system

Abstract

We provide the system. [Solution] A means to obtain the term that the user specifies as causing stress and its alternative expression as configuration data, Means for receiving audio or text data, A means of analyzing received data and identifying stress terms based on configuration data, A means of replacing identified stress terms with alternative expressions specified by the user, A means of displaying the replaced data in the user interface, A system that includes this.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] With the spread of the Internet, in modern times when individual values and cultural backgrounds are diversified, there is a problem that terms and phrases in communication cause stress. In particular, since the criteria for words that cause stress vary among people, an innocent word may cause misunderstanding and damage human relationships. In view of such a situation, it is required to realize stress reduction tailored to individual users.

Means for Solving the Problems

[0005] This invention provides a system in which a user inputs terms that cause them stress and their alternative expressions, and the system acquires this as configuration data. By receiving voice or text data and analyzing it, the system accurately identifies the stressful terms set by the user. Furthermore, it replaces the identified terms with the alternative expressions specified by the user, outputting them in a natural way while considering the context. This reduces stress for the user and enables smooth communication.

[0006] A "user" is an individual or group that uses this system to set words that cause them stress or alternative expressions for those words.

[0007] "Stressful terms" are specific words or phrases that cause users discomfort or mental distress.

[0008] "Alternative expressions" are other words or phrases set by the user that are used instead of terms that cause stress.

[0009] "Configuration data" refers to data containing information about terms that users specify as causing stress, along with their alternative expressions.

[0010] "Audio or text data" refers to information provided by a user or another person, expressed in audio or text format.

[0011] "Analyzing" refers to the process of identifying stress-related terms based on the configuration data in the received data.

[0012] "Context" refers to the surrounding circumstances and situations in which a particular word or phrase is used, and it is an element that forms the natural flow of communication. [Brief explanation of the drawing]

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

Embodiments for Carrying Out the Invention

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

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

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

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

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

[0019] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor and 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), or Bluetooth (registered trademark), etc.

[0020] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0021] [First Embodiment]

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

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

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

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

[0026] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0027] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0028] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0030] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 according to the specific processing program 56 executed on the RAM 30.

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

[0032] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

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

[0034] This invention is a system that aims to reduce stress by identifying terms that cause stress to individual users and replacing those terms with alternative expressions set by the user. An embodiment of this system is shown below.

[0035] First, the user sets stressful terms and their alternative expressions through their device. This set data is stored in a database on the device or in cloud storage. This set data serves as a basis for later analysis.

[0036] Next, the server receives voice or text data. This data includes the content of messages and conversations exchanged in typical communication. The received data is then analyzed, and the server uses natural language processing (NLP) techniques to analyze it. Here, the server accurately identifies user-defined stress terms and selects appropriate alternative expressions based on the user's configuration data.

[0037] The server replaces the identified terms with alternative expressions and sends the formatted result to the terminal. The terminal displays the replaced data on the user interface. This allows the user to communicate using expressions that do not cause them stress.

[0038] As a concrete example, consider a case where a user feels stressed by the word "failure" and configures the system to replace it with "trial." If the server receives a message containing the word "failure," it will replace it with "trial," allowing the user to view the message in a less stressful way. This process allows users to maintain smooth communication while reducing stress.

[0039] The following describes the processing flow.

[0040] Step 1:

[0041] The user inputs stressful terms and their alternative expressions through the terminal's interface and sends this information to the system. The terminal saves the input information as configuration data.

[0042] Step 2:

[0043] The device transfers the saved configuration data to a database or cloud storage, making it accessible for subsequent processing.

[0044] Step 3:

[0045] The server receives message data from users or others in voice or text format. This data is important content for communication with the user.

[0046] Step 4:

[0047] The server sends the received data to its internal natural language processing (NLP) module, which identifies stressful terms by referencing configuration data. This includes word matching and context-based judgments.

[0048] Step 5:

[0049] The server replaces identified stressed terms with user-defined alternative expressions. The replacements are adjusted so that the entire context reads naturally.

[0050] Step 6:

[0051] The server reformats the replaced text or audio data and sends it to the terminal in a format optimized for the user.

[0052] Step 7:

[0053] The terminal receives the replacement results sent from the server and displays them to the user. The user can view the converted message in a stress-free manner.

[0054] (Example 1)

[0055] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0056] In modern society, with the increasing volume of information exchange, certain terminology can cause stress to individual users and hinder smooth communication. To address this problem, there is a need for a system that takes into account the feelings of individual users and automatically replaces specific stressful terms with alternative expressions specified by the user.

[0057] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0058] In this invention, the server includes means for acquiring user-specified stressful terms and their alternative expressions as a recording medium, means for receiving unstructured data, means for analyzing the received unstructured data and identifying specific terms based on the recording medium, means for replacing the identified specific terms with user-specified alternative expressions, and means for displaying the replaced unstructured data on a visualization means. This allows the user to reduce stress and more comfortably browse and interpret information.

[0059] A "user" refers to a person or end-user who uses an information system, and who utilizes specific functions and services according to their role and purpose.

[0060] "Stressful terminology" refers to specific words or phrases that may cause psychological burden or discomfort to users.

[0061] An "alternative expression" refers to another word or phrase specified by the user to replace a specific term, and is used to mitigate the impact of the original term.

[0062] "Recording medium" refers to data storage, cloud services, or other technologies used to store and retain information so that it can be referenced in subsequent processes.

[0063] "Unstructured data" refers to information that is not structured into a specific format, such as audio data or text data, and is a data format that requires analysis and processing.

[0064] "Analysis" refers to the process of examining and investigating received unstructured data using techniques such as natural language processing to extract meaningful information.

[0065] "Identification" refers to the process of accurately extracting or identifying specific information or elements from analyzed data.

[0066] "Substitution" refers to the operation of replacing a specified term with another term, and in this context, it refers to changing to an alternative expression set by the user to reduce stress.

[0067] "Visualization means" refers to devices and technologies used to display processed data in a format that is easy for users to understand.

[0068] The system for implementing this invention consists of a server, a terminal, and a user.

[0069] Users first use their device to set terms they find stressful and their alternative expressions. These settings are saved on the device's internal storage, such as a local database or cloud storage. For example, a user might set the term "failure" to be replaced with "attempt."

[0070] The server receives unstructured data collected from corporate email systems, chat applications, and other sources. This data may include messages and conversations exchanged between users. The received unstructured data is analyzed within the server using natural language processing (NLP) techniques. During the analysis process, the server identifies specific terms based on the recording medium.

[0071] Identified terms are replaced with alternative expressions based on user settings. This replacement process may be supported by a generative AI model, which is optimized to ensure that user-specified alternative expressions function correctly.

[0072] The replaced data is sent from the server to the terminal. On the terminal, the replaced data is displayed via a visualization mechanism, allowing the user to view the message with reduced stress.

[0073] For example, the server replaces the message "The project has failed" with "The project has ended as an attempt," allowing the user to see the message in the new wording on their device.

[0074] An example of a prompt message might be: "Explain how to process terms in a message, with the user configured to replace the term 'failure' with 'attempt'." This prompt guides the system's processing and helps ensure the intended substitution is performed.

[0075] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0076] Step 1:

[0077] The user inputs a term that causes them stress and its alternative expression through their device. The input data includes specific words or phrases and their corresponding modified expressions. This data is stored on a recording medium and serves as a reference point for subsequent processes. Specifically, the user enters a term into the application's input form and sets it to change "failure" to "attempt."

[0078] Step 2:

[0079] The server receives unstructured data. Input includes voice and text messages received through a company's email system and chat services. The server retrieves this data and prepares it for the next analysis step. Specifically, it receives message data from the email server via an internet connection.

[0080] Step 3:

[0081] The server applies Natural Language Processing (NLP) to the unstructured data it receives and performs analysis. The input data includes messages and conversations to be analyzed, and NLP techniques are used to identify specific terms. Specifically, the analysis engine extracts the word "failure" from the message.

[0082] Step 4:

[0083] Based on the analysis results, the server selects an appropriate alternative expression from the configuration data of the recording medium and replaces the identified term. The input is the identified term and its context, and the output is the replaced message. In a specific example, the message "The project ended in failure" is replaced with "The project ended in a trial."

[0084] Step 5:

[0085] The server sends the replaced unstructured data to the terminal. In this process, formatted data is output and prepared for user viewing. Specifically, the replaced message is transferred to the terminal via the internet connection.

[0086] Step 6:

[0087] The terminal uses visualization methods to display the replaced data on the user interface. The input includes formatted unstructured data, and the output is displayed in a user-readable format. Specifically, a message such as "The project has ended in a trial" is displayed on the screen, which the user can view.

[0088] (Application Example 1)

[0089] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0090] In today's information-saturated world, users often encounter terms and expressions within content that they find offensive, leading to stress. In this information-overloaded environment, there is a need to enable users to enjoy content without stress. Traditional technologies often apply a uniform processing to individual terms, making flexible substitution that considers context difficult.

[0091] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0092] In this invention, the server includes means for acquiring user-specified stressful terms and their alternative expressions as configuration data, means for receiving audio or text information, means for analyzing the received data and identifying stressful terms based on the configuration data, means for replacing the identified stressful terms with user-specified alternative expressions, means for displaying the replaced data on a user interface, and means for filtering data provided from a content distribution service. This enables users to visually and comfortably use contextually replaced information while reducing stress.

[0093] A "user" refers to an individual who uses a system to enjoy content while reducing stress.

[0094] "Configuration data" refers to information that records terms that users specify as causing stress, along with their alternative expressions.

[0095] "Audio or text information" includes content related to communication consisting of audio data or text data.

[0096] "Means of analysis" refers to techniques used to identify specific terms from received information and to replace those terms.

[0097] "Stress terms" refer to words or phrases that users have designated as causing them stress.

[0098] "Alternative expressions" refer to terms or phrases specified by the user as replacements for stressful terms.

[0099] "User interface" refers to the environment, including screens and visual display devices, where the replaced data is displayed and the user can actually experience it.

[0100] "Filtering methods" refer to methods or processes for removing or transforming elements from distributed content that users may find offensive.

[0101] A "content distribution service" refers to a platform that provides users with digital information such as videos and ebooks.

[0102] "Visual display devices" refer to devices that provide users with information visually, such as smartphones and smart glasses.

[0103] This invention provides a system that enables users to comfortably enjoy online content while reducing personal stress. The system works as follows:

[0104] First, users use their smartphones or smart glasses to input and configure terms that cause them stress and their alternative expressions into the application. This configuration data is managed within the device and saved to cloud storage as needed.

[0105] The server receives audio and text information from content delivery services. The received data is analyzed using natural language processing libraries such as Python's NLTK and Spacy. During the analysis process, the server identifies stress terms that have been set in advance by the user. Then, it replaces these stress terms with alternative expressions specified by the user and formats the modified data.

[0106] The information replaced by this process is provided to the user through the user interface of a visual display device such as a smartphone or smart glasses. This allows the user to view the content without experiencing stress.

[0107] For example, if a user sets their system to replace the word "war" with "path to peace," then even if the term "war" is used in an ebook or video, the content the user views will display "path to peace."

[0108] An example of a prompt in a generative AI model is: "Please replace the following sentence with an alternative expression based on your settings. Settings: Replace 'war' with 'path to peace'. Sentence: 'Many changes occurred in last year's war.'" Using this prompt, the server can appropriately replace terminology and provide users with less stressful content.

[0109] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0110] Step 1:

[0111] Users input and configure terms they find stressful and their alternative expressions through their device. The entered information is stored on the device or in cloud storage. The input consists of stressful terms and their alternative expressions, while the output is stored on the device as configuration data.

[0112] Step 2:

[0113] The server receives audio or text information from a content distribution service. The input is digital content from the distribution service, and the output is the received text or audio data. The server prepares this as data for further analysis.

