System

Abstract

A system is provided.SOLUTION: A system comprising: means for receiving text input by a user; means for performing sentiment analysis to calculate a sentiment score; means for embellishing the input text with different colors based on the sentiment score; and means for outputting the embellished text to a terminal.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] Although text communication has increased in recent years, it is difficult to accurately convey emotions and nuances using text alone. In particular, misunderstandings of emotions are likely to occur in text-based communication such as email and chat. For this reason, there is a demand for visually expressing the emotions and nuances of text. However, existing systems lack the means to analyze text and visually express emotions by decorating it with appropriate colors. Therefore, the present invention aims to solve this problem. [Means for solving the problem]

[0005] The present invention provides a system that receives text entered by a user, performs sentiment analysis to calculate a sentiment score, decorates the text with different colors based on the sentiment score, and outputs the decorated text to a terminal. In particular, if the sentiment score of the text is positive, it is decorated with green, if it is negative, it is decorated with red, and if it is neutral, it is decorated with yellow. Furthermore, by calculating the sentiment score of the text using natural language processing technology, the system allows the user to intuitively understand the sentiment and nuance of the entered text. This reduces misunderstandings about emotions in text communication and enables more effective communication.

[0006] 1. "User" means a person or entity that provides text input to the system.

[0007] 2. "Input Text" means written words or sentences provided by a user to a system.

[0008] 3. "Sentiment analysis" is the process of analyzing and evaluating the emotional tone and nuance of input text.

[0009] 4. "Sentiment Score" means the numerical data resulting from sentiment analysis that indicates whether text is positive, negative, or neutral.

[0010] 5. "Color Embellishment" refers to applying a specific color to text based on its sentiment score.

[0011] 6. "Terminal" means a device for displaying decorated text, including computers, smartphones, tablets, etc.

[0012] 7. “Natural Language Processing” is the technology used to analyze and understand human language and to calculate sentiment scores for text.

[0013] 8. "Green" is the color used when the sentiment score is positive.

[0014] 9. "Red" is the color used when the sentiment score is negative.

[0015] 10. "Yellow" is the color used when the sentiment score is neutral. [Brief explanation of the drawings]

[0016] [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

[0017] 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.

[0018] First, the terms used in the following description will be explained.

[0019] 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).

[0020] 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.

[0021] 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.

[0022] 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.

[0023] 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."

[0024] [First embodiment]

[0025] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0026] 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.

[0027] 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).

[0028] 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.

[0029] 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.

[0030] 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.

[0031] 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.

[0032] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0033] 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.

[0034] 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.

[0035] 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.

[0036] 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."

[0037] The present invention is a system that analyzes the sentiment of text entered by a user and decorates the text with different colors based on the sentiment score. This system operates between a server and a terminal, and visually expresses the user's input in an easy-to-understand manner.

[0038] System configuration

[0039] 1. The server receives text entered by the user.

[0040] 2. The server performs sentiment analysis and calculates a sentiment score for the input text. Sentiment analysis is performed using natural language processing technology.

[0041] 3. The server decorates the input text by assigning different colors based on the sentiment score: green for positive sentiment, red for negative sentiment, and yellow for neutral sentiment.

[0042] 4. The server generates the decorated text and sends it to the terminal.

[0043] 5. The terminal displays the received decorative text on the screen.

[0044] Program processing explanation

[0045] 1. A user enters text into an input field, for example, "Today is a truly wonderful day!"

[0046] 2. The server receives this input text and begins sentiment analysis, using a natural language processing library such as TextBlob to analyze the sentiment.

[0047] 3. The server receives the analysis result (emotion score). For example, if the score is positive, it is classified as green.

[0048] 4. The server generates the text with green decoration and the text entered by the user colored.

[0049] 5. The server sends the generated decorated text to the terminal, which displays it. The user can visually confirm the decorated text on the screen.

[0050] Specific examples

[0051] Example 1: Positive text

[0052] 1. The user types, "Today is such a great day!"

[0053] 2. The server receives this text and calculates the sentiment score. Since the sentiment score is positive, it is classified as positive.

[0054] 3. The server colors the text green.

[0055] 4. The device will display this green text on the screen, visually indicating positive emotion to the user.

[0056] Example 2: Negative text

[0057] 1. The user types, "I'm feeling very sad today."

[0058] 2. The server receives this text and calculates the sentiment score. Since the sentiment score is negative, it is classified as negative.

[0059] 3. The server colors the text red.

[0060] 4. The device will display this red text on the screen, visually signaling a negative emotion to the user.

[0061] This system visually expresses the emotions and nuances of text, allowing users to intuitively understand the meaning of the text, thereby reducing misunderstandings in text communication and enabling smoother communication.

[0062] The processing flow will be explained below.

[0063] Step 1:

[0064] The user types text into an input field, for example, "Today is a truly wonderful day!"

[0065] Step 2:

[0066] The terminal sends the text entered by the user to the server.

[0067] Step 3:

[0068] The server prepares the input text received from the user for analysis. The server then subjects this text to sentiment analysis.

[0069] Step 4:

[0070] The server performs sentiment analysis using a natural language processing library (e.g., TextBlob). It uses the text analysis capabilities of the TextBlob library to calculate a sentiment score for the input text.

[0071] Step 5:

[0072] The server evaluates the sentiment score and determines whether it is positive, negative, or neutral. The server classifies the sentiment score as either positive, negative, or zero (neutral).

[0073] Step 6:

[0074] The server chooses a corresponding color based on the emotion score: green for a positive emotion score, red for a negative emotion score, and yellow for a zero emotion score.

[0075] Step 7:

[0076] The input text is decorated with a color selected by the server, specifically, the text is colored using the corresponding color code.

[0077] Step 8:

[0078] The server generates the colored text and sends it to the terminal.

[0079] Step 9:

[0080] The terminal displays the colored text received from the server on the screen, allowing the user to visually see the colored text and intuitively understand the emotion and nuance of the input text.

[0081] Example 1

[0082] 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."

[0083] In modern text communication, the sentiment and nuance of a sentence are often misunderstood. Therefore, a method is needed to visually express the sentiment and nuance of a text so that users can intuitively understand it. Also, a means is needed to properly analyze sentiment and decorate the text based on that sentiment.

[0084] 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.

[0085] In this invention, the server includes means for receiving text entered by a user, means for performing sentiment analysis and calculating a sentiment score, means for decorating the input text with different colors based on the sentiment score, means for outputting the decorated text to a terminal, means for performing the sentiment analysis using natural language processing technology, and means for generating the decorated text and sending it to the terminal as an HTTP response. This visually expresses the sentiment and nuance of the text, allowing the user to intuitively understand the sentiment of the sentence.

[0086] "User" refers to a person who uses a system or service.

[0087] A "means" refers to a method or device used to achieve a particular purpose.

[0088] "Server" refers to a computer system that processes and provides data in response to requests from clients.

[0089] "Terminal" refers to an input and display device that can be directly operated by a user.

[0090] "Text" refers to a sentence or character string entered by a user.

[0091] "Sentiment analysis" refers to the process of analyzing the emotional state of input text using natural language processing techniques.

[0092] An "emotion score" is a numerical representation of the emotional state of a piece of text, and can be positive, negative, or neutral.

[0093] "Decorate with different colors" refers to applying specific colors to text depending on the sentiment score.

[0094] "Natural language processing technology" refers to technology that allows computers to analyze, understand, and generate human language.

[0095] An "HTTP response" refers to a server's response message to an HTTP request from a client.

[0096] An "input event" refers to a programmatic action that is triggered when a user interacts with an input device (e.g., a keyboard).

[0097] An "HTTP POST request" refers to one of the HTTP methods used by a client to send data to a server.

[0098] The system aims to analyze the sentiment of text entered by a user and decorate the text with different colors based on the sentiment score. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS The following describes in detail the embodiments of the present invention.

[0099] System configuration

[0100] The system consists of two main components: a server and a terminal. A user inputs text through the terminal, and the server processes the text and returns the results to the terminal.

[0101] Hardware and software used

[0102] server:

[0103] The server receives text from the user, performs sentiment analysis, and decorates the text.

[0104] The server requires a standard web server and a programming language such as Python.

[0105] Uses the TextBlob library as a natural language processing technique.

[0106] You can use Flask or Django as a web framework.

[0107] Device:

[0108] The terminal is used by the user to input text and display the results of processing.

[0109] The device can be a general computer, tablet, or smartphone with a browser.

[0110] It uses HTML, CSS, and JavaScript to provide a simple user interface.

[0111] Program processing explanation

[0112] 1. A user types text into an input field on a device, for example, "Today is a really great day!"

[0113] 2. The device sends this input text to the server as an HTTP POST request.

[0114] 3. The server receives the text sent by the user and performs sentiment analysis using a natural language processing library such as TextBlob.

[0115] 4. The server colors the text based on the resulting sentiment score: green for positive sentiment, red for negative sentiment, and yellow for neutral sentiment.

[0116] 5. The server generates the decorated text and returns it to the device as an HTTP response.

[0117] 6. The terminal displays the received decorative text on the browser screen so that the user can visually confirm it.

[0118] Adding specific examples

[0119] Example 1: Positive text

[0120] The user types, "Today is such a great day!"

[0121] The device sends this text to the server.

[0122] The server calculates the sentiment score and determines it to be positive.

[0123] The server colors this text green and sends it back to the terminal.

[0124] The device displays green text on the browser screen, conveying positive emotions to the user.

[0125] Example 2: Negative text

[0126] The user types, "I'm feeling very sad today."

[0127] The device sends this text to the server.

[0128] The server calculates the sentiment score and determines it to be negative.

[0129] The server colors this text red and sends it back to the terminal.

[0130] The device displays red text on the browser screen, conveying negative emotions to the user.

[0131] Prompt Sentence Examples

[0132] Enter the text "Today is a truly wonderful day!" and run a sentiment analysis to display it in green.

[0133] In this way, the present invention visually expresses the emotion and nuance of text, allowing users to intuitively understand the emotion of a sentence, thereby reducing misunderstandings in text communication and realizing smoother communication.

[0134] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0135] Step 1:

[0136] The user enters text into an input field on the terminal.

[0137] As a concrete example, a user types "Today is a really great day!" When the user confirms the input, an input event is triggered and the terminal stores the entered text in a variable.

[0138] Input: Text entered by the user

[0139] Output: Text stored in a variable in the terminal

[0140] Step 2:

[0141] The terminal sends the entered text data to the server as an HTTP POST request.

[0142] Asynchronous communication is performed using AJAX, and text data is sent to a specified endpoint on the server.

[0143] Input: Text stored in a terminal variable

[0144] Output: HTTP POST request sent to the server

[0145] Step 3:

[0146] The server receives the text sent by the user.

[0147] The server-side API endpoint receives the request and stores the received text data in variables for analysis.

[0148] Input: HTTP POST request

[0149] Output: Text stored in a variable for parsing on the server

[0150] Step 4:

[0151] The server performs sentiment analysis based on the received text.

[0152] Use a natural language processing library such as TextBlob to calculate the sentiment score of the text. For example, for the text "Today is a really great day!", call the TextBlob(text).sentiment method of the TextBlob library to get the sentiment score.

[0153] Input: Text stored in a variable for parsing on the server

[0154] Output: Sentiment score (number indicating positive, negative, or neutral)

[0155] Step 5:

[0156] The server colors the text based on the obtained sentiment score.

[0157] If the sentiment score is positive, the text is colored green, if negative, red, and if neutral, yellow. We also use an HTML template to generate the colored text.

[0158] Input: Sentiment score

[0159] Output: Decorated text (HTML format)

[0160] Step 6:

[0161] The server generates the decorated text and sends it to the terminal as an HTTP response.

[0162] The server returns the generated HTML formatted decorated text as an HTTP response and sends it to the terminal.

[0163] Input: Decorated text (HTML format)

[0164] Output: Decorative text returned as an HTTP response

[0165] Step 7:

[0166] The terminal displays the received decorative text on the screen so that the user can visually confirm it.

[0167] The device uses DOM manipulation to display the returned decorative text in a specific area of ​​the browser, for example, a green-colored "Today is a really great day!"

[0168] Input: Decorative text returned as an HTTP response

[0169] Output: Decorative text displayed in the browser window

[0170] (Application example 1)

[0171] 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."

[0172] Conventional ad delivery systems do not fully consider the impact of text ad content on user emotions, which limits the visual impact of ads and their ability to appeal to user emotions, resulting in the issue of ads not being as effective as they could be.

[0173] 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.

[0174] In this invention, the server includes means for receiving text entered by a user, means for performing sentiment analysis and calculating a sentiment score, means for decorating the entered text with a different color based on the sentiment score, means for outputting the decorated text to a terminal, means for changing the color of the advertisement based on the result of sentiment analysis of the advertisement text, and means for displaying the changed advertisement text on the terminal. This enhances the visual effect of the text advertisement and enables advertisement delivery that strongly appeals to the user's emotions.

[0175] The "means for receiving text entered by the user" is an interface having a function for receiving text data entered by the user and transmitting it to the server.

[0176] The "means for performing sentiment analysis and calculating sentiment scores" is a system that uses natural language processing technology to analyze and quantify the emotional nuances of text.

[0177] The "means for decorating input text with different colors based on the emotion score" is a system having a function for visually highlighting input text using a color corresponding to the emotion score.

[0178] The "means for outputting decorated text to a terminal" is a system having a function for transmitting text data decorated by a server to a user terminal and displaying it.

[0179] The "means for changing the color of an advertisement based on the result of sentiment analysis of the advertisement text" is a system that has the function of analyzing the sentiment score of the advertisement text and dynamically adjusting the color of the advertisement text according to the result.

[0180] The "means for displaying the modified advertising text on the terminal" is a system having a function for displaying color-adjusted advertising text on the user's terminal to visually enhance the advertising effect.

[0181] This invention provides an advertisement distribution system that visually emphasizes advertisement text based on emotion analysis and strongly appeals to the user's emotions. The specific configuration and processing steps of this system are described below.

[0182] Overall system overview

[0183] This system works in cooperation with a server and a terminal. The server performs sentiment analysis of text data and dynamically changes the color of the text according to the results. The decorated text is sent to the terminal and displayed in a visible state for the user.

[0184] Hardware and software used

[0185] Server: Uses the Flask framework (Python). For sentiment analysis, it uses TextBlob, a natural language processing library.

[0186] Client devices: Smartphones, smart glasses, head-mounted displays, etc. These devices have a web browser installed and are processed using JavaScript.

[0187] Program processing

[0188] The server receives the ad text entered by the user and performs sentiment analysis using TextBlob. Based on the sentiment score, the text is colored green, red, or yellow. The colored ad text is then sent from the server to the user's device and displayed in the browser.

[0189] Specific examples

[0190] 1. A user types into the ad dashboard, "This product provides a truly amazing experience!"

[0191] 2. The server receives this text and performs sentiment analysis using the TextBlob library. Since this text contains positive sentiment, the sentiment score is positive.

[0192] 3. The server colors the text green based on the sentiment score. The coloring is achieved using HTML style sheet tags.

[0193] 4. The device receives the decorated text from the server and displays it in the browser, allowing the user to visually recognize positive emotions on the device.

[0194] Prompt Sentence Examples

[0195] Enter the following prompt for the generative AI model:

[0196] Analyze the sentiment of the text entered by the user and decorate it with green for positive sentiment, red for negative sentiment, and yellow for neutral sentiment.

[0197] The above configuration and processing improve the visual effect of the advertising text and realize advertisement delivery that appeals to the user's emotions.

[0198] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0199] Step 1:

[0200] The user accesses the ad management screen and enters ad text in the input field. For example, the user might enter, "This product provides a truly amazing experience!" The input text is sent to the server.

[0201] Step 2:

[0202] The server receives the text data sent by the user. The input data arrives at the server in text format. At this point, the input is the raw text entered by the user.

[0203] Step 3:

[0204] The server uses the TextBlob library to perform sentiment analysis on the text it receives. The server passes the text data to TextBlob and calculates the sentiment score. The input data is the text before analysis begins, and the output data is the analysis result along with the sentiment score.

[0205] Step 4:

[0206] The server decorates the text based on the sentiment score. It colors the text using green if the sentiment score is positive, red if the sentiment score is negative, and yellow if the sentiment score is neutral. The input data is the text with the sentiment score, and the output data is the color-decorated text. This decoration is done using HTML style sheets.

[0207] Step 5:

[0208] The server sends the decorated text to the terminal in JSON format. The input data is the decorated text, and the output data is JSON data formatted for transmission.

[0209] Step 6:

[0210] The terminal parses the JSON data received from the server and parses the decorated text. The terminal parses the received data and converts it into a format that the user can view. The input data is the JSON data sent from the server, and the output data is the parsed decorated text.

[0211] Step 7:

[0212] The terminal displays the decorated advertisement text in the browser, and the user visually confirms it through the terminal screen. The input data is the parsed decorated text, and the output is a visual text advertisement displayed to the user.

[0213] The above steps complete the entire process from inputting advertising text to displaying it. This system makes it easier for users to visually recognize the emotional nuances contained in text advertising.

[0214] 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.

[0215] The present invention is a system that analyzes the emotions of text entered by a user and decorates the text with different colors based on the emotion score, and also combines an emotion engine that recognizes the user's emotions. This system operates between a server and a terminal, and more accurately analyzes user input and presents it in a visually understandable way.

[0216] System configuration

[0217] 1. The server receives text entered by the user, and optionally voice and facial expression data.

[0218] 2. The server uses an emotion recognition module (emotion engine) to classify the user's emotion from the input data and obtain emotion information.

[0219] 3. The server uses a natural language processing library (e.g., TextBlob) to calculate the sentiment score for the input text itself.

[0220] 4. The server reflects the emotional information obtained from the emotion engine in the emotional score of the text.

[0221] 5. The server decorates the input text by assigning different colors based on the sentiment score: green for positive sentiment, red for negative sentiment, and yellow for neutral sentiment.

[0222] 6. The server generates the decorated text and sends it to the terminal.

[0223] 7. The terminal displays the received decorative text on the screen.

[0224] Program processing explanation

[0225] 1. A user types text into an input field, possibly providing voice and facial expression data. For example, they type "Today is a truly wonderful day!"

[0226] 2. The terminal sends the input data from the user to the server.

