System

Abstract

A system is provided.SOLUTION: A system comprising: means for receiving input text including negative expressions; means for detecting negative expressions from the received input text; means for converting the detected negative expressions into positive expressions; and means for reconstructing and outputting text including the converted positive expressions.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] As digital communication advances, negative messages are increasingly having a negative impact on personal and business communication environments. This problem can cause discomfort and stress for many people. It can also disrupt smooth internal and external communication within companies and organizations. Given this background, there is a demand for technology that can convert negative messages into positive ones and provide a healthy, positive communication environment. [Means for solving the problem]

[0005] This invention provides a system that receives input text containing negative expressions, detects negative expressions from the received input text, converts the detected negative expressions into positive expressions, and reconstructs and outputs text containing the converted positive expressions. Specifically, the system includes a means for receiving input text, a means for detecting negative expressions using natural language processing technology, a means for converting the detected negative expressions into positive expressions using AI transformer technology, and a means for reconstructing and outputting the text using a reconstruction algorithm while maintaining the original context. This system enables users to send and receive more positive messages and build good relationships and communication.

[0006] "Negative language" is a statement or phrase that does not evoke a positive emotion or response, but rather evokes discomfort or negative feelings.

[0007] "Input text" refers to sentences or messages that a user types into a digital device and communicates.

[0008] "Means for receiving" refers to the functionality and protocols that allow a digital device to obtain input text from a user and send it to the system.

[0009] A "detection means" is a method or technique that analyzes words or phrases in input text and identifies specific patterns or keywords.

[0010] "Natural language processing technology" is a general term for technologies in the field of computer science that are used to understand, analyze, and generate human language.

[0011] A "means for transformation" is an algorithm or model for replacing detected negative expressions with positive expressions.

[0012] "AI Transformer technology" is an artificial intelligence model trained using large datasets to perform advanced transformations and generation for specific tasks.

[0013] A "reconstruction algorithm" is an algorithm that adapts converted words or phrases to their original context to reconstruct meaningful sentences.

[0014] "Output means" refers to the functionality and protocols for displaying the reconstructed positive text to the user. [Brief explanation of the drawings]

[0015] [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

[0016] 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.

[0017] First, the terms used in the following description will be explained.

[0018] 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).

[0019] 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.

[0020] 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.

[0021] 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.

[0022] 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."

[0023] [First embodiment]

[0024] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0025] 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.

[0026] 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).

[0027] 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.

[0028] 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.

[0029] 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.

[0030] 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.

[0031] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0032] 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.

[0033] 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.

[0034] 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.

[0035] 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."

[0036] This invention is a system for converting negative expressions into positive ones, and is implemented in a configuration including a server, a terminal, and a user. This system supports healthy communication by converting negative messages into positive ones, particularly in a digital communication environment.

[0037] Explanation of program processing

[0038] Receiving input text

[0039] A user types and sends a negative text message into the device, such as "Your proposal is completely useless."

[0040] The terminal captures this input text using an internal program, converts it into a data format (e.g., JSON), and sends it to the server.

[0041] Detecting negative expressions

[0042] The server passes the received text message to an NLP (natural language processing) engine, which analyzes each word and phrase in the message to detect negative expressions. Specifically, the phrase "not at all" is identified as a negative expression.

[0043] Positive transformation

[0044] The server sends the detected negative expressions to an AI positive transformer, which converts the negative expressions into positive ones, for example, converting "not good at all" into "there is room for improvement."

[0045] Reconstructing and outputting messages

[0046] The server then reconstructs the positively transformed text to fit the original context, changing "Your suggestion is no good" to "Your suggestion could be improved."

[0047] The reconstructed positive message is sent to the terminal, which displays the received message to the user.

[0048] Specific examples

[0049] For example, the following occurs:

[0050] 1. The user types "Your proposal is completely useless" into the terminal and presses the send button.

[0051] 2. The device sends this message to the server.

[0052] 3. The server passes the message to an NLP engine, which detects the phrase "not at all" as a negative expression.

[0053] 4. The server sends the detected negative expression to the AI ​​positive transformer, which converts it to "There is room for improvement."

[0054] 5. The server reconstructs the message "Your proposal has room for improvement" and sends it to the device.

[0055] 6. Display a positive message to the user that the device has been rebuilt.

[0056] This system allows users to communicate in a positive manner, improving the atmosphere of digital communication.

[0057] The processing flow will be explained below.

[0058] Step 1:

[0059] The user types a text message into the device interface, for example, "Your suggestion is completely wrong."

[0060] Step 2:

[0061] The device receives the entered text message and converts it into a data format (e.g., JSON) using an internal program.

[0062] Step 3:

[0063] The terminal transmits the converted data to the server via the network.

[0064] Step 4:

[0065] The server parses the received data and extracts the message part. For example, the extracted message is "Your proposal is completely useless."

[0066] Step 5:

[0067] The server passes the extracted message to the NLP engine, which tokenizes and analyzes each word or phrase in the message.

[0068] Step 6:

[0069] The NLP engine identifies negative expressions. For example, "not at all" is detected as a negative expression.

[0070] Step 7:

[0071] The server sends the detected negative expressions to the AI ​​positive transformer, which then transforms the negative expressions into positive ones.

[0072] Step 8:

[0073] The AI ​​positive transformer converts "not good at all" into "there is room for improvement."

[0074] Step 9:

[0075] The server embeds the converted positive expressions into the original message and reconstructs it. For example, "Your proposal is completely useless" is reconstructed as "Your proposal has room for improvement."

[0076] Step 10:

[0077] The server converts the reconstructed positive message back into a data format (e.g., JSON) and sends it to the device.

[0078] Step 11:

[0079] The device analyzes the received data and extracts the reconstructed positive message, for example, "Your proposal has room for improvement."

[0080] Step 12:

[0081] The device will display a positive message to the user that the device has been rebuilt, allowing the user to confirm the message and receive positive feedback.

[0082] In this way, negative messages are transformed into positive expressions, improving the quality of digital communication.

[0083] Example 1

[0084] 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."

[0085] In the conventional digital communication environment, messages containing negative expressions are frequently generated, which causes a decline in the quality of communication and leads to misunderstandings and conflicts. If such negative expressions can be converted into positive expressions, it is possible to promote healthy communication and improve the quality of digital communication.

[0086] 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.

[0087] In this invention, the server includes: means for a user to input input text including negative expressions and for a terminal to transmit the input text; terminal means for converting the input text into a data format and transmitting the data to the server; means for the server to pass the input text received to a natural language processing engine and detect negative expressions; means for the server to transmit the detected negative expressions to a generative AI model and convert them into positive expressions; means for reconstructing the converted positive expressions to fit the original context and transmitting the converted positive expressions to the terminal, and for the terminal to display the reconstructed text. This makes it possible to convert negative messages input by the user into positive expressions and promote healthy communication.

[0088] "User" refers to a person who utilizes the system to enter and send messages.

[0089] A "terminal" refers to the hardware device that a user uses to input and send messages to a server, typically a PC or smartphone.

[0090] "Input text" refers to the message content that a user inputs into a terminal and sends.

[0091] "Data format" refers to the format in which the input text is converted to be sent to the server, an example of which is JSON format.

[0092] "Server" refers to a computing system that parses and processes received input text.

[0093] A "natural language processing engine" refers to a software engine that analyzes text data and detects negative expressions.

[0094] "Negative language" refers to words or phrases that have negative, offensive or derogatory meanings.

[0095] A "generative AI model" refers to an artificial intelligence model that converts negative expressions into positive ones.

[0096] "Positive language" refers to words or phrases that have a positive, constructive, or uplifting meaning.

[0097] "Reconstruction" refers to the process of rearranging the transformed positive expressions to fit the original context.

[0098] "Means for displaying" refers to the functionality of the terminal to make the reconstructed text visible to the user.

[0099] This invention is a system that converts negative expressions into positive ones, including servers, terminals, and users. Particularly in digital communication environments, converting negative messages into positive ones supports healthy communication.

[0100] The system is structured as follows: First, the user inputs a message containing negative language into the device. For example, they input a message such as "Your proposal is completely useless." Next, the device captures this input text, converts it into a data format (e.g., JSON), and sends it to the server. The device can be a Windows PC, a Mac, or a smartphone (iOS, Android).

[0101] The server then passes the received text message to a natural language processing (NLP) engine, such as the Google NLP API or Microsoft Azure Cognitive Services. This engine analyzes each word and phrase in the message to detect negative expressions. For example, the phrase "not at all" is identified as a negative expression.

[0102] The server then sends the detected negative expressions to an AI positive transformer. This transformer is a generative AI model that transforms negative expressions into positive ones. The specific generative AI model used is the OpenAI GPT model. For example, "Not good at all" is transformed into "There is room for improvement."

[0103] The server then reconstructs the positively translated phrase to fit the original context. This changes "Your suggestion is completely wrong" to "Your suggestion could be improved." The reconstructed positive message is then sent back to the device. The device receives this message and displays it to the user.

[0104] Specific examples

[0105] For example, the following occurs:

[0106] 1. The user types "Your proposal is completely useless" into the terminal and presses the send button.

[0107] 2. The device sends this message to the server.

[0108] 3. The server passes the message to an NLP engine, which detects the phrase "not at all" as a negative expression.

[0109] 4. The server sends the detected negative expression to the AI ​​positive transformer, which converts it to "There is room for improvement."

[0110] 5. The server reconstructs the message "Your proposal has room for improvement" and sends it to the device.

[0111] 6. Display a positive message to the user that the device has been rebuilt.

[0112] Prompt Sentence Examples

[0113] Below are some examples of prompts to input to a generative AI model:

[0114] "Transform the following negative message into a positive: 'Your proposal is completely useless.'"

[0115] This will help transform negative messages into positive ones, which is expected to improve the quality of digital communication.

[0116] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0117] Step 1:

[0118] The user enters a message containing negative language and presses the "Send" button. The negative message (e.g., "Your suggestion is completely useless") is provided as input to the terminal. The terminal captures this input text and converts it into a data format such as JSON. Specifically, the terminal generates JSON data such as {"message": "Your suggestion is completely useless"} and prepares it for transmission.

[0119] Step 2:

[0120] The terminal sends the generated JSON data to the server via the network. The JSON data is provided to the server as input. Specifically, the terminal sends data to the server using an HTTPS request, and the server receives the data. As output, the server parses the received JSON data to extract the original message.

[0121] Step 3:

[0122] The server passes the received message to a natural language processing (NLP) engine, which analyzes it to detect negative expressions. The extracted text data is provided to the NLP engine as input. Specifically, the server receives {"message": "Your suggestion is completely no good"} and identifies the phrase "completely no good" as a negative expression. The output is the identified negative expression.

[0123] Step 4:

[0124] The server sends the detected negative expression to the AI ​​positive transformer, requesting that it be transformed into a positive expression. The negative expression (e.g., "not good at all") is provided to the generative AI model as input. Specifically, the server sends the expression "not good at all" to the generative AI model, which then transforms it into "There's room for improvement." A positive expression is generated as output.

[0125] Step 5:

[0126] The server reconstructs the positively transformed phrase to fit the original context and reconstructs the transformed message. The input is a positive expression (e.g., "There is room for improvement") and the original context. Specifically, the server reconstructs the original sentence "Your proposal is no good at all" into "There is room for improvement in your proposal." The output is a reconstructed positive message.

[0127] Step 6:

[0128] The server converts the reconstructed positive message into JSON format and sends it to the device. The reconstructed positive message is provided as input. Specifically, the server converts it into JSON format like {"message": "Your suggestion could be improved"} and sends it to the device. The output is the positive message received by the device.

[0129] Step 7:

[0130] The device displays the received positive message to the user. The received JSON data is provided to the device as input. Specifically, the device displays the parsed message to the user and provides positive feedback such as "Your suggestion has room for improvement." As output, the positive feedback is displayed to the user.

[0131] (Application example 1)

[0132] 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."

[0133] In today's digital communication environment, communication can be adversely affected by negative expressions. This can lead to the risk of damaging interpersonal relationships, and negative feedback can be an obstacle to improving service, particularly in the relationship between customers and store clerks in brick-and-mortar stores. The present invention aims to provide a system that converts such negative expressions into positive ones and supports healthy communication.

[0134] 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.

[0135] In this invention, the server includes means for receiving input text including negative expressions, means for detecting negative expressions from the received input text, means for converting the detected negative expressions into positive expressions, means for reconstructing and outputting the converted text including positive expressions, and means for processing the received feedback in JSON format and displaying the reconstructed positive feedback. This enables communication between customers and store clerks to be smoother and customer satisfaction to be improved by converting negative feedback into positive feedback.

[0136] "Negative expressions" include negative, critical, and passive content, and are elements that worsen the atmosphere of communication.

[0137] "Input text" refers to character string information that a user inputs into a terminal, including messages and feedback.

[0138] The "receiving means" refers to a method or device for acquiring text data entered by a user in a terminal or a server.

[0139] The "detection means" is a technique for identifying and extracting specific patterns or phrases from received text data.

[0140] "Positive expressions" include positive, constructive, and proactive content, and are elements that maintain a good atmosphere for communication.

[0141] "Means for conversion" are methods or techniques for replacing detected negative expressions with positive expressions.

[0142] The "reconstruction and output means" refers to a method or device for adapting the converted positive expression to the original context and displaying it to the user.

[0143] "Feedback" refers to opinions and evaluations provided by customers and users.

[0144] "JSON format" is an abbreviation for JavaScript Object Notation, and is a common format for structuring and representing data.

[0145] A "generative AI model" is a module that uses artificial intelligence technology to generate and convert text.

[0146] A "prompt" is an instruction given to a generative AI model, and serves as a guideline for appropriate conversion.

[0147] This invention is a system that converts negative expressions in digital communication into positive ones and supports healthy communication. Specifically, it configures a feedback system that can be used in physical stores using smartphones. The detailed configuration and operation method of the system are explained below.

[0148] First, the user inputs feedback using a smartphone. For example, the user inputs feedback such as "The product placement in the store is poor." This message is captured by the smartphone and sent to the server.

[0149] The server processes the received text in JSON format and parses the data. The server uses a natural language processing (NLP) engine to detect negative expressions in the text. The phrase "bad" is identified as a negative expression.

[0150] Next, the server converts the detected negative expressions into positive expressions using a generative AI model, such as "gpt-3.5-turbo." The negative "bad" is converted into the positive "it could be better with more effort."

[0151] The transformed positive expressions are then reconstructed to fit the original context, forming the final positive feedback, where a reconstruction algorithm is used to preserve the overall meaning of the text.

[0152] The reconstructed positive feedback is sent back from the server to the smartphone and displayed to the user. The user can check the positive feedback, such as "You could improve the product layout in the store by being more creative," on their smartphone. This system facilitates smooth communication between store staff and customers, improving customer satisfaction.

[0153] Prompt sentences are particularly important when using generative AI models. A specific example of a prompt sentence is "Before conversion: The product placement in the store is poor -> After conversion: ". Using such prompt sentences allows the generative AI model to function appropriately and effectively convert negative expressions.

[0154] The system uses basic hardware such as a smartphone and a server, and software such as Python, JSON, a natural language processing engine, and a generative AI model (e.g., gpt-3.5-turbo) to smoothly detect negative expressions, convert them to positive ones, and reconstruct and display the text.

[0155] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0156] Step 1:

[0157] The user inputs feedback using a smartphone. An example of input is the message "The product placement in the store is poor." The input data is saved as is in text format.

[0158] Step 2:

[0159] The device converts the input feedback into JSON format and sends it to the server. The text data is converted into a JSON object, and the server receives this data.

[0160] Step 3:

[0161] The server analyzes the received text data and detects negative expressions through a natural language processing (NLP) engine. Analysis of the input text identifies the phrase "bad" as a negative expression. The output is a list containing the detected negative phrases.

[0162] Step 4:

[0163] The server sends the detected negative expressions to a generative AI model, which converts them into positive expressions. The model used is "gpt-3.5-turbo." The prompt sentence used is "Before conversion: The product placement in the store is bad -> After conversion: ", and "bad" is converted to "It could be improved with more effort." The output is a positive phrase.

[0164] Step 5:

[0165] The server reconstructs the positive phrases obtained from the generative AI model to fit the original context. Using the reconstruction algorithm, a complete positive feedback is generated: "The product placement in the store could be improved." The output is the reconstructed text.

