system

Abstract

Provide a system. [Solution] a sensor that captures the reaction of the object; analysis means for analyzing the captured data; a voice generating means for generating voice advice based on the analysis result; an output means for outputting the generated voice advice; A system that provides users with real-time voice advice during business negotiations and meetings.

Description

[Technical Field]

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

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

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

[0004] In business situations, the ability to quickly and appropriately grasp the other person's reactions is required to smoothly conduct negotiations and meetings. However, many people find it difficult to accurately grasp the other person's subtle facial expressions and eye movements, which can lead to delayed appropriate responses. This can reduce the effectiveness of negotiations and meetings. [Means for solving the problem]

[0005] The present invention solves the above-mentioned problems by providing a system including a sensor that captures the reaction of a target, an analysis means that analyzes the captured data, a voice generation means that generates voice advice based on the analysis results, and an output means that outputs the generated voice advice. The sensor includes a means for tracking the target's line of sight, and the analysis means includes a means for determining the target's emotion based on the target's facial expression, so that the other person's reaction is analyzed in real time and voice advice such as appropriate talk, speed, and cushion words is provided to the user.

[0006] "Subject reaction" refers to the facial expressions, movements, and other reactions of the other person that are captured by the sensor.

[0007] A "sensor" is a device used to capture the subject's reactions and gaze.

[0008] "Analysis means" refers to technology that analyzes captured data and understands the other person's reactions and gaze.

[0009] "Voice generation means" refers to technology for generating appropriate voice advice based on the analysis results.

[0010] "Output means" refers to a device that provides the generated voice advice to the user in real time.

[0011] The "System" refers to a collection of devices and technologies that include sensors, analysis means, voice generation means, and output means, and that provide real-time voice advice during business negotiations and meetings.

[0012] "Eye tracking means" is a technology that uses sensors to track the position and movement of a subject's eyes and detect the direction of their gaze.

[0013] "Means for determining emotions based on facial expressions" refers to a technology that analyzes captured facial expression data and determines the emotions of the other person. [Brief explanation of the drawings]

[0014] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0015] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

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

[0017] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0018] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0019] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0020] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0021] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0022] [First embodiment]

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

[0024] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0025] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0026] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0027] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0028] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0029] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0031] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0032] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0033] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0034] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0035] This invention relates to a voice advice system for improving communication in business negotiations and meetings. This system analyzes the reactions and gaze of objects in real time and provides voice advice to users based on the results.

[0036] 1. System Configuration

[0037] The system consists of the following main components:

[0038] Device: Functions as audio glasses and has built-in sensors and cameras.

[0039] Server: Processes and analyzes data.

[0040] User: Wears audio glasses and conducts business negotiations and meetings.

[0041] 2. Program Processing

[0042] Detecting the other person's reaction

[0043] The device uses its built-in sensors and camera to capture the face and facial expressions of the person in the meeting or business negotiation. This captured data is initially processed by a facial recognition algorithm to extract important facial features (eyes, nose, mouth, etc.), which can then determine the other person's basic emotional state (interested, confused, etc.).

[0044] Eye tracking

[0045] The device also detects the other person's eye movements, which allows it to determine where the other person is looking and transmits the gaze data to the server, allowing it to understand what the other person is interested in.

[0046] Data analysis

[0047] The server analyzes the received facial feature points and gaze data. By combining facial expression recognition algorithms and gaze analysis algorithms, it can determine the other person's psychological state and interest in real time. For example, it can recognize when the other person is interested or confused.

[0048] Generate and provide voice advice

[0049] The server generates voice advice based on the analysis results. For example, specific advice such as "The other party is interested. Please explain in detail" is generated. This advice is sent to the terminal as digital data, and the terminal provides it to the user by voice.

[0050] Responding to advice

[0051] Based on the provided voice advice, the user can adjust the speed, content, and cushion words of the conversation to proceed with the business negotiation or meeting. For example, if the other party shows interest, the user can respond by adding detailed explanations.

[0052] Specific examples

[0053] Specific examples of business negotiations

[0054] 1. The user (sales representative) wears the audio glasses and attends a sales meeting.

[0055] 2. The device captures the customer's facial expressions and gaze and sends them to the server.

[0056] 3. The server determines that the customer is interested in the product description and generates a voice advice saying, "The customer is interested. Please explain in more detail."

[0057] 4. The device will then give this advice to the user audibly.

[0058] 5. The user follows the advice and provides details about the product.

[0059] Specific examples of meetings

[0060] 1. A user (project manager) wears audio glasses and attends a team meeting.

[0061] 2. The device captures the participants' facial expressions and gaze and sends them to the server.

[0062] 3. The server determines that some team members are losing interest in the content of the discussion and generates audio advice such as, "It seems that some members are losing interest. Try approaching it from a different perspective."

[0063] 4. The device will then give this advice to the user audibly.

[0064] 5. The user follows the advice and approaches and explains the topic from a different perspective.

[0065] As described above, the system of the present invention improves the quality of communication in business negotiations and meetings, and provides support for users to converse with others more effectively.

[0066] The processing flow will be explained below.

[0067] Step 1:

[0068] The user puts on the Audio Glasses. The device (Audio Glasses) initializes the camera and sensors, preparing for a business meeting or negotiation.

[0069] Step 2:

[0070] The device uses a camera to capture the face of the target (business partner or meeting participant), and sensors detect the target's facial expression and eye position in real time.

[0071] Step 3:

[0072] The device extracts key facial features (eyes, nose, mouth, etc.) from the captured facial image data, which is then sent to the server along with the sensor readings.

[0073] Step 4:

[0074] The server analyzes the received facial feature point data and facial expression data, and determines the subject's emotions and reactions (e.g., interest, joy, confusion, etc.) using a facial expression recognition algorithm.

[0075] Step 5:

[0076] The device tracks eye movements, generates gaze data, detects which direction the gaze is directed, and sends this data to a server.

[0077] Step 6:

[0078] The server analyzes the gaze data to determine where the subject is looking, thereby understanding the subject's interests and level of interest.

[0079] Step 7:

[0080] The server generates appropriate voice advice based on the analysis results, such as "The object is of interest. Please explain in more detail."

[0081] Step 8:

[0082] The server transmits the generated voice advice to the terminal as digital data.

[0083] Step 9:

[0084] The device then plays the received audio advice to the user in real time. For example, it could play audio advice such as "The other person seems interested. Please explain in more detail" through earphones.

[0085] Step 10:

[0086] Based on the provided audio advice, users can adjust the speed, content, and cushion words of their own talk to proceed with business negotiations and meetings.

[0087] Step 11:

[0088] After a business meeting or negotiation is over, the device sends data from the entire session (facial expressions, eye contact, voice advice, etc.) to the server.

[0089] Step 12:

[0090] The server analyzes the transmitted data in detail, evaluates the user's performance and the other party's response, and generates feedback that can be used for the next business negotiation or meeting.

[0091] Step 13:

[0092] The server transmits the generated feedback to the user's terminal and provides it in a form that the user can view.

[0093] Example 1

[0094] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0095] In conventional business negotiations and meetings, it is difficult to properly understand the other party's reactions and psychological state and respond accordingly. In particular, it is difficult to read reactions and emotional changes in real time and provide effective advice. This has led to the issue of not being able to maximize the effectiveness of business negotiations and meetings.

[0096] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0097] In this invention, the server includes means for analyzing the captured data, means for generating audio advice based on the analysis results, and means for generating specific advice using a generative AI model. This enables users to accurately grasp the real-time reactions and emotional states of others in business negotiations or meetings and receive appropriate advice in real time.

[0098] A "sensor" is a device for capturing the response of an object.

[0099] An "analysis means" is a method or device for analyzing the captured data.

[0100] The "voice generation means" is a method or device for generating voice advice based on the analysis results.

[0101] The "output means" is a device for providing the generated voice advice to the user.

[0102] A "communication means" is a method or device for transmitting captured data to a server.

[0103] "Real-time analysis means" refers to a method or device for analyzing data received at a server in real time.

[0104] A "generative AI model" is an artificial intelligence-based model that generates specific advice based on analysis results.

[0105] A "user" is a person who wears the audio glasses and conducts business negotiations or meetings.

[0106] The "object" is the person with whom you are negotiating or meeting.

[0107] "Facial feature points" are points on important parts of the face (such as the eyes, nose, and mouth).

[0108] "Gaze data" refers to data relating to the movement of the other person's eyes.

[0109] An "facial expression recognition algorithm" is a computational method for analyzing facial expressions and determining emotions, etc.

[0110] A "gaze analysis algorithm" is a calculation method for analyzing the other person's line of sight.

[0111] This invention relates to a voice advice system for improving communication in business negotiations and meetings. This system analyzes the reactions and gaze of objects in real time and provides voice advice to users based on the results.

[0112] System configuration

[0113] The system consists of the following main components:

[0114] Device: Functions as audio glasses and has built-in sensors and cameras, including high-resolution camera sensors and facial recognition software.

[0115] Server: Processes and analyzes data. Preferably, Google® Cloud Platform AI tools and TENSORFLOW® models are used.

[0116] User: Wears audio glasses and conducts business negotiations and meetings.

[0117] Program processing

[0118] Face and expression capture and initial processing

[0119] The device uses built-in sensors and cameras to capture the face and facial expressions of the other person, for example, using high-resolution camera sensors and facial expression recognition software. This captured data is then processed in real time to extract key facial features (such as eyes, nose, and mouth).

[0120] Eye gaze detection and data transmission

[0121] The device detects the other person's eye movements using tracking technology, such as eye-tracking technology. This gaze data and facial feature point data are sent to the server via a secure communication protocol (e.g., SSL / TLS).

[0122] Data reception and analysis

[0123] The server receives the data sent from the device and analyzes it using a deep learning algorithm for facial expression recognition and gaze analysis. The analysis can be performed using Google Cloud Platform's AI tools and TensorFlow models.

[0124] Generate voice advice

[0125] The server generates voice advice based on the analysis results. For example, advice such as "The business partner is interested. Please provide more specific details." This advice is generated using a generative AI model (e.g., GPT-3 (registered trademark)).

[0126] Sending and viewing advice

[0127] The server sends the generated advice as digital data to the terminal, which then outputs the advice to the user as audio. Bone conduction earphones can be used for audio output.

[0128] User response

[0129] The user can proceed with the business negotiation or meeting based on the provided voice advice. The quality of the business negotiation or meeting can be improved by the user responding in real time.

[0130] Specific examples

[0131] Specific examples of business negotiations

[0132] 1. The user (salesperson) puts on the audio glasses and begins explaining the product to the customer.

[0133] 2. The device captures the customer's face and facial expressions and tracks their gaze.

[0134] 3. The server receives the captured data and determines that the customer is interested in the product description.

[0135] 4. The server generates a voice advice saying, "Customer is interested. Please provide more details."

[0136] 5. The device will then give this advice to the user audibly.

[0137] 6. The user follows the advice and provides additional information about the product and specific usage examples.

[0138] Specific examples of meetings

[0139] 1. A user (project manager) wears audio glasses and attends a team meeting.

[0140] 2. The device captures team members' faces and expressions and tracks their gaze.

[0141] 3. The server receives and analyzes the captured data and determines that some team members are losing interest in the discussion.

[0142] 4. The server generates a voice advice saying, "It seems like some of your members are losing interest. Try approaching them from a different perspective."

[0143] 5. The device will then give this advice to the user audibly.

[0144] 6. The user follows the advice and explains the topic from a different perspective.

[0145] Prompt Sentence Examples

[0146] Here are some example prompts for the generative AI model:

[0147] "Analyze the customer's facial recognition results and gaze data to generate appropriate sales advice. If the customer is interested, provide advice that encourages further clarification."

[0148] As described above, this system utilizes the latest AI technology and hardware to improve the quality of communication during business negotiations and meetings.

[0149] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0150] Step 1:

[0151] The device uses its built-in sensors and camera to capture the face and facial expressions of the target (e.g., business partner or meeting participant). This input data is initially processed in real time using a facial recognition algorithm. Specifically, the data is processed to extract facial feature points (e.g., eyes, nose, mouth) and understand emotional states (e.g., interest, confusion). The output of this processing is feature point data and basic emotional state information.

[0152] Step 2:

[0153] The device also uses built-in eye tracking technology to detect the subject's eye movements, which are recorded as gaze data. This gaze data is also collected in real time and sent to the server along with the simultaneously captured facial feature point data. The specific operation is to identify where the subject is looking. The input of this process is eye movements, and the output is gaze data.

[0154] Step 3:

[0155] The server receives facial feature point data and gaze data sent from the device. Immediately after receiving the data, it analyzes it using a deep learning-based facial expression recognition algorithm and gaze analysis algorithm. Specific examples include using Google Cloud Platform's AI tools and TensorFlow models. The input to this process is the received data, and the output is the analysis results. The analysis results determine the subject's mental state and interests in real time.

[0156] Step 4:

[0157] The server uses a generative AI model (e.g., GPT-3) to generate appropriate voice advice based on the analysis results. Specifically, it generates advice such as, "The business partner is interested. Please explain in more detail." The input of this process is the analysis results, and the output is the generated voice advice.

[0158] Step 5:

[0159] The server transmits the generated audio advice as digital data back to the terminal. The terminal receives this digital data and provides the advice to the user by audio using an audio output device (e.g., bone conduction earphones). The input of this process is the audio advice data, and the output is the audio advice that the user hears.

[0160] Step 6:

[0161] The user adjusts the progress of a business negotiation or meeting based on the provided voice advice. Specifically, the content, speed, and cushion words of the talk are changed in real time. The input of this process is the voice advice, and the output is the adjusted communication content.

[0162] Through the above steps, this system can support effective real-time communication during business negotiations and meetings.

[0163] (Application example 1)

[0164] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0165] Traditional customer service in commercial facilities relies heavily on the experience and skills of staff, making it difficult to accurately grasp customer needs and interests and respond appropriately to them, which can lead to lower customer satisfaction and lost sales opportunities.

[0166] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0167] In this invention, the server includes a sensor that captures the reaction of the object, an analysis means that analyzes the captured data, a voice generation means that generates voice advice based on the analysis results, an output means that outputs the generated voice advice, and a means that provides the voice advice to staff in the commercial facility in real time. This enables staff to quickly grasp customer reactions and take appropriate measures, improving the quality of customer service and increasing customer satisfaction.

[0168] "Subject" refers to the customer whose responses the system captures and analyzes the data.

[0169] "Sensor" refers to a device for capturing the reaction and gaze of a subject.

[0170] "Analysis means" refers to a method or device for analyzing data captured by a sensor and determining the interests and emotions of a subject.

[0171] "Voice generation means" refers to a method or apparatus for generating audio advice based on data obtained from the analysis means.

[0172] "Output means" refers to a method or device for providing the generated audio advice to the user in real time.

[0173] "Commercial establishment" refers to a place where customer interactions and transactions take place.

[0174] "Staff" refers to employees who deal with customers within a commercial facility.

[0175] "Real-time" refers to providing immediate responses or results without delay.

[0176] The system of the present invention aims to support customer service in commercial facilities. To this end, it analyzes the reactions of customers in real time and provides voice advice to staff based on the results.

[0177] System configuration

[0178] The system mainly consists of the following components:

[0179] Sensor: A device built into smart glasses that captures the subject's reactions and gaze.

[0180] Analysis means: A method or device for analyzing data captured by the sensor. Specifically, a face recognition algorithm using OpenCV and Dlib, an eye tracking algorithm using Tobii Gaze SDK, and an expression analysis algorithm using Azure® Cognitive Services Face API are used.

[0181] Voice Generation Means: A method or device for generating voice advice based on data obtained from the Analysis Means. Amazon Polly is used here.

[0182] Output means: A device for providing the generated voice advice to the user in real time, through the speaker of the smart glasses.

[0183] Specific Examples

[0184] 1. Situation: A staff member in a brick-and-mortar store is wearing smart glasses and introducing a new product to a customer.

[0185] 2. Capture: The camera in the smart glasses captures the customer's face and gaze.

[0186] 3. Analysis: The captured data is sent to the server and initially processed by face recognition algorithms (OpenCV and Dlib), followed by facial expression analysis using Azure Cognitive Services Face API, and gaze data is analyzed using the Tobii Gaze SDK.

[0187] 4. Advice generation: The emotional state and interest level are determined based on the analysis results. For example, if the customer is interested in the product description, a voice advice such as "The customer is interested. Please explain in more detail" is generated.

[0188] 5. Delivery: The generated voice advice is provided to staff in real time through the smart glasses speaker.

[0189] Prompt Sentence Examples

[0190] For generative AI models, use prompts like this:

[0191] Explain how to use a voice advice system in business negotiations and meetings to analyze customer responses in real time and provide appropriate customer service advice to staff. For example, explain with specific scenarios what advice should be given when a customer shows interest.

[0192] As described above, the system of the present invention can effectively support customer service within a commercial facility, improve customer satisfaction, and maximize sales opportunities.

[0193] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0194] Step 1:

[0195] The camera in the smart glasses captures the customer's face and gaze. The input is the camera image of the smart glasses, and the output is the captured image data, which is used for subsequent analysis.

[0196] Step 2:

[0197] The device receives the captured image data and uses a facial recognition algorithm (OpenCV and Dlib) to detect facial landmarks. The input is image data and the output is landmark data. Specifically, facial feature points such as eyes, nose, and mouth are detected.

[0198] Step 3:

[0199] The terminal sends landmark data and image data to the server. The input is landmark data and image data, and the output is data sent to the server. This data is used for further analysis on the server.

[0200] Step 4:

[0201] The server analyzes the received landmark data and image data and uses the Azure Cognitive Services Face API to analyze the customer's facial expression. The input is landmark data and image data, and the output is the customer's emotional data, which includes emotional states such as happiness, surprise, and confusion.

[0202] Step 5:

[0203] The server analyzes the customer's gaze data using the Tobii Gaze SDK. The input is image data, and the output is gaze data. This determines which direction the customer is looking.

[0204] Step 6:

[0205] The server integrates the facial expression data and gaze data to comprehensively determine the customer's level of interest and emotional state. The input is facial expression data and gaze data, and the output is comprehensive judgment data. This data indicates whether the customer is interested in a particular product or whether they are confused.

[0206] Step 7:

[0207] The server generates voice advice based on the overall assessment data. Amazon Polly is used as the voice generation method. The input is the overall assessment data, and the output is the generated voice data. Specifically, advice such as "The customer is interested. Please explain in more detail" is generated.

