system

Abstract

We provide the system. [Solution] A means of accepting voice input, A means for converting the voice input into text data, A means for translating the text data into multiple languages, A means of generating phrases suitable for negotiation, Means for outputting the translated text data and the generated phrase, 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 chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] During overseas travel, the language barrier faced by many travelers makes local negotiations difficult and increases travel stress. In particular, in situations such as shopping in the market, negotiating transportation fares, and upgrading accommodation facilities, language problems significantly hinder the travel experience. Also, negotiations may not proceed smoothly, and transactions may end under unfavorable conditions. Technical means for improving these situations and enabling travelers to conduct more comfortable and smooth negotiations are required.

Means for Solving the Problems

[0005] This invention provides a system that accepts voice input, converts it into text data, and translates it into multiple languages. This system overcomes language barriers by processing the user's voice input in real time and providing appropriate translations. Furthermore, it generates and presents phrases suitable for negotiation using historical data and pattern recognition algorithms. This allows the user to gain an advantageous position in negotiations and conduct transactions smoothly.

[0006] "Voice input" is a method of providing information as voice data by having the user speak into it.

[0007] "Text data" refers to a data format that converts voice input into written text.

[0008] Translation is the process of converting text data from one language into another language.

[0009] A "phrase" is a short sentence or expression that is appropriate for a specific situation and is used to effectively advance negotiations.

[0010] A "pattern recognition algorithm" is a computational method for finding specific patterns or trends within data.

[0011] "Output" refers to the act or method of notifying the user of the processed results. [Brief explanation of the drawing]

[0012] [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] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This 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 a plurality of emotions are mapped. [Figure 10] It shows an emotion map to which a plurality of 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

[0013] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described according to the accompanying drawings.

[0014] First, the language used in the following description will be explained.

[0015] In the following embodiments, the numbered 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.

[0016] In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0017] In the following embodiments, the numbered storage is one or more non-volatile storage devices that store various programs, various parameters, and the like. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like.

[0018] In the following embodiments, the numbered communication I / F (Interface) is an interface that includes a communication processor, an antenna, and the like. 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).

[0019] 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."

[0020] [First Embodiment]

[0021] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0022] 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.

[0023] 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).

[0024] 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.

[0025] 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.

[0026] 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.

[0027] 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.

[0028] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0029] 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.

[0030] 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.

[0031] 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.

[0032] 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".

[0033] The system based on this invention is an application that provides support to users in overcoming language barriers and negotiation obstacles while traveling abroad. It is primarily available on mobile devices such as smartphones and tablets. Users begin using the app by launching it and selecting a negotiation scenario that suits their purpose. For example, consider using the app when shopping at a market.

[0034] First, when a user says, "Can you lower the price of this product?", the device recognizes the voice and converts the voice data into text data. This text data is sent to a server, which receives it and begins processing. The server uses natural language processing technology to analyze the meaning of the text and understand the user's intent. Then, based on the scene selected by the user, the server provides a translation appropriate to the local language and generates phrases suitable for negotiation. This phrase generation uses expressions predicted to be effective in actual negotiations based on past data and pattern recognition algorithms.

[0035] For example, the server generates a common local negotiation phrase, such as "Is it possible to lower the price of that item a little more?", along with the translation, and sends it to the terminal. The terminal displays this on its user interface and, if it has a voice assistant function, also outputs it aloud. The user can then use this to continue their conversation with the local seller.

[0036] In this way, the system helps users easily overcome language barriers and negotiate on more favorable terms. The system is designed to process information in real time and provide users with rapid feedback.

[0037] The following describes the processing flow.

[0038] Step 1:

[0039] The user launches the application. The user selects a desired negotiation scenario (e.g., shopping at a market). The device performs the necessary settings according to the selected scenario.

[0040] Step 2:

[0041] The user speaks, inputting the content they want to negotiate via voice. The device receives the voice input via its microphone and uses speech recognition to convert the voice data into text data.

[0042] Step 3:

[0043] The terminal sends the converted text data to the server. The server receives the text data and begins analysis.

[0044] Step 4:

[0045] The server uses natural language processing technology to analyze the meaning of received text data and understand the user's intent. The server then performs translation into the corresponding multilingual languages.

[0046] Step 5:

[0047] The server generates translation results and phrases suitable for negotiation. This uses a pattern recognition algorithm to derive negotiation phrases tailored to specific situations.

[0048] Step 6:

[0049] The server sends the generated translations and phrases to the device. The device receives them and displays them in the user interface. If a voice assistant is available, the information is also output audibly.

[0050] Step 7:

[0051] Users negotiate with locals based on the information displayed on their devices. The information obtained from the app is helpful in negotiations.

[0052] (Example 1)

[0053] 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."

[0054] Many individuals face language barriers and negotiation difficulties when traveling abroad or conducting international business. In particular, communication with people from different cultures and with different customs can lead to linguistic misunderstandings and complicate negotiations. Therefore, real-time language translation and the provision of appropriate phrases for negotiations are essential.

[0055] 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.

[0056] In this invention, the server includes means for receiving voice input, means for converting the voice input into text data, means for analyzing the text data using natural language processing, means for translating the text data into multiple languages, means for generating expressions suitable for negotiation using a generation AI model, and means for outputting the generated expressions via a user interface. This enables users to easily engage in real-time interlingual communication and negotiation.

[0057] A "means for receiving voice input" refers to a mechanism that recognizes the user's voice and inputs it into a format that the system can process.

[0058] "Means" refers to the methods or devices used to achieve a specific objective.

[0059] "Means of converting to text data" refers to a function that analyzes audio data and converts it into a string format.

[0060] "Methods of analysis using natural language processing" refer to techniques that utilize generative AI models and other algorithms to understand and interpret the meaning of text data.

[0061] "Means of translation into multiple languages" refers to functions that convert text data into multiple languages, facilitating communication between people who speak different languages.

[0062] "A method for generating expressions suitable for negotiation using an AI model" refers to a technique that generates effective negotiation phrases for specific cultures and situations based on past data and pattern recognition technology.

[0063] "Means of outputting via a user interface" refers to an interface for presenting generated information to the user visually or audibly.

[0064] This invention provides support for users to overcome language barriers in overseas travel and international business situations. It is primarily implemented on mobile devices such as smartphones and tablets. First, the user launches the application on their device and then starts the system by selecting a negotiation scenario appropriate to the situation in which they are using it.

[0065] For example, in a scenario where a user is shopping at a market, they might speak into the device and say, "Can you lower the price of this item?" The device then receives this voice input using its microphone and converts the voice data into text data using speech recognition software (e.g., a speech recognition API).

[0066] The converted text data is sent to a server via the internet. The server analyzes the text data using natural language processing technology and processes it using a generative AI model to understand the user's intent. At each stage, for example, APIs for natural language processing can be utilized.

[0067] The server then translates the text data into different languages ​​and simultaneously generates expressions suitable for negotiation. This process utilizes historical data and pattern recognition techniques. The generated translations and phrases are then sent to the terminal.

[0068] Finally, the device displays the generated information through the user interface and, in some cases, provides voice output using the voice assistant function. This function allows the user to visually and audibly verify the translation and negotiation phrases.

[0069] As a concrete example, consider a scenario where a user is buying wine at a market in France and asks, "Can I get a small discount on this wine?" An example of a prompt in response to this statement is, "Generate friendly negotiation phrases for buying wine at a French market." Based on this prompt, the system provides an efficient and culturally appropriate response.

[0070] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0071] Step 1:

[0072] The user launches the application. The user selects a negotiation scene from a menu provided on the device screen. The selected scene is saved on the device and used as contextual information in subsequent processes. The main input in this step is the user's selection. The output is information about the selected scene.

[0073] Step 2:

[0074] The user inputs questions or requests by voice. The device captures the voice data using the microphone and converts the voice into text data using a speech recognition API. At this stage, the input is raw voice data, and the output is the user's spoken content represented in text format.

[0075] Step 3:

[0076] The terminal sends text data to the server. Specifically, the data is securely transmitted over the internet using the HTTPS protocol. The input is the text data from the terminal, and the output is this data received by the server.

[0077] Step 4:

[0078] The server analyzes the received text data using a natural language processing engine. A generative AI model is used to understand the user's intent and obtain detailed analysis results. The input is text data, and the output is analysis results including the user's intent and contextual information.

[0079] Step 5:

[0080] The server translates the text into the required language based on the analysis results. It also uses a generative AI model to generate phrases suitable for negotiation. The input to this process is the analysis results, and the output is translated content containing expressions suitable for negotiation.

[0081] Step 6:

[0082] The server sends the generated content to the terminal. This procedure uses a secure protocol to prevent the leakage of user information. The input is the generated content, and the output is the information received by the terminal.

[0083] Step 7:

[0084] The terminal displays the received content on the user interface and, if necessary, outputs it audibly using a voice assistant. Input is content from the server, and output is the visual and auditory presentation of information to the user. This allows the user to instantly use translations and appropriate negotiation phrases.

[0085] (Application Example 1)

[0086] 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."

[0087] Language barriers remain a significant challenge when traveling or making business visits to different cultural regions. In particular, in situations requiring direct communication, such as negotiations or purchasing goods, a lack of language comprehension often prevents achieving desired results. Furthermore, smooth negotiations require appropriate phrases based on the situation and context, and there is a need for technology that automatically supports this. Additionally, there is a need for systems that utilize the user's visual information to assist in on-site situational judgment.

[0088] 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.

[0089] In this invention, the server includes means for receiving voice input, means for converting voice input into text data, means for translating text data into multiple languages, means for generating phrases suitable for negotiation, means for outputting translated text data and generated phrases, means for capturing and analyzing visual data, means for obtaining information from captured visual data, and means for presenting content in real time based on visual data. This enables users to obtain appropriate negotiation phrases based on local language and visual information, allowing for smooth communication across language and cultural barriers.

[0090] "Voice input" is a means for users to communicate information to a system through their voice.

[0091] "Converting to text data" refers to the process of changing voice input into written text.

[0092] "Translating into multiple languages" is the act of converting text expressed in one language into another language.

[0093] "Generating phrases suitable for negotiation" means creating appropriate language expressions for different situations in order to facilitate smooth negotiations.

[0094] "Translated text data" refers to character data that has been converted from the original language to another language.

[0095] "Outputting the generated phrase" refers to the process of showing the created linguistic expression to the user.

[0096] "Capturing and analyzing visual data" means collecting visual information using cameras or other means and interpreting it.

[0097] "Acquiring information from captured visual data" means drawing useful insights based on visually perceived data.

[0098] "Presenting content in real time based on visual data" means presenting the content obtained from visual information to the user immediately.

[0099] The system for carrying out this invention uses a mobile terminal including smart glasses. The user wears the smart glasses and provides visual and auditory input. The server converts the auditory input into text data using speech recognition software. This text data is then translated into multiple languages ​​required by the user using translation software. General natural language processing techniques are used as the translation engine. In this process, the server utilizes historical data and pattern recognition algorithms to generate phrases suitable for negotiation. The generated phrases are displayed in real time on the smart glasses' display and also output as audio.

[0100] Furthermore, the device's camera acquires visual data, which is then analyzed using libraries such as OpenCV. This analysis captures detailed information about the product the user is viewing and information that could be advantageous in negotiations. Based on this information, the server processes it to generate the most suitable negotiation phrases for the user.

[0101] As a concrete example, when a user is considering purchasing pottery in a foreign market, the smart glasses recognize the pottery using visual data and ask via voice input, "Can you lower the price of this beautiful vase?" In response, the translated text "¿Puede bajar el precio de este hermoso jarrón un poco más?" is displayed on the glasses and also provided audibly. In this way, the user can smoothly negotiate with the local seller.

[0102] Example prompt: "Translate phrases that could be used in negotiations in a Spanish market from English to Spanish, and output them in a version suitable for negotiations in Spain."

[0103] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0104] Step 1:

[0105] The device acquires voice input from the user via the microphone. The voice input is entered into the device in the form of voice data. The device uses voice recognition software to convert this voice data into text data. The converted text data is then sent to the next processing step.

[0106] Step 2:

[0107] The server receives the converted text data. The server uses natural language processing techniques to analyze the meaning of the text data. This analysis process involves linguistic analysis to specifically understand the intent of the input text. The analysis results are then passed on to the next translation process.