[0166] Step 6:

[0167] The server converts the reconstructed positive feedback into JSON format and sends it to the device. The device receives this data and displays it to the user. The user can then confirm the final positive feedback. A message saying, "You could improve things by being more creative with the product placement in the store," is displayed on the user's smartphone.

[0168] 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.

[0169] This invention combines a system that converts negative expressions into positive ones with an emotion engine that recognizes the user's emotions. The system is implemented mainly in a configuration including a server, a terminal, and a user. This system improves the quality of messages in digital communication environments and performs appropriate conversions that take into account the user's emotional state.

[0170] Explanation of program processing

[0171] Receiving input text and recognizing emotions

[0172] The user types a text message into the device interface and sends it, for example, "Your suggestion is completely wrong."

[0173] The terminal receives this input text, converts it into a data format (e.g., JSON) using an internal program, and sends it to the server.

[0174] Negative Expression Detection and Emotion Evaluation

[0175] The server passes the received text message to an NLP (natural language processing) engine for analysis. During this process, each word and phrase in the message is tokenized to detect negative expressions. For example, "not at all" is identified as a negative expression.

[0176] At the same time, the server also sends a message to the emotion engine, which recognizes and identifies the user's emotion, such as "anger," "frustration," or "sadness."

[0177] Positive transformation and adjustment

[0178] The server then sends the detected negative expressions to an AI positive transformer, which converts them into positive expressions. For example, "Not good at all" is converted into "There's room for improvement."

[0179] Based on the user's emotions identified by the emotion engine, the AI ​​Positive Transformer adjusts the transformation process, for example, if the user is very angry, it will choose expressions that will soften that emotion.

[0180] Reconstructing and outputting messages

[0181] The server reconstructs the original message by embedding the positively transformed text in it, e.g., "Your proposal is completely useless" is reconstructed as "Your proposal could be improved."

[0182] The reconstructed positive message is converted back into data format and sent to the terminal.

[0183] The device analyzes the received data and displays a reconstructed positive message to the user, who can confirm the message and receive positive feedback.

[0184] Specific examples

[0185] For example, the following occurs:

[0186] 1. The user types "Your proposal is completely useless" into the terminal and presses the send button.

[0187] 2. The device sends this message to the server.

[0188] 3. The server passes the message to an NLP engine, which detects the phrase "not at all" as a negative expression.

[0189] 4. At the same time, the server sends the message to the emotion engine, which identifies the emotion "anger."

[0190] 5. The server sends the detected negative expression to the AI ​​positive transformer, which converts it into "There is room for improvement." The AI ​​then selects a softer expression to ease the user's "anger."

[0191] 6. The server reconstructs the message "Your proposal has room for improvement" and sends it to the device.

[0192] 7. Display a positive message to the user that the device has been rebuilt.

[0193] This system enables positive communication that takes into account the user's emotions, improving the quality of digital communication.

[0194] The processing flow will be explained below.

[0195] Step 1:

[0196] The user types a text message into the device interface, for example, "Your suggestion is completely wrong."

[0197] Step 2:

[0198] The device receives the entered text message and converts it into a data format (e.g., JSON) using an internal program.

[0199] Step 3:

[0200] The terminal transmits the converted data to the server via the network.

[0201] Step 4:

[0202] The server parses the received data and extracts the message part. The extracted message is "Your proposal is completely useless."

[0203] Step 5:

[0204] The server passes the extracted message to the NLP engine, which tokenizes and analyzes each word or phrase in the message.

[0205] Step 6:

[0206] The NLP engine identifies negative expressions. For example, the phrase "not good at all" is detected as a negative expression.

[0207] Step 7:

[0208] The server passes the entire message to the emotion engine, which analyzes and identifies the user's emotion, for example, "anger."

[0209] Step 8:

[0210] The server sends the detected negative expressions and identified emotion information to the AI ​​positive transformer, which converts the negative expressions into positive ones.

[0211] Step 9:

[0212] The AI ​​positive transformer converts "Not good at all" into "There's room for improvement." It chooses a softer expression to ease the user's "anger."

[0213] Step 10:

[0214] The server embeds the converted positive expressions into the original message and reconstructs it. For example, "Your proposal is completely useless" is reconstructed as "Your proposal has room for improvement."

[0215] Step 11:

[0216] The server converts the reconstructed positive message back into a data format (e.g., JSON) and sends it to the device.

[0217] Step 12:

[0218] The device analyzes the received data and extracts the reconstructed positive message: "Your proposal has room for improvement."

[0219] Step 13:

[0220] The device will display a positive message to the user that the device has been rebuilt, allowing the user to confirm the message and receive positive feedback.

[0221] In this way, negative messages are converted into positive expressions, and communication that takes the user's feelings into consideration is realized.

[0222] Example 2

[0223] 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."

[0224] In modern digital communication, negative expressions can lead to misunderstandings and breakdowns in communication. Furthermore, unilateral changes to messages that ignore the user's feelings can further lead to misunderstandings. This invention aims to improve the quality of digital communication by converting negative expressions into positive ones and taking the user's feelings into consideration.

[0225] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes means for receiving input text including negative expressions, means for detecting negative expressions from the received input text, means for converting the detected negative expressions into positive expressions, means for recognizing and identifying the user's emotions, means for adjusting the positive expressions based on the recognized emotions, and means for reconstructing and outputting the converted text including the positive expressions. This enables appropriate communication that converts negative expressions into positive ones and takes the user's emotions into consideration.

[0226] "Input text" refers to sentences or messages that a user enters and sends using a terminal.

[0227] "Negative expressions" are words or phrases that have a negative feeling or intent, such as "not good at all."

[0228] "Natural language processing technology" is a general term for technologies that enable computers to understand, interpret, and generate human language. It is also known as NLP.

[0229] An "emotion engine" is a technology or software that analyzes and identifies emotions from user input text.

[0230] An "AI positive transformer" is an artificial intelligence model or algorithm that transforms negative expressions into positive ones.

[0231] A "reconstruction algorithm" is a method or technology for reconstructing converted text into appropriate sentences while maintaining the original context.

[0232] A "terminal" is a device that a user uses to send input text, such as a smartphone or computer.

[0233] A "server" is a computer system that receives and processes data sent from a terminal.

[0234] "User" means a person who uses the System to send and receive text messages.

[0235] This invention is a system that converts negative expressions into positive ones and recognizes and responds to user emotions. This system mainly includes a server, a terminal, and a user. Here, we will explain in detail how each element works together and processes data.

[0236] Receiving and sending input text

[0237] A user inputs and sends a text message through the device. For example, they input "Your suggestion is completely useless." The device receives this text, converts it into JSON format, and sends it to the server. The hardware used for this is a common communication device such as a smartphone or computer.

[0238] Natural Language Processing and Emotion Recognition

[0239] The server passes the received text message to an NLP (natural language processing) engine for analysis. Specifically, it uses an NLP engine such as SpaCy or BERT to tokenize each word or phrase in the message and identify negative expressions. For example, "not at all" is detected as a negative expression.

[0240] At the same time, the server sends the text to an emotion engine (e.g., IBM Watson, Microsoft Azure Text Analytics) to recognize the user's emotions, such as "anger," "frustration," or "sadness."

[0241] Positive transformation and adjustment

[0242] The server sends the detected negative expressions to the AI ​​positive transformer for transformation. For example, "Not good at all" is transformed into "There is room for improvement." The AI ​​positive transformer selects expressions that will soften the user's emotions. In this case, if the user is very angry, a softer expression will be used.

[0243] Reconstructing and outputting messages

[0244] The server reconstructs the positively converted text while preserving the original context and sends it to the device. For example, "Your suggestion is completely wrong" is reconstructed to "Your suggestion has room for improvement." The device receives this message and displays it to the user. The user can confirm this message and receive positive feedback.

[0245] Examples and prompts

[0246] For example, consider the following scenario:

[0247] 1. The user types "Your proposal is completely useless" into the terminal and presses the send button.

[0248] 2. The device converts this message into JSON format and sends it to the server.

[0249] 3. The server analyzes the message received using an NLP engine and identifies the negative expression "not good at all."

[0250] 4. At the same time, the server sends the text to the emotion engine, which identifies the emotion "anger."

[0251] 5. The server sends "Not good at all" to the AI ​​positive transformer, which converts it to "There's room for improvement."

[0252] 6. The server sends the reconstructed message to the device.

[0253] 7. The terminal displays a message to the user that the terminal has been rebuilt.

[0254] Example prompt: "Please translate the user's message, 'Your suggestion is completely useless,' into a more positive one. Also, if the user is very angry, please choose a more neutral response."

[0255] This system converts negative expressions in digital communication into positive ones, enabling communication that takes the user's emotions into consideration.

[0256] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0257] Step 1:

[0258] The user enters a text message into the device and presses the send button. For example, "Your suggestion is completely useless." The entered text is sent to the cloud. The device converts this text into JSON format and sends it to the server. The text entered by the user is stored in the database.

[0259] Step 2:

[0260] The server passes the received text to an NLP engine (e.g., SpaCy or BERT). The NLP engine tokenizes and analyzes the text. Specifically, each word or phrase is separated into a token, and "not at all" is identified as a negative expression. This process analyzes the data and detects negative keywords in the text.

[0261] Step 3:

[0262] At the same time, the server passes the text to an emotion engine (e.g., IBM Watson, Microsoft Azure Text Analytics) to evaluate the user's emotions. The emotion engine performs sentiment analysis on the text and identifies emotions such as "anger," "frustration," and "sadness." For example, "anger" is identified from the phrase "not at all." The analysis results are generated and passed to the next processing step.

[0263] Step 4:

[0264] The server sends the detected negative expressions to the AI ​​Positive Transformer, which converts the negative expressions into positive ones. Specifically, "Not good at all" is converted into "There is room for improvement." The AI ​​model performs this conversion and outputs the results. This process generates positive expressions.

[0265] Step 5:

[0266] The server adjusts the positive transformation based on the user's emotion identified by the emotion engine. For example, if the user is very angry, it selects phrases that will alleviate that emotion. This adjustment selects appropriate expressions to alleviate the emotion. The adjusted positive expressions are passed to the next processing step.

[0267] Step 6:

[0268] The server then reconstructs the positively transformed text while preserving the original context. For example, "Your suggestion is completely wrong" is reconstructed as "Your suggestion could be improved." The reconstruction algorithm is used to adjust the text to maintain proper grammar and context.

[0269] Step 7:

[0270] The server converts the reconstructed positive text into JSON format and sends it to the device, which then forwards the final positive message to the device.

[0271] Step 8:

[0272] The device analyzes the received positive text and displays it to the user. Specifically, the reconstructed message "Your suggestion has room for improvement" is displayed on the user's display. The user can confirm this message and receive positive feedback.

[0273] This detailed processing allows the system to improve the quality of digital communication and generate positive messages that take the user's emotions into account.

[0274] (Application example 2)

[0275] 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."

[0276] Complaints and negative opinions from customers often occur in physical stores, and responding to them appropriately is a difficult task. Responding to these negative expressions in a positive way is important for improving customer satisfaction, but conventional systems do not appropriately adjust to emotions. Therefore, there is a risk that customer service robots will receive negative complaints and respond inappropriately, thereby amplifying customer dissatisfaction.

[0277] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0278] In this invention, the server includes means for receiving input text including negative expressions, means for detecting negative expressions from the received input text, means for converting the detected negative expressions into positive expressions, means for recognizing a user's emotional state, means for adjusting the positive conversion according to the recognized emotional state of the user, and means for reconstructing and outputting the converted text including positive expressions. This enables a customer service robot in a physical store to convert negative complaints into positive ones and provide an appropriate response that takes into consideration the emotional state of the customer.

[0279] "Negative expressions" are linguistic expressions entered by users that are negative, critical, or dissatisfied.

[0280] "Positive expressions" are linguistic expressions that convey positive, encouraging, and affirmative content to the user.

[0281] "Input text" refers to a sentence or message that a user inputs into the system.

[0282] "Detection means" refers to technical means for identifying specific elements or specific emotional expressions from input text.

[0283] "Means of transformation" are technical means for replacing negative expressions with positive expressions.

[0284] "Reconstructive means" are technical means of reorganizing the transformed positive expressions to fit their original context.

[0285] "User's emotional state" refers to the user's psychological and emotional state as indicated through the input text.

[0286] A "means for recognizing an emotional state" is a technical means for identifying and assessing the emotion of a user's input text.

[0287] The "means for adjusting positive conversion" is a technical means for appropriately correcting the conversion process from negative expressions to positive expressions in accordance with the user's recognized emotional state.

[0288] The present invention relates to a customer service robot system for use in brick-and-mortar stores, which improves customer satisfaction by converting negative complaint messages into positive ones and providing appropriate responses depending on the customer's emotional state.

[0289] *Program generation

[0290] The system includes the following programs:

[0291] 1. A means for receiving input text containing negative expressions

[0292] 2. A method for detecting negative expressions from received input text

[0293] 3. A means of converting detected negative expressions into positive expressions

[0294] 4. Means of Recognizing the User's Emotional State

[0295] 5. A means of adjusting positive transformations according to the perceived emotional state of the user

[0296] 6. A method for reconstructing and outputting text containing the converted positive expressions

[0297] ※Explanation of processing

[0298] This system is comprised of a customer service robot in a physical store, a terminal where users input information, a server, and the robot's own internal program.

[0299] First, the user inputs a complaint or question to the robot in text format. For example, a negative message such as "Your service is completely useless" is input. The robot's terminal receives this message, converts it into a data format (such as JSON), and sends it to the server.

[0300] The server then analyzes this received text through NLP (Natural Language Processing) tools, where each word and phrase is tokenized to detect negative expressions, while an emotion engine evaluates the user's emotional state and identifies emotions such as "anger," "frustration," or "sadness."

[0301] The server then sends the detected negative expressions to an AI positive transformer, which converts them into positive expressions. This conversion process is adjusted according to the user's emotional state. For example, "Not good at all" is converted into "There is room for improvement."

[0302] Finally, the server embeds the converted text into the original message and reconstructs it. This reconstructed positive message is converted back into data format and sent to the robot's terminal. The terminal displays this message to the customer, allowing them to receive positive feedback.

[0303] *Hardware and software used

[0304] The system includes a customer-facing robot in a physical store. The robot includes a terminal to receive customer input and process it appropriately. The specific software used is Python and the transformers library (models: sentiment-analysis, text2text-generation).

[0305] *Examples of specific examples and prompts

[0306] For example, if a customer types, "Your service is terrible," this message will be detected as a negative expression and converted to, "There is room for improvement," and then presented to the customer.

[0307] Example of an input prompt for a generative AI model:

[0308] Original: Your service is absolutely terrible

[0309] Negative expression: Not good at all

[0310] Positive transformation: There is room for improvement.

[0311] In this way, this system is effective in enabling customer service robots to provide appropriate responses in physical stores and improve customer satisfaction.

[0312] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0313] Step 1:

[0314] The user inputs a text message and sends it to the terminal. Specifically, the user inputs a negative message such as "Your service is completely useless" into the terminal interface and presses the send button. The input data is in text format, and this message is passed to the next step.

[0315] Step 2:

[0316] The terminal converts the text message received from the user into a data format (such as JSON) and sends it to the server. For example, the message "Your service is completely useless" is converted into JSON format and sent to the server. Here, the input data is a text message, and the output is JSON format data.

[0317] Step 3:

[0318] The server passes the received text message to an NLP engine for analysis. Specifically, it uses a natural language processing tool (the transformers library) to tokenize the message and detect negative expressions. In this step, "Zentsu dame" is identified as a negative expression. The input data is the text message in JSON format, and the output data is a list of tokenized text and negative expressions.

[0319] Step 4:

[0320] At the same time, the server sends the received message to the emotion engine to evaluate the user's emotional state. Specifically, it uses an emotion recognition model to analyze the emotion of the message and identify emotions such as "anger" or "frustration." The input data is tokenized text, and the output data is a list of emotional states.

[0321] Step 5:

[0322] The server sends the detected negative expression and the user's emotional state to the AI ​​positive transformer, which converts it into a positive expression. The conversion process is adjusted based on the user's emotional state. Specifically, the negative expression "Not good at all" is converted to "There is room for improvement." The input data are negative expressions and the user's emotional state, and the output data are positive expressions.

[0323] Step 6:

[0324] The server reconstructs the text containing the transformed positive phrases. Specifically, it embeds new positive phrases while preserving the context of the original message. In this step, "Your service is terrible" is reconstructed into "Your service could use some improvement." The input data is the positive phrases, and the output data is the reconstructed text message.