[0114] Step 3:

[0115] The server analyzes the received audio or text information using natural language processing libraries (e.g., Python's NLTK or Spacy). The input is the received text or audio data, and the data processing performed by the server includes tokenization, part-of-speech analysis, and contextual understanding. The output is the analyzed text information.

[0116] Step 4:

[0117] The server identifies user-defined stress terms from the analyzed data. The input is parsed text information, which the server uses to detect terms that cause stress. The output is a list of words and phrases identified as stress terms.

[0118] Step 5:

[0119] The server replaces identified stressed terms with alternative expressions specified by the user. The input is a list of stressed terms, and the server refers to the corresponding alternative expressions and performs the replacement. As a result of this data calculation, the output is the replaced character information.

[0120] Step 6:

[0121] The server formats the replaced data and sends it to the terminal. The terminal displays this data in the user interface. The input is the replaced text information, and the output on the terminal is the content displayed to the user. The user can then view the content without any stress.

[0122] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0123] This invention is a system that combines a function to replace terms that cause stress to the user with alternative expressions, with an emotion engine that analyzes the user's emotions. This system supports more appropriate communication.

[0124] First, the user uses their device to set terms that they find stressful and preferred alternative expressions. This setting data is stored on the device and used for subsequent language processing.

[0125] The server receives a voice or text message. At this stage, the emotion engine analyzes the user's voice tone and facial expressions to identify their emotional state. For example, it detects changes in emotions such as joy, anger, sadness, and happiness in real time while the user is speaking.

[0126] The server analyzes the content and determines if it contains terms that the user has identified as causing stress. Based on the user's emotional state obtained from the emotion engine, it selects the most appropriate alternative expression and performs substitution operations as needed. Substitutions based on emotional fluctuations can, for example, use lighter words when the user is relaxed and gentler expressions when they are stressed.

[0127] The final message data, including the alternative expressions generated in this way, is sent to the terminal and displayed on the user interface. This allows the user to receive messages with reduced stress.

[0128] As a specific use case, consider a situation where a user listening to an explanation during a meeting feels stressed by the word "difficult." When the emotion engine detects that the user is feeling tense, the server selects a milder alternative expression, such as "challenge," and adjusts the message to reduce the burden of communication.

[0129] This invention enables personalized communication support that takes user emotions into consideration, resulting in smoother information transmission.

[0130] The following describes the processing flow.

[0131] Step 1:

[0132] The user uses the terminal interface to input stressful terms and their alternative expressions, and saves this configuration data to the system.

[0133] Step 2:

[0134] The terminal saves the entered configuration data to an internal database or cloud storage on a remote server, making it accessible for future processes.

[0135] Step 3:

[0136] The device captures the user's voice and facial expressions in real time and sends them to the emotion engine. The emotion engine analyzes this data to identify the user's emotional state.

[0137] Step 4:

[0138] The server receives messages as audio or text data and sends this data to a natural language processing (NLP) engine. The NLP engine analyzes the received data and compares it with the user's configuration data to identify terms that cause stress.

[0139] Step 5:

[0140] The server implements personalized campaigns based on the user's emotional state, derived from the emotion engine, replacing stressful terms with appropriate alternative expressions. Different expressions are selected depending on whether the user is relaxed or stressed.

[0141] Step 6:

[0142] The server reformats the text or audio data after the replacement is complete and sends it to the terminal in the format best suited to the user's request.

[0143] Step 7:

[0144] The device receives the replaced message data and displays it in the user interface. Users can then view messages filtered based on their emotions and preferences.

[0145] (Example 2)

[0146] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0147] Traditional communication systems often lacked the means to select appropriate alternative expressions when users felt stressed by certain terms, resulting in a compromised communication experience. Furthermore, simply substituting terms without considering the user's emotional state was highly likely to create unnatural dialogue.

[0148] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0149] In this invention, the server includes means for acquiring user-specified terms that evoke emotions and their alternative expressions as configuration data, means for receiving voice or text information, and means for identifying the user's emotional state using an emotion analysis function. This enables the selection of the most appropriate alternative expression according to the user's emotional state.

[0150] A "user" refers to a person who uses a system to customize specific terminology and expressions and communicates with them.

[0151] "Emotion-evoking terminology" refers to expressions that evoke specific emotional responses in users, particularly those that cause stress.

[0152] An "alternative expression" refers to a different expression that replaces the original term to reduce user stress and facilitate communication.

[0153] "Configuration data" refers to digital data used to store information about terms specified by the user and their alternative expressions.

[0154] "Voice or text information" refers to the content of a message delivered by a user, including both audio and text input.

[0155] "Sentiment analysis function" refers to an algorithm or technology that determines the emotional state of a user from their voice tone and text content, and then selects an appropriate alternative expression based on that information.

[0156] "User interface" refers to the visual or manipulative means by which a user interacts with a system and confirms the information after replacement.

[0157] This invention is a system that facilitates communication while taking into account the user's emotional state. The system includes a terminal, a server, and an emotion analysis function to appropriately replace terms that the user finds stressful.

[0158] First, the user uses their device to set specific terms that cause stress and their corresponding alternative expressions. This information is stored on the device as configuration data. For example, the user might enter a setting to replace the term "difficult" with "challenge."

[0159] The server receives messages via voice or text input. After receiving a message, it uses sentiment analysis to identify the user's emotional state (e.g., tension, relaxation) from the tone of voice and the context of the text. This analysis helps determine what kinds of expressions the user is most receptive to.

[0160] If a term in the received information corresponds to a term that the user has identified as causing stress, the server will select the most appropriate alternative expression based on the emotional state and replace the term. For example, if the user identifies their emotional state as relaxed, the term can be replaced with a lighter word.

[0161] The adjusted messages are then delivered to the user on the device via the user interface. This allows the user to experience more comfortable and less stressful communication.

[0162] For example, if the word "difficult" is used during a meeting and the user perceives it as causing tension, the server will replace it with the word "challenge" to reduce the burden of the conversation. An example of a prompt to the generative AI model that enables this process could be the request, "Please rephrase this into a conversation that the user can listen to in a relaxed manner."

[0163] This system allows users to receive personalized, emotion-based communication support, thereby improving the quality of information transmission.

[0164] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0165] Step 1:

[0166] The user uses their device to set stressful terms and their corresponding alternative expressions. This creates input configuration data, which is then stored on the device. In this step, the user performs specific actions, such as setting the alternative expression for the term "difficult" to "challenge."

[0167] Step 2:

[0168] The server receives messages from the user in voice or text format. The input is either an audio file or text data. The server receives this and prepares it for the next parsing step. The specific action involves the user speaking into a terminal or sending a text message.

[0169] Step 3:

[0170] The server uses sentiment analysis functionality to identify the user's emotional state from the received audio or text. The input is the audio or text data received in step 2, and the output is the user's emotional state (e.g., tense, relaxed). The specific operation involves analyzing changes in voice tone and keywords in the text based on the sentiment analysis algorithm.

[0171] Step 4:

[0172] The server analyzes the received message data and refers to the configuration data to identify whether it contains stress-causing terms. The input is the received message data and configuration data, and the output is the presence or absence of stress-causing terms. The specific operation involves using text analysis software to verify the occurrence of terms.

[0173] Step 5:

[0174] Once stressful terms are identified, the server selects the most appropriate alternative expression based on the user's emotional state and replaces the stressful terms. The input is the result of step 4, and the output is the replaced message data. The specific operation is to execute the replacement algorithm according to the emotional state.

[0175] Step 6:

[0176] The server finally sends the replaced message data to the terminal, which then displays it on the user interface. The input is the replaced message data, and the output is the message displayed on the user interface. The terminal's concrete action is to represent the new message on the screen and provide it visually to the user.

[0177] This series of processes allows users to receive information in a less stressful way.

[0178] (Application Example 2)

[0179] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0180] Communication in modern society takes place in diverse environments, but friction can arise from stressful expressions and vocabulary. Furthermore, changes in emotional states often hinder effective communication. Especially in real-world environments such as physical stores, the quality of customer communication directly impacts business, requiring flexible responses tailored to customer emotions. This invention aims to solve these communication challenges and provide a system that enables appropriate information transmission.

[0181] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0182] In this invention, the server includes means for acquiring user-specified stressful terms and their alternative expressions as configuration data, means for receiving audio or text data, means for analyzing the received data and identifying stressful terms based on the configuration data, means for replacing the identified stressful terms with user-specified alternative expressions, means for displaying the replaced data on a user interface, and means for analyzing a person's emotional state using emotion recognition means in a real-world scenario and determining the expression to be replaced. This enables smoother communication through optimal expressions that correspond to emotions while reducing stress.

[0183] A "user" is the entity that uses the system to configure settings to reduce stress and receives alternative representations.

[0184] "Stressful terminology" refers to words or expressions that cause users to feel discomfort or tension in specific situations.

[0185] "Alternative expressions" are words or phrases used to replace stressful terms, conveying a milder and more positive impression.

[0186] "Voice or text data" refers to linguistic information in user communication, which is received and analyzed by the system.

[0187] "Configuration data" refers to data containing combinations of terms that users register as causing them stress and their alternative expressions.

[0188] "Emotion recognition means" refers to technology that analyzes a user's emotional state in real time and supports the selection of appropriate expressions.

[0189] A "user interface" refers to the screen or device through which information is exchanged between the user and the system.

[0190] A "real-world scenario" refers to a situation in which the system is used in an environment that exists physically, rather than virtually.

[0191] The system for implementing this invention supports communication between staff members in physical stores equipped with smart glasses and customers. The server retrieves stressful terms and corresponding alternative expressions set by the user (staff). It also receives the user's voice and text data, analyzes this data to identify stressful terms, and replaces them with alternative expressions. The smart glasses worn by the user analyze the customer's facial expressions and voice through a camera and microphone using an emotion recognition engine, and support appropriate language use in real time.

[0192] The hardware used includes smart glasses equipped with a camera, microphone, and display, which are used for data collection and display. The software uses OpenCV and TENSORFLOW® for emotion recognition, Google® Cloud Speech-to-Text for speech recognition, and spaCy for natural language processing. For data processing, the camera and microphone are used to acquire customer facial expressions and voice data, which is then analyzed using emotion recognition technology. Based on the resulting emotion data, the server selects the most appropriate alternative expression and conveys it to the user.

[0193] As a concrete example, when a store staff member (a user) communicates with a customer who is stressed by the phrase "We are out of stock," the emotion recognition engine detects the customer's dissatisfaction, and the server then suggests an alternative expression that includes positive information, such as "It's currently sold out, but we have it in stock soon."

[0194] An example of a prompt for a generative AI model is, "Analyze the customer's emotions from their facial expressions and suggest words to alleviate their stress." In this way, flexible communication tailored to the emotional state is achieved.

[0195] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0196] Step 1:

[0197] Users can set terms they find stressful and preferred alternative expressions on their device.

[0198] The user enters a pair of terms as configuration data, and the terminal saves it. The saved data is used in subsequent processing.

[0199] Step 2:

[0200] The server receives voice or text data.

[0201] This data will be used as input and prepared for analysis. Specifically, the audio data will be converted into text data using a speech recognition engine.

[0202] Step 3:

[0203] The server analyzes the received text data and identifies stress terms based on the configuration data.

[0204] The system takes a specified term as input and selects a predefined alternative expression. This process utilizes a natural language processing library to understand the context and extract the appropriate term.

[0205] Step 4:

[0206] The server performs emotion recognition based on data obtained from the camera and microphone, and analyzes the user's emotional state.

[0207] The system analyzes input facial expression data and voice tone, and an emotion recognition engine outputs an emotion label. This label then influences subsequent expression selections.

[0208] Step 5:

[0209] The server selects the most appropriate alternative expression based on the identified emotional state and contextual information.

[0210] An AI model generates appropriate alternative expressions using emotion labels and contextual information as input. The output is a new message candidate to be presented to the user.

[0211] Step 6:

[0212] The selected message is displayed in the user interface.

[0213] The user receives this outputted information through the smart glasses' display. They review the displayed information and use it to facilitate smooth communication with customers.