[0227] 3. The server prepares the received input text, and if possible, speech and facial expression data, for analysis. The server then subjects this input data to emotion recognition.

[0228] 4. The server uses the emotion recognition module (emotion engine) to recognize the user's emotions and obtain emotion information.

[0229] 5. The server uses a natural language processing library to calculate a sentiment score for the input text.

[0230] 6. The server reflects the emotion information obtained from the emotion engine into the emotion score. This process makes the emotion score of the text more accurate.

[0231] 7. The server selects a color based on the emotion score: green if the emotion score is positive, red if negative, and yellow if zero.

[0232] 8. The server decorates the input text with a color selected by the server, generating a colored version of the text entered by the user.

[0233] 9. The server sends the colored text to the terminal, which displays it, allowing the user to visually confirm the decorated text on the screen.

[0234] Specific examples

[0235] Example 1: Positive text and facial expressions

[0236] 1. The user types "Today is a really great day!" and simultaneously provides smiley face data.

[0237] 2. The server receives this input data and uses an emotion engine to recognize positive emotions from the user's facial expressions.

[0238] 3. The server calculates the sentiment score using natural language processing and obtains a positive result.

[0239] 4. The server integrates the sentiment information and the text score to determine the final sentiment score as positive.

[0240] 5. The server colors the text in green and sends it to the device.

[0241] 6. The device will display this green text on the screen, visually indicating positive emotion to the user.

[0242] Example 2: Negative text and audio

[0243] 1. The user types, "I feel very sad today," and simultaneously provides voice data in a sad-sounding voice.

[0244] 2. The server receives this input data and uses an emotion engine to recognize negative emotions from the user's voice.

[0245] 3. The server calculates the sentiment score using natural language processing and obtains a negative result.

[0246] 4. The server integrates the sentiment information and the text score to determine the final sentiment score as negative.

[0247] 5. The server colors the text in red and sends it to the terminal.

[0248] 6. The device will display this red text on the screen, visually signaling a negative emotion to the user.

[0249] This system not only visually expresses the emotions and nuances of text, but also incorporates other emotional information from the user, enabling more accurate and effective emotional expression, thereby reducing misunderstandings in text communication and realizing smoother communication.

[0250] The processing flow will be explained below.

[0251] Step 1:

[0252] A user enters text into an input field and optionally provides voice and facial expression data, for example, typing "Today is a truly beautiful day!" and capturing a smile with the camera.

[0253] Step 2:

[0254] The terminal transmits the input text from the user, as well as the captured voice and facial expression data, to the server.

[0255] Step 3:

[0256] The server prepares the received input text, and if possible, voice and facial expression data, for analysis. It then begins emotion recognition based on this input data.

[0257] Step 4:

[0258] The server uses an emotion recognition module (emotion engine) to recognize the user's emotions from voice and facial expression data. For example, a smile is judged as positive, and a sad expression is judged as negative.

[0259] Step 5:

[0260] The server uses a natural language processing library to calculate the sentiment score of the input text, for example, by parsing the text using TextBlob and obtaining its sentiment score.

[0261] Step 6:

[0262] The server combines the emotion information obtained from the emotion engine and the emotion score of the text obtained from the natural language processing library to determine the final emotion score. If the emotion information is positive and the emotion score is also positive, the final score is determined to be positive.

[0263] Step 7:

[0264] The server chooses a color for the text based on the final sentiment score: green if the final sentiment score is positive, red if negative, and yellow if neutral.

[0265] Step 8:

[0266] The server decorates the input text with a color selected by the server, and generates the user's input text in color.

[0267] Step 9:

[0268] The server sends colored text to the terminal, which receives it and displays the decorated text on the screen.

[0269] Step 10:

[0270] The user visually checks the decorated text on the device screen, which allows the user to intuitively understand the sentiment and nuance of the input text.

[0271] Example 2

[0272] 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."

[0273] Conventional text sentiment analysis systems only target text and cannot consider non-text information such as the user's voice or facial expressions. As a result, emotion recognition accuracy is low, making it difficult to accurately reflect the user's actual emotions. Furthermore, technology that decorates text with different colors based on emotion scores can only reflect a subset of emotions, limiting the scope of visual feedback.

[0274] 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.

[0275] In this invention, the server includes means for receiving text, voice, and facial expression data input by a user, means for recognizing the user's emotions from the input data using a sentiment analysis engine and obtaining emotional information, means for calculating an emotional score for the text using natural language processing technology, means for integrating the emotional information and the emotional score to calculate a final emotional score, means for decorating the input text with different colors based on the emotional score, and means for outputting the decorated text to a terminal. This makes it possible to more accurately reflect the user's emotions by taking into account not only text but also non-text information such as voice and facial expressions. Furthermore, visual feedback based on the emotional score can be expressed in a variety of ways, improving the quality of communication.

[0276] A "user" is an entity that uses the system to input data such as text, voice, and facial expressions.

[0277] An "input field" is an interface that allows a user to input data such as text, voice, and facial expressions.

[0278] A "terminal" is a device that allows a user to input data and check the output from the server. Examples include PCs, smartphones, and tablets.

[0279] A "server" is a remote computer system that receives, analyzes, and processes input data.

[0280] An "emotion analysis engine" is a software module that recognizes a user's emotions from input data and obtains emotional information.

[0281] "Natural language processing technology" is a technology for analyzing text data and calculating structure and sentiment scores.

[0282] An "emotion score" is a number calculated based on the content of text data, and indicates an emotional state such as positive, negative, or neutral.

[0283] "Decorating with different colors" means displaying text in different colors based on the sentiment score to provide visual feedback.

[0284] "Decoration" refers to changing the display format of text based on the sentiment score, specifically by adding color.

[0285] The "output means" is a mechanism for sending data processed by the server to a terminal so that the user can visually confirm the results.

[0286] The present invention is a system that receives text, voice, and facial expression data entered by a user, performs emotion analysis to calculate an emotion score, and decorates the text with different colors based on the result. Specifically, it operates in cooperation with a server and a terminal.

[0287] Hardware and software used

[0288] The terminal must have an input field for users to input data. This input field supports text input, voice input, and facial expression (image) input. A browser or dedicated application can be used on the terminal.

[0289] The server requires the following software and libraries:

[0290] Emotion recognition engine: Used to recognize emotions from voice and facial expression data. Possible open source software include OpenCV and Dlib.

[0291] Natural language processing libraries: Used to calculate sentiment scores for text data, such as TextBlob and NLTK (Natural Language Toolkit).

[0292] Web server: Infrastructure for receiving data from devices and returning processed results. For example, frameworks such as Flask and Django can be used.

[0293] System Operation

[0294] The user enters text into the device's input field and optionally provides voice and facial expression data. The device then sends this input data to the server. The server temporarily stores the received data and prepares it for analysis. An emotion recognition engine is used to analyze the voice and facial expression data to obtain emotional information. A natural language processing library is also used to calculate an emotional score for the text data.

[0295] The emotion information and the emotion score of the text are integrated to generate a final emotion score. Based on this final emotion score, the text is decorated with different colors: green for positive emotion, red for negative emotion, and yellow for neutral emotion. The server generates this decorated text and sends it to the device. The device displays the decorated text on the screen to provide visual feedback to the user.

[0296] Specific examples

[0297] Example prompt sentence:

[0298] 1. Enter the text "Today is such a great day!" and a smiling face.

[0299] How it works: The server receives this text and an image of a smiling face, and the emotion recognition engine recognizes the smiling face as a positive emotion. The natural language processing library calculates the emotion score of the text as positive, and the combined result is a high positive score. The server colors the text in green and sends it to the device. The device displays the green text.

[0300] 2. Enter the text "I'm feeling very sad today" and a sad voice.

[0301] How it works: The server receives this text and voice data, and the emotion recognition engine recognizes negative emotions from the tone of the voice. The natural language processing library calculates the emotion score of the text as negative, and the combined result is a negative score. The server colors the text in red and sends it to the device. The device displays the red text.

[0302] This system can provide more accurate and varied visual feedback by taking into account not only text but also non-textual information such as voice and facial expressions, which can significantly improve the quality of communication and reduce misunderstandings in text communication.

[0303] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0304] Step 1:

[0305] The user inputs text into an input field on the terminal, and inputs voice and facial expression data as needed.

[0306] Specific behavior: The user types the text "Today is a wonderful day!" and takes a photo of a smiling face or records audio.

[0307] Input: Text, voice, facial expression data

[0308] Output: Input data sent to the terminal

[0309] Step 2:

[0310] The device sends the input text, voice, and facial expression data to the server.

[0311] Specific operation: The device sends the user's input data to the server via an HTTP request.

[0312] Input: User input data (text, voice, facial expression data)

[0313] Output: Input data sent to the server

[0314] Step 3:

[0315] The server prepares the received data for analysis.

[0316] Specific operation: The server temporarily stores the data received and performs format conversion and data preprocessing as necessary.

[0317] Input: Input data from the device (text, voice, facial expression data)

[0318] Output: Prepared data for emotion recognition

[0319] Step 4:

[0320] The server uses an emotion recognition engine to recognize the user's emotions from voice and facial expression data and obtains emotion information.

[0321] How it works: The emotion recognition engine uses OpenCV for facial expression analysis to recognize positive emotions from smiles, and also uses voice analysis to analyze emotions from the tone of the voice.

[0322] Input: Preprocessed speech and facial expression data

[0323] Output: Emotion information (e.g., positive, negative)

[0324] Step 5:

[0325] The server uses a natural language processing library to calculate a sentiment score for the text.

[0326] What it does: The natural language processing library TextBlob parses the text "Today is a truly wonderful day!" and calculates a positive score (e.g., 0.85).

[0327] Input: Text data

[0328] Output: Sentiment score of the text

[0329] Step 6:

[0330] The server integrates the sentiment information and the sentiment score of the text to generate a final sentiment score.

[0331] Specific behavior: Integrate emotion information (e.g., positive information from smile data) with the text emotion score (e.g., 0.85) to raise the final score to 0.95.

[0332] Input: Sentiment information, sentiment score of text

[0333] Output: Final sentiment score

[0334] Step 7:

[0335] The server selects an appropriate color for the input text based on the emotion score.

[0336] Specific behavior: A final sentiment score of 0.95 is considered positive and the color green is selected.

[0337] Input: Final sentiment score

[0338] Output: Selected color (e.g. green)

[0339] Step 8:

[0340] The server decorates the text with the selected color and generates the decorated text.

[0341] What it does: Uses HTML and CSS to color the text "Today is a truly beautiful day!" in green.

[0342] Input: Text data, selected color

[0343] Output: Decorated text

[0344] Step 9:

[0345] The server sends the decorated text to the device, which displays it on the screen.

[0346] What it does: The server sends the generated HTML code to the device, which then renders it in the device's browser, displaying green text.

[0347] Input: Decorated text data

[0348] Output: Fancy text displayed on the terminal

[0349] This specific processing step allows the user to visually confirm the emotional state of the text, leading to more effective communication.

[0350] (Application example 2)

[0351] 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."

[0352] In text communication, it is difficult to accurately express a user's emotions, leading to misunderstandings. Furthermore, there is a lack of methods for monitoring a user's emotional state in real time and detecting dangerous situations early in public places and workplaces. Furthermore, there is no system that can centrally analyze various input data, including voice and facial expression data, provided by the user to enhance security. The purpose of this invention is to solve these problems and realize more accurate and effective emotional expression and a safer environment.

[0353] The identification process by the identification 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 entered by a user, means for performing emotion analysis and calculating an emotion score, means for decorating the input text with different colors based on the emotion score, means for outputting the decorated text to the terminal, means for receiving voice or facial expression data entered by the user, means for analyzing the voice or facial expression data to obtain emotion information, means for reflecting the emotion information obtained from the voice or facial expression data in the emotion score of the text, means for displaying a warning when a negative emotion is recognized, and means for sending a notification to a security officer as necessary. This makes it possible to analyze a user's emotions from multiple angles and provide accurate emotional expressions, while also increasing safety in public places and workplaces.

[0354] The "means for receiving text entered by the user" is a method for transmitting text data entered by the user via a keyboard, voice input, or the like to the server.

[0355] "Means for performing sentiment analysis and calculating sentiment scores" refers to a method for quantifying the emotional tendencies of text data using natural language processing technology and machine learning algorithms.

[0356] The "means for decorating input text with different colors based on sentiment score" is a method for highlighting text data with specific colors (e.g., green, red, yellow) depending on the analyzed sentiment score.

[0357] The "means for outputting decorated text to a terminal" is a method for transmitting colored text to a user's terminal and displaying it.

[0358] The "means for receiving voice or facial expression data input by the user" refers to a method for transmitting voice data or facial expression data such as camera images provided by the user to the server.

[0359] The "means for acquiring emotional information by analyzing voice or facial expression data" refers to a method for determining the emotional state of a user from voice data or facial expression data using voice recognition technology or image recognition technology.

[0360] "Means for reflecting emotional information obtained from voice or facial expression data in the emotional score of text" is a method for adjusting the emotional score of text based on emotional data obtained from voice or facial expression.

[0361] The "means for displaying a warning when a negative emotion is recognized" is a method for displaying a warning message on a terminal when the emotion analysis determines that the user is in a negative state.

[0362] "Means for sending notifications to security personnel as needed" refers to a method for sending alerts or notification messages to security personnel when the system detects an abnormality.

[0363] This invention is a system that analyzes text, voice, and facial expression data entered by a user, calculates an emotion score based on the data, and decorates the text to reflect the emotion recognition results. This system also has the function of displaying a warning if a negative emotion is recognized and sending a notification to a security officer as necessary. A specific embodiment of this system is described below.

[0364] System configuration

[0365] Hardware used

[0366] Smartphones (e.g., iPhone (registered trademark), ANDROID (registered trademark) devices)

[0367] Robots (e.g., SoftBank Robotics' Pepper)

[0368] Software used

[0369] Emotion recognition module (e.g., Microsoft® Azure® Emotion API)

[0370] Natural language processing libraries (e.g. TextBlob)

[0371] Notification systems (e.g., Twilio API)

[0372] Processing Description

[0373] 1. User Input

[0374] A user can enter text into an input field via a smartphone or robot and provide voice and facial expression data. For example, a user can enter the text "Someone is chasing me!" and show a frightened expression.

[0375] 2. Data Transmission

[0376] A smartphone or robot sends input data (text, voice, facial expression data) from the user to a server, which then receives the data and prepares it for analysis.

[0377] 3. Data Analysis

[0378] The server analyzes the received data. It uses a natural language processing library to calculate an emotion score for the text, and an emotion recognition module to extract emotion information from voice and facial expression data. For example, it uses voice recognition and image recognition technology to determine the user's emotional state.

[0379] 4. Emotional information integration

[0380] The server integrates the emotional information obtained from the emotion recognition module into the text's emotional score, which is classified as positive, negative, or neutral.

[0381] 5. Text Styling

[0382] The text is decorated based on the emotion score. For example, if the emotion score is positive, the text is decorated with green, if it is negative, the text is decorated with red, and if it is neutral, the text is decorated with yellow.

[0383] 6. Warnings and Notifications

[0384] The server uses the Twilio API to display an alert on a smartphone or robot if a negative emotion is detected, and optionally sends a notification to a security officer.

[0385] Specific examples

[0386] Example 1: Dangerous text and facial expressions

[0387] 1. The user types, "Someone's chasing me!" and displays a terrified expression.

[0388] 2. The server receives this input data and uses the emotion engine to recognize the emotion of fear from the user's facial expression.

[0389] 3. The server calculates the sentiment score using natural language processing and obtains a negative result.

[0390] 4. The server integrates the sentiment information and the text score to determine the final sentiment score as negative.

[0391] 5. Your smartphone or robot will display a warning and say "Danger detected" on the screen.

[0392] 6. The server uses the Twilio API to send a notification to the security officer.

[0393] Prompt Sentence Examples

[0394] "The emotion recognition module analyzes the text, voice, and facial expression data entered by the user, and if a negative emotion is detected, a warning is displayed and a notification is sent to security personnel."

[0395] In this way, by implementing the present invention, it is possible to analyze the user's emotions from multiple angles, and provide accurate emotional expression and a safe environment.

[0396] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0397] Step 1:

[0398] The user inputs text, voice, and facial expression data

[0399] The user inputs text via a smartphone or robot and provides voice and facial expression data. For example, the user inputs the text "Someone is chasing me!" and shows a frightened expression. This data becomes the input data.

[0400] Step 2:

[0401] The device sends the input data to the server

[0402] The device sends the input text, voice data, and facial expression data to the server. At this time, the data is encrypted and transferred securely. The text, voice, and facial expression are sent to the server as input data.

[0403] Step 3:

[0404] The server analyzes the text data

[0405] The server uses a natural language processing library such as TextBlob to analyze the text data and calculate the emotion score. The input is the text data, and the output is the emotion score.

[0406] Step 4:

[0407] The server analyzes voice and facial expression data

[0408] The server uses voice recognition and image recognition technology (e.g., Microsoft Azure Emotion API) to analyze voice and facial expression data and obtain emotional information. The input is voice and facial expression data, and the output is emotional information.

[0409] Step 5:

[0410] The server integrates emotional information

[0411] The server integrates the emotion score obtained from the text data with the emotion information obtained from the voice and facial expression data to determine the final emotion score. The input is the emotion score and emotion information of the text, and the output is the integrated emotion score.

[0412] Step 6:

[0413] Server-styled text

[0414] The server decorates the text with different colors based on the final sentiment score: green for positive sentiment, red for negative sentiment, and yellow for neutral sentiment. The input is the integrated sentiment score and the text, and the output is the decorated text.

[0415] Step 7:

[0416] The server sends the decorated text to the terminal.

[0417] The server sends the decorated text to the terminal. The input is the decorated text, and the output is data containing the decorated text.

[0418] Step 8:

[0419] The terminal displays decorated text

[0420] The terminal receives the decorated text and displays it on the screen. The user can visually confirm the text colored according to the emotional state. The input is the decorated text, and the output is the colored text displayed on the terminal screen.

[0421] Step 9:

[0422] Server displays warning

[0423] If a negative emotion is detected, the server displays a warning on the device, such as "Danger detected." The input is the negative emotion score, and the output is a warning message on the device.