[0325] Step 7:

[0326] The reconstructed positive message is converted back into a data format (such as JSON) and sent to the terminal. The terminal analyzes the received data and displays the reconstructed positive message to the user. For example, the message "Your service has room for improvement" is displayed to the user. The input data is the reconstructed text message, and the output data is the positive message displayed to the user.

[0327] The above steps make it possible to realize a system that can convert negative complaint messages into positive ones and provide an appropriate response depending on the user's emotional state.

[0328] 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.

[0329] 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.

[0330] 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.

[0331] [Second embodiment]

[0332] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0333] 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.

[0334] 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).

[0335] 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.

[0336] 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.

[0337] 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).

[0338] 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.

[0339] 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.

[0340] 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.

[0341] 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.

[0342] 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.

[0343] 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."

[0344] This invention is a system for converting negative expressions into positive ones, and is implemented in a configuration including a server, a terminal, and a user. This system supports healthy communication by converting negative messages into positive ones, particularly in a digital communication environment.

[0345] Explanation of program processing

[0346] Receiving input text

[0347] A user types and sends a negative text message into the device, such as "Your proposal is completely useless."

[0348] The terminal captures this input text using an internal program, converts it into a data format (e.g., JSON), and sends it to the server.

[0349] Detecting negative expressions

[0350] The server passes the received text message to an NLP (natural language processing) engine, which analyzes each word and phrase in the message to detect negative expressions. Specifically, the phrase "not at all" is identified as a negative expression.

[0351] Positive transformation

[0352] The server sends the detected negative expressions to an AI positive transformer, which converts the negative expressions into positive ones, for example, converting "not good at all" into "there is room for improvement."

[0353] Reconstructing and outputting messages

[0354] The server then reconstructs the positively transformed text to fit the original context, changing "Your suggestion is no good" to "Your suggestion could be improved."

[0355] The reconstructed positive message is sent to the terminal, which displays the received message to the user.

[0356] Specific examples

[0357] For example, the following occurs:

[0358] 1. The user types "Your proposal is completely useless" into the terminal and presses the send button.

[0359] 2. The device sends this message to the server.

[0360] 3. The server passes the message to an NLP engine, which detects the phrase "not at all" as a negative expression.

[0361] 4. The server sends the detected negative expression to the AI ​​positive transformer, which converts it to "There is room for improvement."

[0362] 5. The server reconstructs the message "Your proposal has room for improvement" and sends it to the device.

[0363] 6. Display a positive message to the user that the device has been rebuilt.

[0364] This system allows users to communicate in a positive manner, improving the atmosphere of digital communication.

[0365] The processing flow will be explained below.

[0366] Step 1:

[0367] The user types a text message into the device interface, for example, "Your suggestion is completely wrong."

[0368] Step 2:

[0369] The device receives the entered text message and converts it into a data format (e.g., JSON) using an internal program.

[0370] Step 3:

[0371] The terminal transmits the converted data to the server via the network.

[0372] Step 4:

[0373] The server parses the received data and extracts the message part. For example, the extracted message is "Your proposal is completely useless."

[0374] Step 5:

[0375] The server passes the extracted message to the NLP engine, which tokenizes and analyzes each word or phrase in the message.

[0376] Step 6:

[0377] The NLP engine identifies negative expressions. For example, "not at all" is detected as a negative expression.

[0378] Step 7:

[0379] The server sends the detected negative expressions to the AI ​​positive transformer, which then transforms the negative expressions into positive ones.

[0380] Step 8:

[0381] The AI ​​positive transformer converts "not good at all" into "there is room for improvement."

[0382] Step 9:

[0383] The server embeds the converted positive expressions into the original message and reconstructs it. For example, "Your proposal is completely useless" is reconstructed as "Your proposal has room for improvement."

[0384] Step 10:

[0385] The server converts the reconstructed positive message back into a data format (e.g., JSON) and sends it to the device.

[0386] Step 11:

[0387] The device analyzes the received data and extracts the reconstructed positive message, for example, "Your proposal has room for improvement."

[0388] Step 12:

[0389] The device will display a positive message to the user that the device has been rebuilt, allowing the user to confirm the message and receive positive feedback.

[0390] In this way, negative messages are transformed into positive expressions, improving the quality of digital communication.

[0391] Example 1

[0392] 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."

[0393] In the conventional digital communication environment, messages containing negative expressions are frequently generated, which causes a decline in the quality of communication and leads to misunderstandings and conflicts. If such negative expressions can be converted into positive expressions, it is possible to promote healthy communication and improve the quality of digital communication.

[0394] 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.

[0395] In this invention, the server includes: means for a user to input input text including negative expressions and for a terminal to transmit the input text; terminal means for converting the input text into a data format and transmitting the data to the server; means for the server to pass the input text received to a natural language processing engine and detect negative expressions; means for the server to transmit the detected negative expressions to a generative AI model and convert them into positive expressions; means for reconstructing the converted positive expressions to fit the original context and transmitting the converted positive expressions to the terminal, and for the terminal to display the reconstructed text. This makes it possible to convert negative messages input by the user into positive expressions and promote healthy communication.

[0396] "User" refers to a person who utilizes the system to enter and send messages.

[0397] A "terminal" refers to the hardware device that a user uses to input and send messages to a server, typically a PC or smartphone.

[0398] "Input text" refers to the message content that a user inputs into a terminal and sends.

[0399] "Data format" refers to the format in which the input text is converted to be sent to the server, an example of which is JSON format.

[0400] "Server" refers to a computing system that parses and processes received input text.

[0401] A "natural language processing engine" refers to a software engine that analyzes text data and detects negative expressions.

[0402] "Negative language" refers to words or phrases that have negative, offensive or derogatory meanings.

[0403] A "generative AI model" refers to an artificial intelligence model that converts negative expressions into positive ones.

[0404] "Positive language" refers to words or phrases that have a positive, constructive, or uplifting meaning.

[0405] "Reconstruction" refers to the process of rearranging the transformed positive expressions to fit the original context.

[0406] "Means for displaying" refers to the functionality of the terminal to make the reconstructed text visible to the user.

[0407] This invention is a system that converts negative expressions into positive ones, including servers, terminals, and users. Particularly in digital communication environments, converting negative messages into positive ones supports healthy communication.

[0408] The system is structured as follows: First, the user inputs a message containing negative language into the device. For example, they input a message such as "Your proposal is completely useless." Next, the device captures this input text, converts it into a data format (e.g., JSON), and sends it to the server. The device can be a Windows PC, a Mac, or a smartphone (iOS, Android).

[0409] The server then passes the received text message to a natural language processing (NLP) engine, such as the Google NLP API or Microsoft Azure Cognitive Services. This engine analyzes each word and phrase in the message to detect negative expressions. For example, the phrase "not at all" is identified as a negative expression.

[0410] The server then sends the detected negative expressions to an AI positive transformer. This transformer is a generative AI model that transforms negative expressions into positive ones. The specific generative AI model used is the OpenAI GPT model. For example, "Not good at all" is transformed into "There is room for improvement."

[0411] The server then reconstructs the positively translated phrase to fit the original context. This changes "Your suggestion is completely wrong" to "Your suggestion could be improved." The reconstructed positive message is then sent back to the device. The device receives this message and displays it to the user.

[0412] Specific examples

[0413] For example, the following occurs:

[0414] 1. The user types "Your proposal is completely useless" into the terminal and presses the send button.

[0415] 2. The device sends this message to the server.

[0416] 3. The server passes the message to an NLP engine, which detects the phrase "not at all" as a negative expression.

[0417] 4. The server sends the detected negative expression to the AI ​​positive transformer, which converts it to "There is room for improvement."

[0418] 5. The server reconstructs the message "Your proposal has room for improvement" and sends it to the device.

[0419] 6. Display a positive message to the user that the device has been rebuilt.

[0420] Prompt Sentence Examples

[0421] Below are some examples of prompts to input to a generative AI model:

[0422] "Transform the following negative message into a positive: 'Your proposal is completely useless.'"

[0423] This will help transform negative messages into positive ones, which is expected to improve the quality of digital communication.

[0424] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0425] Step 1:

[0426] The user enters a message containing negative language and presses the "Send" button. The negative message (e.g., "Your suggestion is completely useless") is provided as input to the terminal. The terminal captures this input text and converts it into a data format such as JSON. Specifically, the terminal generates JSON data such as {"message": "Your suggestion is completely useless"} and prepares it for transmission.

[0427] Step 2:

[0428] The terminal sends the generated JSON data to the server via the network. The JSON data is provided to the server as input. Specifically, the terminal sends data to the server using an HTTPS request, and the server receives the data. As output, the server parses the received JSON data to extract the original message.

[0429] Step 3:

[0430] The server passes the received message to a natural language processing (NLP) engine, which analyzes it to detect negative expressions. The extracted text data is provided to the NLP engine as input. Specifically, the server receives {"message": "Your suggestion is completely no good"} and identifies the phrase "completely no good" as a negative expression. The output is the identified negative expression.

[0431] Step 4:

[0432] The server sends the detected negative expression to the AI ​​positive transformer, requesting that it be transformed into a positive expression. The negative expression (e.g., "not good at all") is provided to the generative AI model as input. Specifically, the server sends the expression "not good at all" to the generative AI model, which then transforms it into "There's room for improvement." A positive expression is generated as output.

[0433] Step 5:

[0434] The server reconstructs the positively transformed phrase to fit the original context and reconstructs the transformed message. The input is a positive expression (e.g., "There is room for improvement") and the original context. Specifically, the server reconstructs the original sentence "Your proposal is no good at all" into "There is room for improvement in your proposal." The output is a reconstructed positive message.

[0435] Step 6:

[0436] The server converts the reconstructed positive message into JSON format and sends it to the device. The reconstructed positive message is provided as input. Specifically, the server converts it into JSON format like {"message": "Your suggestion could be improved"} and sends it to the device. The output is the positive message received by the device.

[0437] Step 7:

[0438] The device displays the received positive message to the user. The received JSON data is provided to the device as input. Specifically, the device displays the parsed message to the user and provides positive feedback such as "Your suggestion has room for improvement." As output, the positive feedback is displayed to the user.

[0439] (Application example 1)

[0440] 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."

[0441] In today's digital communication environment, communication can be adversely affected by negative expressions. This can lead to the risk of damaging interpersonal relationships, and negative feedback can be an obstacle to improving service, particularly in the relationship between customers and store clerks in brick-and-mortar stores. The present invention aims to provide a system that converts such negative expressions into positive ones and supports healthy communication.

[0442] 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.

[0443] In this invention, the server includes means for receiving input text including negative expressions, means for detecting negative expressions from the received input text, means for converting the detected negative expressions into positive expressions, means for reconstructing and outputting the converted text including positive expressions, and means for processing the received feedback in JSON format and displaying the reconstructed positive feedback. This enables communication between customers and store clerks to be smoother and customer satisfaction to be improved by converting negative feedback into positive feedback.

[0444] "Negative expressions" include negative, critical, and passive content, and are elements that worsen the atmosphere of communication.

[0445] "Input text" refers to character string information that a user inputs into a terminal, including messages and feedback.

[0446] The "receiving means" refers to a method or device for acquiring text data entered by a user in a terminal or a server.

[0447] The "detection means" is a technique for identifying and extracting specific patterns or phrases from received text data.

[0448] "Positive expressions" include positive, constructive, and proactive content, and are elements that maintain a good atmosphere for communication.

[0449] "Means for conversion" are methods or techniques for replacing detected negative expressions with positive expressions.

[0450] The "reconstruction and output means" refers to a method or device for adapting the converted positive expression to the original context and displaying it to the user.

[0451] "Feedback" refers to opinions and evaluations provided by customers and users.

[0452] "JSON format" is an abbreviation for JavaScript Object Notation, and is a common format for structuring and representing data.

[0453] A "generative AI model" is a module that uses artificial intelligence technology to generate and convert text.

[0454] A "prompt" is an instruction given to a generative AI model, and serves as a guideline for appropriate conversion.

[0455] This invention is a system that converts negative expressions in digital communication into positive ones and supports healthy communication. Specifically, it configures a feedback system that can be used in physical stores using smartphones. The detailed configuration and operation method of the system are explained below.

[0456] First, the user inputs feedback using a smartphone. For example, the user inputs feedback such as "The product placement in the store is poor." This message is captured by the smartphone and sent to the server.

[0457] The server processes the received text in JSON format and parses the data. The server uses a natural language processing (NLP) engine to detect negative expressions in the text. The phrase "bad" is identified as a negative expression.

[0458] Next, the server converts the detected negative expressions into positive expressions using a generative AI model, such as "gpt-3.5-turbo." The negative "bad" is converted into the positive "it could be better with more effort."

[0459] The transformed positive expressions are then reconstructed to fit the original context, forming the final positive feedback, where a reconstruction algorithm is used to preserve the overall meaning of the text.

[0460] The reconstructed positive feedback is sent back from the server to the smartphone and displayed to the user. The user can check the positive feedback, such as "You could improve the product layout in the store by being more creative," on their smartphone. This system facilitates smooth communication between store staff and customers, improving customer satisfaction.

[0461] Prompt sentences are particularly important when using generative AI models. A specific example of a prompt sentence is "Before conversion: The product placement in the store is poor -> After conversion: ". Using such prompt sentences allows the generative AI model to function appropriately and effectively convert negative expressions.

[0462] The system uses basic hardware such as a smartphone and a server, and software such as Python, JSON, a natural language processing engine, and a generative AI model (e.g., gpt-3.5-turbo) to smoothly detect negative expressions, convert them to positive ones, and reconstruct and display the text.

[0463] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0464] Step 1:

[0465] The user inputs feedback using a smartphone. An example of input is the message "The product placement in the store is poor." The input data is saved as is in text format.

[0466] Step 2:

[0467] The device converts the input feedback into JSON format and sends it to the server. The text data is converted into a JSON object, and the server receives this data.

[0468] Step 3:

[0469] The server analyzes the received text data and detects negative expressions through a natural language processing (NLP) engine. Analysis of the input text identifies the phrase "bad" as a negative expression. The output is a list containing the detected negative phrases.

[0470] Step 4:

[0471] The server sends the detected negative expressions to a generative AI model, which converts them into positive expressions. The model used is "gpt-3.5-turbo." The prompt sentence used is "Before conversion: The product placement in the store is bad -> After conversion: ", and "bad" is converted to "It could be improved with more effort." The output is a positive phrase.

[0472] Step 5:

[0473] The server reconstructs the positive phrases obtained from the generative AI model to fit the original context. Using the reconstruction algorithm, a complete positive feedback is generated: "The product placement in the store could be improved." The output is the reconstructed text.

[0474] Step 6:

[0475] The server converts the reconstructed positive feedback into JSON format and sends it to the device. The device receives this data and displays it to the user. The user can then confirm the final positive feedback. A message saying, "You could improve things by being more creative with the product placement in the store," is displayed on the user's smartphone.

[0476] 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.

[0477] This invention combines a system that converts negative expressions into positive ones with an emotion engine that recognizes the user's emotions. The system is implemented mainly in a configuration including a server, a terminal, and a user. This system improves the quality of messages in digital communication environments and performs appropriate conversions that take into account the user's emotional state.

[0478] Explanation of program processing

[0479] Receiving input text and recognizing emotions

[0480] The user types a text message into the device interface and sends it, for example, "Your suggestion is completely wrong."

[0481] The terminal receives this input text, converts it into a data format (e.g., JSON) using an internal program, and sends it to the server.

[0482] Negative Expression Detection and Emotion Evaluation

[0483] The server passes the received text message to an NLP (natural language processing) engine for analysis. During this process, each word and phrase in the message is tokenized to detect negative expressions. For example, "not at all" is identified as a negative expression.

[0484] At the same time, the server also sends a message to the emotion engine, which recognizes and identifies the user's emotion, such as "anger," "frustration," or "sadness."

[0485] Positive transformation and adjustment

[0486] The server then sends the detected negative expressions to an AI positive transformer, which converts them into positive expressions. For example, "Not good at all" is converted into "There's room for improvement."

[0487] Based on the user's emotions identified by the emotion engine, the AI ​​Positive Transformer adjusts the transformation process, for example, if the user is very angry, it will choose expressions that will soften that emotion.

[0488] Reconstructing and outputting messages

[0489] The server reconstructs the original message by embedding the positively transformed text in it, e.g., "Your proposal is completely useless" is reconstructed as "Your proposal could be improved."

[0490] The reconstructed positive message is converted back into data format and sent to the terminal.