[0214] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0215] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0216] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0217] [Second Embodiment]

[0218] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0219] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0220] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0221] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0222] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0223] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0224] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0225] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0226] The specific processing program 56 is an example of a "program" relating to the technology of this 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.

[0227] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0228] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0229] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. 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".

[0230] This invention is a system that aims to reduce stress by identifying terms that cause stress to individual users and replacing those terms with alternative expressions set by the user. An embodiment of this system is shown below.

[0231] First, the user sets stressful terms and their alternative expressions through their device. This set data is stored in a database on the device or in cloud storage. This set data serves as a basis for later analysis.

[0232] Next, the server receives voice or text data. This data includes the content of messages and conversations exchanged in typical communication. The received data is then analyzed, and the server uses natural language processing (NLP) techniques to analyze it. Here, the server accurately identifies user-defined stress terms and selects appropriate alternative expressions based on the user's configuration data.

[0233] The server replaces the identified terms with alternative expressions and sends the formatted result to the terminal. The terminal displays the replaced data on the user interface. This allows the user to communicate using expressions that do not cause them stress.

[0234] As a concrete example, consider a case where a user feels stressed by the word "failure" and configures the system to replace it with "trial." If the server receives a message containing the word "failure," it will replace it with "trial," allowing the user to view the message in a less stressful way. This process allows users to maintain smooth communication while reducing stress.

[0235] The following describes the processing flow.

[0236] Step 1:

[0237] The user inputs stressful terms and their alternative expressions through the terminal's interface and sends this information to the system. The terminal saves the input information as configuration data.

[0238] Step 2:

[0239] The device transfers the saved configuration data to a database or cloud storage, making it accessible for subsequent processing.

[0240] Step 3:

[0241] The server receives message data from users or others in voice or text format. This data is important content for communication with the user.

[0242] Step 4:

[0243] The server sends the received data to its internal natural language processing (NLP) module, which identifies stressful terms by referencing configuration data. This includes word matching and context-based judgments.

[0244] Step 5:

[0245] The server replaces identified stressed terms with user-defined alternative expressions. The replacements are adjusted so that the entire context reads naturally.

[0246] Step 6:

[0247] The server reformats the replaced text or audio data and sends it to the terminal in a format optimized for the user.

[0248] Step 7:

[0249] The terminal receives the replacement results sent from the server and displays them to the user. The user can view the converted message in a stress-free manner.

[0250] (Example 1)

[0251] Next, we will describe Example 1. 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."

[0252] In modern society, with the increasing volume of information exchange, certain terminology can cause stress to individual users and hinder smooth communication. To address this problem, there is a need for a system that takes into account the feelings of individual users and automatically replaces specific stressful terms with alternative expressions specified by the user.

[0253] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0254] In this invention, the server includes means for acquiring user-specified stressful terms and their alternative expressions as a recording medium, means for receiving unstructured data, means for analyzing the received unstructured data and identifying specific terms based on the recording medium, means for replacing the identified specific terms with user-specified alternative expressions, and means for displaying the replaced unstructured data on a visualization means. This allows the user to reduce stress and more comfortably browse and interpret information.

[0255] A "user" refers to a person or end-user who uses an information system, and who utilizes specific functions and services according to their role and purpose.

[0256] "Stressful terminology" refers to specific words or phrases that may cause psychological burden or discomfort to users.

[0257] An "alternative expression" refers to another word or phrase specified by the user to replace a specific term, and is used to mitigate the impact of the original term.

[0258] "Recording medium" refers to data storage, cloud services, or other technologies used to store and retain information so that it can be referenced in subsequent processes.

[0259] "Unstructured data" refers to information that is not structured into a specific format, such as audio data or text data, and is a data format that requires analysis and processing.

[0260] "Analysis" refers to the process of examining and investigating received unstructured data using techniques such as natural language processing to extract meaningful information.

[0261] "Identification" refers to the process of accurately extracting or identifying specific information or elements from analyzed data.

[0262] "Substitution" refers to the operation of replacing a specified term with another term, and in this context, it refers to changing to an alternative expression set by the user to reduce stress.

[0263] "Visualization means" refers to devices and technologies used to display processed data in a format that is easy for users to understand.

[0264] The system for implementing this invention consists of a server, a terminal, and a user.

[0265] Users first use their device to set terms they find stressful and their alternative expressions. These settings are saved on the device's internal storage, such as a local database or cloud storage. For example, a user might set the term "failure" to be replaced with "attempt."

[0266] The server receives unstructured data collected from corporate email systems, chat applications, and other sources. This data may include messages and conversations exchanged between users. The received unstructured data is analyzed within the server using natural language processing (NLP) techniques. During the analysis process, the server identifies specific terms based on the recording medium.

[0267] Identified terms are replaced with alternative expressions based on user settings. This replacement process may be supported by a generative AI model, which is optimized to ensure that user-specified alternative expressions function correctly.

[0268] The replaced data is sent from the server to the terminal. On the terminal, the replaced data is displayed via a visualization mechanism, allowing the user to view the message with reduced stress.

[0269] For example, the server replaces the message "The project has failed" with "The project has ended as an attempt," allowing the user to see the message in the new wording on their device.

[0270] An example of a prompt message might be: "Explain how to process terms in a message, with the user configured to replace the term 'failure' with 'attempt'." This prompt guides the system's processing and helps ensure the intended substitution is performed.

[0271] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0272] Step 1:

[0273] The user inputs a term that causes them stress and its alternative expression through their device. The input data includes specific words or phrases and their corresponding modified expressions. This data is stored on a recording medium and serves as a reference point for subsequent processes. Specifically, the user enters a term into the application's input form and sets it to change "failure" to "attempt."

[0274] Step 2:

[0275] The server receives unstructured data. Input includes voice and text messages received through a company's email system and chat services. The server retrieves this data and prepares it for the next analysis step. Specifically, it receives message data from the email server via an internet connection.

[0276] Step 3:

[0277] The server applies Natural Language Processing (NLP) to the unstructured data it receives and performs analysis. The input data includes messages and conversations to be analyzed, and NLP techniques are used to identify specific terms. Specifically, the analysis engine extracts the word "failure" from the message.

[0278] Step 4:

[0279] Based on the analysis results, the server selects an appropriate alternative expression from the configuration data of the recording medium and replaces the identified term. The input is the identified term and its context, and the output is the replaced message. In a specific example, the message "The project ended in failure" is replaced with "The project ended in a trial."

[0280] Step 5:

[0281] The server sends the replaced unstructured data to the terminal. In this process, formatted data is output and prepared for user viewing. Specifically, the replaced message is transferred to the terminal via the internet connection.

[0282] Step 6:

[0283] The terminal uses visualization methods to display the replaced data on the user interface. The input includes formatted unstructured data, and the output is displayed in a user-readable format. Specifically, a message such as "The project has ended in a trial" is displayed on the screen, which the user can view.

[0284] (Application Example 1)

[0285] Next, Application Example 1 will be described. In the following description, the data processing device 12 is referred to as a "server", and the smart glasses 214 are referred to as a "terminal".

[0286] In modern times when information is provided in excess, users often feel stressed because terms and expressions that make them feel uncomfortable are often included in the content. In such an information overload environment, it is required to enable users to enjoy content without stress. In the conventional technology, there has been a problem that the same processing is often applied to individual terms, and flexible replacement considering the context is difficult.

[0287] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0288] In this invention, the server includes means for acquiring, as setting data, terms that cause stress specified by the user and their alternative expressions, means for receiving voice or character information, means for analyzing the received data and specifying stress terms based on the setting data, means for replacing the specified stress terms with alternative expressions specified by the user, means for displaying the replaced data on the user interface, and means for filtering data provided from the content distribution service. As a result, the user can visually and comfortably use the replaced information according to the context while reducing stress.

[0289] The "user" refers to an individual who enjoys content while reducing stress by using the system.

[0290] The "setting data" refers to information recording terms that cause stress specified by the user and their alternative expressions.

[0291] "Audio or text information" includes content related to communication consisting of audio data or text data.

[0292] "Means of analysis" refers to techniques used to identify specific terms from received information and to replace those terms.

[0293] "Stress terms" refer to words or phrases that users have designated as causing them stress.

[0294] "Alternative expressions" refer to terms or phrases specified by the user as replacements for stressful terms.

[0295] "User interface" refers to the environment, including screens and visual display devices, where the replaced data is displayed and the user can actually experience it.

[0296] "Filtering methods" refer to methods or processes for removing or transforming elements from distributed content that users may find offensive.

[0297] A "content distribution service" refers to a platform that provides users with digital information such as videos and ebooks.

[0298] "Visual display devices" refer to devices that provide users with information visually, such as smartphones and smart glasses.

[0299] This invention provides a system that enables users to comfortably enjoy online content while reducing personal stress. The system works as follows:

[0300] First, users use their smartphones or smart glasses to input and configure terms that cause them stress and their alternative expressions into the application. This configuration data is managed within the device and saved to cloud storage as needed.

[0301] The server receives audio and text information from the content delivery service. The received data is analyzed using natural language processing libraries such as NLTK and Spacy in Python. During the analysis process, the server identifies the stress terms pre-set by the user. Subsequently, the stress terms are replaced with the alternative expressions specified by the user, and the modified data is formatted.

[0302] The information replaced in this process is provided to the user through the user interfaces of visual display devices such as smartphones and smart glasses. As a result, the user can view the content without feeling stressed.

[0303] As a specific example, if the user sets the word "war" to be replaced with "the path to peace", even if the term "war" is used in an e-book or video, the content viewed by the user will be displayed as "the path to peace".

[0304] An example of a prompt sentence in the generative AI model is "Please replace the following sentence with alternative expressions based on your settings. Settings: 'war' is replaced with 'the path to peace'. Sentence: 'Many changes occurred in the war last year.'" By using this prompt, the server can appropriately replace the terms and provide the user with stress-free content.

[0305] The flow of the specific process in Application Example 1 will be described using Figure 12.

[0306] Step 1:

[0307] The user inputs and sets the terms that cause stress to themselves and their alternative expressions through the terminal. The input information is stored in the terminal or cloud storage. The input is the stress term and the alternative expression, and the output is stored in the terminal as setting data.

[0308] Step 2:

[0309] The server receives audio or text information from a content distribution service. The input is digital content from the distribution service, and the output is the received text or audio data. The server prepares this as data for further analysis.

[0310] Step 3:

[0311] The server analyzes the received audio or text information using natural language processing libraries (e.g., Python's NLTK or Spacy). The input is the received text or audio data, and the data processing performed by the server includes tokenization, part-of-speech analysis, and contextual understanding. The output is the analyzed text information.

[0312] Step 4:

[0313] The server identifies user-defined stress terms from the analyzed data. The input is parsed text information, which the server uses to detect terms that cause stress. The output is a list of words and phrases identified as stress terms.

[0314] Step 5:

[0315] The server replaces identified stressed terms with alternative expressions specified by the user. The input is a list of stressed terms, and the server refers to the corresponding alternative expressions and performs the replacement. As a result of this data calculation, the output is the replaced character information.

[0316] Step 6:

[0317] The server formats the replaced data and sends it to the terminal. The terminal displays this data in the user interface. The input is the replaced text information, and the output on the terminal is the content displayed to the user. The user can then view the content without any stress.

[0318] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0319] This invention is a system that combines a function to replace terms that cause stress to the user with alternative expressions, with an emotion engine that analyzes the user's emotions. This system supports more appropriate communication.

[0320] First, the user uses their device to set terms that they find stressful and preferred alternative expressions. This setting data is stored on the device and used for subsequent language processing.

[0321] The server receives a voice or text message. At this stage, the emotion engine analyzes the user's voice tone and facial expressions to identify their emotional state. For example, it detects changes in emotions such as joy, anger, sadness, and happiness in real time while the user is speaking.

