used

[1046] Device:

[1047] Hardware: devices such as smartphones, tablets, and computers

[1048] Software: Calendar apps (e.g., Google Calendar), email apps (e.g., general email clients), photo gallery apps, natural language processing libraries (e.g., spaCy), image analysis libraries (e.g., OpenCV)

[1049] server:

[1050] Hardware: Cloud server (e.g. AWS, Google Cloud Platform)

[1051] Software: Data analysis engines, generative AI models (e.g., GPT-3), social media APIs (e.g., Twitter API, Instagram API)

[1052] System processing flow

[1053] Data collection:

[1054] The device retrieves schedule information from the user's calendar app via API. For example, if a user adds an event called "December 25th: Christmas Party" to Google Calendar, the device retrieves this information.

[1055] The device retrieves email data from the email app and uses natural language processing technology to extract important keywords and topics, such as an email that says, "A new project has been started."

[1056] The device retrieves recently taken photos from the photo gallery along with their metadata (date taken, location, etc.), including a photo of a Christmas tree, for example.

[1057] Data transmission and analysis:

[1058] The devices send the collected data over the Internet to a server, which analyzes the data and consolidates the information.

[1059] The server extracts specific dates and events from the schedule data, analyzes email data using natural language processing techniques, and uses image analysis techniques on photo data.

[1060] Generate social media posts:

[1061] The server uses a generative AI model (e.g., GPT-3) based on the analysis results to generate a draft of the content to post on social media. For example, it generates a sentence such as, "Today is December 25th, Christmas party day! A new project has also started. I'll post a photo of the Christmas tree. Christmas New Project."

[1062] View and correct drafts:

[1063] The terminal displays the generated draft to the user and provides an interface that allows the user to check and correct it.

[1064] The user can review the generated draft and make corrections as necessary. For example, they could correct it to "Today is December 25th, Christmas party day! We've started a new project and are having a lot of fun. I'll post a picture of the Christmas tree. Christmas, new project, fun time."

[1065] Final check and auto-post:

[1066] The device sends the corrected post content to the server.

[1067] The server performs a final check of the content and automatically posts it to SNS using the SNS API.

[1068] Examples of concrete examples and prompts

[1069] Examples:

[1070] "December 25th: Christmas Party" has been added to the user's schedule.

[1071] The user has received an email with the topic "A new project has been started."

[1072] The user's recent photos include a "photo of a Christmas tree."

[1073] Example prompt for a generative AI model:

[1074] "Based on the user's schedule, email, and photo data, please generate social media post content in the following format.

[1075] Schedule: December 25th, Christmas Party

[1076] Email Topic: New project started

[1077] Photo: Christmas tree photo

[1078] Example of generated result: Today is December 25th, Christmas party day! A new project has also started. I will post a photo of the Christmas tree. Christmas New Project"

[1079] In this way, the present invention significantly reduces the burden on users and enables efficient and effective posting to SNS.

[1080] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1081] Program processing flow

[1082] Step 1:

[1083] Data collection

[1084] 1.1 Schedule data collection:

[1085] The device collects schedule information and obtains event information through a calendar app (e.g., Google Calendar) installed on the user's device. Specifically, the device calls the Google Calendar API to obtain event data stored in the user's calendar. The input for this operation is the API key and user ID, and the output is the schedule information registered in the calendar.

[1086] Input: API key, user ID

[1087] Output: Event information such as "December 25th: Christmas Party"

[1088] 1.2 Email Data Collection:

[1089] The device collects email data from the email app. The device accesses the user's mailbox and retrieves new or relevant emails. NLP techniques are used to extract important keywords and topics from the email body. The input for this operation is email account information and a rule set (e.g., "new project"), and the output is the analyzed keywords and topics.

[1090] Input: Email account information, rule set

[1091] Output: Important keywords and topics, such as "A new project has been launched."

[1092] 1.3 Photographic Data Collection:

[1093] The device collects recently taken photos and their metadata (such as the date of taking the photos, location information, etc.) from the photo gallery. The input of this operation is the path to the photo folder and access permissions, and the output is the photo data and its metadata.

[1094] Input: Path to photo folder, access permissions

[1095] Output: A photo of a Christmas tree and metadata such as "Date taken: December 25th, Location: Home"

[1096] Step 2:

[1097] Sending data

[1098] 2.1 Data transmission to the server:

[1099] The terminal transmits the collected schedule data, email data, and photo data to the server via the Internet. The input of this operation is the collected data, and the output is the transmission completion status.

[1100] Input: Schedule data, email data, photo data

[1101] Output: Data transmission completion status

[1102] Step 3:

[1103] Data analysis

[1104] 3.1 Schedule Data Analysis:

[1105] The server analyzes the received schedule data and extracts specific date and event information. Specifically, the server parses the schedule data and identifies important dates and event names. The input of this operation is the schedule data, and the output is the analyzed event information.

[1106] Input: Schedule data

[1107] Output: Event information such as "December 25th: Christmas Party"

[1108] 3.2 Email Data Analysis:

[1109] The server uses natural language processing techniques (e.g., spaCy) to analyze the email data and extract important keywords and topics. The input of this operation is the email data, and the output is the extracted keywords and topics.

[1110] Input: Email data

[1111] Output: Important keywords, such as "new project"

[1112] 3.3 Image analysis:

[1113] The server analyzes the photo data using image analysis techniques (e.g., OpenCV) to extract its content and metadata. The input to this operation is the photo data and metadata, and the output is the extracted image content information.

[1114] Input: Photo data, metadata

[1115] Output: Information such as "Christmas tree", "Photo date: December 25th, Location: Home", etc.

[1116] Step 4:

[1117] Generating social media posts

[1118] 4.1 Social Media Post Generation:

[1119] The server integrates all the analyzed data and generates a draft of the social media post using a generative AI model (e.g., GPT-3). The input of this operation is the analyzed events, keywords, and image information, and the output is the social media post draft.

[1120] Input: Analyzed event information, keywords, image information

[1121] Output: A draft social media post that reads, "Today is December 25th, Christmas party day! A new project has started. I'll post a picture of my Christmas tree. Christmas New Project."

[1122] Step 5:

[1123] Viewing and correcting drafts

[1124] 5.1 Viewing and correcting drafts:

[1125] The terminal displays the generated SNS post draft to the user and provides an interface for correction. The user uses this interface to check and correct the draft. The input of the operation is the generated SNS post draft, and the output is the draft corrected by the user.

[1126] Input: Generated social media post draft

[1127] Output: Revised social media post draft (e.g., "Today is December 25th, Christmas party day! I've started a new project and am having a lot of fun. I'm posting a picture of my Christmas tree. Christmas. New project. Fun time.")

[1128] Step 6:

[1129] Final confirmation and automatic posting

[1130] 6.1 Final Review and Submission:

[1131] The device sends the corrected post content to the server, which then performs a final check using the SNS API and automatically posts it to the SNS. The input for the operation is the corrected SNS post draft, and the output is the status of the post completion to the SNS.

[1132] Input: Revised social media post draft

[1133] Output: SNS post completion status

[1134] (Application example 1)

[1135] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1136] In today's world, posting to social media accounts for an important marketing strategy, but manually posting requires time and effort. Furthermore, in certain fields, such as the food delivery industry, continuous and timely information transmission is required, placing a significant burden on users. This invention aims to solve these problems by providing a system that automatically generates social media posting content and allows users to easily review and correct the content.

[1137] 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.

[1138] In this invention, the server includes means for collecting user data, means for analyzing the collected data, means for automatically generating SNS post content based on the analysis results, means for displaying the generated post content to the user so that the user can confirm and correct it, means for final confirmation of the corrected post content and automatically posting it to the SNS, means for integrating various electronic data and using natural language processing and image analysis techniques to generate SNS post content, and means for providing a correction interface on the user management terminal, thereby reducing the burden on the user and enabling timely and effective SNS posting.

[1139] "User data" refers to various information related to a user, such as the user's schedule data, email data, and photo data, which are stored on an electronic device.

[1140] "Collection means" refers to the software and hardware components used to obtain the required data from a user's device.

[1141] "Means of analysis" refers to algorithms and analytical techniques used to integrate and filter matching data based on collected data.

[1142] "Means for generating content to post on SNS" refers to a program that automatically creates text and media content to post on SNS on behalf of the user based on the analysis results.

[1143] "Means for enabling review and correction" refers to a software configuration that provides an interface for users to review the generated posted content and edit or correct it as necessary.

[1144] "Means for final confirmation and automatic posting to SNS" refers to the API and program for final confirmation of the corrected data and automatically posting it through the user's SNS account.

[1145] "Natural language processing technology" refers to artificial intelligence technology for analyzing text data and extracting meaningful information.

[1146] "Image analysis technology" refers to technology for analyzing photographs and image data and extracting their content and metadata.

[1147] "User management terminal" refers to a device owned by a user and used for configuration and operation.

[1148] "Correction interface" refers to a software configuration that provides a user-friendly screen for users to correct and edit the content of their generated SNS posts.

[1149] "Means for automatically obtaining new menu and promotional information" refers to APIs and data mining technologies for automatically collecting information on new products and campaigns offered by food delivery services and other services.

[1150] This invention is a system that collects and analyzes user data and automatically generates content to post on social media. The system of this invention is composed of a user terminal for collecting data, a server for analyzing the data and generating content to post on social media, and a terminal for providing a user correction interface.

[1151] 1. Overall system configuration

[1152] The system relies on the following components:

[1153] User terminal: A device for collecting user schedule data, email data, photo data, new menus and promotional information.

[1154] Server: Has the core function of analyzing collected data and automatically generating content to post on social media.

[1155] Terminal that provides the correction interface: A device that displays the generated SNS post content to the user and allows them to check and correct it.

[1156] 2. Data Collection

[1157] First, the user's device retrieves the user's schedule data using the Google Calendar API. Email data is collected via an email client API, and then important keywords and topics are extracted using NLP technology. For photo data, the Google Photos API is used to collect recently taken photos and their metadata. Additionally, new menu items and promotional information from food delivery services are automatically retrieved.

[1158] 3. Data Analysis

[1159] The collected data is sent to a server for analysis. The server uses NLP technology to extract important keywords and topics from the email data, and image analysis technology to analyze the content of the photo data. This generates integrated information from the user's schedule, email content, photo data, new menus, and promotional information.