[0491] The device analyzes the received data and displays a reconstructed positive message to the user, who can confirm the message and receive positive feedback.

[0492] Specific examples

[0493] For example, the following occurs:

[0494] 1. The user types "Your proposal is completely useless" into the terminal and presses the send button.

[0495] 2. The device sends this message to the server.

[0496] 3. The server passes the message to an NLP engine, which detects the phrase "not at all" as a negative expression.

[0497] 4. At the same time, the server sends the message to the emotion engine, which identifies the emotion "anger."

[0498] 5. The server sends the detected negative expression to the AI ​​positive transformer, which converts it into "There is room for improvement." The AI ​​then selects a softer expression to ease the user's "anger."

[0499] 6. The server reconstructs the message "Your proposal has room for improvement" and sends it to the device.

[0500] 7. Display a positive message to the user that the device has been rebuilt.

[0501] This system enables positive communication that takes into account the user's emotions, improving the quality of digital communication.

[0502] The processing flow will be explained below.

[0503] Step 1:

[0504] The user types a text message into the device interface, for example, "Your suggestion is completely wrong."

[0505] Step 2:

[0506] The device receives the entered text message and converts it into a data format (e.g., JSON) using an internal program.

[0507] Step 3:

[0508] The terminal transmits the converted data to the server via the network.

[0509] Step 4:

[0510] The server parses the received data and extracts the message part. The extracted message is "Your proposal is completely useless."

[0511] Step 5:

[0512] The server passes the extracted message to the NLP engine, which tokenizes and analyzes each word or phrase in the message.

[0513] Step 6:

[0514] The NLP engine identifies negative expressions. For example, the phrase "not good at all" is detected as a negative expression.

[0515] Step 7:

[0516] The server passes the entire message to the emotion engine, which analyzes and identifies the user's emotion, for example, "anger."

[0517] Step 8:

[0518] The server sends the detected negative expressions and identified emotion information to the AI ​​positive transformer, which converts the negative expressions into positive ones.

[0519] Step 9:

[0520] The AI ​​positive transformer converts "Not good at all" into "There's room for improvement." It chooses a softer expression to ease the user's "anger."

[0521] Step 10:

[0522] The server embeds the converted positive expressions into the original message and reconstructs it. For example, "Your proposal is completely useless" is reconstructed as "Your proposal has room for improvement."

[0523] Step 11:

[0524] The server converts the reconstructed positive message back into a data format (e.g., JSON) and sends it to the device.

[0525] Step 12:

[0526] The device analyzes the received data and extracts the reconstructed positive message: "Your proposal has room for improvement."

[0527] Step 13:

[0528] The device will display a positive message to the user that the device has been rebuilt, allowing the user to confirm the message and receive positive feedback.

[0529] In this way, negative messages are converted into positive expressions, and communication that takes the user's feelings into consideration is realized.

[0530] Example 2

[0531] 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."

[0532] In modern digital communication, negative expressions can lead to misunderstandings and breakdowns in communication. Furthermore, unilateral changes to messages that ignore the user's feelings can further lead to misunderstandings. This invention aims to improve the quality of digital communication by converting negative expressions into positive ones and taking the user's feelings into consideration.

[0533] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes means for receiving input text including negative expressions, means for detecting negative expressions from the received input text, means for converting the detected negative expressions into positive expressions, means for recognizing and identifying the user's emotions, means for adjusting the positive expressions based on the recognized emotions, and means for reconstructing and outputting the converted text including the positive expressions. This enables appropriate communication that converts negative expressions into positive ones and takes the user's emotions into consideration.

[0534] "Input text" refers to sentences or messages that a user enters and sends using a terminal.

[0535] "Negative expressions" are words or phrases that have a negative feeling or intent, such as "not good at all."

[0536] "Natural language processing technology" is a general term for technologies that enable computers to understand, interpret, and generate human language. It is also known as NLP.

[0537] An "emotion engine" is a technology or software that analyzes and identifies emotions from user input text.

[0538] An "AI positive transformer" is an artificial intelligence model or algorithm that transforms negative expressions into positive ones.

[0539] A "reconstruction algorithm" is a method or technology for reconstructing converted text into appropriate sentences while maintaining the original context.

[0540] A "terminal" is a device that a user uses to send input text, such as a smartphone or computer.

[0541] A "server" is a computer system that receives and processes data sent from a terminal.

[0542] "User" means a person who uses the System to send and receive text messages.

[0543] This invention is a system that converts negative expressions into positive ones and recognizes and responds to user emotions. This system mainly includes a server, a terminal, and a user. Here, we will explain in detail how each element works together and processes data.

[0544] Receiving and sending input text

[0545] A user inputs and sends a text message through the device. For example, they input "Your suggestion is completely useless." The device receives this text, converts it into JSON format, and sends it to the server. The hardware used for this is a common communication device such as a smartphone or computer.

[0546] Natural Language Processing and Emotion Recognition

[0547] The server passes the received text message to an NLP (natural language processing) engine for analysis. Specifically, it uses an NLP engine such as SpaCy or BERT to tokenize each word or phrase in the message and identify negative expressions. For example, "not at all" is detected as a negative expression.

[0548] At the same time, the server sends the text to an emotion engine (e.g., IBM Watson, Microsoft Azure Text Analytics) to recognize the user's emotions, such as "anger," "frustration," or "sadness."

[0549] Positive transformation and adjustment

[0550] The server sends the detected negative expressions to the AI ​​positive transformer for transformation. For example, "Not good at all" is transformed into "There is room for improvement." The AI ​​positive transformer selects expressions that will soften the user's emotions. In this case, if the user is very angry, a softer expression will be used.

[0551] Reconstructing and outputting messages

[0552] The server reconstructs the positively converted text while preserving the original context and sends it to the device. For example, "Your suggestion is completely wrong" is reconstructed to "Your suggestion has room for improvement." The device receives this message and displays it to the user. The user can confirm this message and receive positive feedback.

[0553] Examples and prompts

[0554] For example, consider the following scenario:

[0555] 1. The user types "Your proposal is completely useless" into the terminal and presses the send button.

[0556] 2. The device converts this message into JSON format and sends it to the server.

[0557] 3. The server analyzes the message received using an NLP engine and identifies the negative expression "not good at all."

[0558] 4. At the same time, the server sends the text to the emotion engine, which identifies the emotion "anger."

[0559] 5. The server sends "Not good at all" to the AI ​​positive transformer, which converts it to "There's room for improvement."

[0560] 6. The server sends the reconstructed message to the device.

[0561] 7. The terminal displays a message to the user that the terminal has been rebuilt.

[0562] Example prompt: "Please translate the user's message, 'Your suggestion is completely useless,' into a more positive one. Also, if the user is very angry, please choose a more neutral response."

[0563] This system converts negative expressions in digital communication into positive ones, enabling communication that takes the user's emotions into consideration.

[0564] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0565] Step 1:

[0566] The user enters a text message into the device and presses the send button. For example, "Your suggestion is completely useless." The entered text is sent to the cloud. The device converts this text into JSON format and sends it to the server. The text entered by the user is stored in the database.

[0567] Step 2:

[0568] The server passes the received text to an NLP engine (e.g., SpaCy or BERT). The NLP engine tokenizes and analyzes the text. Specifically, each word or phrase is separated into a token, and "not at all" is identified as a negative expression. This process analyzes the data and detects negative keywords in the text.

[0569] Step 3:

[0570] At the same time, the server passes the text to an emotion engine (e.g., IBM Watson, Microsoft Azure Text Analytics) to evaluate the user's emotions. The emotion engine performs sentiment analysis on the text and identifies emotions such as "anger," "frustration," and "sadness." For example, "anger" is identified from the phrase "not at all." The analysis results are generated and passed to the next processing step.

[0571] Step 4:

[0572] The server sends the detected negative expressions to the AI ​​Positive Transformer, which converts the negative expressions into positive ones. Specifically, "Not good at all" is converted into "There is room for improvement." The AI ​​model performs this conversion and outputs the results. This process generates positive expressions.

[0573] Step 5:

[0574] The server adjusts the positive transformation based on the user's emotion identified by the emotion engine. For example, if the user is very angry, it selects phrases that will alleviate that emotion. This adjustment selects appropriate expressions to alleviate the emotion. The adjusted positive expressions are passed to the next processing step.

[0575] Step 6:

[0576] The server then reconstructs the positively transformed text while preserving the original context. For example, "Your suggestion is completely wrong" is reconstructed as "Your suggestion could be improved." The reconstruction algorithm is used to adjust the text to maintain proper grammar and context.

[0577] Step 7:

[0578] The server converts the reconstructed positive text into JSON format and sends it to the device, which then forwards the final positive message to the device.

[0579] Step 8:

[0580] The device analyzes the received positive text and displays it to the user. Specifically, the reconstructed message "Your suggestion has room for improvement" is displayed on the user's display. The user can confirm this message and receive positive feedback.

[0581] This detailed processing allows the system to improve the quality of digital communication and generate positive messages that take the user's emotions into account.

[0582] (Application example 2)

[0583] 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."

[0584] Complaints and negative opinions from customers often occur in physical stores, and responding to them appropriately is a difficult task. Responding to these negative expressions in a positive way is important for improving customer satisfaction, but conventional systems do not appropriately adjust to emotions. Therefore, there is a risk that customer service robots will receive negative complaints and respond inappropriately, thereby amplifying customer dissatisfaction.

[0585] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0586] In this invention, the server includes means for receiving input text including negative expressions, means for detecting negative expressions from the received input text, means for converting the detected negative expressions into positive expressions, means for recognizing a user's emotional state, means for adjusting the positive conversion according to the recognized emotional state of the user, and means for reconstructing and outputting the converted text including positive expressions. This enables a customer service robot in a physical store to convert negative complaints into positive ones and provide an appropriate response that takes into consideration the emotional state of the customer.

[0587] "Negative expressions" are linguistic expressions entered by users that are negative, critical, or dissatisfied.

[0588] "Positive expressions" are linguistic expressions that convey positive, encouraging, and affirmative content to the user.

[0589] "Input text" refers to a sentence or message that a user inputs into the system.

[0590] "Detection means" refers to technical means for identifying specific elements or specific emotional expressions from input text.

[0591] "Means of transformation" are technical means for replacing negative expressions with positive expressions.

[0592] "Reconstructive means" are technical means of reorganizing the transformed positive expressions to fit their original context.

[0593] "User's emotional state" refers to the user's psychological and emotional state as indicated through the input text.

[0594] A "means for recognizing an emotional state" is a technical means for identifying and assessing the emotion of a user's input text.

[0595] The "means for adjusting positive conversion" is a technical means for appropriately correcting the conversion process from negative expressions to positive expressions in accordance with the user's recognized emotional state.

[0596] The present invention relates to a customer service robot system for use in brick-and-mortar stores, which improves customer satisfaction by converting negative complaint messages into positive ones and providing appropriate responses depending on the customer's emotional state.

[0597] *Program generation

[0598] The system includes the following programs:

[0599] 1. A means for receiving input text containing negative expressions

[0600] 2. A method for detecting negative expressions from received input text

[0601] 3. A means of converting detected negative expressions into positive expressions

[0602] 4. Means of Recognizing the User's Emotional State

[0603] 5. A means of adjusting positive transformations according to the perceived emotional state of the user

[0604] 6. A method for reconstructing and outputting text containing the converted positive expressions

[0605] ※Explanation of processing

[0606] This system is comprised of a customer service robot in a physical store, a terminal where users input information, a server, and the robot's own internal program.

[0607] First, the user inputs a complaint or question to the robot in text format. For example, a negative message such as "Your service is completely useless" is input. The robot's terminal receives this message, converts it into a data format (such as JSON), and sends it to the server.

[0608] The server then analyzes this received text through NLP (Natural Language Processing) tools, where each word and phrase is tokenized to detect negative expressions, while an emotion engine evaluates the user's emotional state and identifies emotions such as "anger," "frustration," or "sadness."

[0609] The server then sends the detected negative expressions to an AI positive transformer, which converts them into positive expressions. This conversion process is adjusted according to the user's emotional state. For example, "Not good at all" is converted into "There is room for improvement."

[0610] Finally, the server embeds the converted text into the original message and reconstructs it. This reconstructed positive message is converted back into data format and sent to the robot's terminal. The terminal displays this message to the customer, allowing them to receive positive feedback.

[0611] *Hardware and software used

[0612] The system includes a customer-facing robot in a physical store. The robot includes a terminal to receive customer input and process it appropriately. The specific software used is Python and the transformers library (models: sentiment-analysis, text2text-generation).

[0613] *Examples of specific examples and prompts

[0614] For example, if a customer types, "Your service is terrible," this message will be detected as a negative expression and converted to, "There is room for improvement," and then presented to the customer.

[0615] Example of an input prompt for a generative AI model:

[0616] Original: Your service is absolutely terrible

[0617] Negative expression: Not good at all

[0618] Positive transformation: There is room for improvement.

[0619] In this way, this system is effective in enabling customer service robots to provide appropriate responses in physical stores and improve customer satisfaction.

[0620] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0621] Step 1:

[0622] The user inputs a text message and sends it to the terminal. Specifically, the user inputs a negative message such as "Your service is completely useless" into the terminal interface and presses the send button. The input data is in text format, and this message is passed to the next step.

[0623] Step 2:

[0624] The terminal converts the text message received from the user into a data format (such as JSON) and sends it to the server. For example, the message "Your service is completely useless" is converted into JSON format and sent to the server. Here, the input data is a text message, and the output is JSON format data.

[0625] Step 3:

[0626] The server passes the received text message to an NLP engine for analysis. Specifically, it uses a natural language processing tool (the transformers library) to tokenize the message and detect negative expressions. In this step, "Zentsu dame" is identified as a negative expression. The input data is the text message in JSON format, and the output data is a list of tokenized text and negative expressions.

[0627] Step 4:

[0628] At the same time, the server sends the received message to the emotion engine to evaluate the user's emotional state. Specifically, it uses an emotion recognition model to analyze the emotion of the message and identify emotions such as "anger" or "frustration." The input data is tokenized text, and the output data is a list of emotional states.

[0629] Step 5:

[0630] The server sends the detected negative expression and the user's emotional state to the AI ​​positive transformer, which converts it into a positive expression. The conversion process is adjusted based on the user's emotional state. Specifically, the negative expression "Not good at all" is converted to "There is room for improvement." The input data are negative expressions and the user's emotional state, and the output data are positive expressions.

[0631] Step 6:

[0632] The server reconstructs the text containing the transformed positive phrases. Specifically, it embeds new positive phrases while preserving the context of the original message. In this step, "Your service is terrible" is reconstructed into "Your service could use some improvement." The input data is the positive phrases, and the output data is the reconstructed text message.

[0633] Step 7:

[0634] The reconstructed positive message is converted back into a data format (such as JSON) and sent to the terminal. The terminal analyzes the received data and displays the reconstructed positive message to the user. For example, the message "Your service has room for improvement" is displayed to the user. The input data is the reconstructed text message, and the output data is the positive message displayed to the user.

[0635] The above steps make it possible to realize a system that can convert negative complaint messages into positive ones and provide an appropriate response depending on the user's emotional state.

[0636] 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.

[0637] 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.

[0638] 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.

[0639] [Third embodiment]

[0640] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0641] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0642] 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).

[0643] 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.

[0644] 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.

[0645] 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).

[0646] 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.

[0647] 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.

[0648] 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.

[0649] 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.

[0650] 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.

[0651] 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."

[0652] This invention is a system for converting negative expressions into positive ones, and is implemented in a configuration including a server, a terminal, and a user. This system supports healthy communication by converting negative messages into positive ones, particularly in a digital communication environment.

[0653] Explanation of program processing

[0654] Receiving input text

[0655] A user types and sends a negative text message into the device, such as "Your proposal is completely useless."

[0656] The terminal captures this input text using an internal program, converts it into a data format (e.g., JSON), and sends it to the server.

[0657] Detecting negative expressions

[0658] The server passes the received text message to an NLP (natural language processing) engine, which analyzes each word and phrase in the message to detect negative expressions. Specifically, the phrase "not at all" is identified as a negative expression.

[0659] Positive transformation

[0660] The server sends the detected negative expressions to an AI positive transformer, which converts the negative expressions into positive ones, for example, converting "not good at all" into "there is room for improvement."

[0661] Reconstructing and outputting messages

[0662] The server then reconstructs the positively transformed text to fit the original context, changing "Your suggestion is no good" to "Your suggestion could be improved."

