System

Abstract

A system is provided.SOLUTION: A system comprising: means for receiving text data input by a user; means for analyzing the text data; means for automatically generating a new text based on the text data; means for storing the new text in a database; means for collecting evaluation data for the new text; means for aggregating the evaluation data; means for generating a ranking based on the aggregation result; and means for displaying the ranking to a user.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] In conventional quote generation systems, the evaluation of quotes entered by users is subjective, raising concerns about the fairness and reliability of the evaluations. Furthermore, the quality and inspiring power of the generated quotes are limited. As a result, quotes that enrich the user's heart cannot be effectively generated, which can lead to a decrease in satisfaction. The present invention aims to solve these problems by providing a fairer and more reliable evaluation system and a system that automatically generates high-quality quotes. [Means for solving the problem]

[0005] The present invention solves the above-mentioned problems by providing a system including: means for receiving text data entered by a user; means for analyzing the text data; means for automatically generating new texts based on the text data; means for storing the new texts in a database; means for collecting evaluation data for the new texts; means for aggregating the evaluation data; means for generating rankings based on the aggregation results; and means for displaying the rankings to users. In particular, the system generates high-quality quotes using a natural language processing algorithm, and further collects and aggregates evaluation data from users in the form of five-point ratings and comments, thereby realizing a fair and reliable evaluation system.

[0006] "User" refers to the individual who operates the system and enters and rates quotes.

[0007] "Text data" refers to text information such as quotes entered by users and their background explanations.

[0008] "Means of analysis" refers to the process of analyzing text data using methods such as natural language processing to understand its content and intent.

[0009] "Means of automatic generation" refers to the process of automatically generating new quotes and sentences using an algorithm based on analyzed text data.

[0010] "Database" refers to a data storage system for storing and managing generated quotes and related information.

[0011] "Evaluation data" refers to the data of the evaluation results that users make of the generated quotes, including a five-point rating and comments.

[0012] "Means of aggregation" refers to the process of statistically analyzing the collected evaluation data and calculating evaluation scores and rankings.

[0013] "Ranking" refers to the results of ranking the evaluation of famous quotes based on the aggregated results. [Brief explanation of the drawings]

[0014] [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

[0015] 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.

[0016] First, the terms used in the following description will be explained.

[0017] 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).

[0018] 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.

[0019] 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.

[0020] 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.

[0021] 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."

[0022] [First embodiment]

[0023] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0024] 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.

[0025] 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).

[0026] 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.

[0027] 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.

[0028] 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.

[0029] 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.

[0030] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0031] 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.

[0032] 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.

[0033] 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.

[0034] 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."

[0035] The system of the present invention analyzes quotes and text data entered by users and provides a process for automatically generating new quotes. This system is realized through the cooperation of a terminal and a server.

[0036] 1. User Input

[0037] The user accesses a dedicated interface on the device and enters the quote and its context. For example, the user might enter:

[0038] Quote: "Dreams aren't meant to be dreamed, they're meant to be chased."

[0039] Context: "I created this quote to celebrate the effort of pursuing your dreams."

[0040] The terminal converts this input data into JSON format and sends it to the server.

[0041] 2. Sending and analyzing quote data

[0042] The server receives and analyzes the JSON data sent from the device. It uses natural language processing (NLP) to perform a structural analysis of the quote and extract keywords and sentiments.

[0043] 3. Automatic generation of quotes

[0044] The server automatically generates new quotes based on the analysis results. For example, for the input quote "Dreams are not meant to be seen, but to be chased," it generates the following result:

[0045] "Those who keep chasing their dreams will win, not those who just dream."

[0046] The generated quotes are stored in a database.

[0047] 4. Save the latest quotes

[0048] The server stores the generated quotes in a database and makes them available for users to view.

[0049] 5. User Ratings

[0050] Other users can access an interface to rate the generated quotes. Users can rate them by entering a five-point rating and a comment. For example, a user might rate them as follows:

[0051] Rating: 4

[0052] Comment: "A very relatable quote!"

[0053] The terminal transmits this evaluation data to the server.

[0054] 6. Collection and compilation of evaluation data

[0055] The server collects and statistically analyzes the received evaluation data, aggregates the evaluation scores, and calculates an average evaluation score for each quote.

[0056] 7. Generating and Displaying Rankings

[0057] The server then creates a ranking of the most popular quotes based on the results of the calculation, and displays the rankings on a web page or in an application for users to view.

[0058] In this way, the system can fairly evaluate quotes submitted by users and automatically generate new, high-quality quotes. This allows users to have a spiritually enriching experience. As a specific example, if the quotes generated from the user input mentioned above are rated by many users and ranked highly, it is expected to generate great emotion and empathy.

[0059] The processing flow will be explained below.

[0060] Step 1:

[0061] The user accesses a dedicated interface on the device and inputs a quote and its background information, which is then converted to JSON format on the device.

[0062] Step 2:

[0063] The terminal sends the data converted into JSON format to the server using an HTTP POST request.

[0064] Step 3:

[0065] The server receives the JSON data and adds it to a queue to be parsed. The received data is logged.

[0066] Step 4:

[0067] The server uses natural language processing (NLP) algorithms to analyze the quote text, specifically extracting keywords, analyzing sentiment, and identifying intent.

[0068] Step 5:

[0069] The server automatically generates new quotes based on the analysis results. The generation algorithm utilizes quotes from an existing database and parameters obtained from the analysis results.

[0070] Step 6:

[0071] The server stores the generated quote in a database and assigns a unique identifier to it. It also stores the date and time of generation and the original quote data.

[0072] Step 7:

[0073] Other users can access the rating interface and rate the generated quotes by entering a 5-point rating and comments.

[0074] Step 8:

[0075] The device collects user evaluation data and sends it to the server in JSON format.

[0076] Step 9:

[0077] The server stores the received evaluation data in a database and automatically updates the evaluation score.

[0078] Step 10:

[0079] The server aggregates the evaluation data at regular intervals and calculates the average evaluation score for each quote using a statistical algorithm.

[0080] Step 11:

[0081] The server generates a ranking of quotes based on the results of the calculation. The rankings are sorted in descending order of evaluation score.

[0082] Step 12:

[0083] The server notifies the user of the ranking results and displays them on a web page or application, allowing the user to view the results from their own device.

[0084] In this way, the system carries out a series of processes from the user's quote input to evaluation, compilation, and display of rankings.

[0085] Example 1

[0086] 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."

[0087] While many sentences and quotes are available on the Internet, their quality and value vary. There is a need for a system that can automatically generate new, high-quality quotes and sentences based on user-entered text, compile evaluations of these, and provide them in a ranking format, thereby enabling the creation and sharing of valuable content.

[0088] 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.

[0089] In this invention, the server includes: means for receiving text data entered by a user through an interface; means for converting the text data into JSON format; means for transmitting the data converted into JSON format to the server; means for the server to receive the JSON data; means for the server to analyze the text data using natural language processing technology and extract keywords and emotions; means for automatically generating new text based on the analysis results using a generative AI model; means for saving the new text in a database; means for collecting data on evaluations of the new text by other users through the user interface; means for the server to statistically analyze the evaluation data and calculate an evaluation score; means for generating a ranking of highly rated texts based on the aggregation results; and means for displaying the ranking to the user. This makes it possible to automatically generate high-quality quotes and texts and share their value through evaluations.

[0090] "User" refers to a person who uses the system to input quotes and sentences and give ratings.

[0091] An "interface" refers to a dedicated screen on a web page or application that allows users to enter quotes or text.

[0092] "Text data" refers to the text information of quotes and their background explanations entered by the user.

[0093] "JSON format" is an abbreviation for JavaScript (registered trademark) Object Notation, and refers to a data exchange format that is easy to read for both humans and machines.

[0094] "Server" refers to the computer system that receives and analyzes data from users, and generates and stores new text.

[0095] "Natural language processing technology" refers to technology that enables computers to understand and generate human language and perform various tasks.

[0096] "Generative AI model" refers to an artificial intelligence model trained to automatically generate new text based on a given prompt.

[0097] "Analysis results" refers to keywords and emotional information extracted from quote data analyzed using natural language processing technology.

[0098] "Database" refers to a collection of information that allows for efficient management and retrieval of saved new quotes and evaluation data.

[0099] "Evaluation data" refers to feedback information such as scores and comments made by users on generated quotes.

[0100] "Statistical analysis" refers to the process of numerically processing evaluation data to reveal trends and patterns in the data.

[0101] The "evaluation score" refers to a numerical representation of the quality of a quote or piece of writing based on user feedback.

[0102] "Ranking" refers to a list of quotes and sentences ranked based on their evaluation scores.

[0103] The system of the present invention analyzes quotes and text data entered by users and provides a process for automatically generating new quotes. This system is realized through the cooperation of a terminal and a server.

[0104] User Input

[0105] The user accesses a dedicated interface on the terminal and inputs the quote and its background information. For example, they might input:

[0106] Quote: "Dreams aren't meant to be dreamed, they're meant to be chased."

[0107] Context: "I created this quote to celebrate the effort of pursuing your dreams."

[0108] The terminal converts this input data into JSON format and sends it to the server.

[0109] Sending and analyzing quote data

[0110] The server receives and analyzes the JSON data sent from the device. The server then performs a structural analysis of the quote using natural language processing (NLP). Specific software used for NLP includes Python's "NLTK" and "spaCy." This allows the server to extract keywords and sentiment from the quote.

[0111] Automatic generation of quotes

[0112] The server automatically generates new quotes using a generative AI model based on the analysis results. Generative AI models used include "GPT-3 (registered trademark)" and "BERT." For example, the following prompt sentence is input to the generative AI model:

[0113] Quote: "Dreams aren't meant to be dreamed, they're meant to be chased."

[0114] Context: "I created this quote to celebrate the effort of pursuing your dreams."

[0115] New auto-generated quote:

[0116] The generative AI model generates new quotes based on this prompt, such as the following:

[0117] "Those who keep chasing their dreams will win, not those who just dream."

[0118] The generated quotes are stored in a database by the server.

[0119] Save the latest quotes

[0120] The server stores the generated quotes in a database and makes them available for users to view. For this purpose, a database management system such as "MySQL (registered trademark)" or "PostgreSQL" is used.

[0121] User Rating

[0122] Other users have access to a dedicated interface to rate the generated quotes. Users can rate them by entering a five-point scale and a comment. For example, they can rate them as follows:

[0123] Rating: 4

[0124] Comment: "A very relatable quote!"

[0125] The terminal converts this evaluation data into JSON format and sends it to the server.

[0126] Collection and compilation of evaluation data

[0127] The server analyzes the received evaluation data, stores it in a database, and aggregates it using data analysis libraries such as Pandas and NumPy to calculate an average evaluation score for each quote.

[0128] Generating and displaying rankings

[0129] The server then creates a ranking of the most popular quotes based on the results of the calculation, and displays the rankings for users to view on a web page or application.

[0130] In this way, the system can fairly evaluate user-submitted quotes and automatically generate new, high-quality quotes, a process that provides users with a spiritually enriching experience.

[0131] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0132] Step 1:

[0133] The user accesses a dedicated interface on the terminal and inputs a quote and its background information. The quote and background information based on the user's input are then returned. For example, enter the following:

[0134] Quote: "Dreams aren't meant to be dreamed, they're meant to be chased."

[0135] Context: "I created this quote to celebrate the effort of pursuing your dreams."

[0136] The terminal converts this input data into JSON format, and the converted JSON data is returned.

[0137] Step 2:

[0138] The terminal sends the converted JSON data to the server as an HTTP request. For example, it sends the following JSON data:

[0139] json

[0140] {

[0141] "quote": "Dreams aren't meant to be seen, they're meant to be chased.",

[0142] "background": "This quote was created to celebrate the effort of pursuing your dreams."

[0143] }

[0144] The server parses the received HTTP request and extracts the JSON data. The extracted JSON data is returned.

[0145] Step 3:

[0146] The server uses natural language processing (NLP) technology to structurally analyze the quotes. For NLP, it uses Python's "NLTK" and "spaCy." Keywords and emotions are extracted from the analyzed data. For example, keywords such as "dream" and "chasing" as well as positive emotions are extracted.

[0147] Step 4:

[0148] The server prepares to use a generative AI model (e.g., "GPT-3") based on the extracted keywords and emotions. It generates a prompt and inputs it into the AI ​​model. A new quote is generated based on this prompt. Example of a prompt:

[0149] Quote: "Dreams aren't meant to be dreamed, they're meant to be chased."

[0150] Context: "I created this quote to celebrate the effort of pursuing your dreams."

[0151] New auto-generated quote:

[0152] One example of a new quote that was generated is, "Those who keep chasing their dreams will win, not those who just dream."

[0153] Step 5:

[0154] The server stores the generated new quotes in a database so that users can view and rate them later.

[0155] Step 6:

[0156] Other users have access to a dedicated interface to rate the generated quotes. Users can rate them by entering a five-point scale and a comment. For example, they can rate them as follows:

[0157] Rating: 4

[0158] Comment: "A very relatable quote!"

[0159] The device converts this evaluation data into JSON format and sends it to the server, where the converted JSON data is obtained.

[0160] Step 7:

[0161] The server analyzes the received evaluation data and stores it in a database. From the analyzed evaluation data, it calculates the evaluation score for each quote. The server aggregates the evaluation scores using "Pandas" and "NumPy." The aggregated score data is obtained.

[0162] Step 8:

[0163] The server then creates a ranking list of the most highly rated quotes based on the results of the calculation. The rankings are displayed on a web page or application so that users can view them. A list of ranked quotes is obtained.

[0164] (Application example 1)

[0165] 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."

[0166] Conventional quote and sentence generation systems have the ability to generate new quotes and sentences based on user input, but they lack the ability to evaluate the generated sentences or display rankings, making it difficult to improve user satisfaction and form a community. Furthermore, they lack a mechanism for evaluating the quality of the generated quotes and providing high-quality quotes.

[0167] 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.

[0168] In this invention, the server includes means for receiving text data entered by a user, means for analyzing the text data, means for automatically generating new texts based on the text data, means for saving the new texts in a database, means for presenting the new texts to users, means for collecting evaluation data for the new texts, means for aggregating the evaluation data, means for generating rankings based on the aggregation results, and means for displaying the rankings to users. This improves the quality of quotes automatically generated based on text entered by a user, and also collects evaluations from users to generate rankings, thereby improving user satisfaction and enabling the formation of a community.

[0169] "Means for receiving text data entered by the user" refers to a function for electronically receiving text data entered by the user through the interface.

[0170] The "means for analyzing text data" is a function for analyzing received text data using natural language processing technology and understanding its structure and meaning.

[0171] "Means for automatically generating new sentences" is a function for automatically generating new sentences based on the analysis results.

[0172] The "means for saving new sentences to a database" is a function for recording the generated new sentences in a database so that they can be accessed later.

[0173] "Means for presenting new text to the user" refers to a function for visually or electronically displaying the generated new text to the user.

[0174] "Means for collecting evaluation data" is a function for collecting evaluations and feedback from users on new text.

[0175] The "means for aggregating evaluation data" is a function for analyzing collected evaluation data and aggregating it as statistical information.

[0176] The "means for generating rankings" is a function for determining the ranking of new sentences based on the evaluation data and displaying them in a ranking format.

[0177] The "means for displaying rankings to users" is a function for displaying the generated rankings so that users can view them.

[0178] To realize the system of this invention, it is necessary to build a system based on a smartphone application. This system receives quotes and text data entered by the user, analyzes them, and provides a process for automatically generating new quotes. Specifically, the following hardware and software are used:

[0179] Hardware and Software Used

[0180] Smartphone: iOS or ANDROID (registered trademark) device

[0181] Server: AWS (registered trademark) or Google (registered trademark) Cloud Platform

[0182] Backend: Python / Django or Node.js

[0183] Natural Language Processing library: spaCy or NLTK

[0184] Database: PostgreSQL or MongoDB

[0185] Frontend: React Native or Flutter(R)

[0186] Specific operation of the system

[0187] 1. User Input

[0188] Users input a quote and its background through a dedicated interface in a smartphone application. For example, they can input a quote such as "Dreams are not meant to be seen, but to be chased." This input data is converted into JSON format and sent to the server.

[0189] 2. Quote data analysis

[0190] The server receives the JSON data and analyzes it using a natural language processing library (e.g., spaCy or NLTK), which understands the structure of the sentence and extracts keywords and sentiment.

[0191] 3. Automatic generation of quotes

[0192] Based on the analysis results, a generative AI model is used to automatically generate new quotes. For example, in response to the input quote, "Dreams are not meant to be seen, but to be pursued," the generated quote would be, "Those who continue to chase their dreams will achieve victory, not those who simply dream."

[0193] 4. Save and display quotes

[0194] The generated quotes are stored in a database and can be viewed by users through the application.

[0195] 5. Ratings and Rankings

[0196] Other users can rate the generated quotes on a five-point scale and add comments. This rating data is sent to the server and statistically analyzed, after which a ranking is generated and displayed.

[0197] Specific examples

[0198] A user inputs "Dreams are not meant to be seen, but to be pursued," and then adds the background explanation "I created this quote to praise the effort of pursuing one's dreams." This causes the server to generate a new quote: "Those who continue to pursue their dreams will achieve victory, not those who simply dream."

[0199] Prompt Sentence Examples

[0200] Generate a new quote based on the following quote:

[0201] Quote: Dreams aren't meant to be seen, they're meant to be chased.

[0202] Background: This quote was created to celebrate the effort of chasing your dreams.

[0203] The above is a specific description of an embodiment of the present invention. This configuration improves the quality of quotes automatically generated based on text entered by users, collects user ratings and generates rankings, and enables increased user satisfaction and community formation.

[0204] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0205] Step 1:

[0206] A user inputs a quote and its background information through the smartphone application interface. This input data is converted to JSON format by the device and sent to the server. For example, a user inputs a quote such as "Dreams are not meant to be seen, but to be pursued" and a background information such as "I created this quote to praise the effort of pursuing one's dreams," and the data is sent to the server as JSON data (e.g., {"quote": "Dreams are not meant to be seen, but to be pursued.", "background": "I created this quote to praise the effort of pursuing one's dreams."}).

[0207] Step 2:

[0208] The server parses the received JSON data. In this parsing process, a natural language processing library (e.g., spaCy or NLTK) is used to analyze the input sentence and extract its structure and keywords. For example, keywords such as "dream," "see," and "chase" are extracted from the quote, "Dreams are not for dreaming, but for chasing."

[0209] Step 3:

[0210] The server automatically generates new quotes using a generative AI model based on the analysis results. In this process, the generative AI model uses the keywords and emotional information extracted in the previous step as prompts to generate new quotes. For example, in response to the statement, "Dreams are not meant to be seen, but to be pursued," the new quote generated is, "Those who continue to chase their dreams will achieve victory, not those who simply dream."

[0211] Step 4:

[0212] The generated quotes are stored in a database by the server. This allows new quotes to be stored in the database and made available for future reference or presentation. For example, the generated quote, its creation date, and evaluation information are recorded in the database.

[0213] Step 5:

[0214] The server prepares the generated quote for presentation to the user. Specifically, it generates data for visually displaying the generated quote on a web page or application. For example, the quote text, rating buttons, comment input fields, etc. are displayed on the screen.

[0215] Step 6:

[0216] Other users can view the generated quotes and rate or comment on them. They enter their rating in the 5-point rating system and comment field, and then send the data from their device to the server. For example, a user can enter "Rating: 4" and "Comment: This is a quote I really relate to!", which is then sent to the server as JSON data.

[0217] Step 7:

[0218] The server aggregates the received evaluation data and calculates an evaluation score for each quote, for example, by statistically processing the evaluation data for each quote and calculating an average evaluation score.

[0219] Step 8:

[0220] The server generates a ranking based on the results of the calculation and displays it to the user. Highly rated quotes are displayed at the top of the ranking. For example, the generated ranking data is displayed in list format on the user's app screen, and the user can view it.

[0221] These steps improve the performance and quality of quotes that are automatically generated based on text entered by users, and also enable rankings to be displayed based on user ratings.

[0222] 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.

[0223] The system of the present invention analyzes quotes and text data entered by the user and automatically generates new quotes, and also uses an emotion engine to recognize the user's emotions and reflect them in the analysis and generation process. This system is realized through the cooperation of a terminal, a server, and an emotion engine.

[0224] 1. User Input

[0225] The user accesses a dedicated interface on the device and enters the quote and its context. For example, the user might enter:

[0226] Quote: "Dreams aren't meant to be dreamed, they're meant to be chased."

[0227] Context: "I created this quote to celebrate the effort of pursuing your dreams."

[0228] The terminal converts this input data into JSON format and sends it to the server.

[0229] 2. Sending and analyzing quote data

[0230] The server receives the JSON data and adds it to a queue for analysis. The received data is logged. Natural language processing (NLP) algorithms are then used to analyze the quote text. Specifically, they extract keywords, perform sentiment analysis, and identify intent.

[0231] 3. Operation of the Emotion Engine

[0232] As part of the analysis process, the emotion engine recognizes the sentiment of the quote. For example, it determines whether the quote entered by the user contains positive emotions such as "effort" or "dream." The emotion engine uses NLP algorithms to perform the sentiment analysis and returns the results to the server.