[1160] 4.Generate social media posting content

[1161] The server automatically generates the content to post on social media based on the analysis results. For example, the generated post might look something like, "Today is December 25th, Christmas party day! I've started a new project and am having a lot of fun. I'll post a photo of my Christmas tree. Christmas, new project, fun time." Natural language generation technology is used to generate this content.

[1162] 5. Viewing and correcting drafts

[1163] An interface for displaying the generated draft is provided on the user's device. The user can check the draft content through this interface and make corrections as necessary. For example, a user can change the content "Today is December 25th, Christmas party day! A new project has started and I'm having a lot of fun. I'll post a picture of the Christmas tree. Christmas New project Fun time" to "Today is December 25th, Christmas party day! A new project has started and I'm having a lot of fun. I'll post a picture of the Christmas tree. Christmas New project Fun time."

[1164] 6. Final confirmation and automatic posting

[1165] The corrected content is sent back to the server, where it is checked for final approval and then automatically posted to the social networking site using the social networking site API, such as Twitter or Instagram.

[1166] Example prompt sentence:

[1167] "Use calendar, email, and photo data to generate posts about the user's schedule. Analyze using the following information."

[1168] Schedule data: (event details, date and time)

[1169] Email data: (Email content)

[1170] Photo data: (date, time, location)

[1171] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1172] Step 1:

[1173] The user's device collects user data. Specifically, it uses the Google Calendar API to obtain schedule data and the email client API to obtain email data. It also uses the Google Photos API to obtain photo data and metadata, and automatically collects new menu items and promotional information for food delivery services. This input data is temporarily stored on the user's device and later sent to the server.

[1174] Step 2:

[1175] The user device sends the collected data to the server. The data sent includes schedule data, email data, photo data, and menu / promotion information. The server receives this data and prepares it for analysis.

[1176] Step 3:

[1177] The schedule data received by the server is analyzed. Specifically, event content and date information are analyzed to extract important events. For example, event information such as "December 25th: Christmas Party" is the subject of analysis. The analysis results are passed on to the next processing step.

[1178] Step 4:

[1179] The server analyzes the email data. Natural language processing (NLP) technology is used to extract important keywords and topics from the email content. For example, the keyword "new project" is extracted from an email that reads "A new project has been started." The analysis results are applied to the next processing step.

[1180] Step 5:

[1181] The server analyzes the photo data. It uses image analysis technology to determine the content of the photo and extract relevant information, including metadata (date and time of the photo, location information). For example, a photo of a Christmas tree is analyzed to determine its metadata. The analysis results are passed on to the next processing step.

[1182] Step 6:

[1183] The server integrates the collected and analyzed results and generates the content to post on social media. The generated post content is written in natural language and combines schedule, email, and photo information. For example, the generated content might read, "Today is December 25th, Christmas party day! A new project has also started. I'll post a photo of the Christmas tree. Christmas New Project." This generative AI model generates the content to post on social media based on the specified prompt.

[1184] Step 7:

[1185] The server sends the generated draft of the SNS post to the user's device. The user's device displays the draft and provides an interface that allows the user to check and correct the content. The user uses this interface to edit and modify the generated post content.

[1186] Step 8:

[1187] The user resubmits the corrected post to the server. The server receives the corrections and performs a final check. Once this check is complete, the post is automatically posted to the social networking site using the social networking site API (e.g., Twitter API or Instagram API).

[1188] 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.

[1189] This invention relates to a system that collects and analyzes a user's schedule data, email data, and photo data, understands the user's emotions using an emotion engine, and automatically generates content to post to an SNS. The generated content is displayed to the user, who can then check and correct it, before it is automatically posted to the SNS.

[1190] System Implementation

[1191] This system consists of a device that collects data, a server that analyzes the data and recognizes emotions, and a device that the user can use to make corrections. The functions of each component and specific examples of their operation are shown below.

[1192] Program processing flow

[1193] The device collects the user's schedule data, email data, and photo data, and sends the collected data to the server. The following describes how each data is processed.

[1194] 1. Collecting schedule data

[1195] The device retrieves event data from the user's calendar app and sends it to the server via an API.

[1196] Example: Use the Google Calendar API to retrieve event information such as "December 25th: Christmas Party."

[1197] 2. Email Data Collection

[1198] The device retrieves the user's email data from the email app and extracts important keywords.

[1199] The device uses NLP (natural language processing) technology to extract keywords and topics, and then uses an emotion analysis engine to recognize the user's emotions and send them to the server.

[1200] Example: Parsing an email that says "A new project has been launched" and extracting relevant topics and emotions (e.g., excitement or joy).

[1201] 3. Photo data collection

[1202] The device accesses the user's photo gallery to collect recently taken photos and obtains the photo's metadata (date and time of taking, location information).

[1203] The device uses image analysis technology and an emotion analysis engine to recognize the content and emotions of the photo and send them to the server.

[1204] Example: Analyzing a photo of a Christmas tree and recognizing associated emotions (e.g., joy and celebration).

[1205] The server analyzes the received data, extracts and synthesizes important information and user sentiment. The analysis includes:

[1206] 1. Data Analysis

[1207] The server analyzes the schedule data, email data, and photo data to extract important events and topics.

[1208] Using natural language processing technology and a sentiment analysis engine, information including user emotions is extracted and content is generated.

[1209] 2. Generating social media posts

[1210] The server generates a draft of a social media post based on the analysis results and emotional information. For example, it creates a post by combining schedule events, email topics, photo content, and associated emotions.

[1211] Example: By combining events based on the user's schedule, excitement based on sentiment analysis, and recently taken photos, we can generate a draft that reads, "Today is December 25th, Christmas party day! We've started a new project and are having a lot of fun. I'll post a photo of the Christmas tree. Christmas, new project, fun times."

[1212] The terminal displays the generated draft to the user, who can then check the contents and make corrections as necessary.

[1213] 1. Viewing and correcting drafts

[1214] The terminal displays the draft to the user and provides an interface for review and correction, allowing the user to edit the draft and make any necessary corrections.

[1215] Example: User reviews draft and amends it to "Today is December 25th, Christmas party day! I've started a new project and am having so much fun. I'm going to post a picture of my Christmas tree. Christmas New project Fun times."

[1216] Finally, the device sends the corrected post content to the server, which performs a final check and then automatically posts it to the social networking site.

[1217] 1. Final confirmation and automatic posting

[1218] The terminal transmits the corrected content to the server.

[1219] The server performs a final check of the content and automatically posts it using the SNS API.

[1220] Example: Post the confirmed content to a social networking site via a social networking site API (e.g., Twitter API, Instagram API).

[1221] In this way, the system takes into consideration the user's feelings, reduces the burden on the user, and enables timely and effective posting on social media.

[1222] The processing flow will be explained below.

[1223] Step 1:

[1224] The device collects the user's schedule data. The device calls the calendar app's API to obtain the user's event information. For example, this data might be "December 25th: Christmas Party."

[1225] Step 2:

[1226] The device collects the user's email data. It then connects to a mail server to retrieve new emails. The retrieved email data is analyzed using natural language processing technology to extract important keywords and topics. Furthermore, a sentiment analysis engine is used to recognize the sentiment of the email.

[1227] Step 3:

[1228] The device accesses the user's photo gallery and collects recently taken photos. It also obtains the photo's metadata (e.g., date and time of the photo, location information). It then uses image analysis technology and an emotion analysis engine to recognize the content of the photo and its associated emotions. For example, it can recognize emotions such as "blessing" and "happiness" from a photo of a Christmas tree.

[1229] Step 4:

[1230] The schedule data, email data, and photo data collected by the device are sent to the server in bulk as data packets using secure protocols such as SSL / TLS.

[1231] Step 5:

[1232] The server analyzes the schedule data it receives. It analyzes the event date, time, name, location, etc. in detail and evaluates their importance. For example, it extracts information such as "December 25th: Christmas Party" and marks it as an important date.

[1233] Step 6:

[1234] The server uses natural language processing technology to analyze the email data it receives and extracts important keywords and topics. It also uses a sentiment analysis engine to extract the emotion of the email (e.g., joy, sadness, excitement). For example, the emotion of an email that says, "A new project starts today," is recognized as "excitement."

[1235] Step 7:

[1236] The server uses image analysis technology to analyze the received photo data and recognize the content of the photo. It then uses an emotion analysis engine to extract the emotion associated with the photo and tag the photo. For example, a photo of a Christmas tree can be tagged with "Christmas tree" and the emotion "blessing."

[1237] Step 8:

[1238] The server generates a draft of a social media post based on the analysis results. It integrates the collected and analyzed schedule, email, and photo data to generate an automatically generated sentence. For example, it generates a draft that reads, "Today is December 25th, Christmas party day! I've started a new project and am having a lot of fun. I'll post a photo of my Christmas tree. Christmas, new project, fun time."

[1239] Step 9:

[1240] The server sends the generated draft to the terminal, which displays the draft to the user and provides an interface for the user to review and correct it. The UI includes a text editor for editing.

[1241] Step 10:

[1242] Users can review the draft and make corrections as needed. They can change the wording or add new hashtags. For example, they could correct it to "Today is December 25th, Christmas party day! I've started a new project and I'm having a lot of fun. I'm posting a picture of my Christmas tree. Christmas. New project. Fun time."

[1243] Step 11:

[1244] The terminal sends the corrected content to the server using a secure protocol.

[1245] Step 12:

[1246] The server performs a final check of the corrected post content received and automatically posts it to social media. The post is actually made using a social media API (e.g., Twitter API, Instagram API). For example, the following content might be automatically posted: "Today is December 25th, Christmas party day! We've started a new project and are having a lot of fun. I'll post a photo of the Christmas tree. Christmas, new project, fun times."

[1247] These are the specific processing steps of this system. By using this system, users can easily and timely post appropriate, emotional posts to social media.

[1248] Example 2

[1249] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1250] In today's social media, timely posting and high-quality content are important. However, it is a significant burden for users to think about appropriate content and post content that reflects their emotions every time. There is a need for a system that can reduce this burden and automatically post social media content that accurately reflects users' emotions. However, existing systems lack sufficient integration of user data and emotion analysis, which leads to issues with the quality of posted content.

[1251] 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.

[1252] In this invention, the server includes means for collecting user data, means for analyzing the collected data and recognizing emotions, and means for automatically generating content to post to an SNS based on the analysis results, thereby reducing the burden on users and enabling high-quality SNS posts that accurately reflect their emotions.

[1253] "User" refers to an individual or organization that uses the system to post on social media.

[1254] "Data" includes information such as the user's schedule data, email data, and image data.