[0208] Step 8:

[0209] The generated voice data is sent to the terminal and provided to the user through the speaker of the smart glasses. The input is the generated voice data, and the output is the provision of voice advice. The user responds to the customer based on this advice.

[0210] In this way, it is possible to analyze customer reactions in real time and provide appropriate advice, thereby improving the quality of customer service within commercial facilities.

[0211] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0212] This invention relates to a voice advice system for improving communication in business negotiations and meetings. This system analyzes the reactions and gaze of objects in real time, and provides voice advice to the user based on the results. It also incorporates an emotion engine that recognizes the user's emotions.

[0213] 1. System Configuration

[0214] The system consists of the following main components:

[0215] Device: Functions as audio glasses and has built-in sensors and cameras.

[0216] Server: Processes and analyzes data.

[0217] User: Wears audio glasses and conducts business negotiations and meetings.

[0218] Emotion engine: Recognizes emotions by analyzing the user's facial expressions and tone of voice.

[0219] 2. Program Processing

[0220] Detecting the other person's reaction

[0221] The device uses its built-in sensors and camera to capture the face and facial expressions of the person you're meeting or negotiating with. This captured data is then processed by a facial recognition algorithm to extract key facial features (eyes, nose, mouth, etc.). This data is then used to understand the person's basic emotional state (interested, confused, etc.).

[0222] Eye tracking

[0223] The device tracks the other person's eye movements to determine which direction they are looking, and the gaze data is sent to a server where it is used to understand what the other person is interested in.

[0224] Recognizing user emotions with an emotion engine

[0225] The emotion engine analyzes the user's facial expression data and voice tone to recognize the user's emotions, for example, determining whether the user is nervous, relaxed, confused, etc.

[0226] Data analysis

[0227] The server analyzes the received facial feature points, facial expression data, gaze data, and user emotion data. By combining a facial expression recognition algorithm and a gaze analysis algorithm, it determines the other person's mental state and interest in real time.

[0228] Generate and provide voice advice

[0229] The server generates voice advice based on the analysis results. For example, specific advice such as "The other person is interested. Please explain in more detail" or "You seem nervous. Please speak a little more slowly" is generated. This advice is sent to the terminal as digital data, and the terminal provides it to the user by voice.

[0230] Responding to advice

[0231] Based on the provided voice advice, the user can adjust the speed, content, and cushion words of their own talk to proceed with business negotiations or meetings. For example, if the other party shows interest, they can add detailed explanations, or if the user is nervous, they can slow down the pace.

[0232] Specific examples

[0233] Specific examples of business negotiations

[0234] 1. The user (sales representative) wears the audio glasses and attends a sales meeting.

[0235] 2. The device captures the customer's facial expressions and gaze and sends them to the server.

[0236] 3. The emotion engine recognizes that the user is nervous.

[0237] 4. The server determines that the customer is interested in the product explanation and that the user is nervous, and generates voice advice saying, "The customer is interested. Please explain in more detail. You seem nervous. Please speak more slowly."

[0238] 5. The device will then give this advice to the user audibly.

[0239] 6. The user follows the advice and adjusts their speaking speed while explaining the product details.

[0240] Specific examples of meetings

[0241] 1. A user (project manager) wears audio glasses and attends a team meeting.

[0242] 2. The device captures the participants' facial expressions and gaze and sends them to the server.

[0243] 3. The emotion engine recognizes that the user is relaxed.

[0244] 4. The server determines that some of the team members are losing interest in the content of the discussion and generates voice advice such as, "It seems that some of the members are losing interest. Try approaching it from a different perspective."

[0245] 5. The device will then give this advice to the user audibly.

[0246] 6. The user follows the advice and approaches and explains the topic from a different perspective.

[0247] As described above, the system of the present invention improves the quality of communication in business negotiations and meetings, and provides support for users to converse more effectively with others. This system can analyze the emotions and reactions of both the user and the other party in real time and provide appropriate advice.

[0248] The processing flow will be explained below.

[0249] Step 1:

[0250] The user puts on the Audio Glasses. The device (Audio Glasses) initializes the camera and sensors, preparing for a business meeting or negotiation.

[0251] Step 2:

[0252] The device uses a camera to capture the face of the target (business partner or meeting participant), and sensors detect the target's facial expression and eye position in real time.

[0253] Step 3:

[0254] The device extracts key facial features (eyes, nose, mouth, etc.) from the captured facial image data, which is then sent to the server along with the sensor readings.

[0255] Step 4:

[0256] The emotion engine analyzes the user's facial expression data and voice tone to recognize the user's emotions, such as tension, relaxation, confusion, etc.

[0257] Step 5:

[0258] The device tracks eye movements, generates gaze data, detects which direction the gaze is directed, and sends this data to a server.

[0259] Step 6:

[0260] The server analyzes the received facial feature data, facial expression data, user emotion data, and gaze data. By combining a facial expression recognition algorithm and a gaze analysis algorithm, it determines the psychological state and interest of the target (business partner or meeting participant) in real time.

[0261] Step 7:

[0262] The server generates voice advice based on the analysis results, such as "The object is interested. Please explain in more detail" or "You seem nervous. Please speak a little more slowly."

[0263] Step 8:

[0264] The server transmits the generated voice advice to the terminal as digital data.

[0265] Step 9:

[0266] The device then plays the received audio advice to the user in real time. For example, the audio advice may say, "The other person seems interested. Please explain in more detail."

[0267] Step 10:

[0268] Based on the provided audio advice, users can adjust the speed, content, and cushion words of their own talk to proceed with business negotiations and meetings.

[0269] Step 11:

[0270] After a business meeting or negotiation is over, the device sends data from the entire session (facial expressions, eye contact, voice advice, etc.) to the server.

[0271] Step 12:

[0272] The server analyzes the transmitted data in detail, evaluates the user's performance and the other party's response, and generates feedback that can be used for the next business negotiation or meeting.

[0273] Step 13:

[0274] The server transmits the generated feedback to the user's terminal and provides it in a form that the user can view.

[0275] Through the above processing steps, the system can provide users with appropriate advice in real time, improving the effectiveness of business negotiations and meetings.

[0276] Example 2

[0277] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0278] In business negotiations and meetings, there is a need to improve the quality of communication by analyzing the other party's facial expressions, gaze, and user emotions in real time and providing appropriate voice advice based on that. However, current systems have difficulty understanding the other party's situation and providing appropriate feedback, making it difficult for users to respond optimally.

[0279] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a sensor that captures the reaction of an object, an analysis means that analyzes the captured data, a voice generation means that generates voice advice based on the analysis result, an output means that outputs the generated voice advice, and an emotion engine that analyzes the emotion of the user. This enables the user to grasp the reaction and emotion of the other party in real time during a business negotiation or meeting and receive appropriate feedback.

[0280] A "sensor that captures the reaction of an object" is a device that uses a camera or optical sensor to capture the face, facial expression, and gaze of the other party in real time during a business negotiation or meeting.

[0281] "Analysis means for analyzing captured data" refers to software and hardware that processes facial feature data, gaze data, and facial expression data acquired by the sensor to identify the other person's emotional state and the object of their attention.

[0282] The "voice generation means for generating voice advice based on the analysis results" refers to an algorithm or software that generates advice on specific actions and talk content in business negotiations or meetings based on data from the analysis means.

[0283] The "output means for outputting the generated voice advice" refers to an audio device or interface for conveying the voice advice to the user, and corresponds to earphones or speakers.

[0284] An "emotion engine that analyzes user emotions" is a system that includes software and algorithms that analyze the user's facial expressions, tone of voice, etc. to recognize the user's emotional state (tension, relaxation, etc.).

[0285] "Facial feature point data" refers to data regarding the position and shape of facial features (eyes, nose, mouth, etc.) captured by a camera or sensor.

[0286] "Gaze data" refers to data collected by sensors about the direction of an object's gaze and the location of its focus.

[0287] "Facial expression data" is data that reflects captured changes in facial expressions and subtle movements, and is used to determine the emotional state of the other person.

[0288] "Real-time" means that the system processes and analyzes data instantly and provides near-instant output (such as voice advice).

[0289] This invention relates to a voice advice system for improving communication in business negotiations and meetings. This system analyzes the reactions and gaze of objects in real time, and provides voice advice to the user based on the results. It also incorporates an emotion engine that recognizes the user's emotions.

[0290] System configuration

[0291] The system consists of the following main components:

[0292] Device: Functions as audio glasses and has built-in sensors and cameras.

[0293] Server: A computing device that processes and analyzes data.

[0294] User: The person wearing the audio glasses who conducts business negotiations and meetings.

[0295] Emotion engine: Software that recognizes emotions by analyzing a user's facial expressions and tone of voice.

[0296] Program processing

[0297] The system operates in the following steps:

[0298] Detecting the other person's reaction

[0299] The device uses its built-in sensors and camera to capture the face and facial expressions of the person in the meeting or business negotiation. This captured data is initially processed by a facial recognition algorithm (e.g., OpenCV) to extract important facial features (eyes, nose, mouth, etc.). This data is used to understand the person's basic emotional state (interested, confused, etc.).

[0300] Eye tracking

[0301] The device tracks the other person's eye movements to determine which direction they are looking, and the gaze data is sent to a server where it is used to understand what the other person is interested in.

[0302] Recognizing user emotions with an emotion engine

[0303] The emotion engine analyzes the user's facial expression data and voice tone to recognize the user's emotions, for example, determining whether the user is nervous, relaxed, confused, etc.

[0304] Data analysis

[0305] The server analyzes the received facial feature points, facial expression data, gaze data, and user emotional data. By combining a facial expression recognition algorithm (e.g., DeepFace) with a gaze analysis algorithm, it can determine the other person's mental state and interest in real time.

[0306] Generate and provide voice advice

[0307] The server generates voice advice based on the analysis results. For example, specific advice such as "The other person is interested. Please explain in more detail" or "You seem nervous. Please speak a little more slowly" is generated. This advice is sent to the terminal as digital data, and the terminal provides it to the user by voice.

[0308] Responding to advice

[0309] Based on the provided voice advice, the user can adjust the speed, content, and cushion words of their own talk to proceed with business negotiations or meetings. For example, if the other party shows interest, they can add detailed explanations, or if the user is nervous, they can slow down the pace.

[0310] Specific examples

[0311] Specific examples of business negotiations

[0312] 1. The user (sales representative) wears the audio glasses and attends a sales meeting.

[0313] 2. The device captures the customer's facial expressions and gaze and sends them to the server.

[0314] 3. The emotion engine recognizes that the user is nervous.

[0315] 4. The server determines that the customer is interested in the product explanation and that the user is nervous, and generates voice advice saying, "The customer is interested. Please explain in more detail. You seem nervous. Please speak more slowly."

[0316] 5. The device will then give this advice to the user audibly.

[0317] 6. The user follows the advice and adjusts their speaking speed while explaining the product details.

[0318] Specific examples of meetings

[0319] 1. A user (project manager) wears audio glasses and attends a team meeting.

[0320] 2. The device captures the participants' facial expressions and gaze and sends them to the server.

[0321] 3. The emotion engine recognizes that the user is relaxed.

[0322] 4. The server determines that some of the team members are losing interest in the content of the discussion and generates voice advice such as, "It seems that some of the members are losing interest. Try approaching it from a different perspective."

[0323] 5. The device will then give this advice to the user audibly.

[0324] 6. The user follows the advice and approaches and explains the topic from a different perspective.

[0325] Example prompts for generative AI models

[0326] Below are some example prompts to be input to the generative AI model:

[0327] Sales: Describe a system that uses audio glasses to monitor a customer's facial expressions and gaze in real time during a sales meeting. What specific voice advice is provided if the user is nervous?

[0328] Meetings: In team meetings, analyze users' facial expressions and gaze in real time and provide audio advice on what to do if someone seems disengaged.

[0329] The system described above can improve the quality of communication during business negotiations and meetings. Users can grasp the other party's reactions and emotions in real time and receive appropriate advice, enabling more effective dialogue.

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

[0331] Step 1: Capture their reactions

[0332] Input: Real-time video of the object during a business meeting or negotiation

[0333] Specific operation: The device uses its built-in sensors and camera to capture the faces and expressions of people involved in business negotiations or meetings.

[0334] Data processing: The captured video data is processed using a facial recognition algorithm (e.g., OpenCV) to extract important facial features (eyes, nose, mouth, etc.).

[0335] Output: Facial feature point data is generated.

[0336] Step 2: Eye tracking

[0337] Input: Facial feature point data of the target

[0338] Specific operation: The device tracks the other person's eye movements in real time.

[0339] Data processing: Tracks pupil movement and processes it to identify the focus of attention. Eye gaze analysis algorithms are used.

[0340] Output: Gaze data is generated and sent to the server.

[0341] Step 3: Recognizing user emotions

[0342] Input: User's facial expression data and voice tone

[0343] Specific behavior: The emotion engine captures and analyzes the user's facial expressions and voice.

[0344] Data processing: Analyzes the user's facial expression data and voice to determine their emotional state (tension, relaxation, etc.).

[0345] Output: The user's emotion data is generated and sent to the server.

[0346] Step 4: Analyze the data

[0347] Input: Facial feature point data, gaze data, user emotion data

[0348] Specific operation: The server combines and analyzes the various data it receives.

[0349] Data processing: Integrates facial expression recognition algorithms (e.g., DeepFace) and gaze analysis algorithms to determine the other person's psychological state and interest in real time.

[0350] Output: The analysis results are generated.

[0351] Step 5: Generate audio advice

[0352] Input: Analysis results

[0353] Specific operation: The server generates voice advice based on the analysis results.

[0354] Data processing: An advice generation algorithm is used to generate appropriate voice advice (e.g., "The customer is interested. Please explain in more detail.").

[0355] Output: Audio advice data is generated and sent to the terminal.

[0356] Step 6: Providing audio advice

[0357] Input: Voice advice data

[0358] Specific operation: The terminal receives the voice advice data and provides it to the user.

[0359] Data processing: Play it back as audio data and notify the user.

[0360] Output: The user receives a voice prompt.

[0361] Step 7: Responding to Advice

[0362] Input: Voice advice

[0363] Specific Action: The user adjusts their behavior based on the provided voice advice.

[0364] Data processing: Users can adjust the speed, content, and cushion words of their talk to optimize the progress of business negotiations and meetings.

[0365] Output: The user's adaptive behavior is output.

[0366] (Application example 2)

[0367] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0368] In manufacturing sites, there is a lack of means to grasp an operator's work efficiency and mental state in real time and provide appropriate advice. If an operator feels confused or tense while working, this can lead to a decrease in work efficiency and an increased likelihood of errors. With conventional systems, it has been difficult to analyze an operator's emotional state in real time and provide appropriate advice.

[0369] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0370] In this invention, the server includes a sensing device that includes means for capturing the reaction of the subject, an evaluation device that includes means for analyzing the captured data, a voice synthesis device that includes means for generating voice advice based on the analysis result, and a presentation device that includes means for outputting the generated voice advice. This makes it possible to analyze the emotional state of operators in a manufacturing site in real time and provide appropriate advice.

[0371] "Target person" refers to the operator who works at the workplace or manufacturing site.

[0372] A "sensing device" is a device for capturing a subject's reactions, particularly facial expressions and eye movements.

[0373] An "assessment tool" is a tool for analyzing the captured data and determining the subject's emotional state and reactions.

[0374] The "voice synthesis means" is a means for generating appropriate voice advice for the subject based on the analysis results obtained by the evaluation means.

[0375] The "presentation means" is a means for providing the generated voice advice to the subject as voice.

[0376] A "manufacturing floor" is a place where industrial products are manufactured, often by operators performing manual or mechanical work.

[0377] "Real-time" refers to data capture, analysis, and advice occurring near-instantaneously or with very little delay.

[0378] "Advice" is information that suggests to the target person the best course of action or next step in a particular situation.

[0379] "Data" refers to information captured by the sensing device, such as the subject's facial expressions and eye movements.

[0380] This invention is a system that analyzes the work efficiency and mental state of operators in manufacturing sites in real time and provides support by providing appropriate voice advice.

[0381] System configuration

[0382] The system consists of the following main components:

[0383] Sensing device: A device used to capture the subject's facial expressions and gaze, such as a webcam.

[0384] Evaluation method: Software that analyzes data obtained from the sensory device and determines the subject's emotional state. Specifically, image processing libraries such as OpenCV and deep learning models are used.

[0385] Speech synthesis means: A means for generating appropriate voice advice for the subject based on the analysis results of the evaluation means. A natural language generation tool such as OpenAI (registered trademark) API is used.

[0386] Presentation means: A means for providing the generated audio advice to the target person by voice, such as a speaker.

[0387] Server: A central computer that manages all data processing and communication.

[0388] Operating procedure

[0389] 1. Data capture:

[0390] The sensing device captures video of the operator in real time.

[0391] 2. Data Analysis:

[0392] The captured data is analyzed by an evaluation tool to determine the subject's emotional state based on their facial expressions and gaze, using OpenCV to extract facial feature points and a deep learning model to classify the emotion.

[0393] 3. Generate voice advice:

[0394] Based on the analysis results, the speech synthesis means generates appropriate voice advice, for example, using the OpenAI API to generate advice in natural language such as "The operator is relaxing. Please continue working."

[0395] 4. Audio presentation:

[0396] The presentation means provides the generated voice advice to the operator through a speaker.

[0397] Usage example

[0398] As a specific example of use, if an operator working at high speed on a production line looks confused, the system will output the advice, "You look confused. Please check your work." Also, if the system determines that the operator is tired after continuing difficult work, it will provide the advice, "Take a short break."

[0399] Prompt Sentence Examples

[0400] An example of a prompt to generate a voice advice is:

[0401] It provides appropriate voice advice based on the given emotion category: "happy" means "The operator is relaxed, please continue working", "sad" means "The operator is confused, please check your work", and "angry" means "The operator is annoyed, please relax and continue working".

[0402] This system allows operators at the manufacturing site to receive appropriate support, improving work efficiency.

[0403] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0404] Step 1:

[0405] Data Capture

[0406] Input: Video data of the subject (operator) captured by the terminal's sensing device.

[0407] How it works: The terminal's webcam captures the operator's facial expressions, gaze, and other information in real time. Specifically, the webcam continuously captures frames and sends them to the system.

[0408] Output: Captured video data.

[0409] Step 2:

[0410] Data Preprocessing

[0411] Input: Captured video data.

[0412] How it works: The device uses image processing libraries such as OpenCV to extract facial feature points from video data, while also performing basic image processing such as noise reduction and contrast adjustment.