[0233] 4. Automatic generation of quotes

[0234] The server automatically generates new quotes based on data including analysis results from the emotion engine. The generation algorithm utilizes parameters obtained from quotes in the existing database and analysis results. For example, for the input quote "Dreams are not meant to be seen, but to be pursued," the server reflects the "positive emotion" found in the emotion analysis and generates the following result:

[0235] "Not only do you have a dream, but you also have to keep working to make that dream come true."

[0236] The generated quotes are stored in a database.

[0237] 5. Save the latest quotes

[0238] The server stores the generated quote in a database and assigns a unique identifier to it. It also stores the date and time of generation and the original quote data.

[0239] 6. User Ratings

[0240] Other users can access the rating interface and rate the generated quotes. Users can rate the quotes by entering a five-point rating and comments. For example, a user might rate the quote as follows:

[0241] Rating: 4

[0242] Comment: "A very relatable quote!"

[0243] The terminal collects this evaluation data and transmits it to the server.

[0244] 7. Collecting evaluation data and applying the emotion engine

[0245] While the server is storing the received evaluation data in a database, the emotion engine analyzes the emotions from the user's evaluation comments and stores them in the database along with the evaluation data. This information will be used as reference information for future quote generation processes.

[0246] 8. Aggregating evaluation data and generating rankings

[0247] The server periodically aggregates the evaluation data and calculates the average evaluation score for each quote. The aggregation is performed using a statistical algorithm. The server also reflects the results of sentiment analysis of the evaluation comments.

[0248] 9. Generating and Displaying Rankings

[0249] The server generates a ranking of quotes based on the results of the calculation. The rankings are sorted in descending order of evaluation score, and the ranking results are notified to the user and displayed on a web page or application. The user can view the results on their own device.

[0250] In this way, the system carries out a series of processes, from user quote input to evaluation, compilation, and ranking display. Thanks to the effect of the emotion engine, the generated quotes are more moving and evoke empathy, enriching the user's heart. As a specific example, if a quote generated from user input is rated by many users and ranks highly, it is expected to generate great emotion and empathy.

[0251] The processing flow will be explained below.

[0252] Step 1:

[0253] The user accesses a dedicated interface on the device and inputs a quote and its background explanation. For example, the user inputs the quote "Dreams are not meant to be seen, but to be pursued" and the background explanation "I created this quote to praise the efforts of pursuing one's dreams."

[0254] Step 2:

[0255] The terminal converts the user input data into JSON format, which looks like this:

[0256] {

[0257] "quote": "Dreams aren't meant to be seen, they're meant to be chased.",

[0258] "background": "This quote was created to celebrate the effort of pursuing your dreams."

[0259] }

[0260] Step 3:

[0261] The device sends JSON format data to the server using an HTTP POST request.

[0262] Step 4:

[0263] The server receives the JSON data and adds it to a queue to be parsed.

[0264] Step 5:

[0265] The server analyzes the quote text using natural language processing (NLP) algorithms, which extract keywords, analyze sentiment, and identify intent.

[0266] Step 6:

[0267] The server then activates the emotion engine as part of the analysis results. The emotion engine recognizes the emotion of the quote and returns the result to the server. For example, it may determine a "positive emotion."

[0268] Step 7:

[0269] The server automatically generates new quotes based on the analysis results from the emotion engine. For example, for the input quote "Dreams are not meant to be seen, but to be chased," it generates the following result:

[0270] "It's important not only to have a dream, but to keep working towards making that dream come true."

[0271] Step 8:

[0272] The server stores the generated quote in a database, along with a unique identifier, the date and time of generation, and the original quote data.

[0273] Step 9:

[0274] Other users can access the rating interface and rate the generated quotes. Users can rate the quotes by entering a 5-point rating and a comment. For example, a user could rate a quote as "Rating: 4" or "Comment: This is a quote I can really relate to!"

[0275] Step 10:

[0276] The device collects user evaluation data and sends it to the server in JSON format.

[0277] Step 11:

[0278] The server stores the received evaluation data in a database. During the evaluation data collection process, the emotion engine analyzes emotions from users' evaluation comments and stores the results in the database along with the evaluation data.

[0279] Step 12:

[0280] The server periodically aggregates the evaluation data and calculates the average evaluation score for each quote, taking into account the results of sentiment analysis of the evaluation comments.

[0281] Step 13:

[0282] The server generates a ranking of quotes based on the results of the calculation. The rankings are sorted in descending order of evaluation score.

[0283] Step 14:

[0284] The server notifies the user of the ranking results and displays them on a web page or application, allowing the user to view the results from their own device.

[0285] This series of processes involves everything from user quote input to evaluation, sentiment analysis, aggregation, and ranking display. By adding the emotion engine, the generated quotes become more moving and relatable, enriching the user's mind.

[0286] Example 2

[0287] 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."

[0288] Conventional text generation systems have difficulty fully reflecting users' emotions and intentions, and the texts they generate often do not meet their expectations. Furthermore, the process of collecting evaluations of generated texts and generating rankings based on those evaluations is cumbersome and inefficient. This has led to problems such as lower user satisfaction and a decrease in the frequency of system use.

[0289] 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.

[0290] In this invention, the server includes a means for adding text data entered by a user to a queue for analysis and analyzing it, a means for analyzing the text data using a natural language processing algorithm, and a means for recognizing the emotion of the text data using an emotion engine. This enables the automatic generation of new text that reflects the user's emotion and intention. The server also includes a means for automatically generating new text based on the recognized emotion, a means for collecting evaluation data for the new text, a means for analyzing the evaluation data using the emotion engine and storing it in a database, a means for generating rankings based on the aggregation results, and a means for displaying the rankings to the user. This makes it easier for the generated text to meet user expectations, further streamlines the evaluation and ranking generation process, and improves the overall user experience.

[0291] "User" refers to a person who uses the system and is responsible for providing quotes and written data and providing evaluation feedback.

[0292] "Text data" refers to text information such as quotes and their background information entered by the user.

[0293] "JSON format" is an abbreviation for JavaScript Object Notation, and is a method for representing data in a structured and human-readable format.

[0294] A "server" is a computer system that receives and analyzes data sent from a terminal via a network and performs the necessary processing.

[0295] The "queue waiting for analysis" is a data structure for temporarily storing data waiting for analysis processing, and is managed by the server.

[0296] "Natural language processing algorithms" refer to technologies and methods for processing and analyzing human language using computers, and are used for keyword extraction, sentiment analysis, and intent identification.

[0297] An "emotion engine" is a software module that analyzes the emotions contained in text data and outputs the results.

[0298] A "generative AI model" is an algorithm or technique that uses artificial intelligence technology to create new sentences or quotes (e.g., a machine learning model).

[0299] A "database" is a data structure for systematically storing, retrieving, and managing data used within a system.

[0300] "Rating data" includes information on the ratings users give to generated quotes and sentences, and specifically refers to five-point ratings and comments.

[0301] "Ranking" refers to a ranked list of quotes and sentences generated based on evaluation data, sorted in order of evaluation score.

[0302] "Purpose-built Interface" means the particular user interface through which a user accesses the system and inputs quotes and text data.

[0303] The system of the present invention provides a series of processes that analyzes text data entered by users, automatically generates new text using an emotion engine, collects evaluations, and generates and displays rankings. The following specific technologies are used in the implementation of this system.

[0304] Hardware and Software Configuration

[0305] 1. Terminal

[0306] Users access the dedicated interface using a device such as a PC, smartphone, or tablet. The dedicated interface provides a form for users to enter quotes and their background information. This input data is converted to JSON format on the device and sent to the server.

[0307] 2. Server

[0308] The server receives the JSON data sent by the user and queues it for parsing. The server runs the following main software modules:

[0309] Natural Language Processing (NLP) algorithms: These use libraries (e.g., spaCy, NLTK, etc.) for keyword extraction, sentiment analysis, and intent identification.

[0310] Emotion engine: Used to recognize the emotion of the quote. For this purpose, NLP techniques and emotion analysis tools (e.g., IBM Watson® Tone Analyzer) are used.

[0311] Generative AI models: Artificial intelligence techniques (e.g., GPT-3) are used to generate new quotes and sentences.

[0312] Database Management System (DBMS): Used to store the generated quotes and evaluation data (e.g., MySQL, PostgreSQL, etc.).

[0313] Program processing flow

[0314] User Input

[0315] Users access a dedicated interface and enter a quote and a description of its context. For example:

[0316] Quote: "Dreams aren't meant to be dreamed, they're meant to be chased."

[0317] Context: "I created this quote to celebrate the effort of pursuing your dreams."

[0318] The terminal converts this input data into JSON format and sends it to the server via an HTTP request.

[0319] Analysis of quote data

[0320] The server adds the received JSON data to a queue for parsing, then uses natural language processing algorithms to parse the quote text and identify keywords, sentiment, and intent, and an emotion engine identifies the sentiment of the quote and returns the results to the server.

[0321] Generating new quotes

[0322] Based on the analysis results from the emotion engine, the server inputs a prompt into the generative AI model to generate a new quote. For example, the following prompt sentence is used:

[0323] Prompt: "Dreams aren't meant to be seen, they're meant to be chased. Generate a new quote with a positive sentiment."

[0324] The generated quotes are stored in a database.

[0325] Collecting ratings and generating rankings

[0326] Other users access the rating interface and rate the generated quotes that are displayed. Ratings are made in the form of a five-point rating and comments. The rating data is analyzed by an emotion engine and stored in a database. The server periodically aggregates the rating data and generates a ranking of quotes using a statistical algorithm. The generated ranking is displayed to users, who can view it from their own devices.

[0327] Specific examples

[0328] The user inputs "Dreams aren't just for dreaming, they're for chasing" and adds a background explanation. The system analyzes this input and generates a new quote that reflects a positive emotion. For example, a quote might be generated: "It's not just about having a dream, it's about continuing to work towards making that dream come true." This quote is then rated by other users, and it receives high ratings and appears at the top of the rankings.

[0329] The above is a mode for carrying out the invention, which is a method for efficiently realizing a series of processes from user input to evaluation and ranking generation in a manner that evokes emotional empathy.

[0330] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0331] Step 1:

[0332] The user accesses the dedicated interface using a terminal and inputs a quote and its background information. The user's input data is converted into JSON format and sent to the server as an HTTP request.

[0333] Input: User enters quote and background information

[0334] Output: JSON format data

[0335] Specific behavior:

[0336] A user accesses a dedicated form in a browser or mobile app.

[0337] Enter a quote and its background.

[0338] The program converts the input data into JSON format.

[0339] The converted data is sent to the server.

[0340] Step 2:

[0341] The server adds the received JSON data to a queue to be parsed and logs it.

[0342] Input: JSON format data sent from the terminal

[0343] Output: Data queued for analysis, log records

[0344] Specific behavior:

[0345] The server receives the HTTP request and reads the JSON data.

[0346] Queue the data for analysis.

[0347] Log the data received with a timestamp.

[0348] Step 3:

[0349] The server uses natural language processing (NLP) algorithms to analyze the quote text, extract keywords, analyze sentiment, and identify intent.

[0350] Input: JSON data retrieved from the queue to be parsed

[0351] Output: Keywords, sentiment, and intent identification

[0352] Specific behavior:

[0353] The server retrieves the data from the queue to be analyzed.

[0354] Analyze the quote text and extract keywords using an NLP library (e.g., spaCy, NLTK).

[0355] Analyze the sentiment of the quote using a sentiment analysis tool.

[0356] Applying algorithms to identify the intent of the text.

[0357] Generate extracted keywords, sentiment analysis results, and intent identification information.

[0358] Step 4:

[0359] The emotion engine recognizes the emotion of the input quote and returns the results to the server.

[0360] Input: Text of the quote and its analysis results

[0361] Output: Emotion recognition result

[0362] Specific behavior:

[0363] The server sends the quote text and its analysis results to an emotion engine (e.g., IBM Watson Tone Analyzer).

[0364] The sentiment engine performs sentiment analysis and assigns a sentiment label, such as "positive."

[0365] The emotion recognition results are returned to the server.

[0366] Step 5:

[0367] The server generates new quotes using a generative AI model based on the results of the emotion engine. For example, instructions are given to the generative AI model using prompt sentences.

[0368] Input: Emotion recognition results, keywords, and intent identification information

[0369] Output: The newly generated quote

[0370] Specific behavior:

[0371] The server inputs prompts into the generative AI model (e.g., GPT-3).

[0372] Prompt: "Dreams aren't meant to be seen, they're meant to be chased. Generate a new quote with a positive sentiment."

[0373] Get the generated quote.

[0374] Save the new quote to the database.

[0375] Step 6:

[0376] The server assigns a unique identifier to the generated quote and stores it in a database along with the date and time of generation and the original quote data.

[0377] Input: Newly generated quote

[0378] Output: Quotation data stored in the database

[0379] Specific behavior:

[0380] A unique ID is assigned to the generated quote.

[0381] The creation date and time and the original quote data are associated.

[0382] The generated quote data is stored in a database.

[0383] Step 7:

[0384] Other users can access the evaluation interface and evaluate the generated quotes. The evaluation data is analyzed by the emotion engine and sent to the server.

[0385] Input: Generated quote displayed in the evaluation interface

[0386] Output: Rating data, rating comments, sentiment analysis results

[0387] Specific behavior:

[0388] Other users access the evaluation interface.

[0389] Rate the generated quotes using a 5-point scale and in the form of comments.

[0390] The evaluation data is analyzed by the emotion engine.

[0391] The analysis results are sent to the server.

[0392] Step 8:

[0393] The server stores the received evaluation data in a database and periodically compiles it to generate rankings.

[0394] Input: Evaluation data, evaluation comments, sentiment analysis results

[0395] Output: Aggregation results, rankings

[0396] Specific behavior:

[0397] The server stores the rating data in a database.

[0398] The evaluation data is periodically aggregated using an aggregation algorithm.

[0399] Calculate the average rating score for each quote.

[0400] Rankings are generated that also reflect the results of sentiment analysis.

[0401] Step 9:

[0402] The server notifies and displays the rankings generated by the server to the user, who can then view the ranking results from their own devices.

[0403] Input: Aggregation results, ranking data

[0404] Output: The ranking displayed to the user

[0405] Specific behavior:

[0406] The server notifies the user of the ranking results.

[0407] The ranking results are displayed on a web page or application.

[0408] The user views the ranking results from their device.

[0409] (Application example 2)

[0410] 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."

[0411] Conventional quote generation systems do not adequately consider the user's emotions when generating quotes, making it difficult to generate sentences that move or resonate with users. Furthermore, the system for improving and optimizing quotes using user evaluation data is insufficient, resulting in inconsistent quality of generated sentences.

[0412] The specification process by the specification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for receiving text data input by a user, means for analyzing the text data, means for automatically generating new text based on the text data, means for saving the new text in a database, means for collecting evaluation data for the new text, means for aggregating the evaluation data, means for generating rankings based on the aggregation results, means for displaying the rankings to the user, means for analyzing emotions from the text input by the user and the evaluation data, and means for generating prompt sentences based on the analysis results and generating new sentences using an AI model. This makes it possible to automatically generate high-quality quotes and sentences that reflect the user's emotions and further improve the quality of the quotes by utilizing the evaluation data.

[0413] The "means for receiving text data entered by the user" refers to a device or program that allows the user to enter text data via a terminal and transfers the data to the server.

[0414] The "means for analyzing the text data" refers to a system or program that analyzes input text data using natural language processing technology and extracts meaning, emotion, and keywords.

[0415] The "means for automatically generating new sentences based on the sentence data" refers to algorithms and programs for generating new sentences and quotes based on the analysis results and existing data.

[0416] The "means for saving the new sentence in a database" refers to a system or program that registers the automatically generated new sentence in a database, assigns a unique identifier, and saves and manages it.

[0417] The "means for collecting evaluation data for the new text" is a device or program that collects evaluations and comments provided by users for the generated text.

[0418] The "means for aggregating the evaluation data" refers to a device or program for statistically processing the collected evaluation data and generating aggregation results such as average values ​​and rankings.

[0419] The "means for generating rankings based on the aggregation results" refers to a system or program that generates rankings of sentences and quotes based on the aggregated evaluation data.

[0420] The "means for displaying the ranking to the user" refers to a device or program for displaying the generated ranking results on a user interface.

[0421] "Means for analyzing emotions from user input text and evaluation data" refers to a system or program that analyzes emotions from text input by a user and evaluation comments and extracts the results.

[0422] "Means for generating prompt sentences based on the analysis results and generating new sentences using an AI model" refers to a system or program that generates prompt sentences based on the results of sentiment analysis and creates new sentences or quotes using a generative AI model.

[0423] The system for implementing the present invention includes a process for analyzing text data entered by a user and automatically generating new text. The system also generates quotes that reflect the user's emotions and generates rankings based on evaluation data. The system is operated through the cooperation of a terminal, a server, and a sentiment analysis engine.

[0424] First, the user inputs text data via a terminal. The terminal is equipped with a dedicated interface, and the user inputs a quote and its background explanation. This data is converted into JSON format and sent to the server.

[0425] The server adds the received JSON data to a queue for analysis and logs it, then analyzes the text data using natural language processing (NLP) algorithms, specifically extracting keywords from the text, analyzing sentiment, and identifying intent.

[0426] The sentiment analysis engine works as part of the analysis process to recognize emotions from the user's input text. For example, it determines whether the input "Dreams are not for dreaming, but for chasing" contains a positive emotion. The results are returned to the server.

[0427] The server then generates new sentences based on the results of the sentiment analysis. The generation algorithm applies parameters derived from the analysis and quotes from an existing database. It uses a generative AI model to generate prompts that reflect the sentiment. For example, it generates prompts like this:

[0428] Example: "Dreams aren't meant to be seen, they're meant to be chased. <|emotion:positive|>"

[0429] The generated quotes are stored in a database and assigned a unique identifier, along with the date and time of generation and the original quote data.

[0430] Other users then access a dedicated interface to rate the generated quotes. The rating data is entered in the form of a five-point rating and comments, and is sent from the device to the server. This allows the rating data to be collected.

[0431] The server uses a sentiment analysis engine to analyze the sentiment of users' comments while storing the evaluation data in a database. This information will be used as reference for future quote generation processes.

[0432] The evaluation data is collected over a set period of time, and an average evaluation score for each quote is calculated. Furthermore, the results of sentiment analysis of the evaluation comments are also reflected to generate a ranking of quotes. These rankings are sorted in descending order of evaluation score, and are notified to users as well as displayed on web pages and applications.

[0433] As a whole system, the terminal is responsible for accepting data input from users and sending the data to the server. The server is mainly responsible for data analysis, generation, storage, and aggregation of evaluation data and display of rankings. The sentiment analysis engine is a key element in the user sentiment recognition and generation process.

[0434] The main hardware and software used are as follows:

[0435] Hardware: Servers (e.g., Amazon EC2 or Google Cloud), user devices (smartphones, PCs)

[0436] Software: TextBlob (sentiment analysis), Transformers (Hugging Face library), GPT-2 model (generative AI model), Python

[0437] The system of the present invention allows users to create inspirational and relatable quotes, and then share and improve the quality of these quotes with other users through ratings.

[0438] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0439] Step 1:

[0440] The user inputs text data via a terminal. The terminal is equipped with a dedicated interface, and the user inputs quotes and their background information. This data is converted into JSON format and sent to the server.

[0441] Input: User-entered quote and background information

[0442] Output: Data converted to JSON format

[0443] Step 2:

[0444] The server adds the received JSON data to a queue to be parsed and logs it. At this stage, the data is also checked for consistency and integrity.

[0445] Input: JSON format data

[0446] Output: Queueing and logging

[0447] Step 3:

[0448] The server analyzes the text data using natural language processing (NLP) algorithms, specifically extracting keywords from the text, analyzing sentiment, and identifying intent.

[0449] Input: Data retrieved from the analysis queue

[0450] Output: Keywords, sentiment analysis results, intent identification results

[0451] Step 4:

[0452] The emotion analysis engine recognizes the emotion from the user's input text, and the analysis results are returned to the server and used in the next text generation process.

[0453] Input: Text data

[0454] Output: Emotion analysis results

[0455] Step 5:

[0456] The server generates new sentences based on the results of sentiment analysis. The generation algorithm uses a generative AI model and prompts that reflect the sentiment. For example, the following prompts are generated:

[0457] "Dreams aren't meant to be seen, they're meant to be chased. <|emotion:positive|>"

[0458] Input: Sentiment analysis results and keyword data

[0459] Output: The new sentence generated

[0460] Step 6:

[0461] The generated quotes are stored in a database. They are assigned a unique identifier and are saved together with the date and time of generation and the original quote data.

[0462] Input: New sentence generated

[0463] Output: Save to database and give it a unique identifier

[0464] Step 7:

[0465] Other users can access a dedicated interface to rate the generated quotes. Ratings are entered in the form of a five-point scale and comments.

[0466] Input: Generate quote

[0467] Output: Rating data (5-point rating and comments)

[0468] Step 8:

[0469] The server stores the received evaluation data in a database. During this process, it uses a sentiment analysis engine to analyze emotions from the evaluation comments and saves the information in the database.

[0470] Input: Evaluation data

[0471] Output: Evaluation data and analysis results saved in a database

[0472] Step 9:

[0473] The evaluation data is collected over a set period of time, and an average evaluation score for each quote is calculated. Furthermore, the results of sentiment analysis of the evaluation comments are also reflected in the ranking of quotes.