[1255] "Means for collecting" refers to the methods and devices used to obtain the user's schedule data, email data, and image data.

[1256] "Means for analyzing" refers to the methods and devices used to analyze the acquired data, extract important keywords and topics, and further recognize emotions.

[1257] "Means for recognizing emotions" refers to technologies and systems for identifying a user's emotions based on collected data.

[1258] "Generating means" refers to a method or device for automatically generating social media posts based on the analysis results and the recognized emotions.

[1259] "Means for displaying, reviewing, and correcting" refers to a method or device that provides an interface for showing the generated post content to the user, allowing the user to review the content and edit or correct it as necessary.

[1260] "Means for final confirmation and automatic posting to SNS" refers to a method or device for final checking of the posted content confirmed and corrected by the user and automatically posting it to the SNS platform.

[1261] "Natural language processing technology" refers to technology that enables computers to understand and process human language.

[1262] "Schedule data" refers to information about appointments and events recorded on a user's calendar.

[1263] "Email data" refers to the content, subject, sender information, etc. contained in a user's email.

[1264] "Image data" refers to the contents of photos and image files taken by users, as well as their metadata.

[1265] The present invention relates to a system that collects and analyzes a user's schedule data, email data, and image data, understands the user's emotions using an emotion engine, and automatically generates content to post on an SNS. An embodiment of the program for this system will be described in detail below.

[1266] System configuration

[1267] This system consists of a terminal that collects data, a server that analyzes the data and recognizes emotions, and a terminal that allows users to make corrections.

[1268] Hardware and software used

[1269] Hardware:

[1270] Device: The user's smartphone, tablet, or personal computer

[1271] Server: Cloud server or local server

[1272] software:

[1273] Calendar API (e.g. Google Calendar API)

[1274] Email API (e.g. Gmail API)

[1275] Natural language processing technology (e.g., Python NLP libraries)

[1276] Image analysis technology

[1277] Sentiment Analysis Engine

[1278] SNS API (e.g. Twitter API, Instagram API)

[1279] Program processing

[1280] The terminal first collects the user's schedule data, email data, and photo data, and then sends them to the server. This process involves the following specific steps:

[1281] Schedule data collection:

[1282] The device uses the Google Calendar API to retrieve event information from the user's calendar. For example, event information such as "December 25th: Christmas Party" is sent to the server.

[1283] Email Data Collection:

[1284] The device uses the Gmail API to retrieve the user's email data. From the retrieved email data, it uses natural language processing (NLP) technology to extract important keywords and topics, uses a sentiment analysis engine to recognize the user's emotions, and sends the results to the server. For example, it analyzes an email that says "A new project has been launched" and extracts related topics and emotions (e.g., excitement or joy).

[1285] Photo data collection:

[1286] The device accesses the photo gallery to collect recently taken photos, obtains the metadata of the collected photos (date and time of taking, location information), and uses image analysis technology to recognize the content of the photos. The recognized content and associated emotion data are then sent to the server. For example, a photo of a Christmas tree is analyzed to recognize associated emotions (e.g., joy or celebration).

[1287] Analysis and post generation

[1288] The server analyzes and integrates the received data, specifically following the steps below.

[1289] Data Analysis:

[1290] The server uses natural language processing technology and a sentiment analysis engine to extract important events and topics from schedule data, email data, and photo data and recognize user emotions.

[1291] Generate social media posts:

[1292] The server generates a draft social media post based on the analysis results and the recognized emotions, such as "Today is December 25th, Christmas party day! I've started a new project and I'm having a lot of fun. I'll post a photo of my Christmas tree. Christmas, new project, fun time."

[1293] User corrections and final posts

[1294] 1. The device displays the generated draft to the user and provides an interface for confirmation and correction. The user checks the displayed content and makes corrections as necessary. For example, the user could correct it to "Today is December 25th, Christmas party day! We've started a new project and are having a lot of fun. I'll post a photo of the Christmas tree. Christmas, new project, fun time."

[1295] 2. The device sends the corrected content to the server, which then performs a final check and automatically posts the content using a social media API. For example, the confirmed content is posted to a social media platform via the Twitter API or Instagram API.

[1296] Prompt Sentence Examples

[1297] Below is a specific example of a prompt sentence to input to a generative AI model.

[1298] Prompt statement:

[1299] The user has a schedule for "December 25th: Christmas Party." He also received an email saying "A new project has started," which makes him excited. A recent photo he took is of a Christmas tree. Based on this information, generate content to post on social media.

[1300] As a result, this system reduces the burden on users while enabling high-quality, timely SNS posts that accurately reflect their emotions.

[1301] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1302] Step 1:

[1303] The terminal collects the user's schedule data.

[1304] Specific operation: The device uses the Google Calendar API to retrieve event information stored in the user's calendar.

[1305] Input: Calendar API access key and user ID

[1306] Data processing: Converts event information obtained from the calendar API into JSON format.

[1307] Output: Schedule data in JSON format (e.g. "December 25th: Christmas Party")

[1308] Step 2:

[1309] The terminal collects the user's email data.

[1310] What happens: The device uses the Gmail API to access the user's inbox and retrieve the most recent emails.

[1311] Input: Gmail API access key and user ID

[1312] Data processing: The acquired email data is converted into text format and important keywords and topics are extracted using natural language processing (NLP) techniques.

[1313] Output: Analyzed keywords and topics, along with associated sentiment data (e.g., "Project started" and "Excited" from an email saying "A new project has started")

[1314] Step 3:

[1315] The terminal collects the user's photo data.

[1316] What happens: The device accesses the photo gallery to retrieve the most recently taken photos.

[1317] Input: Photo Gallery Access Permission

[1318] Data processing: Analyze the metadata of the acquired photos (date and time of shooting, location information) and use image analysis technology to recognize the content of the photos.

[1319] Output: Emotional data related to the image content (e.g., "joy" or "blessing" from a photo of a Christmas tree)

[1320] Step 4:

[1321] The terminal transmits the collected schedule data, email data, and photo data to the server.

[1322] Specific operation: The device packages the data it previously collected into JSON format and sends it to the server using an HTTP request.

[1323] Input: Schedule data, email data, photo data

[1324] Data processing: Consolidate the data into a single JSON file.

[1325] Output: Consolidated data sent to the server

[1326] Step 5:

[1327] The server analyzes the received data.

[1328] How it works: The server uses natural language processing technology and a sentiment analysis engine to extract important events and topics from schedule data, email data, and photo data, and recognizes the user's emotions.

[1329] Input: Integrated data sent from the device

[1330] Data processing: Analyze the data and integrate the events, keywords, and sentiment extracted from each data source.

[1331] Output: Analysis results and emotion information

[1332] Step 6:

[1333] The server generates a draft of the SNS post based on the analysis results.

[1334] How it works: The server uses the analysis results to generate a draft of the content for the social media post. Using a generative AI model, the draft is created in a natural-sounding sentence format.

[1335] Input: Analysis results and emotion information

[1336] Data processing: Combining the analysis results with emotional information, we generate content for social media posts based on templates.

[1337] Output: Draft social media post (e.g., "Today is December 25th, Christmas party day! I've started a new project and am having a lot of fun. I'm posting a picture of my Christmas tree. Christmas. New project. Fun time.")

[1338] Step 7:

[1339] The terminal displays the generated draft to the user.

[1340] Specific behavior: The device provides an interface for the user to view the generated draft of the social media post.

[1341] Input: Draft content sent from the server

[1342] Data processing: None

[1343] Output: The draft content displayed on the user interface

[1344] Step 8:

[1345] The user checks the displayed draft and makes corrections as necessary.

[1346] What happens: The user reviews the draft and, if necessary, edits the text and makes corrections.

[1347] Input: Displayed draft content

[1348] Data manipulation: User-edited text

[1349] Output: Corrected draft content

[1350] Step 9:

[1351] The terminal transmits the corrected content to the server.

[1352] Specific operation: The device sends the corrected SNS post content back to the server.

[1353] Input: Corrected draft content

[1354] Data processing: None

[1355] Output: Corrections sent to the server

[1356] Step 10:

[1357] The server makes a final check of the content and automatically posts it to social media.

[1358] Specific operation: The server performs a final check and automatically posts using the specified SNS API.

[1359] Input: Corrected social media post content

[1360] Data processing: None

[1361] Output: Posted content on social media (e.g., posted via Twitter API or Instagram API)

[1362] (Application example 2)

[1363] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1364] In recent years, as marketing activities using social networking sites (SNS) have become increasingly popular, there is a particular demand for the automatic generation of personalized advertising content based on the emotions of target users. Conventional systems generate posting content without fully considering user emotions, making it difficult to achieve effective advertising. In addition, the burden on users to manually create posting content is significant. There is a demand for a system that can solve these issues and automatically generate effective advertising content that takes user emotions into account and posts it to SNS.

[1365] 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.

[1366] In this invention, the server includes a means for analyzing a user's emotions, generating advertising content based on the analysis results, and posting the advertising content to an SNS, a means for collecting the user's schedule data, email data, and photo data, and a means for extracting important keywords and topics from the email data using natural language processing technology. This enables automatic generation of personalized advertising content based on emotions and timely posting to an SNS.

[1367] "User's data" is all data related to a user, including information such as the user's schedule, emails, photos, etc.

[1368] "Collection means" refers to a method or device for acquiring a user's schedule, email, photo data, and the like.

[1369] "Means for analysis" refers to methods or devices for processing and analyzing collected data and extracting user sentiment and important keywords.

[1370] "Means for automatically generating content to post on SNS" refers to a method or device for automatically generating content to post on SNS based on the analysis results.

[1371] The "means for enabling confirmation and correction" refers to a method or device for displaying the generated posted content to the user, allowing the user to confirm the content and make corrections as necessary.

[1372] The "means for final confirmation and automatic posting to SNS" refers to a method or device for final confirmation of the corrected content to be posted and automatically posting it to SNS.

[1373] "Means for analyzing emotions" refers to a method or device for extracting and analyzing emotional information from user data.

[1374] "Means for generating advertising content and posting it on SNS" refers to a method or device for generating advertising content based on user emotions and posting it on SNS.

[1375] "Schedule data" refers to data that includes a user's calendar and schedule information.

[1376] "Email Data" means data including the content of emails received or sent by a User.

[1377] "Photo data" refers to data including photos taken by a user and their metadata (such as the date and time of the photo and location information).

[1378] "Natural language processing technology" is a technology for analyzing and processing text and language data and understanding their content.

[1379] A "keyword or topic extraction tool" is a method or device for extracting significant words or themes from email data or other documents.