[0413] Output: Preprocessed feature point data (positions of eyes, mouth, nose, etc.).

[0414] Step 3:

[0415] Emotion analysis

[0416] Input: Preprocessed feature point data.

[0417] How it works: The server uses a deep learning model to analyze the subject's emotions. Specifically, it inputs feature point data into an emotion recognition model and determines the emotion category (e.g., Happy, Sad, Angry).

[0418] Output: Parsed emotion data.

[0419] Step 4:

[0420] Voice advice generation

[0421] Input: Parsed emotion data.

[0422] How it works: The server uses natural language generation tools such as the OpenAI API to generate voice advice based on emotion data. It then inputs specific prompts to the generative AI model to get appropriate advice.

[0423] Output: The generated audio advice.

[0424] Step 5:

[0425] Voice advice provided

[0426] Input: The generated audio advice.

[0427] Operation: The device's presentation means (speaker) outputs the generated voice advice to the operator in real time. Specifically, the device receives the results of speech synthesis and plays them back as voice through the speaker.

[0428] Output: Providing audio advice to the operator.

[0429] This series of processing steps allows operators at the manufacturing site to receive appropriate support in real time, which is expected to improve work efficiency.

[0430] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0431] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0432] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0433] [Second embodiment]

[0434] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0435] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0436] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0437] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0438] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0439] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0440] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0441] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0442] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0443] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0444] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0445] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0446] This invention relates to a voice advice system for improving communication in business negotiations and meetings. This system analyzes the reactions and gaze of objects in real time and provides voice advice to users based on the results.

[0447] 1. System Configuration

[0448] The system consists of the following main components:

[0449] Device: Functions as audio glasses and has built-in sensors and cameras.

[0450] Server: Processes and analyzes data.

[0451] User: Wears audio glasses and conducts business negotiations and meetings.

[0452] 2. Program Processing

[0453] Detecting the other person's reaction

[0454] The device uses its built-in sensors and camera to capture the face and facial expressions of the person in the meeting or business negotiation. This captured data is initially processed by a facial recognition algorithm to extract important facial features (eyes, nose, mouth, etc.), which can then determine the other person's basic emotional state (interested, confused, etc.).

[0455] Eye tracking

[0456] The device also detects the other person's eye movements, which allows it to determine where the other person is looking and transmits the gaze data to the server, allowing it to understand what the other person is interested in.

[0457] Data analysis

[0458] The server analyzes the received facial feature points and gaze data. By combining facial expression recognition algorithms and gaze analysis algorithms, it can determine the other person's psychological state and interest in real time. For example, it can recognize when the other person is interested or confused.

[0459] Generate and provide voice advice

[0460] The server generates voice advice based on the analysis results. For example, specific advice such as "The other party is interested. Please explain in detail" is generated. This advice is sent to the terminal as digital data, and the terminal provides it to the user by voice.

[0461] Responding to advice

[0462] Based on the provided voice advice, the user can adjust the speed, content, and cushion words of the conversation to proceed with the business negotiation or meeting. For example, if the other party shows interest, the user can respond by adding detailed explanations.

[0463] Specific examples

[0464] Specific examples of business negotiations

[0465] 1. The user (sales representative) wears the audio glasses and attends a sales meeting.

[0466] 2. The device captures the customer's facial expressions and gaze and sends them to the server.

[0467] 3. The server determines that the customer is interested in the product description and generates a voice advice saying, "The customer is interested. Please explain in more detail."

[0468] 4. The device will then give this advice to the user audibly.

[0469] 5. The user follows the advice and provides details about the product.

[0470] Specific examples of meetings

[0471] 1. A user (project manager) wears audio glasses and attends a team meeting.

[0472] 2. The device captures the participants' facial expressions and gaze and sends them to the server.

[0473] 3. The server determines that some team members are losing interest in the content of the discussion and generates audio advice such as, "It seems that some members are losing interest. Try approaching it from a different perspective."

[0474] 4. The device will then give this advice to the user audibly.

[0475] 5. The user follows the advice and approaches and explains the topic from a different perspective.

[0476] As described above, the system of the present invention improves the quality of communication in business negotiations and meetings, and provides support for users to converse with others more effectively.

[0477] The processing flow will be explained below.

[0478] Step 1:

[0479] The user puts on the Audio Glasses. The device (Audio Glasses) initializes the camera and sensors, preparing for a business meeting or negotiation.

[0480] Step 2:

[0481] The device uses a camera to capture the face of the target (business partner or meeting participant), and sensors detect the target's facial expression and eye position in real time.

[0482] Step 3:

[0483] The device extracts key facial features (eyes, nose, mouth, etc.) from the captured facial image data, which is then sent to the server along with the sensor readings.

[0484] Step 4:

[0485] The server analyzes the received facial feature point data and facial expression data, and determines the subject's emotions and reactions (e.g., interest, joy, confusion, etc.) using a facial expression recognition algorithm.

[0486] Step 5:

[0487] The device tracks eye movements, generates gaze data, detects which direction the gaze is directed, and sends this data to a server.

[0488] Step 6:

[0489] The server analyzes the gaze data to determine where the subject is looking, thereby understanding the subject's interests and level of interest.

[0490] Step 7:

[0491] The server generates appropriate voice advice based on the analysis results, such as "The object is of interest. Please explain in more detail."

[0492] Step 8:

[0493] The server transmits the generated voice advice to the terminal as digital data.

[0494] Step 9:

[0495] The device then plays the received audio advice to the user in real time. For example, it could play audio advice such as "The other person seems interested. Please explain in more detail" through earphones.

[0496] Step 10:

[0497] Based on the provided audio advice, users can adjust the speed, content, and cushion words of their own talk to proceed with business negotiations and meetings.

[0498] Step 11:

[0499] After a business meeting or negotiation is over, the device sends data from the entire session (facial expressions, eye contact, voice advice, etc.) to the server.

[0500] Step 12:

[0501] The server analyzes the transmitted data in detail, evaluates the user's performance and the other party's response, and generates feedback that can be used for the next business negotiation or meeting.

[0502] Step 13:

[0503] The server transmits the generated feedback to the user's terminal and provides it in a form that the user can view.

[0504] Example 1

[0505] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0506] In conventional business negotiations and meetings, it is difficult to properly understand the other party's reactions and psychological state and respond accordingly. In particular, it is difficult to read reactions and emotional changes in real time and provide effective advice. This has led to the issue of not being able to maximize the effectiveness of business negotiations and meetings.

[0507] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0508] In this invention, the server includes means for analyzing the captured data, means for generating audio advice based on the analysis results, and means for generating specific advice using a generative AI model. This enables users to accurately grasp the real-time reactions and emotional states of others in business negotiations or meetings and receive appropriate advice in real time.

[0509] A "sensor" is a device for capturing the response of an object.

[0510] An "analysis means" is a method or device for analyzing the captured data.

[0511] The "voice generation means" is a method or device for generating voice advice based on the analysis results.

[0512] The "output means" is a device for providing the generated voice advice to the user.

[0513] A "communication means" is a method or device for transmitting captured data to a server.

[0514] "Real-time analysis means" refers to a method or device for analyzing data received at a server in real time.

[0515] A "generative AI model" is an artificial intelligence-based model that generates specific advice based on analysis results.

[0516] A "user" is a person who wears the audio glasses and conducts business negotiations or meetings.

[0517] The "object" is the person with whom you are negotiating or meeting.

[0518] "Facial feature points" are points on important parts of the face (such as the eyes, nose, and mouth).

[0519] "Gaze data" refers to data relating to the movement of the other person's eyes.

[0520] An "facial expression recognition algorithm" is a computational method for analyzing facial expressions and determining emotions, etc.

[0521] A "gaze analysis algorithm" is a calculation method for analyzing the other person's line of sight.

[0522] This invention relates to a voice advice system for improving communication in business negotiations and meetings. This system analyzes the reactions and gaze of objects in real time and provides voice advice to users based on the results.

[0523] System configuration

[0524] The system consists of the following main components:

[0525] Device: Functions as audio glasses and has built-in sensors and cameras, including high-resolution camera sensors and facial recognition software.

[0526] Server: Processes and analyzes data, preferably using Google Cloud Platform AI tools and TensorFlow models.

[0527] User: Wears audio glasses and conducts business negotiations and meetings.

[0528] Program processing

[0529] Face and expression capture and initial processing

[0530] The device uses built-in sensors and cameras to capture the face and facial expressions of the other person, for example, using high-resolution camera sensors and facial expression recognition software. This captured data is then processed in real time to extract key facial features (such as eyes, nose, and mouth).

[0531] Eye gaze detection and data transmission

[0532] The device detects the other person's eye movements using tracking technology, such as eye-tracking technology. This gaze data and facial feature point data are sent to the server via a secure communication protocol (e.g., SSL / TLS).

[0533] Data reception and analysis

[0534] The server receives the data sent from the device and analyzes it using a deep learning algorithm for facial expression recognition and gaze analysis. The analysis can be performed using Google Cloud Platform's AI tools and TensorFlow models.

[0535] Generate voice advice

[0536] The server generates voice advice based on the analysis results. For example, advice such as "The business partner is interested. Please provide more specific details." This advice is generated using a generative AI model (e.g., GPT-3).

[0537] Sending and viewing advice

[0538] The server sends the generated advice as digital data to the terminal, which then outputs the advice to the user as audio. Bone conduction earphones can be used for audio output.

[0539] User response

[0540] The user can proceed with the business negotiation or meeting based on the provided voice advice. The quality of the business negotiation or meeting can be improved by the user responding in real time.

[0541] Specific examples

[0542] Specific examples of business negotiations

[0543] 1. The user (salesperson) puts on the audio glasses and begins explaining the product to the customer.

[0544] 2. The device captures the customer's face and facial expressions and tracks their gaze.

[0545] 3. The server receives the captured data and determines that the customer is interested in the product description.

[0546] 4. The server generates a voice advice saying, "Customer is interested. Please provide more details."

[0547] 5. The device will then give this advice to the user audibly.

[0548] 6. The user follows the advice and provides additional information about the product and specific usage examples.

[0549] Specific examples of meetings

[0550] 1. A user (project manager) wears audio glasses and attends a team meeting.

[0551] 2. The device captures team members' faces and expressions and tracks their gaze.

[0552] 3. The server receives and analyzes the captured data and determines that some team members are losing interest in the discussion.

[0553] 4. The server generates a voice advice saying, "It seems like some of your members are losing interest. Try approaching them from a different perspective."

[0554] 5. The device will then give this advice to the user audibly.

[0555] 6. The user follows the advice and explains the topic from a different perspective.

[0556] Prompt Sentence Examples

[0557] Here are some example prompts for the generative AI model:

[0558] "Analyze the customer's facial recognition results and gaze data to generate appropriate sales advice. If the customer is interested, provide advice that encourages further clarification."

[0559] As described above, this system utilizes the latest AI technology and hardware to improve the quality of communication during business negotiations and meetings.

[0560] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0561] Step 1:

[0562] The device uses its built-in sensors and camera to capture the face and facial expressions of the target (e.g., business partner or meeting participant). This input data is initially processed in real time using a facial recognition algorithm. Specifically, the data is processed to extract facial feature points (e.g., eyes, nose, mouth) and understand emotional states (e.g., interest, confusion). The output of this processing is feature point data and basic emotional state information.

[0563] Step 2:

[0564] The device also uses built-in eye tracking technology to detect the subject's eye movements, which are recorded as gaze data. This gaze data is also collected in real time and sent to the server along with the simultaneously captured facial feature point data. The specific operation is to identify where the subject is looking. The input of this process is eye movements, and the output is gaze data.

[0565] Step 3:

[0566] The server receives facial feature point data and gaze data sent from the device. Immediately after receiving the data, it analyzes it using a deep learning-based facial expression recognition algorithm and gaze analysis algorithm. Specific examples include using Google Cloud Platform's AI tools and TensorFlow models. The input to this process is the received data, and the output is the analysis results. The analysis results determine the subject's mental state and interests in real time.

[0567] Step 4:

[0568] The server uses a generative AI model (e.g., GPT-3) to generate appropriate voice advice based on the analysis results. Specifically, it generates advice such as, "The business partner is interested. Please explain in more detail." The input of this process is the analysis results, and the output is the generated voice advice.

[0569] Step 5:

[0570] The server transmits the generated audio advice as digital data back to the terminal. The terminal receives this digital data and provides the advice to the user by audio using an audio output device (e.g., bone conduction earphones). The input of this process is the audio advice data, and the output is the audio advice that the user hears.

[0571] Step 6:

[0572] The user adjusts the progress of a business negotiation or meeting based on the provided voice advice. Specifically, the content, speed, and cushion words of the talk are changed in real time. The input of this process is the voice advice, and the output is the adjusted communication content.

[0573] Through the above steps, this system can support effective real-time communication during business negotiations and meetings.

[0574] (Application example 1)

[0575] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0576] Traditional customer service in commercial facilities relies heavily on the experience and skills of staff, making it difficult to accurately grasp customer needs and interests and respond appropriately to them, which can lead to lower customer satisfaction and lost sales opportunities.

[0577] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0578] In this invention, the server includes a sensor that captures the reaction of the object, an analysis means that analyzes the captured data, a voice generation means that generates voice advice based on the analysis results, an output means that outputs the generated voice advice, and a means that provides the voice advice to staff in the commercial facility in real time. This enables staff to quickly grasp customer reactions and take appropriate measures, improving the quality of customer service and increasing customer satisfaction.

[0579] "Subject" refers to the customer whose responses the system captures and analyzes the data.

[0580] "Sensor" refers to a device for capturing the reaction and gaze of a subject.

[0581] "Analysis means" refers to a method or device for analyzing data captured by a sensor and determining the interests and emotions of a subject.

[0582] "Voice generation means" refers to a method or apparatus for generating audio advice based on data obtained from the analysis means.

[0583] "Output means" refers to a method or device for providing the generated audio advice to the user in real time.

[0584] "Commercial establishment" refers to a place where customer interactions and transactions take place.

[0585] "Staff" refers to employees who deal with customers within a commercial facility.

[0586] "Real-time" refers to providing immediate responses or results without delay.

[0587] The system of the present invention aims to support customer service in commercial facilities. To this end, it analyzes the reactions of customers in real time and provides voice advice to staff based on the results.

[0588] System configuration

[0589] The system mainly consists of the following components:

[0590] Sensor: A device built into smart glasses that captures the subject's reactions and gaze.

[0591] Analysis means: A method or device for analyzing the data captured by the sensor. Specifically, a face recognition algorithm using OpenCV and Dlib, an eye tracking algorithm using Tobii Gaze SDK, and an expression analysis algorithm using Azure Cognitive Services Face API are used.

[0592] Voice Generation Means: A method or device for generating voice advice based on data obtained from the Analysis Means. Amazon Polly is used here.

[0593] Output means: A device for providing the generated voice advice to the user in real time, through the speaker of the smart glasses.

[0594] Specific Examples

[0595] 1. Situation: A staff member in a brick-and-mortar store is wearing smart glasses and introducing a new product to a customer.

[0596] 2. Capture: The camera in the smart glasses captures the customer's face and gaze.

[0597] 3. Analysis: The captured data is sent to the server and initially processed by face recognition algorithms (OpenCV and Dlib), followed by facial expression analysis using Azure Cognitive Services Face API, and gaze data is analyzed using the Tobii Gaze SDK.

[0598] 4. Advice generation: The emotional state and interest level are determined based on the analysis results. For example, if the customer is interested in the product description, a voice advice such as "The customer is interested. Please explain in more detail" is generated.

[0599] 5. Delivery: The generated voice advice is provided to staff in real time through the smart glasses speaker.

[0600] Prompt Sentence Examples

[0601] For generative AI models, use prompts like this:

[0602] Explain how to use a voice advice system in business negotiations and meetings to analyze customer responses in real time and provide appropriate customer service advice to staff. For example, explain with specific scenarios what advice should be given when a customer shows interest.

[0603] As described above, the system of the present invention can effectively support customer service within a commercial facility, improve customer satisfaction, and maximize sales opportunities.

[0604] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0605] Step 1:

[0606] The camera in the smart glasses captures the customer's face and gaze. The input is the camera image of the smart glasses, and the output is the captured image data, which is used for subsequent analysis.

[0607] Step 2:

[0608] The device receives the captured image data and uses a facial recognition algorithm (OpenCV and Dlib) to detect facial landmarks. The input is image data and the output is landmark data. Specifically, facial feature points such as eyes, nose, and mouth are detected.

[0609] Step 3:

[0610] The terminal sends landmark data and image data to the server. The input is landmark data and image data, and the output is data sent to the server. This data is used for further analysis on the server.

[0611] Step 4:

[0612] The server analyzes the received landmark data and image data and uses the Azure Cognitive Services Face API to analyze the customer's facial expression. The input is landmark data and image data, and the output is the customer's emotional data, which includes emotional states such as happiness, surprise, and confusion.

[0613] Step 5:

[0614] The server analyzes the customer's gaze data using the Tobii Gaze SDK. The input is image data, and the output is gaze data. This determines which direction the customer is looking.

[0615] Step 6:

[0616] The server integrates the facial expression data and gaze data to comprehensively determine the customer's level of interest and emotional state. The input is facial expression data and gaze data, and the output is comprehensive judgment data. This data indicates whether the customer is interested in a particular product or whether they are confused.

[0617] Step 7:

[0618] The server generates voice advice based on the overall assessment data. Amazon Polly is used as the voice generation method. The input is the overall assessment data, and the output is the generated voice data. Specifically, advice such as "The customer is interested. Please explain in more detail" is generated.

[0619] Step 8:

[0620] The generated voice data is sent to the terminal and provided to the user through the speaker of the smart glasses. The input is the generated voice data, and the output is the provision of voice advice. The user responds to the customer based on this advice.

[0621] In this way, it is possible to analyze customer reactions in real time and provide appropriate advice, thereby improving the quality of customer service within commercial facilities.

[0622] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0623] This invention relates to a voice advice system for improving communication in business negotiations and meetings. This system analyzes the reactions and gaze of objects in real time, and provides voice advice to the user based on the results. It also incorporates an emotion engine that recognizes the user's emotions.

[0624] 1. System Configuration

[0625] The system consists of the following main components:

[0626] Device: Functions as audio glasses and has built-in sensors and cameras.

[0627] Server: Processes and analyzes data.

[0628] User: Wears audio glasses and conducts business negotiations and meetings.

[0629] Emotion engine: Recognizes emotions by analyzing the user's facial expressions and tone of voice.