[0474] Input: Evaluation data stored in a database

[0475] Output: Evaluation scores and rankings

[0476] Step 10:

[0477] The generated rankings are notified to the user and displayed on a web page or application, where the user can view these rankings.

[0478] Input: Ranking data

[0479] Output: Display of rankings

[0480] 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.

[0481] 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.

[0482] 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.

[0483] [Second embodiment]

[0484] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0485] 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.

[0486] 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).

[0487] 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.

[0488] 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.

[0489] 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).

[0490] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0491] 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.

[0492] 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.

[0493] 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.

[0494] In the smart glasses 214, 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.

[0495] 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."

[0496] The system of the present invention analyzes quotes and text data entered by users and provides a process for automatically generating new quotes. This system is realized through the cooperation of a terminal and a server.

[0497] 1. User Input

[0498] The user accesses a dedicated interface on the device and enters the quote and its context. For example, the user might enter:

[0499] Quote: "Dreams aren't meant to be dreamed, they're meant to be chased."

[0500] Context: "I created this quote to celebrate the effort of pursuing your dreams."

[0501] The terminal converts this input data into JSON format and sends it to the server.

[0502] 2. Sending and analyzing quote data

[0503] The server receives and analyzes the JSON data sent from the device. It uses natural language processing (NLP) to perform a structural analysis of the quote and extract keywords and sentiments.

[0504] 3. Automatic generation of quotes

[0505] The server automatically generates new quotes based on the analysis results. For example, for the input quote "Dreams are not meant to be seen, but to be chased," it generates the following result:

[0506] "Those who keep chasing their dreams will win, not those who just dream."

[0507] The generated quotes are stored in a database.

[0508] 4. Save the latest quotes

[0509] The server stores the generated quotes in a database and makes them available for users to view.

[0510] 5. User Ratings

[0511] Other users can access an interface to rate the generated quotes. Users can rate them by entering a five-point rating and a comment. For example, a user might rate them as follows:

[0512] Rating: 4

[0513] Comment: "A very relatable quote!"

[0514] The terminal transmits this evaluation data to the server.

[0515] 6. Collection and compilation of evaluation data

[0516] The server collects and statistically analyzes the received evaluation data, aggregates the evaluation scores, and calculates an average evaluation score for each quote.

[0517] 7. Generating and Displaying Rankings

[0518] The server then creates a ranking of the most popular quotes based on the results of the calculation, and displays the rankings on a web page or in an application for users to view.

[0519] In this way, the system can fairly evaluate quotes submitted by users and automatically generate new, high-quality quotes. This allows users to have a spiritually enriching experience. As a specific example, if the quotes generated from the user input mentioned above are rated by many users and ranked highly, it is expected to generate great emotion and empathy.

[0520] The processing flow will be explained below.

[0521] Step 1:

[0522] The user accesses a dedicated interface on the device and inputs a quote and its background information, which is then converted to JSON format on the device.

[0523] Step 2:

[0524] The terminal sends the data converted into JSON format to the server using an HTTP POST request.

[0525] Step 3:

[0526] The server receives the JSON data and adds it to a queue to be parsed. The received data is logged.

[0527] Step 4:

[0528] The server uses natural language processing (NLP) algorithms to analyze the quote text, specifically extracting keywords, analyzing sentiment, and identifying intent.

[0529] Step 5:

[0530] The server automatically generates new quotes based on the analysis results. The generation algorithm utilizes quotes from an existing database and parameters obtained from the analysis results.

[0531] Step 6:

[0532] The server stores the generated quote in a database and assigns a unique identifier to it. It also stores the date and time of generation and the original quote data.

[0533] Step 7:

[0534] Other users can access the rating interface and rate the generated quotes by entering a 5-point rating and comments.

[0535] Step 8:

[0536] The device collects user evaluation data and sends it to the server in JSON format.

[0537] Step 9:

[0538] The server stores the received evaluation data in a database and automatically updates the evaluation score.

[0539] Step 10:

[0540] The server aggregates the evaluation data at regular intervals and calculates the average evaluation score for each quote using a statistical algorithm.

[0541] Step 11:

[0542] The server generates a ranking of quotes based on the results of the calculation. The rankings are sorted in descending order of evaluation score.

[0543] Step 12:

[0544] The server notifies the user of the ranking results and displays them on a web page or application, allowing the user to view the results from their own device.

[0545] In this way, the system carries out a series of processes from the user's quote input to evaluation, compilation, and display of rankings.

[0546] Example 1

[0547] 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."

[0548] While many sentences and quotes are available on the Internet, their quality and value vary. There is a need for a system that can automatically generate new, high-quality quotes and sentences based on user-entered text, compile evaluations of these, and provide them in a ranking format, thereby enabling the creation and sharing of valuable content.

[0549] 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.

[0550] In this invention, the server includes: means for receiving text data entered by a user through an interface; means for converting the text data into JSON format; means for transmitting the data converted into JSON format to the server; means for the server to receive the JSON data; means for the server to analyze the text data using natural language processing technology and extract keywords and emotions; means for automatically generating new text based on the analysis results using a generative AI model; means for saving the new text in a database; means for collecting data on evaluations of the new text by other users through the user interface; means for the server to statistically analyze the evaluation data and calculate an evaluation score; means for generating a ranking of highly rated texts based on the aggregation results; and means for displaying the ranking to the user. This makes it possible to automatically generate high-quality quotes and texts and share their value through evaluations.

[0551] "User" refers to a person who uses the system to input quotes and sentences and give ratings.

[0552] An "interface" refers to a dedicated screen on a web page or application that allows users to enter quotes or text.

[0553] "Text data" refers to the text information of quotes and their background explanations entered by the user.

[0554] "JSON format" stands for JavaScript Object Notation and refers to a data exchange format that is easy to read for both humans and machines.

[0555] "Server" refers to the computer system that receives and analyzes data from users, and generates and stores new text.

[0556] "Natural language processing technology" refers to technology that enables computers to understand and generate human language and perform various tasks.

[0557] "Generative AI model" refers to an artificial intelligence model trained to automatically generate new text based on a given prompt.

[0558] "Analysis results" refers to keywords and emotional information extracted from quote data analyzed using natural language processing technology.

[0559] "Database" refers to a collection of information that allows for efficient management and retrieval of saved new quotes and evaluation data.

[0560] "Evaluation data" refers to feedback information such as scores and comments made by users on generated quotes.

[0561] "Statistical analysis" refers to the process of numerically processing evaluation data to reveal trends and patterns in the data.

[0562] The "evaluation score" refers to a numerical representation of the quality of a quote or piece of writing based on user feedback.

[0563] "Ranking" refers to a list of quotes and sentences ranked based on their evaluation scores.

[0564] The system of the present invention analyzes quotes and text data entered by users and provides a process for automatically generating new quotes. This system is realized through the cooperation of a terminal and a server.

[0565] User Input

[0566] The user accesses a dedicated interface on the terminal and inputs the quote and its background information. For example, they might input:

[0567] Quote: "Dreams aren't meant to be dreamed, they're meant to be chased."

[0568] Context: "I created this quote to celebrate the effort of pursuing your dreams."

[0569] The terminal converts this input data into JSON format and sends it to the server.

[0570] Sending and analyzing quote data

[0571] The server receives and analyzes the JSON data sent from the device. The server then performs a structural analysis of the quote using natural language processing (NLP). Specific software used for NLP includes Python's "NLTK" and "spaCy." This allows the server to extract keywords and sentiment from the quote.

[0572] Automatic generation of quotes

[0573] The server automatically generates new quotes using a generative AI model based on the analysis results. Generative AI models used include "GPT-3" and "BERT." For example, the following prompt sentence is input to the generative AI model:

[0574] Quote: "Dreams aren't meant to be dreamed, they're meant to be chased."

[0575] Context: "I created this quote to celebrate the effort of pursuing your dreams."

[0576] New auto-generated quote:

[0577] The generative AI model generates new quotes based on this prompt, such as the following:

[0578] "Those who keep chasing their dreams will win, not those who just dream."

[0579] The generated quotes are stored in a database by the server.

[0580] Save the latest quotes

[0581] The server stores the generated quotes in a database and makes them available for users to view. For this purpose, a database management system such as "MySQL" or "PostgreSQL" is used.

[0582] User Rating

[0583] Other users have access to a dedicated interface to rate the generated quotes. Users can rate them by entering a five-point scale and a comment. For example, they can rate them as follows:

[0584] Rating: 4

[0585] Comment: "A very relatable quote!"

[0586] The terminal converts this evaluation data into JSON format and sends it to the server.

[0587] Collection and compilation of evaluation data

[0588] The server analyzes the received evaluation data, stores it in a database, and aggregates it using data analysis libraries such as Pandas and NumPy to calculate an average evaluation score for each quote.

[0589] Generating and displaying rankings

[0590] The server then creates a ranking of the most popular quotes based on the results of the calculation, and displays the rankings for users to view on a web page or application.

[0591] In this way, the system can fairly evaluate user-submitted quotes and automatically generate new, high-quality quotes, a process that provides users with a spiritually enriching experience.

[0592] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0593] Step 1:

[0594] The user accesses a dedicated interface on the terminal and inputs a quote and its background information. The quote and background information based on the user's input are then returned. For example, enter the following:

[0595] Quote: "Dreams aren't meant to be dreamed, they're meant to be chased."

[0596] Context: "I created this quote to celebrate the effort of pursuing your dreams."

[0597] The terminal converts this input data into JSON format, and the converted JSON data is returned.

[0598] Step 2:

[0599] The terminal sends the converted JSON data to the server as an HTTP request. For example, it sends the following JSON data:

[0600] json

[0601] {

[0602] "quote": "Dreams aren't meant to be seen, they're meant to be chased.",

[0603] "background": "This quote was created to celebrate the effort of pursuing your dreams."

[0604] }

[0605] The server parses the received HTTP request and extracts the JSON data. The extracted JSON data is returned.

[0606] Step 3:

[0607] The server uses natural language processing (NLP) technology to structurally analyze the quotes. For NLP, it uses Python's "NLTK" and "spaCy." Keywords and emotions are extracted from the analyzed data. For example, keywords such as "dream" and "chasing" as well as positive emotions are extracted.

[0608] Step 4:

[0609] The server prepares to use a generative AI model (e.g., "GPT-3") based on the extracted keywords and emotions. It generates a prompt and inputs it into the AI ​​model. A new quote is generated based on this prompt. Example of a prompt:

[0610] Quote: "Dreams aren't meant to be dreamed, they're meant to be chased."

[0611] Context: "I created this quote to celebrate the effort of pursuing your dreams."

[0612] New auto-generated quote:

[0613] One example of a new quote that was generated is, "Those who keep chasing their dreams will win, not those who just dream."

[0614] Step 5:

[0615] The server stores the generated new quotes in a database so that users can view and rate them later.

[0616] Step 6:

[0617] Other users have access to a dedicated interface to rate the generated quotes. Users can rate them by entering a five-point scale and a comment. For example, they can rate them as follows:

[0618] Rating: 4

[0619] Comment: "A very relatable quote!"

[0620] The device converts this evaluation data into JSON format and sends it to the server, where the converted JSON data is obtained.

[0621] Step 7:

[0622] The server analyzes the received evaluation data and stores it in a database. From the analyzed evaluation data, it calculates the evaluation score for each quote. The server aggregates the evaluation scores using "Pandas" and "NumPy." The aggregated score data is obtained.

[0623] Step 8:

[0624] The server then creates a ranking list of the most highly rated quotes based on the results of the calculation. The rankings are displayed on a web page or application so that users can view them. A list of ranked quotes is obtained.

[0625] (Application example 1)

[0626] 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."

[0627] Conventional quote and sentence generation systems have the ability to generate new quotes and sentences based on user input, but they lack the ability to evaluate the generated sentences or display rankings, making it difficult to improve user satisfaction and form a community. Furthermore, they lack a mechanism for evaluating the quality of the generated quotes and providing high-quality quotes.

[0628] 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.

[0629] In this invention, the server includes means for receiving text data entered by a user, means for analyzing the text data, means for automatically generating new texts based on the text data, means for saving the new texts in a database, means for presenting the new texts to users, means for collecting evaluation data for the new texts, means for aggregating the evaluation data, means for generating rankings based on the aggregation results, and means for displaying the rankings to users. This improves the quality of quotes automatically generated based on text entered by a user, and also collects evaluations from users to generate rankings, thereby improving user satisfaction and enabling the formation of a community.

[0630] "Means for receiving text data entered by the user" refers to a function for electronically receiving text data entered by the user through the interface.

[0631] The "means for analyzing text data" is a function for analyzing received text data using natural language processing technology and understanding its structure and meaning.

[0632] "Means for automatically generating new sentences" is a function for automatically generating new sentences based on the analysis results.

[0633] The "means for saving new sentences to a database" is a function for recording the generated new sentences in a database so that they can be accessed later.

[0634] "Means for presenting new text to the user" refers to a function for visually or electronically displaying the generated new text to the user.

[0635] "Means for collecting evaluation data" is a function for collecting evaluations and feedback from users on new text.

[0636] The "means for aggregating evaluation data" is a function for analyzing collected evaluation data and aggregating it as statistical information.

[0637] The "means for generating rankings" is a function for determining the ranking of new sentences based on the evaluation data and displaying them in a ranking format.

[0638] The "means for displaying rankings to users" is a function for displaying the generated rankings so that users can view them.

[0639] To realize the system of this invention, it is necessary to build a system based on a smartphone application. This system receives quotes and text data entered by the user, analyzes them, and provides a process for automatically generating new quotes. Specifically, the following hardware and software are used:

[0640] Hardware and Software Used

[0641] Smartphone: iOS or Android device

[0642] Server: AWS or Google Cloud Platform

[0643] Backend: Python / Django or Node.js

[0644] Natural Language Processing library: spaCy or NLTK

[0645] Database: PostgreSQL or MongoDB

[0646] Frontend: React Native or Flutter

[0647] Specific operation of the system

[0648] 1. User Input

[0649] Users input a quote and its background through a dedicated interface in a smartphone application. For example, they can input a quote such as "Dreams are not meant to be seen, but to be chased." This input data is converted into JSON format and sent to the server.

[0650] 2. Quote data analysis

[0651] The server receives the JSON data and analyzes it using a natural language processing library (e.g., spaCy or NLTK), which understands the structure of the sentence and extracts keywords and sentiment.

[0652] 3. Automatic generation of quotes

[0653] Based on the analysis results, a generative AI model is used to automatically generate new quotes. For example, in response to the input quote, "Dreams are not meant to be seen, but to be pursued," the generated quote would be, "Those who continue to chase their dreams will achieve victory, not those who simply dream."

[0654] 4. Save and display quotes

[0655] The generated quotes are stored in a database and can be viewed by users through the application.

[0656] 5. Ratings and Rankings

[0657] Other users can rate the generated quotes on a five-point scale and add comments. This rating data is sent to the server and statistically analyzed, after which a ranking is generated and displayed.

[0658] Specific examples

[0659] A user inputs "Dreams are not meant to be seen, but to be pursued," and then adds the background explanation "I created this quote to praise the effort of pursuing one's dreams." This causes the server to generate a new quote: "Those who continue to pursue their dreams will achieve victory, not those who simply dream."

[0660] Prompt Sentence Examples

[0661] Generate a new quote based on the following quote:

[0662] Quote: Dreams aren't meant to be seen, they're meant to be chased.

[0663] Background: This quote was created to celebrate the effort of chasing your dreams.

[0664] The above is a specific description of an embodiment of the present invention. This configuration improves the quality of quotes automatically generated based on text entered by users, collects user ratings and generates rankings, and enables increased user satisfaction and community formation.

[0665] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0666] Step 1:

[0667] A user inputs a quote and its background information through the smartphone application interface. This input data is converted to JSON format by the device and sent to the server. For example, a user inputs a quote such as "Dreams are not meant to be seen, but to be pursued" and a background information such as "I created this quote to praise the effort of pursuing one's dreams," and the data is sent to the server as JSON data (e.g., {"quote": "Dreams are not meant to be seen, but to be pursued.", "background": "I created this quote to praise the effort of pursuing one's dreams."}).

[0668] Step 2:

[0669] The server parses the received JSON data. In this parsing process, a natural language processing library (e.g., spaCy or NLTK) is used to analyze the input sentence and extract its structure and keywords. For example, keywords such as "dream," "see," and "chase" are extracted from the quote, "Dreams are not for dreaming, but for chasing."

[0670] Step 3:

[0671] The server automatically generates new quotes using a generative AI model based on the analysis results. In this process, the generative AI model uses the keywords and emotional information extracted in the previous step as prompts to generate new quotes. For example, in response to the statement, "Dreams are not meant to be seen, but to be pursued," the new quote generated is, "Those who continue to chase their dreams will achieve victory, not those who simply dream."

[0672] Step 4:

[0673] The generated quotes are stored in a database by the server. This allows new quotes to be stored in the database and made available for future reference or presentation. For example, the generated quote, its creation date, and evaluation information are recorded in the database.

[0674] Step 5:

[0675] The server prepares the generated quote for presentation to the user. Specifically, it generates data for visually displaying the generated quote on a web page or application. For example, the quote text, rating buttons, comment input fields, etc. are displayed on the screen.

[0676] Step 6:

[0677] Other users can view the generated quotes and rate or comment on them. They enter their rating in the 5-point rating system and comment field, and then send the data from their device to the server. For example, a user can enter "Rating: 4" and "Comment: This is a quote I really relate to!", which is then sent to the server as JSON data.

[0678] Step 7:

[0679] The server aggregates the received evaluation data and calculates an evaluation score for each quote, for example, by statistically processing the evaluation data for each quote and calculating an average evaluation score.

[0680] Step 8:

[0681] The server generates a ranking based on the results of the calculation and displays it to the user. Highly rated quotes are displayed at the top of the ranking. For example, the generated ranking data is displayed in list format on the user's app screen, and the user can view it.

[0682] These steps improve the performance and quality of quotes that are automatically generated based on text entered by users, and also enable rankings to be displayed based on user ratings.

[0683] 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.

[0684] The system of the present invention analyzes quotes and text data entered by the user and automatically generates new quotes, and also uses an emotion engine to recognize the user's emotions and reflect them in the analysis and generation process. This system is realized through the cooperation of a terminal, a server, and an emotion engine.

[0685] 1. User Input

[0686] The user accesses a dedicated interface on the device and enters the quote and its context. For example, the user might enter:

[0687] Quote: "Dreams aren't meant to be dreamed, they're meant to be chased."

[0688] Context: "I created this quote to celebrate the effort of pursuing your dreams."

[0689] The terminal converts this input data into JSON format and sends it to the server.

[0690] 2. Sending and analyzing quote data

[0691] The server receives the JSON data and adds it to a queue for analysis. The received data is logged. Natural language processing (NLP) algorithms are then used to analyze the quote text. Specifically, they extract keywords, perform sentiment analysis, and identify intent.

[0692] 3. Operation of the Emotion Engine

[0693] As part of the analysis process, the emotion engine recognizes the sentiment of the quote. For example, it determines whether the quote entered by the user contains positive emotions such as "effort" or "dream." The emotion engine uses NLP algorithms to perform the sentiment analysis and returns the results to the server.

[0694] 4. Automatic generation of quotes

[0695] The server automatically generates new quotes based on data including analysis results from the emotion engine. The generation algorithm utilizes parameters obtained from quotes in the existing database and analysis results. For example, for the input quote "Dreams are not meant to be seen, but to be pursued," the server reflects the "positive emotion" found in the emotion analysis and generates the following result:

[0696] "Not only do you have a dream, but you also have to keep working to make that dream come true."

[0697] The generated quotes are stored in a database.

[0698] 5. Save the latest quotes

[0699] The server stores the generated quote in a database and assigns a unique identifier to it. It also stores the date and time of generation and the original quote data.

[0700] 6. User Ratings

[0701] Other users can access the rating interface and rate the generated quotes. Users can rate the quotes by entering a five-point rating and comments. For example, a user might rate the quote as follows:

[0702] Rating: 4

[0703] Comment: "A very relatable quote!"

[0704] The terminal collects this evaluation data and transmits it to the server.

[0705] 7. Collecting evaluation data and applying the emotion engine

[0706] While the server is storing the received evaluation data in a database, the emotion engine analyzes the emotions from the user's evaluation comments and stores them in the database along with the evaluation data. This information will be used as reference information for future quote generation processes.

[0707] 8. Aggregating evaluation data and generating rankings

[0708] The server periodically aggregates the evaluation data and calculates the average evaluation score for each quote. The aggregation is performed using a statistical algorithm. The server also reflects the results of sentiment analysis of the evaluation comments.

[0709] 9. Generating and Displaying Rankings

[0710] The server generates a ranking of quotes based on the results of the calculation. The rankings are sorted in descending order of evaluation score, and the ranking results are notified to the user and displayed on a web page or application. The user can view the results on their own device.

[0711] In this way, the system carries out a series of processes, from user quote input to evaluation, compilation, and ranking display. Thanks to the effect of the emotion engine, the generated quotes are more moving and evoke empathy, enriching the user's heart. As a specific example, if a quote generated from user input is rated by many users and ranks highly, it is expected to generate great emotion and empathy.