[1380] The present invention relates to a system that collects and analyzes a user's schedule data, email data, and photo data, understands the user's emotions using an emotion engine, and automatically generates social media posting content and advertising content. The system is composed of a terminal, a server, and an social media platform. A specific embodiment of this system is described below.

[1381] Data collection

[1382] The user's device collects the user's schedule data through a calendar application (e.g., calendar API), retrieves email data using an email client, and extracts important keywords and topics using natural language processing techniques. Additionally, photo data is collected from the user's photo gallery, and metadata and image analysis techniques are used to understand the content of the photos.

[1383] Data analysis

[1384] The server receives the collected data and analyzes user sentiment using a sentiment analysis engine (e.g., scikit-learn or a logistic regression model). Based on the analysis results, it generates social media posting content and advertising content. The generated content is displayed on the user's device, where the user can review and correct it.

[1385] Social media posts

[1386] The posting content and advertising content that the user has finally confirmed are sent to the server, which then automatically posts the content to the SNS platform using the SNS API (e.g., Twitter API, Instagram API).

[1387] Hardware and Software Used

[1388] Use a calendar API (e.g., Google Calendar API) to retrieve schedule data.

[1389] Email data is analyzed using email clients and natural language processing techniques.

[1390] Analyze the photo data using image analysis techniques (e.g., PIL library).

[1391] The sentiment analysis engine uses scikit-learn's Logistic Regression model.

[1392] Use social media APIs to post the final content to social media platforms.

[1393] Specific examples

[1394] For example, event information such as "December 25th: Christmas Party" is obtained from the user's calendar app, topics such as "A new project has started" are extracted from received emails, and "Photos of Christmas trees" are collected from the photo gallery, and the user's emotions are analyzed as "excitement" or "joy."

[1395] By combining this data, social media post content and advertising content such as "Today is December 25th, Christmas party day! We've started a new project and are having a lot of fun. I'm posting a photo of the Christmas tree. Christmas, new project, fun times" is generated.

[1396] An example prompt might look like this:

[1397] "Build an application that analyzes the user's emotional state based on current schedule data, email data, and photo data, generates optimal advertising content, and posts it to social media."

[1398] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1399] Step 1:

[1400] The device obtains the user's schedule data from the calendar application. Specifically, it calls the calendar API to collect event data and sends it to the server. The input is the calendar API authentication information and the user ID, and the output is a list of schedule data.

[1401] Step 2:

[1402] The device retrieves the user's email data from the email client. The retrieved email data is analyzed using natural language processing technology to extract important keywords and topics. The input is the email account authentication information and the retrieved email body, and the output is the extracted keywords and topics. The analysis results are sent to the server.

[1403] Step 3:

[1404] The device collects the user's photo data from the photo gallery. It uses image analysis technology to analyze the content of the photo and extract metadata (date and time of photo capture, location information). The input is the photo gallery's authentication information and the photo file, and the output is the image content and metadata. These data are also sent to the server.

[1405] Step 4:

[1406] The server receives all the data sent from the devices and analyzes the user's emotions using a sentiment analysis engine. The sentiment analysis engine uses scikit-learn's Logistic Regression model to determine emotions from input text data and image data. The inputs are schedule data, email keywords and topics, and photo content and metadata, and the output is recognized emotional information.

[1407] Step 5:

[1408] The server automatically generates social media posts and advertising content based on the analysis results. It uses a generative AI model to create appropriate sentences according to the context. The input is emotional information and collected data, and the output is the generated post content and advertising content.

[1409] Step 6:

[1410] The terminal displays the generated post content and advertising content to the user, who can then review it and make corrections as necessary. The input is the post content and advertising content sent from the server, and the output is the final content corrected by the user.

[1411] Step 7:

[1412] The server performs a final check of the post content and advertising content corrected by the user, and automatically posts it to the SNS using the SNS API. The input is the finalized content, and the output is the result of posting to the SNS.

[1413] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1414] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1415] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1416] [Fourth embodiment]

[1417] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1418] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1419] 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).

[1420] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1421] 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.

[1422] 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).

[1423] 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.

[1424] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1425] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1426] 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.

[1427] 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.

[1428] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1429] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1430] This invention relates to a system that collects and analyzes users' schedule data, email data, and photo data, and automatically generates content to post to SNS. The generated content is displayed to the user, who can then check and correct it before it is automatically posted to the SNS.

[1431] System Implementation

[1432] This system consists of a terminal that collects data, a server that performs analysis, and a terminal that users use to make corrections. Users use devices that store various types of data, and the system has a means of collecting the necessary information from these devices.

[1433] Program processing flow

[1434] The terminal collects the user's schedule data, email data, and photo data, and sends the collected data to the server. The following describes how each data is processed.

[1435] 1. Collecting schedule data

[1436] The device retrieves the user's event data from the calendar app and sends it to the server via an API.

[1437] Example: Use the Google Calendar API to retrieve event information such as "December 25th: Christmas Party."

[1438] 2. Email Data Collection

[1439] The device retrieves the user's email data from the email app and filters it for important keywords.

[1440] The device uses NLP technology to extract keywords and send them to the server.

[1441] Example: Parse an email that says "A new project has been started" and extract related topics.

[1442] 3. Photo data collection

[1443] The device retrieves the most recently taken photos from the photo gallery and sends the data to the server.

[1444] The device also collects photo metadata (e.g., date taken, location information).

[1445] Example: Getting a photo of a Christmas tree and parsing its metadata.

[1446] The server analyzes the data it receives, extracting and synthesizing key information, including:

[1447] 1. Data Analysis

[1448] The server analyzes the schedule data, email data, and photo data.

[1449] Analyze schedule event information to extract specific dates or events.

[1450] Natural language processing techniques are used to process email data and extract important topics and keywords.

[1451] Image analysis technology is used to analyze photo data and extract the content and related information of the photo.

[1452] 2. Generating social media posts

[1453] The server generates a draft of the SNS post based on the analysis results.

[1454] The generated draft will be written in natural language and will combine information from schedules, emails, and photos.

[1455] Example: Integrate events based on the user's schedule, the launch of a new project via email, and recently taken photos to generate a draft titled "Today is December 25th, Christmas party day! A new project has also started. I'll post a photo of the Christmas tree. Christmas New Project."

[1456] The terminal displays the generated draft to the user, who can then check the contents and make corrections as necessary.

[1457] 1. Viewing and correcting drafts

[1458] The terminal displays the draft to the user and provides an interface that allows revisions to be made.

[1459] Users can view the draft and make edits or corrections.

[1460] Example: User reviews draft and amends it to "Today is December 25th, Christmas party day! I've started a new project and am having a lot of fun. I'm going to post a picture of my Christmas tree. Christmas New project Fun times."

[1461] Finally, the device sends the corrected post content to the server, which then performs a final check and automatically posts it to the social networking site.

[1462] 1. Final confirmation and automatic posting

[1463] The terminal transmits the corrected content to the server.

[1464] The server performs a final check of the content and automatically posts it using the SNS API.

[1465] Example: The confirmed post content is actually posted to social media via the Twitter API or Instagram API.

[1466] In this way, this system reduces the burden on users and enables them to post to social media in a timely and effective manner.

[1467] The processing flow will be explained below.

[1468] Step 1:

[1469] The device collects the user's schedule data. The device calls the calendar app's API to obtain the user's event information. This information includes events for the next week and specific dates.

[1470] Step 2:

[1471] The device collects email data. The device accesses a mail server (for example, by connecting using the IMAP protocol) and retrieves new emails from the user's mailbox. The retrieved email data is then analyzed using natural language processing technology to extract important keywords and topics.

[1472] Step 3:

[1473] The device accesses the user's photo gallery and collects recently taken photos, along with the photo's metadata (e.g., the date and time of the photo, location information), and uses image analysis technology to recognize and tag the content of each photo.

[1474] Step 4:

[1475] The schedule data, email data, and photo data collected by the device are sent to the server in a single packet using a secure protocol.

[1476] Step 5:

[1477] The server analyzes the schedule data it receives. It extracts detailed information such as the date, time, name, and location of the event, and evaluates the importance of the event. For example, it extracts event information such as "December 25th: Christmas Party."

[1478] Step 6:

[1479] The server analyzes the received email data using natural language processing technology to extract important keywords and topics, evaluates the importance of the email based on the extracted keywords and topics, and determines the content to include in the social media post.

[1480] Step 7:

[1481] The server uses image analysis technology to analyze the received photo data and recognize the content of the photo. Based on the recognized content, tags and captions are generated for each photo. For example, a photo of a Christmas tree is tagged as "Christmas tree."

[1482] Step 8:

[1483] The server generates a draft of a social media post based on the analysis results. It integrates the collected and analyzed schedule, email, and photo data to generate an automatically generated text. The generated text is structured based on a template.

[1484] Step 9:

[1485] The server sends the generated draft to the terminal, which displays it to the user so that the user can check and correct it. The UI (user interface) includes a text editor for editing, allowing the user to freely edit the text.

[1486] Step 10:

[1487] The user reviews the draft and makes corrections as necessary, such as changing expressions or adding new hashtags. After reviewing, the user confirms the corrected text.

[1488] Step 11:

[1489] The terminal sends the corrected content to the server using a secure protocol.

[1490] Step 12:

[1491] The server performs a final check of the corrected post content received and automatically posts it to social media. The post is actually made using a social media API (e.g., Twitter API, Instagram API). For example, the following content might be automatically posted: "Today is December 25th, Christmas party day! We've started a new project and are having a lot of fun. I'll post a photo of the Christmas tree. Christmas, new project, fun times."

[1492] These are the specific processing steps of this system. By using this system, users can post to SNS at the appropriate time without any burden.

[1493] Example 1

[1494] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1495] In modern society, posting to social networking sites has become a widespread means of disseminating information, but busy daily schedules make it difficult to post regularly and effectively. In particular, as users' schedules and activities become more and more varied, creating content to post to social networking sites requires a great deal of time and effort. Furthermore, gathering necessary information from multiple data sources (schedules, emails, photos, etc.) and integrating it to create content to post places a significant burden on users. Therefore, there is a need for a method that reduces users' effort and allows them to post to social networking sites efficiently and effectively.

[1496] 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.

[1497] In this invention, the server includes a means for collecting user information, a means for analyzing the collected information, and a means for automatically generating SNS post content based on the analysis results. This enables the automatic generation of integrated SNS post content based on the user's schedule information, email information, and image information. Furthermore, by using a generative AI model, the analysis results are converted into natural and attractive post content, which can then be automatically posted to the SNS after the user has confirmed and corrected it. This allows users to effectively use SNS regardless of their daily busy schedules.