[0108] Step 3:

[0109] The server translates the analyzed text data into the specified language using a multilingual translation engine. The analyzed text is used as input, and translated text data is generated. The translated result is in the local language that the user can use for negotiations, and is then supplied to the next phrase generation process.

[0110] Step 4:

[0111] The server generates phrases suitable for negotiation based on the translated text data. Here, pattern recognition algorithms and historical negotiation data are used to select and generate the most appropriate negotiation phrases. The generated phrases are constructed to be directly useful in negotiations.

[0112] Step 5:

[0113] The terminal receives translated and negotiation phrases sent from the server and displays them on its screen. In addition, it outputs them audibly via a voice assistant function. This allows the user to accurately understand the presented information and conduct negotiations appropriately.

[0114] Step 6:

[0115] The device's camera captures visual data of the object the user is looking at in real time. The input visual data is analyzed using an image processing library (e.g., OpenCV). This process extracts visual features and recognizes products the user is interested in. The information obtained is then used as additional information to support the user's negotiation process.

[0116] 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.

[0117] This invention combines a system that overcomes language barriers and supports smooth negotiations with an emotion engine that recognizes the user's emotions. This system can be used by users when negotiating in markets, taxis, hotels, etc., while traveling abroad. The emotion engine analyzes the user's emotions from voice input and reflects them in the negotiation content and phrase generation.

[0118] The user launches the application on their smartphone and begins negotiations. The smartphone (device) accepts voice input and converts it into text data using its voice recognition function. At this time, the built-in emotion engine analyzes the user's emotions from the voice. For example, if the user is nervous, it works to generate appropriate negotiation phrases based on that information.

[0119] The converted text data and sentiment analysis results are sent to the server. The server analyzes the data, performs translation using natural language processing technology, and generates phrases suitable for negotiation based on the sentiment engine's analysis results. Here, the phrases are adjusted according to the user's emotions; for example, if the user is nervous, words that promote relaxation are selected. Next, the server sends these results back to the terminal.

[0120] The device displays translated text and generated phrases in the user interface, and provides information via voice if a voice assistant is available. This allows users to negotiate with locals while receiving real-time advice tailored to their emotions.

[0121] The emotion engine monitors changes in emotions during negotiations and records the data for future negotiations. This accumulated data is used to generate and translate phrases based on the user's individual negotiation patterns. As a result, the system is designed to optimize itself for the user the more it is used, gradually making negotiations easier.

[0122] The following describes the processing flow.

[0123] Step 1:

[0124] The user launches the application on their smartphone and selects the scenario they want to negotiate. For example, they might choose a market transaction.

[0125] Step 2:

[0126] The user speaks into their smartphone's microphone to input the details of their negotiation. The device receives the voice input and uses speech recognition to convert the speech into text data.

[0127] Step 3:

[0128] The device sends voice data to an emotion engine, which analyzes the user's emotions based on factors such as tone and speed of voice. For example, it might determine that the voice is tense.

[0129] Step 4:

[0130] The device sends the converted text data and sentiment analysis results to the server. The server analyzes the received data and translates the text data into the specified language using natural language processing technology.

[0131] Step 5:

[0132] The server generates phrases suitable for negotiation based on the results of sentiment analysis. The phrases are adjusted according to the user's emotional state. For example, it might create a phrase with a nuance like, "You can negotiate in a relaxed manner."

[0133] Step 6:

[0134] The server sends the translation result and the generated phrase back to the terminal.

[0135] Step 7:

[0136] The device receives data from the server and displays translated text and negotiation phrases on the user interface. Furthermore, if a voice assistant function is available, the information is output aloud.

[0137] Step 8:

[0138] The user uses this information to continue negotiations with local people. The emotion engine continuously monitors changes in emotions during negotiations and accumulates data to improve future negotiations.

[0139] (Example 2)

[0140] 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".

[0141] In international negotiations, language barriers can make it difficult to conduct negotiations as intended. Furthermore, the lack of expressions that adequately reflect user emotions can negatively impact negotiation outcomes. Additionally, the failure to optimize systems based on negotiation experience leads to decreased efficiency, which is another challenge.

[0142] 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.

[0143] In this invention, the server includes means for converting voice input into text data, means for analyzing emotional states, and means for performing translation and generating phrases appropriate to the emotions. This enables smooth negotiations that transcend language barriers and allows for the provision of appropriate phrases according to the user's emotional state. Furthermore, based on the record of emotional changes, optimization can be performed in subsequent negotiations, improving negotiation efficiency.

[0144] "Voice input" refers to information received from the user via voice.

[0145] "Text data" refers to data obtained by analyzing voice input and representing its content as textual information.

[0146] "Emotional state" refers to the psychological state inferred from the user's voice and language, and includes states such as tension and relaxation.

[0147] "Multilingual translation" refers to the process of converting text data from one language into another different language.

[0148] "Phrase generation" refers to the process of creating appropriate linguistic expressions for specific situations and purposes.

[0149] "Output" refers to the act of a system visualizing or auditorily presenting its processing results to the user.

[0150] "Monitoring" refers to the continuous observation and recording of information over a certain period of time.

[0151] "Recording" refers to the act of saving observed data in a format that can be used later.

[0152] "Reflecting in phrase generation and translation" refers to the process of improving linguistic expressions and translation results based on past data.

[0153] This invention is a system that helps users overcome language barriers and conduct negotiations smoothly. Users can initiate voice input using a dedicated application on a mobile device. The mobile device (terminal) uses voice recognition to convert the user's voice into text data. At this time, a built-in emotion engine analyzes the user's emotional state based on the voice data. For example, if the user is nervous, that state is recorded and analyzed.

[0154] The results of this conversion and analysis are transmitted to a central unit (server) using communication technology. The server translates the text into multiple languages ​​using generative AI models and natural language processing technologies. Specifically, general generative AI models and translation APIs are used. At this time, phrases suitable for negotiation are generated based on the analyzed emotional state. For example, for a nervous user, phrases that encourage relaxation are added.

[0155] The server sends the completed translation and phrases back to the terminal. The terminal displays the results on its user interface, and if a voice assistant is available, it can also be used to present the information. Based on this information, the user can conduct actual negotiations more effectively.

[0156] Furthermore, the terminal monitors the user's emotional changes during negotiations and records this data. This emotional data is stored on the server side and provides personalized improvements to phrase generation and translation in subsequent negotiations. In this way, the system is designed to optimize to the user's needs with each use.

[0157] As a concrete example, suppose a user wants to order food at a restaurant and uses voice input on their mobile device to ask, "Do you have vegetarian dishes?" This input is converted to text and translated as "Do you have vegetarian dishes?" At the same time, if the emotion engine detects the user's anxiety, it generates a phrase such as, "We'll explain everything in detail, so please ask if you have any questions."

[0158] An example of a specific prompt for a generative AI model would be: "What are some effective negotiation phrases a customer might use when asking about vegetarian options at a restaurant? Please also consider situations where the user might feel anxious."

[0159] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0160] Step 1:

[0161] The user launches an application on their mobile device and performs voice input. The device's microphone captures the user's voice and temporarily stores it as audio data. In this process, the input is the user's voice, and the output is digitized audio data. Specifically, if the user says, for example, "Please tell me the way to the museum," the voice input is completed.

[0162] Step 2:

[0163] The device uses a speech recognition module to convert speech data into text data. This conversion process analyzes the input speech and generates a string of characters based on an acoustic model. The input is digitized speech data, and the output is the corresponding text data. For example, "Please tell me the way to the museum" is converted to text such as "Could you tell me how to get to the museum?"

[0164] Step 3:

[0165] The device's emotion engine analyzes emotional states from text data. It considers characteristics such as voice tone and speed to evaluate the user's psychological state. The input for this step is voice features, and the output is the evaluation of the user's emotional state. For example, if the voice is trembling, it is judged to be a state of tension.

[0166] Step 4:

[0167] The terminal sends the converted text data and the sentiment assessment results to the server. The data is securely transmitted to the server using a communication protocol. In this process, the input is the text data and sentiment assessment results, and the output is the arrival of the data on the server. The server prepares to analyze the received data.

[0168] Step 5:

[0169] The server inputs the received text data into a generating AI model for translation into a foreign language. In addition, it incorporates the sentiment evaluation results to generate appropriate phrases. In this step, the input is text data and sentiment, while the output is the translated text and sentiment-appropriate phrases. For example, a user who appears nervous might be given the phrase, "Don't worry, we'll give you detailed directions."

[0170] Step 6:

[0171] The server sends the processed translated text and negotiation phrases back to the terminal. It transmits the information to the terminal using a communication protocol, ensuring data security. The input for this step is the aforementioned translated results and phrases, and the output is the arrival of the data at the terminal.

[0172] Step 7:

[0173] The terminal displays the received translated text and phrases on the user interface and also provides audio output using a voice assistant. In this step, the input is the translated results and phrases sent from the server, and the output is the information presented to the user. Specifically, the translated results and phrases are displayed on the screen, and additional audio information is provided to the user.

[0174] Step 8:

[0175] The device monitors the user's emotional changes during negotiations and records emotional data. This data is used for phrase generation and translation in subsequent sessions. The input for this step is new information from the user's voice, and the output is the recorded emotional data.

[0176] (Application Example 2)

[0177] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0178] In international work environments, multinational workers using diverse languages ​​can lead to communication breakdowns and misunderstandings of emotions. Maintaining efficient and smooth work instructions and collaborative systems in such environments is challenging. Furthermore, appropriately understanding workers' emotions and generating instructions tailored to the situation is also a challenge.

[0179] 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.

[0180] In this invention, the server includes means for receiving voice input, means for converting it into text data, and means for translating it into multiple languages. This enables real-time communication and emotionally responsive output between different languages.

[0181] "Means for receiving voice input" refers to a device or process that converts voice into a digital signal and captures it in a format usable by the system.

[0182] "Methods for converting to text data" refers to technologies that analyze audio signals and convert their content into corresponding text formats.

[0183] "Methods for translating into multiple languages" refers to the process of converting text data from its original language to another specified language.

[0184] "Methods for generating phrases suitable for negotiation" refer to technologies that automatically generate expressions to facilitate negotiation and communication in response to specific situations and emotions.

[0185] "Means for outputting translated text data and generated phrases" refers to means for providing the translated content and generated phrases to the user through audio or a display device.

[0186] "Means for analyzing the emotions of workers in a specific environment" refers to technologies that analyze voice and nonverbal signals to identify the worker's current emotional state.

[0187] "Means for generating and outputting appropriate instructions based on analyzed emotions" refers to a technology that creates appropriate and effective instructions based on the results of an emotion analysis of a worker and presents them via voice or display.

[0188] To implement this invention, a terminal equipped with a microphone and speaker is used as hardware for processing voice input. The terminal receives voice input and converts the voice into text data using speech recognition software (e.g., Google® Cloud Speech-to-Text). This text data is then translated into multiple languages ​​using natural language processing technology (e.g., Google Translate API).

[0189] Next, an emotion analysis engine (e.g., Microsoft® Azure® Cognitive Services) analyzes the voice data to determine the worker's emotions. Based on this emotion data, it generates phrases suitable for negotiation or work instructions. The generated phrases are translated into the specified language and output through the terminal's display and speaker.

[0190] The server integrates this data and generates appropriate instructions in real time based on the analysis results. Through this system, users can achieve smooth communication that transcends language barriers. Furthermore, because it can respond to changes in workers' emotions, it is expected to strengthen cooperation in international work environments.

[0191] As a concrete example, in a factory, if a Japanese-speaking supervisor needs to give real-time safety instructions to a Spanish-speaking worker, this system can enable smooth and unambiguous instructions.

[0192] An example of a prompt is: "Consider how to use speech recognition and sentiment analysis to ensure accurate work instructions while facilitating smooth communication in a multilingual environment."

[0193] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0194] Step 1:

[0195] The device uses a microphone to receive the user's voice as input. The received voice data is then prepared for processing as a digital signal.

[0196] Step 2:

[0197] The device uses speech recognition software to convert the audio data into text data. This conversion transforms the audio data into character information with a linguistic structure. The output text data is then passed on to the next process.

[0198] Step 3:

[0199] The server receives text data and performs multilingual translation using natural language processing technology. The input text data is translated into the specified language, and the translated text is generated.

[0200] Step 4:

[0201] The server uses an emotion analysis engine to analyze emotional data from the input voice data. This analysis outputs the user's emotional state as specific parameters.