[0663] The reconstructed positive message is sent to the terminal, which displays the received message to the user.

[0664] Specific examples

[0665] For example, the following occurs:

[0666] 1. The user types "Your proposal is completely useless" into the terminal and presses the send button.

[0667] 2. The device sends this message to the server.

[0668] 3. The server passes the message to an NLP engine, which detects the phrase "not at all" as a negative expression.

[0669] 4. The server sends the detected negative expression to the AI ​​positive transformer, which converts it to "There is room for improvement."

[0670] 5. The server reconstructs the message "Your proposal has room for improvement" and sends it to the device.

[0671] 6. Display a positive message to the user that the device has been rebuilt.

[0672] This system allows users to communicate in a positive manner, improving the atmosphere of digital communication.

[0673] The processing flow will be explained below.

[0674] Step 1:

[0675] The user types a text message into the device interface, for example, "Your suggestion is completely wrong."

[0676] Step 2:

[0677] The device receives the entered text message and converts it into a data format (e.g., JSON) using an internal program.

[0678] Step 3:

[0679] The terminal transmits the converted data to the server via the network.

[0680] Step 4:

[0681] The server parses the received data and extracts the message part. For example, the extracted message is "Your proposal is completely useless."

[0682] Step 5:

[0683] The server passes the extracted message to the NLP engine, which tokenizes and analyzes each word or phrase in the message.

[0684] Step 6:

[0685] The NLP engine identifies negative expressions. For example, "not at all" is detected as a negative expression.

[0686] Step 7:

[0687] The server sends the detected negative expressions to the AI ​​positive transformer, which then transforms the negative expressions into positive ones.

[0688] Step 8:

[0689] The AI ​​positive transformer converts "not good at all" into "there is room for improvement."

[0690] Step 9:

[0691] The server embeds the converted positive expressions into the original message and reconstructs it. For example, "Your proposal is completely useless" is reconstructed as "Your proposal has room for improvement."

[0692] Step 10:

[0693] The server converts the reconstructed positive message back into a data format (e.g., JSON) and sends it to the device.

[0694] Step 11:

[0695] The device analyzes the received data and extracts the reconstructed positive message, for example, "Your proposal has room for improvement."

[0696] Step 12:

[0697] The device will display a positive message to the user that the device has been rebuilt, allowing the user to confirm the message and receive positive feedback.

[0698] In this way, negative messages are transformed into positive expressions, improving the quality of digital communication.

[0699] Example 1

[0700] 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."

[0701] In the conventional digital communication environment, messages containing negative expressions are frequently generated, which causes a decline in the quality of communication and leads to misunderstandings and conflicts. If such negative expressions can be converted into positive expressions, it is possible to promote healthy communication and improve the quality of digital communication.

[0702] 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.

[0703] In this invention, the server includes: means for a user to input input text including negative expressions and for a terminal to transmit the input text; terminal means for converting the input text into a data format and transmitting the data to the server; means for the server to pass the input text received to a natural language processing engine and detect negative expressions; means for the server to transmit the detected negative expressions to a generative AI model and convert them into positive expressions; means for reconstructing the converted positive expressions to fit the original context and transmitting the converted positive expressions to the terminal, and for the terminal to display the reconstructed text. This makes it possible to convert negative messages input by the user into positive expressions and promote healthy communication.

[0704] "User" refers to a person who utilizes the system to enter and send messages.

[0705] A "terminal" refers to the hardware device that a user uses to input and send messages to a server, typically a PC or smartphone.

[0706] "Input text" refers to the message content that a user inputs into a terminal and sends.

[0707] "Data format" refers to the format in which the input text is converted to be sent to the server, an example of which is JSON format.

[0708] "Server" refers to a computing system that parses and processes received input text.

[0709] A "natural language processing engine" refers to a software engine that analyzes text data and detects negative expressions.

[0710] "Negative language" refers to words or phrases that have negative, offensive or derogatory meanings.

[0711] A "generative AI model" refers to an artificial intelligence model that converts negative expressions into positive ones.

[0712] "Positive language" refers to words or phrases that have a positive, constructive, or uplifting meaning.

[0713] "Reconstruction" refers to the process of rearranging the transformed positive expressions to fit the original context.

[0714] "Means for displaying" refers to the functionality of the terminal to make the reconstructed text visible to the user.

[0715] This invention is a system that converts negative expressions into positive ones, including servers, terminals, and users. Particularly in digital communication environments, converting negative messages into positive ones supports healthy communication.

[0716] The system is structured as follows: First, the user inputs a message containing negative language into the device. For example, they input a message such as "Your proposal is completely useless." Next, the device captures this input text, converts it into a data format (e.g., JSON), and sends it to the server. The device can be a Windows PC, a Mac, or a smartphone (iOS, Android).

[0717] The server then passes the received text message to a natural language processing (NLP) engine, such as the Google NLP API or Microsoft Azure Cognitive Services. This engine analyzes each word and phrase in the message to detect negative expressions. For example, the phrase "not at all" is identified as a negative expression.

[0718] The server then sends the detected negative expressions to an AI positive transformer. This transformer is a generative AI model that transforms negative expressions into positive ones. The specific generative AI model used is the OpenAI GPT model. For example, "Not good at all" is transformed into "There is room for improvement."

[0719] The server then reconstructs the positively translated phrase to fit the original context. This changes "Your suggestion is completely wrong" to "Your suggestion could be improved." The reconstructed positive message is then sent back to the device. The device receives this message and displays it to the user.

[0720] Specific examples

[0721] For example, the following occurs:

[0722] 1. The user types "Your proposal is completely useless" into the terminal and presses the send button.

[0723] 2. The device sends this message to the server.

[0724] 3. The server passes the message to an NLP engine, which detects the phrase "not at all" as a negative expression.

[0725] 4. The server sends the detected negative expression to the AI ​​positive transformer, which converts it to "There is room for improvement."

[0726] 5. The server reconstructs the message "Your proposal has room for improvement" and sends it to the device.

[0727] 6. Display a positive message to the user that the device has been rebuilt.

[0728] Prompt Sentence Examples

[0729] Below are some examples of prompts to input to a generative AI model:

[0730] "Transform the following negative message into a positive: 'Your proposal is completely useless.'"

[0731] This will help transform negative messages into positive ones, which is expected to improve the quality of digital communication.

[0732] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0733] Step 1:

[0734] The user enters a message containing negative language and presses the "Send" button. The negative message (e.g., "Your suggestion is completely useless") is provided as input to the terminal. The terminal captures this input text and converts it into a data format such as JSON. Specifically, the terminal generates JSON data such as {"message": "Your suggestion is completely useless"} and prepares it for transmission.

[0735] Step 2:

[0736] The terminal sends the generated JSON data to the server via the network. The JSON data is provided to the server as input. Specifically, the terminal sends data to the server using an HTTPS request, and the server receives the data. As output, the server parses the received JSON data to extract the original message.

[0737] Step 3:

[0738] The server passes the received message to a natural language processing (NLP) engine, which analyzes it to detect negative expressions. The extracted text data is provided to the NLP engine as input. Specifically, the server receives {"message": "Your suggestion is completely no good"} and identifies the phrase "completely no good" as a negative expression. The output is the identified negative expression.

[0739] Step 4:

[0740] The server sends the detected negative expression to the AI ​​positive transformer, requesting that it be transformed into a positive expression. The negative expression (e.g., "not good at all") is provided to the generative AI model as input. Specifically, the server sends the expression "not good at all" to the generative AI model, which then transforms it into "There's room for improvement." A positive expression is generated as output.

[0741] Step 5:

[0742] The server reconstructs the positively transformed phrase to fit the original context and reconstructs the transformed message. The input is a positive expression (e.g., "There is room for improvement") and the original context. Specifically, the server reconstructs the original sentence "Your proposal is no good at all" into "There is room for improvement in your proposal." The output is a reconstructed positive message.

[0743] Step 6:

[0744] The server converts the reconstructed positive message into JSON format and sends it to the device. The reconstructed positive message is provided as input. Specifically, the server converts it into JSON format like {"message": "Your suggestion could be improved"} and sends it to the device. The output is the positive message received by the device.

[0745] Step 7:

[0746] The device displays the received positive message to the user. The received JSON data is provided to the device as input. Specifically, the device displays the parsed message to the user and provides positive feedback such as "Your suggestion has room for improvement." As output, the positive feedback is displayed to the user.

[0747] (Application example 1)

[0748] 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."

[0749] In today's digital communication environment, communication can be adversely affected by negative expressions. This can lead to the risk of damaging interpersonal relationships, and negative feedback can be an obstacle to improving service, particularly in the relationship between customers and store clerks in brick-and-mortar stores. The present invention aims to provide a system that converts such negative expressions into positive ones and supports healthy communication.

[0750] 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.

[0751] In this invention, the server includes means for receiving input text including negative expressions, means for detecting negative expressions from the received input text, means for converting the detected negative expressions into positive expressions, means for reconstructing and outputting the converted text including positive expressions, and means for processing the received feedback in JSON format and displaying the reconstructed positive feedback. This enables communication between customers and store clerks to be smoother and customer satisfaction to be improved by converting negative feedback into positive feedback.

[0752] "Negative expressions" include negative, critical, and passive content, and are elements that worsen the atmosphere of communication.

[0753] "Input text" refers to character string information that a user inputs into a terminal, including messages and feedback.

[0754] The "receiving means" refers to a method or device for acquiring text data entered by a user in a terminal or a server.

[0755] The "detection means" is a technique for identifying and extracting specific patterns or phrases from received text data.

[0756] "Positive expressions" include positive, constructive, and proactive content, and are elements that maintain a good atmosphere for communication.

[0757] "Means for conversion" are methods or techniques for replacing detected negative expressions with positive expressions.

[0758] The "reconstruction and output means" refers to a method or device for adapting the converted positive expression to the original context and displaying it to the user.

[0759] "Feedback" refers to opinions and evaluations provided by customers and users.

[0760] "JSON format" is an abbreviation for JavaScript Object Notation, and is a common format for structuring and representing data.

[0761] A "generative AI model" is a module that uses artificial intelligence technology to generate and convert text.

[0762] A "prompt" is an instruction given to a generative AI model, and serves as a guideline for appropriate conversion.

[0763] This invention is a system that converts negative expressions in digital communication into positive ones and supports healthy communication. Specifically, it configures a feedback system that can be used in physical stores using smartphones. The detailed configuration and operation method of the system are explained below.

[0764] First, the user inputs feedback using a smartphone. For example, the user inputs feedback such as "The product placement in the store is poor." This message is captured by the smartphone and sent to the server.

[0765] The server processes the received text in JSON format and parses the data. The server uses a natural language processing (NLP) engine to detect negative expressions in the text. The phrase "bad" is identified as a negative expression.

[0766] Next, the server converts the detected negative expressions into positive expressions using a generative AI model, such as "gpt-3.5-turbo." The negative "bad" is converted into the positive "it could be better with more effort."

[0767] The transformed positive expressions are then reconstructed to fit the original context, forming the final positive feedback, where a reconstruction algorithm is used to preserve the overall meaning of the text.

[0768] The reconstructed positive feedback is sent back from the server to the smartphone and displayed to the user. The user can check the positive feedback, such as "You could improve the product layout in the store by being more creative," on their smartphone. This system facilitates smooth communication between store staff and customers, improving customer satisfaction.

[0769] Prompt sentences are particularly important when using generative AI models. A specific example of a prompt sentence is "Before conversion: The product placement in the store is poor -> After conversion: ". Using such prompt sentences allows the generative AI model to function appropriately and effectively convert negative expressions.

[0770] The system uses basic hardware such as a smartphone and a server, and software such as Python, JSON, a natural language processing engine, and a generative AI model (e.g., gpt-3.5-turbo) to smoothly detect negative expressions, convert them to positive ones, and reconstruct and display the text.

[0771] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0772] Step 1:

[0773] The user inputs feedback using a smartphone. An example of input is the message "The product placement in the store is poor." The input data is saved as is in text format.

[0774] Step 2:

[0775] The device converts the input feedback into JSON format and sends it to the server. The text data is converted into a JSON object, and the server receives this data.

[0776] Step 3:

[0777] The server analyzes the received text data and detects negative expressions through a natural language processing (NLP) engine. Analysis of the input text identifies the phrase "bad" as a negative expression. The output is a list containing the detected negative phrases.

[0778] Step 4:

[0779] The server sends the detected negative expressions to a generative AI model, which converts them into positive expressions. The model used is "gpt-3.5-turbo." The prompt sentence used is "Before conversion: The product placement in the store is bad -> After conversion: ", and "bad" is converted to "It could be improved with more effort." The output is a positive phrase.

[0780] Step 5:

[0781] The server reconstructs the positive phrases obtained from the generative AI model to fit the original context. Using the reconstruction algorithm, a complete positive feedback is generated: "The product placement in the store could be improved." The output is the reconstructed text.

[0782] Step 6:

[0783] The server converts the reconstructed positive feedback into JSON format and sends it to the device. The device receives this data and displays it to the user. The user can then confirm the final positive feedback. A message saying, "You could improve things by being more creative with the product placement in the store," is displayed on the user's smartphone.

[0784] 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.

[0785] This invention combines a system that converts negative expressions into positive ones with an emotion engine that recognizes the user's emotions. The system is implemented mainly in a configuration including a server, a terminal, and a user. This system improves the quality of messages in digital communication environments and performs appropriate conversions that take into account the user's emotional state.

[0786] Explanation of program processing

[0787] Receiving input text and recognizing emotions

[0788] The user types a text message into the device interface and sends it, for example, "Your suggestion is completely wrong."

[0789] The terminal receives this input text, converts it into a data format (e.g., JSON) using an internal program, and sends it to the server.

[0790] Negative Expression Detection and Emotion Evaluation

[0791] The server passes the received text message to an NLP (natural language processing) engine for analysis. During this process, each word and phrase in the message is tokenized to detect negative expressions. For example, "not at all" is identified as a negative expression.

[0792] At the same time, the server also sends a message to the emotion engine, which recognizes and identifies the user's emotion, such as "anger," "frustration," or "sadness."

[0793] Positive transformation and adjustment

[0794] The server then sends the detected negative expressions to an AI positive transformer, which converts them into positive expressions. For example, "Not good at all" is converted into "There's room for improvement."

[0795] Based on the user's emotions identified by the emotion engine, the AI ​​Positive Transformer adjusts the transformation process, for example, if the user is very angry, it will choose expressions that will soften that emotion.

[0796] Reconstructing and outputting messages

[0797] The server reconstructs the original message by embedding the positively transformed text in it, e.g., "Your proposal is completely useless" is reconstructed as "Your proposal could be improved."

[0798] The reconstructed positive message is converted back into data format and sent to the terminal.

[0799] The device analyzes the received data and displays a reconstructed positive message to the user, who can confirm the message and receive positive feedback.

[0800] Specific examples

[0801] For example, the following occurs:

[0802] 1. The user types "Your proposal is completely useless" into the terminal and presses the send button.

[0803] 2. The device sends this message to the server.

[0804] 3. The server passes the message to an NLP engine, which detects the phrase "not at all" as a negative expression.

[0805] 4. At the same time, the server sends the message to the emotion engine, which identifies the emotion "anger."

[0806] 5. The server sends the detected negative expression to the AI ​​positive transformer, which converts it into "There is room for improvement." The AI ​​then selects a softer expression to ease the user's "anger."

[0807] 6. The server reconstructs the message "Your proposal has room for improvement" and sends it to the device.

[0808] 7. Display a positive message to the user that the device has been rebuilt.

[0809] This system enables positive communication that takes into account the user's emotions, improving the quality of digital communication.

[0810] The processing flow will be explained below.

[0811] Step 1:

[0812] The user types a text message into the device interface, for example, "Your suggestion is completely wrong."

[0813] Step 2:

[0814] The device receives the entered text message and converts it into a data format (e.g., JSON) using an internal program.

[0815] Step 3:

[0816] The terminal transmits the converted data to the server via the network.

[0817] Step 4:

[0818] The server parses the received data and extracts the message part. The extracted message is "Your proposal is completely useless."

[0819] Step 5:

[0820] The server passes the extracted message to the NLP engine, which tokenizes and analyzes each word or phrase in the message.

[0821] Step 6:

[0822] The NLP engine identifies negative expressions. For example, the phrase "not good at all" is detected as a negative expression.

[0823] Step 7:

[0824] The server passes the entire message to the emotion engine, which analyzes and identifies the user's emotion, for example, "anger."