[0322] The server analyzes the content and determines if it contains terms that the user has identified as causing stress. Based on the user's emotional state obtained from the emotion engine, it selects the most appropriate alternative expression and performs substitution operations as needed. Substitutions based on emotional fluctuations can, for example, use lighter words when the user is relaxed and gentler expressions when they are stressed.

[0323] The final message data, including the alternative expressions generated in this way, is sent to the terminal and displayed on the user interface. This allows the user to receive messages with reduced stress.

[0324] As a specific use case, consider a situation where a user listening to an explanation during a meeting feels stressed by the word "difficult." When the emotion engine detects that the user is feeling tense, the server selects a milder alternative expression, such as "challenge," and adjusts the message to reduce the burden of communication.

[0325] This invention enables personalized communication support that takes user emotions into consideration, resulting in smoother information transmission.

[0326] The following describes the processing flow.

[0327] Step 1:

[0328] The user uses the terminal interface to input stressful terms and their alternative expressions, and saves this configuration data to the system.

[0329] Step 2:

[0330] The terminal saves the entered configuration data to an internal database or cloud storage on a remote server, making it accessible for future processes.

[0331] Step 3:

[0332] The device captures the user's voice and facial expressions in real time and sends them to the emotion engine. The emotion engine analyzes this data to identify the user's emotional state.

[0333] Step 4:

[0334] The server receives messages as audio or text data and sends this data to a natural language processing (NLP) engine. The NLP engine analyzes the received data and compares it with the user's configuration data to identify terms that cause stress.

[0335] Step 5:

[0336] The server implements personalized campaigns based on the user's emotional state, derived from the emotion engine, replacing stressful terms with appropriate alternative expressions. Different expressions are selected depending on whether the user is relaxed or stressed.

[0337] Step 6:

[0338] The server reformats the text or audio data after the replacement is complete and sends it to the terminal in the format best suited to the user's request.

[0339] Step 7:

[0340] The device receives the replaced message data and displays it in the user interface. Users can then view messages filtered based on their emotions and preferences.

[0341] (Example 2)

[0342] Next, we will describe Example 2. 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".

[0343] Traditional communication systems often lacked the means to select appropriate alternative expressions when users felt stressed by certain terms, resulting in a compromised communication experience. Furthermore, simply substituting terms without considering the user's emotional state was highly likely to create unnatural dialogue.

[0344] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0345] In this invention, the server includes means for acquiring user-specified terms that evoke emotions and their alternative expressions as configuration data, means for receiving voice or text information, and means for identifying the user's emotional state using an emotion analysis function. This enables the selection of the most appropriate alternative expression according to the user's emotional state.

[0346] A "user" refers to a person who uses a system to customize specific terminology and expressions and communicates with them.

[0347] "Emotion-evoking terminology" refers to expressions that evoke specific emotional responses in users, particularly those that cause stress.

[0348] An "alternative expression" refers to a different expression that replaces the original term to reduce user stress and facilitate communication.

[0349] "Configuration data" refers to digital data used to store information about terms specified by the user and their alternative expressions.

[0350] "Voice or text information" refers to the content of a message delivered by a user, including both audio and text input.

[0351] "Sentiment analysis function" refers to an algorithm or technology that determines the emotional state of a user from their voice tone and text content, and then selects an appropriate alternative expression based on that information.

[0352] "User interface" refers to the visual or manipulative means by which a user interacts with a system and confirms the information after replacement.

[0353] This invention is a system that facilitates communication while taking into account the user's emotional state. The system includes a terminal, a server, and an emotion analysis function to appropriately replace terms that the user finds stressful.

[0354] First, the user uses their device to set specific terms that cause stress and their corresponding alternative expressions. This information is stored on the device as configuration data. For example, the user might enter a setting to replace the term "difficult" with "challenge."

[0355] The server receives messages via voice or text input. After receiving a message, it uses sentiment analysis to identify the user's emotional state (e.g., tension, relaxation) from the tone of voice and the context of the text. This analysis helps determine what kinds of expressions the user is most receptive to.

[0356] If a term in the received information corresponds to a term that the user has identified as causing stress, the server will select the most appropriate alternative expression based on the emotional state and replace the term. For example, if the user identifies their emotional state as relaxed, the term can be replaced with a lighter word.

[0357] The adjusted messages are then delivered to the user on the device via the user interface. This allows the user to experience more comfortable and less stressful communication.

[0358] For example, if the word "difficult" is used during a meeting and the user perceives it as causing tension, the server will replace it with the word "challenge" to reduce the burden of the conversation. An example of a prompt to the generative AI model that enables this process could be the request, "Please rephrase this into a conversation that the user can listen to in a relaxed manner."

[0359] This system allows users to receive personalized, emotion-based communication support, thereby improving the quality of information transmission.

[0360] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0361] Step 1:

[0362] The user uses their device to set stressful terms and their corresponding alternative expressions. This creates input configuration data, which is then stored on the device. In this step, the user performs specific actions, such as setting the alternative expression for the term "difficult" to "challenge."

[0363] Step 2:

[0364] The server receives messages from the user in voice or text format. The input is either an audio file or text data. The server receives this and prepares it for the next parsing step. The specific action involves the user speaking into a terminal or sending a text message.

[0365] Step 3:

[0366] The server uses sentiment analysis functionality to identify the user's emotional state from the received audio or text. The input is the audio or text data received in step 2, and the output is the user's emotional state (e.g., tense, relaxed). The specific operation involves analyzing changes in voice tone and keywords in the text based on the sentiment analysis algorithm.

[0367] Step 4:

[0368] The server analyzes the received message data and refers to the configuration data to identify whether it contains stress-causing terms. The input is the received message data and configuration data, and the output is the presence or absence of stress-causing terms. The specific operation involves using text analysis software to verify the occurrence of terms.

[0369] Step 5:

[0370] Once stressful terms are identified, the server selects the most appropriate alternative expression based on the user's emotional state and replaces the stressful terms. The input is the result of step 4, and the output is the replaced message data. The specific operation is to execute the replacement algorithm according to the emotional state.

[0371] Step 6:

[0372] The server finally sends the replaced message data to the terminal, which then displays it on the user interface. The input is the replaced message data, and the output is the message displayed on the user interface. The terminal's concrete action is to represent the new message on the screen and provide it visually to the user.

[0373] This series of processes allows users to receive information in a less stressful way.

[0374] (Application Example 2)

[0375] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0376] Communication in modern society takes place in diverse environments, but friction can arise from stressful expressions and vocabulary. Furthermore, changes in emotional states often hinder effective communication. Especially in real-world environments such as physical stores, the quality of customer communication directly impacts business, requiring flexible responses tailored to customer emotions. This invention aims to solve these communication challenges and provide a system that enables appropriate information transmission.

[0377] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0378] In this invention, the server includes means for acquiring user-specified stressful terms and their alternative expressions as configuration data, means for receiving audio or text data, means for analyzing the received data and identifying stressful terms based on the configuration data, means for replacing the identified stressful terms with user-specified alternative expressions, means for displaying the replaced data on a user interface, and means for analyzing a person's emotional state using emotion recognition means in a real-world scenario and determining the expression to be replaced. This enables smoother communication through optimal expressions that correspond to emotions while reducing stress.

[0379] A "user" is the entity that uses the system to configure settings to reduce stress and receives alternative representations.

[0380] "Stressful terminology" refers to words or expressions that cause users to feel discomfort or tension in specific situations.

[0381] "Alternative expressions" are words or phrases used to replace stressful terms, conveying a milder and more positive impression.

[0382] "Voice or text data" refers to linguistic information in user communication, which is received and analyzed by the system.

[0383] "Configuration data" refers to data containing combinations of terms that users register as causing them stress and their alternative expressions.

[0384] "Emotion recognition means" refers to technology that analyzes a user's emotional state in real time and supports the selection of appropriate expressions.

[0385] A "user interface" refers to the screen or device through which information is exchanged between the user and the system.

[0386] A "real-world scenario" refers to a situation in which the system is used in an environment that exists physically, rather than virtually.

[0387] The system for implementing this invention supports communication between staff members in physical stores equipped with smart glasses and customers. The server retrieves stressful terms and corresponding alternative expressions set by the user (staff). It also receives the user's voice and text data, analyzes this data to identify stressful terms, and replaces them with alternative expressions. The smart glasses worn by the user analyze the customer's facial expressions and voice through a camera and microphone using an emotion recognition engine, and support appropriate language use in real time.

[0388] The hardware used includes smart glasses equipped with a camera, microphone, and display, which are used for data collection and display. The software uses OpenCV and TensorFlow for emotion recognition, Google Cloud Speech-to-Text for speech recognition, and spaCy for natural language processing. For data processing, the camera and microphone are used to acquire customer facial expressions and voice data, which is then analyzed using emotion recognition technology. Based on the resulting emotion data, the server selects the most appropriate alternative expression and conveys it to the user.

[0389] As a concrete example, when a store staff member (a user) communicates with a customer who is stressed by the phrase "We are out of stock," the emotion recognition engine detects the customer's dissatisfaction, and the server then suggests an alternative expression that includes positive information, such as "It's currently sold out, but we have it in stock soon."

[0390] An example of a prompt for a generative AI model is, "Analyze the customer's emotions from their facial expressions and suggest words to alleviate their stress." In this way, flexible communication tailored to the emotional state is achieved.

[0391] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0392] Step 1:

[0393] Users can set terms they find stressful and preferred alternative expressions on their device.

[0394] The user enters a pair of terms as configuration data, and the terminal saves it. The saved data is used in subsequent processing.

[0395] Step 2:

[0396] The server receives voice or text data.

[0397] This data will be used as input and prepared for analysis. Specifically, the audio data will be converted into text data using a speech recognition engine.

[0398] Step 3:

[0399] The server analyzes the received text data and identifies stress terms based on the configuration data.

[0400] The system takes a specified term as input and selects a predefined alternative expression. This process utilizes a natural language processing library to understand the context and extract the appropriate term.

[0401] Step 4:

[0402] The server performs emotion recognition based on data obtained from the camera and microphone, and analyzes the user's emotional state.

[0403] The system analyzes input facial expression data and voice tone, and an emotion recognition engine outputs an emotion label. This label then influences subsequent expression selections.

[0404] Step 5:

[0405] The server selects the most appropriate alternative expression based on the identified emotional state and contextual information.

[0406] An AI model generates appropriate alternative expressions using emotion labels and contextual information as input. The output is a new message candidate to be presented to the user.

[0407] Step 6:

[0408] The selected message is displayed in the user interface.

[0409] The user receives this outputted information through the smart glasses' display. They review the displayed information and use it to facilitate smooth communication with customers.

[0410] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0411] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0412] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0413] [Third Embodiment]

[0414] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0415] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0416] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0417] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0418] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0419] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0420] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0421] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0422] The specific processing program 56 is an example of a "program" relating to the technology of this 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.

[0423] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0424] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0425] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0426] This invention is a system that aims to reduce stress by identifying terms that cause stress to individual users and replacing those terms with alternative expressions set by the user. An embodiment of this system is shown below.

[0427] First, the user sets stressful terms and their alternative expressions through their device. This set data is stored in a database on the device or in cloud storage. This set data serves as a basis for later analysis.

[0428] Next, the server receives voice or text data. This data includes the content of messages and conversations exchanged in typical communication. The received data is then analyzed, and the server uses natural language processing (NLP) techniques to analyze it. Here, the server accurately identifies user-defined stress terms and selects appropriate alternative expressions based on the user's configuration data.

[0429] The server replaces the identified terms with alternative expressions and sends the formatted result to the terminal. The terminal displays the replaced data on the user interface. This allows the user to communicate using expressions that do not cause them stress.

[0430] As a concrete example, consider a case where a user feels stressed by the word "failure" and configures the system to replace it with "trial." If the server receives a message containing the word "failure," it will replace it with "trial," allowing the user to view the message in a less stressful way. This process allows users to maintain smooth communication while reducing stress.