[0202] Step 5:

[0203] The server generates phrases appropriate to specific situations based on analyzed sentiment data. Using a generative AI model, it creates phrases suitable for negotiations and work instructions, and the generated phrases are then refined and output.

[0204] Step 6:

[0205] The terminal receives the translated text and generated phrases sent from the server. It displays this information on the user interface and outputs it as audio through the speaker. The user can then make decisions based on this information.

[0206] 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.

[0207] 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.

[0208] 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.

[0209] [Second Embodiment]

[0210] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0211] 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.

[0212] 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).

[0213] 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.

[0214] 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.

[0215] 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).

[0216] 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.

[0217] 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.

[0218] 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.

[0219] 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.

[0220] 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.

[0221] 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".

[0222] The system based on this invention is an application that provides support to users in overcoming language barriers and negotiation obstacles while traveling abroad. It is primarily available on mobile devices such as smartphones and tablets. Users begin using the app by launching it and selecting a negotiation scenario that suits their purpose. For example, consider using the app when shopping at a market.

[0223] First, when a user says, "Can you lower the price of this product?", the device recognizes the voice and converts the voice data into text data. This text data is sent to a server, which receives it and begins processing. The server uses natural language processing technology to analyze the meaning of the text and understand the user's intent. Then, based on the scene selected by the user, the server provides a translation appropriate to the local language and generates phrases suitable for negotiation. This phrase generation uses expressions predicted to be effective in actual negotiations based on past data and pattern recognition algorithms.

[0224] For example, the server generates a common local negotiation phrase, such as "Is it possible to lower the price of that item a little more?", along with the translation, and sends it to the terminal. The terminal displays this on its user interface and, if it has a voice assistant function, also outputs it aloud. The user can then use this to continue their conversation with the local seller.

[0225] In this way, the system helps users easily overcome language barriers and negotiate on more favorable terms. The system is designed to process information in real time and provide users with rapid feedback.

[0226] The following describes the processing flow.

[0227] Step 1:

[0228] The user launches the application. The user selects a desired negotiation scenario (e.g., shopping at a market). The device performs the necessary settings according to the selected scenario.

[0229] Step 2:

[0230] The user speaks, inputting the content they want to negotiate via voice. The device receives the voice input via its microphone and uses speech recognition to convert the voice data into text data.

[0231] Step 3:

[0232] The terminal sends the converted text data to the server. The server receives the text data and begins analysis.

[0233] Step 4:

[0234] The server uses natural language processing technology to analyze the meaning of received text data and understand the user's intent. The server then performs translation into the corresponding multilingual languages.

[0235] Step 5:

[0236] The server generates translation results and phrases suitable for negotiation. This uses a pattern recognition algorithm to derive negotiation phrases tailored to specific situations.

[0237] Step 6:

[0238] The server sends the generated translations and phrases to the device. The device receives them and displays them in the user interface. If a voice assistant is available, the information is also output audibly.

[0239] Step 7:

[0240] Users negotiate with locals based on the information displayed on their devices. The information obtained from the app is helpful in negotiations.

[0241] (Example 1)

[0242] 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."

[0243] Many individuals face language barriers and negotiation difficulties when traveling abroad or conducting international business. In particular, communication with people from different cultures and with different customs can lead to linguistic misunderstandings and complicate negotiations. Therefore, real-time language translation and the provision of appropriate phrases for negotiations are essential.

[0244] 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.

[0245] In this invention, the server includes means for receiving voice input, means for converting the voice input into text data, means for analyzing the text data using natural language processing, means for translating the text data into multiple languages, means for generating expressions suitable for negotiation using a generation AI model, and means for outputting the generated expressions via a user interface. This enables users to easily engage in real-time interlingual communication and negotiation.

[0246] A "means for receiving voice input" refers to a mechanism that recognizes the user's voice and inputs it into a format that the system can process.

[0247] "Means" refers to the methods or devices used to achieve a specific objective.

[0248] "Means of converting to text data" refers to a function that analyzes audio data and converts it into a string format.

[0249] "Methods of analysis using natural language processing" refer to techniques that utilize generative AI models and other algorithms to understand and interpret the meaning of text data.

[0250] "Means of translation into multiple languages" refers to functions that convert text data into multiple languages, facilitating communication between people who speak different languages.

[0251] "A method for generating expressions suitable for negotiation using an AI model" refers to a technique that generates effective negotiation phrases for specific cultures and situations based on past data and pattern recognition technology.

[0252] "Means of outputting via a user interface" refers to an interface for presenting generated information to the user visually or audibly.

[0253] This invention provides support for users to overcome language barriers in overseas travel and international business situations. It is primarily implemented on mobile devices such as smartphones and tablets. First, the user launches the application on their device and then starts the system by selecting a negotiation scenario appropriate to the situation in which they are using it.

[0254] For example, in a scenario where a user is shopping at a market, they might speak into the device and say, "Can you lower the price of this item?" The device then receives this voice input using its microphone and converts the voice data into text data using speech recognition software (e.g., a speech recognition API).

[0255] The converted text data is sent to a server via the internet. The server analyzes the text data using natural language processing technology and processes it using a generative AI model to understand the user's intent. At each stage, for example, APIs for natural language processing can be utilized.

[0256] The server then translates the text data into different languages ​​and simultaneously generates expressions suitable for negotiation. This process utilizes historical data and pattern recognition techniques. The generated translations and phrases are then sent to the terminal.

[0257] Finally, the device displays the generated information through the user interface and, in some cases, provides voice output using the voice assistant function. This function allows the user to visually and audibly verify the translation and negotiation phrases.

[0258] As a concrete example, consider a scenario where a user is buying wine at a market in France and asks, "Can I get a small discount on this wine?" An example of a prompt in response to this statement is, "Generate friendly negotiation phrases for buying wine at a French market." Based on this prompt, the system provides an efficient and culturally appropriate response.

[0259] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0260] Step 1:

[0261] The user launches the application. The user selects a negotiation scene from a menu provided on the device screen. The selected scene is saved on the device and used as contextual information in subsequent processes. The main input in this step is the user's selection. The output is information about the selected scene.

[0262] Step 2:

[0263] The user inputs questions or requests by voice. The device captures the voice data using the microphone and converts the voice into text data using a speech recognition API. At this stage, the input is raw voice data, and the output is the user's spoken content represented in text format.

[0264] Step 3:

[0265] The terminal sends text data to the server. Specifically, the data is securely transmitted over the internet using the HTTPS protocol. The input is the text data from the terminal, and the output is this data received by the server.

[0266] Step 4:

[0267] The server analyzes the received text data using a natural language processing engine. A generative AI model is used to understand the user's intent and obtain detailed analysis results. The input is text data, and the output is analysis results including the user's intent and contextual information.

[0268] Step 5:

[0269] The server translates the text into the required language based on the analysis results. It also uses a generative AI model to generate phrases suitable for negotiation. The input to this process is the analysis results, and the output is translated content containing expressions suitable for negotiation.

[0270] Step 6:

[0271] The server sends the generated content to the terminal. This procedure uses a secure protocol to prevent the leakage of user information. The input is the generated content, and the output is the information received by the terminal.

[0272] Step 7:

[0273] The terminal displays the received content on the user interface and, if necessary, outputs it audibly using a voice assistant. Input is content from the server, and output is the visual and auditory presentation of information to the user. This allows the user to instantly use translations and appropriate negotiation phrases.

[0274] (Application Example 1)

[0275] 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 glasses 214 will be referred to as the "terminal."

[0276] Language barriers remain a significant challenge when traveling or making business visits to different cultural regions. In particular, in situations requiring direct communication, such as negotiations or purchasing goods, a lack of language comprehension often prevents achieving desired results. Furthermore, smooth negotiations require appropriate phrases based on the situation and context, and there is a need for technology that automatically supports this. Additionally, there is a need for systems that utilize the user's visual information to assist in on-site situational judgment.

[0277] 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.

[0278] In this invention, the server includes means for receiving voice input, means for converting voice input into text data, means for translating text data into multiple languages, means for generating phrases suitable for negotiation, means for outputting translated text data and generated phrases, means for capturing and analyzing visual data, means for obtaining information from captured visual data, and means for presenting content in real time based on visual data. This enables users to obtain appropriate negotiation phrases based on local language and visual information, allowing for smooth communication across language and cultural barriers.

[0279] "Voice input" is a means for users to communicate information to a system through their voice.

[0280] "Converting to text data" refers to the process of changing voice input into written text.

[0281] "Translating into multiple languages" is the act of converting text expressed in one language into another language.

[0282] "Generating phrases suitable for negotiation" means creating appropriate language expressions for different situations in order to facilitate smooth negotiations.

[0283] "Translated text data" refers to character data converted from the original language to another language.

[0284] "Output the generated phrase" refers to the process of presenting the created language expression to the user.

[0285] "Capture and analyze visual data" means collecting visual information using a camera or the like and interpreting it.

[0286] "Obtain information from the captured visual data" means extracting useful insights based on the visually captured data.

[0287] "Present content in real time based on visual data" means presenting the content obtained from visual information to the user immediately on the spot.

[0288] The system for implementing this invention uses a portable terminal including smart glasses. The user wears the smart glasses and performs visual and voice inputs. The server converts the voice input into text data using voice recognition software. This text data is converted into multiple languages required by the user using translation software. For this, general natural language processing technology as a translation engine is used. At that time, the server utilizes past data and pattern recognition algorithms to generate phrases suitable for negotiation. The generated phrases are displayed in real time on the display of the smart glasses and also output as voice.

[0289] [[ID=二十四]]Furthermore, the camera of the terminal acquires visual data and analyzes the visual data using a library such as the OpenCV library. Through this analysis, detailed information about the product the user is looking at and advantageous information in the negotiation are captured. Based on that information, the server performs processing so that an optimal negotiation phrase for the user is generated.

[0290] As a concrete example, when a user is considering purchasing pottery in a foreign market, the smart glasses recognize the pottery using visual data and ask via voice input, "Can you lower the price of this beautiful vase?" In response, the translated text "¿Puede bajar el precio de este hermoso jarrón un poco más?" is displayed on the glasses and also provided audibly. In this way, the user can smoothly negotiate with the local seller.

[0291] Example prompt: "Translate phrases that could be used in negotiations in a Spanish market from English to Spanish, and output them in a version suitable for negotiations in Spain."

[0292] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0293] Step 1:

[0294] The device acquires voice input from the user via the microphone. The voice input is entered into the device in the form of voice data. The device uses voice recognition software to convert this voice data into text data. The converted text data is then sent to the next processing step.

[0295] Step 2:

[0296] The server receives the converted text data. The server uses natural language processing techniques to analyze the meaning of the text data. This analysis process involves linguistic analysis to specifically understand the intent of the input text. The analysis results are then passed on to the next translation process.

[0297] Step 3:

[0298] The server translates the analyzed text data into the specified language using a multilingual translation engine. The analyzed text is used as input, and translated text data is generated. The translated result is in the local language that the user can use for negotiations, and is then supplied to the next phrase generation process.

[0299] Step 4:

[0300] The server generates phrases suitable for negotiation based on the translated text data. Here, pattern recognition algorithms and historical negotiation data are used to select and generate the most appropriate negotiation phrases. The generated phrases are constructed to be directly useful in negotiations.

[0301] Step 5:

[0302] The terminal receives translated and negotiation phrases sent from the server and displays them on its screen. In addition, it outputs them audibly via a voice assistant function. This allows the user to accurately understand the presented information and conduct negotiations appropriately.

[0303] Step 6:

[0304] The device's camera captures visual data of the object the user is looking at in real time. The input visual data is analyzed using an image processing library (e.g., OpenCV). This process extracts visual features and recognizes products the user is interested in. The information obtained is then used as additional information to support the user's negotiation process.

[0305] 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.

[0306] This invention combines an emotion engine that recognizes the user's emotions with a system that overcomes language barriers and supports smooth negotiations. This system is useful when the user negotiates in a market, taxi, hotel, etc. during overseas travel. The emotion engine analyzes the user's emotions from voice input and reflects them in negotiation content and phrase generation.

[0307] The user launches an application on the smartphone and starts the negotiation. The smartphone (terminal) accepts voice input and converts it into text data by means of a voice recognition function. At this time, the built-in emotion engine analyzes the user's emotions from the voice. For example, when the user is tense, it works to generate corresponding negotiation phrases based on that information.