[0825] Step 8:

[0826] The server sends the detected negative expressions and identified emotion information to the AI ​​positive transformer, which converts the negative expressions into positive ones.

[0827] Step 9:

[0828] The AI ​​positive transformer converts "Not good at all" into "There's room for improvement." It chooses a softer expression to ease the user's "anger."

[0829] Step 10:

[0830] The server embeds the converted positive expressions into the original message and reconstructs it. For example, "Your proposal is completely useless" is reconstructed as "Your proposal has room for improvement."

[0831] Step 11:

[0832] The server converts the reconstructed positive message back into a data format (e.g., JSON) and sends it to the device.

[0833] Step 12:

[0834] The device analyzes the received data and extracts the reconstructed positive message: "Your proposal has room for improvement."

[0835] Step 13:

[0836] The device will display a positive message to the user that the device has been rebuilt, allowing the user to confirm the message and receive positive feedback.

[0837] In this way, negative messages are converted into positive expressions, and communication that takes the user's feelings into consideration is realized.

[0838] Example 2

[0839] 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."

[0840] In modern digital communication, negative expressions can lead to misunderstandings and breakdowns in communication. Furthermore, unilateral changes to messages that ignore the user's feelings can further lead to misunderstandings. This invention aims to improve the quality of digital communication by converting negative expressions into positive ones and taking the user's feelings into consideration.

[0841] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes means for receiving input text including negative expressions, means for detecting negative expressions from the received input text, means for converting the detected negative expressions into positive expressions, means for recognizing and identifying the user's emotions, means for adjusting the positive expressions based on the recognized emotions, and means for reconstructing and outputting the converted text including the positive expressions. This enables appropriate communication that converts negative expressions into positive ones and takes the user's emotions into consideration.

[0842] "Input text" refers to sentences or messages that a user enters and sends using a terminal.

[0843] "Negative expressions" are words or phrases that have a negative feeling or intent, such as "not good at all."

[0844] "Natural language processing technology" is a general term for technologies that enable computers to understand, interpret, and generate human language. It is also known as NLP.

[0845] An "emotion engine" is a technology or software that analyzes and identifies emotions from user input text.

[0846] An "AI positive transformer" is an artificial intelligence model or algorithm that transforms negative expressions into positive ones.

[0847] A "reconstruction algorithm" is a method or technology for reconstructing converted text into appropriate sentences while maintaining the original context.

[0848] A "terminal" is a device that a user uses to send input text, such as a smartphone or computer.

[0849] A "server" is a computer system that receives and processes data sent from a terminal.

[0850] "User" means a person who uses the System to send and receive text messages.

[0851] This invention is a system that converts negative expressions into positive ones and recognizes and responds to user emotions. This system mainly includes a server, a terminal, and a user. Here, we will explain in detail how each element works together and processes data.

[0852] Receiving and sending input text

[0853] A user inputs and sends a text message through the device. For example, they input "Your suggestion is completely useless." The device receives this text, converts it into JSON format, and sends it to the server. The hardware used for this is a common communication device such as a smartphone or computer.

[0854] Natural Language Processing and Emotion Recognition

[0855] The server passes the received text message to an NLP (natural language processing) engine for analysis. Specifically, it uses an NLP engine such as SpaCy or BERT to tokenize each word or phrase in the message and identify negative expressions. For example, "not at all" is detected as a negative expression.

[0856] At the same time, the server sends the text to an emotion engine (e.g., IBM Watson, Microsoft Azure Text Analytics) to recognize the user's emotions, such as "anger," "frustration," or "sadness."

[0857] Positive transformation and adjustment

[0858] The server sends the detected negative expressions to the AI ​​positive transformer for transformation. For example, "Not good at all" is transformed into "There is room for improvement." The AI ​​positive transformer selects expressions that will soften the user's emotions. In this case, if the user is very angry, a softer expression will be used.

[0859] Reconstructing and outputting messages

[0860] The server reconstructs the positively converted text while preserving the original context and sends it to the device. For example, "Your suggestion is completely wrong" is reconstructed to "Your suggestion has room for improvement." The device receives this message and displays it to the user. The user can confirm this message and receive positive feedback.

[0861] Examples and prompts

[0862] For example, consider the following scenario:

[0863] 1. The user types "Your proposal is completely useless" into the terminal and presses the send button.

[0864] 2. The device converts this message into JSON format and sends it to the server.

[0865] 3. The server analyzes the message received using an NLP engine and identifies the negative expression "not good at all."

[0866] 4. At the same time, the server sends the text to the emotion engine, which identifies the emotion "anger."

[0867] 5. The server sends "Not good at all" to the AI ​​positive transformer, which converts it to "There's room for improvement."

[0868] 6. The server sends the reconstructed message to the device.

[0869] 7. The terminal displays a message to the user that the terminal has been rebuilt.

[0870] Example prompt: "Please translate the user's message, 'Your suggestion is completely useless,' into a more positive one. Also, if the user is very angry, please choose a more neutral response."

[0871] This system converts negative expressions in digital communication into positive ones, enabling communication that takes the user's emotions into consideration.

[0872] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0873] Step 1:

[0874] The user enters a text message into the device and presses the send button. For example, "Your suggestion is completely useless." The entered text is sent to the cloud. The device converts this text into JSON format and sends it to the server. The text entered by the user is stored in the database.

[0875] Step 2:

[0876] The server passes the received text to an NLP engine (e.g., SpaCy or BERT). The NLP engine tokenizes and analyzes the text. Specifically, each word or phrase is separated into a token, and "not at all" is identified as a negative expression. This process analyzes the data and detects negative keywords in the text.

[0877] Step 3:

[0878] At the same time, the server passes the text to an emotion engine (e.g., IBM Watson, Microsoft Azure Text Analytics) to evaluate the user's emotions. The emotion engine performs sentiment analysis on the text and identifies emotions such as "anger," "frustration," and "sadness." For example, "anger" is identified from the phrase "not at all." The analysis results are generated and passed to the next processing step.

[0879] Step 4:

[0880] The server sends the detected negative expressions to the AI ​​Positive Transformer, which converts the negative expressions into positive ones. Specifically, "Not good at all" is converted into "There is room for improvement." The AI ​​model performs this conversion and outputs the results. This process generates positive expressions.

[0881] Step 5:

[0882] The server adjusts the positive transformation based on the user's emotion identified by the emotion engine. For example, if the user is very angry, it selects phrases that will alleviate that emotion. This adjustment selects appropriate expressions to alleviate the emotion. The adjusted positive expressions are passed to the next processing step.

[0883] Step 6:

[0884] The server then reconstructs the positively transformed text while preserving the original context. For example, "Your suggestion is completely wrong" is reconstructed as "Your suggestion could be improved." The reconstruction algorithm is used to adjust the text to maintain proper grammar and context.

[0885] Step 7:

[0886] The server converts the reconstructed positive text into JSON format and sends it to the device, which then forwards the final positive message to the device.

[0887] Step 8:

[0888] The device analyzes the received positive text and displays it to the user. Specifically, the reconstructed message "Your suggestion has room for improvement" is displayed on the user's display. The user can confirm this message and receive positive feedback.

[0889] This detailed processing allows the system to improve the quality of digital communication and generate positive messages that take the user's emotions into account.

[0890] (Application example 2)

[0891] 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."

[0892] Complaints and negative opinions from customers often occur in physical stores, and responding to them appropriately is a difficult task. Responding to these negative expressions in a positive way is important for improving customer satisfaction, but conventional systems do not appropriately adjust to emotions. Therefore, there is a risk that customer service robots will receive negative complaints and respond inappropriately, thereby amplifying customer dissatisfaction.

[0893] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0894] In this invention, the server includes means for receiving input text including negative expressions, means for detecting negative expressions from the received input text, means for converting the detected negative expressions into positive expressions, means for recognizing a user's emotional state, means for adjusting the positive conversion according to the recognized emotional state of the user, and means for reconstructing and outputting the converted text including positive expressions. This enables a customer service robot in a physical store to convert negative complaints into positive ones and provide an appropriate response that takes into consideration the emotional state of the customer.

[0895] "Negative expressions" are linguistic expressions entered by users that are negative, critical, or dissatisfied.

[0896] "Positive expressions" are linguistic expressions that convey positive, encouraging, and affirmative content to the user.

[0897] "Input text" refers to a sentence or message that a user inputs into the system.

[0898] "Detection means" refers to technical means for identifying specific elements or specific emotional expressions from input text.

[0899] "Means of transformation" are technical means for replacing negative expressions with positive expressions.

[0900] "Reconstructive means" are technical means of reorganizing the transformed positive expressions to fit their original context.

[0901] "User's emotional state" refers to the user's psychological and emotional state as indicated through the input text.

[0902] A "means for recognizing an emotional state" is a technical means for identifying and assessing the emotion of a user's input text.

[0903] The "means for adjusting positive conversion" is a technical means for appropriately correcting the conversion process from negative expressions to positive expressions in accordance with the user's recognized emotional state.

[0904] The present invention relates to a customer service robot system for use in brick-and-mortar stores, which improves customer satisfaction by converting negative complaint messages into positive ones and providing appropriate responses depending on the customer's emotional state.

[0905] *Program generation

[0906] The system includes the following programs:

[0907] 1. A means for receiving input text containing negative expressions

[0908] 2. A method for detecting negative expressions from received input text

[0909] 3. A means of converting detected negative expressions into positive expressions

[0910] 4. Means of Recognizing the User's Emotional State

[0911] 5. A means of adjusting positive transformations according to the perceived emotional state of the user

[0912] 6. A method for reconstructing and outputting text containing the converted positive expressions

[0913] ※Explanation of processing

[0914] This system is comprised of a customer service robot in a physical store, a terminal where users input information, a server, and the robot's own internal program.

[0915] First, the user inputs a complaint or question to the robot in text format. For example, a negative message such as "Your service is completely useless" is input. The robot's terminal receives this message, converts it into a data format (such as JSON), and sends it to the server.

[0916] The server then analyzes this received text through NLP (Natural Language Processing) tools, where each word and phrase is tokenized to detect negative expressions, while an emotion engine evaluates the user's emotional state and identifies emotions such as "anger," "frustration," or "sadness."

[0917] The server then sends the detected negative expressions to an AI positive transformer, which converts them into positive expressions. This conversion process is adjusted according to the user's emotional state. For example, "Not good at all" is converted into "There is room for improvement."

[0918] Finally, the server embeds the converted text into the original message and reconstructs it. This reconstructed positive message is converted back into data format and sent to the robot's terminal. The terminal displays this message to the customer, allowing them to receive positive feedback.

[0919] *Hardware and software used

[0920] The system includes a customer-facing robot in a physical store. The robot includes a terminal to receive customer input and process it appropriately. The specific software used is Python and the transformers library (models: sentiment-analysis, text2text-generation).

[0921] *Examples of specific examples and prompts

[0922] For example, if a customer types, "Your service is terrible," this message will be detected as a negative expression and converted to, "There is room for improvement," and then presented to the customer.

[0923] Example of an input prompt for a generative AI model:

[0924] Original: Your service is absolutely terrible

[0925] Negative expression: Not good at all

[0926] Positive transformation: There is room for improvement.

[0927] In this way, this system is effective in enabling customer service robots to provide appropriate responses in physical stores and improve customer satisfaction.

[0928] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0929] Step 1:

[0930] The user inputs a text message and sends it to the terminal. Specifically, the user inputs a negative message such as "Your service is completely useless" into the terminal interface and presses the send button. The input data is in text format, and this message is passed to the next step.

[0931] Step 2:

[0932] The terminal converts the text message received from the user into a data format (such as JSON) and sends it to the server. For example, the message "Your service is completely useless" is converted into JSON format and sent to the server. Here, the input data is a text message, and the output is JSON format data.

[0933] Step 3:

[0934] The server passes the received text message to an NLP engine for analysis. Specifically, it uses a natural language processing tool (the transformers library) to tokenize the message and detect negative expressions. In this step, "Zentsu dame" is identified as a negative expression. The input data is the text message in JSON format, and the output data is a list of tokenized text and negative expressions.

[0935] Step 4:

[0936] At the same time, the server sends the received message to the emotion engine to evaluate the user's emotional state. Specifically, it uses an emotion recognition model to analyze the emotion of the message and identify emotions such as "anger" or "frustration." The input data is tokenized text, and the output data is a list of emotional states.

[0937] Step 5:

[0938] The server sends the detected negative expression and the user's emotional state to the AI ​​positive transformer, which converts it into a positive expression. The conversion process is adjusted based on the user's emotional state. Specifically, the negative expression "Not good at all" is converted to "There is room for improvement." The input data are negative expressions and the user's emotional state, and the output data are positive expressions.

[0939] Step 6:

[0940] The server reconstructs the text containing the transformed positive phrases. Specifically, it embeds new positive phrases while preserving the context of the original message. In this step, "Your service is terrible" is reconstructed into "Your service could use some improvement." The input data is the positive phrases, and the output data is the reconstructed text message.

[0941] Step 7:

[0942] The reconstructed positive message is converted back into a data format (such as JSON) and sent to the terminal. The terminal analyzes the received data and displays the reconstructed positive message to the user. For example, the message "Your service has room for improvement" is displayed to the user. The input data is the reconstructed text message, and the output data is the positive message displayed to the user.

[0943] The above steps make it possible to realize a system that can convert negative complaint messages into positive ones and provide an appropriate response depending on the user's emotional state.

[0944] 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.

[0945] 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.

[0946] 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.

[0947] [Fourth embodiment]

[0948] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0949] 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.

[0950] 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).

[0951] 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.

[0952] 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.

[0953] 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).

[0954] 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.

[0955] 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.

[0956] 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.

[0957] 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.

[0958] 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.

[0959] 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.

[0960] 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."

[0961] This invention is a system for converting negative expressions into positive ones, and is implemented in a configuration including a server, a terminal, and a user. This system supports healthy communication by converting negative messages into positive ones, particularly in a digital communication environment.

[0962] Explanation of program processing

[0963] Receiving input text

[0964] A user types and sends a negative text message into the device, such as "Your proposal is completely useless."

[0965] The terminal captures this input text using an internal program, converts it into a data format (e.g., JSON), and sends it to the server.

[0966] Detecting negative expressions

[0967] The server passes the received text message to an NLP (natural language processing) engine, which analyzes each word and phrase in the message to detect negative expressions. Specifically, the phrase "not at all" is identified as a negative expression.

[0968] Positive transformation

[0969] The server sends the detected negative expressions to an AI positive transformer, which converts the negative expressions into positive ones, for example, converting "not good at all" into "there is room for improvement."

[0970] Reconstructing and outputting messages

[0971] The server then reconstructs the positively transformed text to fit the original context, changing "Your suggestion is no good" to "Your suggestion could be improved."

[0972] The reconstructed positive message is sent to the terminal, which displays the received message to the user.

[0973] Specific examples

[0974] For example, the following occurs:

[0975] 1. The user types "Your proposal is completely useless" into the terminal and presses the send button.

[0976] 2. The device sends this message to the server.

[0977] 3. The server passes the message to an NLP engine, which detects the phrase "not at all" as a negative expression.

[0978] 4. The server sends the detected negative expression to the AI ​​positive transformer, which converts it to "There is room for improvement."

[0979] 5. The server reconstructs the message "Your proposal has room for improvement" and sends it to the device.

[0980] 6. Display a positive message to the user that the device has been rebuilt.

[0981] This system allows users to communicate in a positive manner, improving the atmosphere of digital communication.

[0982] The processing flow will be explained below.

[0983] Step 1:

[0984] The user types a text message into the device interface, for example, "Your suggestion is completely wrong."

[0985] Step 2:

[0986] The device receives the entered text message and converts it into a data format (e.g., JSON) using an internal program.

[0987] Step 3:

[0988] The terminal transmits the converted data to the server via the network.

[0989] Step 4:

[0990] The server parses the received data and extracts the message part. For example, the extracted message is "Your proposal is completely useless."

[0991] Step 5:

[0992] The server passes the extracted message to the NLP engine, which tokenizes and analyzes each word or phrase in the message.

[0993] Step 6:

[0994] The NLP engine identifies negative expressions. For example, "not at all" is detected as a negative expression.

[0995] Step 7:

[0996] The server sends the detected negative expressions to the AI ​​positive transformer, which then transforms the negative expressions into positive ones.

[0997] Step 8:

[0998] The AI ​​positive transformer converts "not good at all" into "there is room for improvement."