[0630] 2. Program Processing

[0631] Detecting the other person's reaction

[0632] The device uses its built-in sensors and camera to capture the face and facial expressions of the person you're meeting or negotiating with. This captured data is then processed by a facial recognition algorithm to extract key facial features (eyes, nose, mouth, etc.). This data is then used to understand the person's basic emotional state (interested, confused, etc.).

[0633] Eye tracking

[0634] The device tracks the other person's eye movements to determine which direction they are looking, and the gaze data is sent to a server where it is used to understand what the other person is interested in.

[0635] Recognizing user emotions with an emotion engine

[0636] The emotion engine analyzes the user's facial expression data and voice tone to recognize the user's emotions, for example, determining whether the user is nervous, relaxed, confused, etc.

[0637] Data analysis

[0638] The server analyzes the received facial feature points, facial expression data, gaze data, and user emotion data. By combining a facial expression recognition algorithm and a gaze analysis algorithm, it determines the other person's mental state and interest in real time.

[0639] Generate and provide voice advice

[0640] The server generates voice advice based on the analysis results. For example, specific advice such as "The other person is interested. Please explain in more detail" or "You seem nervous. Please speak a little more slowly" is generated. This advice is sent to the terminal as digital data, and the terminal provides it to the user by voice.

[0641] Responding to advice

[0642] Based on the provided voice advice, the user can adjust the speed, content, and cushion words of their own talk to proceed with business negotiations or meetings. For example, if the other party shows interest, they can add detailed explanations, or if the user is nervous, they can slow down the pace.

[0643] Specific examples

[0644] Specific examples of business negotiations

[0645] 1. The user (sales representative) wears the audio glasses and attends a sales meeting.

[0646] 2. The device captures the customer's facial expressions and gaze and sends them to the server.

[0647] 3. The emotion engine recognizes that the user is nervous.

[0648] 4. The server determines that the customer is interested in the product explanation and that the user is nervous, and generates voice advice saying, "The customer is interested. Please explain in more detail. You seem nervous. Please speak more slowly."

[0649] 5. The device will then give this advice to the user audibly.

[0650] 6. The user follows the advice and adjusts their speaking speed while explaining the product details.

[0651] Specific examples of meetings

[0652] 1. A user (project manager) wears audio glasses and attends a team meeting.

[0653] 2. The device captures the participants' facial expressions and gaze and sends them to the server.

[0654] 3. The emotion engine recognizes that the user is relaxed.

[0655] 4. The server determines that some of the team members are losing interest in the content of the discussion and generates voice advice such as, "It seems that some of the members are losing interest. Try approaching it from a different perspective."

[0656] 5. The device will then give this advice to the user audibly.

[0657] 6. The user follows the advice and approaches and explains the topic from a different perspective.

[0658] As described above, the system of the present invention improves the quality of communication in business negotiations and meetings, and provides support for users to converse more effectively with others. This system can analyze the emotions and reactions of both the user and the other party in real time and provide appropriate advice.

[0659] The processing flow will be explained below.

[0660] Step 1:

[0661] The user puts on the Audio Glasses. The device (Audio Glasses) initializes the camera and sensors, preparing for a business meeting or negotiation.

[0662] Step 2:

[0663] The device uses a camera to capture the face of the target (business partner or meeting participant), and sensors detect the target's facial expression and eye position in real time.

[0664] Step 3:

[0665] The device extracts key facial features (eyes, nose, mouth, etc.) from the captured facial image data, which is then sent to the server along with the sensor readings.

[0666] Step 4:

[0667] The emotion engine analyzes the user's facial expression data and voice tone to recognize the user's emotions, such as tension, relaxation, confusion, etc.

[0668] Step 5:

[0669] The device tracks eye movements, generates gaze data, detects which direction the gaze is directed, and sends this data to a server.

[0670] Step 6:

[0671] The server analyzes the received facial feature data, facial expression data, user emotion data, and gaze data. By combining a facial expression recognition algorithm and a gaze analysis algorithm, it determines the psychological state and interest of the target (business partner or meeting participant) in real time.

[0672] Step 7:

[0673] The server generates voice advice based on the analysis results, such as "The object is interested. Please explain in more detail" or "You seem nervous. Please speak a little more slowly."

[0674] Step 8:

[0675] The server transmits the generated voice advice to the terminal as digital data.

[0676] Step 9:

[0677] The device then plays the received audio advice to the user in real time. For example, the audio advice may say, "The other person seems interested. Please explain in more detail."

[0678] Step 10:

[0679] Based on the provided audio advice, users can adjust the speed, content, and cushion words of their own talk to proceed with business negotiations and meetings.

[0680] Step 11:

[0681] After a business meeting or negotiation is over, the device sends data from the entire session (facial expressions, eye contact, voice advice, etc.) to the server.

[0682] Step 12:

[0683] The server analyzes the transmitted data in detail, evaluates the user's performance and the other party's response, and generates feedback that can be used for the next business negotiation or meeting.

[0684] Step 13:

[0685] The server transmits the generated feedback to the user's terminal and provides it in a form that the user can view.

[0686] Through the above processing steps, the system can provide users with appropriate advice in real time, improving the effectiveness of business negotiations and meetings.

[0687] Example 2

[0688] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0689] In business negotiations and meetings, there is a need to improve the quality of communication by analyzing the other party's facial expressions, gaze, and user emotions in real time and providing appropriate voice advice based on that. However, current systems have difficulty understanding the other party's situation and providing appropriate feedback, making it difficult for users to respond optimally.

[0690] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a sensor that captures the reaction of an object, an analysis means that analyzes the captured data, a voice generation means that generates voice advice based on the analysis result, an output means that outputs the generated voice advice, and an emotion engine that analyzes the emotion of the user. This enables the user to grasp the reaction and emotion of the other party in real time during a business negotiation or meeting and receive appropriate feedback.

[0691] A "sensor that captures the reaction of an object" is a device that uses a camera or optical sensor to capture the face, facial expression, and gaze of the other party in real time during a business negotiation or meeting.

[0692] "Analysis means for analyzing captured data" refers to software and hardware that processes facial feature data, gaze data, and facial expression data acquired by the sensor to identify the other person's emotional state and the object of their attention.

[0693] The "voice generation means for generating voice advice based on the analysis results" refers to an algorithm or software that generates advice on specific actions and talk content in business negotiations or meetings based on data from the analysis means.

[0694] The "output means for outputting the generated voice advice" refers to an audio device or interface for conveying the voice advice to the user, and corresponds to earphones or speakers.

[0695] An "emotion engine that analyzes user emotions" is a system that includes software and algorithms that analyze the user's facial expressions, tone of voice, etc. to recognize the user's emotional state (tension, relaxation, etc.).

[0696] "Facial feature point data" refers to data regarding the position and shape of facial features (eyes, nose, mouth, etc.) captured by a camera or sensor.

[0697] "Gaze data" refers to data collected by sensors about the direction of an object's gaze and the location of its focus.

[0698] "Facial expression data" is data that reflects captured changes in facial expressions and subtle movements, and is used to determine the emotional state of the other person.

[0699] "Real-time" means that the system processes and analyzes data instantly and provides near-instant output (such as voice advice).

[0700] This invention relates to a voice advice system for improving communication in business negotiations and meetings. This system analyzes the reactions and gaze of objects in real time, and provides voice advice to the user based on the results. It also incorporates an emotion engine that recognizes the user's emotions.

[0701] System configuration

[0702] The system consists of the following main components:

[0703] Device: Functions as audio glasses and has built-in sensors and cameras.

[0704] Server: A computing device that processes and analyzes data.

[0705] User: The person wearing the audio glasses who conducts business negotiations and meetings.

[0706] Emotion engine: Software that recognizes emotions by analyzing a user's facial expressions and tone of voice.

[0707] Program processing

[0708] The system operates in the following steps:

[0709] Detecting the other person's reaction

[0710] The device uses its built-in sensors and camera to capture the face and facial expressions of the person in the meeting or business negotiation. This captured data is initially processed by a facial recognition algorithm (e.g., OpenCV) to extract important facial features (eyes, nose, mouth, etc.). This data is used to understand the person's basic emotional state (interested, confused, etc.).

[0711] Eye tracking

[0712] The device tracks the other person's eye movements to determine which direction they are looking, and the gaze data is sent to a server where it is used to understand what the other person is interested in.

[0713] Recognizing user emotions with an emotion engine

[0714] The emotion engine analyzes the user's facial expression data and voice tone to recognize the user's emotions, for example, determining whether the user is nervous, relaxed, confused, etc.

[0715] Data analysis

[0716] The server analyzes the received facial feature points, facial expression data, gaze data, and user emotional data. By combining a facial expression recognition algorithm (e.g., DeepFace) with a gaze analysis algorithm, it can determine the other person's mental state and interest in real time.

[0717] Generate and provide voice advice

[0718] The server generates voice advice based on the analysis results. For example, specific advice such as "The other person is interested. Please explain in more detail" or "You seem nervous. Please speak a little more slowly" is generated. This advice is sent to the terminal as digital data, and the terminal provides it to the user by voice.

[0719] Responding to advice

[0720] Based on the provided voice advice, the user can adjust the speed, content, and cushion words of their own talk to proceed with business negotiations or meetings. For example, if the other party shows interest, they can add detailed explanations, or if the user is nervous, they can slow down the pace.

[0721] Specific examples

[0722] Specific examples of business negotiations

[0723] 1. The user (sales representative) wears the audio glasses and attends a sales meeting.

[0724] 2. The device captures the customer's facial expressions and gaze and sends them to the server.

[0725] 3. The emotion engine recognizes that the user is nervous.

[0726] 4. The server determines that the customer is interested in the product explanation and that the user is nervous, and generates voice advice saying, "The customer is interested. Please explain in more detail. You seem nervous. Please speak more slowly."

[0727] 5. The device will then give this advice to the user audibly.

[0728] 6. The user follows the advice and adjusts their speaking speed while explaining the product details.

[0729] Specific examples of meetings

[0730] 1. A user (project manager) wears audio glasses and attends a team meeting.

[0731] 2. The device captures the participants' facial expressions and gaze and sends them to the server.

[0732] 3. The emotion engine recognizes that the user is relaxed.

[0733] 4. The server determines that some of the team members are losing interest in the content of the discussion and generates voice advice such as, "It seems that some of the members are losing interest. Try approaching it from a different perspective."

[0734] 5. The device will then give this advice to the user audibly.

[0735] 6. The user follows the advice and approaches and explains the topic from a different perspective.

[0736] Example prompts for generative AI models

[0737] Below are some example prompts to be input to the generative AI model:

[0738] Sales: Describe a system that uses audio glasses to monitor a customer's facial expressions and gaze in real time during a sales meeting. What specific voice advice is provided if the user is nervous?

[0739] Meetings: In team meetings, analyze users' facial expressions and gaze in real time and provide audio advice on what to do if someone seems disengaged.

[0740] The system described above can improve the quality of communication during business negotiations and meetings. Users can grasp the other party's reactions and emotions in real time and receive appropriate advice, enabling more effective dialogue.

[0741] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0742] Step 1: Capture their reactions

[0743] Input: Real-time video of the object during a business meeting or negotiation

[0744] Specific operation: The device uses its built-in sensors and camera to capture the faces and expressions of people involved in business negotiations or meetings.

[0745] Data processing: The captured video data is processed using a facial recognition algorithm (e.g., OpenCV) to extract important facial features (eyes, nose, mouth, etc.).

[0746] Output: Facial feature point data is generated.

[0747] Step 2: Eye tracking

[0748] Input: Facial feature point data of the target

[0749] Specific operation: The device tracks the other person's eye movements in real time.

[0750] Data processing: Tracks pupil movement and processes it to identify the focus of attention. Eye gaze analysis algorithms are used.

[0751] Output: Gaze data is generated and sent to the server.

[0752] Step 3: Recognizing user emotions

[0753] Input: User's facial expression data and voice tone

[0754] Specific behavior: The emotion engine captures and analyzes the user's facial expressions and voice.

[0755] Data processing: Analyzes the user's facial expression data and voice to determine their emotional state (tension, relaxation, etc.).

[0756] Output: The user's emotion data is generated and sent to the server.

[0757] Step 4: Analyze the data

[0758] Input: Facial feature point data, gaze data, user emotion data

[0759] Specific operation: The server combines and analyzes the various data it receives.

[0760] Data processing: Integrates facial expression recognition algorithms (e.g., DeepFace) and gaze analysis algorithms to determine the other person's psychological state and interest in real time.

[0761] Output: The analysis results are generated.

[0762] Step 5: Generate audio advice

[0763] Input: Analysis results

[0764] Specific operation: The server generates voice advice based on the analysis results.

[0765] Data processing: An advice generation algorithm is used to generate appropriate voice advice (e.g., "The customer is interested. Please explain in more detail.").

[0766] Output: Audio advice data is generated and sent to the terminal.

[0767] Step 6: Providing audio advice

[0768] Input: Voice advice data

[0769] Specific operation: The terminal receives the voice advice data and provides it to the user.

[0770] Data processing: Play it back as audio data and notify the user.

[0771] Output: The user receives a voice prompt.

[0772] Step 7: Responding to Advice

[0773] Input: Voice advice

[0774] Specific Action: The user adjusts their behavior based on the provided voice advice.

[0775] Data processing: Users can adjust the speed, content, and cushion words of their talk to optimize the progress of business negotiations and meetings.

[0776] Output: The user's adaptive behavior is output.

[0777] (Application example 2)

[0778] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0779] In manufacturing sites, there is a lack of means to grasp an operator's work efficiency and mental state in real time and provide appropriate advice. If an operator feels confused or tense while working, this can lead to a decrease in work efficiency and an increased likelihood of errors. With conventional systems, it has been difficult to analyze an operator's emotional state in real time and provide appropriate advice.

[0780] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0781] In this invention, the server includes a sensing device that includes means for capturing the reaction of the subject, an evaluation device that includes means for analyzing the captured data, a voice synthesis device that includes means for generating voice advice based on the analysis result, and a presentation device that includes means for outputting the generated voice advice. This makes it possible to analyze the emotional state of operators in a manufacturing site in real time and provide appropriate advice.

[0782] "Target person" refers to the operator who works at the workplace or manufacturing site.

[0783] A "sensing device" is a device for capturing a subject's reactions, particularly facial expressions and eye movements.

[0784] An "assessment tool" is a tool for analyzing the captured data and determining the subject's emotional state and reactions.

[0785] The "voice synthesis means" is a means for generating appropriate voice advice for the subject based on the analysis results obtained by the evaluation means.

[0786] The "presentation means" is a means for providing the generated voice advice to the subject as voice.

[0787] A "manufacturing floor" is a place where industrial products are manufactured, often by operators performing manual or mechanical work.

[0788] "Real-time" refers to data capture, analysis, and advice occurring near-instantaneously or with very little delay.

[0789] "Advice" is information that suggests to the target person the best course of action or next step in a particular situation.

[0790] "Data" refers to information captured by the sensing device, such as the subject's facial expressions and eye movements.

[0791] This invention is a system that analyzes the work efficiency and mental state of operators in manufacturing sites in real time and provides support by providing appropriate voice advice.

[0792] System configuration

[0793] The system consists of the following main components:

[0794] Sensing device: A device used to capture the subject's facial expressions and gaze, such as a webcam.

[0795] Evaluation method: Software that analyzes data obtained from the sensory device and determines the subject's emotional state. Specifically, image processing libraries such as OpenCV and deep learning models are used.

[0796] Speech synthesis means: A means for generating appropriate voice advice for the subject based on the analysis results of the evaluation means. Natural language generation tools such as OpenAI API are used.

[0797] Presentation means: A means for providing the generated audio advice to the target person by voice, such as a speaker.

[0798] Server: A central computer that manages all data processing and communication.

[0799] Operating procedure

[0800] 1. Data capture:

[0801] The sensing device captures video of the operator in real time.

[0802] 2. Data Analysis:

[0803] The captured data is analyzed by an evaluation tool to determine the subject's emotional state based on their facial expressions and gaze, using OpenCV to extract facial feature points and a deep learning model to classify the emotion.

[0804] 3. Generate voice advice:

[0805] Based on the analysis results, the speech synthesis means generates appropriate voice advice, for example, using the OpenAI API to generate advice in natural language such as "The operator is relaxing. Please continue working."

[0806] 4. Audio presentation:

[0807] The presentation means provides the generated voice advice to the operator through a speaker.

[0808] Usage example

[0809] As a specific example of use, if an operator working at high speed on a production line looks confused, the system will output the advice, "You look confused. Please check your work." Also, if the system determines that the operator is tired after continuing difficult work, it will provide the advice, "Take a short break."

[0810] Prompt Sentence Examples

[0811] An example of a prompt to generate a voice advice is:

[0812] It provides appropriate voice advice based on the given emotion category: "happy" means "The operator is relaxed, please continue working", "sad" means "The operator is confused, please check your work", and "angry" means "The operator is annoyed, please relax and continue working".

[0813] This system allows operators at the manufacturing site to receive appropriate support, improving work efficiency.

[0814] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0815] Step 1:

[0816] Data Capture

[0817] Input: Video data of the subject (operator) captured by the terminal's sensing device.

[0818] How it works: The terminal's webcam captures the operator's facial expressions, gaze, and other information in real time. Specifically, the webcam continuously captures frames and sends them to the system.

[0819] Output: Captured video data.

[0820] Step 2:

[0821] Data Preprocessing

[0822] Input: Captured video data.

[0823] How it works: The device uses image processing libraries such as OpenCV to extract facial feature points from video data, while also performing basic image processing such as noise reduction and contrast adjustment.

[0824] Output: Preprocessed feature point data (positions of eyes, mouth, nose, etc.).

[0825] Step 3:

[0826] Emotion analysis

[0827] Input: Preprocessed feature point data.

[0828] How it works: The server uses a deep learning model to analyze the subject's emotions. Specifically, it inputs feature point data into an emotion recognition model and determines the emotion category (e.g., Happy, Sad, Angry).

[0829] Output: Parsed emotion data.

[0830] Step 4:

[0831] Voice advice generation

[0832] Input: Parsed emotion data.

[0833] How it works: The server uses natural language generation tools such as the OpenAI API to generate voice advice based on emotion data. It then inputs specific prompts to the generative AI model to get appropriate advice.

[0834] Output: The generated audio advice.

[0835] Step 5:

[0836] Voice advice provided

[0837] Input: The generated audio advice.