[0308] The converted text data and the analysis results of the emotions are sent to the server. The server analyzes the data, performs translation using natural language processing technology, and generates phrases suitable for negotiation based on the analysis results of the emotion engine. Here, the phrases are adjusted according to the user's emotions. For example, when the user is tense, words that encourage relaxation are selected. Next, the server sends these results back to the terminal.

[0309] The terminal displays the translated text and the generated phrases on the user interface, and provides information in voice as well if a voice assistant is available. As a result, the user can negotiate with local people while receiving advice suitable for their emotions in real time.

[0310] The emotion engine monitors changes in emotions during negotiation and records the data in preparation for the next negotiation. This accumulated data is utilized for future phrase generation and translation based on the user's individual negotiation patterns. As a result, the system is designed to be optimized for the user the more it is used, and the negotiation becomes gradually easier.

[0311] The following describes the processing flow.

[0312] Step 1:

[0313] The user launches the application on their smartphone and selects the scenario they want to negotiate. For example, they might choose a market transaction.

[0314] Step 2:

[0315] The user speaks into their smartphone's microphone to input the details of their negotiation. The device receives the voice input and uses speech recognition to convert the speech into text data.

[0316] Step 3:

[0317] The device sends voice data to an emotion engine, which analyzes the user's emotions based on factors such as tone and speed of voice. For example, it might determine that the voice is tense.

[0318] Step 4:

[0319] The device sends the converted text data and sentiment analysis results to the server. The server analyzes the received data and translates the text data into the specified language using natural language processing technology.

[0320] Step 5:

[0321] The server generates phrases suitable for negotiation based on the results of sentiment analysis. The phrases are adjusted according to the user's emotional state. For example, it might create a phrase with a nuance like, "You can negotiate in a relaxed manner."

[0322] Step 6:

[0323] The server sends the translation result and the generated phrase back to the terminal.

[0324] Step 7:

[0325] The device receives data from the server and displays translated text and negotiation phrases on the user interface. Furthermore, if a voice assistant function is available, the information is output aloud.

[0326] Step 8:

[0327] The user uses this information to continue negotiations with local people. The emotion engine continuously monitors changes in emotions during negotiations and accumulates data to improve future negotiations.

[0328] (Example 2)

[0329] 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".

[0330] In international negotiations, language barriers can make it difficult to conduct negotiations as intended. Furthermore, the lack of expressions that adequately reflect user emotions can negatively impact negotiation outcomes. Additionally, the failure to optimize systems based on negotiation experience leads to decreased efficiency, which is another challenge.

[0331] 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.

[0332] In this invention, the server includes means for converting voice input into text data, means for analyzing emotional states, and means for performing translation and generating phrases appropriate to the emotions. This enables smooth negotiations that transcend language barriers and allows for the provision of appropriate phrases according to the user's emotional state. Furthermore, based on the record of emotional changes, optimization can be performed in subsequent negotiations, improving negotiation efficiency.

[0333] "Voice input" refers to information received from the user via voice.

[0334] "Text data" refers to data obtained by analyzing voice input and representing its content as textual information.

[0335] "Emotional state" refers to the psychological state inferred from the user's voice and language, and includes states such as tension and relaxation.

[0336] "Multilingual translation" refers to the process of converting text data from one language into another different language.

[0337] "Phrase generation" refers to the process of creating appropriate linguistic expressions for specific situations and purposes.

[0338] "Output" refers to the act of a system visualizing or auditorily presenting its processing results to the user.

[0339] "Monitoring" refers to the continuous observation and recording of information over a certain period of time.

[0340] "Recording" refers to the act of saving observed data in a format that can be used later.

[0341] "Reflecting in phrase generation and translation" refers to the process of improving linguistic expressions and translation results based on past data.

[0342] This invention is a system that helps users overcome language barriers and conduct negotiations smoothly. Users can initiate voice input using a dedicated application on a mobile device. The mobile device (terminal) uses voice recognition to convert the user's voice into text data. At this time, a built-in emotion engine analyzes the user's emotional state based on the voice data. For example, if the user is nervous, that state is recorded and analyzed.

[0343] The results of this conversion and analysis are transmitted to a central unit (server) using communication technology. The server translates the text into multiple languages ​​using generative AI models and natural language processing technologies. Specifically, general generative AI models and translation APIs are used. At this time, phrases suitable for negotiation are generated based on the analyzed emotional state. For example, for a nervous user, phrases that encourage relaxation are added.

[0344] The server sends the completed translation and phrases back to the terminal. The terminal displays the results on its user interface, and if a voice assistant is available, it can also be used to present the information. Based on this information, the user can conduct actual negotiations more effectively.

[0345] Furthermore, the terminal monitors the user's emotional changes during negotiations and records this data. This emotional data is stored on the server side and provides personalized improvements to phrase generation and translation in subsequent negotiations. In this way, the system is designed to optimize to the user's needs with each use.

[0346] As a concrete example, suppose a user wants to order food at a restaurant and uses voice input on their mobile device to ask, "Do you have vegetarian dishes?" This input is converted to text and translated as "Do you have vegetarian dishes?" At the same time, if the emotion engine detects the user's anxiety, it generates a phrase such as, "We'll explain everything in detail, so please ask if you have any questions."

[0347] An example of a specific prompt for a generative AI model would be: "What are some effective negotiation phrases a customer might use when asking about vegetarian options at a restaurant? Please also consider situations where the user might feel anxious."

[0348] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0349] Step 1:

[0350] The user launches an application on their mobile device and performs voice input. The device's microphone captures the user's voice and temporarily stores it as audio data. In this process, the input is the user's voice, and the output is digitized audio data. Specifically, if the user says, for example, "Please tell me the way to the museum," the voice input is completed.

[0351] Step 2:

[0352] The device uses a speech recognition module to convert speech data into text data. This conversion process analyzes the input speech and generates a string of characters based on an acoustic model. The input is digitized speech data, and the output is the corresponding text data. For example, "Please tell me the way to the museum" is converted to text such as "Could you tell me how to get to the museum?"

[0353] Step 3:

[0354] The device's emotion engine analyzes emotional states from text data. It considers characteristics such as voice tone and speed to evaluate the user's psychological state. The input for this step is voice features, and the output is the evaluation of the user's emotional state. For example, if the voice is trembling, it is judged to be a state of tension.

[0355] Step 4:

[0356] The terminal sends the converted text data and the sentiment assessment results to the server. The data is securely transmitted to the server using a communication protocol. In this process, the input is the text data and sentiment assessment results, and the output is the arrival of the data on the server. The server prepares to analyze the received data.

[0357] Step 5:

[0358] The server inputs the received text data into a generating AI model for translation into a foreign language. In addition, it incorporates the sentiment evaluation results to generate appropriate phrases. In this step, the input is text data and sentiment, while the output is the translated text and sentiment-appropriate phrases. For example, a user who appears nervous might be given the phrase, "Don't worry, we'll give you detailed directions."

[0359] Step 6:

[0360] The server sends the processed translated text and negotiation phrases back to the terminal. It transmits the information to the terminal using a communication protocol, ensuring data security. The input for this step is the aforementioned translated results and phrases, and the output is the arrival of the data at the terminal.

[0361] Step 7:

[0362] The terminal displays the received translated text and phrases on the user interface and also provides audio output using a voice assistant. In this step, the input is the translated results and phrases sent from the server, and the output is the information presented to the user. Specifically, the translated results and phrases are displayed on the screen, and additional audio information is provided to the user.

[0363] Step 8:

[0364] The device monitors the user's emotional changes during negotiations and records emotional data. This data is used for phrase generation and translation in subsequent sessions. The input for this step is new information from the user's voice, and the output is the recorded emotional data.

[0365] (Application Example 2)

[0366] 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."

[0367] In international work environments, multinational workers using diverse languages ​​can lead to communication breakdowns and misunderstandings of emotions. Maintaining efficient and smooth work instructions and collaborative systems in such environments is challenging. Furthermore, appropriately understanding workers' emotions and generating instructions tailored to the situation is also a challenge.

[0368] 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.

[0369] In this invention, the server includes means for receiving voice input, means for converting it into text data, and means for translating it into multiple languages. This enables real-time communication and emotionally responsive output between different languages.

[0370] "Means for receiving voice input" refers to a device or process that converts voice into a digital signal and captures it in a format usable by the system.

[0371] "Methods for converting to text data" refers to technologies that analyze audio signals and convert their content into corresponding text formats.

[0372] "Methods for translating into multiple languages" refers to the process of converting text data from its original language to another specified language.

[0373] "Methods for generating phrases suitable for negotiation" refer to technologies that automatically generate expressions to facilitate negotiation and communication in response to specific situations and emotions.

[0374] "Means for outputting translated text data and generated phrases" refers to means for providing the translated content and generated phrases to the user through audio or a display device.

[0375] "Means for analyzing the emotions of workers in a specific environment" refers to technologies that analyze voice and nonverbal signals to identify the worker's current emotional state.

[0376] "Means for generating and outputting appropriate instructions based on analyzed emotions" refers to a technology that creates appropriate and effective instructions based on the results of an emotion analysis of a worker and presents them via voice or display.

[0377] To implement this invention, a terminal equipped with a microphone and speaker is used as hardware for processing voice input. The terminal receives voice input and converts the voice into text data using speech recognition software (e.g., Google Cloud Speech-to-Text). This text data is then translated into multiple languages ​​using natural language processing technology (e.g., Google Translate API).

[0378] Next, an emotion analysis engine (e.g., Microsoft Azure Cognitive Services) analyzes the voice data to determine the worker's emotions. Based on this emotion data, it generates phrases suitable for negotiation or work instructions. The generated phrases are translated into the specified language and output through the terminal's display and speakers.

[0379] The server integrates this data and generates appropriate instructions in real time based on the analysis results. Through this system, users can achieve smooth communication that transcends language barriers. Furthermore, because it can respond to changes in workers' emotions, it is expected to strengthen cooperation in international work environments.

[0380] As a concrete example, in a factory, if a Japanese-speaking supervisor needs to give real-time safety instructions to a Spanish-speaking worker, this system can enable smooth and unambiguous instructions.

[0381] An example of a prompt is: "Consider how to use speech recognition and sentiment analysis to ensure accurate work instructions while facilitating smooth communication in a multilingual environment."

[0382] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0383] Step 1:

[0384] The device uses a microphone to receive the user's voice as input. The received voice data is then prepared for processing as a digital signal.

[0385] Step 2:

[0386] The device uses speech recognition software to convert the audio data into text data. This conversion transforms the audio data into character information with a linguistic structure. The output text data is then passed on to the next process.

[0387] Step 3:

[0388] The server receives text data and performs multilingual translation using natural language processing technology. The input text data is translated into the specified language, and the translated text is generated.

[0389] Step 4:

[0390] The server uses an emotion analysis engine to analyze emotional data from the input voice data. This analysis outputs the user's emotional state as specific parameters.

[0391] Step 5:

[0392] The server generates phrases appropriate to specific situations based on analyzed sentiment data. Using a generative AI model, it creates phrases suitable for negotiations and work instructions, and the generated phrases are then refined and output.

[0393] Step 6:

[0394] The terminal receives the translated text and generated phrases sent from the server. It displays this information on the user interface and outputs it as audio through the speaker. The user can then make decisions based on this information.

[0395] 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.

[0396] 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.

[0397] 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.

[0398] [Third Embodiment]

[0399] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0400] 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.

[0401] 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).

[0402] 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.

[0403] 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.

[0404] 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).

[0405] 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.

[0406] 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.

[0407] 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.

[0408] 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.

[0409] 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.

[0410] 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".

[0411] The system based on this invention is an application that provides support to users in overcoming language barriers and negotiation obstacles while traveling abroad. It is primarily available on mobile devices such as smartphones and tablets. Users begin using the app by launching it and selecting a negotiation scenario that suits their purpose. For example, consider using the app when shopping at a market.

[0412] First, when a user says, "Can you lower the price of this product?", the device recognizes the voice and converts the voice data into text data. This text data is sent to a server, which receives it and begins processing. The server uses natural language processing technology to analyze the meaning of the text and understand the user's intent. Then, based on the scene selected by the user, the server provides a translation appropriate to the local language and generates phrases suitable for negotiation. This phrase generation uses expressions predicted to be effective in actual negotiations based on past data and pattern recognition algorithms.

[0413] For example, the server generates a common local negotiation phrase, such as "Is it possible to lower the price of that item a little more?", along with the translation, and sends it to the terminal. The terminal displays this on its user interface and, if it has a voice assistant function, also outputs it aloud. The user can then use this to continue their conversation with the local seller.