[0431] The following describes the processing flow.

[0432] Step 1:

[0433] The user inputs stressful terms and their alternative expressions through the terminal's interface and sends this information to the system. The terminal saves the input information as configuration data.

[0434] Step 2:

[0435] The device transfers the saved configuration data to a database or cloud storage, making it accessible for subsequent processing.

[0436] Step 3:

[0437] The server receives message data from users or others in voice or text format. This data is important content for communication with the user.

[0438] Step 4:

[0439] The server sends the received data to its internal natural language processing (NLP) module, which identifies stressful terms by referencing configuration data. This includes word matching and context-based judgments.

[0440] Step 5:

[0441] The server replaces identified stressed terms with user-defined alternative expressions. The replacements are adjusted so that the entire context reads naturally.

[0442] Step 6:

[0443] The server reformats the replaced text or audio data and sends it to the terminal in a format optimized for the user.

[0444] Step 7:

[0445] The terminal receives the replacement results sent from the server and displays them to the user. The user can view the converted message in a stress-free manner.

[0446] (Example 1)

[0447] Next, we will describe Example 1. 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."

[0448] In modern society, with the increasing volume of information exchange, certain terminology can cause stress to individual users and hinder smooth communication. To address this problem, there is a need for a system that takes into account the feelings of individual users and automatically replaces specific stressful terms with alternative expressions specified by the user.

[0449] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0450] In this invention, the server includes means for acquiring user-specified stressful terms and their alternative expressions as a recording medium, means for receiving unstructured data, means for analyzing the received unstructured data and identifying specific terms based on the recording medium, means for replacing the identified specific terms with user-specified alternative expressions, and means for displaying the replaced unstructured data on a visualization means. This allows the user to reduce stress and more comfortably browse and interpret information.

[0451] A "user" refers to a person or end-user who uses an information system, and who utilizes specific functions and services according to their role and purpose.

[0452] "Stressful terminology" refers to specific words or phrases that may cause psychological burden or discomfort to users.

[0453] An "alternative expression" refers to another word or phrase specified by the user to replace a specific term, and is used to mitigate the impact of the original term.

[0454] "Recording medium" refers to data storage, cloud services, or other technologies used to store and retain information so that it can be referenced in subsequent processes.

[0455] "Unstructured data" refers to information that is not structured into a specific format, such as audio data or text data, and is a data format that requires analysis and processing.

[0456] "Analysis" refers to the process of examining and investigating received unstructured data using techniques such as natural language processing to extract meaningful information.

[0457] "Identification" refers to the process of accurately extracting or identifying specific information or elements from analyzed data.

[0458] "Substitution" refers to the operation of replacing a specified term with another term, and in this context, it refers to changing to an alternative expression set by the user to reduce stress.

[0459] "Visualization means" refers to devices and technologies used to display processed data in a format that is easy for users to understand.

[0460] The system for implementing this invention consists of a server, a terminal, and a user.

[0461] Users first use their device to set terms they find stressful and their alternative expressions. These settings are saved on the device's internal storage, such as a local database or cloud storage. For example, a user might set the term "failure" to be replaced with "attempt."

[0462] The server receives unstructured data collected from corporate email systems, chat applications, and other sources. This data may include messages and conversations exchanged between users. The received unstructured data is analyzed within the server using natural language processing (NLP) techniques. During the analysis process, the server identifies specific terms based on the recording medium.

[0463] Identified terms are replaced with alternative expressions based on user settings. This replacement process may be supported by a generative AI model, which is optimized to ensure that user-specified alternative expressions function correctly.

[0464] The replaced data is sent from the server to the terminal. On the terminal, the replaced data is displayed via a visualization mechanism, allowing the user to view the message with reduced stress.

[0465] For example, the server replaces the message "The project has failed" with "The project has ended as an attempt," allowing the user to see the message in the new wording on their device.

[0466] An example of a prompt message might be: "Explain how to process terms in a message, with the user configured to replace the term 'failure' with 'attempt'." This prompt guides the system's processing and helps ensure the intended substitution is performed.

[0467] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0468] Step 1:

[0469] The user inputs a term that causes them stress and its alternative expression through their device. The input data includes specific words or phrases and their corresponding modified expressions. This data is stored on a recording medium and serves as a reference point for subsequent processes. Specifically, the user enters a term into the application's input form and sets it to change "failure" to "attempt."

[0470] Step 2:

[0471] The server receives unstructured data. Input includes voice and text messages received through a company's email system and chat services. The server retrieves this data and prepares it for the next analysis step. Specifically, it receives message data from the email server via an internet connection.

[0472] Step 3:

[0473] The server applies Natural Language Processing (NLP) to the unstructured data it receives and performs analysis. The input data includes messages and conversations to be analyzed, and NLP techniques are used to identify specific terms. Specifically, the analysis engine extracts the word "failure" from the message.

[0474] Step 4:

[0475] Based on the analysis results, the server selects an appropriate alternative expression from the configuration data of the recording medium and replaces the identified term. The input is the identified term and its context, and the output is the replaced message. In a specific example, the message "The project ended in failure" is replaced with "The project ended in a trial."

[0476] Step 5:

[0477] The server sends the replaced unstructured data to the terminal. In this process, formatted data is output and prepared for user viewing. Specifically, the replaced message is transferred to the terminal via the internet connection.

[0478] Step 6:

[0479] The terminal uses visualization methods to display the replaced data on the user interface. The input includes formatted unstructured data, and the output is displayed in a user-readable format. Specifically, a message such as "The project has ended in a trial" is displayed on the screen, which the user can view.

[0480] (Application Example 1)

[0481] Next, we will explain Application Example 1. In the following explanation, 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."

[0482] In today's information-saturated world, users often encounter terms and expressions within content that they find offensive, leading to stress. In this information-overloaded environment, there is a need to enable users to enjoy content without stress. Traditional technologies often apply a uniform processing to individual terms, making flexible substitution that considers context difficult.

[0483] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0484] In this invention, the server includes means for acquiring user-specified stressful terms and their alternative expressions as configuration data, means for receiving audio or text information, means for analyzing the received data and identifying stressful terms based on the configuration data, means for replacing the identified stressful terms with user-specified alternative expressions, means for displaying the replaced data on a user interface, and means for filtering data provided from a content distribution service. This enables users to visually and comfortably use contextually replaced information while reducing stress.

[0485] A "user" refers to an individual who uses a system to enjoy content while reducing stress.

[0486] "Configuration data" refers to information that records terms that users specify as causing stress, along with their alternative expressions.

[0487] "Audio or text information" includes content related to communication consisting of audio data or text data.

[0488] "Means of analysis" refers to techniques used to identify specific terms from received information and to replace those terms.

[0489] "Stress terms" refer to words or phrases that users have designated as causing them stress.

[0490] "Alternative expressions" refer to terms or phrases specified by the user as replacements for stressful terms.

[0491] "User interface" refers to the environment, including screens and visual display devices, where the replaced data is displayed and the user can actually experience it.

[0492] "Filtering methods" refer to methods or processes for removing or transforming elements from distributed content that users may find offensive.

[0493] A "content distribution service" refers to a platform that provides users with digital information such as videos and ebooks.

[0494] "Visual display devices" refer to devices that provide users with information visually, such as smartphones and smart glasses.

[0495] This invention provides a system that enables users to comfortably enjoy online content while reducing personal stress. The system works as follows:

[0496] First, users use their smartphones or smart glasses to input and configure terms that cause them stress and their alternative expressions into the application. This configuration data is managed within the device and saved to cloud storage as needed.

[0497] The server receives audio and text information from content delivery services. The received data is analyzed using natural language processing libraries such as Python's NLTK and Spacy. During the analysis process, the server identifies stress terms that have been set in advance by the user. Then, it replaces these stress terms with alternative expressions specified by the user and formats the modified data.

[0498] The information replaced by this process is provided to the user through the user interface of a visual display device such as a smartphone or smart glasses. This allows the user to view the content without experiencing stress.

[0499] For example, if a user sets their system to replace the word "war" with "path to peace," then even if the term "war" is used in an ebook or video, the content the user views will display "path to peace."

[0500] An example of a prompt in a generative AI model is: "Please replace the following sentence with an alternative expression based on your settings. Settings: Replace 'war' with 'path to peace'. Sentence: 'Many changes occurred in last year's war.'" Using this prompt, the server can appropriately replace terminology and provide users with less stressful content.

[0501] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0502] Step 1:

[0503] Users input and configure terms they find stressful and their alternative expressions through their device. The entered information is stored on the device or in cloud storage. The input consists of stressful terms and their alternative expressions, while the output is stored on the device as configuration data.

[0504] Step 2:

[0505] The server receives audio or text information from a content distribution service. The input is digital content from the distribution service, and the output is the received text or audio data. The server prepares this as data for further analysis.

[0506] Step 3:

[0507] The server analyzes the received audio or text information using natural language processing libraries (e.g., Python's NLTK or Spacy). The input is the received text or audio data, and the data processing performed by the server includes tokenization, part-of-speech analysis, and contextual understanding. The output is the analyzed text information.

[0508] Step 4:

[0509] The server identifies user-defined stress terms from the analyzed data. The input is parsed text information, which the server uses to detect terms that cause stress. The output is a list of words and phrases identified as stress terms.

[0510] Step 5:

[0511] The server replaces identified stressed terms with alternative expressions specified by the user. The input is a list of stressed terms, and the server refers to the corresponding alternative expressions and performs the replacement. As a result of this data calculation, the output is the replaced character information.

[0512] Step 6:

[0513] The server formats the replaced data and sends it to the terminal. The terminal displays this data in the user interface. The input is the replaced text information, and the output on the terminal is the content displayed to the user. The user can then view the content without any stress.

[0514] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0515] This invention is a system that combines a function to replace terms that cause stress to the user with alternative expressions, with an emotion engine that analyzes the user's emotions. This system supports more appropriate communication.

[0516] First, the user uses their device to set terms that they find stressful and preferred alternative expressions. This setting data is stored on the device and used for subsequent language processing.

[0517] The server receives a voice or text message. At this stage, the emotion engine analyzes the user's voice tone and facial expressions to identify their emotional state. For example, it detects changes in emotions such as joy, anger, sadness, and happiness in real time while the user is speaking.

[0518] The server analyzes the content and determines if it contains terms that the user has identified as causing stress. Based on the user's emotional state obtained from the emotion engine, it selects the most appropriate alternative expression and performs substitution operations as needed. Substitutions based on emotional fluctuations can, for example, use lighter words when the user is relaxed and gentler expressions when they are stressed.

[0519] The final message data, including the alternative expressions generated in this way, is sent to the terminal and displayed on the user interface. This allows the user to receive messages with reduced stress.

[0520] As a specific use case, consider a situation where a user listening to an explanation during a meeting feels stressed by the word "difficult." When the emotion engine detects that the user is feeling tense, the server selects a milder alternative expression, such as "challenge," and adjusts the message to reduce the burden of communication.

[0521] This invention enables personalized communication support that takes user emotions into consideration, resulting in smoother information transmission.

[0522] The following describes the processing flow.

[0523] Step 1:

[0524] The user uses the terminal interface to input stressful terms and their alternative expressions, and saves this configuration data to the system.

[0525] Step 2:

[0526] The terminal saves the entered configuration data to an internal database or cloud storage on a remote server, making it accessible for future processes.

[0527] Step 3:

[0528] The device captures the user's voice and facial expressions in real time and sends them to the emotion engine. The emotion engine analyzes this data to identify the user's emotional state.

[0529] Step 4:

[0530] The server receives messages as audio or text data and sends this data to a natural language processing (NLP) engine. The NLP engine analyzes the received data and compares it with the user's configuration data to identify terms that cause stress.

[0531] Step 5:

[0532] The server implements personalized campaigns based on the user's emotional state, derived from the emotion engine, replacing stressful terms with appropriate alternative expressions. Different expressions are selected depending on whether the user is relaxed or stressed.