[0838] Operation: The device's presentation means (speaker) outputs the generated voice advice to the operator in real time. Specifically, the device receives the results of speech synthesis and plays them back as voice through the speaker.

[0839] Output: Providing audio advice to the operator.

[0840] This series of processing steps allows operators at the manufacturing site to receive appropriate support in real time, which is expected to improve work efficiency.

[0841] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0842] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0843] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0844] [Third embodiment]

[0845] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0846] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0847] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0848] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0849] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0850] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0851] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0852] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0853] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0854] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0855] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0856] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[0857] This invention relates to a voice advice system for improving communication in business negotiations and meetings. This system analyzes the reactions and gaze of objects in real time and provides voice advice to users based on the results.

[0858] 1. System Configuration

[0859] The system consists of the following main components:

[0860] Device: Functions as audio glasses and has built-in sensors and cameras.

[0861] Server: Processes and analyzes data.

[0862] User: Wears audio glasses and conducts business negotiations and meetings.

[0863] 2. Program Processing

[0864] Detecting the other person's reaction

[0865] The device uses its built-in sensors and camera to capture the face and facial expressions of the person in the meeting or business negotiation. This captured data is initially processed by a facial recognition algorithm to extract important facial features (eyes, nose, mouth, etc.), which can then determine the other person's basic emotional state (interested, confused, etc.).

[0866] Eye tracking

[0867] The device also detects the other person's eye movements, which allows it to determine where the other person is looking and transmits the gaze data to the server, allowing it to understand what the other person is interested in.

[0868] Data analysis

[0869] The server analyzes the received facial feature points and gaze data. By combining facial expression recognition algorithms and gaze analysis algorithms, it can determine the other person's psychological state and interest in real time. For example, it can recognize when the other person is interested or confused.

[0870] Generate and provide voice advice

[0871] The server generates voice advice based on the analysis results. For example, specific advice such as "The other party is interested. Please explain in detail" is generated. This advice is sent to the terminal as digital data, and the terminal provides it to the user by voice.

[0872] Responding to advice

[0873] Based on the provided voice advice, the user can adjust the speed, content, and cushion words of the conversation to proceed with the business negotiation or meeting. For example, if the other party shows interest, the user can respond by adding detailed explanations.

[0874] Specific examples

[0875] Specific examples of business negotiations

[0876] 1. The user (sales representative) wears the audio glasses and attends a sales meeting.

[0877] 2. The device captures the customer's facial expressions and gaze and sends them to the server.

[0878] 3. The server determines that the customer is interested in the product description and generates a voice advice saying, "The customer is interested. Please explain in more detail."

[0879] 4. The device will then give this advice to the user audibly.

[0880] 5. The user follows the advice and provides details about the product.

[0881] Specific examples of meetings

[0882] 1. A user (project manager) wears audio glasses and attends a team meeting.

[0883] 2. The device captures the participants' facial expressions and gaze and sends them to the server.

[0884] 3. The server determines that some team members are losing interest in the content of the discussion and generates audio advice such as, "It seems that some members are losing interest. Try approaching it from a different perspective."

[0885] 4. The device will then give this advice to the user audibly.

[0886] 5. The user follows the advice and approaches and explains the topic from a different perspective.

[0887] As described above, the system of the present invention improves the quality of communication in business negotiations and meetings, and provides support for users to converse with others more effectively.

[0888] The processing flow will be explained below.

[0889] Step 1:

[0890] The user puts on the Audio Glasses. The device (Audio Glasses) initializes the camera and sensors, preparing for a business meeting or negotiation.

[0891] Step 2:

[0892] The device uses a camera to capture the face of the target (business partner or meeting participant), and sensors detect the target's facial expression and eye position in real time.

[0893] Step 3:

[0894] The device extracts key facial features (eyes, nose, mouth, etc.) from the captured facial image data, which is then sent to the server along with the sensor readings.

[0895] Step 4:

[0896] The server analyzes the received facial feature point data and facial expression data, and determines the subject's emotions and reactions (e.g., interest, joy, confusion, etc.) using a facial expression recognition algorithm.

[0897] Step 5:

[0898] The device tracks eye movements, generates gaze data, detects which direction the gaze is directed, and sends this data to a server.

[0899] Step 6:

[0900] The server analyzes the gaze data to determine where the subject is looking, thereby understanding the subject's interests and level of interest.

[0901] Step 7:

[0902] The server generates appropriate voice advice based on the analysis results, such as "The object is of interest. Please explain in more detail."

[0903] Step 8:

[0904] The server transmits the generated voice advice to the terminal as digital data.

[0905] Step 9:

[0906] The device then plays the received audio advice to the user in real time. For example, it could play audio advice such as "The other person seems interested. Please explain in more detail" through earphones.

[0907] Step 10:

[0908] Based on the provided audio advice, users can adjust the speed, content, and cushion words of their own talk to proceed with business negotiations and meetings.

[0909] Step 11:

[0910] After a business meeting or negotiation is over, the device sends data from the entire session (facial expressions, eye contact, voice advice, etc.) to the server.

[0911] Step 12:

[0912] The server analyzes the transmitted data in detail, evaluates the user's performance and the other party's response, and generates feedback that can be used for the next business negotiation or meeting.

[0913] Step 13:

[0914] The server transmits the generated feedback to the user's terminal and provides it in a form that the user can view.

[0915] Example 1

[0916] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0917] In conventional business negotiations and meetings, it is difficult to properly understand the other party's reactions and psychological state and respond accordingly. In particular, it is difficult to read reactions and emotional changes in real time and provide effective advice. This has led to the issue of not being able to maximize the effectiveness of business negotiations and meetings.

[0918] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0919] In this invention, the server includes means for analyzing the captured data, means for generating audio advice based on the analysis results, and means for generating specific advice using a generative AI model. This enables users to accurately grasp the real-time reactions and emotional states of others in business negotiations or meetings and receive appropriate advice in real time.

[0920] A "sensor" is a device for capturing the response of an object.

[0921] An "analysis means" is a method or device for analyzing the captured data.

[0922] The "voice generation means" is a method or device for generating voice advice based on the analysis results.

[0923] The "output means" is a device for providing the generated voice advice to the user.

[0924] A "communication means" is a method or device for transmitting captured data to a server.

[0925] "Real-time analysis means" refers to a method or device for analyzing data received at a server in real time.

[0926] A "generative AI model" is an artificial intelligence-based model that generates specific advice based on analysis results.

[0927] A "user" is a person who wears the audio glasses and conducts business negotiations or meetings.

[0928] The "object" is the person with whom you are negotiating or meeting.

[0929] "Facial feature points" are points on important parts of the face (such as the eyes, nose, and mouth).

[0930] "Gaze data" refers to data relating to the movement of the other person's eyes.

[0931] An "facial expression recognition algorithm" is a computational method for analyzing facial expressions and determining emotions, etc.

[0932] A "gaze analysis algorithm" is a calculation method for analyzing the other person's line of sight.

[0933] This invention relates to a voice advice system for improving communication in business negotiations and meetings. This system analyzes the reactions and gaze of objects in real time and provides voice advice to users based on the results.

[0934] System configuration

[0935] The system consists of the following main components:

[0936] Device: Functions as audio glasses and has built-in sensors and cameras, including high-resolution camera sensors and facial recognition software.

[0937] Server: Processes and analyzes data, preferably using Google Cloud Platform AI tools and TensorFlow models.

[0938] User: Wears audio glasses and conducts business negotiations and meetings.

[0939] Program processing

[0940] Face and expression capture and initial processing

[0941] The device uses built-in sensors and cameras to capture the face and facial expressions of the other person, for example, using high-resolution camera sensors and facial expression recognition software. This captured data is then processed in real time to extract key facial features (such as eyes, nose, and mouth).

[0942] Eye gaze detection and data transmission

[0943] The device detects the other person's eye movements using tracking technology, such as eye-tracking technology. This gaze data and facial feature point data are sent to the server via a secure communication protocol (e.g., SSL / TLS).

[0944] Data reception and analysis

[0945] The server receives the data sent from the device and analyzes it using a deep learning algorithm for facial expression recognition and gaze analysis. The analysis can be performed using Google Cloud Platform's AI tools and TensorFlow models.

[0946] Generate voice advice

[0947] The server generates voice advice based on the analysis results. For example, advice such as "The business partner is interested. Please provide more specific details." This advice is generated using a generative AI model (e.g., GPT-3).

[0948] Sending and viewing advice

[0949] The server sends the generated advice as digital data to the terminal, which then outputs the advice to the user as audio. Bone conduction earphones can be used for audio output.

[0950] User response

[0951] The user can proceed with the business negotiation or meeting based on the provided voice advice. The quality of the business negotiation or meeting can be improved by the user responding in real time.

[0952] Specific examples

[0953] Specific examples of business negotiations

[0954] 1. The user (salesperson) puts on the audio glasses and begins explaining the product to the customer.

[0955] 2. The device captures the customer's face and facial expressions and tracks their gaze.

[0956] 3. The server receives the captured data and determines that the customer is interested in the product description.

[0957] 4. The server generates a voice advice saying, "Customer is interested. Please provide more details."

[0958] 5. The device will then give this advice to the user audibly.

[0959] 6. The user follows the advice and provides additional information about the product and specific usage examples.

[0960] Specific examples of meetings

[0961] 1. A user (project manager) wears audio glasses and attends a team meeting.

[0962] 2. The device captures team members' faces and expressions and tracks their gaze.

[0963] 3. The server receives and analyzes the captured data and determines that some team members are losing interest in the discussion.

[0964] 4. The server generates a voice advice saying, "It seems like some of your members are losing interest. Try approaching them from a different perspective."

[0965] 5. The device will then give this advice to the user audibly.

[0966] 6. The user follows the advice and explains the topic from a different perspective.

[0967] Prompt Sentence Examples

[0968] Here are some example prompts for the generative AI model:

[0969] "Analyze the customer's facial recognition results and gaze data to generate appropriate sales advice. If the customer is interested, provide advice that encourages further clarification."

[0970] As described above, this system utilizes the latest AI technology and hardware to improve the quality of communication during business negotiations and meetings.

[0971] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0972] Step 1:

[0973] The device uses its built-in sensors and camera to capture the face and facial expressions of the target (e.g., business partner or meeting participant). This input data is initially processed in real time using a facial recognition algorithm. Specifically, the data is processed to extract facial feature points (e.g., eyes, nose, mouth) and understand emotional states (e.g., interest, confusion). The output of this processing is feature point data and basic emotional state information.

[0974] Step 2:

[0975] The device also uses built-in eye tracking technology to detect the subject's eye movements, which are recorded as gaze data. This gaze data is also collected in real time and sent to the server along with the simultaneously captured facial feature point data. The specific operation is to identify where the subject is looking. The input of this process is eye movements, and the output is gaze data.

[0976] Step 3:

[0977] The server receives facial feature point data and gaze data sent from the device. Immediately after receiving the data, it analyzes it using a deep learning-based facial expression recognition algorithm and gaze analysis algorithm. Specific examples include using Google Cloud Platform's AI tools and TensorFlow models. The input to this process is the received data, and the output is the analysis results. The analysis results determine the subject's mental state and interests in real time.

[0978] Step 4:

[0979] The server uses a generative AI model (e.g., GPT-3) to generate appropriate voice advice based on the analysis results. Specifically, it generates advice such as, "The business partner is interested. Please explain in more detail." The input of this process is the analysis results, and the output is the generated voice advice.

[0980] Step 5:

[0981] The server transmits the generated audio advice as digital data back to the terminal. The terminal receives this digital data and provides the advice to the user by audio using an audio output device (e.g., bone conduction earphones). The input of this process is the audio advice data, and the output is the audio advice that the user hears.

[0982] Step 6:

[0983] The user adjusts the progress of a business negotiation or meeting based on the provided voice advice. Specifically, the content, speed, and cushion words of the talk are changed in real time. The input of this process is the voice advice, and the output is the adjusted communication content.

[0984] Through the above steps, this system can support effective real-time communication during business negotiations and meetings.

[0985] (Application example 1)

[0986] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0987] Traditional customer service in commercial facilities relies heavily on the experience and skills of staff, making it difficult to accurately grasp customer needs and interests and respond appropriately to them, which can lead to lower customer satisfaction and lost sales opportunities.

[0988] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0989] In this invention, the server includes a sensor that captures the reaction of the object, an analysis means that analyzes the captured data, a voice generation means that generates voice advice based on the analysis results, an output means that outputs the generated voice advice, and a means that provides the voice advice to staff in the commercial facility in real time. This enables staff to quickly grasp customer reactions and take appropriate measures, improving the quality of customer service and increasing customer satisfaction.

[0990] "Subject" refers to the customer whose responses the system captures and analyzes the data.

[0991] "Sensor" refers to a device for capturing the reaction and gaze of a subject.

[0992] "Analysis means" refers to a method or device for analyzing data captured by a sensor and determining the interests and emotions of a subject.

[0993] "Voice generation means" refers to a method or apparatus for generating audio advice based on data obtained from the analysis means.

[0994] "Output means" refers to a method or device for providing the generated audio advice to the user in real time.

[0995] "Commercial establishment" refers to a place where customer interactions and transactions take place.

[0996] "Staff" refers to employees who deal with customers within a commercial facility.

[0997] "Real-time" refers to providing immediate responses or results without delay.

[0998] The system of the present invention aims to support customer service in commercial facilities. To this end, it analyzes the reactions of customers in real time and provides voice advice to staff based on the results.

[0999] System configuration

[1000] The system mainly consists of the following components:

[1001] Sensor: A device built into smart glasses that captures the subject's reactions and gaze.

[1002] Analysis means: A method or device for analyzing the data captured by the sensor. Specifically, a face recognition algorithm using OpenCV and Dlib, an eye tracking algorithm using Tobii Gaze SDK, and an expression analysis algorithm using Azure Cognitive Services Face API are used.

[1003] Voice Generation Means: A method or device for generating voice advice based on data obtained from the Analysis Means. Amazon Polly is used here.

[1004] Output means: A device for providing the generated voice advice to the user in real time, through the speaker of the smart glasses.

[1005] Specific Examples

[1006] 1. Situation: A staff member in a brick-and-mortar store is wearing smart glasses and introducing a new product to a customer.

[1007] 2. Capture: The camera in the smart glasses captures the customer's face and gaze.

[1008] 3. Analysis: The captured data is sent to the server and initially processed by face recognition algorithms (OpenCV and Dlib), followed by facial expression analysis using Azure Cognitive Services Face API, and gaze data is analyzed using the Tobii Gaze SDK.

[1009] 4. Advice generation: The emotional state and interest level are determined based on the analysis results. For example, if the customer is interested in the product description, a voice advice such as "The customer is interested. Please explain in more detail" is generated.

[1010] 5. Delivery: The generated voice advice is provided to staff in real time through the smart glasses speaker.

[1011] Prompt Sentence Examples

[1012] For generative AI models, use prompts like this:

[1013] Explain how to use a voice advice system in business negotiations and meetings to analyze customer responses in real time and provide appropriate customer service advice to staff. For example, explain with specific scenarios what advice should be given when a customer shows interest.

[1014] As described above, the system of the present invention can effectively support customer service within a commercial facility, improve customer satisfaction, and maximize sales opportunities.

[1015] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1016] Step 1:

[1017] The camera in the smart glasses captures the customer's face and gaze. The input is the camera image of the smart glasses, and the output is the captured image data, which is used for subsequent analysis.

[1018] Step 2:

[1019] The device receives the captured image data and uses a facial recognition algorithm (OpenCV and Dlib) to detect facial landmarks. The input is image data and the output is landmark data. Specifically, facial feature points such as eyes, nose, and mouth are detected.

[1020] Step 3:

[1021] The terminal sends landmark data and image data to the server. The input is landmark data and image data, and the output is data sent to the server. This data is used for further analysis on the server.

[1022] Step 4:

[1023] The server analyzes the received landmark data and image data and uses the Azure Cognitive Services Face API to analyze the customer's facial expression. The input is landmark data and image data, and the output is the customer's emotional data, which includes emotional states such as happiness, surprise, and confusion.

[1024] Step 5:

[1025] The server analyzes the customer's gaze data using the Tobii Gaze SDK. The input is image data, and the output is gaze data. This determines which direction the customer is looking.

[1026] Step 6:

[1027] The server integrates the facial expression data and gaze data to comprehensively determine the customer's level of interest and emotional state. The input is facial expression data and gaze data, and the output is comprehensive judgment data. This data indicates whether the customer is interested in a particular product or whether they are confused.

[1028] Step 7:

[1029] The server generates voice advice based on the overall assessment data. Amazon Polly is used as the voice generation method. The input is the overall assessment data, and the output is the generated voice data. Specifically, advice such as "The customer is interested. Please explain in more detail" is generated.

[1030] Step 8:

[1031] The generated voice data is sent to the terminal and provided to the user through the speaker of the smart glasses. The input is the generated voice data, and the output is the provision of voice advice. The user responds to the customer based on this advice.

[1032] In this way, it is possible to analyze customer reactions in real time and provide appropriate advice, thereby improving the quality of customer service within commercial facilities.

[1033] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1034] This invention relates to a voice advice system for improving communication in business negotiations and meetings. This system analyzes the reactions and gaze of objects in real time, and provides voice advice to the user based on the results. It also incorporates an emotion engine that recognizes the user's emotions.

[1035] 1. System Configuration

[1036] The system consists of the following main components:

[1037] Device: Functions as audio glasses and has built-in sensors and cameras.

[1038] Server: Processes and analyzes data.

[1039] User: Wears audio glasses and conducts business negotiations and meetings.

[1040] Emotion engine: Recognizes emotions by analyzing the user's facial expressions and tone of voice.

[1041] 2. Program Processing

[1042] Detecting the other person's reaction

[1043] The device uses its built-in sensors and camera to capture the face and facial expressions of the person you're meeting or negotiating with. This captured data is then processed by a facial recognition algorithm to extract key facial features (eyes, nose, mouth, etc.). This data is then used to understand the person's basic emotional state (interested, confused, etc.).

[1044] Eye tracking

[1045] The device tracks the other person's eye movements to determine which direction they are looking, and the gaze data is sent to a server where it is used to understand what the other person is interested in.

[1046] Recognizing user emotions with an emotion engine

[1047] The emotion engine analyzes the user's facial expression data and voice tone to recognize the user's emotions, for example, determining whether the user is nervous, relaxed, confused, etc.

[1048] Data analysis

[1049] The server analyzes the received facial feature points, facial expression data, gaze data, and user emotion data. By combining a facial expression recognition algorithm and a gaze analysis algorithm, it determines the other person's mental state and interest in real time.