[0712] The processing flow will be explained below.

[0713] Step 1:

[0714] The user accesses a dedicated interface on the device and inputs a quote and its background explanation. For example, the user inputs the quote "Dreams are not meant to be seen, but to be pursued" and the background explanation "I created this quote to praise the efforts of pursuing one's dreams."

[0715] Step 2:

[0716] The terminal converts the user input data into JSON format, which looks like this:

[0717] {

[0718] "quote": "Dreams aren't meant to be seen, they're meant to be chased.",

[0719] "background": "This quote was created to celebrate the effort of pursuing your dreams."

[0720] }

[0721] Step 3:

[0722] The device sends JSON format data to the server using an HTTP POST request.

[0723] Step 4:

[0724] The server receives the JSON data and adds it to a queue to be parsed.

[0725] Step 5:

[0726] The server analyzes the quote text using natural language processing (NLP) algorithms, which extract keywords, analyze sentiment, and identify intent.

[0727] Step 6:

[0728] The server then activates the emotion engine as part of the analysis results. The emotion engine recognizes the emotion of the quote and returns the result to the server. For example, it may determine a "positive emotion."

[0729] Step 7:

[0730] The server automatically generates new quotes based on the analysis results from the emotion engine. For example, for the input quote "Dreams are not meant to be seen, but to be chased," it generates the following result:

[0731] "It's important not only to have a dream, but to keep working towards making that dream come true."

[0732] Step 8:

[0733] The server stores the generated quote in a database, along with a unique identifier, the date and time of generation, and the original quote data.

[0734] Step 9:

[0735] Other users can access the rating interface and rate the generated quotes. Users can rate the quotes by entering a 5-point rating and a comment. For example, a user could rate a quote as "Rating: 4" or "Comment: This is a quote I can really relate to!"

[0736] Step 10:

[0737] The device collects user evaluation data and sends it to the server in JSON format.

[0738] Step 11:

[0739] The server stores the received evaluation data in a database. During the evaluation data collection process, the emotion engine analyzes emotions from users' evaluation comments and stores the results in the database along with the evaluation data.

[0740] Step 12:

[0741] The server periodically aggregates the evaluation data and calculates the average evaluation score for each quote, taking into account the results of sentiment analysis of the evaluation comments.

[0742] Step 13:

[0743] The server generates a ranking of quotes based on the results of the calculation. The rankings are sorted in descending order of evaluation score.

[0744] Step 14:

[0745] The server notifies the user of the ranking results and displays them on a web page or application, allowing the user to view the results from their own device.

[0746] This series of processes involves everything from user quote input to evaluation, sentiment analysis, aggregation, and ranking display. By adding the emotion engine, the generated quotes become more moving and relatable, enriching the user's mind.

[0747] Example 2

[0748] 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."

[0749] Conventional text generation systems have difficulty fully reflecting users' emotions and intentions, and the texts they generate often do not meet their expectations. Furthermore, the process of collecting evaluations of generated texts and generating rankings based on those evaluations is cumbersome and inefficient. This has led to problems such as lower user satisfaction and a decrease in the frequency of system use.

[0750] 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.

[0751] In this invention, the server includes a means for adding text data entered by a user to a queue for analysis and analyzing it, a means for analyzing the text data using a natural language processing algorithm, and a means for recognizing the emotion of the text data using an emotion engine. This enables the automatic generation of new text that reflects the user's emotion and intention. The server also includes a means for automatically generating new text based on the recognized emotion, a means for collecting evaluation data for the new text, a means for analyzing the evaluation data using the emotion engine and storing it in a database, a means for generating rankings based on the aggregation results, and a means for displaying the rankings to the user. This makes it easier for the generated text to meet user expectations, further streamlines the evaluation and ranking generation process, and improves the overall user experience.

[0752] "User" refers to a person who uses the system and is responsible for providing quotes and written data and providing evaluation feedback.

[0753] "Text data" refers to text information such as quotes and their background information entered by the user.

[0754] "JSON format" is an abbreviation for JavaScript Object Notation, and is a method for representing data in a structured and human-readable format.

[0755] A "server" is a computer system that receives and analyzes data sent from a terminal via a network and performs the necessary processing.

[0756] The "queue waiting for analysis" is a data structure for temporarily storing data waiting for analysis processing, and is managed by the server.

[0757] "Natural language processing algorithms" refer to technologies and methods for processing and analyzing human language using computers, and are used for keyword extraction, sentiment analysis, and intent identification.

[0758] An "emotion engine" is a software module that analyzes the emotions contained in text data and outputs the results.

[0759] A "generative AI model" is an algorithm or technique that uses artificial intelligence technology to create new sentences or quotes (e.g., a machine learning model).

[0760] A "database" is a data structure for systematically storing, retrieving, and managing data used within a system.

[0761] "Rating data" includes information on the ratings users give to generated quotes and sentences, and specifically refers to five-point ratings and comments.

[0762] "Ranking" refers to a ranked list of quotes and sentences generated based on evaluation data, sorted in order of evaluation score.

[0763] "Purpose-built Interface" means the particular user interface through which a user accesses the system and inputs quotes and text data.

[0764] The system of the present invention provides a series of processes that analyzes text data entered by users, automatically generates new text using an emotion engine, collects evaluations, and generates and displays rankings. The following specific technologies are used in the implementation of this system.

[0765] Hardware and Software Configuration

[0766] 1. Terminal

[0767] Users access the dedicated interface using a device such as a PC, smartphone, or tablet. The dedicated interface provides a form for users to enter quotes and their background information. This input data is converted to JSON format on the device and sent to the server.

[0768] 2. Server

[0769] The server receives the JSON data sent by the user and queues it for parsing. The server runs the following main software modules:

[0770] Natural Language Processing (NLP) algorithms: These use libraries (e.g., spaCy, NLTK, etc.) for keyword extraction, sentiment analysis, and intent identification.

[0771] Emotion engine: Used to recognize the sentiment of quotes. For this purpose, NLP techniques and emotion analysis tools (e.g., IBM Watson Tone Analyzer) are used.

[0772] Generative AI models: Artificial intelligence techniques (e.g., GPT-3) are used to generate new quotes and sentences.

[0773] Database Management System (DBMS): Used to store the generated quotes and evaluation data (e.g., MySQL, PostgreSQL, etc.).

[0774] Program processing flow

[0775] User Input

[0776] Users access a dedicated interface and enter a quote and a description of its context. For example:

[0777] Quote: "Dreams aren't meant to be dreamed, they're meant to be chased."

[0778] Context: "I created this quote to celebrate the effort of pursuing your dreams."

[0779] The terminal converts this input data into JSON format and sends it to the server via an HTTP request.

[0780] Analysis of quote data

[0781] The server adds the received JSON data to a queue for parsing, then uses natural language processing algorithms to parse the quote text and identify keywords, sentiment, and intent, and an emotion engine identifies the sentiment of the quote and returns the results to the server.

[0782] Generating new quotes

[0783] Based on the analysis results from the emotion engine, the server inputs a prompt into the generative AI model to generate a new quote. For example, the following prompt sentence is used:

[0784] Prompt: "Dreams aren't meant to be seen, they're meant to be chased. Generate a new quote with a positive sentiment."

[0785] The generated quotes are stored in a database.

[0786] Collecting ratings and generating rankings

[0787] Other users access the rating interface and rate the generated quotes that are displayed. Ratings are made in the form of a five-point rating and comments. The rating data is analyzed by an emotion engine and stored in a database. The server periodically aggregates the rating data and generates a ranking of quotes using a statistical algorithm. The generated ranking is displayed to users, who can view it from their own devices.

[0788] Specific examples

[0789] The user inputs "Dreams aren't just for dreaming, they're for chasing" and adds a background explanation. The system analyzes this input and generates a new quote that reflects a positive emotion. For example, a quote might be generated: "It's not just about having a dream, it's about continuing to work towards making that dream come true." This quote is then rated by other users, and it receives high ratings and appears at the top of the rankings.

[0790] The above is a mode for carrying out the invention, which is a method for efficiently realizing a series of processes from user input to evaluation and ranking generation in a manner that evokes emotional empathy.

[0791] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0792] Step 1:

[0793] The user accesses the dedicated interface using a terminal and inputs a quote and its background information. The user's input data is converted into JSON format and sent to the server as an HTTP request.

[0794] Input: User enters quote and background information

[0795] Output: JSON format data

[0796] Specific behavior:

[0797] A user accesses a dedicated form in a browser or mobile app.

[0798] Enter a quote and its background.

[0799] The program converts the input data into JSON format.

[0800] The converted data is sent to the server.

[0801] Step 2:

[0802] The server adds the received JSON data to a queue to be parsed and logs it.

[0803] Input: JSON format data sent from the terminal

[0804] Output: Data queued for analysis, log records

[0805] Specific behavior:

[0806] The server receives the HTTP request and reads the JSON data.

[0807] Queue the data for analysis.

[0808] Log the data received with a timestamp.

[0809] Step 3:

[0810] The server uses natural language processing (NLP) algorithms to analyze the quote text, extract keywords, analyze sentiment, and identify intent.

[0811] Input: JSON data retrieved from the queue to be parsed

[0812] Output: Keywords, sentiment, and intent identification

[0813] Specific behavior:

[0814] The server retrieves the data from the queue to be analyzed.

[0815] Analyze the quote text and extract keywords using an NLP library (e.g., spaCy, NLTK).

[0816] Analyze the sentiment of the quote using a sentiment analysis tool.

[0817] Applying algorithms to identify the intent of the text.

[0818] Generate extracted keywords, sentiment analysis results, and intent identification information.

[0819] Step 4:

[0820] The emotion engine recognizes the emotion of the input quote and returns the results to the server.

[0821] Input: Text of the quote and its analysis results

[0822] Output: Emotion recognition result

[0823] Specific behavior:

[0824] The server sends the quote text and its analysis results to an emotion engine (e.g., IBM Watson Tone Analyzer).

[0825] The sentiment engine performs sentiment analysis and assigns a sentiment label, such as "positive."

[0826] The emotion recognition results are returned to the server.

[0827] Step 5:

[0828] The server generates new quotes using a generative AI model based on the results of the emotion engine. For example, instructions are given to the generative AI model using prompt sentences.

[0829] Input: Emotion recognition results, keywords, and intent identification information

[0830] Output: The newly generated quote

[0831] Specific behavior:

[0832] The server inputs prompts into the generative AI model (e.g., GPT-3).

[0833] Prompt: "Dreams aren't meant to be seen, they're meant to be chased. Generate a new quote with a positive sentiment."

[0834] Get the generated quote.

[0835] Save the new quote to the database.

[0836] Step 6:

[0837] The server assigns a unique identifier to the generated quote and stores it in a database along with the date and time of generation and the original quote data.

[0838] Input: Newly generated quote

[0839] Output: Quotation data stored in the database

[0840] Specific behavior:

[0841] A unique ID is assigned to the generated quote.

[0842] The creation date and time and the original quote data are associated.

[0843] The generated quote data is stored in a database.

[0844] Step 7:

[0845] Other users can access the evaluation interface and evaluate the generated quotes. The evaluation data is analyzed by the emotion engine and sent to the server.

[0846] Input: Generated quote displayed in the evaluation interface

[0847] Output: Rating data, rating comments, sentiment analysis results

[0848] Specific behavior:

[0849] Other users access the evaluation interface.

[0850] Rate the generated quotes using a 5-point scale and in the form of comments.

[0851] The evaluation data is analyzed by the emotion engine.

[0852] The analysis results are sent to the server.

[0853] Step 8:

[0854] The server stores the received evaluation data in a database and periodically compiles it to generate rankings.

[0855] Input: Evaluation data, evaluation comments, sentiment analysis results

[0856] Output: Aggregation results, rankings

[0857] Specific behavior:

[0858] The server stores the rating data in a database.

[0859] The evaluation data is periodically aggregated using an aggregation algorithm.

[0860] Calculate the average rating score for each quote.

[0861] Rankings are generated that also reflect the results of sentiment analysis.

[0862] Step 9:

[0863] The server notifies and displays the rankings generated by the server to the user, who can then view the ranking results from their own devices.

[0864] Input: Aggregation results, ranking data

[0865] Output: The ranking displayed to the user

[0866] Specific behavior:

[0867] The server notifies the user of the ranking results.

[0868] The ranking results are displayed on a web page or application.

[0869] The user views the ranking results from their device.

[0870] (Application example 2)

[0871] 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."

[0872] Conventional quote generation systems do not adequately consider the user's emotions when generating quotes, making it difficult to generate sentences that move or resonate with users. Furthermore, the system for improving and optimizing quotes using user evaluation data is insufficient, resulting in inconsistent quality of generated sentences.

[0873] The specification process by the specification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for receiving text data input by a user, means for analyzing the text data, means for automatically generating new text based on the text data, means for saving the new text in a database, means for collecting evaluation data for the new text, means for aggregating the evaluation data, means for generating rankings based on the aggregation results, means for displaying the rankings to the user, means for analyzing emotions from the text input by the user and the evaluation data, and means for generating prompt sentences based on the analysis results and generating new sentences using an AI model. This makes it possible to automatically generate high-quality quotes and sentences that reflect the user's emotions and further improve the quality of the quotes by utilizing the evaluation data.

[0874] The "means for receiving text data entered by the user" refers to a device or program that allows the user to enter text data via a terminal and transfers the data to the server.

[0875] The "means for analyzing the text data" refers to a system or program that analyzes input text data using natural language processing technology and extracts meaning, emotion, and keywords.

[0876] The "means for automatically generating new sentences based on the sentence data" refers to algorithms and programs for generating new sentences and quotes based on the analysis results and existing data.

[0877] The "means for saving the new sentence in a database" refers to a system or program that registers the automatically generated new sentence in a database, assigns a unique identifier, and saves and manages it.

[0878] The "means for collecting evaluation data for the new text" is a device or program that collects evaluations and comments provided by users for the generated text.

[0879] The "means for aggregating the evaluation data" refers to a device or program for statistically processing the collected evaluation data and generating aggregation results such as average values ​​and rankings.

[0880] The "means for generating rankings based on the aggregation results" refers to a system or program that generates rankings of sentences and quotes based on the aggregated evaluation data.

[0881] The "means for displaying the ranking to the user" refers to a device or program for displaying the generated ranking results on a user interface.

[0882] "Means for analyzing emotions from user input text and evaluation data" refers to a system or program that analyzes emotions from text input by a user and evaluation comments and extracts the results.

[0883] "Means for generating prompt sentences based on the analysis results and generating new sentences using an AI model" refers to a system or program that generates prompt sentences based on the results of sentiment analysis and creates new sentences or quotes using a generative AI model.

[0884] The system for implementing the present invention includes a process for analyzing text data entered by a user and automatically generating new text. The system also generates quotes that reflect the user's emotions and generates rankings based on evaluation data. The system is operated through the cooperation of a terminal, a server, and a sentiment analysis engine.

[0885] First, the user inputs text data via a terminal. The terminal is equipped with a dedicated interface, and the user inputs a quote and its background explanation. This data is converted into JSON format and sent to the server.

[0886] The server adds the received JSON data to a queue for analysis and logs it, then analyzes the text data using natural language processing (NLP) algorithms, specifically extracting keywords from the text, analyzing sentiment, and identifying intent.

[0887] The sentiment analysis engine works as part of the analysis process to recognize emotions from the user's input text. For example, it determines whether the input "Dreams are not for dreaming, but for chasing" contains a positive emotion. The results are returned to the server.

[0888] The server then generates new sentences based on the results of the sentiment analysis. The generation algorithm applies parameters derived from the analysis and quotes from an existing database. It uses a generative AI model to generate prompts that reflect the sentiment. For example, it generates prompts like this:

[0889] Example: "Dreams aren't meant to be seen, they're meant to be chased. <|emotion:positive|>"

[0890] The generated quotes are stored in a database and assigned a unique identifier, along with the date and time of generation and the original quote data.

[0891] Other users then access a dedicated interface to rate the generated quotes. The rating data is entered in the form of a five-point rating and comments, and is sent from the device to the server. This allows the rating data to be collected.

[0892] The server uses a sentiment analysis engine to analyze the sentiment of users' comments while storing the evaluation data in a database. This information will be used as reference for future quote generation processes.

[0893] The evaluation data is collected over a set period of time, and an average evaluation score for each quote is calculated. Furthermore, the results of sentiment analysis of the evaluation comments are also reflected to generate a ranking of quotes. These rankings are sorted in descending order of evaluation score, and are notified to users as well as displayed on web pages and applications.

[0894] As a whole system, the terminal is responsible for accepting data input from users and sending the data to the server. The server is mainly responsible for data analysis, generation, storage, and aggregation of evaluation data and display of rankings. The sentiment analysis engine is a key element in the user sentiment recognition and generation process.

[0895] The main hardware and software used are as follows:

[0896] Hardware: Servers (e.g., Amazon EC2 or Google Cloud), user devices (smartphones, PCs)

[0897] Software: TextBlob (sentiment analysis), Transformers (Hugging Face library), GPT-2 model (generative AI model), Python

[0898] The system of the present invention allows users to create inspirational and relatable quotes, and then share and improve the quality of these quotes with other users through ratings.

[0899] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0900] Step 1:

[0901] The user inputs text data via a terminal. The terminal is equipped with a dedicated interface, and the user inputs quotes and their background information. This data is converted into JSON format and sent to the server.

[0902] Input: User-entered quote and background information

[0903] Output: Data converted to JSON format

[0904] Step 2:

[0905] The server adds the received JSON data to a queue to be parsed and logs it. At this stage, the data is also checked for consistency and integrity.

[0906] Input: JSON format data

[0907] Output: Queueing and logging

[0908] Step 3:

[0909] The server analyzes the text data using natural language processing (NLP) algorithms, specifically extracting keywords from the text, analyzing sentiment, and identifying intent.

[0910] Input: Data retrieved from the analysis queue

[0911] Output: Keywords, sentiment analysis results, intent identification results

[0912] Step 4:

[0913] The emotion analysis engine recognizes the emotion from the user's input text, and the analysis results are returned to the server and used in the next text generation process.

[0914] Input: Text data

[0915] Output: Emotion analysis results

[0916] Step 5:

[0917] The server generates new sentences based on the results of sentiment analysis. The generation algorithm uses a generative AI model and prompts that reflect the sentiment. For example, the following prompts are generated:

[0918] "Dreams aren't meant to be seen, they're meant to be chased. <|emotion:positive|>"

[0919] Input: Sentiment analysis results and keyword data

[0920] Output: The new sentence generated

[0921] Step 6:

[0922] The generated quotes are stored in a database. They are assigned a unique identifier and are saved together with the date and time of generation and the original quote data.

[0923] Input: New sentence generated

[0924] Output: Save to database and give it a unique identifier

[0925] Step 7:

[0926] Other users can access a dedicated interface to rate the generated quotes. Ratings are entered in the form of a five-point scale and comments.

[0927] Input: Generate quote

[0928] Output: Rating data (5-point rating and comments)

[0929] Step 8:

[0930] The server stores the received evaluation data in a database. During this process, it uses a sentiment analysis engine to analyze emotions from the evaluation comments and saves the information in the database.

[0931] Input: Evaluation data

[0932] Output: Evaluation data and analysis results saved in a database

[0933] Step 9:

[0934] The evaluation data is collected over a set period of time, and an average evaluation score for each quote is calculated. Furthermore, the results of sentiment analysis of the evaluation comments are also reflected in the ranking of quotes.

[0935] Input: Evaluation data stored in a database

[0936] Output: Evaluation scores and rankings

[0937] Step 10:

[0938] The generated rankings are notified to the user and displayed on a web page or application, where the user can view these rankings.

[0939] Input: Ranking data

[0940] Output: Display of rankings

[0941] 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.

[0942] 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.

[0943] 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.

[0944] [Third embodiment]

[0945] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0946] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0947] 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).

[0948] 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.

[0949] 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.

[0950] 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).

[0951] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0952] 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.

[0953] 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.

[0954] 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.

[0955] 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.

[0956] 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."

[0957] The system of the present invention analyzes quotes and text data entered by users and provides a process for automatically generating new quotes. This system is realized through the cooperation of a terminal and a server.

[0958] 1. User Input

[0959] The user accesses a dedicated interface on the device and enters the quote and its context. For example, the user might enter:

[0960] Quote: "Dreams aren't meant to be dreamed, they're meant to be chased."

[0961] Context: "I created this quote to celebrate the effort of pursuing your dreams."

[0962] The terminal converts this input data into JSON format and sends it to the server.

[0963] 2. Sending and analyzing quote data

[0964] The server receives and analyzes the JSON data sent from the device. It uses natural language processing (NLP) to perform a structural analysis of the quote and extract keywords and sentiments.

[0965] 3. Automatic generation of quotes

[0966] The server automatically generates new quotes based on the analysis results. For example, for the input quote "Dreams are not meant to be seen, but to be chased," it generates the following result:

[0967] "Those who keep chasing their dreams will win, not those who just dream."

[0968] The generated quotes are stored in a database.

[0969] 4. Save the latest quotes

[0970] The server stores the generated quotes in a database and makes them available for users to view.