[1498] "User" refers to any individual or entity that uses the System.

[1499] "Information" refers to data related to a user, and specifically includes schedule information, e-mail information, and image information.

[1500] "Collection methods" refers to methods for securing and obtaining user information, including calendar apps, email apps, photo galleries, etc.

[1501] "Analysis methods" refer to methods used to analyze collected information and extract important keywords and topics, including natural language processing and image analysis techniques.

[1502] "Generation means" refers to a method for automatically generating a draft of the content of a SNS post from the analyzed data.

[1503] "SNS post content" refers to a post that includes text and images generated based on user information.

[1504] "Correctable interface" refers to an interface that allows users to check and edit the content of SNS posts they have created.

[1505] "SNS API" refers to an application programming interface that allows a program to access a specific SNS platform and automatically post to it.

[1506] "Natural language processing technology" is a technology for analyzing text data such as email information and extracting important keywords and topics.

[1507] "Image analysis technology" is a technology for analyzing image data and extracting its contents and related information.

[1508] A "generative AI model" refers to an artificial intelligence model that automatically generates sentences written in natural language based on given data.

[1509] MODE FOR CARRYING OUT THE INVENTION

[1510] This invention is a system that collects and analyzes users' schedule information, email information, and image information, and automatically generates content to post on social media based on this information. The system consists of a terminal that collects user information, a server that analyzes the collected information, and a terminal on which the user can make corrections.

[1511] Hardware and software used

[1512] Device:

[1513] Hardware: devices such as smartphones, tablets, and computers

[1514] Software: Calendar apps (e.g., Google Calendar), email apps (e.g., general email clients), photo gallery apps, natural language processing libraries (e.g., spaCy), image analysis libraries (e.g., OpenCV)

[1515] server:

[1516] Hardware: Cloud server (e.g. AWS, Google Cloud Platform)

[1517] Software: Data analysis engines, generative AI models (e.g., GPT-3), social media APIs (e.g., Twitter API, Instagram API)

[1518] System processing flow

[1519] Data collection:

[1520] The device retrieves schedule information from the user's calendar app via API. For example, if a user adds an event called "December 25th: Christmas Party" to Google Calendar, the device retrieves this information.

[1521] The device retrieves email data from the email app and uses natural language processing technology to extract important keywords and topics, such as an email that says, "A new project has been started."

[1522] The device retrieves recently taken photos from the photo gallery along with their metadata (date taken, location, etc.), including a photo of a Christmas tree, for example.

[1523] Data transmission and analysis:

[1524] The devices send the collected data over the Internet to a server, which analyzes the data and consolidates the information.

[1525] The server extracts specific dates and events from the schedule data, analyzes email data using natural language processing techniques, and uses image analysis techniques on photo data.

[1526] Generate social media posts:

[1527] The server uses a generative AI model (e.g., GPT-3) based on the analysis results to generate a draft of the content to post on social media. For example, it generates a sentence such as, "Today is December 25th, Christmas party day! A new project has also started. I'll post a photo of the Christmas tree. Christmas New Project."

[1528] View and correct drafts:

[1529] The terminal displays the generated draft to the user and provides an interface that allows the user to check and correct it.

[1530] The user can review the generated draft and make corrections as necessary. For example, they could correct it to "Today is December 25th, Christmas party day! We've started a new project and are having a lot of fun. I'll post a picture of the Christmas tree. Christmas, new project, fun time."

[1531] Final check and auto-post:

[1532] The device sends the corrected post content to the server.

[1533] The server performs a final check of the content and automatically posts it to SNS using the SNS API.

[1534] Examples of concrete examples and prompts

[1535] Examples:

[1536] "December 25th: Christmas Party" has been added to the user's schedule.

[1537] The user has received an email with the topic "A new project has been started."

[1538] The user's recent photos include a "photo of a Christmas tree."

[1539] Example prompt for a generative AI model:

[1540] "Based on the user's schedule, email, and photo data, please generate social media post content in the following format.

[1541] Schedule: December 25th, Christmas Party

[1542] Email Topic: New project started

[1543] Photo: Christmas tree photo

[1544] Example of generated result: Today is December 25th, Christmas party day! A new project has also started. I will post a photo of the Christmas tree. Christmas New Project"

[1545] In this way, the present invention significantly reduces the burden on users and enables efficient and effective posting to SNS.

[1546] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1547] Program processing flow

[1548] Step 1:

[1549] Data collection

[1550] 1.1 Schedule data collection:

[1551] The device collects schedule information and obtains event information through a calendar app (e.g., Google Calendar) installed on the user's device. Specifically, the device calls the Google Calendar API to obtain event data stored in the user's calendar. The input for this operation is the API key and user ID, and the output is the schedule information registered in the calendar.

[1552] Input: API key, user ID

[1553] Output: Event information such as "December 25th: Christmas Party"

[1554] 1.2 Email Data Collection:

[1555] The device collects email data from the email app. The device accesses the user's mailbox and retrieves new or relevant emails. NLP techniques are used to extract important keywords and topics from the email body. The input for this operation is email account information and a rule set (e.g., "new project"), and the output is the analyzed keywords and topics.

[1556] Input: Email account information, rule set

[1557] Output: Important keywords and topics, such as "A new project has been launched."

[1558] 1.3 Photographic Data Collection:

[1559] The device collects recently taken photos and their metadata (such as the date of taking the photos, location information, etc.) from the photo gallery. The input of this operation is the path to the photo folder and access permissions, and the output is the photo data and its metadata.

[1560] Input: Path to photo folder, access permissions

[1561] Output: A photo of a Christmas tree and metadata such as "Date taken: December 25th, Location: Home"

[1562] Step 2:

[1563] Sending data

[1564] 2.1 Data transmission to the server:

[1565] The terminal transmits the collected schedule data, email data, and photo data to the server via the Internet. The input of this operation is the collected data, and the output is the transmission completion status.

[1566] Input: Schedule data, email data, photo data

[1567] Output: Data transmission completion status

[1568] Step 3:

[1569] Data analysis

[1570] 3.1 Schedule Data Analysis:

[1571] The server analyzes the received schedule data and extracts specific date and event information. Specifically, the server parses the schedule data and identifies important dates and event names. The input of this operation is the schedule data, and the output is the analyzed event information.

[1572] Input: Schedule data

[1573] Output: Event information such as "December 25th: Christmas Party"

[1574] 3.2 Email Data Analysis:

[1575] The server uses natural language processing techniques (e.g., spaCy) to analyze the email data and extract important keywords and topics. The input of this operation is the email data, and the output is the extracted keywords and topics.

[1576] Input: Email data

[1577] Output: Important keywords, such as "new project"

[1578] 3.3 Image analysis:

[1579] The server analyzes the photo data using image analysis techniques (e.g., OpenCV) to extract its content and metadata. The input to this operation is the photo data and metadata, and the output is the extracted image content information.

[1580] Input: Photo data, metadata

[1581] Output: Information such as "Christmas tree", "Photo date: December 25th, Location: Home", etc.

[1582] Step 4:

[1583] Generating social media posts

[1584] 4.1 Social Media Post Generation:

[1585] The server integrates all the analyzed data and generates a draft of the social media post using a generative AI model (e.g., GPT-3). The input of this operation is the analyzed events, keywords, and image information, and the output is the social media post draft.

[1586] Input: Analyzed event information, keywords, image information

[1587] Output: A draft social media post that reads, "Today is December 25th, Christmas party day! A new project has started. I'll post a picture of my Christmas tree. Christmas New Project."

[1588] Step 5:

[1589] Viewing and correcting drafts

[1590] 5.1 Viewing and correcting drafts:

[1591] The terminal displays the generated SNS post draft to the user and provides an interface for correction. The user uses this interface to check and correct the draft. The input of the operation is the generated SNS post draft, and the output is the draft corrected by the user.

[1592] Input: Generated social media post draft

[1593] Output: Revised social media post draft (e.g., "Today is December 25th, Christmas party day! I've started a new project and am having a lot of fun. I'm posting a picture of my Christmas tree. Christmas. New project. Fun time.")

[1594] Step 6:

[1595] Final confirmation and automatic posting

[1596] 6.1 Final Review and Submission:

[1597] The device sends the corrected post content to the server, which then performs a final check using the SNS API and automatically posts it to the SNS. The input for the operation is the corrected SNS post draft, and the output is the status of the post completion to the SNS.

[1598] Input: Revised social media post draft

[1599] Output: SNS post completion status

[1600] (Application example 1)

[1601] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1602] In today's world, posting to social media accounts for an important marketing strategy, but manually posting requires time and effort. Furthermore, in certain fields, such as the food delivery industry, continuous and timely information transmission is required, placing a significant burden on users. This invention aims to solve these problems by providing a system that automatically generates social media posting content and allows users to easily review and correct the content.

[1603] 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.

[1604] In this invention, the server includes means for collecting user data, means for analyzing the collected data, means for automatically generating SNS post content based on the analysis results, means for displaying the generated post content to the user so that the user can confirm and correct it, means for final confirmation of the corrected post content and automatically posting it to the SNS, means for integrating various electronic data and using natural language processing and image analysis techniques to generate SNS post content, and means for providing a correction interface on the user management terminal, thereby reducing the burden on the user and enabling timely and effective SNS posting.

[1605] "User data" refers to various information related to a user, such as the user's schedule data, email data, and photo data, which are stored on an electronic device.

[1606] "Collection means" refers to the software and hardware components used to obtain the required data from a user's device.

[1607] "Means of analysis" refers to algorithms and analytical techniques used to integrate and filter matching data based on collected data.

[1608] "Means for generating content to post on SNS" refers to a program that automatically creates text and media content to post on SNS on behalf of the user based on the analysis results.

[1609] "Means for enabling review and correction" refers to a software configuration that provides an interface for users to review the generated posted content and edit or correct it as necessary.

[1610] "Means for final confirmation and automatic posting to SNS" refers to the API and program for final confirmation of the corrected data and automatically posting it through the user's SNS account.

[1611] "Natural language processing technology" refers to artificial intelligence technology for analyzing text data and extracting meaningful information.

[1612] "Image analysis technology" refers to technology for analyzing photographs and image data and extracting their content and metadata.

[1613] "User management terminal" refers to a device owned by a user and used for configuration and operation.