[0999] Step 9:

[1000] The server embeds the converted positive expressions into the original message and reconstructs it. For example, "Your proposal is completely useless" is reconstructed as "Your proposal has room for improvement."

[1001] Step 10:

[1002] The server converts the reconstructed positive message back into a data format (e.g., JSON) and sends it to the device.

[1003] Step 11:

[1004] The device analyzes the received data and extracts the reconstructed positive message, for example, "Your proposal has room for improvement."

[1005] Step 12:

[1006] The device will display a positive message to the user that the device has been rebuilt, allowing the user to confirm the message and receive positive feedback.

[1007] In this way, negative messages are transformed into positive expressions, improving the quality of digital communication.

[1008] Example 1

[1009] 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."

[1010] In the conventional digital communication environment, messages containing negative expressions are frequently generated, which causes a decline in the quality of communication and leads to misunderstandings and conflicts. If such negative expressions can be converted into positive expressions, it is possible to promote healthy communication and improve the quality of digital communication.

[1011] 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.

[1012] In this invention, the server includes: means for a user to input input text including negative expressions and for a terminal to transmit the input text; terminal means for converting the input text into a data format and transmitting the data to the server; means for the server to pass the input text received to a natural language processing engine and detect negative expressions; means for the server to transmit the detected negative expressions to a generative AI model and convert them into positive expressions; means for reconstructing the converted positive expressions to fit the original context and transmitting the converted positive expressions to the terminal, and for the terminal to display the reconstructed text. This makes it possible to convert negative messages input by the user into positive expressions and promote healthy communication.

[1013] "User" refers to a person who utilizes the system to enter and send messages.

[1014] A "terminal" refers to the hardware device that a user uses to input and send messages to a server, typically a PC or smartphone.

[1015] "Input text" refers to the message content that a user inputs into a terminal and sends.

[1016] "Data format" refers to the format in which the input text is converted to be sent to the server, an example of which is JSON format.

[1017] "Server" refers to a computing system that parses and processes received input text.

[1018] A "natural language processing engine" refers to a software engine that analyzes text data and detects negative expressions.

[1019] "Negative language" refers to words or phrases that have negative, offensive or derogatory meanings.

[1020] A "generative AI model" refers to an artificial intelligence model that converts negative expressions into positive ones.

[1021] "Positive language" refers to words or phrases that have a positive, constructive, or uplifting meaning.

[1022] "Reconstruction" refers to the process of rearranging the transformed positive expressions to fit the original context.

[1023] "Means for displaying" refers to the functionality of the terminal to make the reconstructed text visible to the user.

[1024] This invention is a system that converts negative expressions into positive ones, including servers, terminals, and users. Particularly in digital communication environments, converting negative messages into positive ones supports healthy communication.

[1025] The system is structured as follows: First, the user inputs a message containing negative language into the device. For example, they input a message such as "Your proposal is completely useless." Next, the device captures this input text, converts it into a data format (e.g., JSON), and sends it to the server. The device can be a Windows PC, a Mac, or a smartphone (iOS, Android).

[1026] The server then passes the received text message to a natural language processing (NLP) engine, such as the Google NLP API or Microsoft Azure Cognitive Services. This engine analyzes each word and phrase in the message to detect negative expressions. For example, the phrase "not at all" is identified as a negative expression.

[1027] The server then sends the detected negative expressions to an AI positive transformer. This transformer is a generative AI model that transforms negative expressions into positive ones. The specific generative AI model used is the OpenAI GPT model. For example, "Not good at all" is transformed into "There is room for improvement."

[1028] The server then reconstructs the positively translated phrase to fit the original context. This changes "Your suggestion is completely wrong" to "Your suggestion could be improved." The reconstructed positive message is then sent back to the device. The device receives this message and displays it to the user.

[1029] Specific examples

[1030] For example, the following occurs:

[1031] 1. The user types "Your proposal is completely useless" into the terminal and presses the send button.

[1032] 2. The device sends this message to the server.

[1033] 3. The server passes the message to an NLP engine, which detects the phrase "not at all" as a negative expression.

[1034] 4. The server sends the detected negative expression to the AI ​​positive transformer, which converts it to "There is room for improvement."

[1035] 5. The server reconstructs the message "Your proposal has room for improvement" and sends it to the device.

[1036] 6. Display a positive message to the user that the device has been rebuilt.

[1037] Prompt Sentence Examples

[1038] Below are some examples of prompts to input to a generative AI model:

[1039] "Transform the following negative message into a positive: 'Your proposal is completely useless.'"

[1040] This will help transform negative messages into positive ones, which is expected to improve the quality of digital communication.

[1041] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1042] Step 1:

[1043] The user enters a message containing negative language and presses the "Send" button. The negative message (e.g., "Your suggestion is completely useless") is provided as input to the terminal. The terminal captures this input text and converts it into a data format such as JSON. Specifically, the terminal generates JSON data such as {"message": "Your suggestion is completely useless"} and prepares it for transmission.

[1044] Step 2:

[1045] The terminal sends the generated JSON data to the server via the network. The JSON data is provided to the server as input. Specifically, the terminal sends data to the server using an HTTPS request, and the server receives the data. As output, the server parses the received JSON data to extract the original message.

[1046] Step 3:

[1047] The server passes the received message to a natural language processing (NLP) engine, which analyzes it to detect negative expressions. The extracted text data is provided to the NLP engine as input. Specifically, the server receives {"message": "Your suggestion is completely no good"} and identifies the phrase "completely no good" as a negative expression. The output is the identified negative expression.

[1048] Step 4:

[1049] The server sends the detected negative expression to the AI ​​positive transformer, requesting that it be transformed into a positive expression. The negative expression (e.g., "not good at all") is provided to the generative AI model as input. Specifically, the server sends the expression "not good at all" to the generative AI model, which then transforms it into "There's room for improvement." A positive expression is generated as output.

[1050] Step 5:

[1051] The server reconstructs the positively transformed phrase to fit the original context and reconstructs the transformed message. The input is a positive expression (e.g., "There is room for improvement") and the original context. Specifically, the server reconstructs the original sentence "Your proposal is no good at all" into "There is room for improvement in your proposal." The output is a reconstructed positive message.

[1052] Step 6:

[1053] The server converts the reconstructed positive message into JSON format and sends it to the device. The reconstructed positive message is provided as input. Specifically, the server converts it into JSON format like {"message": "Your suggestion could be improved"} and sends it to the device. The output is the positive message received by the device.

[1054] Step 7:

[1055] The device displays the received positive message to the user. The received JSON data is provided to the device as input. Specifically, the device displays the parsed message to the user and provides positive feedback such as "Your suggestion has room for improvement." As output, the positive feedback is displayed to the user.

[1056] (Application example 1)

[1057] 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."

[1058] In today's digital communication environment, communication can be adversely affected by negative expressions. This can lead to the risk of damaging interpersonal relationships, and negative feedback can be an obstacle to improving service, particularly in the relationship between customers and store clerks in brick-and-mortar stores. The present invention aims to provide a system that converts such negative expressions into positive ones and supports healthy communication.

[1059] 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.

[1060] In this invention, the server includes means for receiving input text including negative expressions, means for detecting negative expressions from the received input text, means for converting the detected negative expressions into positive expressions, means for reconstructing and outputting the converted text including positive expressions, and means for processing the received feedback in JSON format and displaying the reconstructed positive feedback. This enables communication between customers and store clerks to be smoother and customer satisfaction to be improved by converting negative feedback into positive feedback.

[1061] "Negative expressions" include negative, critical, and passive content, and are elements that worsen the atmosphere of communication.

[1062] "Input text" refers to character string information that a user inputs into a terminal, including messages and feedback.

[1063] The "receiving means" refers to a method or device for acquiring text data entered by a user in a terminal or a server.

[1064] The "detection means" is a technique for identifying and extracting specific patterns or phrases from received text data.

[1065] "Positive expressions" include positive, constructive, and proactive content, and are elements that maintain a good atmosphere for communication.

[1066] "Means for conversion" are methods or techniques for replacing detected negative expressions with positive expressions.

[1067] The "reconstruction and output means" refers to a method or device for adapting the converted positive expression to the original context and displaying it to the user.

[1068] "Feedback" refers to opinions and evaluations provided by customers and users.

[1069] "JSON format" is an abbreviation for JavaScript Object Notation, and is a common format for structuring and representing data.

[1070] A "generative AI model" is a module that uses artificial intelligence technology to generate and convert text.

[1071] A "prompt" is an instruction given to a generative AI model, and serves as a guideline for appropriate conversion.

[1072] This invention is a system that converts negative expressions in digital communication into positive ones and supports healthy communication. Specifically, it configures a feedback system that can be used in physical stores using smartphones. The detailed configuration and operation method of the system are explained below.

[1073] First, the user inputs feedback using a smartphone. For example, the user inputs feedback such as "The product placement in the store is poor." This message is captured by the smartphone and sent to the server.

[1074] The server processes the received text in JSON format and parses the data. The server uses a natural language processing (NLP) engine to detect negative expressions in the text. The phrase "bad" is identified as a negative expression.

[1075] Next, the server converts the detected negative expressions into positive expressions using a generative AI model, such as "gpt-3.5-turbo." The negative "bad" is converted into the positive "it could be better with more effort."

[1076] The transformed positive expressions are then reconstructed to fit the original context, forming the final positive feedback, where a reconstruction algorithm is used to preserve the overall meaning of the text.

[1077] The reconstructed positive feedback is sent back from the server to the smartphone and displayed to the user. The user can check the positive feedback, such as "You could improve the product layout in the store by being more creative," on their smartphone. This system facilitates smooth communication between store staff and customers, improving customer satisfaction.

[1078] Prompt sentences are particularly important when using generative AI models. A specific example of a prompt sentence is "Before conversion: The product placement in the store is poor -> After conversion: ". Using such prompt sentences allows the generative AI model to function appropriately and effectively convert negative expressions.

[1079] The system uses basic hardware such as a smartphone and a server, and software such as Python, JSON, a natural language processing engine, and a generative AI model (e.g., gpt-3.5-turbo) to smoothly detect negative expressions, convert them to positive ones, and reconstruct and display the text.

[1080] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1081] Step 1:

[1082] The user inputs feedback using a smartphone. An example of input is the message "The product placement in the store is poor." The input data is saved as is in text format.

[1083] Step 2:

[1084] The device converts the input feedback into JSON format and sends it to the server. The text data is converted into a JSON object, and the server receives this data.

[1085] Step 3:

[1086] The server analyzes the received text data and detects negative expressions through a natural language processing (NLP) engine. Analysis of the input text identifies the phrase "bad" as a negative expression. The output is a list containing the detected negative phrases.

[1087] Step 4:

[1088] The server sends the detected negative expressions to a generative AI model, which converts them into positive expressions. The model used is "gpt-3.5-turbo." The prompt sentence used is "Before conversion: The product placement in the store is bad -> After conversion: ", and "bad" is converted to "It could be improved with more effort." The output is a positive phrase.

[1089] Step 5:

[1090] The server reconstructs the positive phrases obtained from the generative AI model to fit the original context. Using the reconstruction algorithm, a complete positive feedback is generated: "The product placement in the store could be improved." The output is the reconstructed text.

[1091] Step 6:

[1092] The server converts the reconstructed positive feedback into JSON format and sends it to the device. The device receives this data and displays it to the user. The user can then confirm the final positive feedback. A message saying, "You could improve things by being more creative with the product placement in the store," is displayed on the user's smartphone.

[1093] 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.

[1094] This invention combines a system that converts negative expressions into positive ones with an emotion engine that recognizes the user's emotions. The system is implemented mainly in a configuration including a server, a terminal, and a user. This system improves the quality of messages in digital communication environments and performs appropriate conversions that take into account the user's emotional state.

[1095] Explanation of program processing

[1096] Receiving input text and recognizing emotions

[1097] The user types a text message into the device interface and sends it, for example, "Your suggestion is completely wrong."

[1098] The terminal receives this input text, converts it into a data format (e.g., JSON) using an internal program, and sends it to the server.

[1099] Negative Expression Detection and Emotion Evaluation

[1100] The server passes the received text message to an NLP (natural language processing) engine for analysis. During this process, each word and phrase in the message is tokenized to detect negative expressions. For example, "not at all" is identified as a negative expression.

[1101] At the same time, the server also sends a message to the emotion engine, which recognizes and identifies the user's emotion, such as "anger," "frustration," or "sadness."

[1102] Positive transformation and adjustment

[1103] The server then sends the detected negative expressions to an AI positive transformer, which converts them into positive expressions. For example, "Not good at all" is converted into "There's room for improvement."

[1104] Based on the user's emotions identified by the emotion engine, the AI ​​Positive Transformer adjusts the transformation process, for example, if the user is very angry, it will choose expressions that will soften that emotion.

[1105] Reconstructing and outputting messages

[1106] The server reconstructs the original message by embedding the positively transformed text in it, e.g., "Your proposal is completely useless" is reconstructed as "Your proposal could be improved."

[1107] The reconstructed positive message is converted back into data format and sent to the terminal.

[1108] The device analyzes the received data and displays a reconstructed positive message to the user, who can confirm the message and receive positive feedback.

[1109] Specific examples

[1110] For example, the following occurs:

[1111] 1. The user types "Your proposal is completely useless" into the terminal and presses the send button.

[1112] 2. The device sends this message to the server.

[1113] 3. The server passes the message to an NLP engine, which detects the phrase "not at all" as a negative expression.

[1114] 4. At the same time, the server sends the message to the emotion engine, which identifies the emotion "anger."

[1115] 5. The server sends the detected negative expression to the AI ​​positive transformer, which converts it into "There is room for improvement." The AI ​​then selects a softer expression to ease the user's "anger."

[1116] 6. The server reconstructs the message "Your proposal has room for improvement" and sends it to the device.

[1117] 7. Display a positive message to the user that the device has been rebuilt.

[1118] This system enables positive communication that takes into account the user's emotions, improving the quality of digital communication.

[1119] The processing flow will be explained below.

[1120] Step 1:

[1121] The user types a text message into the device interface, for example, "Your suggestion is completely wrong."

[1122] Step 2:

[1123] The device receives the entered text message and converts it into a data format (e.g., JSON) using an internal program.

[1124] Step 3:

[1125] The terminal transmits the converted data to the server via the network.

[1126] Step 4:

[1127] The server parses the received data and extracts the message part. The extracted message is "Your proposal is completely useless."

[1128] Step 5:

[1129] The server passes the extracted message to the NLP engine, which tokenizes and analyzes each word or phrase in the message.

[1130] Step 6:

[1131] The NLP engine identifies negative expressions. For example, the phrase "not good at all" is detected as a negative expression.

[1132] Step 7:

[1133] The server passes the entire message to the emotion engine, which analyzes and identifies the user's emotion, for example, "anger."

[1134] Step 8:

[1135] The server sends the detected negative expressions and identified emotion information to the AI ​​positive transformer, which converts the negative expressions into positive ones.

[1136] Step 9:

[1137] The AI ​​positive transformer converts "Not good at all" into "There's room for improvement." It chooses a softer expression to ease the user's "anger."

[1138] Step 10:

[1139] The server embeds the converted positive expressions into the original message and reconstructs it. For example, "Your proposal is completely useless" is reconstructed as "Your proposal has room for improvement."

[1140] Step 11:

[1141] The server converts the reconstructed positive message back into a data format (e.g., JSON) and sends it to the device.

[1142] Step 12:

[1143] The device analyzes the received data and extracts the reconstructed positive message: "Your proposal has room for improvement."

[1144] Step 13:

[1145] The device will display a positive message to the user that the device has been rebuilt, allowing the user to confirm the message and receive positive feedback.

[1146] In this way, negative messages are converted into positive expressions, and communication that takes the user's feelings into consideration is realized.

[1147] Example 2

[1148] 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."

[1149] In modern digital communication, negative expressions can lead to misunderstandings and breakdowns in communication. Furthermore, unilateral changes to messages that ignore the user's feelings can further lead to misunderstandings. This invention aims to improve the quality of digital communication by converting negative expressions into positive ones and taking the user's feelings into consideration.

[1150] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes means for receiving input text including negative expressions, means for detecting negative expressions from the received input text, means for converting the detected negative expressions into positive expressions, means for recognizing and identifying the user's emotions, means for adjusting the positive expressions based on the recognized emotions, and means for reconstructing and outputting the converted text including the positive expressions. This enables appropriate communication that converts negative expressions into positive ones and takes the user's emotions into consideration.