[0533] Step 6:

[0534] The server reformats the text or audio data after the replacement is complete and sends it to the terminal in the format best suited to the user's request.

[0535] Step 7:

[0536] The device receives the replaced message data and displays it in the user interface. Users can then view messages filtered based on their emotions and preferences.

[0537] (Example 2)

[0538] Next, we will describe Example 2. 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."

[0539] Traditional communication systems often lacked the means to select appropriate alternative expressions when users felt stressed by certain terms, resulting in a compromised communication experience. Furthermore, simply substituting terms without considering the user's emotional state was highly likely to create unnatural dialogue.

[0540] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0541] In this invention, the server includes means for acquiring user-specified terms that evoke emotions and their alternative expressions as configuration data, means for receiving voice or text information, and means for identifying the user's emotional state using an emotion analysis function. This enables the selection of the most appropriate alternative expression according to the user's emotional state.

[0542] A "user" refers to a person who uses a system to customize specific terminology and expressions and communicates with them.

[0543] "Emotion-evoking terminology" refers to expressions that evoke specific emotional responses in users, particularly those that cause stress.

[0544] An "alternative expression" refers to a different expression that replaces the original term to reduce user stress and facilitate communication.

[0545] "Configuration data" refers to digital data used to store information about terms specified by the user and their alternative expressions.

[0546] "Voice or text information" refers to the content of a message delivered by a user, including both audio and text input.

[0547] "Sentiment analysis function" refers to an algorithm or technology that determines the emotional state of a user from their voice tone and text content, and then selects an appropriate alternative expression based on that information.

[0548] "User interface" refers to the visual or manipulative means by which a user interacts with a system and confirms the information after replacement.

[0549] This invention is a system that facilitates communication while taking into account the user's emotional state. The system includes a terminal, a server, and an emotion analysis function to appropriately replace terms that the user finds stressful.

[0550] First, the user uses their device to set specific terms that cause stress and their corresponding alternative expressions. This information is stored on the device as configuration data. For example, the user might enter a setting to replace the term "difficult" with "challenge."

[0551] The server receives messages via voice or text input. After receiving a message, it uses sentiment analysis to identify the user's emotional state (e.g., tension, relaxation) from the tone of voice and the context of the text. This analysis helps determine what kinds of expressions the user is most receptive to.

[0552] If a term in the received information corresponds to a term that the user has identified as causing stress, the server will select the most appropriate alternative expression based on the emotional state and replace the term. For example, if the user identifies their emotional state as relaxed, the term can be replaced with a lighter word.

[0553] The adjusted messages are then delivered to the user on the device via the user interface. This allows the user to experience more comfortable and less stressful communication.

[0554] For example, if the word "difficult" is used during a meeting and the user perceives it as causing tension, the server will replace it with the word "challenge" to reduce the burden of the conversation. An example of a prompt to the generative AI model that enables this process could be the request, "Please rephrase this into a conversation that the user can listen to in a relaxed manner."

[0555] This system allows users to receive personalized, emotion-based communication support, thereby improving the quality of information transmission.

[0556] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0557] Step 1:

[0558] The user uses their device to set stressful terms and their corresponding alternative expressions. This creates input configuration data, which is then stored on the device. In this step, the user performs specific actions, such as setting the alternative expression for the term "difficult" to "challenge."

[0559] Step 2:

[0560] The server receives messages from the user in voice or text format. The input is either an audio file or text data. The server receives this and prepares it for the next parsing step. The specific action involves the user speaking into a terminal or sending a text message.

[0561] Step 3:

[0562] The server uses sentiment analysis functionality to identify the user's emotional state from the received audio or text. The input is the audio or text data received in step 2, and the output is the user's emotional state (e.g., tense, relaxed). The specific operation involves analyzing changes in voice tone and keywords in the text based on the sentiment analysis algorithm.

[0563] Step 4:

[0564] The server analyzes the received message data and refers to the configuration data to identify whether it contains stress-causing terms. The input is the received message data and configuration data, and the output is the presence or absence of stress-causing terms. The specific operation involves using text analysis software to verify the occurrence of terms.

[0565] Step 5:

[0566] Once stressful terms are identified, the server selects the most appropriate alternative expression based on the user's emotional state and replaces the stressful terms. The input is the result of step 4, and the output is the replaced message data. The specific operation is to execute the replacement algorithm according to the emotional state.

[0567] Step 6:

[0568] The server finally sends the replaced message data to the terminal, which then displays it on the user interface. The input is the replaced message data, and the output is the message displayed on the user interface. The terminal's concrete action is to represent the new message on the screen and provide it visually to the user.

[0569] This series of processes allows users to receive information in a less stressful way.

[0570] (Application Example 2)

[0571] Next, we will explain application example 2. In the following explanation, 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."

[0572] Communication in modern society takes place in diverse environments, but friction can arise from stressful expressions and vocabulary. Furthermore, changes in emotional states often hinder effective communication. Especially in real-world environments such as physical stores, the quality of customer communication directly impacts business, requiring flexible responses tailored to customer emotions. This invention aims to solve these communication challenges and provide a system that enables appropriate information transmission.

[0573] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0574] In this invention, the server includes means for acquiring user-specified stressful terms and their alternative expressions as configuration data, means for receiving audio or text data, means for analyzing the received data and identifying stressful terms based on the configuration data, means for replacing the identified stressful terms with user-specified alternative expressions, means for displaying the replaced data on a user interface, and means for analyzing a person's emotional state using emotion recognition means in a real-world scenario and determining the expression to be replaced. This enables smoother communication through optimal expressions that correspond to emotions while reducing stress.

[0575] A "user" is the entity that uses the system to configure settings to reduce stress and receives alternative representations.

[0576] "Stressful terminology" refers to words or expressions that cause users to feel discomfort or tension in specific situations.

[0577] "Alternative expressions" are words or phrases used to replace stressful terms, conveying a milder and more positive impression.

[0578] "Voice or text data" refers to linguistic information in user communication, which is received and analyzed by the system.

[0579] "Configuration data" refers to data containing combinations of terms that users register as causing them stress and their alternative expressions.

[0580] "Emotion recognition means" refers to technology that analyzes a user's emotional state in real time and supports the selection of appropriate expressions.

[0581] A "user interface" refers to the screen or device through which information is exchanged between the user and the system.

[0582] A "real-world scenario" refers to a situation in which the system is used in an environment that exists physically, rather than virtually.

[0583] The system for implementing this invention supports communication between staff members in physical stores equipped with smart glasses and customers. The server retrieves stressful terms and corresponding alternative expressions set by the user (staff). It also receives the user's voice and text data, analyzes this data to identify stressful terms, and replaces them with alternative expressions. The smart glasses worn by the user analyze the customer's facial expressions and voice through a camera and microphone using an emotion recognition engine, and support appropriate language use in real time.

[0584] The hardware used includes smart glasses equipped with a camera, microphone, and display, which are used for data collection and display. The software uses OpenCV and TensorFlow for emotion recognition, Google Cloud Speech-to-Text for speech recognition, and spaCy for natural language processing. For data processing, the camera and microphone are used to acquire customer facial expressions and voice data, which is then analyzed using emotion recognition technology. Based on the resulting emotion data, the server selects the most appropriate alternative expression and conveys it to the user.

[0585] As a concrete example, when a store staff member (a user) communicates with a customer who is stressed by the phrase "We are out of stock," the emotion recognition engine detects the customer's dissatisfaction, and the server then suggests an alternative expression that includes positive information, such as "It's currently sold out, but we have it in stock soon."

[0586] An example of a prompt for a generative AI model is, "Analyze the customer's emotions from their facial expressions and suggest words to alleviate their stress." In this way, flexible communication tailored to the emotional state is achieved.

[0587] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0588] Step 1:

[0589] Users can set terms they find stressful and preferred alternative expressions on their device.

[0590] The user enters a pair of terms as configuration data, and the terminal saves it. The saved data is used in subsequent processing.

[0591] Step 2:

[0592] The server receives voice or text data.

[0593] This data will be used as input and prepared for analysis. Specifically, the audio data will be converted into text data using a speech recognition engine.

[0594] Step 3:

[0595] The server analyzes the received text data and identifies stress terms based on the configuration data.

[0596] The system takes a specified term as input and selects a predefined alternative expression. This process utilizes a natural language processing library to understand the context and extract the appropriate term.

[0597] Step 4:

[0598] The server performs emotion recognition based on data obtained from the camera and microphone, and analyzes the user's emotional state.

[0599] The system analyzes input facial expression data and voice tone, and an emotion recognition engine outputs an emotion label. This label then influences subsequent expression selections.

[0600] Step 5:

[0601] The server selects the most appropriate alternative expression based on the identified emotional state and contextual information.

[0602] An AI model generates appropriate alternative expressions using emotion labels and contextual information as input. The output is a new message candidate to be presented to the user.

[0603] Step 6:

[0604] The selected message is displayed in the user interface.

[0605] The user receives this outputted information through the smart glasses' display. They review the displayed information and use it to facilitate smooth communication with customers.

[0606] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0607] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0608] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0609] [Fourth Embodiment]

[0610] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0611] As shown in Figure 7, the 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.

[0612] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0613] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0614] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0615] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0616] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0617] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive 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 robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0618] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0619] The specific processing program 56 is an example of a "program" relating to the technology of this 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.

[0620] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0621] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0622] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0623] This invention is a system that aims to reduce stress by identifying terms that cause stress to individual users and replacing those terms with alternative expressions set by the user. An embodiment of this system is shown below.

[0624] First, the user sets stressful terms and their alternative expressions through their device. This set data is stored in a database on the device or in cloud storage. This set data serves as a basis for subsequent analysis.

[0625] Next, the server receives voice or text data. This data includes the content of messages and conversations exchanged in typical communication. The received data is then analyzed, and the server uses natural language processing (NLP) techniques to analyze it. Here, the server accurately identifies user-defined stress terms and selects appropriate alternative expressions based on the user's configuration data.

[0626] The server replaces the identified terms with alternative expressions and sends the formatted result to the terminal. The terminal displays the replaced data on the user interface. This allows the user to communicate using expressions that do not cause them stress.

[0627] As a concrete example, consider a case where a user feels stressed by the word "failure" and configures the system to replace it with "trial." If the server receives a message containing the word "failure," it will replace it with "trial," allowing the user to view the message in a less stressful way. This process allows users to maintain smooth communication while reducing stress.

[0628] The following describes the processing flow.

[0629] Step 1:

[0630] The user inputs stressful terms and their alternative expressions through the terminal's interface and sends this information to the system. The terminal saves the input information as configuration data.

[0631] Step 2:

[0632] The device transfers the saved configuration data to a database or cloud storage, making it accessible for subsequent processing.

[0633] Step 3:

[0634] The server receives message data from users or others in voice or text format. This data is important content for communication with the user.

[0635] Step 4:

[0636] The server sends the received data to its internal natural language processing (NLP) module, which identifies stressful terms by referencing configuration data. This includes word matching and context-based judgments.

[0637] Step 5:

[0638] The server replaces identified stressed terms with user-defined alternative expressions. The replacements are adjusted so that the entire context reads naturally.

[0639] Step 6:

[0640] The server reformats the replaced text or audio data and sends it to the terminal in a format optimized for the user.

[0641] Step 7:

[0642] The terminal receives the replacement results sent from the server and displays them to the user. The user can view the converted message in a stress-free manner.

[0643] (Example 1)

[0644] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0645] In modern society, with the increasing volume of information exchange, certain terminology can cause stress to individual users and hinder smooth communication. To address this problem, there is a need for a system that takes into account the feelings of individual users and automatically replaces specific stressful terms with alternative expressions specified by the user.

[0646] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0647] In this invention, the server includes means for acquiring user-specified stressful terms and their alternative expressions as a recording medium, means for receiving unstructured data, means for analyzing the received unstructured data and identifying specific terms based on the recording medium, means for replacing the identified specific terms with user-specified alternative expressions, and means for displaying the replaced unstructured data on a visualization means. This allows the user to reduce stress and more comfortably browse and interpret information.