[0414] In this way, the system helps users easily overcome language barriers and negotiate on more favorable terms. The system is designed to process information in real time and provide users with rapid feedback.

[0415] The following describes the processing flow.

[0416] Step 1:

[0417] The user launches the application. The user selects a desired negotiation scenario (e.g., shopping at a market). The device performs the necessary settings according to the selected scenario.

[0418] Step 2:

[0419] The user speaks, inputting the content they want to negotiate via voice. The device receives the voice input via its microphone and uses speech recognition to convert the voice data into text data.

[0420] Step 3:

[0421] The terminal sends the converted text data to the server. The server receives the text data and begins analysis.

[0422] Step 4:

[0423] The server uses natural language processing technology to analyze the meaning of received text data and understand the user's intent. The server then performs translation into the corresponding multilingual languages.

[0424] Step 5:

[0425] The server generates translation results and phrases suitable for negotiation. This uses a pattern recognition algorithm to derive negotiation phrases tailored to specific situations.

[0426] Step 6:

[0427] The server sends the generated translations and phrases to the device. The device receives them and displays them in the user interface. If a voice assistant is available, the information is also output audibly.

[0428] Step 7:

[0429] Users negotiate with locals based on the information displayed on their devices. The information obtained from the app is helpful in negotiations.

[0430] (Example 1)

[0431] 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."

[0432] Many individuals face language barriers and negotiation difficulties when traveling abroad or conducting international business. In particular, communication with people from different cultures and with different customs can lead to linguistic misunderstandings and complicate negotiations. Therefore, real-time language translation and the provision of appropriate phrases for negotiations are essential.

[0433] 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.

[0434] In this invention, the server includes means for receiving voice input, means for converting the voice input into text data, means for analyzing the text data using natural language processing, means for translating the text data into multiple languages, means for generating expressions suitable for negotiation using a generation AI model, and means for outputting the generated expressions via a user interface. This enables users to easily engage in real-time interlingual communication and negotiation.

[0435] A "means for receiving voice input" refers to a mechanism that recognizes the user's voice and inputs it into a format that the system can process.

[0436] "Means" refers to the methods or devices used to achieve a specific objective.

[0437] "Means of converting to text data" refers to a function that analyzes audio data and converts it into a string format.

[0438] "Methods of analysis using natural language processing" refer to techniques that utilize generative AI models and other algorithms to understand and interpret the meaning of text data.

[0439] "Means of translation into multiple languages" refers to functions that convert text data into multiple languages, facilitating communication between people who speak different languages.

[0440] "A method for generating expressions suitable for negotiation using an AI model" refers to a technique that generates effective negotiation phrases for specific cultures and situations based on past data and pattern recognition technology.

[0441] "Means of outputting via a user interface" refers to an interface for presenting generated information to the user visually or audibly.

[0442] This invention provides support for users to overcome language barriers in overseas travel and international business situations. It is primarily implemented on mobile devices such as smartphones and tablets. First, the user launches the application on their device and then starts the system by selecting a negotiation scenario appropriate to the situation in which they are using it.

[0443] For example, in a scenario where a user is shopping at a market, they might speak into the device and say, "Can you lower the price of this item?" The device then receives this voice input using its microphone and converts the voice data into text data using speech recognition software (e.g., a speech recognition API).

[0444] The converted text data is sent to a server via the internet. The server analyzes the text data using natural language processing technology and processes it using a generative AI model to understand the user's intent. At each stage, for example, APIs for natural language processing can be utilized.

[0445] The server then translates the text data into different languages ​​and simultaneously generates expressions suitable for negotiation. This process utilizes historical data and pattern recognition techniques. The generated translations and phrases are then sent to the terminal.

[0446] Finally, the device displays the generated information through the user interface and, in some cases, provides voice output using the voice assistant function. This function allows the user to visually and audibly verify the translation and negotiation phrases.

[0447] As a concrete example, consider a scenario where a user is buying wine at a market in France and asks, "Can I get a small discount on this wine?" An example of a prompt in response to this statement is, "Generate friendly negotiation phrases for buying wine at a French market." Based on this prompt, the system provides an efficient and culturally appropriate response.

[0448] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0449] Step 1:

[0450] The user launches the application. The user selects a negotiation scene from a menu provided on the device screen. The selected scene is saved on the device and used as contextual information in subsequent processes. The main input in this step is the user's selection. The output is information about the selected scene.

[0451] Step 2:

[0452] The user inputs questions or requests by voice. The device captures the voice data using the microphone and converts the voice into text data using a speech recognition API. At this stage, the input is raw voice data, and the output is the user's spoken content represented in text format.

[0453] Step 3:

[0454] The terminal sends text data to the server. Specifically, the data is securely transmitted over the internet using the HTTPS protocol. The input is the text data from the terminal, and the output is this data received by the server.

[0455] Step 4:

[0456] The server analyzes the received text data using a natural language processing engine. A generative AI model is used to understand the user's intent and obtain detailed analysis results. The input is text data, and the output is analysis results including the user's intent and contextual information.

[0457] Step 5:

[0458] The server translates the text into the required language based on the analysis results. It also uses a generative AI model to generate phrases suitable for negotiation. The input to this process is the analysis results, and the output is translated content containing expressions suitable for negotiation.

[0459] Step 6:

[0460] The server sends the generated content to the terminal. This procedure uses a secure protocol to prevent the leakage of user information. The input is the generated content, and the output is the information received by the terminal.

[0461] Step 7:

[0462] The terminal displays the received content on the user interface and, if necessary, outputs it audibly using a voice assistant. Input is content from the server, and output is the visual and auditory presentation of information to the user. This allows the user to instantly use translations and appropriate negotiation phrases.

[0463] (Application Example 1)

[0464] 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."

[0465] Language barriers remain a significant challenge when traveling or making business visits to different cultural regions. In particular, in situations requiring direct communication, such as negotiations or purchasing goods, a lack of language comprehension often prevents achieving desired results. Furthermore, smooth negotiations require appropriate phrases based on the situation and context, and there is a need for technology that automatically supports this. Additionally, there is a need for systems that utilize the user's visual information to assist in on-site situational judgment.

[0466] 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.

[0467] In this invention, the server includes means for receiving voice input, means for converting voice input into text data, means for translating text data into multiple languages, means for generating phrases suitable for negotiation, means for outputting translated text data and generated phrases, means for capturing and analyzing visual data, means for obtaining information from captured visual data, and means for presenting content in real time based on visual data. This enables users to obtain appropriate negotiation phrases based on local language and visual information, allowing for smooth communication across language and cultural barriers.

[0468] "Voice input" is a means for users to communicate information to a system through their voice.

[0469] "Converting to text data" refers to the process of changing voice input into written text.

[0470] "Translating into multiple languages" is the act of converting text expressed in one language into another language.

[0471] "Generating phrases suitable for negotiation" means creating appropriate language expressions for different situations in order to facilitate smooth negotiations.

[0472] "Translated text data" refers to character data that has been converted from the original language to another language.

[0473] "Outputting the generated phrase" refers to the process of showing the created linguistic expression to the user.

[0474] "Capturing and analyzing visual data" means collecting visual information using cameras or other means and interpreting it.

[0475] "Acquiring information from captured visual data" means drawing useful insights based on visually perceived data.

[0476] "Presenting content in real time based on visual data" means presenting the content obtained from visual information to the user immediately.

[0477] The system for carrying out this invention uses a mobile terminal including smart glasses. The user wears the smart glasses and provides visual and auditory input. The server converts the auditory input into text data using speech recognition software. This text data is then translated into multiple languages ​​required by the user using translation software. General natural language processing techniques are used as the translation engine. In this process, the server utilizes historical data and pattern recognition algorithms to generate phrases suitable for negotiation. The generated phrases are displayed in real time on the smart glasses' display and also output as audio.

[0478] Furthermore, the device's camera acquires visual data, which is then analyzed using libraries such as OpenCV. This analysis captures detailed information about the product the user is viewing and information that could be advantageous in negotiations. Based on this information, the server processes it to generate the most suitable negotiation phrases for the user.

[0479] As a concrete example, when a user is considering purchasing pottery in a foreign market, the smart glasses recognize the pottery using visual data and ask via voice input, "Can you lower the price of this beautiful vase?" In response, the translated text "¿Puede bajar el precio de este hermoso jarrón un poco más?" is displayed on the glasses and also provided audibly. In this way, the user can smoothly negotiate with the local seller.

[0480] Example prompt: "Translate phrases that could be used in negotiations in a Spanish market from English to Spanish, and output them in a version suitable for negotiations in Spain."

[0481] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0482] Step 1:

[0483] The device acquires voice input from the user via the microphone. The voice input is entered into the device in the form of voice data. The device uses voice recognition software to convert this voice data into text data. The converted text data is then sent to the next processing step.

[0484] Step 2:

[0485] The server receives the converted text data. The server uses natural language processing techniques to analyze the meaning of the text data. This analysis process involves linguistic analysis to specifically understand the intent of the input text. The analysis results are then passed on to the next translation process.

[0486] Step 3:

[0487] The server translates the analyzed text data into the specified language using a multilingual translation engine. The analyzed text is used as input, and translated text data is generated. The translated result is in the local language that the user can use for negotiations, and is then supplied to the next phrase generation process.

[0488] Step 4:

[0489] The server generates phrases suitable for negotiation based on the translated text data. Here, pattern recognition algorithms and historical negotiation data are used to select and generate the most appropriate negotiation phrases. The generated phrases are constructed to be directly useful in negotiations.

[0490] Step 5:

[0491] The terminal receives translated and negotiation phrases sent from the server and displays them on its screen. In addition, it outputs them audibly via a voice assistant function. This allows the user to accurately understand the presented information and conduct negotiations appropriately.

[0492] Step 6:

[0493] The device's camera captures visual data of the object the user is looking at in real time. The input visual data is analyzed using an image processing library (e.g., OpenCV). This process extracts visual features and recognizes products the user is interested in. The information obtained is then used as additional information to support the user's negotiation process.

[0494] 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.

[0495] This invention combines a system that overcomes language barriers and supports smooth negotiations with an emotion engine that recognizes the user's emotions. This system can be used by users when negotiating in markets, taxis, hotels, etc., while traveling abroad. The emotion engine analyzes the user's emotions from voice input and reflects them in the negotiation content and phrase generation.

[0496] The user launches the application on their smartphone and begins negotiations. The smartphone (device) accepts voice input and converts it into text data using its voice recognition function. At this time, the built-in emotion engine analyzes the user's emotions from the voice. For example, if the user is nervous, it works to generate appropriate negotiation phrases based on that information.

[0497] The converted text data and sentiment analysis results are sent to the server. The server analyzes the data, performs translation using natural language processing technology, and generates phrases suitable for negotiation based on the sentiment engine's analysis results. Here, the phrases are adjusted according to the user's emotions; for example, if the user is nervous, words that promote relaxation are selected. Next, the server sends these results back to the terminal.

[0498] The device displays translated text and generated phrases in the user interface, and provides information via voice if a voice assistant is available. This allows users to negotiate with locals while receiving real-time advice tailored to their emotions.

[0499] The emotion engine monitors changes in emotions during negotiations and records the data for future negotiations. This accumulated data is used to generate and translate phrases based on the user's individual negotiation patterns. As a result, the system is designed to optimize itself for the user the more it is used, gradually making negotiations easier.

[0500] The following describes the processing flow.

[0501] Step 1:

[0502] The user launches the application on their smartphone and selects the scenario they want to negotiate. For example, they might choose a market transaction.

[0503] Step 2:

[0504] The user speaks into their smartphone's microphone to input the details of their negotiation. The device receives the voice input and uses speech recognition to convert the speech into text data.

[0505] Step 3:

[0506] The device sends voice data to an emotion engine, which analyzes the user's emotions based on factors such as tone and speed of voice. For example, it might determine that the voice is tense.

[0507] Step 4:

[0508] The device sends the converted text data and sentiment analysis results to the server. The server analyzes the received data and translates the text data into the specified language using natural language processing technology.

[0509] Step 5:

[0510] The server generates phrases suitable for negotiation based on the results of sentiment analysis. The phrases are adjusted according to the user's emotional state. For example, it might create a phrase with a nuance like, "You can negotiate in a relaxed manner."

[0511] Step 6:

[0512] The server sends the translation result and the generated phrase back to the terminal.