[0424] Step 10:

[0425] The server sends a notification to the security officer

[0426] The server uses the Twilio API to send notifications to security personnel depending on the situation. For example, if a threat is detected, an alert is sent to the personnel via email or SMS. The input is the negative sentiment score and the alert content, and the output is a notification to the security personnel.

[0427] At each step, appropriate data processing or calculation is performed based on the input data, and the respective outputs are obtained to achieve the overall processing.

[0428] 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.

[0429] 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.

[0430] 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.

[0431] [Second embodiment]

[0432] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0433] 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.

[0434] 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).

[0435] 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.

[0436] 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.

[0437] 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).

[0438] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0439] 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.

[0440] 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.

[0441] 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.

[0442] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0443] 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."

[0444] The present invention is a system that analyzes the sentiment of text entered by a user and decorates the text with different colors based on the sentiment score. This system operates between a server and a terminal, and visually expresses the user's input in an easy-to-understand manner.

[0445] System configuration

[0446] 1. The server receives text entered by the user.

[0447] 2. The server performs sentiment analysis and calculates a sentiment score for the input text. Sentiment analysis is performed using natural language processing technology.

[0448] 3. The server decorates the input text by assigning different colors based on the sentiment score: green for positive sentiment, red for negative sentiment, and yellow for neutral sentiment.

[0449] 4. The server generates the decorated text and sends it to the terminal.

[0450] 5. The terminal displays the received decorative text on the screen.

[0451] Program processing explanation

[0452] 1. A user enters text into an input field, for example, "Today is a truly wonderful day!"

[0453] 2. The server receives this input text and begins sentiment analysis, using a natural language processing library such as TextBlob to analyze the sentiment.

[0454] 3. The server receives the analysis result (emotion score). For example, if the score is positive, it is classified as green.

[0455] 4. The server generates the text with green decoration and the text entered by the user colored.

[0456] 5. The server sends the generated decorated text to the terminal, which displays it. The user can visually confirm the decorated text on the screen.

[0457] Specific examples

[0458] Example 1: Positive text

[0459] 1. The user types, "Today is such a great day!"

[0460] 2. The server receives this text and calculates the sentiment score. Since the sentiment score is positive, it is classified as positive.

[0461] 3. The server colors the text green.

[0462] 4. The device will display this green text on the screen, visually indicating positive emotion to the user.

[0463] Example 2: Negative text

[0464] 1. The user types, "I'm feeling very sad today."

[0465] 2. The server receives this text and calculates the sentiment score. Since the sentiment score is negative, it is classified as negative.

[0466] 3. The server colors the text red.

[0467] 4. The device will display this red text on the screen, visually signaling a negative emotion to the user.

[0468] This system visually expresses the emotions and nuances of text, allowing users to intuitively understand the meaning of the text, thereby reducing misunderstandings in text communication and enabling smoother communication.

[0469] The processing flow will be explained below.

[0470] Step 1:

[0471] The user types text into an input field, for example, "Today is a truly wonderful day!"

[0472] Step 2:

[0473] The terminal sends the text entered by the user to the server.

[0474] Step 3:

[0475] The server prepares the input text received from the user for analysis. The server then subjects this text to sentiment analysis.

[0476] Step 4:

[0477] The server performs sentiment analysis using a natural language processing library (e.g., TextBlob). It uses the text analysis capabilities of the TextBlob library to calculate a sentiment score for the input text.

[0478] Step 5:

[0479] The server evaluates the sentiment score and determines whether it is positive, negative, or neutral. The server classifies the sentiment score as either positive, negative, or zero (neutral).

[0480] Step 6:

[0481] The server chooses a corresponding color based on the emotion score: green for a positive emotion score, red for a negative emotion score, and yellow for a zero emotion score.

[0482] Step 7:

[0483] The input text is decorated with a color selected by the server, specifically, the text is colored using the corresponding color code.

[0484] Step 8:

[0485] The server generates the colored text and sends it to the terminal.

[0486] Step 9:

[0487] The terminal displays the colored text received from the server on the screen, allowing the user to visually see the colored text and intuitively understand the emotion and nuance of the input text.

[0488] Example 1

[0489] 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."

[0490] In modern text communication, the sentiment and nuance of a sentence are often misunderstood. Therefore, a method is needed to visually express the sentiment and nuance of a text so that users can intuitively understand it. Also, a means is needed to properly analyze sentiment and decorate the text based on that sentiment.

[0491] 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.

[0492] In this invention, the server includes means for receiving text entered by a user, means for performing sentiment analysis and calculating a sentiment score, means for decorating the input text with different colors based on the sentiment score, means for outputting the decorated text to a terminal, means for performing the sentiment analysis using natural language processing technology, and means for generating the decorated text and sending it to the terminal as an HTTP response. This visually expresses the sentiment and nuance of the text, allowing the user to intuitively understand the sentiment of the sentence.

[0493] "User" refers to a person who uses a system or service.

[0494] A "means" refers to a method or device used to achieve a particular purpose.

[0495] "Server" refers to a computer system that processes and provides data in response to requests from clients.

[0496] "Terminal" refers to an input and display device that can be directly operated by a user.

[0497] "Text" refers to a sentence or character string entered by a user.

[0498] "Sentiment analysis" refers to the process of analyzing the emotional state of input text using natural language processing techniques.

[0499] An "emotion score" is a numerical representation of the emotional state of a piece of text, and can be positive, negative, or neutral.

[0500] "Decorate with different colors" refers to applying specific colors to text depending on the sentiment score.

[0501] "Natural language processing technology" refers to technology that allows computers to analyze, understand, and generate human language.

[0502] An "HTTP response" refers to a server's response message to an HTTP request from a client.

[0503] An "input event" refers to a programmatic action that is triggered when a user interacts with an input device (e.g., a keyboard).

[0504] An "HTTP POST request" refers to one of the HTTP methods used by a client to send data to a server.

[0505] The system aims to analyze the sentiment of text entered by a user and decorate the text with different colors based on the sentiment score. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS The following describes in detail the embodiments of the present invention.

[0506] System configuration

[0507] The system consists of two main components: a server and a terminal. A user inputs text through the terminal, and the server processes the text and returns the results to the terminal.

[0508] Hardware and software used

[0509] server:

[0510] The server receives text from the user, performs sentiment analysis, and decorates the text.

[0511] The server requires a standard web server and a programming language such as Python.

[0512] Uses the TextBlob library as a natural language processing technique.

[0513] You can use Flask or Django as a web framework.

[0514] Device:

[0515] The terminal is used by the user to input text and display the results of processing.

[0516] The device can be a general computer, tablet, or smartphone with a browser.

[0517] It uses HTML, CSS, and JavaScript to provide a simple user interface.

[0518] Program processing explanation

[0519] 1. A user types text into an input field on a device, for example, "Today is a really great day!"

[0520] 2. The device sends this input text to the server as an HTTP POST request.

[0521] 3. The server receives the text sent by the user and performs sentiment analysis using a natural language processing library such as TextBlob.

[0522] 4. The server colors the text based on the resulting sentiment score: green for positive sentiment, red for negative sentiment, and yellow for neutral sentiment.

[0523] 5. The server generates the decorated text and returns it to the device as an HTTP response.

[0524] 6. The terminal displays the received decorative text on the browser screen so that the user can visually confirm it.

[0525] Adding specific examples

[0526] Example 1: Positive text

[0527] The user types, "Today is such a great day!"

[0528] The device sends this text to the server.

[0529] The server calculates the sentiment score and determines it to be positive.

[0530] The server colors this text green and sends it back to the terminal.

[0531] The device displays green text on the browser screen, conveying positive emotions to the user.

[0532] Example 2: Negative text

[0533] The user types, "I'm feeling very sad today."

[0534] The device sends this text to the server.

[0535] The server calculates the sentiment score and determines it to be negative.

[0536] The server colors this text red and sends it back to the terminal.

[0537] The device displays red text on the browser screen, conveying negative emotions to the user.

[0538] Prompt Sentence Examples

[0539] Enter the text "Today is a truly wonderful day!" and run a sentiment analysis to display it in green.

[0540] In this way, the present invention visually expresses the emotion and nuance of text, allowing users to intuitively understand the emotion of a sentence, thereby reducing misunderstandings in text communication and realizing smoother communication.

[0541] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0542] Step 1:

[0543] The user enters text into an input field on the terminal.

[0544] As a concrete example, a user types "Today is a really great day!" When the user confirms the input, an input event is triggered and the terminal stores the entered text in a variable.

[0545] Input: Text entered by the user

[0546] Output: Text stored in a variable in the terminal

[0547] Step 2:

[0548] The terminal sends the entered text data to the server as an HTTP POST request.

[0549] Asynchronous communication is performed using AJAX, and text data is sent to a specified endpoint on the server.

[0550] Input: Text stored in a terminal variable

[0551] Output: HTTP POST request sent to the server

[0552] Step 3:

[0553] The server receives the text sent by the user.

[0554] The server-side API endpoint receives the request and stores the received text data in variables for analysis.

[0555] Input: HTTP POST request

[0556] Output: Text stored in a variable for parsing on the server

[0557] Step 4:

[0558] The server performs sentiment analysis based on the received text.

[0559] Use a natural language processing library such as TextBlob to calculate the sentiment score of the text. For example, for the text "Today is a really great day!", call the TextBlob(text).sentiment method of the TextBlob library to get the sentiment score.

[0560] Input: Text stored in a variable for parsing on the server

[0561] Output: Sentiment score (number indicating positive, negative, or neutral)

[0562] Step 5:

[0563] The server colors the text based on the obtained sentiment score.

[0564] If the sentiment score is positive, the text is colored green, if negative, red, and if neutral, yellow. We also use an HTML template to generate the colored text.

[0565] Input: Sentiment score

[0566] Output: Decorated text (HTML format)

[0567] Step 6:

[0568] The server generates the decorated text and sends it to the terminal as an HTTP response.

[0569] The server returns the generated HTML formatted decorated text as an HTTP response and sends it to the terminal.

[0570] Input: Decorated text (HTML format)

[0571] Output: Decorative text returned as an HTTP response

[0572] Step 7:

[0573] The terminal displays the received decorative text on the screen so that the user can visually confirm it.

[0574] The device uses DOM manipulation to display the returned decorative text in a specific area of ​​the browser, for example, a green-colored "Today is a really great day!"

[0575] Input: Decorative text returned as an HTTP response

[0576] Output: Decorative text displayed in the browser window

[0577] (Application example 1)

[0578] 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."

[0579] Conventional ad delivery systems do not fully consider the impact of text ad content on user emotions, which limits the visual impact of ads and their ability to appeal to user emotions, resulting in the issue of ads not being as effective as they could be.

[0580] 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.

[0581] In this invention, the server includes means for receiving text entered by a user, means for performing sentiment analysis and calculating a sentiment score, means for decorating the entered text with a different color based on the sentiment score, means for outputting the decorated text to a terminal, means for changing the color of the advertisement based on the result of sentiment analysis of the advertisement text, and means for displaying the changed advertisement text on the terminal. This enhances the visual effect of the text advertisement and enables advertisement delivery that strongly appeals to the user's emotions.

[0582] The "means for receiving text entered by the user" is an interface having a function for receiving text data entered by the user and transmitting it to the server.

[0583] The "means for performing sentiment analysis and calculating sentiment scores" is a system that uses natural language processing technology to analyze and quantify the emotional nuances of text.

[0584] The "means for decorating input text with different colors based on the emotion score" is a system having a function for visually highlighting input text using a color corresponding to the emotion score.

[0585] The "means for outputting decorated text to a terminal" is a system having a function for transmitting text data decorated by a server to a user terminal and displaying it.

[0586] The "means for changing the color of an advertisement based on the result of sentiment analysis of the advertisement text" is a system that has the function of analyzing the sentiment score of the advertisement text and dynamically adjusting the color of the advertisement text according to the result.

[0587] The "means for displaying the modified advertising text on the terminal" is a system having a function for displaying color-adjusted advertising text on the user's terminal to visually enhance the advertising effect.

[0588] This invention provides an advertisement distribution system that visually emphasizes advertisement text based on emotion analysis and strongly appeals to the user's emotions. The specific configuration and processing steps of this system are described below.

[0589] Overall system overview

[0590] This system works in cooperation with a server and a terminal. The server performs sentiment analysis of text data and dynamically changes the color of the text according to the results. The decorated text is sent to the terminal and displayed in a visible state for the user.

[0591] Hardware and software used

[0592] Server: Uses the Flask framework (Python). For sentiment analysis, it uses TextBlob, a natural language processing library.

[0593] Client devices: Smartphones, smart glasses, head-mounted displays, etc. These devices have a web browser installed and are processed using JavaScript.

[0594] Program processing

[0595] The server receives the ad text entered by the user and performs sentiment analysis using TextBlob. Based on the sentiment score, the text is colored green, red, or yellow. The colored ad text is then sent from the server to the user's device and displayed in the browser.

[0596] Specific examples

[0597] 1. A user types into the ad dashboard, "This product provides a truly amazing experience!"

[0598] 2. The server receives this text and performs sentiment analysis using the TextBlob library. Since this text contains positive sentiment, the sentiment score is positive.

[0599] 3. The server colors the text green based on the sentiment score. The coloring is achieved using HTML style sheet tags.

[0600] 4. The device receives the decorated text from the server and displays it in the browser, allowing the user to visually recognize positive emotions on the device.

[0601] Prompt Sentence Examples

[0602] Enter the following prompt for the generative AI model:

[0603] Analyze the sentiment of the text entered by the user and decorate it with green for positive sentiment, red for negative sentiment, and yellow for neutral sentiment.

[0604] The above configuration and processing improve the visual effect of the advertising text and realize advertisement delivery that appeals to the user's emotions.

[0605] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0606] Step 1:

[0607] The user accesses the ad management screen and enters ad text in the input field. For example, the user might enter, "This product provides a truly amazing experience!" The input text is sent to the server.

[0608] Step 2:

[0609] The server receives the text data sent by the user. The input data arrives at the server in text format. At this point, the input is the raw text entered by the user.

[0610] Step 3:

[0611] The server uses the TextBlob library to perform sentiment analysis on the text it receives. The server passes the text data to TextBlob and calculates the sentiment score. The input data is the text before analysis begins, and the output data is the analysis result along with the sentiment score.

[0612] Step 4:

[0613] The server decorates the text based on the sentiment score. It colors the text using green if the sentiment score is positive, red if the sentiment score is negative, and yellow if the sentiment score is neutral. The input data is the text with the sentiment score, and the output data is the color-decorated text. This decoration is done using HTML style sheets.

[0614] Step 5:

[0615] The server sends the decorated text to the terminal in JSON format. The input data is the decorated text, and the output data is JSON data formatted for transmission.

[0616] Step 6:

[0617] The terminal parses the JSON data received from the server and parses the decorated text. The terminal parses the received data and converts it into a format that the user can view. The input data is the JSON data sent from the server, and the output data is the parsed decorated text.

[0618] Step 7:

[0619] The terminal displays the decorated advertisement text in the browser, and the user visually confirms it through the terminal screen. The input data is the parsed decorated text, and the output is a visual text advertisement displayed to the user.

[0620] The above steps complete the entire process from inputting advertising text to displaying it. This system makes it easier for users to visually recognize the emotional nuances contained in text advertising.

[0621] 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.

[0622] The present invention is a system that analyzes the emotions of text entered by a user and decorates the text with different colors based on the emotion score, and also combines an emotion engine that recognizes the user's emotions. This system operates between a server and a terminal, and more accurately analyzes user input and presents it in a visually understandable way.

[0623] System configuration

[0624] 1. The server receives text entered by the user, and optionally voice and facial expression data.

[0625] 2. The server uses an emotion recognition module (emotion engine) to classify the user's emotion from the input data and obtain emotion information.

[0626] 3. The server uses a natural language processing library (e.g., TextBlob) to calculate the sentiment score for the input text itself.

[0627] 4. The server reflects the emotional information obtained from the emotion engine in the emotional score of the text.

[0628] 5. The server decorates the input text by assigning different colors based on the sentiment score: green for positive sentiment, red for negative sentiment, and yellow for neutral sentiment.

[0629] 6. The server generates the decorated text and sends it to the terminal.

[0630] 7. The terminal displays the received decorative text on the screen.

[0631] Program processing explanation

[0632] 1. A user types text into an input field, possibly providing voice and facial expression data. For example, they type "Today is a truly wonderful day!"

[0633] 2. The terminal sends the input data from the user to the server.

[0634] 3. The server prepares the received input text, and if possible, speech and facial expression data, for analysis. The server then subjects this input data to emotion recognition.

[0635] 4. The server uses the emotion recognition module (emotion engine) to recognize the user's emotions and obtain emotion information.

[0636] 5. The server uses a natural language processing library to calculate a sentiment score for the input text.

[0637] 6. The server reflects the emotion information obtained from the emotion engine into the emotion score. This process makes the emotion score of the text more accurate.

[0638] 7. The server selects a color based on the emotion score: green if the emotion score is positive, red if negative, and yellow if zero.

[0639] 8. The server decorates the input text with a color selected by the server, generating a colored version of the text entered by the user.

[0640] 9. The server sends the colored text to the terminal, which displays it, allowing the user to visually confirm the decorated text on the screen.

[0641] Specific examples

[0642] Example 1: Positive text and facial expressions

[0643] 1. The user types "Today is a really great day!" and simultaneously provides smiley face data.

[0644] 2. The server receives this input data and uses an emotion engine to recognize positive emotions from the user's facial expressions.

[0645] 3. The server calculates the sentiment score using natural language processing and obtains a positive result.

[0646] 4. The server integrates the sentiment information and the text score to determine the final sentiment score as positive.

[0647] 5. The server colors the text in green and sends it to the device.

[0648] 6. The device will display this green text on the screen, visually indicating positive emotion to the user.

[0649] Example 2: Negative text and audio

[0650] 1. The user types, "I feel very sad today," and simultaneously provides voice data in a sad-sounding voice.

[0651] 2. The server receives this input data and uses an emotion engine to recognize negative emotions from the user's voice.

[0652] 3. The server calculates the sentiment score using natural language processing and obtains a negative result.

[0653] 4. The server integrates the sentiment information and the text score to determine the final sentiment score as negative.

[0654] 5. The server colors the text in red and sends it to the terminal.