[1050] Generate and provide voice advice

[1051] The server generates voice advice based on the analysis results. For example, specific advice such as "The other person is interested. Please explain in more detail" or "You seem nervous. Please speak a little more slowly" is generated. This advice is sent to the terminal as digital data, and the terminal provides it to the user by voice.

[1052] Responding to advice

[1053] Based on the provided voice advice, the user can adjust the speed, content, and cushion words of their own talk to proceed with business negotiations or meetings. For example, if the other party shows interest, they can add detailed explanations, or if the user is nervous, they can slow down the pace.

[1054] Specific examples

[1055] Specific examples of business negotiations

[1056] 1. The user (sales representative) wears the audio glasses and attends a sales meeting.

[1057] 2. The device captures the customer's facial expressions and gaze and sends them to the server.

[1058] 3. The emotion engine recognizes that the user is nervous.

[1059] 4. The server determines that the customer is interested in the product explanation and that the user is nervous, and generates voice advice saying, "The customer is interested. Please explain in more detail. You seem nervous. Please speak more slowly."

[1060] 5. The device will then give this advice to the user audibly.

[1061] 6. The user follows the advice and adjusts their speaking speed while explaining the product details.

[1062] Specific examples of meetings

[1063] 1. A user (project manager) wears audio glasses and attends a team meeting.

[1064] 2. The device captures the participants' facial expressions and gaze and sends them to the server.

[1065] 3. The emotion engine recognizes that the user is relaxed.

[1066] 4. The server determines that some of the team members are losing interest in the content of the discussion and generates voice advice such as, "It seems that some of the members are losing interest. Try approaching it from a different perspective."

[1067] 5. The device will then give this advice to the user audibly.

[1068] 6. The user follows the advice and approaches and explains the topic from a different perspective.

[1069] As described above, the system of the present invention improves the quality of communication in business negotiations and meetings, and provides support for users to converse more effectively with others. This system can analyze the emotions and reactions of both the user and the other party in real time and provide appropriate advice.

[1070] The processing flow will be explained below.

[1071] Step 1:

[1072] The user puts on the Audio Glasses. The device (Audio Glasses) initializes the camera and sensors, preparing for a business meeting or negotiation.

[1073] Step 2:

[1074] The device uses a camera to capture the face of the target (business partner or meeting participant), and sensors detect the target's facial expression and eye position in real time.

[1075] Step 3:

[1076] The device extracts key facial features (eyes, nose, mouth, etc.) from the captured facial image data, which is then sent to the server along with the sensor readings.

[1077] Step 4:

[1078] The emotion engine analyzes the user's facial expression data and voice tone to recognize the user's emotions, such as tension, relaxation, confusion, etc.

[1079] Step 5:

[1080] The device tracks eye movements, generates gaze data, detects which direction the gaze is directed, and sends this data to a server.

[1081] Step 6:

[1082] The server analyzes the received facial feature data, facial expression data, user emotion data, and gaze data. By combining a facial expression recognition algorithm and a gaze analysis algorithm, it determines the psychological state and interest of the target (business partner or meeting participant) in real time.

[1083] Step 7:

[1084] The server generates voice advice based on the analysis results, such as "The object is interested. Please explain in more detail" or "You seem nervous. Please speak a little more slowly."

[1085] Step 8:

[1086] The server transmits the generated voice advice to the terminal as digital data.

[1087] Step 9:

[1088] The device then plays the received audio advice to the user in real time. For example, the audio advice may say, "The other person seems interested. Please explain in more detail."

[1089] Step 10:

[1090] Based on the provided audio advice, users can adjust the speed, content, and cushion words of their own talk to proceed with business negotiations and meetings.

[1091] Step 11:

[1092] After a business meeting or negotiation is over, the device sends data from the entire session (facial expressions, eye contact, voice advice, etc.) to the server.

[1093] Step 12:

[1094] The server analyzes the transmitted data in detail, evaluates the user's performance and the other party's response, and generates feedback that can be used for the next business negotiation or meeting.

[1095] Step 13:

[1096] The server transmits the generated feedback to the user's terminal and provides it in a form that the user can view.

[1097] Through the above processing steps, the system can provide users with appropriate advice in real time, improving the effectiveness of business negotiations and meetings.

[1098] Example 2

[1099] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1100] In business negotiations and meetings, there is a need to improve the quality of communication by analyzing the other party's facial expressions, gaze, and user emotions in real time and providing appropriate voice advice based on that. However, current systems have difficulty understanding the other party's situation and providing appropriate feedback, making it difficult for users to respond optimally.

[1101] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a sensor that captures the reaction of an object, an analysis means that analyzes the captured data, a voice generation means that generates voice advice based on the analysis result, an output means that outputs the generated voice advice, and an emotion engine that analyzes the emotion of the user. This enables the user to grasp the reaction and emotion of the other party in real time during a business negotiation or meeting and receive appropriate feedback.

[1102] A "sensor that captures the reaction of an object" is a device that uses a camera or optical sensor to capture the face, facial expression, and gaze of the other party in real time during a business negotiation or meeting.

[1103] "Analysis means for analyzing captured data" refers to software and hardware that processes facial feature data, gaze data, and facial expression data acquired by the sensor to identify the other person's emotional state and the object of their attention.

[1104] The "voice generation means for generating voice advice based on the analysis results" refers to an algorithm or software that generates advice on specific actions and talk content in business negotiations or meetings based on data from the analysis means.

[1105] The "output means for outputting the generated voice advice" refers to an audio device or interface for conveying the voice advice to the user, and corresponds to earphones or speakers.

[1106] An "emotion engine that analyzes user emotions" is a system that includes software and algorithms that analyze the user's facial expressions, tone of voice, etc. to recognize the user's emotional state (tension, relaxation, etc.).

[1107] "Facial feature point data" refers to data regarding the position and shape of facial features (eyes, nose, mouth, etc.) captured by a camera or sensor.

[1108] "Gaze data" refers to data collected by sensors about the direction of an object's gaze and the location of its focus.

[1109] "Facial expression data" is data that reflects captured changes in facial expressions and subtle movements, and is used to determine the emotional state of the other person.

[1110] "Real-time" means that the system processes and analyzes data instantly and provides near-instant output (such as voice advice).

[1111] This invention relates to a voice advice system for improving communication in business negotiations and meetings. This system analyzes the reactions and gaze of objects in real time, and provides voice advice to the user based on the results. It also incorporates an emotion engine that recognizes the user's emotions.

[1112] System configuration

[1113] The system consists of the following main components:

[1114] Device: Functions as audio glasses and has built-in sensors and cameras.

[1115] Server: A computing device that processes and analyzes data.

[1116] User: The person wearing the audio glasses who conducts business negotiations and meetings.

[1117] Emotion engine: Software that recognizes emotions by analyzing a user's facial expressions and tone of voice.

[1118] Program processing

[1119] The system operates in the following steps:

[1120] Detecting the other person's reaction

[1121] The device uses its built-in sensors and camera to capture the face and facial expressions of the person in the meeting or business negotiation. This captured data is initially processed by a facial recognition algorithm (e.g., OpenCV) to extract important facial features (eyes, nose, mouth, etc.). This data is used to understand the person's basic emotional state (interested, confused, etc.).

[1122] Eye tracking

[1123] The device tracks the other person's eye movements to determine which direction they are looking, and the gaze data is sent to a server where it is used to understand what the other person is interested in.

[1124] Recognizing user emotions with an emotion engine

[1125] The emotion engine analyzes the user's facial expression data and voice tone to recognize the user's emotions, for example, determining whether the user is nervous, relaxed, confused, etc.

[1126] Data analysis

[1127] The server analyzes the received facial feature points, facial expression data, gaze data, and user emotional data. By combining a facial expression recognition algorithm (e.g., DeepFace) with a gaze analysis algorithm, it can determine the other person's mental state and interest in real time.

[1128] Generate and provide voice advice

[1129] The server generates voice advice based on the analysis results. For example, specific advice such as "The other person is interested. Please explain in more detail" or "You seem nervous. Please speak a little more slowly" is generated. This advice is sent to the terminal as digital data, and the terminal provides it to the user by voice.

[1130] Responding to advice

[1131] Based on the provided voice advice, the user can adjust the speed, content, and cushion words of their own talk to proceed with business negotiations or meetings. For example, if the other party shows interest, they can add detailed explanations, or if the user is nervous, they can slow down the pace.

[1132] Specific examples

[1133] Specific examples of business negotiations

[1134] 1. The user (sales representative) wears the audio glasses and attends a sales meeting.

[1135] 2. The device captures the customer's facial expressions and gaze and sends them to the server.

[1136] 3. The emotion engine recognizes that the user is nervous.

[1137] 4. The server determines that the customer is interested in the product explanation and that the user is nervous, and generates voice advice saying, "The customer is interested. Please explain in more detail. You seem nervous. Please speak more slowly."

[1138] 5. The device will then give this advice to the user audibly.

[1139] 6. The user follows the advice and adjusts their speaking speed while explaining the product details.

[1140] Specific examples of meetings

[1141] 1. A user (project manager) wears audio glasses and attends a team meeting.

[1142] 2. The device captures the participants' facial expressions and gaze and sends them to the server.

[1143] 3. The emotion engine recognizes that the user is relaxed.

[1144] 4. The server determines that some of the team members are losing interest in the content of the discussion and generates voice advice such as, "It seems that some of the members are losing interest. Try approaching it from a different perspective."

[1145] 5. The device will then give this advice to the user audibly.

[1146] 6. The user follows the advice and approaches and explains the topic from a different perspective.

[1147] Example prompts for generative AI models

[1148] Below are some example prompts to be input to the generative AI model:

[1149] Sales: Describe a system that uses audio glasses to monitor a customer's facial expressions and gaze in real time during a sales meeting. What specific voice advice is provided if the user is nervous?

[1150] Meetings: In team meetings, analyze users' facial expressions and gaze in real time and provide audio advice on what to do if someone seems disengaged.

[1151] The system described above can improve the quality of communication during business negotiations and meetings. Users can grasp the other party's reactions and emotions in real time and receive appropriate advice, enabling more effective dialogue.

[1152] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1153] Step 1: Capture their reactions

[1154] Input: Real-time video of the object during a business meeting or negotiation

[1155] Specific operation: The device uses its built-in sensors and camera to capture the faces and expressions of people involved in business negotiations or meetings.

[1156] Data processing: The captured video data is processed using a facial recognition algorithm (e.g., OpenCV) to extract important facial features (eyes, nose, mouth, etc.).

[1157] Output: Facial feature point data is generated.

[1158] Step 2: Eye tracking

[1159] Input: Facial feature point data of the target

[1160] Specific operation: The device tracks the other person's eye movements in real time.

[1161] Data processing: Tracks pupil movement and processes it to identify the focus of attention. Eye gaze analysis algorithms are used.

[1162] Output: Gaze data is generated and sent to the server.

[1163] Step 3: Recognizing user emotions

[1164] Input: User's facial expression data and voice tone

[1165] Specific behavior: The emotion engine captures and analyzes the user's facial expressions and voice.

[1166] Data processing: Analyzes the user's facial expression data and voice to determine their emotional state (tension, relaxation, etc.).

[1167] Output: The user's emotion data is generated and sent to the server.

[1168] Step 4: Analyze the data

[1169] Input: Facial feature point data, gaze data, user emotion data

[1170] Specific operation: The server combines and analyzes the various data it receives.

[1171] Data processing: Integrates facial expression recognition algorithms (e.g., DeepFace) and gaze analysis algorithms to determine the other person's psychological state and interest in real time.

[1172] Output: The analysis results are generated.

[1173] Step 5: Generate audio advice

[1174] Input: Analysis results

[1175] Specific operation: The server generates voice advice based on the analysis results.

[1176] Data processing: An advice generation algorithm is used to generate appropriate voice advice (e.g., "The customer is interested. Please explain in more detail.").

[1177] Output: Audio advice data is generated and sent to the terminal.

[1178] Step 6: Providing audio advice

[1179] Input: Voice advice data

[1180] Specific operation: The terminal receives the voice advice data and provides it to the user.

[1181] Data processing: Play it back as audio data and notify the user.

[1182] Output: The user receives a voice prompt.

[1183] Step 7: Responding to Advice

[1184] Input: Voice advice

[1185] Specific Action: The user adjusts their behavior based on the provided voice advice.

[1186] Data processing: Users can adjust the speed, content, and cushion words of their talk to optimize the progress of business negotiations and meetings.

[1187] Output: The user's adaptive behavior is output.

[1188] (Application example 2)

[1189] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1190] In manufacturing sites, there is a lack of means to grasp an operator's work efficiency and mental state in real time and provide appropriate advice. If an operator feels confused or tense while working, this can lead to a decrease in work efficiency and an increased likelihood of errors. With conventional systems, it has been difficult to analyze an operator's emotional state in real time and provide appropriate advice.

[1191] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1192] In this invention, the server includes a sensing device that includes means for capturing the reaction of the subject, an evaluation device that includes means for analyzing the captured data, a voice synthesis device that includes means for generating voice advice based on the analysis result, and a presentation device that includes means for outputting the generated voice advice. This makes it possible to analyze the emotional state of operators in a manufacturing site in real time and provide appropriate advice.

[1193] "Target person" refers to the operator who works at the workplace or manufacturing site.

[1194] A "sensing device" is a device for capturing a subject's reactions, particularly facial expressions and eye movements.

[1195] An "assessment tool" is a tool for analyzing the captured data and determining the subject's emotional state and reactions.

[1196] The "voice synthesis means" is a means for generating appropriate voice advice for the subject based on the analysis results obtained by the evaluation means.

[1197] The "presentation means" is a means for providing the generated voice advice to the subject as voice.

[1198] A "manufacturing floor" is a place where industrial products are manufactured, often by operators performing manual or mechanical work.

[1199] "Real-time" refers to data capture, analysis, and advice occurring near-instantaneously or with very little delay.

[1200] "Advice" is information that suggests to the target person the best course of action or next step in a particular situation.

[1201] "Data" refers to information captured by the sensing device, such as the subject's facial expressions and eye movements.

[1202] This invention is a system that analyzes the work efficiency and mental state of operators in manufacturing sites in real time and provides support by providing appropriate voice advice.

[1203] System configuration

[1204] The system consists of the following main components:

[1205] Sensing device: A device used to capture the subject's facial expressions and gaze, such as a webcam.

[1206] Evaluation method: Software that analyzes data obtained from the sensory device and determines the subject's emotional state. Specifically, image processing libraries such as OpenCV and deep learning models are used.

[1207] Speech synthesis means: A means for generating appropriate voice advice for the subject based on the analysis results of the evaluation means. Natural language generation tools such as OpenAI API are used.

[1208] Presentation means: A means for providing the generated audio advice to the target person by voice, such as a speaker.

[1209] Server: A central computer that manages all data processing and communication.

[1210] Operating procedure

[1211] 1. Data capture:

[1212] The sensing device captures video of the operator in real time.

[1213] 2. Data Analysis:

[1214] The captured data is analyzed by an evaluation tool to determine the subject's emotional state based on their facial expressions and gaze, using OpenCV to extract facial feature points and a deep learning model to classify the emotion.

[1215] 3. Generate voice advice:

[1216] Based on the analysis results, the speech synthesis means generates appropriate voice advice, for example, using the OpenAI API to generate advice in natural language such as "The operator is relaxing. Please continue working."

[1217] 4. Audio presentation:

[1218] The presentation means provides the generated voice advice to the operator through a speaker.

[1219] Usage example

[1220] As a specific example of use, if an operator working at high speed on a production line looks confused, the system will output the advice, "You look confused. Please check your work." Also, if the system determines that the operator is tired after continuing difficult work, it will provide the advice, "Take a short break."

[1221] Prompt Sentence Examples

[1222] An example of a prompt to generate a voice advice is:

[1223] It provides appropriate voice advice based on the given emotion category: "happy" means "The operator is relaxed, please continue working", "sad" means "The operator is confused, please check your work", and "angry" means "The operator is annoyed, please relax and continue working".

[1224] This system allows operators at the manufacturing site to receive appropriate support, improving work efficiency.

[1225] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1226] Step 1:

[1227] Data Capture

[1228] Input: Video data of the subject (operator) captured by the terminal's sensing device.

[1229] How it works: The terminal's webcam captures the operator's facial expressions, gaze, and other information in real time. Specifically, the webcam continuously captures frames and sends them to the system.

[1230] Output: Captured video data.

[1231] Step 2:

[1232] Data Preprocessing

[1233] Input: Captured video data.

[1234] How it works: The device uses image processing libraries such as OpenCV to extract facial feature points from video data, while also performing basic image processing such as noise reduction and contrast adjustment.

[1235] Output: Preprocessed feature point data (positions of eyes, mouth, nose, etc.).

[1236] Step 3:

[1237] Emotion analysis

[1238] Input: Preprocessed feature point data.

[1239] How it works: The server uses a deep learning model to analyze the subject's emotions. Specifically, it inputs feature point data into an emotion recognition model and determines the emotion category (e.g., Happy, Sad, Angry).

[1240] Output: Parsed emotion data.

[1241] Step 4:

[1242] Voice advice generation

[1243] Input: Parsed emotion data.

[1244] How it works: The server uses natural language generation tools such as the OpenAI API to generate voice advice based on emotion data. It then inputs specific prompts to the generative AI model to get appropriate advice.

[1245] Output: The generated audio advice.

[1246] Step 5:

[1247] Voice advice provided

[1248] Input: The generated audio advice.

[1249] Operation: The device's presentation means (speaker) outputs the generated voice advice to the operator in real time. Specifically, the device receives the results of speech synthesis and plays them back as voice through the speaker.

[1250] Output: Providing audio advice to the operator.

[1251] This series of processing steps allows operators at the manufacturing site to receive appropriate support in real time, which is expected to improve work efficiency.

[1252] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1253] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1254] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1255] [Fourth embodiment]

[1256] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1257] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1258] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1259] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1260] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1261] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1262] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1263] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1264] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1265] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1266] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1267] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1268] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1269] This invention relates to a voice advice system for improving communication in business negotiations and meetings. This system analyzes the reactions and gaze of objects in real time and provides voice advice to users based on the results.

[1270] 1. System Configuration

[1271] The system consists of the following main components:

[1272] Device: Functions as audio glasses and has built-in sensors and cameras.

[1273] Server: Processes and analyzes data.

[1274] User: Wears audio glasses and conducts business negotiations and meetings.