[0971] 5. User Ratings

[0972] Other users can access an interface to rate the generated quotes. Users can rate them by entering a five-point rating and a comment. For example, a user might rate them as follows:

[0973] Rating: 4

[0974] Comment: "A very relatable quote!"

[0975] The terminal transmits this evaluation data to the server.

[0976] 6. Collection and compilation of evaluation data

[0977] The server collects and statistically analyzes the received evaluation data, aggregates the evaluation scores, and calculates an average evaluation score for each quote.

[0978] 7. Generating and Displaying Rankings

[0979] The server then creates a ranking of the most popular quotes based on the results of the calculation, and displays the rankings on a web page or in an application for users to view.

[0980] In this way, the system can fairly evaluate quotes submitted by users and automatically generate new, high-quality quotes. This allows users to have a spiritually enriching experience. As a specific example, if the quotes generated from the user input mentioned above are rated by many users and ranked highly, it is expected to generate great emotion and empathy.

[0981] The processing flow will be explained below.

[0982] Step 1:

[0983] The user accesses a dedicated interface on the device and inputs a quote and its background information, which is then converted to JSON format on the device.

[0984] Step 2:

[0985] The terminal sends the data converted into JSON format to the server using an HTTP POST request.

[0986] Step 3:

[0987] The server receives the JSON data and adds it to a queue to be parsed. The received data is logged.

[0988] Step 4:

[0989] The server uses natural language processing (NLP) algorithms to analyze the quote text, specifically extracting keywords, analyzing sentiment, and identifying intent.

[0990] Step 5:

[0991] The server automatically generates new quotes based on the analysis results. The generation algorithm utilizes quotes from an existing database and parameters obtained from the analysis results.

[0992] Step 6:

[0993] The server stores the generated quote in a database and assigns a unique identifier to it. It also stores the date and time of generation and the original quote data.

[0994] Step 7:

[0995] Other users can access the rating interface and rate the generated quotes by entering a 5-point rating and comments.

[0996] Step 8:

[0997] The device collects user evaluation data and sends it to the server in JSON format.

[0998] Step 9:

[0999] The server stores the received evaluation data in a database and automatically updates the evaluation score.

[1000] Step 10:

[1001] The server aggregates the evaluation data at regular intervals and calculates the average evaluation score for each quote using a statistical algorithm.

[1002] Step 11:

[1003] The server generates a ranking of quotes based on the results of the calculation. The rankings are sorted in descending order of evaluation score.

[1004] Step 12:

[1005] The server notifies the user of the ranking results and displays them on a web page or application, allowing the user to view the results from their own device.

[1006] In this way, the system carries out a series of processes from the user's quote input to evaluation, compilation, and display of rankings.

[1007] Example 1

[1008] 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."

[1009] While many sentences and quotes are available on the Internet, their quality and value vary. There is a need for a system that can automatically generate new, high-quality quotes and sentences based on user-entered text, compile evaluations of these, and provide them in a ranking format, thereby enabling the creation and sharing of valuable content.

[1010] 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.

[1011] In this invention, the server includes: means for receiving text data entered by a user through an interface; means for converting the text data into JSON format; means for transmitting the data converted into JSON format to the server; means for the server to receive the JSON data; means for the server to analyze the text data using natural language processing technology and extract keywords and emotions; means for automatically generating new text based on the analysis results using a generative AI model; means for saving the new text in a database; means for collecting data on evaluations of the new text by other users through the user interface; means for the server to statistically analyze the evaluation data and calculate an evaluation score; means for generating a ranking of highly rated texts based on the aggregation results; and means for displaying the ranking to the user. This makes it possible to automatically generate high-quality quotes and texts and share their value through evaluations.

[1012] "User" refers to a person who uses the system to input quotes and sentences and give ratings.

[1013] An "interface" refers to a dedicated screen on a web page or application that allows users to enter quotes or text.

[1014] "Text data" refers to the text information of quotes and their background explanations entered by the user.

[1015] "JSON format" stands for JavaScript Object Notation and refers to a data exchange format that is easy to read for both humans and machines.

[1016] "Server" refers to the computer system that receives and analyzes data from users, and generates and stores new text.

[1017] "Natural language processing technology" refers to technology that enables computers to understand and generate human language and perform various tasks.

[1018] "Generative AI model" refers to an artificial intelligence model trained to automatically generate new text based on a given prompt.

[1019] "Analysis results" refers to keywords and emotional information extracted from quote data analyzed using natural language processing technology.

[1020] "Database" refers to a collection of information that allows for efficient management and retrieval of saved new quotes and evaluation data.

[1021] "Evaluation data" refers to feedback information such as scores and comments made by users on generated quotes.

[1022] "Statistical analysis" refers to the process of numerically processing evaluation data to reveal trends and patterns in the data.

[1023] The "evaluation score" refers to a numerical representation of the quality of a quote or piece of writing based on user feedback.

[1024] "Ranking" refers to a list of quotes and sentences ranked based on their evaluation scores.

[1025] The system of the present invention analyzes quotes and text data entered by users and provides a process for automatically generating new quotes. This system is realized through the cooperation of a terminal and a server.

[1026] User Input

[1027] The user accesses a dedicated interface on the terminal and inputs the quote and its background information. For example, they might input:

[1028] Quote: "Dreams aren't meant to be dreamed, they're meant to be chased."

[1029] Context: "I created this quote to celebrate the effort of pursuing your dreams."

[1030] The terminal converts this input data into JSON format and sends it to the server.

[1031] Sending and analyzing quote data

[1032] The server receives and analyzes the JSON data sent from the device. The server then performs a structural analysis of the quote using natural language processing (NLP). Specific software used for NLP includes Python's "NLTK" and "spaCy." This allows the server to extract keywords and sentiment from the quote.

[1033] Automatic generation of quotes

[1034] The server automatically generates new quotes using a generative AI model based on the analysis results. Generative AI models used include "GPT-3" and "BERT." For example, the following prompt sentence is input to the generative AI model:

[1035] Quote: "Dreams aren't meant to be dreamed, they're meant to be chased."

[1036] Context: "I created this quote to celebrate the effort of pursuing your dreams."

[1037] New auto-generated quote:

[1038] The generative AI model generates new quotes based on this prompt, such as the following:

[1039] "Those who keep chasing their dreams will win, not those who just dream."

[1040] The generated quotes are stored in a database by the server.

[1041] Save the latest quotes

[1042] The server stores the generated quotes in a database and makes them available for users to view. For this purpose, a database management system such as "MySQL" or "PostgreSQL" is used.

[1043] User Rating

[1044] Other users have access to a dedicated interface to rate the generated quotes. Users can rate them by entering a five-point scale and a comment. For example, they can rate them as follows:

[1045] Rating: 4

[1046] Comment: "A very relatable quote!"

[1047] The terminal converts this evaluation data into JSON format and sends it to the server.

[1048] Collection and compilation of evaluation data

[1049] The server analyzes the received evaluation data, stores it in a database, and aggregates it using data analysis libraries such as Pandas and NumPy to calculate an average evaluation score for each quote.

[1050] Generating and displaying rankings

[1051] The server then creates a ranking of the most popular quotes based on the results of the calculation, and displays the rankings for users to view on a web page or application.

[1052] In this way, the system can fairly evaluate user-submitted quotes and automatically generate new, high-quality quotes, a process that provides users with a spiritually enriching experience.

[1053] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1054] Step 1:

[1055] The user accesses a dedicated interface on the terminal and inputs a quote and its background information. The quote and background information based on the user's input are then returned. For example, enter the following:

[1056] Quote: "Dreams aren't meant to be dreamed, they're meant to be chased."

[1057] Context: "I created this quote to celebrate the effort of pursuing your dreams."

[1058] The terminal converts this input data into JSON format, and the converted JSON data is returned.

[1059] Step 2:

[1060] The terminal sends the converted JSON data to the server as an HTTP request. For example, it sends the following JSON data:

[1061] json

[1062] {

[1063] "quote": "Dreams aren't meant to be seen, they're meant to be chased.",

[1064] "background": "This quote was created to celebrate the effort of pursuing your dreams."

[1065] }

[1066] The server parses the received HTTP request and extracts the JSON data. The extracted JSON data is returned.

[1067] Step 3:

[1068] The server uses natural language processing (NLP) technology to structurally analyze the quotes. For NLP, it uses Python's "NLTK" and "spaCy." Keywords and emotions are extracted from the analyzed data. For example, keywords such as "dream" and "chasing" as well as positive emotions are extracted.

[1069] Step 4:

[1070] The server prepares to use a generative AI model (e.g., "GPT-3") based on the extracted keywords and emotions. It generates a prompt and inputs it into the AI ​​model. A new quote is generated based on this prompt. Example of a prompt:

[1071] Quote: "Dreams aren't meant to be dreamed, they're meant to be chased."

[1072] Context: "I created this quote to celebrate the effort of pursuing your dreams."

[1073] New auto-generated quote:

[1074] One example of a new quote that was generated is, "Those who keep chasing their dreams will win, not those who just dream."

[1075] Step 5:

[1076] The server stores the generated new quotes in a database so that users can view and rate them later.

[1077] Step 6:

[1078] Other users have access to a dedicated interface to rate the generated quotes. Users can rate them by entering a five-point scale and a comment. For example, they can rate them as follows:

[1079] Rating: 4

[1080] Comment: "A very relatable quote!"

[1081] The device converts this evaluation data into JSON format and sends it to the server, where the converted JSON data is obtained.

[1082] Step 7:

[1083] The server analyzes the received evaluation data and stores it in a database. From the analyzed evaluation data, it calculates the evaluation score for each quote. The server aggregates the evaluation scores using "Pandas" and "NumPy." The aggregated score data is obtained.

[1084] Step 8:

[1085] The server then creates a ranking list of the most highly rated quotes based on the results of the calculation. The rankings are displayed on a web page or application so that users can view them. A list of ranked quotes is obtained.

[1086] (Application example 1)

[1087] 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."

[1088] Conventional quote and sentence generation systems have the ability to generate new quotes and sentences based on user input, but they lack the ability to evaluate the generated sentences or display rankings, making it difficult to improve user satisfaction and form a community. Furthermore, they lack a mechanism for evaluating the quality of the generated quotes and providing high-quality quotes.

[1089] 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.

[1090] In this invention, the server includes means for receiving text data entered by a user, means for analyzing the text data, means for automatically generating new texts based on the text data, means for saving the new texts in a database, means for presenting the new texts to users, means for collecting evaluation data for the new texts, means for aggregating the evaluation data, means for generating rankings based on the aggregation results, and means for displaying the rankings to users. This improves the quality of quotes automatically generated based on text entered by a user, and also collects evaluations from users to generate rankings, thereby improving user satisfaction and enabling the formation of a community.

[1091] "Means for receiving text data entered by the user" refers to a function for electronically receiving text data entered by the user through the interface.

[1092] The "means for analyzing text data" is a function for analyzing received text data using natural language processing technology and understanding its structure and meaning.

[1093] "Means for automatically generating new sentences" is a function for automatically generating new sentences based on the analysis results.

[1094] The "means for saving new sentences to a database" is a function for recording the generated new sentences in a database so that they can be accessed later.

[1095] "Means for presenting new text to the user" refers to a function for visually or electronically displaying the generated new text to the user.

[1096] "Means for collecting evaluation data" is a function for collecting evaluations and feedback from users on new text.

[1097] The "means for aggregating evaluation data" is a function for analyzing collected evaluation data and aggregating it as statistical information.

[1098] The "means for generating rankings" is a function for determining the ranking of new sentences based on the evaluation data and displaying them in a ranking format.

[1099] The "means for displaying rankings to users" is a function for displaying the generated rankings so that users can view them.

[1100] To realize the system of this invention, it is necessary to build a system based on a smartphone application. This system receives quotes and text data entered by the user, analyzes them, and provides a process for automatically generating new quotes. Specifically, the following hardware and software are used:

[1101] Hardware and Software Used

[1102] Smartphone: iOS or Android device

[1103] Server: AWS or Google Cloud Platform

[1104] Backend: Python / Django or Node.js

[1105] Natural Language Processing library: spaCy or NLTK

[1106] Database: PostgreSQL or MongoDB

[1107] Frontend: React Native or Flutter

[1108] Specific operation of the system

[1109] 1. User Input

[1110] Users input a quote and its background through a dedicated interface in a smartphone application. For example, they can input a quote such as "Dreams are not meant to be seen, but to be chased." This input data is converted into JSON format and sent to the server.

[1111] 2. Quote data analysis

[1112] The server receives the JSON data and analyzes it using a natural language processing library (e.g., spaCy or NLTK), which understands the structure of the sentence and extracts keywords and sentiment.

[1113] 3. Automatic generation of quotes

[1114] Based on the analysis results, a generative AI model is used to automatically generate new quotes. For example, in response to the input quote, "Dreams are not meant to be seen, but to be pursued," the generated quote would be, "Those who continue to chase their dreams will achieve victory, not those who simply dream."

[1115] 4. Save and display quotes

[1116] The generated quotes are stored in a database and can be viewed by users through the application.

[1117] 5. Ratings and Rankings

[1118] Other users can rate the generated quotes on a five-point scale and add comments. This rating data is sent to the server and statistically analyzed, after which a ranking is generated and displayed.

[1119] Specific examples

[1120] A user inputs "Dreams are not meant to be seen, but to be pursued," and then adds the background explanation "I created this quote to praise the effort of pursuing one's dreams." This causes the server to generate a new quote: "Those who continue to pursue their dreams will achieve victory, not those who simply dream."

[1121] Prompt Sentence Examples

[1122] Generate a new quote based on the following quote:

[1123] Quote: Dreams aren't meant to be seen, they're meant to be chased.

[1124] Background: This quote was created to celebrate the effort of chasing your dreams.

[1125] The above is a specific description of an embodiment of the present invention. This configuration improves the quality of quotes automatically generated based on text entered by users, collects user ratings and generates rankings, and enables increased user satisfaction and community formation.

[1126] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1127] Step 1:

[1128] A user inputs a quote and its background information through the smartphone application interface. This input data is converted to JSON format by the device and sent to the server. For example, a user inputs a quote such as "Dreams are not meant to be seen, but to be pursued" and a background information such as "I created this quote to praise the effort of pursuing one's dreams," and the data is sent to the server as JSON data (e.g., {"quote": "Dreams are not meant to be seen, but to be pursued.", "background": "I created this quote to praise the effort of pursuing one's dreams."}).

[1129] Step 2:

[1130] The server parses the received JSON data. In this parsing process, a natural language processing library (e.g., spaCy or NLTK) is used to analyze the input sentence and extract its structure and keywords. For example, keywords such as "dream," "see," and "chase" are extracted from the quote, "Dreams are not for dreaming, but for chasing."

[1131] Step 3:

[1132] The server automatically generates new quotes using a generative AI model based on the analysis results. In this process, the generative AI model uses the keywords and emotional information extracted in the previous step as prompts to generate new quotes. For example, in response to the statement, "Dreams are not meant to be seen, but to be pursued," the new quote generated is, "Those who continue to chase their dreams will achieve victory, not those who simply dream."

[1133] Step 4:

[1134] The generated quotes are stored in a database by the server. This allows new quotes to be stored in the database and made available for future reference or presentation. For example, the generated quote, its creation date, and evaluation information are recorded in the database.

[1135] Step 5:

[1136] The server prepares the generated quote for presentation to the user. Specifically, it generates data for visually displaying the generated quote on a web page or application. For example, the quote text, rating buttons, comment input fields, etc. are displayed on the screen.

[1137] Step 6:

[1138] Other users can view the generated quotes and rate or comment on them. They enter their rating in the 5-point rating system and comment field, and then send the data from their device to the server. For example, a user can enter "Rating: 4" and "Comment: This is a quote I really relate to!", which is then sent to the server as JSON data.

[1139] Step 7:

[1140] The server aggregates the received evaluation data and calculates an evaluation score for each quote, for example, by statistically processing the evaluation data for each quote and calculating an average evaluation score.

[1141] Step 8:

[1142] The server generates a ranking based on the results of the calculation and displays it to the user. Highly rated quotes are displayed at the top of the ranking. For example, the generated ranking data is displayed in list format on the user's app screen, and the user can view it.

[1143] These steps improve the performance and quality of quotes that are automatically generated based on text entered by users, and also enable rankings to be displayed based on user ratings.

[1144] 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.

[1145] The system of the present invention analyzes quotes and text data entered by the user and automatically generates new quotes, and also uses an emotion engine to recognize the user's emotions and reflect them in the analysis and generation process. This system is realized through the cooperation of a terminal, a server, and an emotion engine.

[1146] 1. User Input

[1147] The user accesses a dedicated interface on the device and enters the quote and its context. For example, the user might enter:

[1148] Quote: "Dreams aren't meant to be dreamed, they're meant to be chased."

[1149] Context: "I created this quote to celebrate the effort of pursuing your dreams."

[1150] The terminal converts this input data into JSON format and sends it to the server.

[1151] 2. Sending and analyzing quote data

[1152] The server receives the JSON data and adds it to a queue for analysis. The received data is logged. Natural language processing (NLP) algorithms are then used to analyze the quote text. Specifically, they extract keywords, perform sentiment analysis, and identify intent.

[1153] 3. Operation of the Emotion Engine

[1154] As part of the analysis process, the emotion engine recognizes the sentiment of the quote. For example, it determines whether the quote entered by the user contains positive emotions such as "effort" or "dream." The emotion engine uses NLP algorithms to perform the sentiment analysis and returns the results to the server.

[1155] 4. Automatic generation of quotes

[1156] The server automatically generates new quotes based on data including analysis results from the emotion engine. The generation algorithm utilizes parameters obtained from quotes in the existing database and analysis results. For example, for the input quote "Dreams are not meant to be seen, but to be pursued," the server reflects the "positive emotion" found in the emotion analysis and generates the following result:

[1157] "Not only do you have a dream, but you also have to keep working to make that dream come true."

[1158] The generated quotes are stored in a database.

[1159] 5. Save the latest quotes

[1160] The server stores the generated quote in a database and assigns a unique identifier to it. It also stores the date and time of generation and the original quote data.

[1161] 6. User Ratings

[1162] Other users can access the rating interface and rate the generated quotes. Users can rate the quotes by entering a five-point rating and comments. For example, a user might rate the quote as follows:

[1163] Rating: 4

[1164] Comment: "A very relatable quote!"

[1165] The terminal collects this evaluation data and transmits it to the server.

[1166] 7. Collecting evaluation data and applying the emotion engine

[1167] While the server is storing the received evaluation data in a database, the emotion engine analyzes the emotions from the user's evaluation comments and stores them in the database along with the evaluation data. This information will be used as reference information for future quote generation processes.

[1168] 8. Aggregating evaluation data and generating rankings

[1169] The server periodically aggregates the evaluation data and calculates the average evaluation score for each quote. The aggregation is performed using a statistical algorithm. The server also reflects the results of sentiment analysis of the evaluation comments.

[1170] 9. Generating and Displaying Rankings

[1171] The server generates a ranking of quotes based on the results of the calculation. The rankings are sorted in descending order of evaluation score, and the ranking results are notified to the user and displayed on a web page or application. The user can view the results on their own device.

[1172] In this way, the system carries out a series of processes, from user quote input to evaluation, compilation, and ranking display. Thanks to the effect of the emotion engine, the generated quotes are more moving and evoke empathy, enriching the user's heart. As a specific example, if a quote generated from user input is rated by many users and ranks highly, it is expected to generate great emotion and empathy.

[1173] The processing flow will be explained below.

[1174] Step 1:

[1175] The user accesses a dedicated interface on the device and inputs a quote and its background explanation. For example, the user inputs the quote "Dreams are not meant to be seen, but to be pursued" and the background explanation "I created this quote to praise the efforts of pursuing one's dreams."

[1176] Step 2:

[1177] The terminal converts the user input data into JSON format, which looks like this:

[1178] {

[1179] "quote": "Dreams aren't meant to be seen, they're meant to be chased.",

[1180] "background": "This quote was created to celebrate the effort of pursuing your dreams."

[1181] }

[1182] Step 3:

[1183] The device sends JSON format data to the server using an HTTP POST request.

[1184] Step 4:

[1185] The server receives the JSON data and adds it to a queue to be parsed.

[1186] Step 5:

[1187] The server analyzes the quote text using natural language processing (NLP) algorithms, which extract keywords, analyze sentiment, and identify intent.

[1188] Step 6:

[1189] The server then activates the emotion engine as part of the analysis results. The emotion engine recognizes the emotion of the quote and returns the result to the server. For example, it may determine a "positive emotion."

[1190] Step 7:

[1191] The server automatically generates new quotes based on the analysis results from the emotion engine. For example, for the input quote "Dreams are not meant to be seen, but to be chased," it generates the following result:

[1192] "It's important not only to have a dream, but to keep working towards making that dream come true."

[1193] Step 8:

[1194] The server stores the generated quote in a database, along with a unique identifier, the date and time of generation, and the original quote data.

[1195] Step 9:

[1196] Other users can access the rating interface and rate the generated quotes. Users can rate the quotes by entering a 5-point rating and a comment. For example, a user could rate a quote as "Rating: 4" or "Comment: This is a quote I can really relate to!"

[1197] Step 10:

[1198] The device collects user evaluation data and sends it to the server in JSON format.