[0655] 6. The device will display this red text on the screen, visually signaling a negative emotion to the user.

[0656] This system not only visually expresses the emotions and nuances of text, but also incorporates other emotional information from the user, enabling more accurate and effective emotional expression, thereby reducing misunderstandings in text communication and realizing smoother communication.

[0657] The processing flow will be explained below.

[0658] Step 1:

[0659] A user enters text into an input field and optionally provides voice and facial expression data, for example, typing "Today is a truly beautiful day!" and capturing a smile with the camera.

[0660] Step 2:

[0661] The terminal transmits the input text from the user, as well as the captured voice and facial expression data, to the server.

[0662] Step 3:

[0663] The server prepares the received input text, and if possible, voice and facial expression data, for analysis. It then begins emotion recognition based on this input data.

[0664] Step 4:

[0665] The server uses an emotion recognition module (emotion engine) to recognize the user's emotions from voice and facial expression data. For example, a smile is judged as positive, and a sad expression is judged as negative.

[0666] Step 5:

[0667] The server uses a natural language processing library to calculate the sentiment score of the input text, for example, by parsing the text using TextBlob and obtaining its sentiment score.

[0668] Step 6:

[0669] The server combines the emotion information obtained from the emotion engine and the emotion score of the text obtained from the natural language processing library to determine the final emotion score. If the emotion information is positive and the emotion score is also positive, the final score is determined to be positive.

[0670] Step 7:

[0671] The server chooses a color for the text based on the final sentiment score: green if the final sentiment score is positive, red if negative, and yellow if neutral.

[0672] Step 8:

[0673] The server decorates the input text with a color selected by the server, and generates the user's input text in color.

[0674] Step 9:

[0675] The server sends colored text to the terminal, which receives it and displays the decorated text on the screen.

[0676] Step 10:

[0677] The user visually checks the decorated text on the device screen, which allows the user to intuitively understand the sentiment and nuance of the input text.

[0678] Example 2

[0679] 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."

[0680] Conventional text sentiment analysis systems only target text and cannot consider non-text information such as the user's voice or facial expressions. As a result, emotion recognition accuracy is low, making it difficult to accurately reflect the user's actual emotions. Furthermore, technology that decorates text with different colors based on emotion scores can only reflect a subset of emotions, limiting the scope of visual feedback.

[0681] 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.

[0682] In this invention, the server includes means for receiving text, voice, and facial expression data input by a user, means for recognizing the user's emotions from the input data using a sentiment analysis engine and obtaining emotional information, means for calculating an emotional score for the text using natural language processing technology, means for integrating the emotional information and the emotional score to calculate a final emotional score, means for decorating the input text with different colors based on the emotional score, and means for outputting the decorated text to a terminal. This makes it possible to more accurately reflect the user's emotions by taking into account not only text but also non-text information such as voice and facial expressions. Furthermore, visual feedback based on the emotional score can be expressed in a variety of ways, improving the quality of communication.

[0683] A "user" is an entity that uses the system to input data such as text, voice, and facial expressions.

[0684] An "input field" is an interface that allows a user to input data such as text, voice, and facial expressions.

[0685] A "terminal" is a device that allows a user to input data and check the output from the server. Examples include PCs, smartphones, and tablets.

[0686] A "server" is a remote computer system that receives, analyzes, and processes input data.

[0687] An "emotion analysis engine" is a software module that recognizes a user's emotions from input data and obtains emotional information.

[0688] "Natural language processing technology" is a technology for analyzing text data and calculating structure and sentiment scores.

[0689] An "emotion score" is a number calculated based on the content of text data, and indicates an emotional state such as positive, negative, or neutral.

[0690] "Decorating with different colors" means displaying text in different colors based on the sentiment score to provide visual feedback.

[0691] "Decoration" refers to changing the display format of text based on the sentiment score, specifically by adding color.

[0692] The "output means" is a mechanism for sending data processed by the server to a terminal so that the user can visually confirm the results.

[0693] The present invention is a system that receives text, voice, and facial expression data entered by a user, performs emotion analysis to calculate an emotion score, and decorates the text with different colors based on the result. Specifically, it operates in cooperation with a server and a terminal.

[0694] Hardware and software used

[0695] The terminal must have an input field for users to input data. This input field supports text input, voice input, and facial expression (image) input. A browser or dedicated application can be used on the terminal.

[0696] The server requires the following software and libraries:

[0697] Emotion recognition engine: Used to recognize emotions from voice and facial expression data. Possible open source software include OpenCV and Dlib.

[0698] Natural language processing libraries: Used to calculate sentiment scores for text data, such as TextBlob and NLTK (Natural Language Toolkit).

[0699] Web server: Infrastructure for receiving data from devices and returning processed results. For example, frameworks such as Flask and Django can be used.

[0700] System Operation

[0701] The user enters text into the device's input field and optionally provides voice and facial expression data. The device then sends this input data to the server. The server temporarily stores the received data and prepares it for analysis. An emotion recognition engine is used to analyze the voice and facial expression data to obtain emotional information. A natural language processing library is also used to calculate an emotional score for the text data.

[0702] The emotion information and the emotion score of the text are integrated to generate a final emotion score. Based on this final emotion score, the text is decorated with different colors: green for positive emotion, red for negative emotion, and yellow for neutral emotion. The server generates this decorated text and sends it to the device. The device displays the decorated text on the screen to provide visual feedback to the user.

[0703] Specific examples

[0704] Example prompt sentence:

[0705] 1. Enter the text "Today is such a great day!" and a smiling face.

[0706] How it works: The server receives this text and an image of a smiling face, and the emotion recognition engine recognizes the smiling face as a positive emotion. The natural language processing library calculates the emotion score of the text as positive, and the combined result is a high positive score. The server colors the text in green and sends it to the device. The device displays the green text.

[0707] 2. Enter the text "I'm feeling very sad today" and a sad voice.

[0708] How it works: The server receives this text and voice data, and the emotion recognition engine recognizes negative emotions from the tone of the voice. The natural language processing library calculates the emotion score of the text as negative, and the combined result is a negative score. The server colors the text in red and sends it to the device. The device displays the red text.

[0709] This system can provide more accurate and varied visual feedback by taking into account not only text but also non-textual information such as voice and facial expressions, which can significantly improve the quality of communication and reduce misunderstandings in text communication.

[0710] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0711] Step 1:

[0712] The user inputs text into an input field on the terminal, and inputs voice and facial expression data as needed.

[0713] Specific behavior: The user types the text "Today is a wonderful day!" and takes a photo of a smiling face or records audio.

[0714] Input: Text, voice, facial expression data

[0715] Output: Input data sent to the terminal

[0716] Step 2:

[0717] The device sends the input text, voice, and facial expression data to the server.

[0718] Specific operation: The device sends the user's input data to the server via an HTTP request.

[0719] Input: User input data (text, voice, facial expression data)

[0720] Output: Input data sent to the server

[0721] Step 3:

[0722] The server prepares the received data for analysis.

[0723] Specific operation: The server temporarily stores the data received and performs format conversion and data preprocessing as necessary.

[0724] Input: Input data from the device (text, voice, facial expression data)

[0725] Output: Prepared data for emotion recognition

[0726] Step 4:

[0727] The server uses an emotion recognition engine to recognize the user's emotions from voice and facial expression data and obtains emotion information.

[0728] How it works: The emotion recognition engine uses OpenCV for facial expression analysis to recognize positive emotions from smiles, and also uses voice analysis to analyze emotions from the tone of the voice.

[0729] Input: Preprocessed speech and facial expression data

[0730] Output: Emotion information (e.g., positive, negative)

[0731] Step 5:

[0732] The server uses a natural language processing library to calculate a sentiment score for the text.

[0733] What it does: The natural language processing library TextBlob parses the text "Today is a truly wonderful day!" and calculates a positive score (e.g., 0.85).

[0734] Input: Text data

[0735] Output: Sentiment score of the text

[0736] Step 6:

[0737] The server integrates the sentiment information and the sentiment score of the text to generate a final sentiment score.

[0738] Specific behavior: Integrate emotion information (e.g., positive information from smile data) with the text emotion score (e.g., 0.85) to raise the final score to 0.95.

[0739] Input: Sentiment information, sentiment score of text

[0740] Output: Final sentiment score

[0741] Step 7:

[0742] The server selects an appropriate color for the input text based on the emotion score.

[0743] Specific behavior: A final sentiment score of 0.95 is considered positive and the color green is selected.

[0744] Input: Final sentiment score

[0745] Output: Selected color (e.g. green)

[0746] Step 8:

[0747] The server decorates the text with the selected color and generates the decorated text.

[0748] What it does: Uses HTML and CSS to color the text "Today is a truly beautiful day!" in green.

[0749] Input: Text data, selected color

[0750] Output: Decorated text

[0751] Step 9:

[0752] The server sends the decorated text to the device, which displays it on the screen.

[0753] What it does: The server sends the generated HTML code to the device, which then renders it in the device's browser, displaying green text.

[0754] Input: Decorated text data

[0755] Output: Fancy text displayed on the terminal

[0756] This specific processing step allows the user to visually confirm the emotional state of the text, leading to more effective communication.

[0757] (Application example 2)

[0758] 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."

[0759] In text communication, it is difficult to accurately express a user's emotions, leading to misunderstandings. Furthermore, there is a lack of methods for monitoring a user's emotional state in real time and detecting dangerous situations early in public places and workplaces. Furthermore, there is no system that can centrally analyze various input data, including voice and facial expression data, provided by the user to enhance security. The purpose of this invention is to solve these problems and realize more accurate and effective emotional expression and a safer environment.

[0760] The identification process by the identification 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 entered by a user, means for performing emotion analysis and calculating an emotion score, means for decorating the input text with different colors based on the emotion score, means for outputting the decorated text to the terminal, means for receiving voice or facial expression data entered by the user, means for analyzing the voice or facial expression data to obtain emotion information, means for reflecting the emotion information obtained from the voice or facial expression data in the emotion score of the text, means for displaying a warning when a negative emotion is recognized, and means for sending a notification to a security officer as necessary. This makes it possible to analyze a user's emotions from multiple angles and provide accurate emotional expressions, while also increasing safety in public places and workplaces.

[0761] The "means for receiving text entered by the user" is a method for transmitting text data entered by the user via a keyboard, voice input, or the like to the server.

[0762] "Means for performing sentiment analysis and calculating sentiment scores" refers to a method for quantifying the emotional tendencies of text data using natural language processing technology and machine learning algorithms.

[0763] The "means for decorating input text with different colors based on sentiment score" is a method for highlighting text data with specific colors (e.g., green, red, yellow) depending on the analyzed sentiment score.

[0764] The "means for outputting decorated text to a terminal" is a method for transmitting colored text to a user's terminal and displaying it.

[0765] The "means for receiving voice or facial expression data input by the user" refers to a method for transmitting voice data or facial expression data such as camera images provided by the user to the server.

[0766] The "means for acquiring emotional information by analyzing voice or facial expression data" refers to a method for determining the emotional state of a user from voice data or facial expression data using voice recognition technology or image recognition technology.

[0767] "Means for reflecting emotional information obtained from voice or facial expression data in the emotional score of text" is a method for adjusting the emotional score of text based on emotional data obtained from voice or facial expression.

[0768] The "means for displaying a warning when a negative emotion is recognized" is a method for displaying a warning message on a terminal when the emotion analysis determines that the user is in a negative state.

[0769] "Means for sending notifications to security personnel as needed" refers to a method for sending alerts or notification messages to security personnel when the system detects an abnormality.

[0770] This invention is a system that analyzes text, voice, and facial expression data entered by a user, calculates an emotion score based on the data, and decorates the text to reflect the emotion recognition results. This system also has the function of displaying a warning if a negative emotion is recognized and sending a notification to a security officer as necessary. A specific embodiment of this system is described below.

[0771] System configuration

[0772] Hardware used

[0773] Smartphones (e.g. iPhone, Android devices)

[0774] Robots (e.g., SoftBank Robotics' Pepper)

[0775] Software used

[0776] Emotion recognition module (e.g. Microsoft Azure Emotion API)

[0777] Natural language processing libraries (e.g. TextBlob)

[0778] Notification systems (e.g., Twilio API)

[0779] Processing Description

[0780] 1. User Input

[0781] A user can enter text into an input field via a smartphone or robot and provide voice and facial expression data. For example, a user can enter the text "Someone is chasing me!" and show a frightened expression.

[0782] 2. Data Transmission

[0783] A smartphone or robot sends input data (text, voice, facial expression data) from the user to a server, which then receives the data and prepares it for analysis.

[0784] 3. Data Analysis

[0785] The server analyzes the received data. It uses a natural language processing library to calculate an emotion score for the text, and an emotion recognition module to extract emotion information from voice and facial expression data. For example, it uses voice recognition and image recognition technology to determine the user's emotional state.

[0786] 4. Emotional information integration

[0787] The server integrates the emotional information obtained from the emotion recognition module into the text's emotional score, which is classified as positive, negative, or neutral.

[0788] 5. Text Styling

[0789] The text is decorated based on the emotion score. For example, if the emotion score is positive, the text is decorated with green, if it is negative, the text is decorated with red, and if it is neutral, the text is decorated with yellow.

[0790] 6. Warnings and Notifications

[0791] The server uses the Twilio API to display an alert on a smartphone or robot if a negative emotion is detected, and optionally sends a notification to a security officer.

[0792] Specific examples

[0793] Example 1: Dangerous text and facial expressions

[0794] 1. The user types, "Someone's chasing me!" and displays a terrified expression.

[0795] 2. The server receives this input data and uses the emotion engine to recognize the emotion of fear from the user's facial expression.

[0796] 3. The server calculates the sentiment score using natural language processing and obtains a negative result.

[0797] 4. The server integrates the sentiment information and the text score to determine the final sentiment score as negative.

[0798] 5. Your smartphone or robot will display a warning and say "Danger detected" on the screen.

[0799] 6. The server uses the Twilio API to send a notification to the security officer.

[0800] Prompt Sentence Examples

[0801] "The emotion recognition module analyzes the text, voice, and facial expression data entered by the user, and if a negative emotion is detected, a warning is displayed and a notification is sent to security personnel."

[0802] In this way, by implementing the present invention, it is possible to analyze the user's emotions from multiple angles, and provide accurate emotional expression and a safe environment.

[0803] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0804] Step 1:

[0805] The user inputs text, voice, and facial expression data

[0806] The user inputs text via a smartphone or robot and provides voice and facial expression data. For example, the user inputs the text "Someone is chasing me!" and shows a frightened expression. This data becomes the input data.

[0807] Step 2:

[0808] The device sends the input data to the server

[0809] The device sends the input text, voice data, and facial expression data to the server. At this time, the data is encrypted and transferred securely. The text, voice, and facial expression are sent to the server as input data.

[0810] Step 3:

[0811] The server analyzes the text data

[0812] The server uses a natural language processing library such as TextBlob to analyze the text data and calculate the emotion score. The input is the text data, and the output is the emotion score.

[0813] Step 4:

[0814] The server analyzes voice and facial expression data

[0815] The server uses voice recognition and image recognition technology (e.g., Microsoft Azure Emotion API) to analyze voice and facial expression data and obtain emotional information. The input is voice and facial expression data, and the output is emotional information.

[0816] Step 5:

[0817] The server integrates emotional information

[0818] The server integrates the emotion score obtained from the text data with the emotion information obtained from the voice and facial expression data to determine the final emotion score. The input is the emotion score and emotion information of the text, and the output is the integrated emotion score.

[0819] Step 6:

[0820] Server-styled text

[0821] The server decorates the text with different colors based on the final sentiment score: green for positive sentiment, red for negative sentiment, and yellow for neutral sentiment. The input is the integrated sentiment score and the text, and the output is the decorated text.

[0822] Step 7:

[0823] The server sends the decorated text to the terminal.

[0824] The server sends the decorated text to the terminal. The input is the decorated text, and the output is data containing the decorated text.

[0825] Step 8:

[0826] The terminal displays decorated text

[0827] The terminal receives the decorated text and displays it on the screen. The user can visually confirm the text colored according to the emotional state. The input is the decorated text, and the output is the colored text displayed on the terminal screen.

[0828] Step 9:

[0829] Server displays warning

[0830] If a negative emotion is detected, the server displays a warning on the device, such as "Danger detected." The input is the negative emotion score, and the output is a warning message on the device.

[0831] Step 10:

[0832] The server sends a notification to the security officer

[0833] The server uses the Twilio API to send notifications to security personnel depending on the situation. For example, if a threat is detected, an alert is sent to the personnel via email or SMS. The input is the negative sentiment score and the alert content, and the output is a notification to the security personnel.

[0834] At each step, appropriate data processing or calculation is performed based on the input data, and the respective outputs are obtained to achieve the overall processing.

[0835] 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.

[0836] 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.

[0837] 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.

[0838] [Third embodiment]

[0839] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0840] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0841] 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).

[0842] 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.

[0843] 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.

[0844] 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).

[0845] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0846] 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.

[0847] 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.

[0848] 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.

[0849] 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.

[0850] 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."

[0851] The present invention is a system that analyzes the sentiment of text entered by a user and decorates the text with different colors based on the sentiment score. This system operates between a server and a terminal, and visually expresses the user's input in an easy-to-understand manner.

[0852] System configuration

[0853] 1. The server receives text entered by the user.

[0854] 2. The server performs sentiment analysis and calculates a sentiment score for the input text. Sentiment analysis is performed using natural language processing technology.

[0855] 3. The server decorates the input text by assigning different colors based on the sentiment score: green for positive sentiment, red for negative sentiment, and yellow for neutral sentiment.

[0856] 4. The server generates the decorated text and sends it to the terminal.

[0857] 5. The terminal displays the received decorative text on the screen.

[0858] Program processing explanation

[0859] 1. A user enters text into an input field, for example, "Today is a truly wonderful day!"

[0860] 2. The server receives this input text and begins sentiment analysis, using a natural language processing library such as TextBlob to analyze the sentiment.

[0861] 3. The server receives the analysis result (emotion score). For example, if the score is positive, it is classified as green.

[0862] 4. The server generates the text with green decoration and the text entered by the user colored.

[0863] 5. The server sends the generated decorated text to the terminal, which displays it. The user can visually confirm the decorated text on the screen.