[1614] "Correction interface" refers to a software configuration that provides a user-friendly screen for users to correct and edit the content of their generated SNS posts.

[1615] "Means for automatically obtaining new menu and promotional information" refers to APIs and data mining technologies for automatically collecting information on new products and campaigns offered by food delivery services and other services.

[1616] This invention is a system that collects and analyzes user data and automatically generates content to post on social media. The system of this invention is composed of a user terminal for collecting data, a server for analyzing the data and generating content to post on social media, and a terminal for providing a user correction interface.

[1617] 1. Overall system configuration

[1618] The system relies on the following components:

[1619] User terminal: A device for collecting user schedule data, email data, photo data, new menus and promotional information.

[1620] Server: Has the core function of analyzing collected data and automatically generating content to post on social media.

[1621] Terminal that provides the correction interface: A device that displays the generated SNS post content to the user and allows them to check and correct it.

[1622] 2. Data Collection

[1623] First, the user's device retrieves the user's schedule data using the Google Calendar API. Email data is collected via an email client API, and then important keywords and topics are extracted using NLP technology. For photo data, the Google Photos API is used to collect recently taken photos and their metadata. Additionally, new menu items and promotional information from food delivery services are automatically retrieved.

[1624] 3. Data Analysis

[1625] The collected data is sent to a server for analysis. The server uses NLP technology to extract important keywords and topics from the email data, and image analysis technology to analyze the content of the photo data. This generates integrated information from the user's schedule, email content, photo data, new menus, and promotional information.

[1626] 4.Generate social media posting content

[1627] The server automatically generates the content to post on social media based on the analysis results. For example, the generated post might look something like, "Today is December 25th, Christmas party day! I've started a new project and am having a lot of fun. I'll post a photo of my Christmas tree. Christmas, new project, fun time." Natural language generation technology is used to generate this content.

[1628] 5. Viewing and correcting drafts

[1629] An interface for displaying the generated draft is provided on the user's device. The user can check the draft content through this interface and make corrections as necessary. For example, a user can change the content "Today is December 25th, Christmas party day! A new project has started and I'm having a lot of fun. I'll post a picture of the Christmas tree. Christmas New project Fun time" to "Today is December 25th, Christmas party day! A new project has started and I'm having a lot of fun. I'll post a picture of the Christmas tree. Christmas New project Fun time."

[1630] 6. Final confirmation and automatic posting

[1631] The corrected content is sent back to the server, where it is checked for final approval and then automatically posted to the social networking site using the social networking site API, such as Twitter or Instagram.

[1632] Example prompt sentence:

[1633] "Use calendar, email, and photo data to generate posts about the user's schedule. Analyze using the following information."

[1634] Schedule data: (event details, date and time)

[1635] Email data: (Email content)

[1636] Photo data: (date, time, location)

[1637] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1638] Step 1:

[1639] The user's device collects user data. Specifically, it uses the Google Calendar API to obtain schedule data and the email client API to obtain email data. It also uses the Google Photos API to obtain photo data and metadata, and automatically collects new menu items and promotional information for food delivery services. This input data is temporarily stored on the user's device and later sent to the server.

[1640] Step 2:

[1641] The user device sends the collected data to the server. The data sent includes schedule data, email data, photo data, and menu / promotion information. The server receives this data and prepares it for analysis.

[1642] Step 3:

[1643] The schedule data received by the server is analyzed. Specifically, event content and date information are analyzed to extract important events. For example, event information such as "December 25th: Christmas Party" is the subject of analysis. The analysis results are passed on to the next processing step.

[1644] Step 4:

[1645] The server analyzes the email data. Natural language processing (NLP) technology is used to extract important keywords and topics from the email content. For example, the keyword "new project" is extracted from an email that reads "A new project has been started." The analysis results are applied to the next processing step.

[1646] Step 5:

[1647] The server analyzes the photo data. It uses image analysis technology to determine the content of the photo and extract relevant information, including metadata (date and time of the photo, location information). For example, a photo of a Christmas tree is analyzed to determine its metadata. The analysis results are passed on to the next processing step.

[1648] Step 6:

[1649] The server integrates the collected and analyzed results and generates the content to post on social media. The generated post content is written in natural language and combines schedule, email, and photo information. For example, the generated content might read, "Today is December 25th, Christmas party day! A new project has also started. I'll post a photo of the Christmas tree. Christmas New Project." This generative AI model generates the content to post on social media based on the specified prompt.

[1650] Step 7:

[1651] The server sends the generated draft of the SNS post to the user's device. The user's device displays the draft and provides an interface that allows the user to check and correct the content. The user uses this interface to edit and modify the generated post content.

[1652] Step 8:

[1653] The user resubmits the corrected post to the server. The server receives the corrections and performs a final check. Once this check is complete, the post is automatically posted to the social networking site using the social networking site API (e.g., Twitter API or Instagram API).

[1654] 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.

[1655] This invention relates to a system that collects and analyzes a user's schedule data, email data, and photo data, understands the user's emotions using an emotion engine, and automatically generates content to post to an SNS. The generated content is displayed to the user, who can then check and correct it, before it is automatically posted to the SNS.

[1656] System Implementation

[1657] This system consists of a device that collects data, a server that analyzes the data and recognizes emotions, and a device that the user can use to make corrections. The functions of each component and specific examples of their operation are shown below.

[1658] Program processing flow

[1659] The device collects the user's schedule data, email data, and photo data, and sends the collected data to the server. The following describes how each data is processed.

[1660] 1. Collecting schedule data

[1661] The device retrieves event data from the user's calendar app and sends it to the server via an API.

[1662] Example: Use the Google Calendar API to retrieve event information such as "December 25th: Christmas Party."

[1663] 2. Email Data Collection

[1664] The device retrieves the user's email data from the email app and extracts important keywords.

[1665] The device uses NLP (natural language processing) technology to extract keywords and topics, and then uses an emotion analysis engine to recognize the user's emotions and send them to the server.

[1666] Example: Parsing an email that says "A new project has been launched" and extracting relevant topics and emotions (e.g., excitement or joy).

[1667] 3. Photo data collection

[1668] The device accesses the user's photo gallery to collect recently taken photos and obtains the photo's metadata (date and time of taking, location information).

[1669] The device uses image analysis technology and an emotion analysis engine to recognize the content and emotions of the photo and send them to the server.

[1670] Example: Analyzing a photo of a Christmas tree and recognizing associated emotions (e.g., joy and celebration).

[1671] The server analyzes the received data, extracts and synthesizes important information and user sentiment. The analysis includes:

[1672] 1. Data Analysis

[1673] The server analyzes the schedule data, email data, and photo data to extract important events and topics.

[1674] Using natural language processing technology and a sentiment analysis engine, information including user emotions is extracted and content is generated.

[1675] 2. Generating social media posts

[1676] The server generates a draft of a social media post based on the analysis results and emotional information. For example, it creates a post by combining schedule events, email topics, photo content, and associated emotions.

[1677] Example: By combining events based on the user's schedule, excitement based on sentiment analysis, and recently taken photos, we can generate a draft that reads, "Today is December 25th, Christmas party day! We've started a new project and are having a lot of fun. I'll post a photo of the Christmas tree. Christmas, new project, fun times."

[1678] The terminal displays the generated draft to the user, who can then check the contents and make corrections as necessary.

[1679] 1. Viewing and correcting drafts

[1680] The terminal displays the draft to the user and provides an interface for review and correction, allowing the user to edit the draft and make any necessary corrections.

[1681] Example: User reviews draft and amends it to "Today is December 25th, Christmas party day! I've started a new project and am having so much fun. I'm going to post a picture of my Christmas tree. Christmas New project Fun times."

[1682] Finally, the device sends the corrected post content to the server, which performs a final check and then automatically posts it to the social networking site.

[1683] 1. Final confirmation and automatic posting

[1684] The terminal transmits the corrected content to the server.

[1685] The server performs a final check of the content and automatically posts it using the SNS API.

[1686] Example: Post the confirmed content to a social networking site via a social networking site API (e.g., Twitter API, Instagram API).

[1687] In this way, the system takes into consideration the user's feelings, reduces the burden on the user, and enables timely and effective posting on social media.

[1688] The processing flow will be explained below.

[1689] Step 1:

[1690] The device collects the user's schedule data. The device calls the calendar app's API to obtain the user's event information. For example, this data might be "December 25th: Christmas Party."

[1691] Step 2:

[1692] The device collects the user's email data. It then connects to a mail server to retrieve new emails. The retrieved email data is analyzed using natural language processing technology to extract important keywords and topics. Furthermore, a sentiment analysis engine is used to recognize the sentiment of the email.

[1693] Step 3:

[1694] The device accesses the user's photo gallery and collects recently taken photos. It also obtains the photo's metadata (e.g., date and time of the photo, location information). It then uses image analysis technology and an emotion analysis engine to recognize the content of the photo and its associated emotions. For example, it can recognize emotions such as "blessing" and "happiness" from a photo of a Christmas tree.

[1695] Step 4:

[1696] The schedule data, email data, and photo data collected by the device are sent to the server in bulk as data packets using secure protocols such as SSL / TLS.

[1697] Step 5:

[1698] The server analyzes the schedule data it receives. It analyzes the event date, time, name, location, etc. in detail and evaluates their importance. For example, it extracts information such as "December 25th: Christmas Party" and marks it as an important date.

[1699] Step 6:

[1700] The server uses natural language processing technology to analyze the email data it receives and extracts important keywords and topics. It also uses a sentiment analysis engine to extract the emotion of the email (e.g., joy, sadness, excitement). For example, the emotion of an email that says, "A new project starts today," is recognized as "excitement."

[1701] Step 7:

[1702] The server uses image analysis technology to analyze the received photo data and recognize the content of the photo. It then uses an emotion analysis engine to extract the emotion associated with the photo and tag the photo. For example, a photo of a Christmas tree can be tagged with "Christmas tree" and the emotion "blessing."

[1703] Step 8:

[1704] The server generates a draft of a social media post based on the analysis results. It integrates the collected and analyzed schedule, email, and photo data to generate an automatically generated sentence. For example, it generates a draft that reads, "Today is December 25th, Christmas party day! I've started a new project and am having a lot of fun. I'll post a photo of my Christmas tree. Christmas, new project, fun time."

[1705] Step 9:

[1706] The server sends the generated draft to the terminal, which displays the draft to the user and provides an interface for the user to review and correct it. The UI includes a text editor for editing.