[0513] Step 7:

[0514] The device receives data from the server and displays translated text and negotiation phrases on the user interface. Furthermore, if a voice assistant function is available, the information is output aloud.

[0515] Step 8:

[0516] The user uses this information to continue negotiations with local people. The emotion engine continuously monitors changes in emotions during negotiations and accumulates data to improve future negotiations.

[0517] (Example 2)

[0518] 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."

[0519] In international negotiations, language barriers can make it difficult to conduct negotiations as intended. Furthermore, the lack of expressions that adequately reflect user emotions can negatively impact negotiation outcomes. Additionally, the failure to optimize systems based on negotiation experience leads to decreased efficiency, which is another challenge.

[0520] 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.

[0521] In this invention, the server includes means for converting voice input into text data, means for analyzing emotional states, and means for performing translation and generating phrases appropriate to the emotions. This enables smooth negotiations that transcend language barriers and allows for the provision of appropriate phrases according to the user's emotional state. Furthermore, based on the record of emotional changes, optimization can be performed in subsequent negotiations, improving negotiation efficiency.

[0522] "Voice input" refers to information received from the user via voice.

[0523] "Text data" refers to data obtained by analyzing voice input and representing its content as textual information.

[0524] "Emotional state" refers to the psychological state inferred from the user's voice and language, and includes states such as tension and relaxation.

[0525] "Multilingual translation" refers to the process of converting text data from one language into another different language.

[0526] "Phrase generation" refers to the process of creating appropriate linguistic expressions for specific situations and purposes.

[0527] "Output" refers to the act of a system visualizing or auditorily presenting its processing results to the user.

[0528] "Monitoring" refers to the continuous observation and recording of information over a certain period of time.

[0529] "Recording" refers to the act of saving observed data in a format that can be used later.

[0530] "Reflecting in phrase generation and translation" refers to the process of improving linguistic expressions and translation results based on past data.

[0531] This invention is a system that helps users overcome language barriers and conduct negotiations smoothly. Users can initiate voice input using a dedicated application on a mobile device. The mobile device (terminal) uses voice recognition to convert the user's voice into text data. At this time, a built-in emotion engine analyzes the user's emotional state based on the voice data. For example, if the user is nervous, that state is recorded and analyzed.

[0532] The results of this conversion and analysis are transmitted to a central unit (server) using communication technology. The server translates the text into multiple languages ​​using generative AI models and natural language processing technologies. Specifically, general generative AI models and translation APIs are used. At this time, phrases suitable for negotiation are generated based on the analyzed emotional state. For example, for a nervous user, phrases that encourage relaxation are added.

[0533] The server sends the completed translation and phrases back to the terminal. The terminal displays the results on its user interface, and if a voice assistant is available, it can also be used to present the information. Based on this information, the user can conduct actual negotiations more effectively.

[0534] Furthermore, the terminal monitors the user's emotional changes during negotiations and records this data. This emotional data is stored on the server side and provides personalized improvements to phrase generation and translation in subsequent negotiations. In this way, the system is designed to optimize to the user's needs with each use.

[0535] As a concrete example, suppose a user wants to order food at a restaurant and uses voice input on their mobile device to ask, "Do you have vegetarian dishes?" This input is converted to text and translated as "Do you have vegetarian dishes?" At the same time, if the emotion engine detects the user's anxiety, it generates a phrase such as, "We'll explain everything in detail, so please ask if you have any questions."

[0536] An example of a specific prompt for a generative AI model would be: "What are some effective negotiation phrases a customer might use when asking about vegetarian options at a restaurant? Please also consider situations where the user might feel anxious."

[0537] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0538] Step 1:

[0539] The user launches an application on their mobile device and performs voice input. The device's microphone captures the user's voice and temporarily stores it as audio data. In this process, the input is the user's voice, and the output is digitized audio data. Specifically, if the user says, for example, "Please tell me the way to the museum," the voice input is completed.

[0540] Step 2:

[0541] The device uses a speech recognition module to convert speech data into text data. This conversion process analyzes the input speech and generates a string of characters based on an acoustic model. The input is digitized speech data, and the output is the corresponding text data. For example, "Please tell me the way to the museum" is converted to text such as "Could you tell me how to get to the museum?"

[0542] Step 3:

[0543] The device's emotion engine analyzes emotional states from text data. It considers characteristics such as voice tone and speed to evaluate the user's psychological state. The input for this step is voice features, and the output is the evaluation of the user's emotional state. For example, if the voice is trembling, it is judged to be a state of tension.

[0544] Step 4:

[0545] The terminal sends the converted text data and the sentiment assessment results to the server. The data is securely transmitted to the server using a communication protocol. In this process, the input is the text data and sentiment assessment results, and the output is the arrival of the data on the server. The server prepares to analyze the received data.

[0546] Step 5:

[0547] The server inputs the received text data into a generating AI model for translation into a foreign language. In addition, it incorporates the sentiment evaluation results to generate appropriate phrases. In this step, the input is text data and sentiment, while the output is the translated text and sentiment-appropriate phrases. For example, a user who appears nervous might be given the phrase, "Don't worry, we'll give you detailed directions."

[0548] Step 6:

[0549] The server sends the processed translated text and negotiation phrases back to the terminal. It transmits the information to the terminal using a communication protocol, ensuring data security. The input for this step is the aforementioned translated results and phrases, and the output is the arrival of the data at the terminal.

[0550] Step 7:

[0551] The terminal displays the received translated text and phrases on the user interface and also provides audio output using a voice assistant. In this step, the input is the translated results and phrases sent from the server, and the output is the information presented to the user. Specifically, the translated results and phrases are displayed on the screen, and additional audio information is provided to the user.

[0552] Step 8:

[0553] The device monitors the user's emotional changes during negotiations and records emotional data. This data is used for phrase generation and translation in subsequent sessions. The input for this step is new information from the user's voice, and the output is the recorded emotional data.

[0554] (Application Example 2)

[0555] 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."

[0556] In international work environments, multinational workers using diverse languages ​​can lead to communication breakdowns and misunderstandings of emotions. Maintaining efficient and smooth work instructions and collaborative systems in such environments is challenging. Furthermore, appropriately understanding workers' emotions and generating instructions tailored to the situation is also a challenge.

[0557] 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.

[0558] In this invention, the server includes means for receiving voice input, means for converting it into text data, and means for translating it into multiple languages. This enables real-time communication and emotionally responsive output between different languages.

[0559] "Means for receiving voice input" refers to a device or process that converts voice into a digital signal and captures it in a format usable by the system.

[0560] "Methods for converting to text data" refers to technologies that analyze audio signals and convert their content into corresponding text formats.

[0561] "Methods for translating into multiple languages" refers to the process of converting text data from its original language to another specified language.

[0562] "Methods for generating phrases suitable for negotiation" refer to technologies that automatically generate expressions to facilitate negotiation and communication in response to specific situations and emotions.

[0563] "Means for outputting translated text data and generated phrases" refers to means for providing the translated content and generated phrases to the user through audio or a display device.

[0564] "Means for analyzing the emotions of workers in a specific environment" refers to technologies that analyze voice and nonverbal signals to identify the worker's current emotional state.

[0565] "Means for generating and outputting appropriate instructions based on analyzed emotions" refers to a technology that creates appropriate and effective instructions based on the results of an emotion analysis of a worker and presents them via voice or display.

[0566] To implement this invention, a terminal equipped with a microphone and speaker is used as hardware for processing voice input. The terminal receives voice input and converts the voice into text data using speech recognition software (e.g., Google Cloud Speech-to-Text). This text data is then translated into multiple languages ​​using natural language processing technology (e.g., Google Translate API).

[0567] Next, an emotion analysis engine (e.g., Microsoft Azure Cognitive Services) analyzes the voice data to determine the worker's emotions. Based on this emotion data, it generates phrases suitable for negotiation or work instructions. The generated phrases are translated into the specified language and output through the terminal's display and speakers.

[0568] The server integrates this data and generates appropriate instructions in real time based on the analysis results. Through this system, users can achieve smooth communication that transcends language barriers. Furthermore, because it can respond to changes in workers' emotions, it is expected to strengthen cooperation in international work environments.

[0569] As a concrete example, in a factory, if a Japanese-speaking supervisor needs to give real-time safety instructions to a Spanish-speaking worker, this system can enable smooth and unambiguous instructions.

[0570] An example of a prompt is: "Consider how to use speech recognition and sentiment analysis to ensure accurate work instructions while facilitating smooth communication in a multilingual environment."

[0571] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0572] Step 1:

[0573] The device uses a microphone to receive the user's voice as input. The received voice data is then prepared for processing as a digital signal.

[0574] Step 2:

[0575] The device uses speech recognition software to convert the audio data into text data. This conversion transforms the audio data into character information with a linguistic structure. The output text data is then passed on to the next process.

[0576] Step 3:

[0577] The server receives text data and performs multilingual translation using natural language processing technology. The input text data is translated into the specified language, and the translated text is generated.

[0578] Step 4:

[0579] The server uses an emotion analysis engine to analyze emotional data from the input voice data. This analysis outputs the user's emotional state as specific parameters.

[0580] Step 5:

[0581] The server generates phrases appropriate to specific situations based on analyzed sentiment data. Using a generative AI model, it creates phrases suitable for negotiations and work instructions, and the generated phrases are then refined and output.

[0582] Step 6:

[0583] The terminal receives the translated text and generated phrases sent from the server. It displays this information on the user interface and outputs it as audio through the speaker. The user can then make decisions based on this information.

[0584] 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.

[0585] 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.

[0586] 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.

[0587] [Fourth Embodiment]

[0588] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0589] 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.

[0590] 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).

[0591] 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.

[0592] 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.

[0593] 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).

[0594] 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.

[0595] 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.

[0596] 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.

[0597] 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.

[0598] 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.

[0599] 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.

[0600] 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".

[0601] The system based on this invention is an application that provides support to users in overcoming language barriers and negotiation obstacles while traveling abroad. It is primarily available on mobile devices such as smartphones and tablets. Users begin using the app by launching it and selecting a negotiation scenario that suits their purpose. For example, consider using the app when shopping at a market.

[0602] First, when a user says, "Can you lower the price of this product?", the device recognizes the voice and converts the voice data into text data. This text data is sent to a server, which receives it and begins processing. The server uses natural language processing technology to analyze the meaning of the text and understand the user's intent. Then, based on the scene selected by the user, the server provides a translation appropriate to the local language and generates phrases suitable for negotiation. This phrase generation uses expressions predicted to be effective in actual negotiations based on past data and pattern recognition algorithms.

[0603] For example, the server generates a common local negotiation phrase, such as "Is it possible to lower the price of that item a little more?", along with the translation, and sends it to the terminal. The terminal displays this on its user interface and, if it has a voice assistant function, also outputs it aloud. The user can then use this to continue their conversation with the local seller.

[0604] In this way, the system helps users easily overcome language barriers and negotiate on more favorable terms. The system is designed to process information in real time and provide users with rapid feedback.

[0605] The following describes the processing flow.

[0606] Step 1:

[0607] The user launches the application. The user selects a desired negotiation scenario (e.g., shopping at a market). The device performs the necessary settings according to the selected scenario.

[0608] Step 2:

[0609] The user speaks, inputting the content they want to negotiate via voice. The device receives the voice input via its microphone and uses speech recognition to convert the voice data into text data.

[0610] Step 3:

[0611] The terminal sends the converted text data to the server. The server receives the text data and begins analysis.

[0612] Step 4:

[0613] The server uses natural language processing technology to analyze the meaning of received text data and understand the user's intent. The server then performs translation into the corresponding multilingual languages.

[0614] Step 5:

[0615] The server generates translation results and phrases suitable for negotiation. This uses a pattern recognition algorithm to derive negotiation phrases tailored to specific situations.

[0616] Step 6:

[0617] The server sends the generated translations and phrases to the device. The device receives them and displays them in the user interface. If a voice assistant is available, the information is also output audibly.

[0618] Step 7:

[0619] Users negotiate with locals based on the information displayed on their devices. The information obtained from the app is helpful in negotiations.

[0620] (Example 1)

[0621] 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".

[0622] Many individuals face language barriers and negotiation difficulties when traveling abroad or conducting international business. In particular, communication with people from different cultures and with different customs can lead to linguistic misunderstandings and complicate negotiations. Therefore, real-time language translation and the provision of appropriate phrases for negotiations are essential.

[0623] 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.