[1199] Step 11:

[1200] The server stores the received evaluation data in a database. During the evaluation data collection process, the emotion engine analyzes emotions from users' evaluation comments and stores the results in the database along with the evaluation data.

[1201] Step 12:

[1202] The server periodically aggregates the evaluation data and calculates the average evaluation score for each quote, taking into account the results of sentiment analysis of the evaluation comments.

[1203] Step 13:

[1204] The server generates a ranking of quotes based on the results of the calculation. The rankings are sorted in descending order of evaluation score.

[1205] Step 14:

[1206] The server notifies the user of the ranking results and displays them on a web page or application, allowing the user to view the results from their own device.

[1207] This series of processes involves everything from user quote input to evaluation, sentiment analysis, aggregation, and ranking display. By adding the emotion engine, the generated quotes become more moving and relatable, enriching the user's mind.

[1208] Example 2

[1209] 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."

[1210] Conventional text generation systems have difficulty fully reflecting users' emotions and intentions, and the texts they generate often do not meet their expectations. Furthermore, the process of collecting evaluations of generated texts and generating rankings based on those evaluations is cumbersome and inefficient. This has led to problems such as lower user satisfaction and a decrease in the frequency of system use.

[1211] 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.

[1212] In this invention, the server includes a means for adding text data entered by a user to a queue for analysis and analyzing it, a means for analyzing the text data using a natural language processing algorithm, and a means for recognizing the emotion of the text data using an emotion engine. This enables the automatic generation of new text that reflects the user's emotion and intention. The server also includes a means for automatically generating new text based on the recognized emotion, a means for collecting evaluation data for the new text, a means for analyzing the evaluation data using the emotion engine and storing it in a database, a means for generating rankings based on the aggregation results, and a means for displaying the rankings to the user. This makes it easier for the generated text to meet user expectations, further streamlines the evaluation and ranking generation process, and improves the overall user experience.

[1213] "User" refers to a person who uses the system and is responsible for providing quotes and written data and providing evaluation feedback.

[1214] "Text data" refers to text information such as quotes and their background information entered by the user.

[1215] "JSON format" is an abbreviation for JavaScript Object Notation, and is a method for representing data in a structured and human-readable format.

[1216] A "server" is a computer system that receives and analyzes data sent from a terminal via a network and performs the necessary processing.

[1217] The "queue waiting for analysis" is a data structure for temporarily storing data waiting for analysis processing, and is managed by the server.

[1218] "Natural language processing algorithms" refer to technologies and methods for processing and analyzing human language using computers, and are used for keyword extraction, sentiment analysis, and intent identification.

[1219] An "emotion engine" is a software module that analyzes the emotions contained in text data and outputs the results.

[1220] A "generative AI model" is an algorithm or technique that uses artificial intelligence technology to create new sentences or quotes (e.g., a machine learning model).

[1221] A "database" is a data structure for systematically storing, retrieving, and managing data used within a system.

[1222] "Rating data" includes information on the ratings users give to generated quotes and sentences, and specifically refers to five-point ratings and comments.

[1223] "Ranking" refers to a ranked list of quotes and sentences generated based on evaluation data, sorted in order of evaluation score.

[1224] "Purpose-built Interface" means the particular user interface through which a user accesses the system and inputs quotes and text data.

[1225] The system of the present invention provides a series of processes that analyzes text data entered by users, automatically generates new text using an emotion engine, collects evaluations, and generates and displays rankings. The following specific technologies are used in the implementation of this system.

[1226] Hardware and Software Configuration

[1227] 1. Terminal

[1228] Users access the dedicated interface using a device such as a PC, smartphone, or tablet. The dedicated interface provides a form for users to enter quotes and their background information. This input data is converted to JSON format on the device and sent to the server.

[1229] 2. Server

[1230] The server receives the JSON data sent by the user and queues it for parsing. The server runs the following main software modules:

[1231] Natural Language Processing (NLP) algorithms: These use libraries (e.g., spaCy, NLTK, etc.) for keyword extraction, sentiment analysis, and intent identification.

[1232] Emotion engine: Used to recognize the sentiment of quotes. For this purpose, NLP techniques and emotion analysis tools (e.g., IBM Watson Tone Analyzer) are used.

[1233] Generative AI models: Artificial intelligence techniques (e.g., GPT-3) are used to generate new quotes and sentences.

[1234] Database Management System (DBMS): Used to store the generated quotes and evaluation data (e.g., MySQL, PostgreSQL, etc.).

[1235] Program processing flow

[1236] User Input

[1237] Users access a dedicated interface and enter a quote and a description of its context. For example:

[1238] Quote: "Dreams aren't meant to be dreamed, they're meant to be chased."

[1239] Context: "I created this quote to celebrate the effort of pursuing your dreams."

[1240] The terminal converts this input data into JSON format and sends it to the server via an HTTP request.

[1241] Analysis of quote data

[1242] The server adds the received JSON data to a queue for parsing, then uses natural language processing algorithms to parse the quote text and identify keywords, sentiment, and intent, and an emotion engine identifies the sentiment of the quote and returns the results to the server.

[1243] Generating new quotes

[1244] Based on the analysis results from the emotion engine, the server inputs a prompt into the generative AI model to generate a new quote. For example, the following prompt sentence is used:

[1245] Prompt: "Dreams aren't meant to be seen, they're meant to be chased. Generate a new quote with a positive sentiment."

[1246] The generated quotes are stored in a database.

[1247] Collecting ratings and generating rankings

[1248] Other users access the rating interface and rate the generated quotes that are displayed. Ratings are made in the form of a five-point rating and comments. The rating data is analyzed by an emotion engine and stored in a database. The server periodically aggregates the rating data and generates a ranking of quotes using a statistical algorithm. The generated ranking is displayed to users, who can view it from their own devices.

[1249] Specific examples

[1250] The user inputs "Dreams aren't just for dreaming, they're for chasing" and adds a background explanation. The system analyzes this input and generates a new quote that reflects a positive emotion. For example, a quote might be generated: "It's not just about having a dream, it's about continuing to work towards making that dream come true." This quote is then rated by other users, and it receives high ratings and appears at the top of the rankings.

[1251] The above is a mode for carrying out the invention, which is a method for efficiently realizing a series of processes from user input to evaluation and ranking generation in a manner that evokes emotional empathy.

[1252] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1253] Step 1:

[1254] The user accesses the dedicated interface using a terminal and inputs a quote and its background information. The user's input data is converted into JSON format and sent to the server as an HTTP request.

[1255] Input: User enters quote and background information

[1256] Output: JSON format data

[1257] Specific behavior:

[1258] A user accesses a dedicated form in a browser or mobile app.

[1259] Enter a quote and its background.

[1260] The program converts the input data into JSON format.

[1261] The converted data is sent to the server.

[1262] Step 2:

[1263] The server adds the received JSON data to a queue to be parsed and logs it.

[1264] Input: JSON format data sent from the terminal

[1265] Output: Data queued for analysis, log records

[1266] Specific behavior:

[1267] The server receives the HTTP request and reads the JSON data.

[1268] Queue the data for analysis.

[1269] Log the data received with a timestamp.

[1270] Step 3:

[1271] The server uses natural language processing (NLP) algorithms to analyze the quote text, extract keywords, analyze sentiment, and identify intent.

[1272] Input: JSON data retrieved from the queue to be parsed

[1273] Output: Keywords, sentiment, and intent identification

[1274] Specific behavior:

[1275] The server retrieves the data from the queue to be analyzed.

[1276] Analyze the quote text and extract keywords using an NLP library (e.g., spaCy, NLTK).

[1277] Analyze the sentiment of the quote using a sentiment analysis tool.

[1278] Applying algorithms to identify the intent of the text.

[1279] Generate extracted keywords, sentiment analysis results, and intent identification information.

[1280] Step 4:

[1281] The emotion engine recognizes the emotion of the input quote and returns the results to the server.

[1282] Input: Text of the quote and its analysis results

[1283] Output: Emotion recognition result

[1284] Specific behavior:

[1285] The server sends the quote text and its analysis results to an emotion engine (e.g., IBM Watson Tone Analyzer).

[1286] The sentiment engine performs sentiment analysis and assigns a sentiment label, such as "positive."

[1287] The emotion recognition results are returned to the server.

[1288] Step 5:

[1289] The server generates new quotes using a generative AI model based on the results of the emotion engine. For example, instructions are given to the generative AI model using prompt sentences.

[1290] Input: Emotion recognition results, keywords, and intent identification information

[1291] Output: The newly generated quote

[1292] Specific behavior:

[1293] The server inputs prompts into the generative AI model (e.g., GPT-3).

[1294] Prompt: "Dreams aren't meant to be seen, they're meant to be chased. Generate a new quote with a positive sentiment."

[1295] Get the generated quote.

[1296] Save the new quote to the database.

[1297] Step 6:

[1298] The server assigns a unique identifier to the generated quote and stores it in a database along with the date and time of generation and the original quote data.

[1299] Input: Newly generated quote

[1300] Output: Quotation data stored in the database

[1301] Specific behavior:

[1302] A unique ID is assigned to the generated quote.

[1303] The creation date and time and the original quote data are associated.

[1304] The generated quote data is stored in a database.

[1305] Step 7:

[1306] Other users can access the evaluation interface and evaluate the generated quotes. The evaluation data is analyzed by the emotion engine and sent to the server.

[1307] Input: Generated quote displayed in the evaluation interface

[1308] Output: Rating data, rating comments, sentiment analysis results

[1309] Specific behavior:

[1310] Other users access the evaluation interface.

[1311] Rate the generated quotes using a 5-point scale and in the form of comments.

[1312] The evaluation data is analyzed by the emotion engine.

[1313] The analysis results are sent to the server.

[1314] Step 8:

[1315] The server stores the received evaluation data in a database and periodically compiles it to generate rankings.

[1316] Input: Evaluation data, evaluation comments, sentiment analysis results

[1317] Output: Aggregation results, rankings

[1318] Specific behavior:

[1319] The server stores the rating data in a database.

[1320] The evaluation data is periodically aggregated using an aggregation algorithm.

[1321] Calculate the average rating score for each quote.

[1322] Rankings are generated that also reflect the results of sentiment analysis.

[1323] Step 9:

[1324] The server notifies and displays the rankings generated by the server to the user, who can then view the ranking results from their own devices.

[1325] Input: Aggregation results, ranking data

[1326] Output: The ranking displayed to the user

[1327] Specific behavior:

[1328] The server notifies the user of the ranking results.

[1329] The ranking results are displayed on a web page or application.

[1330] The user views the ranking results from their device.

[1331] (Application example 2)

[1332] 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."

[1333] Conventional quote generation systems do not adequately consider the user's emotions when generating quotes, making it difficult to generate sentences that move or resonate with users. Furthermore, the system for improving and optimizing quotes using user evaluation data is insufficient, resulting in inconsistent quality of generated sentences.

[1334] The specification process by the specification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for receiving text data input by a user, means for analyzing the text data, means for automatically generating new text based on the text data, means for saving the new text in a database, means for collecting evaluation data for the new text, means for aggregating the evaluation data, means for generating rankings based on the aggregation results, means for displaying the rankings to the user, means for analyzing emotions from the text input by the user and the evaluation data, and means for generating prompt sentences based on the analysis results and generating new sentences using an AI model. This makes it possible to automatically generate high-quality quotes and sentences that reflect the user's emotions and further improve the quality of the quotes by utilizing the evaluation data.

[1335] The "means for receiving text data entered by the user" refers to a device or program that allows the user to enter text data via a terminal and transfers the data to the server.

[1336] The "means for analyzing the text data" refers to a system or program that analyzes input text data using natural language processing technology and extracts meaning, emotion, and keywords.

[1337] The "means for automatically generating new sentences based on the sentence data" refers to algorithms and programs for generating new sentences and quotes based on the analysis results and existing data.

[1338] The "means for saving the new sentence in a database" refers to a system or program that registers the automatically generated new sentence in a database, assigns a unique identifier, and saves and manages it.

[1339] The "means for collecting evaluation data for the new text" is a device or program that collects evaluations and comments provided by users for the generated text.

[1340] The "means for aggregating the evaluation data" refers to a device or program for statistically processing the collected evaluation data and generating aggregation results such as average values ​​and rankings.

[1341] The "means for generating rankings based on the aggregation results" refers to a system or program that generates rankings of sentences and quotes based on the aggregated evaluation data.

[1342] The "means for displaying the ranking to the user" refers to a device or program for displaying the generated ranking results on a user interface.

[1343] "Means for analyzing emotions from user input text and evaluation data" refers to a system or program that analyzes emotions from text input by a user and evaluation comments and extracts the results.

[1344] "Means for generating prompt sentences based on the analysis results and generating new sentences using an AI model" refers to a system or program that generates prompt sentences based on the results of sentiment analysis and creates new sentences or quotes using a generative AI model.

[1345] The system for implementing the present invention includes a process for analyzing text data entered by a user and automatically generating new text. The system also generates quotes that reflect the user's emotions and generates rankings based on evaluation data. The system is operated through the cooperation of a terminal, a server, and a sentiment analysis engine.

[1346] First, the user inputs text data via a terminal. The terminal is equipped with a dedicated interface, and the user inputs a quote and its background explanation. This data is converted into JSON format and sent to the server.

[1347] The server adds the received JSON data to a queue for analysis and logs it, then analyzes the text data using natural language processing (NLP) algorithms, specifically extracting keywords from the text, analyzing sentiment, and identifying intent.

[1348] The sentiment analysis engine works as part of the analysis process to recognize emotions from the user's input text. For example, it determines whether the input "Dreams are not for dreaming, but for chasing" contains a positive emotion. The results are returned to the server.

[1349] The server then generates new sentences based on the results of the sentiment analysis. The generation algorithm applies parameters derived from the analysis and quotes from an existing database. It uses a generative AI model to generate prompts that reflect the sentiment. For example, it generates prompts like this:

[1350] Example: "Dreams aren't meant to be seen, they're meant to be chased. <|emotion:positive|>"

[1351] The generated quotes are stored in a database and assigned a unique identifier, along with the date and time of generation and the original quote data.

[1352] Other users then access a dedicated interface to rate the generated quotes. The rating data is entered in the form of a five-point rating and comments, and is sent from the device to the server. This allows the rating data to be collected.

[1353] The server uses a sentiment analysis engine to analyze the sentiment of users' comments while storing the evaluation data in a database. This information will be used as reference for future quote generation processes.

[1354] The evaluation data is collected over a set period of time, and an average evaluation score for each quote is calculated. Furthermore, the results of sentiment analysis of the evaluation comments are also reflected to generate a ranking of quotes. These rankings are sorted in descending order of evaluation score, and are notified to users as well as displayed on web pages and applications.

[1355] As a whole system, the terminal is responsible for accepting data input from users and sending the data to the server. The server is mainly responsible for data analysis, generation, storage, and aggregation of evaluation data and display of rankings. The sentiment analysis engine is a key element in the user sentiment recognition and generation process.

[1356] The main hardware and software used are as follows:

[1357] Hardware: Servers (e.g., Amazon EC2 or Google Cloud), user devices (smartphones, PCs)

[1358] Software: TextBlob (sentiment analysis), Transformers (Hugging Face library), GPT-2 model (generative AI model), Python

[1359] The system of the present invention allows users to create inspirational and relatable quotes, and then share and improve the quality of these quotes with other users through ratings.

[1360] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1361] Step 1:

[1362] The user inputs text data via a terminal. The terminal is equipped with a dedicated interface, and the user inputs quotes and their background information. This data is converted into JSON format and sent to the server.

[1363] Input: User-entered quote and background information

[1364] Output: Data converted to JSON format

[1365] Step 2:

[1366] The server adds the received JSON data to a queue to be parsed and logs it. At this stage, the data is also checked for consistency and integrity.

[1367] Input: JSON format data

[1368] Output: Queueing and logging

[1369] Step 3:

[1370] The server analyzes the text data using natural language processing (NLP) algorithms, specifically extracting keywords from the text, analyzing sentiment, and identifying intent.

[1371] Input: Data retrieved from the analysis queue

[1372] Output: Keywords, sentiment analysis results, intent identification results

[1373] Step 4:

[1374] The emotion analysis engine recognizes the emotion from the user's input text, and the analysis results are returned to the server and used in the next text generation process.

[1375] Input: Text data

[1376] Output: Emotion analysis results

[1377] Step 5:

[1378] The server generates new sentences based on the results of sentiment analysis. The generation algorithm uses a generative AI model and prompts that reflect the sentiment. For example, the following prompts are generated:

[1379] "Dreams aren't meant to be seen, they're meant to be chased. <|emotion:positive|>"

[1380] Input: Sentiment analysis results and keyword data

[1381] Output: The new sentence generated

[1382] Step 6:

[1383] The generated quotes are stored in a database. They are assigned a unique identifier and are saved together with the date and time of generation and the original quote data.

[1384] Input: New sentence generated

[1385] Output: Save to database and give it a unique identifier

[1386] Step 7:

[1387] Other users can access a dedicated interface to rate the generated quotes. Ratings are entered in the form of a five-point scale and comments.

[1388] Input: Generate quote

[1389] Output: Rating data (5-point rating and comments)

[1390] Step 8:

[1391] The server stores the received evaluation data in a database. During this process, it uses a sentiment analysis engine to analyze emotions from the evaluation comments and saves the information in the database.

[1392] Input: Evaluation data

[1393] Output: Evaluation data and analysis results saved in a database

[1394] Step 9:

[1395] The evaluation data is collected over a set period of time, and an average evaluation score for each quote is calculated. Furthermore, the results of sentiment analysis of the evaluation comments are also reflected in the ranking of quotes.

[1396] Input: Evaluation data stored in a database

[1397] Output: Evaluation scores and rankings

[1398] Step 10:

[1399] The generated rankings are notified to the user and displayed on a web page or application, where the user can view these rankings.

[1400] Input: Ranking data

[1401] Output: Display of rankings

[1402] 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.

[1403] 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.

[1404] 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.

[1405] [Fourth embodiment]

[1406] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1407] 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.

[1408] 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).

[1409] 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.

[1410] 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.

[1411] 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).

[1412] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1413] 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.

[1414] 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.

[1415] 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.

[1416] 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.

[1417] 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.

[1418] 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."

[1419] The system of the present invention analyzes quotes and text data entered by users and provides a process for automatically generating new quotes. This system is realized through the cooperation of a terminal and a server.

[1420] 1. User Input

[1421] The user accesses a dedicated interface on the device and enters the quote and its context. For example, the user might enter:

[1422] Quote: "Dreams aren't meant to be dreamed, they're meant to be chased."

[1423] Context: "I created this quote to celebrate the effort of pursuing your dreams."

[1424] The terminal converts this input data into JSON format and sends it to the server.

[1425] 2. Sending and analyzing quote data

[1426] The server receives and analyzes the JSON data sent from the device. It uses natural language processing (NLP) to perform a structural analysis of the quote and extract keywords and sentiments.

[1427] 3. Automatic generation of quotes

[1428] The server automatically generates new quotes based on the analysis results. For example, for the input quote "Dreams are not meant to be seen, but to be chased," it generates the following result:

[1429] "Those who keep chasing their dreams will win, not those who just dream."

[1430] The generated quotes are stored in a database.

[1431] 4. Save the latest quotes

[1432] The server stores the generated quotes in a database and makes them available for users to view.

[1433] 5. User Ratings

[1434] Other users can access an interface to rate the generated quotes. Users can rate them by entering a five-point rating and a comment. For example, a user might rate them as follows:

[1435] Rating: 4

[1436] Comment: "A very relatable quote!"

[1437] The terminal transmits this evaluation data to the server.

[1438] 6. Collection and compilation of evaluation data

[1439] The server collects and statistically analyzes the received evaluation data, aggregates the evaluation scores, and calculates an average evaluation score for each quote.

[1440] 7. Generating and Displaying Rankings

[1441] The server then creates a ranking of the most popular quotes based on the results of the calculation, and displays the rankings on a web page or in an application for users to view.

[1442] In this way, the system can fairly evaluate quotes submitted by users and automatically generate new, high-quality quotes. This allows users to have a spiritually enriching experience. As a specific example, if the quotes generated from the user input mentioned above are rated by many users and ranked highly, it is expected to generate great emotion and empathy.

[1443] The processing flow will be explained below.

[1444] Step 1:

[1445] The user accesses a dedicated interface on the device and inputs a quote and its background information, which is then converted to JSON format on the device.

[1446] Step 2:

[1447] The terminal sends the data converted into JSON format to the server using an HTTP POST request.

[1448] Step 3:

[1449] The server receives the JSON data and adds it to a queue to be parsed. The received data is logged.

[1450] Step 4:

[1451] The server uses natural language processing (NLP) algorithms to analyze the quote text, specifically extracting keywords, analyzing sentiment, and identifying intent.

[1452] Step 5:

[1453] The server automatically generates new quotes based on the analysis results. The generation algorithm utilizes quotes from an existing database and parameters obtained from the analysis results.

[1454] Step 6:

[1455] The server stores the generated quote in a database and assigns a unique identifier to it. It also stores the date and time of generation and the original quote data.

[1456] Step 7:

[1457] Other users can access the rating interface and rate the generated quotes by entering a 5-point rating and comments.

[1458] Step 8:

[1459] The device collects user evaluation data and sends it to the server in JSON format.