[1707] Step 10:

[1708] Users can review the draft and make corrections as needed. They can change the wording or add new hashtags. For example, they could correct it to "Today is December 25th, Christmas party day! I've started a new project and I'm having a lot of fun. I'm posting a picture of my Christmas tree. Christmas. New project. Fun time."

[1709] Step 11:

[1710] The terminal sends the corrected content to the server using a secure protocol.

[1711] Step 12:

[1712] The server performs a final check of the corrected post content received and automatically posts it to social media. The post is actually made using a social media API (e.g., Twitter API, Instagram API). For example, the following content might be automatically posted: "Today is December 25th, Christmas party day! We've started a new project and are having a lot of fun. I'll post a photo of the Christmas tree. Christmas, new project, fun times."

[1713] These are the specific processing steps of this system. By using this system, users can easily and timely post appropriate, emotional posts to social media.

[1714] Example 2

[1715] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1716] In today's social media, timely posting and high-quality content are important. However, it is a significant burden for users to think about appropriate content and post content that reflects their emotions every time. There is a need for a system that can reduce this burden and automatically post social media content that accurately reflects users' emotions. However, existing systems lack sufficient integration of user data and emotion analysis, which leads to issues with the quality of posted content.

[1717] 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.

[1718] In this invention, the server includes means for collecting user data, means for analyzing the collected data and recognizing emotions, and means for automatically generating content to post to an SNS based on the analysis results, thereby reducing the burden on users and enabling high-quality SNS posts that accurately reflect their emotions.

[1719] "User" refers to an individual or organization that uses the system to post on social media.

[1720] "Data" includes information such as the user's schedule data, email data, and image data.

[1721] "Means for collecting" refers to the methods and devices used to obtain the user's schedule data, email data, and image data.

[1722] "Means for analyzing" refers to the methods and devices used to analyze the acquired data, extract important keywords and topics, and further recognize emotions.

[1723] "Means for recognizing emotions" refers to technologies and systems for identifying a user's emotions based on collected data.

[1724] "Generating means" refers to a method or device for automatically generating social media posts based on the analysis results and the recognized emotions.

[1725] "Means for displaying, reviewing, and correcting" refers to a method or device that provides an interface for showing the generated post content to the user, allowing the user to review the content and edit or correct it as necessary.

[1726] "Means for final confirmation and automatic posting to SNS" refers to a method or device for final checking of the posted content confirmed and corrected by the user and automatically posting it to the SNS platform.

[1727] "Natural language processing technology" refers to technology that enables computers to understand and process human language.

[1728] "Schedule data" refers to information about appointments and events recorded on a user's calendar.

[1729] "Email data" refers to the content, subject, sender information, etc. contained in a user's email.

[1730] "Image data" refers to the contents of photos and image files taken by users, as well as their metadata.

[1731] The present invention relates to a system that collects and analyzes a user's schedule data, email data, and image data, understands the user's emotions using an emotion engine, and automatically generates content to post on an SNS. An embodiment of the program for this system will be described in detail below.

[1732] System configuration

[1733] This system consists of a terminal that collects data, a server that analyzes the data and recognizes emotions, and a terminal that allows users to make corrections.

[1734] Hardware and software used

[1735] Hardware:

[1736] Device: The user's smartphone, tablet, or personal computer

[1737] Server: Cloud server or local server

[1738] software:

[1739] Calendar API (e.g. Google Calendar API)

[1740] Email API (e.g. Gmail API)

[1741] Natural language processing technology (e.g., Python NLP libraries)

[1742] Image analysis technology

[1743] Sentiment Analysis Engine

[1744] SNS API (e.g. Twitter API, Instagram API)

[1745] Program processing

[1746] The terminal first collects the user's schedule data, email data, and photo data, and then sends them to the server. This process involves the following specific steps:

[1747] Schedule data collection:

[1748] The device uses the Google Calendar API to retrieve event information from the user's calendar. For example, event information such as "December 25th: Christmas Party" is sent to the server.

[1749] Email Data Collection:

[1750] The device uses the Gmail API to retrieve the user's email data. From the retrieved email data, it uses natural language processing (NLP) technology to extract important keywords and topics, uses a sentiment analysis engine to recognize the user's emotions, and sends the results to the server. For example, it analyzes an email that says "A new project has been launched" and extracts related topics and emotions (e.g., excitement or joy).

[1751] Photo data collection:

[1752] The device accesses the photo gallery to collect recently taken photos, obtains the metadata of the collected photos (date and time of taking, location information), and uses image analysis technology to recognize the content of the photos. The recognized content and associated emotion data are then sent to the server. For example, a photo of a Christmas tree is analyzed to recognize associated emotions (e.g., joy or celebration).

[1753] Analysis and post generation

[1754] The server analyzes and integrates the received data, specifically following the steps below.

[1755] Data Analysis:

[1756] The server uses natural language processing technology and a sentiment analysis engine to extract important events and topics from schedule data, email data, and photo data and recognize user emotions.

[1757] Generate social media posts:

[1758] The server generates a draft social media post based on the analysis results and the recognized emotions, such as "Today is December 25th, Christmas party day! I've started a new project and I'm having a lot of fun. I'll post a photo of my Christmas tree. Christmas, new project, fun time."

[1759] User corrections and final posts

[1760] 1. The device displays the generated draft to the user and provides an interface for confirmation and correction. The user checks the displayed content and makes corrections as necessary. For example, the user could correct it to "Today is December 25th, Christmas party day! We've started a new project and are having a lot of fun. I'll post a photo of the Christmas tree. Christmas, new project, fun time."

[1761] 2. The device sends the corrected content to the server, which then performs a final check and automatically posts the content using a social media API. For example, the confirmed content is posted to a social media platform via the Twitter API or Instagram API.

[1762] Prompt Sentence Examples

[1763] Below is a specific example of a prompt sentence to input to a generative AI model.

[1764] Prompt statement:

[1765] The user has a schedule for "December 25th: Christmas Party." He also received an email saying "A new project has started," which makes him excited. A recent photo he took is of a Christmas tree. Based on this information, generate content to post on social media.

[1766] As a result, this system reduces the burden on users while enabling high-quality, timely SNS posts that accurately reflect their emotions.

[1767] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1768] Step 1:

[1769] The terminal collects the user's schedule data.

[1770] Specific operation: The device uses the Google Calendar API to retrieve event information stored in the user's calendar.

[1771] Input: Calendar API access key and user ID

[1772] Data processing: Converts event information obtained from the calendar API into JSON format.

[1773] Output: Schedule data in JSON format (e.g. "December 25th: Christmas Party")

[1774] Step 2:

[1775] The terminal collects the user's email data.

[1776] What happens: The device uses the Gmail API to access the user's inbox and retrieve the most recent emails.

[1777] Input: Gmail API access key and user ID

[1778] Data processing: The acquired email data is converted into text format and important keywords and topics are extracted using natural language processing (NLP) techniques.

[1779] Output: Analyzed keywords and topics, along with associated sentiment data (e.g., "Project started" and "Excited" from an email saying "A new project has started")

[1780] Step 3:

[1781] The terminal collects the user's photo data.

[1782] What happens: The device accesses the photo gallery to retrieve the most recently taken photos.

[1783] Input: Photo Gallery Access Permission

[1784] Data processing: Analyze the metadata of the acquired photos (date and time of shooting, location information) and use image analysis technology to recognize the content of the photos.

[1785] Output: Emotional data related to the image content (e.g., "joy" or "blessing" from a photo of a Christmas tree)

[1786] Step 4:

[1787] The terminal transmits the collected schedule data, email data, and photo data to the server.

[1788] Specific operation: The device packages the data it previously collected into JSON format and sends it to the server using an HTTP request.

[1789] Input: Schedule data, email data, photo data

[1790] Data processing: Consolidate the data into a single JSON file.

[1791] Output: Consolidated data sent to the server

[1792] Step 5:

[1793] The server analyzes the received data.

[1794] How it works: The server uses natural language processing technology and a sentiment analysis engine to extract important events and topics from schedule data, email data, and photo data, and recognizes the user's emotions.

[1795] Input: Integrated data sent from the device

[1796] Data processing: Analyze the data and integrate the events, keywords, and sentiment extracted from each data source.

[1797] Output: Analysis results and emotion information

[1798] Step 6:

[1799] The server generates a draft of the SNS post based on the analysis results.

[1800] How it works: The server uses the analysis results to generate a draft of the content for the social media post. Using a generative AI model, the draft is created in a natural-sounding sentence format.

[1801] Input: Analysis results and emotion information

[1802] Data processing: Combining the analysis results with emotional information, we generate content for social media posts based on templates.

[1803] Output: Draft social media post (e.g., "Today is December 25th, Christmas party day! I've started a new project and am having a lot of fun. I'm posting a picture of my Christmas tree. Christmas. New project. Fun time.")

[1804] Step 7:

[1805] The terminal displays the generated draft to the user.

[1806] Specific behavior: The device provides an interface for the user to view the generated draft of the social media post.

[1807] Input: Draft content sent from the server

[1808] Data processing: None

[1809] Output: The draft content displayed on the user interface

[1810] Step 8:

[1811] The user checks the displayed draft and makes corrections as necessary.

[1812] What happens: The user reviews the draft and, if necessary, edits the text and makes corrections.

[1813] Input: Displayed draft content

[1814] Data manipulation: User-edited text

[1815] Output: Corrected draft content

[1816] Step 9:

[1817] The terminal transmits the corrected content to the server.

[1818] Specific operation: The device sends the corrected SNS post content back to the server.

[1819] Input: Corrected draft content

[1820] Data processing: None

[1821] Output: Corrections sent to the server

[1822] Step 10:

[1823] The server makes a final check of the content and automatically posts it to social media.

[1824] Specific operation: The server performs a final check and automatically posts using the specified SNS API.

[1825] Input: Corrected social media post content

[1826] Data processing: None

[1827] Output: Posted content on social media (e.g., posted via Twitter API or Instagram API)

[1828] (Application example 2)

[1829] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1830] In recent years, as marketing activities using social networking sites (SNS) have become increasingly popular, there is a particular demand for the automatic generation of personalized advertising content based on the emotions of target users. Conventional systems generate posting content without fully considering user emotions, making it difficult to achieve effective advertising. In addition, the burden on users to manually create posting content is significant. There is a demand for a system that can solve these issues and automatically generate effective advertising content that takes user emotions into account and posts it to SNS.

[1831] 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.