[0624] In this invention, the server includes means for receiving voice input, means for converting the voice input into text data, means for analyzing the text data using natural language processing, means for translating the text data into multiple languages, means for generating expressions suitable for negotiation using a generation AI model, and means for outputting the generated expressions via a user interface. This enables users to easily engage in real-time interlingual communication and negotiation.

[0625] A "means for receiving voice input" refers to a mechanism that recognizes the user's voice and inputs it into a format that the system can process.

[0626] "Means" refers to the methods or devices used to achieve a specific objective.

[0627] "Means of converting to text data" refers to a function that analyzes audio data and converts it into a string format.

[0628] "Methods of analysis using natural language processing" refer to techniques that utilize generative AI models and other algorithms to understand and interpret the meaning of text data.

[0629] "Means of translation into multiple languages" refers to functions that convert text data into multiple languages, facilitating communication between people who speak different languages.

[0630] "A method for generating expressions suitable for negotiation using an AI model" refers to a technique that generates effective negotiation phrases for specific cultures and situations based on past data and pattern recognition technology.

[0631] "Means of outputting via a user interface" refers to an interface for presenting generated information to the user visually or audibly.

[0632] This invention provides support for users to overcome language barriers in overseas travel and international business situations. It is primarily implemented on mobile devices such as smartphones and tablets. First, the user launches the application on their device and then starts the system by selecting a negotiation scenario appropriate to the situation in which they are using it.

[0633] For example, in a scenario where a user is shopping at a market, they might speak into the device and say, "Can you lower the price of this item?" The device then receives this voice input using its microphone and converts the voice data into text data using speech recognition software (e.g., a speech recognition API).

[0634] The converted text data is sent to a server via the internet. The server analyzes the text data using natural language processing technology and processes it using a generative AI model to understand the user's intent. At each stage, for example, APIs for natural language processing can be utilized.

[0635] The server then translates the text data into different languages ​​and simultaneously generates expressions suitable for negotiation. This process utilizes historical data and pattern recognition techniques. The generated translations and phrases are then sent to the terminal.

[0636] Finally, the device displays the generated information through the user interface and, in some cases, provides voice output using the voice assistant function. This function allows the user to visually and audibly verify the translation and negotiation phrases.

[0637] As a concrete example, consider a scenario where a user is buying wine at a market in France and asks, "Can I get a small discount on this wine?" An example of a prompt in response to this statement is, "Generate friendly negotiation phrases for buying wine at a French market." Based on this prompt, the system provides an efficient and culturally appropriate response.

[0638] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0639] Step 1:

[0640] The user launches the application. The user selects a negotiation scene from a menu provided on the device screen. The selected scene is saved on the device and used as contextual information in subsequent processes. The main input in this step is the user's selection. The output is information about the selected scene.

[0641] Step 2:

[0642] The user inputs questions or requests by voice. The device captures the voice data using the microphone and converts the voice into text data using a speech recognition API. At this stage, the input is raw voice data, and the output is the user's spoken content represented in text format.

[0643] Step 3:

[0644] The terminal sends text data to the server. Specifically, the data is securely transmitted over the internet using the HTTPS protocol. The input is the text data from the terminal, and the output is this data received by the server.

[0645] Step 4:

[0646] The server analyzes the received text data using a natural language processing engine. A generative AI model is used to understand the user's intent and obtain detailed analysis results. The input is text data, and the output is analysis results including the user's intent and contextual information.

[0647] Step 5:

[0648] The server translates the text into the required language based on the analysis results. It also uses a generative AI model to generate phrases suitable for negotiation. The input to this process is the analysis results, and the output is translated content containing expressions suitable for negotiation.

[0649] Step 6:

[0650] The server sends the generated content to the terminal. This procedure uses a secure protocol to prevent the leakage of user information. The input is the generated content, and the output is the information received by the terminal.

[0651] Step 7:

[0652] The terminal displays the received content on the user interface and, if necessary, outputs it audibly using a voice assistant. Input is content from the server, and output is the visual and auditory presentation of information to the user. This allows the user to instantly use translations and appropriate negotiation phrases.

[0653] (Application Example 1)

[0654] 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".

[0655] Language barriers remain a significant challenge when traveling or making business visits to different cultural regions. In particular, in situations requiring direct communication, such as negotiations or purchasing goods, a lack of language comprehension often prevents achieving desired results. Furthermore, smooth negotiations require appropriate phrases based on the situation and context, and there is a need for technology that automatically supports this. Additionally, there is a need for systems that utilize the user's visual information to assist in on-site situational judgment.

[0656] 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.

[0657] In this invention, the server includes means for receiving voice input, means for converting voice input into text data, means for translating text data into multiple languages, means for generating phrases suitable for negotiation, means for outputting translated text data and generated phrases, means for capturing and analyzing visual data, means for obtaining information from captured visual data, and means for presenting content in real time based on visual data. This enables users to obtain appropriate negotiation phrases based on local language and visual information, allowing for smooth communication across language and cultural barriers.

[0658] "Voice input" is a means for users to communicate information to a system through their voice.

[0659] "Converting to text data" refers to the process of changing voice input into written text.

[0660] "Translating into multiple languages" is the act of converting text expressed in one language into another language.

[0661] "Generating phrases suitable for negotiation" means creating appropriate language expressions for different situations in order to facilitate smooth negotiations.

[0662] "Translated text data" refers to character data that has been converted from the original language to another language.

[0663] "Outputting the generated phrase" refers to the process of showing the created linguistic expression to the user.

[0664] "Capturing and analyzing visual data" means collecting visual information using cameras or other means and interpreting it.

[0665] "Acquiring information from captured visual data" means drawing useful insights based on visually perceived data.

[0666] "Presenting content in real time based on visual data" means presenting the content obtained from visual information to the user immediately.

[0667] The system for carrying out this invention uses a mobile terminal including smart glasses. The user wears the smart glasses and provides visual and auditory input. The server converts the auditory input into text data using speech recognition software. This text data is then translated into multiple languages ​​required by the user using translation software. General natural language processing techniques are used as the translation engine. In this process, the server utilizes historical data and pattern recognition algorithms to generate phrases suitable for negotiation. The generated phrases are displayed in real time on the smart glasses' display and also output as audio.

[0668] Furthermore, the device's camera acquires visual data, which is then analyzed using libraries such as OpenCV. This analysis captures detailed information about the product the user is viewing and information that could be advantageous in negotiations. Based on this information, the server processes it to generate the most suitable negotiation phrases for the user.

[0669] As a concrete example, when a user is considering purchasing pottery in a foreign market, the smart glasses recognize the pottery using visual data and ask via voice input, "Can you lower the price of this beautiful vase?" In response, the translated text "¿Puede bajar el precio de este hermoso jarrón un poco más?" is displayed on the glasses and also provided audibly. In this way, the user can smoothly negotiate with the local seller.

[0670] Example prompt: "Translate phrases that could be used in negotiations in a Spanish market from English to Spanish, and output them in a version suitable for negotiations in Spain."

[0671] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0672] Step 1:

[0673] The device acquires voice input from the user via the microphone. The voice input is entered into the device in the form of voice data. The device uses voice recognition software to convert this voice data into text data. The converted text data is then sent to the next processing step.

[0674] Step 2:

[0675] The server receives the converted text data. The server uses natural language processing techniques to analyze the meaning of the text data. This analysis process involves linguistic analysis to specifically understand the intent of the input text. The analysis results are then passed on to the next translation process.

[0676] Step 3:

[0677] The server translates the analyzed text data into the specified language using a multilingual translation engine. The analyzed text is used as input, and translated text data is generated. The translated result is in the local language that the user can use for negotiations, and is then supplied to the next phrase generation process.

[0678] Step 4:

[0679] The server generates phrases suitable for negotiation based on the translated text data. Here, pattern recognition algorithms and historical negotiation data are used to select and generate the most appropriate negotiation phrases. The generated phrases are constructed to be directly useful in negotiations.

[0680] Step 5:

[0681] The terminal receives translated and negotiation phrases sent from the server and displays them on its screen. In addition, it outputs them audibly via a voice assistant function. This allows the user to accurately understand the presented information and conduct negotiations appropriately.

[0682] Step 6:

[0683] The device's camera captures visual data of the object the user is looking at in real time. The input visual data is analyzed using an image processing library (e.g., OpenCV). This process extracts visual features and recognizes products the user is interested in. The information obtained is then used as additional information to support the user's negotiation process.

[0684] 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.

[0685] This invention combines a system that overcomes language barriers and supports smooth negotiations with an emotion engine that recognizes the user's emotions. This system can be used by users when negotiating in markets, taxis, hotels, etc., while traveling abroad. The emotion engine analyzes the user's emotions from voice input and reflects them in the negotiation content and phrase generation.

[0686] The user launches the application on their smartphone and begins negotiations. The smartphone (device) accepts voice input and converts it into text data using its voice recognition function. At this time, the built-in emotion engine analyzes the user's emotions from the voice. For example, if the user is nervous, it works to generate appropriate negotiation phrases based on that information.

[0687] The converted text data and sentiment analysis results are sent to the server. The server analyzes the data, performs translation using natural language processing technology, and generates phrases suitable for negotiation based on the sentiment engine's analysis results. Here, the phrases are adjusted according to the user's emotions; for example, if the user is nervous, words that promote relaxation are selected. Next, the server sends these results back to the terminal.

[0688] The device displays translated text and generated phrases in the user interface, and provides information via voice if a voice assistant is available. This allows users to negotiate with locals while receiving real-time advice tailored to their emotions.

[0689] The emotion engine monitors changes in emotions during negotiations and records the data for future negotiations. This accumulated data is used to generate and translate phrases based on the user's individual negotiation patterns. As a result, the system is designed to optimize itself for the user the more it is used, gradually making negotiations easier.

[0690] The following describes the processing flow.

[0691] Step 1:

[0692] The user launches the application on their smartphone and selects the scenario they want to negotiate. For example, they might choose a market transaction.

[0693] Step 2:

[0694] The user speaks into their smartphone's microphone to input the details of their negotiation. The device receives the voice input and uses speech recognition to convert the speech into text data.

[0695] Step 3:

[0696] The device sends voice data to an emotion engine, which analyzes the user's emotions based on factors such as tone and speed of voice. For example, it might determine that the voice is tense.

[0697] Step 4:

[0698] The device sends the converted text data and sentiment analysis results to the server. The server analyzes the received data and translates the text data into the specified language using natural language processing technology.

[0699] Step 5:

[0700] The server generates phrases suitable for negotiation based on the results of sentiment analysis. The phrases are adjusted according to the user's emotional state. For example, it might create a phrase with a nuance like, "You can negotiate in a relaxed manner."

[0701] Step 6:

[0702] The server sends the translation result and the generated phrase back to the terminal.

[0703] Step 7:

[0704] The device receives data from the server and displays translated text and negotiation phrases on the user interface. Furthermore, if a voice assistant function is available, the information is output aloud.

[0705] Step 8:

[0706] The user uses this information to continue negotiations with local people. The emotion engine continuously monitors changes in emotions during negotiations and accumulates data to improve future negotiations.

[0707] (Example 2)

[0708] 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".

[0709] In international negotiations, language barriers can make it difficult to conduct negotiations as intended. Furthermore, the lack of expressions that adequately reflect user emotions can negatively impact negotiation outcomes. Additionally, the failure to optimize systems based on negotiation experience leads to decreased efficiency, which is another challenge.

[0710] 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.

[0711] In this invention, the server includes means for converting voice input into text data, means for analyzing emotional states, and means for performing translation and generating phrases appropriate to the emotions. This enables smooth negotiations that transcend language barriers and allows for the provision of appropriate phrases according to the user's emotional state. Furthermore, based on the record of emotional changes, optimization can be performed in subsequent negotiations, improving negotiation efficiency.

[0712] "Voice input" refers to information received from the user via voice.

[0713] "Text data" refers to data obtained by analyzing voice input and representing its content as textual information.

[0714] "Emotional state" refers to the psychological state inferred from the user's voice and language, and includes states such as tension and relaxation.

[0715] "Multilingual translation" refers to the process of converting text data from one language into another different language.

[0716] "Phrase generation" refers to the process of creating appropriate linguistic expressions for specific situations and purposes.

[0717] "Output" refers to the act of a system visualizing or auditorily presenting its processing results to the user.

[0718] "Monitoring" refers to the continuous observation and recording of information over a certain period of time.

[0719] "Recording" refers to the act of saving observed data in a format that can be used later.