[0864] Specific examples

[0865] Example 1: Positive text

[0866] 1. The user types, "Today is such a great day!"

[0867] 2. The server receives this text and calculates the sentiment score. Since the sentiment score is positive, it is classified as positive.

[0868] 3. The server colors the text green.

[0869] 4. The device will display this green text on the screen, visually indicating positive emotion to the user.

[0870] Example 2: Negative text

[0871] 1. The user types, "I'm feeling very sad today."

[0872] 2. The server receives this text and calculates the sentiment score. Since the sentiment score is negative, it is classified as negative.

[0873] 3. The server colors the text red.

[0874] 4. The device will display this red text on the screen, visually signaling a negative emotion to the user.

[0875] This system visually expresses the emotions and nuances of text, allowing users to intuitively understand the meaning of the text, thereby reducing misunderstandings in text communication and enabling smoother communication.

[0876] The processing flow will be explained below.

[0877] Step 1:

[0878] The user types text into an input field, for example, "Today is a truly wonderful day!"

[0879] Step 2:

[0880] The terminal sends the text entered by the user to the server.

[0881] Step 3:

[0882] The server prepares the input text received from the user for analysis. The server then subjects this text to sentiment analysis.

[0883] Step 4:

[0884] The server performs sentiment analysis using a natural language processing library (e.g., TextBlob). It uses the text analysis capabilities of the TextBlob library to calculate a sentiment score for the input text.

[0885] Step 5:

[0886] The server evaluates the sentiment score and determines whether it is positive, negative, or neutral. The server classifies the sentiment score as either positive, negative, or zero (neutral).

[0887] Step 6:

[0888] The server chooses a corresponding color based on the emotion score: green for a positive emotion score, red for a negative emotion score, and yellow for a zero emotion score.

[0889] Step 7:

[0890] The input text is decorated with a color selected by the server, specifically, the text is colored using the corresponding color code.

[0891] Step 8:

[0892] The server generates the colored text and sends it to the terminal.

[0893] Step 9:

[0894] The terminal displays the colored text received from the server on the screen, allowing the user to visually see the colored text and intuitively understand the emotion and nuance of the input text.

[0895] Example 1

[0896] 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."

[0897] In modern text communication, the sentiment and nuance of a sentence are often misunderstood. Therefore, a method is needed to visually express the sentiment and nuance of a text so that users can intuitively understand it. Also, a means is needed to properly analyze sentiment and decorate the text based on that sentiment.

[0898] 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.

[0899] In this invention, the server includes means for receiving text entered by a user, means for performing sentiment analysis and calculating a sentiment score, means for decorating the input text with different colors based on the sentiment score, means for outputting the decorated text to a terminal, means for performing the sentiment analysis using natural language processing technology, and means for generating the decorated text and sending it to the terminal as an HTTP response. This visually expresses the sentiment and nuance of the text, allowing the user to intuitively understand the sentiment of the sentence.

[0900] "User" refers to a person who uses a system or service.

[0901] A "means" refers to a method or device used to achieve a particular purpose.

[0902] "Server" refers to a computer system that processes and provides data in response to requests from clients.

[0903] "Terminal" refers to an input and display device that can be directly operated by a user.

[0904] "Text" refers to a sentence or character string entered by a user.

[0905] "Sentiment analysis" refers to the process of analyzing the emotional state of input text using natural language processing techniques.

[0906] An "emotion score" is a numerical representation of the emotional state of a piece of text, and can be positive, negative, or neutral.

[0907] "Decorate with different colors" refers to applying specific colors to text depending on the sentiment score.

[0908] "Natural language processing technology" refers to technology that allows computers to analyze, understand, and generate human language.

[0909] An "HTTP response" refers to a server's response message to an HTTP request from a client.

[0910] An "input event" refers to a programmatic action that is triggered when a user interacts with an input device (e.g., a keyboard).

[0911] An "HTTP POST request" refers to one of the HTTP methods used by a client to send data to a server.

[0912] The system aims to analyze the sentiment of text entered by a user and decorate the text with different colors based on the sentiment score. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS The following describes in detail the embodiments of the present invention.

[0913] System configuration

[0914] The system consists of two main components: a server and a terminal. A user inputs text through the terminal, and the server processes the text and returns the results to the terminal.

[0915] Hardware and software used

[0916] server:

[0917] The server receives text from the user, performs sentiment analysis, and decorates the text.

[0918] The server requires a standard web server and a programming language such as Python.

[0919] Uses the TextBlob library as a natural language processing technique.

[0920] You can use Flask or Django as a web framework.

[0921] Device:

[0922] The terminal is used by the user to input text and display the results of processing.

[0923] The device can be a general computer, tablet, or smartphone with a browser.

[0924] It uses HTML, CSS, and JavaScript to provide a simple user interface.

[0925] Program processing explanation

[0926] 1. A user types text into an input field on a device, for example, "Today is a really great day!"

[0927] 2. The device sends this input text to the server as an HTTP POST request.

[0928] 3. The server receives the text sent by the user and performs sentiment analysis using a natural language processing library such as TextBlob.

[0929] 4. The server colors the text based on the resulting sentiment score: green for positive sentiment, red for negative sentiment, and yellow for neutral sentiment.

[0930] 5. The server generates the decorated text and returns it to the device as an HTTP response.

[0931] 6. The terminal displays the received decorative text on the browser screen so that the user can visually confirm it.

[0932] Adding specific examples

[0933] Example 1: Positive text

[0934] The user types, "Today is such a great day!"

[0935] The device sends this text to the server.

[0936] The server calculates the sentiment score and determines it to be positive.

[0937] The server colors this text green and sends it back to the terminal.

[0938] The device displays green text on the browser screen, conveying positive emotions to the user.

[0939] Example 2: Negative text

[0940] The user types, "I'm feeling very sad today."

[0941] The device sends this text to the server.

[0942] The server calculates the sentiment score and determines it to be negative.

[0943] The server colors this text red and sends it back to the terminal.

[0944] The device displays red text on the browser screen, conveying negative emotions to the user.

[0945] Prompt Sentence Examples

[0946] Enter the text "Today is a truly wonderful day!" and run a sentiment analysis to display it in green.

[0947] In this way, the present invention visually expresses the emotion and nuance of text, allowing users to intuitively understand the emotion of a sentence, thereby reducing misunderstandings in text communication and realizing smoother communication.

[0948] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0949] Step 1:

[0950] The user enters text into an input field on the terminal.

[0951] As a concrete example, a user types "Today is a really great day!" When the user confirms the input, an input event is triggered and the terminal stores the entered text in a variable.

[0952] Input: Text entered by the user

[0953] Output: Text stored in a variable in the terminal

[0954] Step 2:

[0955] The terminal sends the entered text data to the server as an HTTP POST request.

[0956] Asynchronous communication is performed using AJAX, and text data is sent to a specified endpoint on the server.

[0957] Input: Text stored in a terminal variable

[0958] Output: HTTP POST request sent to the server

[0959] Step 3:

[0960] The server receives the text sent by the user.

[0961] The server-side API endpoint receives the request and stores the received text data in variables for analysis.

[0962] Input: HTTP POST request

[0963] Output: Text stored in a variable for parsing on the server

[0964] Step 4:

[0965] The server performs sentiment analysis based on the received text.

[0966] Use a natural language processing library such as TextBlob to calculate the sentiment score of the text. For example, for the text "Today is a really great day!", call the TextBlob(text).sentiment method of the TextBlob library to get the sentiment score.

[0967] Input: Text stored in a variable for parsing on the server

[0968] Output: Sentiment score (number indicating positive, negative, or neutral)

[0969] Step 5:

[0970] The server colors the text based on the obtained sentiment score.

[0971] If the sentiment score is positive, the text is colored green, if negative, red, and if neutral, yellow. We also use an HTML template to generate the colored text.

[0972] Input: Sentiment score

[0973] Output: Decorated text (HTML format)

[0974] Step 6:

[0975] The server generates the decorated text and sends it to the terminal as an HTTP response.

[0976] The server returns the generated HTML formatted decorated text as an HTTP response and sends it to the terminal.

[0977] Input: Decorated text (HTML format)

[0978] Output: Decorative text returned as an HTTP response

[0979] Step 7:

[0980] The terminal displays the received decorative text on the screen so that the user can visually confirm it.

[0981] The device uses DOM manipulation to display the returned decorative text in a specific area of ​​the browser, for example, a green-colored "Today is a really great day!"

[0982] Input: Decorative text returned as an HTTP response

[0983] Output: Decorative text displayed in the browser window

[0984] (Application example 1)

[0985] 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."

[0986] Conventional ad delivery systems do not fully consider the impact of text ad content on user emotions, which limits the visual impact of ads and their ability to appeal to user emotions, resulting in the issue of ads not being as effective as they could be.

[0987] 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.

[0988] In this invention, the server includes means for receiving text entered by a user, means for performing sentiment analysis and calculating a sentiment score, means for decorating the entered text with a different color based on the sentiment score, means for outputting the decorated text to a terminal, means for changing the color of the advertisement based on the result of sentiment analysis of the advertisement text, and means for displaying the changed advertisement text on the terminal. This enhances the visual effect of the text advertisement and enables advertisement delivery that strongly appeals to the user's emotions.

[0989] The "means for receiving text entered by the user" is an interface having a function for receiving text data entered by the user and transmitting it to the server.

[0990] The "means for performing sentiment analysis and calculating sentiment scores" is a system that uses natural language processing technology to analyze and quantify the emotional nuances of text.

[0991] The "means for decorating input text with different colors based on the emotion score" is a system having a function for visually highlighting input text using a color corresponding to the emotion score.

[0992] The "means for outputting decorated text to a terminal" is a system having a function for transmitting text data decorated by a server to a user terminal and displaying it.

[0993] The "means for changing the color of an advertisement based on the result of sentiment analysis of the advertisement text" is a system that has the function of analyzing the sentiment score of the advertisement text and dynamically adjusting the color of the advertisement text according to the result.

[0994] The "means for displaying the modified advertising text on the terminal" is a system having a function for displaying color-adjusted advertising text on the user's terminal to visually enhance the advertising effect.

[0995] This invention provides an advertisement distribution system that visually emphasizes advertisement text based on emotion analysis and strongly appeals to the user's emotions. The specific configuration and processing steps of this system are described below.

[0996] Overall system overview

[0997] This system works in cooperation with a server and a terminal. The server performs sentiment analysis of text data and dynamically changes the color of the text according to the results. The decorated text is sent to the terminal and displayed in a visible state for the user.

[0998] Hardware and software used

[0999] Server: Uses the Flask framework (Python). For sentiment analysis, it uses TextBlob, a natural language processing library.

[1000] Client devices: Smartphones, smart glasses, head-mounted displays, etc. These devices have a web browser installed and are processed using JavaScript.

[1001] Program processing

[1002] The server receives the ad text entered by the user and performs sentiment analysis using TextBlob. Based on the sentiment score, the text is colored green, red, or yellow. The colored ad text is then sent from the server to the user's device and displayed in the browser.

[1003] Specific examples

[1004] 1. A user types into the ad dashboard, "This product provides a truly amazing experience!"

[1005] 2. The server receives this text and performs sentiment analysis using the TextBlob library. Since this text contains positive sentiment, the sentiment score is positive.

[1006] 3. The server colors the text green based on the sentiment score. The coloring is achieved using HTML style sheet tags.

[1007] 4. The device receives the decorated text from the server and displays it in the browser, allowing the user to visually recognize positive emotions on the device.

[1008] Prompt Sentence Examples

[1009] Enter the following prompt for the generative AI model:

[1010] Analyze the sentiment of the text entered by the user and decorate it with green for positive sentiment, red for negative sentiment, and yellow for neutral sentiment.

[1011] The above configuration and processing improve the visual effect of the advertising text and realize advertisement delivery that appeals to the user's emotions.

[1012] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1013] Step 1:

[1014] The user accesses the ad management screen and enters ad text in the input field. For example, the user might enter, "This product provides a truly amazing experience!" The input text is sent to the server.

[1015] Step 2:

[1016] The server receives the text data sent by the user. The input data arrives at the server in text format. At this point, the input is the raw text entered by the user.

[1017] Step 3:

[1018] The server uses the TextBlob library to perform sentiment analysis on the text it receives. The server passes the text data to TextBlob and calculates the sentiment score. The input data is the text before analysis begins, and the output data is the analysis result along with the sentiment score.

[1019] Step 4:

[1020] The server decorates the text based on the sentiment score. It colors the text using green if the sentiment score is positive, red if the sentiment score is negative, and yellow if the sentiment score is neutral. The input data is the text with the sentiment score, and the output data is the color-decorated text. This decoration is done using HTML style sheets.

[1021] Step 5:

[1022] The server sends the decorated text to the terminal in JSON format. The input data is the decorated text, and the output data is JSON data formatted for transmission.

[1023] Step 6:

[1024] The terminal parses the JSON data received from the server and parses the decorated text. The terminal parses the received data and converts it into a format that the user can view. The input data is the JSON data sent from the server, and the output data is the parsed decorated text.

[1025] Step 7:

[1026] The terminal displays the decorated advertisement text in the browser, and the user visually confirms it through the terminal screen. The input data is the parsed decorated text, and the output is a visual text advertisement displayed to the user.

[1027] The above steps complete the entire process from inputting advertising text to displaying it. This system makes it easier for users to visually recognize the emotional nuances contained in text advertising.

[1028] 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.

[1029] The present invention is a system that analyzes the emotions of text entered by a user and decorates the text with different colors based on the emotion score, and also combines an emotion engine that recognizes the user's emotions. This system operates between a server and a terminal, and more accurately analyzes user input and presents it in a visually understandable way.

[1030] System configuration

[1031] 1. The server receives text entered by the user, and optionally voice and facial expression data.

[1032] 2. The server uses an emotion recognition module (emotion engine) to classify the user's emotion from the input data and obtain emotion information.

[1033] 3. The server uses a natural language processing library (e.g., TextBlob) to calculate the sentiment score for the input text itself.

[1034] 4. The server reflects the emotional information obtained from the emotion engine in the emotional score of the text.

[1035] 5. The server decorates the input text by assigning different colors based on the sentiment score: green for positive sentiment, red for negative sentiment, and yellow for neutral sentiment.

[1036] 6. The server generates the decorated text and sends it to the terminal.

[1037] 7. The terminal displays the received decorative text on the screen.

[1038] Program processing explanation

[1039] 1. A user types text into an input field, possibly providing voice and facial expression data. For example, they type "Today is a truly wonderful day!"

[1040] 2. The terminal sends the input data from the user to the server.

[1041] 3. The server prepares the received input text, and if possible, speech and facial expression data, for analysis. The server then subjects this input data to emotion recognition.

[1042] 4. The server uses the emotion recognition module (emotion engine) to recognize the user's emotions and obtain emotion information.

[1043] 5. The server uses a natural language processing library to calculate a sentiment score for the input text.

[1044] 6. The server reflects the emotion information obtained from the emotion engine into the emotion score. This process makes the emotion score of the text more accurate.

[1045] 7. The server selects a color based on the emotion score: green if the emotion score is positive, red if negative, and yellow if zero.

[1046] 8. The server decorates the input text with a color selected by the server, generating a colored version of the text entered by the user.

[1047] 9. The server sends the colored text to the terminal, which displays it, allowing the user to visually confirm the decorated text on the screen.

[1048] Specific examples

[1049] Example 1: Positive text and facial expressions

[1050] 1. The user types "Today is a really great day!" and simultaneously provides smiley face data.

[1051] 2. The server receives this input data and uses an emotion engine to recognize positive emotions from the user's facial expressions.

[1052] 3. The server calculates the sentiment score using natural language processing and obtains a positive result.

[1053] 4. The server integrates the sentiment information and the text score to determine the final sentiment score as positive.

[1054] 5. The server colors the text in green and sends it to the device.

[1055] 6. The device will display this green text on the screen, visually indicating positive emotion to the user.

[1056] Example 2: Negative text and audio

[1057] 1. The user types, "I feel very sad today," and simultaneously provides voice data in a sad-sounding voice.

[1058] 2. The server receives this input data and uses an emotion engine to recognize negative emotions from the user's voice.

[1059] 3. The server calculates the sentiment score using natural language processing and obtains a negative result.

[1060] 4. The server integrates the sentiment information and the text score to determine the final sentiment score as negative.

[1061] 5. The server colors the text in red and sends it to the terminal.

[1062] 6. The device will display this red text on the screen, visually signaling a negative emotion to the user.

[1063] This system not only visually expresses the emotions and nuances of text, but also incorporates other emotional information from the user, enabling more accurate and effective emotional expression, thereby reducing misunderstandings in text communication and realizing smoother communication.

[1064] The processing flow will be explained below.

[1065] Step 1:

[1066] A user enters text into an input field and optionally provides voice and facial expression data, for example, typing "Today is a truly beautiful day!" and capturing a smile with the camera.

[1067] Step 2:

[1068] The terminal transmits the input text from the user, as well as the captured voice and facial expression data, to the server.

[1069] Step 3:

[1070] The server prepares the received input text, and if possible, voice and facial expression data, for analysis. It then begins emotion recognition based on this input data.

[1071] Step 4:

[1072] The server uses an emotion recognition module (emotion engine) to recognize the user's emotions from voice and facial expression data. For example, a smile is judged as positive, and a sad expression is judged as negative.

[1073] Step 5:

[1074] The server uses a natural language processing library to calculate the sentiment score of the input text, for example, by parsing the text using TextBlob and obtaining its sentiment score.

[1075] Step 6:

[1076] The server combines the emotion information obtained from the emotion engine and the emotion score of the text obtained from the natural language processing library to determine the final emotion score. If the emotion information is positive and the emotion score is also positive, the final score is determined to be positive.

[1077] Step 7:

[1078] The server chooses a color for the text based on the final sentiment score: green if the final sentiment score is positive, red if negative, and yellow if neutral.

[1079] Step 8:

[1080] The server decorates the input text with a color selected by the server, and generates the user's input text in color.

[1081] Step 9:

[1082] The server sends colored text to the terminal, which receives it and displays the decorated text on the screen.

[1083] Step 10:

[1084] The user visually checks the decorated text on the device screen, which allows the user to intuitively understand the sentiment and nuance of the input text.

[1085] Example 2

[1086] 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."