[1151] "Input text" refers to sentences or messages that a user enters and sends using a terminal.

[1152] "Negative expressions" are words or phrases that have a negative feeling or intent, such as "not good at all."

[1153] "Natural language processing technology" is a general term for technologies that enable computers to understand, interpret, and generate human language. It is also known as NLP.

[1154] An "emotion engine" is a technology or software that analyzes and identifies emotions from user input text.

[1155] An "AI positive transformer" is an artificial intelligence model or algorithm that transforms negative expressions into positive ones.

[1156] A "reconstruction algorithm" is a method or technology for reconstructing converted text into appropriate sentences while maintaining the original context.

[1157] A "terminal" is a device that a user uses to send input text, such as a smartphone or computer.

[1158] A "server" is a computer system that receives and processes data sent from a terminal.

[1159] "User" means a person who uses the System to send and receive text messages.

[1160] This invention is a system that converts negative expressions into positive ones and recognizes and responds to user emotions. This system mainly includes a server, a terminal, and a user. Here, we will explain in detail how each element works together and processes data.

[1161] Receiving and sending input text

[1162] A user inputs and sends a text message through the device. For example, they input "Your suggestion is completely useless." The device receives this text, converts it into JSON format, and sends it to the server. The hardware used for this is a common communication device such as a smartphone or computer.

[1163] Natural Language Processing and Emotion Recognition

[1164] The server passes the received text message to an NLP (natural language processing) engine for analysis. Specifically, it uses an NLP engine such as SpaCy or BERT to tokenize each word or phrase in the message and identify negative expressions. For example, "not at all" is detected as a negative expression.

[1165] At the same time, the server sends the text to an emotion engine (e.g., IBM Watson, Microsoft Azure Text Analytics) to recognize the user's emotions, such as "anger," "frustration," or "sadness."

[1166] Positive transformation and adjustment

[1167] The server sends the detected negative expressions to the AI ​​positive transformer for transformation. For example, "Not good at all" is transformed into "There is room for improvement." The AI ​​positive transformer selects expressions that will soften the user's emotions. In this case, if the user is very angry, a softer expression will be used.

[1168] Reconstructing and outputting messages

[1169] The server reconstructs the positively converted text while preserving the original context and sends it to the device. For example, "Your suggestion is completely wrong" is reconstructed to "Your suggestion has room for improvement." The device receives this message and displays it to the user. The user can confirm this message and receive positive feedback.

[1170] Examples and prompts

[1171] For example, consider the following scenario:

[1172] 1. The user types "Your proposal is completely useless" into the terminal and presses the send button.

[1173] 2. The device converts this message into JSON format and sends it to the server.

[1174] 3. The server analyzes the message received using an NLP engine and identifies the negative expression "not good at all."

[1175] 4. At the same time, the server sends the text to the emotion engine, which identifies the emotion "anger."

[1176] 5. The server sends "Not good at all" to the AI ​​positive transformer, which converts it to "There's room for improvement."

[1177] 6. The server sends the reconstructed message to the device.

[1178] 7. The terminal displays a message to the user that the terminal has been rebuilt.

[1179] Example prompt: "Please translate the user's message, 'Your suggestion is completely useless,' into a more positive one. Also, if the user is very angry, please choose a more neutral response."

[1180] This system converts negative expressions in digital communication into positive ones, enabling communication that takes the user's emotions into consideration.

[1181] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1182] Step 1:

[1183] The user enters a text message into the device and presses the send button. For example, "Your suggestion is completely useless." The entered text is sent to the cloud. The device converts this text into JSON format and sends it to the server. The text entered by the user is stored in the database.

[1184] Step 2:

[1185] The server passes the received text to an NLP engine (e.g., SpaCy or BERT). The NLP engine tokenizes and analyzes the text. Specifically, each word or phrase is separated into a token, and "not at all" is identified as a negative expression. This process analyzes the data and detects negative keywords in the text.

[1186] Step 3:

[1187] At the same time, the server passes the text to an emotion engine (e.g., IBM Watson, Microsoft Azure Text Analytics) to evaluate the user's emotions. The emotion engine performs sentiment analysis on the text and identifies emotions such as "anger," "frustration," and "sadness." For example, "anger" is identified from the phrase "not at all." The analysis results are generated and passed to the next processing step.

[1188] Step 4:

[1189] The server sends the detected negative expressions to the AI ​​Positive Transformer, which converts the negative expressions into positive ones. Specifically, "Not good at all" is converted into "There is room for improvement." The AI ​​model performs this conversion and outputs the results. This process generates positive expressions.

[1190] Step 5:

[1191] The server adjusts the positive transformation based on the user's emotion identified by the emotion engine. For example, if the user is very angry, it selects phrases that will alleviate that emotion. This adjustment selects appropriate expressions to alleviate the emotion. The adjusted positive expressions are passed to the next processing step.

[1192] Step 6:

[1193] The server then reconstructs the positively transformed text while preserving the original context. For example, "Your suggestion is completely wrong" is reconstructed as "Your suggestion could be improved." The reconstruction algorithm is used to adjust the text to maintain proper grammar and context.

[1194] Step 7:

[1195] The server converts the reconstructed positive text into JSON format and sends it to the device, which then forwards the final positive message to the device.

[1196] Step 8:

[1197] The device analyzes the received positive text and displays it to the user. Specifically, the reconstructed message "Your suggestion has room for improvement" is displayed on the user's display. The user can confirm this message and receive positive feedback.

[1198] This detailed processing allows the system to improve the quality of digital communication and generate positive messages that take the user's emotions into account.

[1199] (Application example 2)

[1200] 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."

[1201] Complaints and negative opinions from customers often occur in physical stores, and responding to them appropriately is a difficult task. Responding to these negative expressions in a positive way is important for improving customer satisfaction, but conventional systems do not appropriately adjust to emotions. Therefore, there is a risk that customer service robots will receive negative complaints and respond inappropriately, thereby amplifying customer dissatisfaction.

[1202] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1203] In this invention, the server includes means for receiving input text including negative expressions, means for detecting negative expressions from the received input text, means for converting the detected negative expressions into positive expressions, means for recognizing a user's emotional state, means for adjusting the positive conversion according to the recognized emotional state of the user, and means for reconstructing and outputting the converted text including positive expressions. This enables a customer service robot in a physical store to convert negative complaints into positive ones and provide an appropriate response that takes into consideration the emotional state of the customer.

[1204] "Negative expressions" are linguistic expressions entered by users that are negative, critical, or dissatisfied.

[1205] "Positive expressions" are linguistic expressions that convey positive, encouraging, and affirmative content to the user.

[1206] "Input text" refers to a sentence or message that a user inputs into the system.

[1207] "Detection means" refers to technical means for identifying specific elements or specific emotional expressions from input text.

[1208] "Means of transformation" are technical means for replacing negative expressions with positive expressions.

[1209] "Reconstructive means" are technical means of reorganizing the transformed positive expressions to fit their original context.

[1210] "User's emotional state" refers to the user's psychological and emotional state as indicated through the input text.

[1211] A "means for recognizing an emotional state" is a technical means for identifying and assessing the emotion of a user's input text.

[1212] The "means for adjusting positive conversion" is a technical means for appropriately correcting the conversion process from negative expressions to positive expressions in accordance with the user's recognized emotional state.

[1213] The present invention relates to a customer service robot system for use in brick-and-mortar stores, which improves customer satisfaction by converting negative complaint messages into positive ones and providing appropriate responses depending on the customer's emotional state.

[1214] *Program generation

[1215] The system includes the following programs:

[1216] 1. A means for receiving input text containing negative expressions

[1217] 2. A method for detecting negative expressions from received input text

[1218] 3. A means of converting detected negative expressions into positive expressions

[1219] 4. Means of Recognizing the User's Emotional State

[1220] 5. A means of adjusting positive transformations according to the perceived emotional state of the user

[1221] 6. A method for reconstructing and outputting text containing the converted positive expressions

[1222] ※Explanation of processing

[1223] This system is comprised of a customer service robot in a physical store, a terminal where users input information, a server, and the robot's own internal program.

[1224] First, the user inputs a complaint or question to the robot in text format. For example, a negative message such as "Your service is completely useless" is input. The robot's terminal receives this message, converts it into a data format (such as JSON), and sends it to the server.

[1225] The server then analyzes this received text through NLP (Natural Language Processing) tools, where each word and phrase is tokenized to detect negative expressions, while an emotion engine evaluates the user's emotional state and identifies emotions such as "anger," "frustration," or "sadness."

[1226] The server then sends the detected negative expressions to an AI positive transformer, which converts them into positive expressions. This conversion process is adjusted according to the user's emotional state. For example, "Not good at all" is converted into "There is room for improvement."

[1227] Finally, the server embeds the converted text into the original message and reconstructs it. This reconstructed positive message is converted back into data format and sent to the robot's terminal. The terminal displays this message to the customer, allowing them to receive positive feedback.

[1228] *Hardware and software used

[1229] The system includes a customer-facing robot in a physical store. The robot includes a terminal to receive customer input and process it appropriately. The specific software used is Python and the transformers library (models: sentiment-analysis, text2text-generation).

[1230] *Examples of specific examples and prompts

[1231] For example, if a customer types, "Your service is terrible," this message will be detected as a negative expression and converted to, "There is room for improvement," and then presented to the customer.

[1232] Example of an input prompt for a generative AI model:

[1233] Original: Your service is absolutely terrible

[1234] Negative expression: Not good at all

[1235] Positive transformation: There is room for improvement.

[1236] In this way, this system is effective in enabling customer service robots to provide appropriate responses in physical stores and improve customer satisfaction.

[1237] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1238] Step 1:

[1239] The user inputs a text message and sends it to the terminal. Specifically, the user inputs a negative message such as "Your service is completely useless" into the terminal interface and presses the send button. The input data is in text format, and this message is passed to the next step.

[1240] Step 2:

[1241] The terminal converts the text message received from the user into a data format (such as JSON) and sends it to the server. For example, the message "Your service is completely useless" is converted into JSON format and sent to the server. Here, the input data is a text message, and the output is JSON format data.

[1242] Step 3:

[1243] The server passes the received text message to an NLP engine for analysis. Specifically, it uses a natural language processing tool (the transformers library) to tokenize the message and detect negative expressions. In this step, "Zentsu dame" is identified as a negative expression. The input data is the text message in JSON format, and the output data is a list of tokenized text and negative expressions.

[1244] Step 4:

[1245] At the same time, the server sends the received message to the emotion engine to evaluate the user's emotional state. Specifically, it uses an emotion recognition model to analyze the emotion of the message and identify emotions such as "anger" or "frustration." The input data is tokenized text, and the output data is a list of emotional states.

[1246] Step 5:

[1247] The server sends the detected negative expression and the user's emotional state to the AI ​​positive transformer, which converts it into a positive expression. The conversion process is adjusted based on the user's emotional state. Specifically, the negative expression "Not good at all" is converted to "There is room for improvement." The input data are negative expressions and the user's emotional state, and the output data are positive expressions.

[1248] Step 6:

[1249] The server reconstructs the text containing the transformed positive phrases. Specifically, it embeds new positive phrases while preserving the context of the original message. In this step, "Your service is terrible" is reconstructed into "Your service could use some improvement." The input data is the positive phrases, and the output data is the reconstructed text message.

[1250] Step 7:

[1251] The reconstructed positive message is converted back into a data format (such as JSON) and sent to the terminal. The terminal analyzes the received data and displays the reconstructed positive message to the user. For example, the message "Your service has room for improvement" is displayed to the user. The input data is the reconstructed text message, and the output data is the positive message displayed to the user.

[1252] The above steps make it possible to realize a system that can convert negative complaint messages into positive ones and provide an appropriate response depending on the user's emotional state.

[1253] 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.

[1254] 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.

[1255] 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.

[1256] 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.

[1257] 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.

[1258] 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.

[1259] 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).

[1260] 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.

[1261] 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."

[1262] 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.

[1263] 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).

[1264] 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.

[1265] 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.

[1266] 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.

[1267] 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.

[1268] 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.

[1269] 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.

[1270] 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.

[1271] 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.

[1272] 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.

[1273] 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.

[1274] The following is further disclosed regarding the above embodiment.

[1275] (Claim 1)

[1276] means for receiving input text including negative expressions;

[1277] means for detecting negative expressions from the received input text;

[1278] A means of converting detected negative expressions into positive expressions;

[1279] The system includes means for reconstructing and outputting text containing the transformed positive expressions.

[1280] (Claim 2)

[1281] The system of claim 1, wherein natural language processing techniques are used to detect negative expressions from the received input text.

[1282] (Claim 3)

[1283] The system of claim 1, wherein a reconstruction algorithm is used to preserve the original context when reconstructing and outputting text containing the converted positive expressions.

[1284] "Example 1"

[1285] (Claim 1)

[1286] A means for a user to input an input text including a negative expression and for the terminal to transmit the input text;

[1287] terminal means for converting input text into a data format and transmitting the data to a server;

[1288] A means for passing the input text received by the server to a natural language processing engine to detect negative expressions;

[1289] A means for the server to send the detected negative expressions to a generative AI model and convert them into positive expressions;

[1290] The system includes means for reconstructing the transformed positive expression to fit the original context, transmitting the reconstructed text to a terminal, and the terminal displaying the reconstructed text.

[1291] (Claim 2)

[1292] The system of claim 1, wherein natural language processing techniques are used to detect negative expressions from the received input text.

[1293] (Claim 3)

[1294] The system of claim 1, wherein a reconstruction algorithm is used to preserve the original context when reconstructing and outputting text containing the converted positive expressions.

[1295] "Application Example 1"

[1296] (Claim 1)

[1297] means for receiving input text including negative expressions;

[1298] means for detecting negative expressions from the received input text;

[1299] A means of converting detected negative expressions into positive expressions;

[1300] means for reconstructing and outputting the text containing the converted positive expressions;

[1301] A means to process the received feedback in JSON format and display the reconstructed positive feedback;

[1302] A system including:

[1303] (Claim 2)

[1304] The system of claim 1, wherein natural language processing techniques are used to detect negative expressions from the received input text.

[1305] (Claim 3)

[1306] The system of claim 1, wherein a reconstruction algorithm is used to reconstruct and output the text containing the converted positive expressions.

[1307] (Claim 4)

[1308] 10. The system of claim 1, further comprising means for transforming the reconstructed positive feedback with a generative AI model.

[1309] (Claim 5)

[1310] The system of claim 4, wherein the positive transformation is performed using a prompt sentence for the generative AI model.

[1311] "Example 2: Combining Emotion Engines"

[1312] (Claim 1)

[1313] means for receiving input text including negative expressions;

[1314] means for detecting negative expressions from the received input text;

[1315] A means of converting detected negative expressions into positive expressions;

[1316] means for recognizing and identifying a user's emotions;

[1317] a means of adjusting positive expressions based on perceived emotions;

[1318] The system includes means for reconstructing and outputting text containing the transformed positive expressions.

[1319] (Claim 2)

[1320] The system of claim 1, wherein natural language processing techniques are used to detect negative expressions from the received input text.

[1321] (Claim 3)

[1322] The system of claim 1, wherein a reconstruction algorithm is used to preserve the original context when reconstructing and outputting text containing the converted positive expressions.

[1323] "Application example 2 when combining emotion engines"

[1324] (Claim 1)

[1325] means for receiving input text including negative expressions;

[1326] means for detecting negative expressions from the received input text;

[1327] A means of converting detected negative expressions into positive expressions;

[1328] means for recognizing the emotional state of a user;

[1329] means for adjusting the positive transformation in response to the recognized emotional state of the user;

[1330] The system includes means for reconstructing and outputting text containing the transformed positive expressions.

[1331] (Claim 2)

[1332] The system of claim 1, wherein natural language processing techniques are used to detect negative expressions from the received input text.

[1333] (Claim 3)

[1334] The system of claim 1, wherein a reconstruction algorithm is used to preserve the original context when reconstructing and outputting text containing the converted positive expressions. [Explanation of symbols]

[1335] 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 input text including negative expressions; means for detecting negative expressions from the received input text; A means of converting detected negative expressions into positive expressions; The system includes means for reconstructing and outputting text containing the transformed positive expressions.

2. The system of claim 1 , wherein natural language processing techniques are used to detect negative expressions from the received input text.

3. 2. The system of claim 1, wherein a reconstruction algorithm is used to preserve the original context when reconstructing and outputting the text containing the converted positive expressions.

Images