[0648] A "user" refers to a person or end-user who uses an information system, and who utilizes specific functions and services according to their role and purpose.

[0649] "Stressful terminology" refers to specific words or phrases that may cause psychological burden or discomfort to users.

[0650] An "alternative expression" refers to another word or phrase specified by the user to replace a specific term, and is used to mitigate the impact of the original term.

[0651] "Recording medium" refers to data storage, cloud services, or other technologies used to store and retain information so that it can be referenced in subsequent processes.

[0652] "Unstructured data" refers to information that is not structured into a specific format, such as audio data or text data, and is a data format that requires analysis and processing.

[0653] "Analysis" refers to the process of examining and investigating received unstructured data using techniques such as natural language processing to extract meaningful information.

[0654] "Identification" refers to the process of accurately extracting or identifying specific information or elements from analyzed data.

[0655] "Substitution" refers to the operation of replacing a specified term with another term, and in this context, it refers to changing to an alternative expression set by the user to reduce stress.

[0656] "Visualization means" refers to devices and technologies used to display processed data in a format that is easy for users to understand.

[0657] The system for implementing this invention consists of a server, a terminal, and a user.

[0658] Users first use their device to set terms they find stressful and their alternative expressions. These settings are saved on the device's internal storage, such as a local database or cloud storage. For example, a user might set the term "failure" to be replaced with "attempt."

[0659] The server receives unstructured data collected from corporate email systems, chat applications, and other sources. This data may include messages and conversations exchanged between users. The received unstructured data is analyzed within the server using natural language processing (NLP) techniques. During the analysis process, the server identifies specific terms based on the recording medium.

[0660] Identified terms are replaced with alternative expressions based on user settings. This replacement process may be supported by a generative AI model, which is optimized to ensure that user-specified alternative expressions function correctly.

[0661] The replaced data is sent from the server to the terminal. On the terminal, the replaced data is displayed via a visualization mechanism, allowing the user to view the message with reduced stress.

[0662] For example, the server replaces the message "The project has failed" with "The project has ended as an attempt," allowing the user to see the message in the new wording on their device.

[0663] An example of a prompt message might be: "Explain how to process terms in a message, with the user configured to replace the term 'failure' with 'attempt'." This prompt guides the system's processing and helps ensure the intended substitution is performed.

[0664] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0665] Step 1:

[0666] The user inputs a term that causes them stress and its alternative expression through their device. The input data includes specific words or phrases and their corresponding modified expressions. This data is stored on a recording medium and serves as a reference for subsequent processes. Specifically, the user enters a term into the application's input form and sets it to change "failure" to "attempt."

[0667] Step 2:

[0668] The server receives unstructured data. Input includes voice and text messages received through a company's email system and chat services. The server retrieves this data and prepares it for the next analysis step. Specifically, it receives message data from the email server via an internet connection.

[0669] Step 3:

[0670] The server applies Natural Language Processing (NLP) to the unstructured data it receives and performs analysis. The input data includes messages and conversations to be analyzed, and NLP techniques are used to identify specific terms. Specifically, the analysis engine extracts the word "failure" from the message.

[0671] Step 4:

[0672] Based on the analysis results, the server selects an appropriate alternative expression from the configuration data of the recording medium and replaces the identified term. The input is the identified term and its context, and the output is the replaced message. In a specific example, the message "The project ended in failure" is replaced with "The project ended in a trial."

[0673] Step 5:

[0674] The server sends the replaced unstructured data to the terminal. In this process, formatted data is output and prepared for user viewing. Specifically, the replaced message is transferred to the terminal via the internet connection.

[0675] Step 6:

[0676] The terminal uses visualization methods to display the replaced data on the user interface. The input includes formatted unstructured data, and the output is displayed in a user-readable format. Specifically, a message such as "The project has ended in a trial" is displayed on the screen, which the user can view.

[0677] (Application Example 1)

[0678] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0679] In today's information-saturated world, users often encounter terms and expressions within content that they find offensive, leading to stress. In this information-overloaded environment, there is a need to enable users to enjoy content without stress. Traditional technologies often apply a uniform processing to individual terms, making flexible substitution that considers context difficult.

[0680] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0681] In this invention, the server includes means for acquiring user-specified stressful terms and their alternative expressions as configuration data, means for receiving audio or text information, means for analyzing the received data and identifying stressful terms based on the configuration data, means for replacing the identified stressful terms with user-specified alternative expressions, means for displaying the replaced data on a user interface, and means for filtering data provided from a content distribution service. This enables users to visually and comfortably use contextually replaced information while reducing stress.

[0682] A "user" refers to an individual who uses a system to enjoy content while reducing stress.

[0683] "Configuration data" refers to information that records terms that users specify as causing stress, along with their alternative expressions.

[0684] "Audio or text information" includes content related to communication consisting of audio data or text data.

[0685] "Means of analysis" refers to techniques used to identify specific terms from received information and to replace those terms.

[0686] "Stress terms" refer to words or phrases that users have designated as causing them stress.

[0687] "Alternative expressions" refer to terms or phrases specified by the user as replacements for stressful terms.

[0688] "User interface" refers to the environment, including screens and visual display devices, where the replaced data is displayed and the user can actually experience it.

[0689] "Filtering methods" refer to methods or processes for removing or transforming elements from distributed content that users may find offensive.

[0690] A "content distribution service" refers to a platform that provides users with digital information such as videos and ebooks.

[0691] "Visual display devices" refer to devices that provide users with information visually, such as smartphones and smart glasses.

[0692] This invention provides a system that enables users to comfortably enjoy online content while reducing personal stress. The system works as follows:

[0693] First, users use their smartphones or smart glasses to input and configure terms that cause them stress and their alternative expressions into the application. This configuration data is managed within the device and saved to cloud storage as needed.

[0694] The server receives audio and text information from content delivery services. The received data is analyzed using natural language processing libraries such as Python's NLTK and Spacy. During the analysis process, the server identifies stress terms that have been set in advance by the user. Then, it replaces these stress terms with alternative expressions specified by the user and formats the modified data.

[0695] The information replaced by this process is provided to the user through the user interface of a visual display device such as a smartphone or smart glasses. This allows the user to view the content without experiencing stress.

[0696] For example, if a user sets their system to replace the word "war" with "path to peace," then even if the term "war" is used in an ebook or video, the content the user views will display "path to peace."

[0697] An example of a prompt in a generative AI model is: "Please replace the following sentence with an alternative expression based on your settings. Settings: Replace 'war' with 'path to peace'. Sentence: 'Many changes occurred in last year's war.'" Using this prompt, the server can appropriately replace terminology and provide users with less stressful content.

[0698] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0699] Step 1:

[0700] Users input and configure terms they find stressful and their alternative expressions through their device. The entered information is stored on the device or in cloud storage. The input consists of stressful terms and their alternative expressions, while the output is stored on the device as configuration data.

[0701] Step 2:

[0702] The server receives audio or text information from a content distribution service. The input is digital content from the distribution service, and the output is the received text or audio data. The server prepares this as data for further analysis.

[0703] Step 3:

[0704] The server analyzes the received audio or text information using natural language processing libraries (e.g., Python's NLTK or Spacy). The input is the received text or audio data, and the data processing performed by the server includes tokenization, part-of-speech analysis, and contextual understanding. The output is the analyzed text information.

[0705] Step 4:

[0706] The server identifies user-defined stress terms from the analyzed data. The input is parsed text information, which the server uses to detect terms that cause stress. The output is a list of words and phrases identified as stress terms.

[0707] Step 5:

[0708] The server replaces identified stressed terms with alternative expressions specified by the user. The input is a list of stressed terms, and the server refers to the corresponding alternative expressions and performs the replacement. As a result of this data calculation, the output is the replaced character information.

[0709] Step 6:

[0710] The server formats the replaced data and sends it to the terminal. The terminal displays this data in the user interface. The input is the replaced text information, and the output on the terminal is the content displayed to the user. The user can then view the content without any stress.

[0711] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0712] This invention is a system that combines a function to replace terms that cause stress to the user with alternative expressions, with an emotion engine that analyzes the user's emotions. This system supports more appropriate communication.

[0713] First, the user uses their device to set terms that they find stressful and preferred alternative expressions. This setting data is stored on the device and used for subsequent language processing.

[0714] The server receives a voice or text message. At this stage, the emotion engine analyzes the user's voice tone and facial expressions to identify their emotional state. For example, it detects changes in emotions such as joy, anger, sadness, and happiness in real time while the user is speaking.

[0715] The server analyzes the content and determines if it contains terms that the user has identified as causing stress. Based on the user's emotional state obtained from the emotion engine, it selects the most appropriate alternative expression and performs substitution operations as needed. Substitutions based on emotional fluctuations can, for example, use lighter words when the user is relaxed and gentler expressions when they are stressed.

[0716] The final message data, including the alternative expressions generated in this way, is sent to the terminal and displayed on the user interface. This allows the user to receive messages with reduced stress.

[0717] As a specific use case, consider a situation where a user listening to an explanation during a meeting feels stressed by the word "difficult." When the emotion engine detects that the user is feeling tense, the server selects a milder alternative expression, such as "challenge," and adjusts the message to reduce the burden of communication.

[0718] This invention enables personalized communication support that takes user emotions into consideration, resulting in smoother information transmission.

[0719] The following describes the processing flow.

[0720] Step 1:

[0721] The user uses the terminal interface to input stressful terms and their alternative expressions, and saves this configuration data to the system.

[0722] Step 2:

[0723] The terminal saves the entered configuration data to an internal database or cloud storage on a remote server, making it accessible for future processes.

[0724] Step 3:

[0725] The device captures the user's voice and facial expressions in real time and sends them to the emotion engine. The emotion engine analyzes this data to identify the user's emotional state.

[0726] Step 4:

[0727] The server receives messages as audio or text data and sends this data to a natural language processing (NLP) engine. The NLP engine analyzes the received data and compares it with the user's configuration data to identify terms that cause stress.

[0728] Step 5:

[0729] The server implements personalized campaigns based on the user's emotional state, derived from the emotion engine, replacing stressful terms with appropriate alternative expressions. Different expressions are selected depending on whether the user is relaxed or stressed.

[0730] Step 6:

[0731] The server reformats the text or audio data after the replacement is complete and sends it to the terminal in the format best suited to the user's request.

[0732] Step 7:

[0733] The device receives the replaced message data and displays it in the user interface. Users can then view messages filtered based on their emotions and preferences.

[0734] (Example 2)

[0735] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0736] Traditional communication systems often lacked the means to select appropriate alternative expressions when users felt stressed by certain terms, resulting in a compromised communication experience. Furthermore, simply substituting terms without considering the user's emotional state was highly likely to create unnatural dialogue.

[0737] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0738] In this invention, the server includes means for acquiring user-specified terms that evoke emotions and their alternative expressions as configuration data, means for receiving voice or text information, and means for identifying the user's emotional state using an emotion analysis function. This enables the selection of the most appropriate alternative expression according to the user's emotional state.

[0739] A "user" refers to a person who uses a system to customize specific terminology and expressions and communicates with them.

[0740] "Emotion-evoking terminology" refers to expressions that evoke specific emotional responses in users, particularly those that cause stress.

[0741] An "alternative expression" refers to a different expression that replaces the original term to reduce user stress and facilitate communication.

[0742] "Configuration data" refers to digital data used to store information about terms specified by the user and their alternative expressions.

[0743] "Voice or text information" refers to the content of a message delivered by a user, including both audio and text input.

[0744] "Sentiment analysis function" refers to an algorithm or technology that determines the emotional state of a user from their voice tone and text content, and then selects an appropriate alternative expression based on that information.

[0745] "User interface" refers to the visual or manipulative means by which a user interacts with a system and confirms the information after replacement.