[1275] 2. Program Processing

[1276] Detecting the other person's reaction

[1277] The device uses its built-in sensors and camera to capture the face and facial expressions of the person in the meeting or business negotiation. This captured data is initially processed by a facial recognition algorithm to extract important facial features (eyes, nose, mouth, etc.), which can then determine the other person's basic emotional state (interested, confused, etc.).

[1278] Eye tracking

[1279] The device also detects the other person's eye movements, which allows it to determine where the other person is looking and transmits the gaze data to the server, allowing it to understand what the other person is interested in.

[1280] Data analysis

[1281] The server analyzes the received facial feature points and gaze data. By combining facial expression recognition algorithms and gaze analysis algorithms, it can determine the other person's psychological state and interest in real time. For example, it can recognize when the other person is interested or confused.

[1282] Generate and provide voice advice

[1283] The server generates voice advice based on the analysis results. For example, specific advice such as "The other party is interested. Please explain in detail" is generated. This advice is sent to the terminal as digital data, and the terminal provides it to the user by voice.

[1284] Responding to advice

[1285] Based on the provided voice advice, the user can adjust the speed, content, and cushion words of the conversation to proceed with the business negotiation or meeting. For example, if the other party shows interest, the user can respond by adding detailed explanations.

[1286] Specific examples

[1287] Specific examples of business negotiations

[1288] 1. The user (sales representative) wears the audio glasses and attends a sales meeting.

[1289] 2. The device captures the customer's facial expressions and gaze and sends them to the server.

[1290] 3. The server determines that the customer is interested in the product description and generates a voice advice saying, "The customer is interested. Please explain in more detail."

[1291] 4. The device will then give this advice to the user audibly.

[1292] 5. The user follows the advice and provides details about the product.

[1293] Specific examples of meetings

[1294] 1. A user (project manager) wears audio glasses and attends a team meeting.

[1295] 2. The device captures the participants' facial expressions and gaze and sends them to the server.

[1296] 3. The server determines that some team members are losing interest in the content of the discussion and generates audio advice such as, "It seems that some members are losing interest. Try approaching it from a different perspective."

[1297] 4. The device will then give this advice to the user audibly.

[1298] 5. The user follows the advice and approaches and explains the topic from a different perspective.

[1299] As described above, the system of the present invention improves the quality of communication in business negotiations and meetings, and provides support for users to converse with others more effectively.

[1300] The processing flow will be explained below.

[1301] Step 1:

[1302] The user puts on the Audio Glasses. The device (Audio Glasses) initializes the camera and sensors, preparing for a business meeting or negotiation.

[1303] Step 2:

[1304] The device uses a camera to capture the face of the target (business partner or meeting participant), and sensors detect the target's facial expression and eye position in real time.

[1305] Step 3:

[1306] The device extracts key facial features (eyes, nose, mouth, etc.) from the captured facial image data, which is then sent to the server along with the sensor readings.

[1307] Step 4:

[1308] The server analyzes the received facial feature point data and facial expression data, and determines the subject's emotions and reactions (e.g., interest, joy, confusion, etc.) using a facial expression recognition algorithm.

[1309] Step 5:

[1310] The device tracks eye movements, generates gaze data, detects which direction the gaze is directed, and sends this data to a server.

[1311] Step 6:

[1312] The server analyzes the gaze data to determine where the subject is looking, thereby understanding the subject's interests and level of interest.

[1313] Step 7:

[1314] The server generates appropriate voice advice based on the analysis results, such as "The object is of interest. Please explain in more detail."

[1315] Step 8:

[1316] The server transmits the generated voice advice to the terminal as digital data.

[1317] Step 9:

[1318] The device then plays the received audio advice to the user in real time. For example, it could play audio advice such as "The other person seems interested. Please explain in more detail" through earphones.

[1319] Step 10:

[1320] Based on the provided audio advice, users can adjust the speed, content, and cushion words of their own talk to proceed with business negotiations and meetings.

[1321] Step 11:

[1322] After a business meeting or negotiation is over, the device sends data from the entire session (facial expressions, eye contact, voice advice, etc.) to the server.

[1323] Step 12:

[1324] The server analyzes the transmitted data in detail, evaluates the user's performance and the other party's response, and generates feedback that can be used for the next business negotiation or meeting.

[1325] Step 13:

[1326] The server transmits the generated feedback to the user's terminal and provides it in a form that the user can view.

[1327] Example 1

[1328] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1329] In conventional business negotiations and meetings, it is difficult to properly understand the other party's reactions and psychological state and respond accordingly. In particular, it is difficult to read reactions and emotional changes in real time and provide effective advice. This has led to the issue of not being able to maximize the effectiveness of business negotiations and meetings.

[1330] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1331] In this invention, the server includes means for analyzing the captured data, means for generating audio advice based on the analysis results, and means for generating specific advice using a generative AI model. This enables users to accurately grasp the real-time reactions and emotional states of others in business negotiations or meetings and receive appropriate advice in real time.

[1332] A "sensor" is a device for capturing the response of an object.

[1333] An "analysis means" is a method or device for analyzing the captured data.

[1334] The "voice generation means" is a method or device for generating voice advice based on the analysis results.

[1335] The "output means" is a device for providing the generated voice advice to the user.

[1336] A "communication means" is a method or device for transmitting captured data to a server.

[1337] "Real-time analysis means" refers to a method or device for analyzing data received at a server in real time.

[1338] A "generative AI model" is an artificial intelligence-based model that generates specific advice based on analysis results.

[1339] A "user" is a person who wears the audio glasses and conducts business negotiations or meetings.

[1340] The "object" is the person with whom you are negotiating or meeting.

[1341] "Facial feature points" are points on important parts of the face (such as the eyes, nose, and mouth).

[1342] "Gaze data" refers to data relating to the movement of the other person's eyes.

[1343] An "facial expression recognition algorithm" is a computational method for analyzing facial expressions and determining emotions, etc.

[1344] A "gaze analysis algorithm" is a calculation method for analyzing the other person's line of sight.

[1345] This invention relates to a voice advice system for improving communication in business negotiations and meetings. This system analyzes the reactions and gaze of objects in real time and provides voice advice to users based on the results.

[1346] System configuration

[1347] The system consists of the following main components:

[1348] Device: Functions as audio glasses and has built-in sensors and cameras, including high-resolution camera sensors and facial recognition software.

[1349] Server: Processes and analyzes data, preferably using Google Cloud Platform AI tools and TensorFlow models.

[1350] User: Wears audio glasses and conducts business negotiations and meetings.

[1351] Program processing

[1352] Face and expression capture and initial processing

[1353] The device uses built-in sensors and cameras to capture the face and facial expressions of the other person, for example, using high-resolution camera sensors and facial expression recognition software. This captured data is then processed in real time to extract key facial features (such as eyes, nose, and mouth).

[1354] Eye gaze detection and data transmission

[1355] The device detects the other person's eye movements using tracking technology, such as eye-tracking technology. This gaze data and facial feature point data are sent to the server via a secure communication protocol (e.g., SSL / TLS).

[1356] Data reception and analysis

[1357] The server receives the data sent from the device and analyzes it using a deep learning algorithm for facial expression recognition and gaze analysis. The analysis can be performed using Google Cloud Platform's AI tools and TensorFlow models.

[1358] Generate voice advice

[1359] The server generates voice advice based on the analysis results. For example, advice such as "The business partner is interested. Please provide more specific details." This advice is generated using a generative AI model (e.g., GPT-3).

[1360] Sending and viewing advice

[1361] The server sends the generated advice as digital data to the terminal, which then outputs the advice to the user as audio. Bone conduction earphones can be used for audio output.

[1362] User response

[1363] The user can proceed with the business negotiation or meeting based on the provided voice advice. The quality of the business negotiation or meeting can be improved by the user responding in real time.

[1364] Specific examples

[1365] Specific examples of business negotiations

[1366] 1. The user (salesperson) puts on the audio glasses and begins explaining the product to the customer.

[1367] 2. The device captures the customer's face and facial expressions and tracks their gaze.

[1368] 3. The server receives the captured data and determines that the customer is interested in the product description.

[1369] 4. The server generates a voice advice saying, "Customer is interested. Please provide more details."

[1370] 5. The device will then give this advice to the user audibly.

[1371] 6. The user follows the advice and provides additional information about the product and specific usage examples.

[1372] Specific examples of meetings

[1373] 1. A user (project manager) wears audio glasses and attends a team meeting.

[1374] 2. The device captures team members' faces and expressions and tracks their gaze.

[1375] 3. The server receives and analyzes the captured data and determines that some team members are losing interest in the discussion.

[1376] 4. The server generates a voice advice saying, "It seems like some of your members are losing interest. Try approaching them from a different perspective."

[1377] 5. The device will then give this advice to the user audibly.

[1378] 6. The user follows the advice and explains the topic from a different perspective.

[1379] Prompt Sentence Examples

[1380] Here are some example prompts for the generative AI model:

[1381] "Analyze the customer's facial recognition results and gaze data to generate appropriate sales advice. If the customer is interested, provide advice that encourages further clarification."

[1382] As described above, this system utilizes the latest AI technology and hardware to improve the quality of communication during business negotiations and meetings.

[1383] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1384] Step 1:

[1385] The device uses its built-in sensors and camera to capture the face and facial expressions of the target (e.g., business partner or meeting participant). This input data is initially processed in real time using a facial recognition algorithm. Specifically, the data is processed to extract facial feature points (e.g., eyes, nose, mouth) and understand emotional states (e.g., interest, confusion). The output of this processing is feature point data and basic emotional state information.

[1386] Step 2:

[1387] The device also uses built-in eye tracking technology to detect the subject's eye movements, which are recorded as gaze data. This gaze data is also collected in real time and sent to the server along with the simultaneously captured facial feature point data. The specific operation is to identify where the subject is looking. The input of this process is eye movements, and the output is gaze data.

[1388] Step 3:

[1389] The server receives facial feature point data and gaze data sent from the device. Immediately after receiving the data, it analyzes it using a deep learning-based facial expression recognition algorithm and gaze analysis algorithm. Specific examples include using Google Cloud Platform's AI tools and TensorFlow models. The input to this process is the received data, and the output is the analysis results. The analysis results determine the subject's mental state and interests in real time.

[1390] Step 4:

[1391] The server uses a generative AI model (e.g., GPT-3) to generate appropriate voice advice based on the analysis results. Specifically, it generates advice such as, "The business partner is interested. Please explain in more detail." The input of this process is the analysis results, and the output is the generated voice advice.

[1392] Step 5:

[1393] The server transmits the generated audio advice as digital data back to the terminal. The terminal receives this digital data and provides the advice to the user by audio using an audio output device (e.g., bone conduction earphones). The input of this process is the audio advice data, and the output is the audio advice that the user hears.

[1394] Step 6:

[1395] The user adjusts the progress of a business negotiation or meeting based on the provided voice advice. Specifically, the content, speed, and cushion words of the talk are changed in real time. The input of this process is the voice advice, and the output is the adjusted communication content.

[1396] Through the above steps, this system can support effective real-time communication during business negotiations and meetings.

[1397] (Application example 1)

[1398] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1399] Traditional customer service in commercial facilities relies heavily on the experience and skills of staff, making it difficult to accurately grasp customer needs and interests and respond appropriately to them, which can lead to lower customer satisfaction and lost sales opportunities.

[1400] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1401] In this invention, the server includes a sensor that captures the reaction of the object, an analysis means that analyzes the captured data, a voice generation means that generates voice advice based on the analysis results, an output means that outputs the generated voice advice, and a means that provides the voice advice to staff in the commercial facility in real time. This enables staff to quickly grasp customer reactions and take appropriate measures, improving the quality of customer service and increasing customer satisfaction.

[1402] "Subject" refers to the customer whose responses the system captures and analyzes the data.

[1403] "Sensor" refers to a device for capturing the reaction and gaze of a subject.

[1404] "Analysis means" refers to a method or device for analyzing data captured by a sensor and determining the interests and emotions of a subject.

[1405] "Voice generation means" refers to a method or apparatus for generating audio advice based on data obtained from the analysis means.

[1406] "Output means" refers to a method or device for providing the generated audio advice to the user in real time.

[1407] "Commercial establishment" refers to a place where customer interactions and transactions take place.

[1408] "Staff" refers to employees who deal with customers within a commercial facility.

[1409] "Real-time" refers to providing immediate responses or results without delay.

[1410] The system of the present invention aims to support customer service in commercial facilities. To this end, it analyzes the reactions of customers in real time and provides voice advice to staff based on the results.

[1411] System configuration

[1412] The system mainly consists of the following components:

[1413] Sensor: A device built into smart glasses that captures the subject's reactions and gaze.

[1414] Analysis means: A method or device for analyzing the data captured by the sensor. Specifically, a face recognition algorithm using OpenCV and Dlib, an eye tracking algorithm using Tobii Gaze SDK, and an expression analysis algorithm using Azure Cognitive Services Face API are used.

[1415] Voice Generation Means: A method or device for generating voice advice based on data obtained from the Analysis Means. Amazon Polly is used here.

[1416] Output means: A device for providing the generated voice advice to the user in real time, through the speaker of the smart glasses.

[1417] Specific Examples

[1418] 1. Situation: A staff member in a brick-and-mortar store is wearing smart glasses and introducing a new product to a customer.

[1419] 2. Capture: The camera in the smart glasses captures the customer's face and gaze.

[1420] 3. Analysis: The captured data is sent to the server and initially processed by face recognition algorithms (OpenCV and Dlib), followed by facial expression analysis using Azure Cognitive Services Face API, and gaze data is analyzed using the Tobii Gaze SDK.

[1421] 4. Advice generation: The emotional state and interest level are determined based on the analysis results. For example, if the customer is interested in the product description, a voice advice such as "The customer is interested. Please explain in more detail" is generated.

[1422] 5. Delivery: The generated voice advice is provided to staff in real time through the smart glasses speaker.

[1423] Prompt Sentence Examples

[1424] For generative AI models, use prompts like this:

[1425] Explain how to use a voice advice system in business negotiations and meetings to analyze customer responses in real time and provide appropriate customer service advice to staff. For example, explain with specific scenarios what advice should be given when a customer shows interest.

[1426] As described above, the system of the present invention can effectively support customer service within a commercial facility, improve customer satisfaction, and maximize sales opportunities.

[1427] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1428] Step 1:

[1429] The camera in the smart glasses captures the customer's face and gaze. The input is the camera image of the smart glasses, and the output is the captured image data, which is used for subsequent analysis.

[1430] Step 2:

[1431] The device receives the captured image data and uses a facial recognition algorithm (OpenCV and Dlib) to detect facial landmarks. The input is image data and the output is landmark data. Specifically, facial feature points such as eyes, nose, and mouth are detected.

[1432] Step 3:

[1433] The terminal sends landmark data and image data to the server. The input is landmark data and image data, and the output is data sent to the server. This data is used for further analysis on the server.

[1434] Step 4:

[1435] The server analyzes the received landmark data and image data and uses the Azure Cognitive Services Face API to analyze the customer's facial expression. The input is landmark data and image data, and the output is the customer's emotional data, which includes emotional states such as happiness, surprise, and confusion.

[1436] Step 5:

[1437] The server analyzes the customer's gaze data using the Tobii Gaze SDK. The input is image data, and the output is gaze data. This determines which direction the customer is looking.

[1438] Step 6:

[1439] The server integrates the facial expression data and gaze data to comprehensively determine the customer's level of interest and emotional state. The input is facial expression data and gaze data, and the output is comprehensive judgment data. This data indicates whether the customer is interested in a particular product or whether they are confused.

[1440] Step 7:

[1441] The server generates voice advice based on the overall assessment data. Amazon Polly is used as the voice generation method. The input is the overall assessment data, and the output is the generated voice data. Specifically, advice such as "The customer is interested. Please explain in more detail" is generated.

[1442] Step 8:

[1443] The generated voice data is sent to the terminal and provided to the user through the speaker of the smart glasses. The input is the generated voice data, and the output is the provision of voice advice. The user responds to the customer based on this advice.

[1444] In this way, it is possible to analyze customer reactions in real time and provide appropriate advice, thereby improving the quality of customer service within commercial facilities.

[1445] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1446] This invention relates to a voice advice system for improving communication in business negotiations and meetings. This system analyzes the reactions and gaze of objects in real time, and provides voice advice to the user based on the results. It also incorporates an emotion engine that recognizes the user's emotions.

[1447] 1. System Configuration

[1448] The system consists of the following main components:

[1449] Device: Functions as audio glasses and has built-in sensors and cameras.

[1450] Server: Processes and analyzes data.

[1451] User: Wears audio glasses and conducts business negotiations and meetings.

[1452] Emotion engine: Recognizes emotions by analyzing the user's facial expressions and tone of voice.

[1453] 2. Program Processing

[1454] Detecting the other person's reaction

[1455] The device uses its built-in sensors and camera to capture the face and facial expressions of the person you're meeting or negotiating with. This captured data is then processed by a facial recognition algorithm to extract key facial features (eyes, nose, mouth, etc.). This data is then used to understand the person's basic emotional state (interested, confused, etc.).

[1456] Eye tracking

[1457] The device tracks the other person's eye movements to determine which direction they are looking, and the gaze data is sent to a server where it is used to understand what the other person is interested in.

[1458] Recognizing user emotions with an emotion engine

[1459] The emotion engine analyzes the user's facial expression data and voice tone to recognize the user's emotions, for example, determining whether the user is nervous, relaxed, confused, etc.

[1460] Data analysis

[1461] The server analyzes the received facial feature points, facial expression data, gaze data, and user emotion data. By combining a facial expression recognition algorithm and a gaze analysis algorithm, it determines the other person's mental state and interest in real time.

[1462] Generate and provide voice advice

[1463] The server generates voice advice based on the analysis results. For example, specific advice such as "The other person is interested. Please explain in more detail" or "You seem nervous. Please speak a little more slowly" is generated. This advice is sent to the terminal as digital data, and the terminal provides it to the user by voice.

[1464] Responding to advice

[1465] Based on the provided voice advice, the user can adjust the speed, content, and cushion words of their own talk to proceed with business negotiations or meetings. For example, if the other party shows interest, they can add detailed explanations, or if the user is nervous, they can slow down the pace.

[1466] Specific examples

[1467] Specific examples of business negotiations

[1468] 1. The user (sales representative) wears the audio glasses and attends a sales meeting.

[1469] 2. The device captures the customer's facial expressions and gaze and sends them to the server.