[1087] Conventional text sentiment analysis systems only target text and cannot consider non-text information such as the user's voice or facial expressions. As a result, emotion recognition accuracy is low, making it difficult to accurately reflect the user's actual emotions. Furthermore, technology that decorates text with different colors based on emotion scores can only reflect a subset of emotions, limiting the scope of visual feedback.

[1088] 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.

[1089] In this invention, the server includes means for receiving text, voice, and facial expression data input by a user, means for recognizing the user's emotions from the input data using a sentiment analysis engine and obtaining emotional information, means for calculating an emotional score for the text using natural language processing technology, means for integrating the emotional information and the emotional score to calculate a final emotional score, means for decorating the input text with different colors based on the emotional score, and means for outputting the decorated text to a terminal. This makes it possible to more accurately reflect the user's emotions by taking into account not only text but also non-text information such as voice and facial expressions. Furthermore, visual feedback based on the emotional score can be expressed in a variety of ways, improving the quality of communication.

[1090] A "user" is an entity that uses the system to input data such as text, voice, and facial expressions.

[1091] An "input field" is an interface that allows a user to input data such as text, voice, and facial expressions.

[1092] A "terminal" is a device that allows a user to input data and check the output from the server. Examples include PCs, smartphones, and tablets.

[1093] A "server" is a remote computer system that receives, analyzes, and processes input data.

[1094] An "emotion analysis engine" is a software module that recognizes a user's emotions from input data and obtains emotional information.

[1095] "Natural language processing technology" is a technology for analyzing text data and calculating structure and sentiment scores.

[1096] An "emotion score" is a number calculated based on the content of text data, and indicates an emotional state such as positive, negative, or neutral.

[1097] "Decorating with different colors" means displaying text in different colors based on the sentiment score to provide visual feedback.

[1098] "Decoration" refers to changing the display format of text based on the sentiment score, specifically by adding color.

[1099] The "output means" is a mechanism for sending data processed by the server to a terminal so that the user can visually confirm the results.

[1100] The present invention is a system that receives text, voice, and facial expression data entered by a user, performs emotion analysis to calculate an emotion score, and decorates the text with different colors based on the result. Specifically, it operates in cooperation with a server and a terminal.

[1101] Hardware and software used

[1102] The terminal must have an input field for users to input data. This input field supports text input, voice input, and facial expression (image) input. A browser or dedicated application can be used on the terminal.

[1103] The server requires the following software and libraries:

[1104] Emotion recognition engine: Used to recognize emotions from voice and facial expression data. Possible open source software include OpenCV and Dlib.

[1105] Natural language processing libraries: Used to calculate sentiment scores for text data, such as TextBlob and NLTK (Natural Language Toolkit).

[1106] Web server: Infrastructure for receiving data from devices and returning processed results. For example, frameworks such as Flask and Django can be used.

[1107] System Operation

[1108] The user enters text into the device's input field and optionally provides voice and facial expression data. The device then sends this input data to the server. The server temporarily stores the received data and prepares it for analysis. An emotion recognition engine is used to analyze the voice and facial expression data to obtain emotional information. A natural language processing library is also used to calculate an emotional score for the text data.

[1109] The emotion information and the emotion score of the text are integrated to generate a final emotion score. Based on this final emotion score, the text is decorated with different colors: green for positive emotion, red for negative emotion, and yellow for neutral emotion. The server generates this decorated text and sends it to the device. The device displays the decorated text on the screen to provide visual feedback to the user.

[1110] Specific examples

[1111] Example prompt sentence:

[1112] 1. Enter the text "Today is such a great day!" and a smiling face.

[1113] How it works: The server receives this text and an image of a smiling face, and the emotion recognition engine recognizes the smiling face as a positive emotion. The natural language processing library calculates the emotion score of the text as positive, and the combined result is a high positive score. The server colors the text in green and sends it to the device. The device displays the green text.

[1114] 2. Enter the text "I'm feeling very sad today" and a sad voice.

[1115] How it works: The server receives this text and voice data, and the emotion recognition engine recognizes negative emotions from the tone of the voice. The natural language processing library calculates the emotion score of the text as negative, and the combined result is a negative score. The server colors the text in red and sends it to the device. The device displays the red text.

[1116] This system can provide more accurate and varied visual feedback by taking into account not only text but also non-textual information such as voice and facial expressions, which can significantly improve the quality of communication and reduce misunderstandings in text communication.

[1117] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1118] Step 1:

[1119] The user inputs text into an input field on the terminal, and inputs voice and facial expression data as needed.

[1120] Specific behavior: The user types the text "Today is a wonderful day!" and takes a photo of a smiling face or records audio.

[1121] Input: Text, voice, facial expression data

[1122] Output: Input data sent to the terminal

[1123] Step 2:

[1124] The device sends the input text, voice, and facial expression data to the server.

[1125] Specific operation: The device sends the user's input data to the server via an HTTP request.

[1126] Input: User input data (text, voice, facial expression data)

[1127] Output: Input data sent to the server

[1128] Step 3:

[1129] The server prepares the received data for analysis.

[1130] Specific operation: The server temporarily stores the data received and performs format conversion and data preprocessing as necessary.

[1131] Input: Input data from the device (text, voice, facial expression data)

[1132] Output: Prepared data for emotion recognition

[1133] Step 4:

[1134] The server uses an emotion recognition engine to recognize the user's emotions from voice and facial expression data and obtains emotion information.

[1135] How it works: The emotion recognition engine uses OpenCV for facial expression analysis to recognize positive emotions from smiles, and also uses voice analysis to analyze emotions from the tone of the voice.

[1136] Input: Preprocessed speech and facial expression data

[1137] Output: Emotion information (e.g., positive, negative)

[1138] Step 5:

[1139] The server uses a natural language processing library to calculate a sentiment score for the text.

[1140] What it does: The natural language processing library TextBlob parses the text "Today is a truly wonderful day!" and calculates a positive score (e.g., 0.85).

[1141] Input: Text data

[1142] Output: Sentiment score of the text

[1143] Step 6:

[1144] The server integrates the sentiment information and the sentiment score of the text to generate a final sentiment score.

[1145] Specific behavior: Integrate emotion information (e.g., positive information from smile data) with the text emotion score (e.g., 0.85) to raise the final score to 0.95.

[1146] Input: Sentiment information, sentiment score of text

[1147] Output: Final sentiment score

[1148] Step 7:

[1149] The server selects an appropriate color for the input text based on the emotion score.

[1150] Specific behavior: A final sentiment score of 0.95 is considered positive and the color green is selected.

[1151] Input: Final sentiment score

[1152] Output: Selected color (e.g. green)

[1153] Step 8:

[1154] The server decorates the text with the selected color and generates the decorated text.

[1155] What it does: Uses HTML and CSS to color the text "Today is a truly beautiful day!" in green.

[1156] Input: Text data, selected color

[1157] Output: Decorated text

[1158] Step 9:

[1159] The server sends the decorated text to the device, which displays it on the screen.

[1160] What it does: The server sends the generated HTML code to the device, which then renders it in the device's browser, displaying green text.

[1161] Input: Decorated text data

[1162] Output: Fancy text displayed on the terminal

[1163] This specific processing step allows the user to visually confirm the emotional state of the text, leading to more effective communication.

[1164] (Application example 2)

[1165] 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."

[1166] In text communication, it is difficult to accurately express a user's emotions, leading to misunderstandings. Furthermore, there is a lack of methods for monitoring a user's emotional state in real time and detecting dangerous situations early in public places and workplaces. Furthermore, there is no system that can centrally analyze various input data, including voice and facial expression data, provided by the user to enhance security. The purpose of this invention is to solve these problems and realize more accurate and effective emotional expression and a safer environment.

[1167] The identification process by the identification 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 entered by a user, means for performing emotion analysis and calculating an emotion score, means for decorating the input text with different colors based on the emotion score, means for outputting the decorated text to the terminal, means for receiving voice or facial expression data entered by the user, means for analyzing the voice or facial expression data to obtain emotion information, means for reflecting the emotion information obtained from the voice or facial expression data in the emotion score of the text, means for displaying a warning when a negative emotion is recognized, and means for sending a notification to a security officer as necessary. This makes it possible to analyze a user's emotions from multiple angles and provide accurate emotional expressions, while also increasing safety in public places and workplaces.

[1168] The "means for receiving text entered by the user" is a method for transmitting text data entered by the user via a keyboard, voice input, or the like to the server.

[1169] "Means for performing sentiment analysis and calculating sentiment scores" refers to a method for quantifying the emotional tendencies of text data using natural language processing technology and machine learning algorithms.

[1170] The "means for decorating input text with different colors based on sentiment score" is a method for highlighting text data with specific colors (e.g., green, red, yellow) depending on the analyzed sentiment score.

[1171] The "means for outputting decorated text to a terminal" is a method for transmitting colored text to a user's terminal and displaying it.

[1172] The "means for receiving voice or facial expression data input by the user" refers to a method for transmitting voice data or facial expression data such as camera images provided by the user to the server.

[1173] The "means for acquiring emotional information by analyzing voice or facial expression data" refers to a method for determining the emotional state of a user from voice data or facial expression data using voice recognition technology or image recognition technology.

[1174] "Means for reflecting emotional information obtained from voice or facial expression data in the emotional score of text" is a method for adjusting the emotional score of text based on emotional data obtained from voice or facial expression.

[1175] The "means for displaying a warning when a negative emotion is recognized" is a method for displaying a warning message on a terminal when the emotion analysis determines that the user is in a negative state.

[1176] "Means for sending notifications to security personnel as needed" refers to a method for sending alerts or notification messages to security personnel when the system detects an abnormality.

[1177] This invention is a system that analyzes text, voice, and facial expression data entered by a user, calculates an emotion score based on the data, and decorates the text to reflect the emotion recognition results. This system also has the function of displaying a warning if a negative emotion is recognized and sending a notification to a security officer as necessary. A specific embodiment of this system is described below.

[1178] System configuration

[1179] Hardware used

[1180] Smartphones (e.g. iPhone, Android devices)

[1181] Robots (e.g., SoftBank Robotics' Pepper)

[1182] Software used

[1183] Emotion recognition module (e.g. Microsoft Azure Emotion API)

[1184] Natural language processing libraries (e.g. TextBlob)

[1185] Notification systems (e.g., Twilio API)

[1186] Processing Description

[1187] 1. User Input

[1188] A user can enter text into an input field via a smartphone or robot and provide voice and facial expression data. For example, a user can enter the text "Someone is chasing me!" and show a frightened expression.

[1189] 2. Data Transmission

[1190] A smartphone or robot sends input data (text, voice, facial expression data) from the user to a server, which then receives the data and prepares it for analysis.

[1191] 3. Data Analysis

[1192] The server analyzes the received data. It uses a natural language processing library to calculate an emotion score for the text, and an emotion recognition module to extract emotion information from voice and facial expression data. For example, it uses voice recognition and image recognition technology to determine the user's emotional state.

[1193] 4. Emotional information integration

[1194] The server integrates the emotional information obtained from the emotion recognition module into the text's emotional score, which is classified as positive, negative, or neutral.

[1195] 5. Text Styling

[1196] The text is decorated based on the emotion score. For example, if the emotion score is positive, the text is decorated with green, if it is negative, the text is decorated with red, and if it is neutral, the text is decorated with yellow.

[1197] 6. Warnings and Notifications

[1198] The server uses the Twilio API to display an alert on a smartphone or robot if a negative emotion is detected, and optionally sends a notification to a security officer.

[1199] Specific examples

[1200] Example 1: Dangerous text and facial expressions

[1201] 1. The user types, "Someone's chasing me!" and displays a terrified expression.

[1202] 2. The server receives this input data and uses the emotion engine to recognize the emotion of fear from the user's facial expression.

[1203] 3. The server calculates the sentiment score using natural language processing and obtains a negative result.

[1204] 4. The server integrates the sentiment information and the text score to determine the final sentiment score as negative.

[1205] 5. Your smartphone or robot will display a warning and say "Danger detected" on the screen.

[1206] 6. The server uses the Twilio API to send a notification to the security officer.

[1207] Prompt Sentence Examples

[1208] "The emotion recognition module analyzes the text, voice, and facial expression data entered by the user, and if a negative emotion is detected, a warning is displayed and a notification is sent to security personnel."

[1209] In this way, by implementing the present invention, it is possible to analyze the user's emotions from multiple angles, and provide accurate emotional expression and a safe environment.

[1210] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1211] Step 1:

[1212] The user inputs text, voice, and facial expression data

[1213] The user inputs text via a smartphone or robot and provides voice and facial expression data. For example, the user inputs the text "Someone is chasing me!" and shows a frightened expression. This data becomes the input data.

[1214] Step 2:

[1215] The device sends the input data to the server

[1216] The device sends the input text, voice data, and facial expression data to the server. At this time, the data is encrypted and transferred securely. The text, voice, and facial expression are sent to the server as input data.

[1217] Step 3:

[1218] The server analyzes the text data

[1219] The server uses a natural language processing library such as TextBlob to analyze the text data and calculate the emotion score. The input is the text data, and the output is the emotion score.

[1220] Step 4:

[1221] The server analyzes voice and facial expression data

[1222] The server uses voice recognition and image recognition technology (e.g., Microsoft Azure Emotion API) to analyze voice and facial expression data and obtain emotional information. The input is voice and facial expression data, and the output is emotional information.

[1223] Step 5:

[1224] The server integrates emotional information

[1225] The server integrates the emotion score obtained from the text data with the emotion information obtained from the voice and facial expression data to determine the final emotion score. The input is the emotion score and emotion information of the text, and the output is the integrated emotion score.

[1226] Step 6:

[1227] Server-styled text

[1228] The server decorates the text with different colors based on the final sentiment score: green for positive sentiment, red for negative sentiment, and yellow for neutral sentiment. The input is the integrated sentiment score and the text, and the output is the decorated text.

[1229] Step 7:

[1230] The server sends the decorated text to the terminal.

[1231] The server sends the decorated text to the terminal. The input is the decorated text, and the output is data containing the decorated text.

[1232] Step 8:

[1233] The terminal displays decorated text

[1234] The terminal receives the decorated text and displays it on the screen. The user can visually confirm the text colored according to the emotional state. The input is the decorated text, and the output is the colored text displayed on the terminal screen.

[1235] Step 9:

[1236] Server displays warning

[1237] If a negative emotion is detected, the server displays a warning on the device, such as "Danger detected." The input is the negative emotion score, and the output is a warning message on the device.

[1238] Step 10:

[1239] The server sends a notification to the security officer

[1240] The server uses the Twilio API to send notifications to security personnel depending on the situation. For example, if a threat is detected, an alert is sent to the personnel via email or SMS. The input is the negative sentiment score and the alert content, and the output is a notification to the security personnel.

[1241] At each step, appropriate data processing or calculation is performed based on the input data, and the respective outputs are obtained to achieve the overall processing.

[1242] 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.

[1243] 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.

[1244] 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.

[1245] [Fourth embodiment]

[1246] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1247] 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.

[1248] 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).

[1249] 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.

[1250] 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.

[1251] 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).

[1252] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1253] 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.

[1254] 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.

[1255] 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.

[1256] 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.

[1257] 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.

[1258] 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."

[1259] The present invention is a system that analyzes the sentiment of text entered by a user and decorates the text with different colors based on the sentiment score. This system operates between a server and a terminal, and visually expresses the user's input in an easy-to-understand manner.

[1260] System configuration

[1261] 1. The server receives text entered by the user.

[1262] 2. The server performs sentiment analysis and calculates a sentiment score for the input text. Sentiment analysis is performed using natural language processing technology.

[1263] 3. The server decorates the input text by assigning different colors based on the sentiment score: green for positive sentiment, red for negative sentiment, and yellow for neutral sentiment.

[1264] 4. The server generates the decorated text and sends it to the terminal.

[1265] 5. The terminal displays the received decorative text on the screen.

[1266] Program processing explanation

[1267] 1. A user enters text into an input field, for example, "Today is a truly wonderful day!"

[1268] 2. The server receives this input text and begins sentiment analysis, using a natural language processing library such as TextBlob to analyze the sentiment.

[1269] 3. The server receives the analysis result (emotion score). For example, if the score is positive, it is classified as green.

[1270] 4. The server generates the text with green decoration and the text entered by the user colored.

[1271] 5. The server sends the generated decorated text to the terminal, which displays it. The user can visually confirm the decorated text on the screen.

[1272] Specific examples

[1273] Example 1: Positive text

[1274] 1. The user types, "Today is such a great day!"

[1275] 2. The server receives this text and calculates the sentiment score. Since the sentiment score is positive, it is classified as positive.

[1276] 3. The server colors the text green.

[1277] 4. The device will display this green text on the screen, visually indicating positive emotion to the user.

[1278] Example 2: Negative text

[1279] 1. The user types, "I'm feeling very sad today."

[1280] 2. The server receives this text and calculates the sentiment score. Since the sentiment score is negative, it is classified as negative.

[1281] 3. The server colors the text red.

[1282] 4. The device will display this red text on the screen, visually signaling a negative emotion to the user.

[1283] This system visually expresses the emotions and nuances of text, allowing users to intuitively understand the meaning of the text, thereby reducing misunderstandings in text communication and enabling smoother communication.

[1284] The processing flow will be explained below.

[1285] Step 1:

[1286] The user types text into an input field, for example, "Today is a truly wonderful day!"

[1287] Step 2:

[1288] The terminal sends the text entered by the user to the server.

[1289] Step 3:

[1290] The server prepares the input text received from the user for analysis. The server then subjects this text to sentiment analysis.

[1291] Step 4:

[1292] The server performs sentiment analysis using a natural language processing library (e.g., TextBlob). It uses the text analysis capabilities of the TextBlob library to calculate a sentiment score for the input text.

[1293] Step 5:

[1294] The server evaluates the sentiment score and determines whether it is positive, negative, or neutral. The server classifies the sentiment score as either positive, negative, or zero (neutral).

[1295] Step 6:

[1296] The server chooses a corresponding color based on the emotion score: green for a positive emotion score, red for a negative emotion score, and yellow for a zero emotion score.

[1297] Step 7:

[1298] The input text is decorated with a color selected by the server, specifically, the text is colored using the corresponding color code.

[1299] Step 8:

[1300] The server generates the colored text and sends it to the terminal.