[0720] "Reflecting in phrase generation and translation" refers to the process of improving linguistic expressions and translation results based on past data.

[0721] This invention is a system that helps users overcome language barriers and conduct negotiations smoothly. Users can initiate voice input using a dedicated application on a mobile device. The mobile device (terminal) uses voice recognition to convert the user's voice into text data. At this time, a built-in emotion engine analyzes the user's emotional state based on the voice data. For example, if the user is nervous, that state is recorded and analyzed.

[0722] The results of this conversion and analysis are transmitted to a central unit (server) using communication technology. The server translates the text into multiple languages ​​using generative AI models and natural language processing technologies. Specifically, general generative AI models and translation APIs are used. At this time, phrases suitable for negotiation are generated based on the analyzed emotional state. For example, for a nervous user, phrases that encourage relaxation are added.

[0723] The server sends the completed translation and phrases back to the terminal. The terminal displays the results on its user interface, and if a voice assistant is available, it can also be used to present the information. Based on this information, the user can conduct actual negotiations more effectively.

[0724] Furthermore, the terminal monitors the user's emotional changes during negotiations and records this data. This emotional data is stored on the server side and provides personalized improvements to phrase generation and translation in subsequent negotiations. In this way, the system is designed to optimize to the user's needs with each use.

[0725] As a concrete example, suppose a user wants to order food at a restaurant and uses voice input on their mobile device to ask, "Do you have vegetarian dishes?" This input is converted to text and translated as "Do you have vegetarian dishes?" At the same time, if the emotion engine detects the user's anxiety, it generates a phrase such as, "We'll explain everything in detail, so please ask if you have any questions."

[0726] An example of a specific prompt for a generative AI model would be: "What are some effective negotiation phrases a customer might use when asking about vegetarian options at a restaurant? Please also consider situations where the user might feel anxious."

[0727] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0728] Step 1:

[0729] The user launches an application on their mobile device and performs voice input. The device's microphone captures the user's voice and temporarily stores it as audio data. In this process, the input is the user's voice, and the output is digitized audio data. Specifically, if the user says, for example, "Please tell me the way to the museum," the voice input is completed.

[0730] Step 2:

[0731] The device uses a speech recognition module to convert speech data into text data. This conversion process analyzes the input speech and generates a string of characters based on an acoustic model. The input is digitized speech data, and the output is the corresponding text data. For example, "Please tell me the way to the museum" is converted to text such as "Could you tell me how to get to the museum?"

[0732] Step 3:

[0733] The device's emotion engine analyzes emotional states from text data. It considers characteristics such as voice tone and speed to evaluate the user's psychological state. The input for this step is voice features, and the output is the evaluation of the user's emotional state. For example, if the voice is trembling, it is judged to be a state of tension.

[0734] Step 4:

[0735] The terminal sends the converted text data and the sentiment assessment results to the server. The data is securely transmitted to the server using a communication protocol. In this process, the input is the text data and sentiment assessment results, and the output is the arrival of the data on the server. The server prepares to analyze the received data.

[0736] Step 5:

[0737] The server inputs the received text data into a generating AI model for translation into a foreign language. In addition, it incorporates the sentiment evaluation results to generate appropriate phrases. In this step, the input is text data and sentiment, while the output is the translated text and sentiment-appropriate phrases. For example, a user who appears nervous might be given the phrase, "Don't worry, we'll give you detailed directions."

[0738] Step 6:

[0739] The server sends the processed translated text and negotiation phrases back to the terminal. It transmits the information to the terminal using a communication protocol, ensuring data security. The input for this step is the aforementioned translated results and phrases, and the output is the arrival of the data at the terminal.

[0740] Step 7:

[0741] The terminal displays the received translated text and phrases on the user interface and also provides audio output using a voice assistant. In this step, the input is the translated results and phrases sent from the server, and the output is the information presented to the user. Specifically, the translated results and phrases are displayed on the screen, and additional audio information is provided to the user.

[0742] Step 8:

[0743] The device monitors the user's emotional changes during negotiations and records emotional data. This data is used for phrase generation and translation in subsequent sessions. The input for this step is new information from the user's voice, and the output is the recorded emotional data.

[0744] (Application Example 2)

[0745] 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".

[0746] In international work environments, multinational workers using diverse languages ​​can lead to communication breakdowns and misunderstandings of emotions. Maintaining efficient and smooth work instructions and collaborative systems in such environments is challenging. Furthermore, appropriately understanding workers' emotions and generating instructions tailored to the situation is also a challenge.

[0747] 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.

[0748] In this invention, the server includes means for receiving voice input, means for converting it into text data, and means for translating it into multiple languages. This enables real-time communication and emotionally responsive output between different languages.

[0749] "Means for receiving voice input" refers to a device or process that converts voice into a digital signal and captures it in a format usable by the system.

[0750] "Methods for converting to text data" refers to technologies that analyze audio signals and convert their content into corresponding text formats.

[0751] "Methods for translating into multiple languages" refers to the process of converting text data from its original language to another specified language.

[0752] "Methods for generating phrases suitable for negotiation" refer to technologies that automatically generate expressions to facilitate negotiation and communication in response to specific situations and emotions.

[0753] "Means for outputting translated text data and generated phrases" refers to means for providing the translated content and generated phrases to the user through audio or a display device.

[0754] "Means for analyzing the emotions of workers in a specific environment" refers to technologies that analyze voice and nonverbal signals to identify the worker's current emotional state.

[0755] "Means for generating and outputting appropriate instructions based on analyzed emotions" refers to a technology that creates appropriate and effective instructions based on the results of an emotion analysis of a worker and presents them via voice or display.

[0756] To implement this invention, a terminal equipped with a microphone and speaker is used as hardware for processing voice input. The terminal receives voice input and converts the voice into text data using speech recognition software (e.g., Google Cloud Speech-to-Text). This text data is then translated into multiple languages ​​using natural language processing technology (e.g., Google Translate API).

[0757] Next, an emotion analysis engine (e.g., Microsoft Azure Cognitive Services) analyzes the voice data to determine the worker's emotions. Based on this emotion data, it generates phrases suitable for negotiation or work instructions. The generated phrases are translated into the specified language and output through the terminal's display and speakers.

[0758] The server integrates this data and generates appropriate instructions in real time based on the analysis results. Through this system, users can achieve smooth communication that transcends language barriers. Furthermore, because it can respond to changes in workers' emotions, it is expected to strengthen cooperation in international work environments.

[0759] As a concrete example, in a factory, if a Japanese-speaking supervisor needs to give real-time safety instructions to a Spanish-speaking worker, this system can enable smooth and unambiguous instructions.

[0760] An example of a prompt is: "Consider how to use speech recognition and sentiment analysis to ensure accurate work instructions while facilitating smooth communication in a multilingual environment."

[0761] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0762] Step 1:

[0763] The device uses a microphone to receive the user's voice as input. The received voice data is then prepared for processing as a digital signal.

[0764] Step 2:

[0765] The device uses speech recognition software to convert the audio data into text data. This conversion transforms the audio data into character information with a linguistic structure. The output text data is then passed on to the next process.

[0766] Step 3:

[0767] The server receives text data and performs multilingual translation using natural language processing technology. The input text data is translated into the specified language, and the translated text is generated.

[0768] Step 4:

[0769] The server uses an emotion analysis engine to analyze emotional data from the input voice data. This analysis outputs the user's emotional state as specific parameters.

[0770] Step 5:

[0771] The server generates phrases appropriate to specific situations based on analyzed sentiment data. Using a generative AI model, it creates phrases suitable for negotiations and work instructions, and the generated phrases are then refined and output.

[0772] Step 6:

[0773] The terminal receives the translated text and generated phrases sent from the server. It displays this information on the user interface and outputs it as audio through the speaker. The user can then make decisions based on this information.

[0774] 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.

[0775] 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.

[0776] 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.

[0777] 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.

[0778] 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.

[0779] 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.

[0780] 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.

[0781] 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.

[0782] 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."

[0783] 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.

[0784] 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.

[0785] 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.

[0786] 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.

[0787] 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.

[0788] 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.

[0789] 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.

[0790] 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.

[0791] 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.

[0792] 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.

[0793] 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.

[0794] 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.

[0795] The following is further disclosed regarding the embodiments described above.

[0796] (Claim 1)

[0797] A means of accepting voice input,

[0798] A means for converting the voice input into text data,

[0799] A means for translating the text data into multiple languages,

[0800] A means of generating phrases suitable for negotiation,

[0801] Means for outputting the translated text data and the generated phrase,

[0802] A system that includes this.

[0803] (Claim 2)

[0804] The system according to claim 1, wherein the means for receiving voice input sets up different scenarios according to the user's selection.

[0805] (Claim 3)

[0806] The system according to claim 1, wherein the means for generating phrases suitable for the negotiation uses past data and a pattern recognition algorithm.

[0807] "Example 1"

[0808] (Claim 1)

[0809] A means of accepting voice input,

[0810] A means for converting the voice input into text data,

[0811] A means for analyzing the text data using natural language processing,

[0812] A means for translating the text data into multiple languages,

[0813] A method for generating expressions suitable for negotiation using an AI model,

[0814] A means for outputting the generated expression via a user interface,

[0815] A system that includes this.

[0816] (Claim 2)

[0817] The system according to claim 1, wherein the means for receiving voice input sets up different negotiation scenarios according to the user's selection.

[0818] (Claim 3)

[0819] The system according to claim 1, wherein the means for generating expressions suitable for the negotiations uses past information and pattern recognition technology.

[0820] "Application Example 1"

[0821] (Claim 1)

[0822] A means of accepting voice input,

[0823] A means for converting the voice input into text data,

[0824] A means for translating the text data into multiple languages,

[0825] A means of generating phrases suitable for negotiation,

[0826] Means for outputting the translated text data and the generated phrase,

[0827] Means for capturing and analyzing visual data,

[0828] Means for obtaining information from captured visual data,

[0829] A means for presenting content in real time based on the aforementioned visual data,

[0830] A system that includes this.

[0831] (Claim 2)

[0832] The system according to claim 1, wherein the means for receiving voice input sets up different scenarios according to the user's selection.

[0833] (Claim 3)

[0834] The system according to claim 1, wherein the means for generating phrases suitable for the negotiation uses past data and a pattern recognition algorithm.

[0835] "Example 2 of combining an emotion engine"

[0836] (Claim 1)

[0837] A means of accepting voice input,

[0838] A means for converting the voice input into text data,

[0839] A means for analyzing emotional states from the text data,

[0840] A means for translating the text data into multiple languages,

[0841] A means for generating phrases suitable for negotiation according to the emotional state,

[0842] Means for outputting the translated text data and the generated phrase,

[0843] A means of monitoring the user's emotional state and recording its changes,

[0844] A means of reflecting the recorded emotional data in the next phrase generation or translation,

[0845] A system that includes this.

[0846] (Claim 2)

[0847] The system according to claim 1, wherein the means for receiving voice input sets up different scenarios according to the user's selection.

[0848] (Claim 3)

[0849] The system according to claim 1, wherein the means for generating phrases suitable for the negotiation uses past data and a pattern recognition algorithm.

[0850] "Application example 2 when combining with an emotional engine"

[0851] (Claim 1)

[0852] A means of accepting voice input,

[0853] A means for converting the voice input into text data,

[0854] A means for translating the text data into multiple languages,

[0855] A means of generating phrases suitable for negotiation,

[0856] Means for outputting the translated text data and the generated phrase,

[0857] A means of analyzing the emotions of workers in a specific environment,

[0858] A means of generating and outputting appropriate instructions based on analyzed emotions,

[0859] A system that includes this.

[0860] (Claim 2)

[0861] The system according to claim 1, wherein the means for receiving the voice input makes settings according to the conditions of the work site.

[0862] (Claim 3)

[0863] The system according to claim 1, wherein the means for generating phrases suitable for the negotiation uses past data and algorithms to facilitate smooth communication between workers. [Explanation of Symbols]

[0864] 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 accepting voice input, A means for converting the voice input into text data, A means for translating the text data into multiple languages, A means of generating phrases suitable for negotiation, Means for outputting the translated text data and the generated phrase, A system that includes this.

2. The system according to claim 1, wherein the means for receiving voice input sets different scenarios according to the user's selection.

3. The system according to claim 1, wherein the means for generating phrases suitable for the negotiation uses past data and a pattern recognition algorithm.

Images