[1460] Step 9:

[1461] The server stores the received evaluation data in a database and automatically updates the evaluation score.

[1462] Step 10:

[1463] The server aggregates the evaluation data at regular intervals and calculates the average evaluation score for each quote using a statistical algorithm.

[1464] Step 11:

[1465] The server generates a ranking of quotes based on the results of the calculation. The rankings are sorted in descending order of evaluation score.

[1466] Step 12:

[1467] The server notifies the user of the ranking results and displays them on a web page or application, allowing the user to view the results from their own device.

[1468] In this way, the system carries out a series of processes from the user's quote input to evaluation, compilation, and display of rankings.

[1469] Example 1

[1470] 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."

[1471] While many sentences and quotes are available on the Internet, their quality and value vary. There is a need for a system that can automatically generate new, high-quality quotes and sentences based on user-entered text, compile evaluations of these, and provide them in a ranking format, thereby enabling the creation and sharing of valuable content.

[1472] 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.

[1473] In this invention, the server includes: means for receiving text data entered by a user through an interface; means for converting the text data into JSON format; means for transmitting the data converted into JSON format to the server; means for the server to receive the JSON data; means for the server to analyze the text data using natural language processing technology and extract keywords and emotions; means for automatically generating new text based on the analysis results using a generative AI model; means for saving the new text in a database; means for collecting data on evaluations of the new text by other users through the user interface; means for the server to statistically analyze the evaluation data and calculate an evaluation score; means for generating a ranking of highly rated texts based on the aggregation results; and means for displaying the ranking to the user. This makes it possible to automatically generate high-quality quotes and texts and share their value through evaluations.

[1474] "User" refers to a person who uses the system to input quotes and sentences and give ratings.

[1475] An "interface" refers to a dedicated screen on a web page or application that allows users to enter quotes or text.

[1476] "Text data" refers to the text information of quotes and their background explanations entered by the user.

[1477] "JSON format" stands for JavaScript Object Notation and refers to a data exchange format that is easy to read for both humans and machines.

[1478] "Server" refers to the computer system that receives and analyzes data from users, and generates and stores new text.

[1479] "Natural language processing technology" refers to technology that enables computers to understand and generate human language and perform various tasks.

[1480] "Generative AI model" refers to an artificial intelligence model trained to automatically generate new text based on a given prompt.

[1481] "Analysis results" refers to keywords and emotional information extracted from quote data analyzed using natural language processing technology.

[1482] "Database" refers to a collection of information that allows for efficient management and retrieval of saved new quotes and evaluation data.

[1483] "Evaluation data" refers to feedback information such as scores and comments made by users on generated quotes.

[1484] "Statistical analysis" refers to the process of numerically processing evaluation data to reveal trends and patterns in the data.

[1485] The "evaluation score" refers to a numerical representation of the quality of a quote or piece of writing based on user feedback.

[1486] "Ranking" refers to a list of quotes and sentences ranked based on their evaluation scores.

[1487] The system of the present invention analyzes quotes and text data entered by users and provides a process for automatically generating new quotes. This system is realized through the cooperation of a terminal and a server.

[1488] User Input

[1489] The user accesses a dedicated interface on the terminal and inputs the quote and its background information. For example, they might input:

[1490] Quote: "Dreams aren't meant to be dreamed, they're meant to be chased."

[1491] Context: "I created this quote to celebrate the effort of pursuing your dreams."

[1492] The terminal converts this input data into JSON format and sends it to the server.

[1493] Sending and analyzing quote data

[1494] The server receives and analyzes the JSON data sent from the device. The server then performs a structural analysis of the quote using natural language processing (NLP). Specific software used for NLP includes Python's "NLTK" and "spaCy." This allows the server to extract keywords and sentiment from the quote.

[1495] Automatic generation of quotes

[1496] The server automatically generates new quotes using a generative AI model based on the analysis results. Generative AI models used include "GPT-3" and "BERT." For example, the following prompt sentence is input to the generative AI model:

[1497] Quote: "Dreams aren't meant to be dreamed, they're meant to be chased."

[1498] Context: "I created this quote to celebrate the effort of pursuing your dreams."

[1499] New auto-generated quote:

[1500] The generative AI model generates new quotes based on this prompt, such as the following:

[1501] "Those who keep chasing their dreams will win, not those who just dream."

[1502] The generated quotes are stored in a database by the server.

[1503] Save the latest quotes

[1504] The server stores the generated quotes in a database and makes them available for users to view. For this purpose, a database management system such as "MySQL" or "PostgreSQL" is used.

[1505] User Rating

[1506] Other users have access to a dedicated interface to rate the generated quotes. Users can rate them by entering a five-point scale and a comment. For example, they can rate them as follows:

[1507] Rating: 4

[1508] Comment: "A very relatable quote!"

[1509] The terminal converts this evaluation data into JSON format and sends it to the server.

[1510] Collection and compilation of evaluation data

[1511] The server analyzes the received evaluation data, stores it in a database, and aggregates it using data analysis libraries such as Pandas and NumPy to calculate an average evaluation score for each quote.

[1512] Generating and displaying rankings

[1513] The server then creates a ranking of the most popular quotes based on the results of the calculation, and displays the rankings for users to view on a web page or application.

[1514] In this way, the system can fairly evaluate user-submitted quotes and automatically generate new, high-quality quotes, a process that provides users with a spiritually enriching experience.

[1515] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1516] Step 1:

[1517] The user accesses a dedicated interface on the terminal and inputs a quote and its background information. The quote and background information based on the user's input are then returned. For example, enter the following:

[1518] Quote: "Dreams aren't meant to be dreamed, they're meant to be chased."

[1519] Context: "I created this quote to celebrate the effort of pursuing your dreams."

[1520] The terminal converts this input data into JSON format, and the converted JSON data is returned.

[1521] Step 2:

[1522] The terminal sends the converted JSON data to the server as an HTTP request. For example, it sends the following JSON data:

[1523] json

[1524] {

[1525] "quote": "Dreams aren't meant to be seen, they're meant to be chased.",

[1526] "background": "This quote was created to celebrate the effort of pursuing your dreams."

[1527] }

[1528] The server parses the received HTTP request and extracts the JSON data. The extracted JSON data is returned.

[1529] Step 3:

[1530] The server uses natural language processing (NLP) technology to structurally analyze the quotes. For NLP, it uses Python's "NLTK" and "spaCy." Keywords and emotions are extracted from the analyzed data. For example, keywords such as "dream" and "chasing" as well as positive emotions are extracted.

[1531] Step 4:

[1532] The server prepares to use a generative AI model (e.g., "GPT-3") based on the extracted keywords and emotions. It generates a prompt and inputs it into the AI ​​model. A new quote is generated based on this prompt. Example of a prompt:

[1533] Quote: "Dreams aren't meant to be dreamed, they're meant to be chased."

[1534] Context: "I created this quote to celebrate the effort of pursuing your dreams."

[1535] New auto-generated quote:

[1536] One example of a new quote that was generated is, "Those who keep chasing their dreams will win, not those who just dream."

[1537] Step 5:

[1538] The server stores the generated new quotes in a database so that users can view and rate them later.

[1539] Step 6:

[1540] Other users have access to a dedicated interface to rate the generated quotes. Users can rate them by entering a five-point scale and a comment. For example, they can rate them as follows:

[1541] Rating: 4

[1542] Comment: "A very relatable quote!"

[1543] The device converts this evaluation data into JSON format and sends it to the server, where the converted JSON data is obtained.

[1544] Step 7:

[1545] The server analyzes the received evaluation data and stores it in a database. From the analyzed evaluation data, it calculates the evaluation score for each quote. The server aggregates the evaluation scores using "Pandas" and "NumPy." The aggregated score data is obtained.

[1546] Step 8:

[1547] The server then creates a ranking list of the most highly rated quotes based on the results of the calculation. The rankings are displayed on a web page or application so that users can view them. A list of ranked quotes is obtained.

[1548] (Application example 1)

[1549] 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."

[1550] Conventional quote and sentence generation systems have the ability to generate new quotes and sentences based on user input, but they lack the ability to evaluate the generated sentences or display rankings, making it difficult to improve user satisfaction and form a community. Furthermore, they lack a mechanism for evaluating the quality of the generated quotes and providing high-quality quotes.

[1551] 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.

[1552] In this invention, the server includes means for receiving text data entered by a user, means for analyzing the text data, means for automatically generating new texts based on the text data, means for saving the new texts in a database, means for presenting the new texts to users, means for collecting evaluation data for the new texts, means for aggregating the evaluation data, means for generating rankings based on the aggregation results, and means for displaying the rankings to users. This improves the quality of quotes automatically generated based on text entered by a user, and also collects evaluations from users to generate rankings, thereby improving user satisfaction and enabling the formation of a community.

[1553] "Means for receiving text data entered by the user" refers to a function for electronically receiving text data entered by the user through the interface.

[1554] The "means for analyzing text data" is a function for analyzing received text data using natural language processing technology and understanding its structure and meaning.

[1555] "Means for automatically generating new sentences" is a function for automatically generating new sentences based on the analysis results.

[1556] The "means for saving new sentences to a database" is a function for recording the generated new sentences in a database so that they can be accessed later.

[1557] "Means for presenting new text to the user" refers to a function for visually or electronically displaying the generated new text to the user.

[1558] "Means for collecting evaluation data" is a function for collecting evaluations and feedback from users on new text.

[1559] The "means for aggregating evaluation data" is a function for analyzing collected evaluation data and aggregating it as statistical information.

[1560] The "means for generating rankings" is a function for determining the ranking of new sentences based on the evaluation data and displaying them in a ranking format.

[1561] The "means for displaying rankings to users" is a function for displaying the generated rankings so that users can view them.

[1562] To realize the system of this invention, it is necessary to build a system based on a smartphone application. This system receives quotes and text data entered by the user, analyzes them, and provides a process for automatically generating new quotes. Specifically, the following hardware and software are used:

[1563] Hardware and Software Used

[1564] Smartphone: iOS or Android device

[1565] Server: AWS or Google Cloud Platform

[1566] Backend: Python / Django or Node.js

[1567] Natural Language Processing library: spaCy or NLTK

[1568] Database: PostgreSQL or MongoDB

[1569] Frontend: React Native or Flutter

[1570] Specific operation of the system

[1571] 1. User Input

[1572] Users input a quote and its background through a dedicated interface in a smartphone application. For example, they can input a quote such as "Dreams are not meant to be seen, but to be chased." This input data is converted into JSON format and sent to the server.

[1573] 2. Quote data analysis

[1574] The server receives the JSON data and analyzes it using a natural language processing library (e.g., spaCy or NLTK), which understands the structure of the sentence and extracts keywords and sentiment.

[1575] 3. Automatic generation of quotes

[1576] Based on the analysis results, a generative AI model is used to automatically generate new quotes. For example, in response to the input quote, "Dreams are not meant to be seen, but to be pursued," the generated quote would be, "Those who continue to chase their dreams will achieve victory, not those who simply dream."

[1577] 4. Save and display quotes

[1578] The generated quotes are stored in a database and can be viewed by users through the application.

[1579] 5. Ratings and Rankings

[1580] Other users can rate the generated quotes on a five-point scale and add comments. This rating data is sent to the server and statistically analyzed, after which a ranking is generated and displayed.

[1581] Specific examples

[1582] A user inputs "Dreams are not meant to be seen, but to be pursued," and then adds the background explanation "I created this quote to praise the effort of pursuing one's dreams." This causes the server to generate a new quote: "Those who continue to pursue their dreams will achieve victory, not those who simply dream."

[1583] Prompt Sentence Examples

[1584] Generate a new quote based on the following quote:

[1585] Quote: Dreams aren't meant to be seen, they're meant to be chased.

[1586] Background: This quote was created to celebrate the effort of chasing your dreams.

[1587] The above is a specific description of an embodiment of the present invention. This configuration improves the quality of quotes automatically generated based on text entered by users, collects user ratings and generates rankings, and enables increased user satisfaction and community formation.

[1588] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1589] Step 1:

[1590] A user inputs a quote and its background information through the smartphone application interface. This input data is converted to JSON format by the device and sent to the server. For example, a user inputs a quote such as "Dreams are not meant to be seen, but to be pursued" and a background information such as "I created this quote to praise the effort of pursuing one's dreams," and the data is sent to the server as JSON data (e.g., {"quote": "Dreams are not meant to be seen, but to be pursued.", "background": "I created this quote to praise the effort of pursuing one's dreams."}).

[1591] Step 2:

[1592] The server parses the received JSON data. In this parsing process, a natural language processing library (e.g., spaCy or NLTK) is used to analyze the input sentence and extract its structure and keywords. For example, keywords such as "dream," "see," and "chase" are extracted from the quote, "Dreams are not for dreaming, but for chasing."

[1593] Step 3:

[1594] The server automatically generates new quotes using a generative AI model based on the analysis results. In this process, the generative AI model uses the keywords and emotional information extracted in the previous step as prompts to generate new quotes. For example, in response to the statement, "Dreams are not meant to be seen, but to be pursued," the new quote generated is, "Those who continue to chase their dreams will achieve victory, not those who simply dream."

[1595] Step 4:

[1596] The generated quotes are stored in a database by the server. This allows new quotes to be stored in the database and made available for future reference or presentation. For example, the generated quote, its creation date, and evaluation information are recorded in the database.

[1597] Step 5:

[1598] The server prepares the generated quote for presentation to the user. Specifically, it generates data for visually displaying the generated quote on a web page or application. For example, the quote text, rating buttons, comment input fields, etc. are displayed on the screen.

[1599] Step 6:

[1600] Other users can view the generated quotes and rate or comment on them. They enter their rating in the 5-point rating system and comment field, and then send the data from their device to the server. For example, a user can enter "Rating: 4" and "Comment: This is a quote I really relate to!", which is then sent to the server as JSON data.

[1601] Step 7:

[1602] The server aggregates the received evaluation data and calculates an evaluation score for each quote, for example, by statistically processing the evaluation data for each quote and calculating an average evaluation score.

[1603] Step 8:

[1604] The server generates a ranking based on the results of the calculation and displays it to the user. Highly rated quotes are displayed at the top of the ranking. For example, the generated ranking data is displayed in list format on the user's app screen, and the user can view it.

[1605] These steps improve the performance and quality of quotes that are automatically generated based on text entered by users, and also enable rankings to be displayed based on user ratings.

[1606] 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.

[1607] The system of the present invention analyzes quotes and text data entered by the user and automatically generates new quotes, and also uses an emotion engine to recognize the user's emotions and reflect them in the analysis and generation process. This system is realized through the cooperation of a terminal, a server, and an emotion engine.

[1608] 1. User Input

[1609] The user accesses a dedicated interface on the device and enters the quote and its context. For example, the user might enter:

[1610] Quote: "Dreams aren't meant to be dreamed, they're meant to be chased."

[1611] Context: "I created this quote to celebrate the effort of pursuing your dreams."

[1612] The terminal converts this input data into JSON format and sends it to the server.

[1613] 2. Sending and analyzing quote data

[1614] The server receives the JSON data and adds it to a queue for analysis. The received data is logged. Natural language processing (NLP) algorithms are then used to analyze the quote text. Specifically, they extract keywords, perform sentiment analysis, and identify intent.

[1615] 3. Operation of the Emotion Engine

[1616] As part of the analysis process, the emotion engine recognizes the sentiment of the quote. For example, it determines whether the quote entered by the user contains positive emotions such as "effort" or "dream." The emotion engine uses NLP algorithms to perform the sentiment analysis and returns the results to the server.

[1617] 4. Automatic generation of quotes

[1618] The server automatically generates new quotes based on data including analysis results from the emotion engine. The generation algorithm utilizes parameters obtained from quotes in the existing database and analysis results. For example, for the input quote "Dreams are not meant to be seen, but to be pursued," the server reflects the "positive emotion" found in the emotion analysis and generates the following result:

[1619] "Not only do you have a dream, but you also have to keep working to make that dream come true."

[1620] The generated quotes are stored in a database.

[1621] 5. Save the latest quotes

[1622] The server stores the generated quote in a database and assigns a unique identifier to it. It also stores the date and time of generation and the original quote data.

[1623] 6. User Ratings

[1624] Other users can access the rating interface and rate the generated quotes. Users can rate the quotes by entering a five-point rating and comments. For example, a user might rate the quote as follows:

[1625] Rating: 4

[1626] Comment: "A very relatable quote!"

[1627] The terminal collects this evaluation data and transmits it to the server.

[1628] 7. Collecting evaluation data and applying the emotion engine

[1629] While the server is storing the received evaluation data in a database, the emotion engine analyzes the emotions from the user's evaluation comments and stores them in the database along with the evaluation data. This information will be used as reference information for future quote generation processes.

[1630] 8. Aggregating evaluation data and generating rankings

[1631] The server periodically aggregates the evaluation data and calculates the average evaluation score for each quote. The aggregation is performed using a statistical algorithm. The server also reflects the results of sentiment analysis of the evaluation comments.

[1632] 9. Generating and Displaying Rankings

[1633] The server generates a ranking of quotes based on the results of the calculation. The rankings are sorted in descending order of evaluation score, and the ranking results are notified to the user and displayed on a web page or application. The user can view the results on their own device.

[1634] In this way, the system carries out a series of processes, from user quote input to evaluation, compilation, and ranking display. Thanks to the effect of the emotion engine, the generated quotes are more moving and evoke empathy, enriching the user's heart. As a specific example, if a quote generated from user input is rated by many users and ranks highly, it is expected to generate great emotion and empathy.

[1635] The processing flow will be explained below.

[1636] Step 1:

[1637] The user accesses a dedicated interface on the device and inputs a quote and its background explanation. For example, the user inputs the quote "Dreams are not meant to be seen, but to be pursued" and the background explanation "I created this quote to praise the efforts of pursuing one's dreams."

[1638] Step 2:

[1639] The terminal converts the user input data into JSON format, which looks like this:

[1640] {

[1641] "quote": "Dreams aren't meant to be seen, they're meant to be chased.",

[1642] "background": "This quote was created to celebrate the effort of pursuing your dreams."

[1643] }

[1644] Step 3:

[1645] The device sends JSON format data to the server using an HTTP POST request.

[1646] Step 4:

[1647] The server receives the JSON data and adds it to a queue to be parsed.

[1648] Step 5:

[1649] The server analyzes the quote text using natural language processing (NLP) algorithms, which extract keywords, analyze sentiment, and identify intent.

[1650] Step 6:

[1651] The server then activates the emotion engine as part of the analysis results. The emotion engine recognizes the emotion of the quote and returns the result to the server. For example, it may determine a "positive emotion."

[1652] Step 7:

[1653] The server automatically generates new quotes based on the analysis results from the emotion engine. For example, for the input quote "Dreams are not meant to be seen, but to be chased," it generates the following result:

[1654] "It's important not only to have a dream, but to keep working towards making that dream come true."

[1655] Step 8:

[1656] The server stores the generated quote in a database, along with a unique identifier, the date and time of generation, and the original quote data.

[1657] Step 9:

[1658] Other users can access the rating interface and rate the generated quotes. Users can rate the quotes by entering a 5-point rating and a comment. For example, a user could rate a quote as "Rating: 4" or "Comment: This is a quote I can really relate to!"

[1659] Step 10:

[1660] The device collects user evaluation data and sends it to the server in JSON format.

[1661] Step 11:

[1662] The server stores the received evaluation data in a database. During the evaluation data collection process, the emotion engine analyzes emotions from users' evaluation comments and stores the results in the database along with the evaluation data.

[1663] Step 12:

[1664] The server periodically aggregates the evaluation data and calculates the average evaluation score for each quote, taking into account the results of sentiment analysis of the evaluation comments.

[1665] Step 13:

[1666] The server generates a ranking of quotes based on the results of the calculation. The rankings are sorted in descending order of evaluation score.

[1667] Step 14:

[1668] The server notifies the user of the ranking results and displays them on a web page or application, allowing the user to view the results from their own device.

[1669] This series of processes involves everything from user quote input to evaluation, sentiment analysis, aggregation, and ranking display. By adding the emotion engine, the generated quotes become more moving and relatable, enriching the user's mind.

[1670] Example 2

[1671] 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."

[1672] Conventional text generation systems have difficulty fully reflecting users' emotions and intentions, and the texts they generate often do not meet their expectations. Furthermore, the process of collecting evaluations of generated texts and generating rankings based on those evaluations is cumbersome and inefficient. This has led to problems such as lower user satisfaction and a decrease in the frequency of system use.

[1673] 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.

[1674] In this invention, the server includes a means for adding text data entered by a user to a queue for analysis and analyzing it, a means for analyzing the text data using a natural language processing algorithm, and a means for recognizing the emotion of the text data using an emotion engine. This enables the automatic generation of new text that reflects the user's emotion and intention. The server also includes a means for automatically generating new text based on the recognized emotion, a means for collecting evaluation data for the new text, a means for analyzing the evaluation data using the emotion engine and storing it in a database, a means for generating rankings based on the aggregation results, and a means for displaying the rankings to the user. This makes it easier for the generated text to meet user expectations, further streamlines the evaluation and ranking generation process, and improves the overall user experience.

[1675] "User" refers to a person who uses the system and is responsible for providing quotes and written data and providing evaluation feedback.