[0746] This invention is a system that facilitates communication while taking into account the user's emotional state. The system includes a terminal, a server, and an emotion analysis function to appropriately replace terms that the user finds stressful.

[0747] First, the user uses their device to set specific terms that cause stress and their corresponding alternative expressions. This information is stored on the device as configuration data. For example, the user might enter a setting to replace the term "difficult" with "challenge."

[0748] The server receives messages via voice or text input. After receiving a message, it uses sentiment analysis to identify the user's emotional state (e.g., tension, relaxation) from the tone of voice and the context of the text. This analysis helps determine what kinds of expressions the user is most receptive to.

[0749] If a term in the received information corresponds to a term that the user has identified as causing stress, the server will select the most appropriate alternative expression based on the emotional state and replace the term. For example, if the user identifies their emotional state as relaxed, the term can be replaced with a lighter word.

[0750] The adjusted messages are then delivered to the user on the device via the user interface. This allows the user to experience more comfortable and less stressful communication.

[0751] For example, if the word "difficult" is used during a meeting and the user perceives it as causing tension, the server will replace it with the word "challenge" to reduce the burden of the conversation. An example of a prompt to the generative AI model that enables this process could be the request, "Please rephrase this into a conversation that the user can listen to in a relaxed manner."

[0752] This system allows users to receive personalized, emotion-based communication support, thereby improving the quality of information transmission.

[0753] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0754] Step 1:

[0755] The user uses their device to set stressful terms and their corresponding alternative expressions. This creates input configuration data, which is then stored on the device. In this step, the user performs specific actions, such as setting the alternative expression for the term "difficult" to "challenge."

[0756] Step 2:

[0757] The server receives messages from the user in voice or text format. The input is either an audio file or text data. The server receives this and prepares it for the next parsing step. The specific action involves the user speaking into a terminal or sending a text message.

[0758] Step 3:

[0759] The server uses sentiment analysis functionality to identify the user's emotional state from the received audio or text. The input is the audio or text data received in step 2, and the output is the user's emotional state (e.g., tense, relaxed). The specific operation involves analyzing changes in voice tone and keywords in the text based on the sentiment analysis algorithm.

[0760] Step 4:

[0761] The server analyzes the received message data and refers to the configuration data to identify whether it contains stress-causing terms. The input is the received message data and configuration data, and the output is the presence or absence of stress-causing terms. The specific operation involves using text analysis software to verify the occurrence of terms.

[0762] Step 5:

[0763] Once stressful terms are identified, the server selects the most appropriate alternative expression based on the user's emotional state and replaces the stressful terms. The input is the result of step 4, and the output is the replaced message data. The specific operation is to execute the replacement algorithm according to the emotional state.

[0764] Step 6:

[0765] The server finally sends the replaced message data to the terminal, which then displays it on the user interface. The input is the replaced message data, and the output is the message displayed on the user interface. The terminal's concrete action is to represent the new message on the screen and provide it visually to the user.

[0766] This series of processes allows users to receive information in a less stressful way.

[0767] (Application Example 2)

[0768] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0769] Communication in modern society takes place in diverse environments, but friction can arise from stressful expressions and vocabulary. Furthermore, changes in emotional states often hinder effective communication. Especially in real-world environments such as physical stores, the quality of customer communication directly impacts business, requiring flexible responses tailored to customer emotions. This invention aims to solve these communication challenges and provide a system that enables appropriate information transmission.

[0770] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0771] In this invention, the server includes means for acquiring user-specified stressful terms and their alternative expressions as configuration data, means for receiving audio or text data, means for analyzing the received data and identifying stressful terms based on the configuration data, means for replacing the identified stressful terms with user-specified alternative expressions, means for displaying the replaced data on a user interface, and means for analyzing a person's emotional state using emotion recognition means in a real-world scenario and determining the expression to be replaced. This enables smoother communication through optimal expressions that correspond to emotions while reducing stress.

[0772] A "user" is the entity that uses the system to configure settings to reduce stress and receives alternative representations.

[0773] "Stressful terminology" refers to words or expressions that cause users to feel discomfort or tension in specific situations.

[0774] "Alternative expressions" are words or phrases used to replace stressful terms, conveying a milder and more positive impression.

[0775] "Voice or text data" refers to linguistic information in user communication, which is received and analyzed by the system.

[0776] "Configuration data" refers to data containing combinations of terms that users register as causing them stress and their alternative expressions.

[0777] "Emotion recognition means" refers to technology that analyzes a user's emotional state in real time and supports the selection of appropriate expressions.

[0778] A "user interface" refers to the screen or device through which information is exchanged between the user and the system.

[0779] A "real-world scenario" refers to a situation in which the system is used in an environment that exists physically, rather than virtually.

[0780] The system for implementing this invention supports communication between staff members in physical stores equipped with smart glasses and customers. The server retrieves stressful terms and corresponding alternative expressions set by the user (staff). It also receives the user's voice and text data, analyzes this data to identify stressful terms, and replaces them with alternative expressions. The smart glasses worn by the user analyze the customer's facial expressions and voice through a camera and microphone using an emotion recognition engine, and support appropriate language use in real time.

[0781] The hardware used includes smart glasses equipped with a camera, microphone, and display, which are used for data collection and display. The software uses OpenCV and TensorFlow for emotion recognition, Google Cloud Speech-to-Text for speech recognition, and spaCy for natural language processing. For data processing, the camera and microphone are used to acquire customer facial expressions and voice data, which is then analyzed using emotion recognition technology. Based on the resulting emotion data, the server selects the most appropriate alternative expression and conveys it to the user.

[0782] As a concrete example, when a store staff member (a user) communicates with a customer who is stressed by the phrase "We are out of stock," the emotion recognition engine detects the customer's dissatisfaction, and the server then suggests an alternative expression that includes positive information, such as "It's currently sold out, but we have it in stock soon."

[0783] An example of a prompt for a generative AI model is, "Analyze the customer's emotions from their facial expressions and suggest words to alleviate their stress." In this way, flexible communication tailored to the emotional state is achieved.

[0784] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0785] Step 1:

[0786] Users can set terms they find stressful and preferred alternative expressions on their device.

[0787] The user enters a pair of terms as configuration data, and the terminal saves it. The saved data is used in subsequent processing.

[0788] Step 2:

[0789] The server receives voice or text data.

[0790] This data will be used as input and prepared for analysis. Specifically, the audio data will be converted into text data using a speech recognition engine.

[0791] Step 3:

[0792] The server analyzes the received text data and identifies stress terms based on the configuration data.

[0793] The system takes a specified term as input and selects a predefined alternative expression. This process utilizes a natural language processing library to understand the context and extract the appropriate term.

[0794] Step 4:

[0795] The server performs emotion recognition based on data obtained from the camera and microphone, and analyzes the user's emotional state.

[0796] The system analyzes input facial expression data and voice tone, and an emotion recognition engine outputs an emotion label. This label then influences subsequent expression selections.

[0797] Step 5:

[0798] The server selects the most appropriate alternative expression based on the identified emotional state and contextual information.

[0799] An AI model generates appropriate alternative expressions using emotion labels and contextual information as input. The output is a new message candidate to be presented to the user.

[0800] Step 6:

[0801] The selected message is displayed in the user interface.

[0802] The user receives this outputted information through the smart glasses' display. They review the displayed information and use it to facilitate smooth communication with customers.

[0803] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0804] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0805] 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 this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0806] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0807] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0808] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0809] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0810] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0811] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0812] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0813] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0814] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0815] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0816] 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.

[0817] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0818] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0819] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0820] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0821] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0822] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0823] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0824] The following is further disclosed regarding the embodiments described above.

[0825] (Claim 1)

[0826] A means of obtaining user-specified stressful terms and their alternative expressions as configuration data,

[0827] Means for receiving audio or text data,

[0828] A means of analyzing received data and identifying stress terms based on configuration data,

[0829] A means of replacing identified stress terms with alternative expressions specified by the user,

[0830] A means of displaying the replaced data in the user interface,

[0831] A system that includes this.

[0832] (Claim 2)

[0833] The system according to claim 1, wherein the substitution of stressful terms is designed to be optimized on a contextual basis.

[0834] (Claim 3)

[0835] The system according to claim 1, further comprising speech recognition means for converting received audio data into text data.

[0836] "Example 1"

[0837] (Claim 1)

[0838] A means of acquiring the terms that users specify as causing stress and their alternative expressions as a recording medium,

[0839] A means for receiving unstructured data,

[0840] A means for analyzing received unstructured data and identifying specific terms based on the recording medium,

[0841] A means for replacing identified specific terms with alternative expressions specified by the user,

[0842] A means for displaying the substituted unstructured data on a visualization means,

[0843] A system that includes this.

[0844] (Claim 2)

[0845] The system according to claim 1, wherein the substitution of terms is designed to be optimized based on the recording medium.

[0846] (Claim 3)

[0847] The system according to claim 1, further comprising speech recognition means for converting received audio data into unstructured data.

[0848] "Application Example 1"

[0849] (Claim 1)

[0850] A means of obtaining user-specified stressful terms and their alternative expressions as configuration data,

[0851] Means for receiving audio or text information,

[0852] A means of analyzing received data and identifying stress terms based on configuration data,

[0853] A means of replacing identified stress terms with alternative expressions specified by the user,

[0854] A means of displaying the replaced data in the user interface,

[0855] A means of filtering data provided by content distribution services,

[0856] A system that includes this.

[0857] (Claim 2)

[0858] The system according to claim 1, wherein the replacement of received information is designed to be optimized on a contextual basis, and the replaced data is provided to the user using a visual display device.

[0859] (Claim 3)

[0860] The system according to claim 1, further comprising speech recognition means for converting received audio data into text information, and for detecting stressful terms in the delivered audiovisual content and making it viewable with alternative expressions.

[0861] "Example 2 of combining an emotion engine"

[0862] (Claim 1)

[0863] A means of obtaining, as configuration data, terms that evoke emotions specified by the user and their alternative expressions,

[0864] Means for receiving audio or text information,

[0865] A means of identifying a user's emotional state using an emotion analysis function,

[0866] A means of analyzing received information and identifying terms based on configuration data,

[0867] A means of selecting the most appropriate alternative expression according to the user's emotional state and replacing the identified term,

[0868] A means of displaying the replaced information in the user interface,

[0869] A system that includes this.

[0870] (Claim 2)

[0871] The system according to claim 1, which is designed so that term substitution is optimized based on the user's emotional state and context.

[0872] (Claim 3)

[0873] The system according to claim 1, further comprising a speech recognition function that converts received audio information into text information.

[0874] "Application example 2 when combining with an emotional engine"

[0875] (Claim 1)

[0876] A means of obtaining user-specified stressful terms and their alternative expressions as configuration data,

[0877] Means for receiving audio or text data,

[0878] A means of analyzing received data and identifying stress terms based on configuration data,

[0879] A means of replacing identified stress terms with alternative expressions specified by the user,

[0880] A means of displaying the replaced data in the user interface,

[0881] In real-world situations, means for analyzing human emotional states using emotion recognition methods and determining the expressions to be replaced,

[0882] A system that includes this.

[0883] (Claim 2)

[0884] The system according to claim 1, wherein the substitution of stressful terms is designed to be optimized based on context and emotional state.

[0885] (Claim 3)

[0886] The system according to claim 1, further comprising speech recognition means for converting received audio data into text data, and emotion recognition means for analyzing facial expressions in real time. [Explanation of Symbols]

[0887] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means of obtaining user-specified stressful terms and their alternative expressions as configuration data, Means for receiving audio or text data, A means of analyzing received data and identifying stress terms based on configuration data, A means of replacing identified stress terms with alternative expressions specified by the user, A means of displaying the replaced data in the user interface, A system that includes this.

2. The system according to claim 1, wherein the substitution of stressful terms is designed to be optimized on a contextual basis.

3. The system according to claim 1, further comprising speech recognition means for converting received audio data into text data.

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