[1301] Step 9:

[1302] The terminal displays the colored text received from the server on the screen, allowing the user to visually see the colored text and intuitively understand the emotion and nuance of the input text.

[1303] Example 1

[1304] 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."

[1305] In modern text communication, the sentiment and nuance of a sentence are often misunderstood. Therefore, a method is needed to visually express the sentiment and nuance of a text so that users can intuitively understand it. Also, a means is needed to properly analyze sentiment and decorate the text based on that sentiment.

[1306] 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.

[1307] In this invention, the server includes means for receiving text entered by a user, means for performing sentiment analysis and calculating a sentiment score, means for decorating the input text with different colors based on the sentiment score, means for outputting the decorated text to a terminal, means for performing the sentiment analysis using natural language processing technology, and means for generating the decorated text and sending it to the terminal as an HTTP response. This visually expresses the sentiment and nuance of the text, allowing the user to intuitively understand the sentiment of the sentence.

[1308] "User" refers to a person who uses a system or service.

[1309] A "means" refers to a method or device used to achieve a particular purpose.

[1310] "Server" refers to a computer system that processes and provides data in response to requests from clients.

[1311] "Terminal" refers to an input and display device that can be directly operated by a user.

[1312] "Text" refers to a sentence or character string entered by a user.

[1313] "Sentiment analysis" refers to the process of analyzing the emotional state of input text using natural language processing techniques.

[1314] An "emotion score" is a numerical representation of the emotional state of a piece of text, and can be positive, negative, or neutral.

[1315] "Decorate with different colors" refers to applying specific colors to text depending on the sentiment score.

[1316] "Natural language processing technology" refers to technology that allows computers to analyze, understand, and generate human language.

[1317] An "HTTP response" refers to a server's response message to an HTTP request from a client.

[1318] An "input event" refers to a programmatic action that is triggered when a user interacts with an input device (e.g., a keyboard).

[1319] An "HTTP POST request" refers to one of the HTTP methods used by a client to send data to a server.

[1320] The system aims to analyze the sentiment of text entered by a user and decorate the text with different colors based on the sentiment score. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS The following describes in detail the embodiments of the present invention.

[1321] System configuration

[1322] The system consists of two main components: a server and a terminal. A user inputs text through the terminal, and the server processes the text and returns the results to the terminal.

[1323] Hardware and software used

[1324] server:

[1325] The server receives text from the user, performs sentiment analysis, and decorates the text.

[1326] The server requires a standard web server and a programming language such as Python.

[1327] Uses the TextBlob library as a natural language processing technique.

[1328] You can use Flask or Django as a web framework.

[1329] Device:

[1330] The terminal is used by the user to input text and display the results of processing.

[1331] The device can be a general computer, tablet, or smartphone with a browser.

[1332] It uses HTML, CSS, and JavaScript to provide a simple user interface.

[1333] Program processing explanation

[1334] 1. A user types text into an input field on a device, for example, "Today is a really great day!"

[1335] 2. The device sends this input text to the server as an HTTP POST request.

[1336] 3. The server receives the text sent by the user and performs sentiment analysis using a natural language processing library such as TextBlob.

[1337] 4. The server colors the text based on the resulting sentiment score: green for positive sentiment, red for negative sentiment, and yellow for neutral sentiment.

[1338] 5. The server generates the decorated text and returns it to the device as an HTTP response.

[1339] 6. The terminal displays the received decorative text on the browser screen so that the user can visually confirm it.

[1340] Adding specific examples

[1341] Example 1: Positive text

[1342] The user types, "Today is such a great day!"

[1343] The device sends this text to the server.

[1344] The server calculates the sentiment score and determines it to be positive.

[1345] The server colors this text green and sends it back to the terminal.

[1346] The device displays green text on the browser screen, conveying positive emotions to the user.

[1347] Example 2: Negative text

[1348] The user types, "I'm feeling very sad today."

[1349] The device sends this text to the server.

[1350] The server calculates the sentiment score and determines it to be negative.

[1351] The server colors this text red and sends it back to the terminal.

[1352] The device displays red text on the browser screen, conveying negative emotions to the user.

[1353] Prompt Sentence Examples

[1354] Enter the text "Today is a truly wonderful day!" and run a sentiment analysis to display it in green.

[1355] In this way, the present invention visually expresses the emotion and nuance of text, allowing users to intuitively understand the emotion of a sentence, thereby reducing misunderstandings in text communication and realizing smoother communication.

[1356] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1357] Step 1:

[1358] The user enters text into an input field on the terminal.

[1359] As a concrete example, a user types "Today is a really great day!" When the user confirms the input, an input event is triggered and the terminal stores the entered text in a variable.

[1360] Input: Text entered by the user

[1361] Output: Text stored in a variable in the terminal

[1362] Step 2:

[1363] The terminal sends the entered text data to the server as an HTTP POST request.

[1364] Asynchronous communication is performed using AJAX, and text data is sent to a specified endpoint on the server.

[1365] Input: Text stored in a terminal variable

[1366] Output: HTTP POST request sent to the server

[1367] Step 3:

[1368] The server receives the text sent by the user.

[1369] The server-side API endpoint receives the request and stores the received text data in variables for analysis.

[1370] Input: HTTP POST request

[1371] Output: Text stored in a variable for parsing on the server

[1372] Step 4:

[1373] The server performs sentiment analysis based on the received text.

[1374] Use a natural language processing library such as TextBlob to calculate the sentiment score of the text. For example, for the text "Today is a really great day!", call the TextBlob(text).sentiment method of the TextBlob library to get the sentiment score.

[1375] Input: Text stored in a variable for parsing on the server

[1376] Output: Sentiment score (number indicating positive, negative, or neutral)

[1377] Step 5:

[1378] The server colors the text based on the obtained sentiment score.

[1379] If the sentiment score is positive, the text is colored green, if negative, red, and if neutral, yellow. We also use an HTML template to generate the colored text.

[1380] Input: Sentiment score

[1381] Output: Decorated text (HTML format)

[1382] Step 6:

[1383] The server generates the decorated text and sends it to the terminal as an HTTP response.

[1384] The server returns the generated HTML formatted decorated text as an HTTP response and sends it to the terminal.

[1385] Input: Decorated text (HTML format)

[1386] Output: Decorative text returned as an HTTP response

[1387] Step 7:

[1388] The terminal displays the received decorative text on the screen so that the user can visually confirm it.

[1389] The device uses DOM manipulation to display the returned decorative text in a specific area of ​​the browser, for example, a green-colored "Today is a really great day!"

[1390] Input: Decorative text returned as an HTTP response

[1391] Output: Decorative text displayed in the browser window

[1392] (Application example 1)

[1393] 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."

[1394] Conventional ad delivery systems do not fully consider the impact of text ad content on user emotions, which limits the visual impact of ads and their ability to appeal to user emotions, resulting in the issue of ads not being as effective as they could be.

[1395] 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.

[1396] In this invention, the server includes means for receiving text entered by a user, means for performing sentiment analysis and calculating a sentiment score, means for decorating the entered text with a different color based on the sentiment score, means for outputting the decorated text to a terminal, means for changing the color of the advertisement based on the result of sentiment analysis of the advertisement text, and means for displaying the changed advertisement text on the terminal. This enhances the visual effect of the text advertisement and enables advertisement delivery that strongly appeals to the user's emotions.

[1397] The "means for receiving text entered by the user" is an interface having a function for receiving text data entered by the user and transmitting it to the server.

[1398] The "means for performing sentiment analysis and calculating sentiment scores" is a system that uses natural language processing technology to analyze and quantify the emotional nuances of text.

[1399] The "means for decorating input text with different colors based on the emotion score" is a system having a function for visually highlighting input text using a color corresponding to the emotion score.

[1400] The "means for outputting decorated text to a terminal" is a system having a function for transmitting text data decorated by a server to a user terminal and displaying it.

[1401] The "means for changing the color of an advertisement based on the result of sentiment analysis of the advertisement text" is a system that has the function of analyzing the sentiment score of the advertisement text and dynamically adjusting the color of the advertisement text according to the result.

[1402] The "means for displaying the modified advertising text on the terminal" is a system having a function for displaying color-adjusted advertising text on the user's terminal to visually enhance the advertising effect.

[1403] This invention provides an advertisement distribution system that visually emphasizes advertisement text based on emotion analysis and strongly appeals to the user's emotions. The specific configuration and processing steps of this system are described below.

[1404] Overall system overview

[1405] This system works in cooperation with a server and a terminal. The server performs sentiment analysis of text data and dynamically changes the color of the text according to the results. The decorated text is sent to the terminal and displayed in a visible state for the user.

[1406] Hardware and software used

[1407] Server: Uses the Flask framework (Python). For sentiment analysis, it uses TextBlob, a natural language processing library.

[1408] Client devices: Smartphones, smart glasses, head-mounted displays, etc. These devices have a web browser installed and are processed using JavaScript.

[1409] Program processing

[1410] The server receives the ad text entered by the user and performs sentiment analysis using TextBlob. Based on the sentiment score, the text is colored green, red, or yellow. The colored ad text is then sent from the server to the user's device and displayed in the browser.

[1411] Specific examples

[1412] 1. A user types into the ad dashboard, "This product provides a truly amazing experience!"

[1413] 2. The server receives this text and performs sentiment analysis using the TextBlob library. Since this text contains positive sentiment, the sentiment score is positive.

[1414] 3. The server colors the text green based on the sentiment score. The coloring is achieved using HTML style sheet tags.

[1415] 4. The device receives the decorated text from the server and displays it in the browser, allowing the user to visually recognize positive emotions on the device.

[1416] Prompt Sentence Examples

[1417] Enter the following prompt for the generative AI model:

[1418] Analyze the sentiment of the text entered by the user and decorate it with green for positive sentiment, red for negative sentiment, and yellow for neutral sentiment.

[1419] The above configuration and processing improve the visual effect of the advertising text and realize advertisement delivery that appeals to the user's emotions.

[1420] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1421] Step 1:

[1422] The user accesses the ad management screen and enters ad text in the input field. For example, the user might enter, "This product provides a truly amazing experience!" The input text is sent to the server.

[1423] Step 2:

[1424] The server receives the text data sent by the user. The input data arrives at the server in text format. At this point, the input is the raw text entered by the user.

[1425] Step 3:

[1426] The server uses the TextBlob library to perform sentiment analysis on the text it receives. The server passes the text data to TextBlob and calculates the sentiment score. The input data is the text before analysis begins, and the output data is the analysis result along with the sentiment score.

[1427] Step 4:

[1428] The server decorates the text based on the sentiment score. It colors the text using green if the sentiment score is positive, red if the sentiment score is negative, and yellow if the sentiment score is neutral. The input data is the text with the sentiment score, and the output data is the color-decorated text. This decoration is done using HTML style sheets.

[1429] Step 5:

[1430] The server sends the decorated text to the terminal in JSON format. The input data is the decorated text, and the output data is JSON data formatted for transmission.

[1431] Step 6:

[1432] The terminal parses the JSON data received from the server and parses the decorated text. The terminal parses the received data and converts it into a format that the user can view. The input data is the JSON data sent from the server, and the output data is the parsed decorated text.

[1433] Step 7:

[1434] The terminal displays the decorated advertisement text in the browser, and the user visually confirms it through the terminal screen. The input data is the parsed decorated text, and the output is a visual text advertisement displayed to the user.

[1435] The above steps complete the entire process from inputting advertising text to displaying it. This system makes it easier for users to visually recognize the emotional nuances contained in text advertising.

[1436] 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.

[1437] The present invention is a system that analyzes the emotions of text entered by a user and decorates the text with different colors based on the emotion score, and also combines an emotion engine that recognizes the user's emotions. This system operates between a server and a terminal, and more accurately analyzes user input and presents it in a visually understandable way.

[1438] System configuration

[1439] 1. The server receives text entered by the user, and optionally voice and facial expression data.

[1440] 2. The server uses an emotion recognition module (emotion engine) to classify the user's emotion from the input data and obtain emotion information.

[1441] 3. The server uses a natural language processing library (e.g., TextBlob) to calculate the sentiment score for the input text itself.

[1442] 4. The server reflects the emotional information obtained from the emotion engine in the emotional score of the text.

[1443] 5. The server decorates the input text by assigning different colors based on the sentiment score: green for positive sentiment, red for negative sentiment, and yellow for neutral sentiment.

[1444] 6. The server generates the decorated text and sends it to the terminal.

[1445] 7. The terminal displays the received decorative text on the screen.

[1446] Program processing explanation

[1447] 1. A user types text into an input field, possibly providing voice and facial expression data. For example, they type "Today is a truly wonderful day!"

[1448] 2. The terminal sends the input data from the user to the server.

[1449] 3. The server prepares the received input text, and if possible, speech and facial expression data, for analysis. The server then subjects this input data to emotion recognition.

[1450] 4. The server uses the emotion recognition module (emotion engine) to recognize the user's emotions and obtain emotion information.

[1451] 5. The server uses a natural language processing library to calculate a sentiment score for the input text.

[1452] 6. The server reflects the emotion information obtained from the emotion engine into the emotion score. This process makes the emotion score of the text more accurate.

[1453] 7. The server selects a color based on the emotion score: green if the emotion score is positive, red if negative, and yellow if zero.

[1454] 8. The server decorates the input text with a color selected by the server, generating a colored version of the text entered by the user.

[1455] 9. The server sends the colored text to the terminal, which displays it, allowing the user to visually confirm the decorated text on the screen.

[1456] Specific examples

[1457] Example 1: Positive text and facial expressions

[1458] 1. The user types "Today is a really great day!" and simultaneously provides smiley face data.

[1459] 2. The server receives this input data and uses an emotion engine to recognize positive emotions from the user's facial expressions.

[1460] 3. The server calculates the sentiment score using natural language processing and obtains a positive result.

[1461] 4. The server integrates the sentiment information and the text score to determine the final sentiment score as positive.

[1462] 5. The server colors the text in green and sends it to the device.

[1463] 6. The device will display this green text on the screen, visually indicating positive emotion to the user.

[1464] Example 2: Negative text and audio

[1465] 1. The user types, "I feel very sad today," and simultaneously provides voice data in a sad-sounding voice.

[1466] 2. The server receives this input data and uses an emotion engine to recognize negative emotions from the user's voice.

[1467] 3. The server calculates the sentiment score using natural language processing and obtains a negative result.

[1468] 4. The server integrates the sentiment information and the text score to determine the final sentiment score as negative.

[1469] 5. The server colors the text in red and sends it to the terminal.

[1470] 6. The device will display this red text on the screen, visually signaling a negative emotion to the user.

[1471] This system not only visually expresses the emotions and nuances of text, but also incorporates other emotional information from the user, enabling more accurate and effective emotional expression, thereby reducing misunderstandings in text communication and realizing smoother communication.

[1472] The processing flow will be explained below.

[1473] Step 1:

[1474] A user enters text into an input field and optionally provides voice and facial expression data, for example, typing "Today is a truly beautiful day!" and capturing a smile with the camera.

[1475] Step 2:

[1476] The terminal transmits the input text from the user, as well as the captured voice and facial expression data, to the server.

[1477] Step 3:

[1478] The server prepares the received input text, and if possible, voice and facial expression data, for analysis. It then begins emotion recognition based on this input data.

[1479] Step 4:

[1480] The server uses an emotion recognition module (emotion engine) to recognize the user's emotions from voice and facial expression data. For example, a smile is judged as positive, and a sad expression is judged as negative.

[1481] Step 5:

[1482] The server uses a natural language processing library to calculate the sentiment score of the input text, for example, by parsing the text using TextBlob and obtaining its sentiment score.

[1483] Step 6:

[1484] The server combines the emotion information obtained from the emotion engine and the emotion score of the text obtained from the natural language processing library to determine the final emotion score. If the emotion information is positive and the emotion score is also positive, the final score is determined to be positive.

[1485] Step 7:

[1486] The server chooses a color for the text based on the final sentiment score: green if the final sentiment score is positive, red if negative, and yellow if neutral.

[1487] Step 8:

[1488] The server decorates the input text with a color selected by the server, and generates the user's input text in color.

[1489] Step 9:

[1490] The server sends colored text to the terminal, which receives it and displays the decorated text on the screen.

[1491] Step 10:

[1492] The user visually checks the decorated text on the device screen, which allows the user to intuitively understand the sentiment and nuance of the input text.

[1493] Example 2

[1494] 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."

[1495] Conventional text sentiment analysis systems only target text and cannot consider non-text information such as the user's voice or facial expressions. As a result, emotion recognition accuracy is low, making it difficult to accurately reflect the user's actual emotions. Furthermore, technology that decorates text with different colors based on emotion scores can only reflect a subset of emotions, limiting the scope of visual feedback.

[1496] 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.

[1497] In this invention, the server includes means for receiving text, voice, and facial expression data input by a user, means for recognizing the user's emotions from the input data using a sentiment analysis engine and obtaining emotional information, means for calculating an emotional score for the text using natural language processing technology, means for integrating the emotional information and the emotional score to calculate a final emotional score, means for decorating the input text with different colors based on the emotional score, and means for outputting the decorated text to a terminal. This makes it possible to more accurately reflect the user's emotions by taking into account not only text but also non-text information such as voice and facial expressions. Furthermore, visual feedback based on the emotional score can be expressed in a variety of ways, improving the quality of communication.

[1498] A "user" is an entity that uses the system to input data such as text, voice, and facial expressions.

[1499] An "input field" is an interface that allows a user to input data such as text, voice, and facial expressions.

[1500] A "terminal" is a device that allows a user to input data and check the output from the server. Examples include PCs, smartphones, and tablets.

[1501] A "server" is a remote computer system that receives, analyzes, and processes input data.

[1502] An "emotion analysis engine" is a software module that recognizes a user's emotions from input data and obtains emotional information.

[1503] "Natural language processing technology" is a technology for analyzing text data and calculating structure and sentiment scores.

[1504] An "emotion score" is a number calculated based on the content of text data, and indicates an emotional state such as positive, negative, or neutral.

[1505] "Decorating with different colors" means displaying text in different colors based on the sentiment score to provide visual feedback.

[1506] "Decoration" refers to changing the display format of text based on the sentiment score, specifically by adding color.

[1507] The "output means" is a mechanism for sending data processed by the server to a terminal so that the user can visually confirm the results.