[1676] "Text data" refers to text information such as quotes and their background information entered by the user.

[1677] "JSON format" is an abbreviation for JavaScript Object Notation, and is a method for representing data in a structured and human-readable format.

[1678] A "server" is a computer system that receives and analyzes data sent from a terminal via a network and performs the necessary processing.

[1679] The "queue waiting for analysis" is a data structure for temporarily storing data waiting for analysis processing, and is managed by the server.

[1680] "Natural language processing algorithms" refer to technologies and methods for processing and analyzing human language using computers, and are used for keyword extraction, sentiment analysis, and intent identification.

[1681] An "emotion engine" is a software module that analyzes the emotions contained in text data and outputs the results.

[1682] A "generative AI model" is an algorithm or technique that uses artificial intelligence technology to create new sentences or quotes (e.g., a machine learning model).

[1683] A "database" is a data structure for systematically storing, retrieving, and managing data used within a system.

[1684] "Rating data" includes information on the ratings users give to generated quotes and sentences, and specifically refers to five-point ratings and comments.

[1685] "Ranking" refers to a ranked list of quotes and sentences generated based on evaluation data, sorted in order of evaluation score.

[1686] "Purpose-built Interface" means the particular user interface through which a user accesses the system and inputs quotes and text data.

[1687] The system of the present invention provides a series of processes that analyzes text data entered by users, automatically generates new text using an emotion engine, collects evaluations, and generates and displays rankings. The following specific technologies are used in the implementation of this system.

[1688] Hardware and Software Configuration

[1689] 1. Terminal

[1690] Users access the dedicated interface using a device such as a PC, smartphone, or tablet. The dedicated interface provides a form for users to enter quotes and their background information. This input data is converted to JSON format on the device and sent to the server.

[1691] 2. Server

[1692] The server receives the JSON data sent by the user and queues it for parsing. The server runs the following main software modules:

[1693] Natural Language Processing (NLP) algorithms: These use libraries (e.g., spaCy, NLTK, etc.) for keyword extraction, sentiment analysis, and intent identification.

[1694] Emotion engine: Used to recognize the sentiment of quotes. For this purpose, NLP techniques and emotion analysis tools (e.g., IBM Watson Tone Analyzer) are used.

[1695] Generative AI models: Artificial intelligence techniques (e.g., GPT-3) are used to generate new quotes and sentences.

[1696] Database Management System (DBMS): Used to store the generated quotes and evaluation data (e.g., MySQL, PostgreSQL, etc.).

[1697] Program processing flow

[1698] User Input

[1699] Users access a dedicated interface and enter a quote and a description of its context. For example:

[1700] Quote: "Dreams aren't meant to be dreamed, they're meant to be chased."

[1701] Context: "I created this quote to celebrate the effort of pursuing your dreams."

[1702] The terminal converts this input data into JSON format and sends it to the server via an HTTP request.

[1703] Analysis of quote data

[1704] The server adds the received JSON data to a queue for parsing, then uses natural language processing algorithms to parse the quote text and identify keywords, sentiment, and intent, and an emotion engine identifies the sentiment of the quote and returns the results to the server.

[1705] Generating new quotes

[1706] Based on the analysis results from the emotion engine, the server inputs a prompt into the generative AI model to generate a new quote. For example, the following prompt sentence is used:

[1707] Prompt: "Dreams aren't meant to be seen, they're meant to be chased. Generate a new quote with a positive sentiment."

[1708] The generated quotes are stored in a database.

[1709] Collecting ratings and generating rankings

[1710] Other users access the rating interface and rate the generated quotes that are displayed. Ratings are made in the form of a five-point rating and comments. The rating data is analyzed by an emotion engine and stored in a database. The server periodically aggregates the rating data and generates a ranking of quotes using a statistical algorithm. The generated ranking is displayed to users, who can view it from their own devices.

[1711] Specific examples

[1712] The user inputs "Dreams aren't just for dreaming, they're for chasing" and adds a background explanation. The system analyzes this input and generates a new quote that reflects a positive emotion. For example, a quote might be generated: "It's not just about having a dream, it's about continuing to work towards making that dream come true." This quote is then rated by other users, and it receives high ratings and appears at the top of the rankings.

[1713] The above is a mode for carrying out the invention, which is a method for efficiently realizing a series of processes from user input to evaluation and ranking generation in a manner that evokes emotional empathy.

[1714] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1715] Step 1:

[1716] The user accesses the dedicated interface using a terminal and inputs a quote and its background information. The user's input data is converted into JSON format and sent to the server as an HTTP request.

[1717] Input: User enters quote and background information

[1718] Output: JSON format data

[1719] Specific behavior:

[1720] A user accesses a dedicated form in a browser or mobile app.

[1721] Enter a quote and its background.

[1722] The program converts the input data into JSON format.

[1723] The converted data is sent to the server.

[1724] Step 2:

[1725] The server adds the received JSON data to a queue to be parsed and logs it.

[1726] Input: JSON format data sent from the terminal

[1727] Output: Data queued for analysis, log records

[1728] Specific behavior:

[1729] The server receives the HTTP request and reads the JSON data.

[1730] Queue the data for analysis.

[1731] Log the data received with a timestamp.

[1732] Step 3:

[1733] The server uses natural language processing (NLP) algorithms to analyze the quote text, extract keywords, analyze sentiment, and identify intent.

[1734] Input: JSON data retrieved from the queue to be parsed

[1735] Output: Keywords, sentiment, and intent identification

[1736] Specific behavior:

[1737] The server retrieves the data from the queue to be analyzed.

[1738] Analyze the quote text and extract keywords using an NLP library (e.g., spaCy, NLTK).

[1739] Analyze the sentiment of the quote using a sentiment analysis tool.

[1740] Applying algorithms to identify the intent of the text.

[1741] Generate extracted keywords, sentiment analysis results, and intent identification information.

[1742] Step 4:

[1743] The emotion engine recognizes the emotion of the input quote and returns the results to the server.

[1744] Input: Text of the quote and its analysis results

[1745] Output: Emotion recognition result

[1746] Specific behavior:

[1747] The server sends the quote text and its analysis results to an emotion engine (e.g., IBM Watson Tone Analyzer).

[1748] The sentiment engine performs sentiment analysis and assigns a sentiment label, such as "positive."

[1749] The emotion recognition results are returned to the server.

[1750] Step 5:

[1751] The server generates new quotes using a generative AI model based on the results of the emotion engine. For example, instructions are given to the generative AI model using prompt sentences.

[1752] Input: Emotion recognition results, keywords, and intent identification information

[1753] Output: The newly generated quote

[1754] Specific behavior:

[1755] The server inputs prompts into the generative AI model (e.g., GPT-3).

[1756] Prompt: "Dreams aren't meant to be seen, they're meant to be chased. Generate a new quote with a positive sentiment."

[1757] Get the generated quote.

[1758] Save the new quote to the database.

[1759] Step 6:

[1760] The server assigns a unique identifier to the generated quote and stores it in a database along with the date and time of generation and the original quote data.

[1761] Input: Newly generated quote

[1762] Output: Quotation data stored in the database

[1763] Specific behavior:

[1764] A unique ID is assigned to the generated quote.

[1765] The creation date and time and the original quote data are associated.

[1766] The generated quote data is stored in a database.

[1767] Step 7:

[1768] Other users can access the evaluation interface and evaluate the generated quotes. The evaluation data is analyzed by the emotion engine and sent to the server.

[1769] Input: Generated quote displayed in the evaluation interface

[1770] Output: Rating data, rating comments, sentiment analysis results

[1771] Specific behavior:

[1772] Other users access the evaluation interface.

[1773] Rate the generated quotes using a 5-point scale and in the form of comments.

[1774] The evaluation data is analyzed by the emotion engine.

[1775] The analysis results are sent to the server.

[1776] Step 8:

[1777] The server stores the received evaluation data in a database and periodically compiles it to generate rankings.

[1778] Input: Evaluation data, evaluation comments, sentiment analysis results

[1779] Output: Aggregation results, rankings

[1780] Specific behavior:

[1781] The server stores the rating data in a database.

[1782] The evaluation data is periodically aggregated using an aggregation algorithm.

[1783] Calculate the average rating score for each quote.

[1784] Rankings are generated that also reflect the results of sentiment analysis.

[1785] Step 9:

[1786] The server notifies and displays the rankings generated by the server to the user, who can then view the ranking results from their own devices.

[1787] Input: Aggregation results, ranking data

[1788] Output: The ranking displayed to the user

[1789] Specific behavior:

[1790] The server notifies the user of the ranking results.

[1791] The ranking results are displayed on a web page or application.

[1792] The user views the ranking results from their device.

[1793] (Application example 2)

[1794] 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."

[1795] Conventional quote generation systems do not adequately consider the user's emotions when generating quotes, making it difficult to generate sentences that move or resonate with users. Furthermore, the system for improving and optimizing quotes using user evaluation data is insufficient, resulting in inconsistent quality of generated sentences.

[1796] The specification process by the specification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for receiving text data input by a user, means for analyzing the text data, means for automatically generating new text based on the text data, means for saving the new text in a database, means for collecting evaluation data for the new text, means for aggregating the evaluation data, means for generating rankings based on the aggregation results, means for displaying the rankings to the user, means for analyzing emotions from the text input by the user and the evaluation data, and means for generating prompt sentences based on the analysis results and generating new sentences using an AI model. This makes it possible to automatically generate high-quality quotes and sentences that reflect the user's emotions and further improve the quality of the quotes by utilizing the evaluation data.

[1797] The "means for receiving text data entered by the user" refers to a device or program that allows the user to enter text data via a terminal and transfers the data to the server.

[1798] The "means for analyzing the text data" refers to a system or program that analyzes input text data using natural language processing technology and extracts meaning, emotion, and keywords.

[1799] The "means for automatically generating new sentences based on the sentence data" refers to algorithms and programs for generating new sentences and quotes based on the analysis results and existing data.

[1800] The "means for saving the new sentence in a database" refers to a system or program that registers the automatically generated new sentence in a database, assigns a unique identifier, and saves and manages it.

[1801] The "means for collecting evaluation data for the new text" is a device or program that collects evaluations and comments provided by users for the generated text.

[1802] The "means for aggregating the evaluation data" refers to a device or program for statistically processing the collected evaluation data and generating aggregation results such as average values ​​and rankings.

[1803] The "means for generating rankings based on the aggregation results" refers to a system or program that generates rankings of sentences and quotes based on the aggregated evaluation data.

[1804] The "means for displaying the ranking to the user" refers to a device or program for displaying the generated ranking results on a user interface.

[1805] "Means for analyzing emotions from user input text and evaluation data" refers to a system or program that analyzes emotions from text input by a user and evaluation comments and extracts the results.

[1806] "Means for generating prompt sentences based on the analysis results and generating new sentences using an AI model" refers to a system or program that generates prompt sentences based on the results of sentiment analysis and creates new sentences or quotes using a generative AI model.

[1807] The system for implementing the present invention includes a process for analyzing text data entered by a user and automatically generating new text. The system also generates quotes that reflect the user's emotions and generates rankings based on evaluation data. The system is operated through the cooperation of a terminal, a server, and a sentiment analysis engine.

[1808] First, the user inputs text data via a terminal. The terminal is equipped with a dedicated interface, and the user inputs a quote and its background explanation. This data is converted into JSON format and sent to the server.

[1809] The server adds the received JSON data to a queue for analysis and logs it, then analyzes the text data using natural language processing (NLP) algorithms, specifically extracting keywords from the text, analyzing sentiment, and identifying intent.

[1810] The sentiment analysis engine works as part of the analysis process to recognize emotions from the user's input text. For example, it determines whether the input "Dreams are not for dreaming, but for chasing" contains a positive emotion. The results are returned to the server.

[1811] The server then generates new sentences based on the results of the sentiment analysis. The generation algorithm applies parameters derived from the analysis and quotes from an existing database. It uses a generative AI model to generate prompts that reflect the sentiment. For example, it generates prompts like this:

[1812] Example: "Dreams aren't meant to be seen, they're meant to be chased. <|emotion:positive|>"

[1813] The generated quotes are stored in a database and assigned a unique identifier, along with the date and time of generation and the original quote data.

[1814] Other users then access a dedicated interface to rate the generated quotes. The rating data is entered in the form of a five-point rating and comments, and is sent from the device to the server. This allows the rating data to be collected.

[1815] The server uses a sentiment analysis engine to analyze the sentiment of users' comments while storing the evaluation data in a database. This information will be used as reference for future quote generation processes.

[1816] The evaluation data is collected over a set period of time, and an average evaluation score for each quote is calculated. Furthermore, the results of sentiment analysis of the evaluation comments are also reflected to generate a ranking of quotes. These rankings are sorted in descending order of evaluation score, and are notified to users as well as displayed on web pages and applications.

[1817] As a whole system, the terminal is responsible for accepting data input from users and sending the data to the server. The server is mainly responsible for data analysis, generation, storage, and aggregation of evaluation data and display of rankings. The sentiment analysis engine is a key element in the user sentiment recognition and generation process.

[1818] The main hardware and software used are as follows:

[1819] Hardware: Servers (e.g., Amazon EC2 or Google Cloud), user devices (smartphones, PCs)

[1820] Software: TextBlob (sentiment analysis), Transformers (Hugging Face library), GPT-2 model (generative AI model), Python

[1821] The system of the present invention allows users to create inspirational and relatable quotes, and then share and improve the quality of these quotes with other users through ratings.

[1822] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1823] Step 1:

[1824] The user inputs text data via a terminal. The terminal is equipped with a dedicated interface, and the user inputs quotes and their background information. This data is converted into JSON format and sent to the server.

[1825] Input: User-entered quote and background information

[1826] Output: Data converted to JSON format

[1827] Step 2:

[1828] The server adds the received JSON data to a queue to be parsed and logs it. At this stage, the data is also checked for consistency and integrity.

[1829] Input: JSON format data

[1830] Output: Queueing and logging

[1831] Step 3:

[1832] The server analyzes the text data using natural language processing (NLP) algorithms, specifically extracting keywords from the text, analyzing sentiment, and identifying intent.

[1833] Input: Data retrieved from the analysis queue

[1834] Output: Keywords, sentiment analysis results, intent identification results

[1835] Step 4:

[1836] The emotion analysis engine recognizes the emotion from the user's input text, and the analysis results are returned to the server and used in the next text generation process.

[1837] Input: Text data

[1838] Output: Emotion analysis results

[1839] Step 5:

[1840] The server generates new sentences based on the results of sentiment analysis. The generation algorithm uses a generative AI model and prompts that reflect the sentiment. For example, the following prompts are generated:

[1841] "Dreams aren't meant to be seen, they're meant to be chased. <|emotion:positive|>"

[1842] Input: Sentiment analysis results and keyword data

[1843] Output: The new sentence generated

[1844] Step 6:

[1845] The generated quotes are stored in a database. They are assigned a unique identifier and are saved together with the date and time of generation and the original quote data.

[1846] Input: New sentence generated

[1847] Output: Save to database and give it a unique identifier

[1848] Step 7:

[1849] Other users can access a dedicated interface to rate the generated quotes. Ratings are entered in the form of a five-point scale and comments.

[1850] Input: Generate quote

[1851] Output: Rating data (5-point rating and comments)

[1852] Step 8:

[1853] The server stores the received evaluation data in a database. During this process, it uses a sentiment analysis engine to analyze emotions from the evaluation comments and saves the information in the database.

[1854] Input: Evaluation data

[1855] Output: Evaluation data and analysis results saved in a database

[1856] Step 9:

[1857] The evaluation data is collected over a set period of time, and an average evaluation score for each quote is calculated. Furthermore, the results of sentiment analysis of the evaluation comments are also reflected in the ranking of quotes.

[1858] Input: Evaluation data stored in a database

[1859] Output: Evaluation scores and rankings

[1860] Step 10:

[1861] The generated rankings are notified to the user and displayed on a web page or application, where the user can view these rankings.

[1862] Input: Ranking data

[1863] Output: Display of rankings

[1864] 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.

[1865] 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.

[1866] 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.

[1867] 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.

[1868] 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.

[1869] 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.

[1870] 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).

[1871] 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.

[1872] 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."

[1873] 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.

[1874] 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).

[1875] 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.

[1876] 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.

[1877] 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.

[1878] 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.

[1879] 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.

[1880] 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.

[1881] 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.

[1882] 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.

[1883] 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.

[1884] 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.

[1885] The following is further disclosed regarding the above embodiment.

[1886] (Claim 1)

[1887] a means for receiving text data input by a user;

[1888] means for analyzing the text data;

[1889] means for automatically generating new sentences based on the sentence data;

[1890] means for storing said new sentences in a database;

[1891] means for collecting evaluation data for the new text;

[1892] means for aggregating the evaluation data;

[1893] means for generating a ranking based on the counting results;

[1894] means for displaying the ranking to a user;

[1895] A system including:

[1896] (Claim 2)

[1897] The system of claim 1 , wherein the generation of the new sentences is performed using a natural language processing algorithm.

[1898] (Claim 3)

[1899] The system according to claim 1, wherein the user's evaluation data is in a format including a five-point rating and a comment.

[1900] "Example 1"

[1901] (Claim 1)

[1902] a means for receiving text data entered by a user through an interface;

[1903] means for converting the text data into a JSON format;

[1904] means for transmitting the data converted into the JSON format to a server;

[1905] A server receives the JSON data;

[1906] means for the server to analyze the text data using natural language processing technology and extract keywords and emotions;

[1907] means for automatically generating new sentences based on the analysis results using a generative AI model;

[1908] means for storing said new sentences in a database;

[1909] means for collecting data on other users' ratings of the new text through a user interface;

[1910] A server statistically analyzes the evaluation data and calculates an evaluation score;

[1911] a means for generating a ranking of highly evaluated sentences based on the tallying results;

[1912] means for displaying the ranking to a user;

[1913] A system including:

[1914] (Claim 2)

[1915] The system of claim 1, wherein the generation of the new sentences is performed using natural language processing algorithms and generative AI models.

[1916] (Claim 3)

[1917] The system according to claim 1, wherein the user's evaluation data is in a format including a five-point rating and a comment.

[1918] "Application Example 1"

[1919] (Claim 1)

[1920] a means for receiving text data input by a user;

[1921] means for analyzing the text data;

[1922] means for automatically generating new sentences based on the sentence data;

[1923] means for storing said new sentences in a database;

[1924] means for presenting said new sentence to a user;

[1925] means for collecting evaluation data for the new text;

[1926] means for aggregating the evaluation data;

[1927] means for generating a ranking based on the counting results;

[1928] means for displaying the ranking to a user;

[1929] A system including:

[1930] (Claim 2)

[1931] The system of claim 1 , wherein the generation of the new sentences is performed using a natural language processing algorithm.

[1932] (Claim 3)

[1933] The system according to claim 1, wherein the user's evaluation data is in a format including a five-point rating and a comment.

[1934] "Example 2: Combining Emotion Engines"

[1935] (Claim 1)

[1936] a means for receiving text data input by a user;

[1937] means for converting the text data into a JSON format and transmitting the converted data to a server;

[1938] means for adding the text data to a queue for analysis and analyzing it;

[1939] means for analyzing the text of the document data using a natural language processing algorithm;

[1940] means for recognizing emotions in the text data using an emotion engine;

[1941] means for automatically generating new sentences based on the recognized emotions;

[1942] means for storing said new sentences in a database;

[1943] means for collecting evaluation data for the new text;

[1944] means for analyzing the evaluation data using an emotion engine and storing the data in a database;

[1945] means for generating a ranking based on the counting results;

[1946] means for displaying the ranking to a user;

[1947] A system including:

[1948] (Claim 2)

[1949] The system of claim 1, wherein the generation of the new sentences is performed using a generative AI model.

[1950] (Claim 3)

[1951] The system according to claim 1, wherein the user's evaluation data is in a format including a five-point rating and a comment.

[1952] "Application example 2 when combining emotion engines"

[1953] (Claim 1)

[1954] a means for receiving text data input by a user;

[1955] means for analyzing the text data;

[1956] means for automatically generating new sentences based on the sentence data;

[1957] means for storing said new sentences in a database;

[1958] means for collecting evaluation data for the new text;

[1959] means for aggregating the evaluation data;

[1960] means for generating a ranking based on the counting results;

[1961] means for displaying the ranking to a user;

[1962] A means for analyzing emotions from user input sentences and evaluation data;

[1963] A means for generating prompt sentences based on the analysis results and generating new sentences using an AI model;

[1964] A system including:

[1965] (Claim 2)

[1966] The system of claim 1, wherein the generation of the new sentences is performed using natural language processing algorithms and AI models.

[1967] (Claim 3)

[1968] 2. The system according to claim 1, wherein the user's evaluation data is in a format including a multi-level rating and comments. [Explanation of symbols]

[1969] 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 receiving text data input by a user; means for analyzing the text data; means for automatically generating new sentences based on the sentence data; means for storing said new sentences in a database; means for collecting evaluation data for the new text; means for aggregating the evaluation data; means for generating a ranking based on the counting results; means for displaying the ranking to a user; A system including:

2. The system of claim 1 , wherein the generation of the new sentences is performed using a natural language processing algorithm.

3. The system according to claim 1 , wherein the user's evaluation data is in a format including a five-point rating and a comment.

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