[1832] In this invention, the server includes a means for analyzing a user's emotions, generating advertising content based on the analysis results, and posting the advertising content to an SNS, a means for collecting the user's schedule data, email data, and photo data, and a means for extracting important keywords and topics from the email data using natural language processing technology. This enables automatic generation of personalized advertising content based on emotions and timely posting to an SNS.

[1833] "User's data" is all data related to a user, including information such as the user's schedule, emails, photos, etc.

[1834] "Collection means" refers to a method or device for acquiring a user's schedule, email, photo data, and the like.

[1835] "Means for analysis" refers to methods or devices for processing and analyzing collected data and extracting user sentiment and important keywords.

[1836] "Means for automatically generating content to post on SNS" refers to a method or device for automatically generating content to post on SNS based on the analysis results.

[1837] The "means for enabling confirmation and correction" refers to a method or device for displaying the generated posted content to the user, allowing the user to confirm the content and make corrections as necessary.

[1838] The "means for final confirmation and automatic posting to SNS" refers to a method or device for final confirmation of the corrected content to be posted and automatically posting it to SNS.

[1839] "Means for analyzing emotions" refers to a method or device for extracting and analyzing emotional information from user data.

[1840] "Means for generating advertising content and posting it on SNS" refers to a method or device for generating advertising content based on user emotions and posting it on SNS.

[1841] "Schedule data" refers to data that includes a user's calendar and schedule information.

[1842] "Email Data" means data including the content of emails received or sent by a User.

[1843] "Photo data" refers to data including photos taken by a user and their metadata (such as the date and time of the photo and location information).

[1844] "Natural language processing technology" is a technology for analyzing and processing text and language data and understanding their content.

[1845] A "keyword or topic extraction tool" is a method or device for extracting significant words or themes from email data or other documents.

[1846] The present invention relates to a system that collects and analyzes a user's schedule data, email data, and photo data, understands the user's emotions using an emotion engine, and automatically generates social media posting content and advertising content. The system is composed of a terminal, a server, and an social media platform. A specific embodiment of this system is described below.

[1847] Data collection

[1848] The user's device collects the user's schedule data through a calendar application (e.g., calendar API), retrieves email data using an email client, and extracts important keywords and topics using natural language processing techniques. Additionally, photo data is collected from the user's photo gallery, and metadata and image analysis techniques are used to understand the content of the photos.

[1849] Data analysis

[1850] The server receives the collected data and analyzes user sentiment using a sentiment analysis engine (e.g., scikit-learn or a logistic regression model). Based on the analysis results, it generates social media posting content and advertising content. The generated content is displayed on the user's device, where the user can review and correct it.

[1851] Social media posts

[1852] The posting content and advertising content that the user has finally confirmed are sent to the server, which then automatically posts the content to the SNS platform using the SNS API (e.g., Twitter API, Instagram API).

[1853] Hardware and Software Used

[1854] Use a calendar API (e.g., Google Calendar API) to retrieve schedule data.

[1855] Email data is analyzed using email clients and natural language processing techniques.

[1856] Analyze the photo data using image analysis techniques (e.g., PIL library).

[1857] The sentiment analysis engine uses scikit-learn's Logistic Regression model.

[1858] Use social media APIs to post the final content to social media platforms.

[1859] Specific examples

[1860] For example, event information such as "December 25th: Christmas Party" is obtained from the user's calendar app, topics such as "A new project has started" are extracted from received emails, and "Photos of Christmas trees" are collected from the photo gallery, and the user's emotions are analyzed as "excitement" or "joy."

[1861] By combining this data, social media post content and advertising content such as "Today is December 25th, Christmas party day! We've started a new project and are having a lot of fun. I'm posting a photo of the Christmas tree. Christmas, new project, fun times" is generated.

[1862] An example prompt might look like this:

[1863] "Build an application that analyzes the user's emotional state based on current schedule data, email data, and photo data, generates optimal advertising content, and posts it to social media."

[1864] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1865] Step 1:

[1866] The device obtains the user's schedule data from the calendar application. Specifically, it calls the calendar API to collect event data and sends it to the server. The input is the calendar API authentication information and the user ID, and the output is a list of schedule data.

[1867] Step 2:

[1868] The device retrieves the user's email data from the email client. The retrieved email data is analyzed using natural language processing technology to extract important keywords and topics. The input is the email account authentication information and the retrieved email body, and the output is the extracted keywords and topics. The analysis results are sent to the server.

[1869] Step 3:

[1870] The device collects the user's photo data from the photo gallery. It uses image analysis technology to analyze the content of the photo and extract metadata (date and time of photo capture, location information). The input is the photo gallery's authentication information and the photo file, and the output is the image content and metadata. These data are also sent to the server.

[1871] Step 4:

[1872] The server receives all the data sent from the devices and analyzes the user's emotions using a sentiment analysis engine. The sentiment analysis engine uses scikit-learn's Logistic Regression model to determine emotions from input text data and image data. The inputs are schedule data, email keywords and topics, and photo content and metadata, and the output is recognized emotional information.

[1873] Step 5:

[1874] The server automatically generates social media posts and advertising content based on the analysis results. It uses a generative AI model to create appropriate sentences according to the context. The input is emotional information and collected data, and the output is the generated post content and advertising content.

[1875] Step 6:

[1876] The terminal displays the generated post content and advertising content to the user, who can then review it and make corrections as necessary. The input is the post content and advertising content sent from the server, and the output is the final content corrected by the user.

[1877] Step 7:

[1878] The server performs a final check of the post content and advertising content corrected by the user, and automatically posts it to the SNS using the SNS API. The input is the finalized content, and the output is the result of posting to the SNS.

[1879] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1880] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1881] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1882] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1883] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1884] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1885] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1886] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1887] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1888] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1889] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1890] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1891] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1892] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[1893] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1894] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1895] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[1896] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1897] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1898] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1899] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1900] The following is further disclosed regarding the above embodiment.

[1901] (Claim 1)

[1902] a means for collecting user data;

[1903] a means for analyzing the collected data;

[1904] A means to automatically generate SNS posting content based on the analysis results,

[1905] A means for displaying the generated post content to the user so that they can check and correct it;

[1906] A way to check the corrected content and automatically post it to social media.

[1907] A system including:

[1908] (Claim 2)

[1909] 10. The system of claim 1, further comprising means for collecting schedule data, email data, and photo data of a user.

[1910] (Claim 3)

[1911] 2. The system of claim 1, wherein the analyzing means includes means for extracting important keywords or topics from the email data using natural language processing techniques.

[1912] "Example 1"

[1913] (Claim 1)

[1914] a means for collecting information about users;

[1915] a means for analyzing the collected information; and

[1916] A means to automatically generate SNS posting content based on the analysis results,

[1917] A means for displaying the generated post content to the user so that they can check and correct it;

[1918] A way to check the corrected content and automatically post it to social media.

[1919] A system including:

[1920] (Claim 2)

[1921] 10. The system of claim 1, further comprising means for collecting user schedule information, email information, and image information.

[1922] (Claim 3)

[1923] 2. The system of claim 1, wherein the analyzing means includes means for extracting important keywords or topics from the email information using natural language processing techniques.

[1924] (Claim 4)

[1925] 3. The system of claim 2, further comprising means for extracting content and related information from the image information using image analysis techniques.

[1926] (Claim 5)

[1927] The system of claim 1, further comprising means for generating a draft of a social media post from the analysis results using a generative AI model.

[1928] (Claim 6)

[1929] 10. The system of claim 1, further comprising means for displaying the generated draft to a user and providing an interface for enabling corrections.

[1930] (Claim 7)

[1931] The system according to claim 1, further comprising means for automatically posting the corrected posting content using an SNS API.

[1932] "Application Example 1"

[1933] (Claim 1)

[1934] a means for collecting user data;

[1935] a means for analyzing the collected data;

[1936] A means to automatically generate SNS posting content based on the analysis results,

[1937] A means for displaying the generated post content to the user so that they can check and correct it;

[1938] A way to check the corrected content and automatically post it to social media.

[1939] A method that uses natural language processing and image analysis technology to integrate various electronic data and generate social media posting content;

[1940] means for providing a correction interface on a user management terminal;

[1941] A system including:

[1942] (Claim 2)

[1943] means for collecting user schedule data, email data, and photo data;

[1944] A way to automatically obtain new menu and promotion information,

[1945] 10. The system of claim 1.

[1946] (Claim 3)

[1947] The analysis means uses natural language processing techniques to extract important keywords and topics from the email data;

[1948] means for analyzing the metadata of the photograph using image analysis techniques;

[1949] means for providing a user correction interface;

[1950] 10. The system of claim 1.

[1951] "Example 2: Combining Emotion Engines"

[1952] (Claim 1)

[1953] a means for collecting user data;

[1954] A means of analyzing the collected data and recognizing emotions;

[1955] A means to automatically generate SNS posting content based on the analysis results,

[1956] A means for displaying the generated post content to the user so that they can check and correct it;

[1957] A way to check the corrected content and automatically post it to social media.

[1958] A system including:

[1959] (Claim 2)

[1960] 10. The system of claim 1, further comprising means for collecting user schedule data, email data, and image data.

[1961] (Claim 3)

[1962] 2. The system of claim 1, wherein the analyzing means includes means for extracting important keywords and topics from the email data using natural language processing techniques and for recognizing sentiment.

[1963] "Application example 2 when combining emotion engines"

[1964] (Claim 1)

[1965] a means for collecting user data;

[1966] a means for analyzing the collected data;

[1967] A means to automatically generate SNS posting content based on the analysis results,

[1968] A means for displaying the generated post content to the user so that they can check and correct it;

[1969] A way to check the corrected content and automatically post it to social media.

[1970] A means for analyzing user emotions, generating advertising content based on the analysis results, and posting it on SNS;

[1971] A system including:

[1972] (Claim 2)

[1973] 10. The system of claim 1, further comprising means for collecting schedule data, email data, and photo data of a user.

[1974] (Claim 3)

[1975] 2. The system of claim 1, wherein the analyzing means includes means for extracting important keywords or topics from the email data using natural language processing techniques. [Explanation of symbols]

[1976] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. a means for collecting user data; a means for analyzing the collected data; A means to automatically generate SNS posting content based on the analysis results, A means for displaying the generated post content to the user so that the user can check and correct it; A way to check the corrected content and automatically post it to social media. A system including:

2. 10. The system of claim 1, further comprising means for collecting user schedule data, email data, and photo data.

3. 2. The system of claim 1, wherein the analyzing means includes means for extracting important keywords or topics from the email data using natural language processing techniques.

Images