[1508] The present invention is a system that receives text, voice, and facial expression data entered by a user, performs emotion analysis to calculate an emotion score, and decorates the text with different colors based on the result. Specifically, it operates in cooperation with a server and a terminal.

[1509] Hardware and software used

[1510] The terminal must have an input field for users to input data. This input field supports text input, voice input, and facial expression (image) input. A browser or dedicated application can be used on the terminal.

[1511] The server requires the following software and libraries:

[1512] Emotion recognition engine: Used to recognize emotions from voice and facial expression data. Possible open source software include OpenCV and Dlib.

[1513] Natural language processing libraries: Used to calculate sentiment scores for text data, such as TextBlob and NLTK (Natural Language Toolkit).

[1514] Web server: Infrastructure for receiving data from devices and returning processed results. For example, frameworks such as Flask and Django can be used.

[1515] System Operation

[1516] The user enters text into the device's input field and optionally provides voice and facial expression data. The device then sends this input data to the server. The server temporarily stores the received data and prepares it for analysis. An emotion recognition engine is used to analyze the voice and facial expression data to obtain emotional information. A natural language processing library is also used to calculate an emotional score for the text data.

[1517] The emotion information and the emotion score of the text are integrated to generate a final emotion score. Based on this final emotion score, the text is decorated with different colors: green for positive emotion, red for negative emotion, and yellow for neutral emotion. The server generates this decorated text and sends it to the device. The device displays the decorated text on the screen to provide visual feedback to the user.

[1518] Specific examples

[1519] Example prompt sentence:

[1520] 1. Enter the text "Today is such a great day!" and a smiling face.

[1521] How it works: The server receives this text and an image of a smiling face, and the emotion recognition engine recognizes the smiling face as a positive emotion. The natural language processing library calculates the emotion score of the text as positive, and the combined result is a high positive score. The server colors the text in green and sends it to the device. The device displays the green text.

[1522] 2. Enter the text "I'm feeling very sad today" and a sad voice.

[1523] How it works: The server receives this text and voice data, and the emotion recognition engine recognizes negative emotions from the tone of the voice. The natural language processing library calculates the emotion score of the text as negative, and the combined result is a negative score. The server colors the text in red and sends it to the device. The device displays the red text.

[1524] This system can provide more accurate and varied visual feedback by taking into account not only text but also non-textual information such as voice and facial expressions, which can significantly improve the quality of communication and reduce misunderstandings in text communication.

[1525] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1526] Step 1:

[1527] The user inputs text into an input field on the terminal, and inputs voice and facial expression data as needed.

[1528] Specific behavior: The user types the text "Today is a wonderful day!" and takes a photo of a smiling face or records audio.

[1529] Input: Text, voice, facial expression data

[1530] Output: Input data sent to the terminal

[1531] Step 2:

[1532] The device sends the input text, voice, and facial expression data to the server.

[1533] Specific operation: The device sends the user's input data to the server via an HTTP request.

[1534] Input: User input data (text, voice, facial expression data)

[1535] Output: Input data sent to the server

[1536] Step 3:

[1537] The server prepares the received data for analysis.

[1538] Specific operation: The server temporarily stores the data received and performs format conversion and data preprocessing as necessary.

[1539] Input: Input data from the device (text, voice, facial expression data)

[1540] Output: Prepared data for emotion recognition

[1541] Step 4:

[1542] The server uses an emotion recognition engine to recognize the user's emotions from voice and facial expression data and obtains emotion information.

[1543] How it works: The emotion recognition engine uses OpenCV for facial expression analysis to recognize positive emotions from smiles, and also uses voice analysis to analyze emotions from the tone of the voice.

[1544] Input: Preprocessed speech and facial expression data

[1545] Output: Emotion information (e.g., positive, negative)

[1546] Step 5:

[1547] The server uses a natural language processing library to calculate a sentiment score for the text.

[1548] What it does: The natural language processing library TextBlob parses the text "Today is a truly wonderful day!" and calculates a positive score (e.g., 0.85).

[1549] Input: Text data

[1550] Output: Sentiment score of the text

[1551] Step 6:

[1552] The server integrates the sentiment information and the sentiment score of the text to generate a final sentiment score.

[1553] Specific behavior: Integrate emotion information (e.g., positive information from smile data) with the text emotion score (e.g., 0.85) to raise the final score to 0.95.

[1554] Input: Sentiment information, sentiment score of text

[1555] Output: Final sentiment score

[1556] Step 7:

[1557] The server selects an appropriate color for the input text based on the emotion score.

[1558] Specific behavior: A final sentiment score of 0.95 is considered positive and the color green is selected.

[1559] Input: Final sentiment score

[1560] Output: Selected color (e.g. green)

[1561] Step 8:

[1562] The server decorates the text with the selected color and generates the decorated text.

[1563] What it does: Uses HTML and CSS to color the text "Today is a truly beautiful day!" in green.

[1564] Input: Text data, selected color

[1565] Output: Decorated text

[1566] Step 9:

[1567] The server sends the decorated text to the device, which displays it on the screen.

[1568] What it does: The server sends the generated HTML code to the device, which then renders it in the device's browser, displaying green text.

[1569] Input: Decorated text data

[1570] Output: Fancy text displayed on the terminal

[1571] This specific processing step allows the user to visually confirm the emotional state of the text, leading to more effective communication.

[1572] (Application example 2)

[1573] 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."

[1574] In text communication, it is difficult to accurately express a user's emotions, leading to misunderstandings. Furthermore, there is a lack of methods for monitoring a user's emotional state in real time and detecting dangerous situations early in public places and workplaces. Furthermore, there is no system that can centrally analyze various input data, including voice and facial expression data, provided by the user to enhance security. The purpose of this invention is to solve these problems and realize more accurate and effective emotional expression and a safer environment.

[1575] The identification process by the identification 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 entered by a user, means for performing emotion analysis and calculating an emotion score, means for decorating the input text with different colors based on the emotion score, means for outputting the decorated text to the terminal, means for receiving voice or facial expression data entered by the user, means for analyzing the voice or facial expression data to obtain emotion information, means for reflecting the emotion information obtained from the voice or facial expression data in the emotion score of the text, means for displaying a warning when a negative emotion is recognized, and means for sending a notification to a security officer as necessary. This makes it possible to analyze a user's emotions from multiple angles and provide accurate emotional expressions, while also increasing safety in public places and workplaces.

[1576] The "means for receiving text entered by the user" is a method for transmitting text data entered by the user via a keyboard, voice input, or the like to the server.

[1577] "Means for performing sentiment analysis and calculating sentiment scores" refers to a method for quantifying the emotional tendencies of text data using natural language processing technology and machine learning algorithms.

[1578] The "means for decorating input text with different colors based on sentiment score" is a method for highlighting text data with specific colors (e.g., green, red, yellow) depending on the analyzed sentiment score.

[1579] The "means for outputting decorated text to a terminal" is a method for transmitting colored text to a user's terminal and displaying it.

[1580] The "means for receiving voice or facial expression data input by the user" refers to a method for transmitting voice data or facial expression data such as camera images provided by the user to the server.

[1581] The "means for acquiring emotional information by analyzing voice or facial expression data" refers to a method for determining the emotional state of a user from voice data or facial expression data using voice recognition technology or image recognition technology.

[1582] "Means for reflecting emotional information obtained from voice or facial expression data in the emotional score of text" is a method for adjusting the emotional score of text based on emotional data obtained from voice or facial expression.

[1583] The "means for displaying a warning when a negative emotion is recognized" is a method for displaying a warning message on a terminal when the emotion analysis determines that the user is in a negative state.

[1584] "Means for sending notifications to security personnel as needed" refers to a method for sending alerts or notification messages to security personnel when the system detects an abnormality.

[1585] This invention is a system that analyzes text, voice, and facial expression data entered by a user, calculates an emotion score based on the data, and decorates the text to reflect the emotion recognition results. This system also has the function of displaying a warning if a negative emotion is recognized and sending a notification to a security officer as necessary. A specific embodiment of this system is described below.

[1586] System configuration

[1587] Hardware used

[1588] Smartphones (e.g. iPhone, Android devices)

[1589] Robots (e.g., SoftBank Robotics' Pepper)

[1590] Software used

[1591] Emotion recognition module (e.g. Microsoft Azure Emotion API)

[1592] Natural language processing libraries (e.g. TextBlob)

[1593] Notification systems (e.g., Twilio API)

[1594] Processing Description

[1595] 1. User Input

[1596] A user can enter text into an input field via a smartphone or robot and provide voice and facial expression data. For example, a user can enter the text "Someone is chasing me!" and show a frightened expression.

[1597] 2. Data Transmission

[1598] A smartphone or robot sends input data (text, voice, facial expression data) from the user to a server, which then receives the data and prepares it for analysis.

[1599] 3. Data Analysis

[1600] The server analyzes the received data. It uses a natural language processing library to calculate an emotion score for the text, and an emotion recognition module to extract emotion information from voice and facial expression data. For example, it uses voice recognition and image recognition technology to determine the user's emotional state.

[1601] 4. Emotional information integration

[1602] The server integrates the emotional information obtained from the emotion recognition module into the text's emotional score, which is classified as positive, negative, or neutral.

[1603] 5. Text Styling

[1604] The text is decorated based on the emotion score. For example, if the emotion score is positive, the text is decorated with green, if it is negative, the text is decorated with red, and if it is neutral, the text is decorated with yellow.

[1605] 6. Warnings and Notifications

[1606] The server uses the Twilio API to display an alert on a smartphone or robot if a negative emotion is detected, and optionally sends a notification to a security officer.

[1607] Specific examples

[1608] Example 1: Dangerous text and facial expressions

[1609] 1. The user types, "Someone's chasing me!" and displays a terrified expression.

[1610] 2. The server receives this input data and uses the emotion engine to recognize the emotion of fear from the user's facial expression.

[1611] 3. The server calculates the sentiment score using natural language processing and obtains a negative result.

[1612] 4. The server integrates the sentiment information and the text score to determine the final sentiment score as negative.

[1613] 5. Your smartphone or robot will display a warning and say "Danger detected" on the screen.

[1614] 6. The server uses the Twilio API to send a notification to the security officer.

[1615] Prompt Sentence Examples

[1616] "The emotion recognition module analyzes the text, voice, and facial expression data entered by the user, and if a negative emotion is detected, a warning is displayed and a notification is sent to security personnel."

[1617] In this way, by implementing the present invention, it is possible to analyze the user's emotions from multiple angles, and provide accurate emotional expression and a safe environment.

[1618] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1619] Step 1:

[1620] The user inputs text, voice, and facial expression data

[1621] The user inputs text via a smartphone or robot and provides voice and facial expression data. For example, the user inputs the text "Someone is chasing me!" and shows a frightened expression. This data becomes the input data.

[1622] Step 2:

[1623] The device sends the input data to the server

[1624] The device sends the input text, voice data, and facial expression data to the server. At this time, the data is encrypted and transferred securely. The text, voice, and facial expression are sent to the server as input data.

[1625] Step 3:

[1626] The server analyzes the text data

[1627] The server uses a natural language processing library such as TextBlob to analyze the text data and calculate the emotion score. The input is the text data, and the output is the emotion score.

[1628] Step 4:

[1629] The server analyzes voice and facial expression data

[1630] The server uses voice recognition and image recognition technology (e.g., Microsoft Azure Emotion API) to analyze voice and facial expression data and obtain emotional information. The input is voice and facial expression data, and the output is emotional information.

[1631] Step 5:

[1632] The server integrates emotional information

[1633] The server integrates the emotion score obtained from the text data with the emotion information obtained from the voice and facial expression data to determine the final emotion score. The input is the emotion score and emotion information of the text, and the output is the integrated emotion score.

[1634] Step 6:

[1635] Server-styled text

[1636] The server decorates the text with different colors based on the final sentiment score: green for positive sentiment, red for negative sentiment, and yellow for neutral sentiment. The input is the integrated sentiment score and the text, and the output is the decorated text.

[1637] Step 7:

[1638] The server sends the decorated text to the terminal.

[1639] The server sends the decorated text to the terminal. The input is the decorated text, and the output is data containing the decorated text.

[1640] Step 8:

[1641] The terminal displays decorated text

[1642] The terminal receives the decorated text and displays it on the screen. The user can visually confirm the text colored according to the emotional state. The input is the decorated text, and the output is the colored text displayed on the terminal screen.

[1643] Step 9:

[1644] Server displays warning

[1645] If a negative emotion is detected, the server displays a warning on the device, such as "Danger detected." The input is the negative emotion score, and the output is a warning message on the device.

[1646] Step 10:

[1647] The server sends a notification to the security officer

[1648] The server uses the Twilio API to send notifications to security personnel depending on the situation. For example, if a threat is detected, an alert is sent to the personnel via email or SMS. The input is the negative sentiment score and the alert content, and the output is a notification to the security personnel.

[1649] At each step, appropriate data processing or calculation is performed based on the input data, and the respective outputs are obtained to achieve the overall processing.

[1650] 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.

[1651] 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.

[1652] 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.

[1653] 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.

[1654] 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.

[1655] 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.

[1656] 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).

[1657] 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.

[1658] 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."

[1659] 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.

[1660] 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).

[1661] 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.

[1662] 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.

[1663] 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.

[1664] 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.

[1665] 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.

[1666] 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.

[1667] 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.

[1668] 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.

[1669] 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.

[1670] 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.

[1671] The following is further disclosed regarding the above embodiment.

[1672] (Claim 1)

[1673] means for receiving user-entered text;

[1674] means for performing sentiment analysis and calculating a sentiment score;

[1675] means for decorating the input text with different colors based on the sentiment score;

[1676] A system including means for outputting decorated text to a terminal.

[1677] (Claim 2)

[1678] a means for coloring the text in green if the sentiment score is positive;

[1679] a means for coloring the text in red if the sentiment score is negative;

[1680] 10. The system of claim 1, further comprising means for coloring text yellow if the sentiment score of the text is neutral.

[1681] (Claim 3)

[1682] 10. The system of claim 1, further comprising means for calculating a sentiment score for the text using natural language processing techniques.

[1683] "Example 1"

[1684] (Claim 1)

[1685] means for receiving user-entered text;

[1686] means for performing sentiment analysis and calculating a sentiment score;

[1687] means for decorating the input text with different colors based on the sentiment score;

[1688] means for outputting the decorated text to a terminal;

[1689] means for performing the sentiment analysis using natural language processing techniques;

[1690] The system includes a means for generating the decorated text and transmitting it to a terminal as an HTTP response.

[1691] (Claim 2)

[1692] a means for coloring the text in green if the sentiment score is positive;

[1693] a means for coloring the text in red if the sentiment score is negative;

[1694] 10. The system of claim 1, further comprising means for coloring text yellow if the sentiment score of the text is neutral.

[1695] (Claim 3)

[1696] a means for triggering an input event when a user enters text into the input field;

[1697] 10. The system of claim 1, wherein the terminal includes means for sending the text to the server in an HTTP POST request.

[1698] "Application Example 1"

[1699] (Claim 1)

[1700] means for receiving user-entered text;

[1701] means for performing sentiment analysis and calculating a sentiment score;

[1702] means for decorating the input text with different colors based on the sentiment score;

[1703] means for outputting the decorated text to a terminal;

[1704] means for varying the color of the advertisement based on a sentiment analysis of the advertisement text;

[1705] The system includes means for displaying the modified advertising text on the terminal.

[1706] (Claim 2)

[1707] a means for coloring the text in green if the sentiment score is positive;

[1708] a means for coloring the text in red if the sentiment score is negative;

[1709] 10. The system of claim 1, further comprising means for coloring text yellow if the sentiment score of the text is neutral.

[1710] (Claim 3)

[1711] 10. The system of claim 1, further comprising means for calculating a sentiment score for the text using natural language processing techniques.

[1712] "Example 2: Combining Emotion Engines"

[1713] (Claim 1)

[1714] means for receiving text, voice, and facial expression data input by a user;

[1715] means for recognizing user emotions from input data using a sentiment analysis engine to obtain sentiment information;

[1716] A means for calculating a sentiment score of text using natural language processing techniques;

[1717] a means for integrating the emotion information and the emotion score to calculate a final emotion score;

[1718] means for decorating the input text with different colors based on the sentiment score;

[1719] A system including means for outputting decorated text to a terminal.

[1720] (Claim 2)

[1721] a means for coloring the text in green if the sentiment score is positive;

[1722] a means for coloring the text in red if the sentiment score is negative;

[1723] 10. The system of claim 1, further comprising means for coloring text yellow if the sentiment score of the text is neutral.

[1724] (Claim 3)

[1725] 10. The system of claim 1, further comprising means for recognizing emotions from voice and facial expressions using an emotion recognition engine.

[1726] "Application example 2 when combining emotion engines"

[1727] (Claim 1)

[1728] means for receiving user-entered text;

[1729] means for performing sentiment analysis and calculating a sentiment score;

[1730] means for decorating the input text with different colors based on the sentiment score;

[1731] means for outputting the decorated text to a terminal;

[1732] means for receiving voice or facial expression data input by a user;

[1733] A means for analyzing voice or facial expression data to acquire emotional information;

[1734] A means for reflecting emotion information obtained from voice or facial expression data in an emotion score of the text;

[1735] a means of displaying a warning when negative emotions are recognized;

[1736] The system includes a means to send notifications to security personnel as needed.

[1737] (Claim 2)

[1738] a means for coloring the text in green if the sentiment score is positive;

[1739] a means for coloring the text in red if the sentiment score is negative;

[1740] 10. The system of claim 1, further comprising means for coloring text yellow if the sentiment score of the text is neutral.

[1741] (Claim 3)

[1742] 10. The system of claim 1, further comprising means for calculating a sentiment score for the text using natural language processing techniques. [Explanation of symbols]

[1743] 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. means for receiving user-entered text; means for performing sentiment analysis and calculating a sentiment score; means for decorating the input text with different colors based on the sentiment score; A system including means for outputting decorated text to a terminal.

2. a means for coloring the text in green if the sentiment score is positive; a means for coloring the text in red if the sentiment score is negative; 10. The system of claim 1, further comprising means for coloring text in yellow if the sentiment score of the text is neutral.

3. The system of claim 1 , further comprising means for calculating a sentiment score for the text using natural language processing techniques.

Images