[1470] 3. The emotion engine recognizes that the user is nervous.

[1471] 4. The server determines that the customer is interested in the product explanation and that the user is nervous, and generates voice advice saying, "The customer is interested. Please explain in more detail. You seem nervous. Please speak more slowly."

[1472] 5. The device will then give this advice to the user audibly.

[1473] 6. The user follows the advice and adjusts their speaking speed while explaining the product details.

[1474] Specific examples of meetings

[1475] 1. A user (project manager) wears audio glasses and attends a team meeting.

[1476] 2. The device captures the participants' facial expressions and gaze and sends them to the server.

[1477] 3. The emotion engine recognizes that the user is relaxed.

[1478] 4. The server determines that some of the team members are losing interest in the content of the discussion and generates voice advice such as, "It seems that some of the members are losing interest. Try approaching it from a different perspective."

[1479] 5. The device will then give this advice to the user audibly.

[1480] 6. The user follows the advice and approaches and explains the topic from a different perspective.

[1481] As described above, the system of the present invention improves the quality of communication in business negotiations and meetings, and provides support for users to converse more effectively with others. This system can analyze the emotions and reactions of both the user and the other party in real time and provide appropriate advice.

[1482] The processing flow will be explained below.

[1483] Step 1:

[1484] The user puts on the Audio Glasses. The device (Audio Glasses) initializes the camera and sensors, preparing for a business meeting or negotiation.

[1485] Step 2:

[1486] The device uses a camera to capture the face of the target (business partner or meeting participant), and sensors detect the target's facial expression and eye position in real time.

[1487] Step 3:

[1488] The device extracts key facial features (eyes, nose, mouth, etc.) from the captured facial image data, which is then sent to the server along with the sensor readings.

[1489] Step 4:

[1490] The emotion engine analyzes the user's facial expression data and voice tone to recognize the user's emotions, such as tension, relaxation, confusion, etc.

[1491] Step 5:

[1492] The device tracks eye movements, generates gaze data, detects which direction the gaze is directed, and sends this data to a server.

[1493] Step 6:

[1494] The server analyzes the received facial feature data, facial expression data, user emotion data, and gaze data. By combining a facial expression recognition algorithm and a gaze analysis algorithm, it determines the psychological state and interest of the target (business partner or meeting participant) in real time.

[1495] Step 7:

[1496] The server generates voice advice based on the analysis results, such as "The object is interested. Please explain in more detail" or "You seem nervous. Please speak a little more slowly."

[1497] Step 8:

[1498] The server transmits the generated voice advice to the terminal as digital data.

[1499] Step 9:

[1500] The device then plays the received audio advice to the user in real time. For example, the audio advice may say, "The other person seems interested. Please explain in more detail."

[1501] Step 10:

[1502] Based on the provided audio advice, users can adjust the speed, content, and cushion words of their own talk to proceed with business negotiations and meetings.

[1503] Step 11:

[1504] After a business meeting or negotiation is over, the device sends data from the entire session (facial expressions, eye contact, voice advice, etc.) to the server.

[1505] Step 12:

[1506] The server analyzes the transmitted data in detail, evaluates the user's performance and the other party's response, and generates feedback that can be used for the next business negotiation or meeting.

[1507] Step 13:

[1508] The server transmits the generated feedback to the user's terminal and provides it in a form that the user can view.

[1509] Through the above processing steps, the system can provide users with appropriate advice in real time, improving the effectiveness of business negotiations and meetings.

[1510] Example 2

[1511] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1512] In business negotiations and meetings, there is a need to improve the quality of communication by analyzing the other party's facial expressions, gaze, and user emotions in real time and providing appropriate voice advice based on that. However, current systems have difficulty understanding the other party's situation and providing appropriate feedback, making it difficult for users to respond optimally.

[1513] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a sensor that captures the reaction of an object, an analysis means that analyzes the captured data, a voice generation means that generates voice advice based on the analysis result, an output means that outputs the generated voice advice, and an emotion engine that analyzes the emotion of the user. This enables the user to grasp the reaction and emotion of the other party in real time during a business negotiation or meeting and receive appropriate feedback.

[1514] A "sensor that captures the reaction of an object" is a device that uses a camera or optical sensor to capture the face, facial expression, and gaze of the other party in real time during a business negotiation or meeting.

[1515] "Analysis means for analyzing captured data" refers to software and hardware that processes facial feature data, gaze data, and facial expression data acquired by the sensor to identify the other person's emotional state and the object of their attention.

[1516] The "voice generation means for generating voice advice based on the analysis results" refers to an algorithm or software that generates advice on specific actions and talk content in business negotiations or meetings based on data from the analysis means.

[1517] The "output means for outputting the generated voice advice" refers to an audio device or interface for conveying the voice advice to the user, and corresponds to earphones or speakers.

[1518] An "emotion engine that analyzes user emotions" is a system that includes software and algorithms that analyze the user's facial expressions, tone of voice, etc. to recognize the user's emotional state (tension, relaxation, etc.).

[1519] "Facial feature point data" refers to data regarding the position and shape of facial features (eyes, nose, mouth, etc.) captured by a camera or sensor.

[1520] "Gaze data" refers to data collected by sensors about the direction of an object's gaze and the location of its focus.

[1521] "Facial expression data" is data that reflects captured changes in facial expressions and subtle movements, and is used to determine the emotional state of the other person.

[1522] "Real-time" means that the system processes and analyzes data instantly and provides near-instant output (such as voice advice).

[1523] This invention relates to a voice advice system for improving communication in business negotiations and meetings. This system analyzes the reactions and gaze of objects in real time, and provides voice advice to the user based on the results. It also incorporates an emotion engine that recognizes the user's emotions.

[1524] System configuration

[1525] The system consists of the following main components:

[1526] Device: Functions as audio glasses and has built-in sensors and cameras.

[1527] Server: A computing device that processes and analyzes data.

[1528] User: The person wearing the audio glasses who conducts business negotiations and meetings.

[1529] Emotion engine: Software that recognizes emotions by analyzing a user's facial expressions and tone of voice.

[1530] Program processing

[1531] The system operates in the following steps:

[1532] Detecting the other person's reaction

[1533] The device uses its built-in sensors and camera to capture the face and facial expressions of the person in the meeting or business negotiation. This captured data is initially processed by a facial recognition algorithm (e.g., OpenCV) to extract important facial features (eyes, nose, mouth, etc.). This data is used to understand the person's basic emotional state (interested, confused, etc.).

[1534] Eye tracking

[1535] The device tracks the other person's eye movements to determine which direction they are looking, and the gaze data is sent to a server where it is used to understand what the other person is interested in.

[1536] Recognizing user emotions with an emotion engine

[1537] The emotion engine analyzes the user's facial expression data and voice tone to recognize the user's emotions, for example, determining whether the user is nervous, relaxed, confused, etc.

[1538] Data analysis

[1539] The server analyzes the received facial feature points, facial expression data, gaze data, and user emotional data. By combining a facial expression recognition algorithm (e.g., DeepFace) with a gaze analysis algorithm, it can determine the other person's mental state and interest in real time.

[1540] Generate and provide voice advice

[1541] The server generates voice advice based on the analysis results. For example, specific advice such as "The other person is interested. Please explain in more detail" or "You seem nervous. Please speak a little more slowly" is generated. This advice is sent to the terminal as digital data, and the terminal provides it to the user by voice.

[1542] Responding to advice

[1543] Based on the provided voice advice, the user can adjust the speed, content, and cushion words of their own talk to proceed with business negotiations or meetings. For example, if the other party shows interest, they can add detailed explanations, or if the user is nervous, they can slow down the pace.

[1544] Specific examples

[1545] Specific examples of business negotiations

[1546] 1. The user (sales representative) wears the audio glasses and attends a sales meeting.

[1547] 2. The device captures the customer's facial expressions and gaze and sends them to the server.

[1548] 3. The emotion engine recognizes that the user is nervous.

[1549] 4. The server determines that the customer is interested in the product explanation and that the user is nervous, and generates voice advice saying, "The customer is interested. Please explain in more detail. You seem nervous. Please speak more slowly."

[1550] 5. The device will then give this advice to the user audibly.

[1551] 6. The user follows the advice and adjusts their speaking speed while explaining the product details.

[1552] Specific examples of meetings

[1553] 1. A user (project manager) wears audio glasses and attends a team meeting.

[1554] 2. The device captures the participants' facial expressions and gaze and sends them to the server.

[1555] 3. The emotion engine recognizes that the user is relaxed.

[1556] 4. The server determines that some of the team members are losing interest in the content of the discussion and generates voice advice such as, "It seems that some of the members are losing interest. Try approaching it from a different perspective."

[1557] 5. The device will then give this advice to the user audibly.

[1558] 6. The user follows the advice and approaches and explains the topic from a different perspective.

[1559] Example prompts for generative AI models

[1560] Below are some example prompts to be input to the generative AI model:

[1561] Sales: Describe a system that uses audio glasses to monitor a customer's facial expressions and gaze in real time during a sales meeting. What specific voice advice is provided if the user is nervous?

[1562] Meetings: In team meetings, analyze users' facial expressions and gaze in real time and provide audio advice on what to do if someone seems disengaged.

[1563] The system described above can improve the quality of communication during business negotiations and meetings. Users can grasp the other party's reactions and emotions in real time and receive appropriate advice, enabling more effective dialogue.

[1564] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1565] Step 1: Capture their reactions

[1566] Input: Real-time video of the object during a business meeting or negotiation

[1567] Specific operation: The device uses its built-in sensors and camera to capture the faces and expressions of people involved in business negotiations or meetings.

[1568] Data processing: The captured video data is processed using a facial recognition algorithm (e.g., OpenCV) to extract important facial features (eyes, nose, mouth, etc.).

[1569] Output: Facial feature point data is generated.

[1570] Step 2: Eye tracking

[1571] Input: Facial feature point data of the target

[1572] Specific operation: The device tracks the other person's eye movements in real time.

[1573] Data processing: Tracks pupil movement and processes it to identify the focus of attention. Eye gaze analysis algorithms are used.

[1574] Output: Gaze data is generated and sent to the server.

[1575] Step 3: Recognizing user emotions

[1576] Input: User's facial expression data and voice tone

[1577] Specific behavior: The emotion engine captures and analyzes the user's facial expressions and voice.

[1578] Data processing: Analyzes the user's facial expression data and voice to determine their emotional state (tension, relaxation, etc.).

[1579] Output: The user's emotion data is generated and sent to the server.

[1580] Step 4: Analyze the data

[1581] Input: Facial feature point data, gaze data, user emotion data

[1582] Specific operation: The server combines and analyzes the various data it receives.

[1583] Data processing: Integrates facial expression recognition algorithms (e.g., DeepFace) and gaze analysis algorithms to determine the other person's psychological state and interest in real time.

[1584] Output: The analysis results are generated.

[1585] Step 5: Generate audio advice

[1586] Input: Analysis results

[1587] Specific operation: The server generates voice advice based on the analysis results.

[1588] Data processing: An advice generation algorithm is used to generate appropriate voice advice (e.g., "The customer is interested. Please explain in more detail.").

[1589] Output: Audio advice data is generated and sent to the terminal.

[1590] Step 6: Providing audio advice

[1591] Input: Voice advice data

[1592] Specific operation: The terminal receives the voice advice data and provides it to the user.

[1593] Data processing: Play it back as audio data and notify the user.

[1594] Output: The user receives a voice prompt.

[1595] Step 7: Responding to Advice

[1596] Input: Voice advice

[1597] Specific Action: The user adjusts their behavior based on the provided voice advice.

[1598] Data processing: Users can adjust the speed, content, and cushion words of their talk to optimize the progress of business negotiations and meetings.

[1599] Output: The user's adaptive behavior is output.

[1600] (Application example 2)

[1601] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1602] In manufacturing sites, there is a lack of means to grasp an operator's work efficiency and mental state in real time and provide appropriate advice. If an operator feels confused or tense while working, this can lead to a decrease in work efficiency and an increased likelihood of errors. With conventional systems, it has been difficult to analyze an operator's emotional state in real time and provide appropriate advice.

[1603] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1604] In this invention, the server includes a sensing device that includes means for capturing the reaction of the subject, an evaluation device that includes means for analyzing the captured data, a voice synthesis device that includes means for generating voice advice based on the analysis result, and a presentation device that includes means for outputting the generated voice advice. This makes it possible to analyze the emotional state of operators in a manufacturing site in real time and provide appropriate advice.

[1605] "Target person" refers to the operator who works at the workplace or manufacturing site.

[1606] A "sensing device" is a device for capturing a subject's reactions, particularly facial expressions and eye movements.

[1607] An "assessment tool" is a tool for analyzing the captured data and determining the subject's emotional state and reactions.

[1608] The "voice synthesis means" is a means for generating appropriate voice advice for the subject based on the analysis results obtained by the evaluation means.

[1609] The "presentation means" is a means for providing the generated voice advice to the subject as voice.

[1610] A "manufacturing floor" is a place where industrial products are manufactured, often by operators performing manual or mechanical work.

[1611] "Real-time" refers to data capture, analysis, and advice occurring near-instantaneously or with very little delay.

[1612] "Advice" is information that suggests to the target person the best course of action or next step in a particular situation.

[1613] "Data" refers to information captured by the sensing device, such as the subject's facial expressions and eye movements.

[1614] This invention is a system that analyzes the work efficiency and mental state of operators in manufacturing sites in real time and provides support by providing appropriate voice advice.

[1615] System configuration

[1616] The system consists of the following main components:

[1617] Sensing device: A device used to capture the subject's facial expressions and gaze, such as a webcam.

[1618] Evaluation method: Software that analyzes data obtained from the sensory device and determines the subject's emotional state. Specifically, image processing libraries such as OpenCV and deep learning models are used.

[1619] Speech synthesis means: A means for generating appropriate voice advice for the subject based on the analysis results of the evaluation means. Natural language generation tools such as OpenAI API are used.

[1620] Presentation means: A means for providing the generated audio advice to the target person by voice, such as a speaker.

[1621] Server: A central computer that manages all data processing and communication.

[1622] Operating procedure

[1623] 1. Data capture:

[1624] The sensing device captures video of the operator in real time.

[1625] 2. Data Analysis:

[1626] The captured data is analyzed by an evaluation tool to determine the subject's emotional state based on their facial expressions and gaze, using OpenCV to extract facial feature points and a deep learning model to classify the emotion.

[1627] 3. Generate voice advice:

[1628] Based on the analysis results, the speech synthesis means generates appropriate voice advice, for example, using the OpenAI API to generate advice in natural language such as "The operator is relaxing. Please continue working."

[1629] 4. Audio presentation:

[1630] The presentation means provides the generated voice advice to the operator through a speaker.

[1631] Usage example

[1632] As a specific example of use, if an operator working at high speed on a production line looks confused, the system will output the advice, "You look confused. Please check your work." Also, if the system determines that the operator is tired after continuing difficult work, it will provide the advice, "Take a short break."

[1633] Prompt Sentence Examples

[1634] An example of a prompt to generate a voice advice is:

[1635] It provides appropriate voice advice based on the given emotion category: "happy" means "The operator is relaxed, please continue working", "sad" means "The operator is confused, please check your work", and "angry" means "The operator is annoyed, please relax and continue working".

[1636] This system allows operators at the manufacturing site to receive appropriate support, improving work efficiency.

[1637] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1638] Step 1:

[1639] Data Capture

[1640] Input: Video data of the subject (operator) captured by the terminal's sensing device.

[1641] How it works: The terminal's webcam captures the operator's facial expressions, gaze, and other information in real time. Specifically, the webcam continuously captures frames and sends them to the system.

[1642] Output: Captured video data.

[1643] Step 2:

[1644] Data Preprocessing

[1645] Input: Captured video data.

[1646] How it works: The device uses image processing libraries such as OpenCV to extract facial feature points from video data, while also performing basic image processing such as noise reduction and contrast adjustment.

[1647] Output: Preprocessed feature point data (positions of eyes, mouth, nose, etc.).

[1648] Step 3:

[1649] Emotion analysis

[1650] Input: Preprocessed feature point data.

[1651] How it works: The server uses a deep learning model to analyze the subject's emotions. Specifically, it inputs feature point data into an emotion recognition model and determines the emotion category (e.g., Happy, Sad, Angry).

[1652] Output: Parsed emotion data.

[1653] Step 4:

[1654] Voice advice generation

[1655] Input: Parsed emotion data.

[1656] How it works: The server uses natural language generation tools such as the OpenAI API to generate voice advice based on emotion data. It then inputs specific prompts to the generative AI model to get appropriate advice.

[1657] Output: The generated audio advice.

[1658] Step 5:

[1659] Voice advice provided

[1660] Input: The generated audio advice.

[1661] Operation: The device's presentation means (speaker) outputs the generated voice advice to the operator in real time. Specifically, the device receives the results of speech synthesis and plays them back as voice through the speaker.

[1662] Output: Providing audio advice to the operator.

[1663] This series of processing steps allows operators at the manufacturing site to receive appropriate support in real time, which is expected to improve work efficiency.

[1664] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1665] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1666] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1667] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1668] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1669] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1670] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1671] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1672] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1673] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1674] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1675] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1676] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1677] 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.

[1678] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1679] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1680] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[1681] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1682] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1683] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1684] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1685] The following is further disclosed regarding the above embodiment.

[1686] (Claim 1)

[1687] a sensor that captures the reaction of the object;

[1688] analysis means for analyzing the captured data;

[1689] a voice generating means for generating voice advice based on the analysis result;

[1690] an output means for outputting the generated voice advice;

[1691] A system that provides users with real-time voice advice during business negotiations and meetings.

[1692] (Claim 2)

[1693] 10. The system of claim 1, wherein the sensor includes means for tracking the gaze of an object.

[1694] (Claim 3)

[1695] 2. The system according to claim 1, wherein the analyzing means includes means for determining emotions based on facial expressions of the target object.

[1696] "Example 1"

[1697] (Claim 1)

[1698] a sensor that captures the reaction of the object;

[1699] analysis means for analyzing the captured data;

[1700] a voice generating means for generating voice advice based on the analysis result;

[1701] an output means for outputting the generated voice advice;

[1702] a communication means for transmitting the captured data to a server;

[1703] means for analyzing the received data in real time in the server;

[1704] A means for generating specific advice using a generative AI model based on the analysis results; and

[1705] A system having an output means for providing real-time audio advice to a user.

[1706] (Claim 2)

[1707] 10. The system of claim 1, wherein the sensor includes means for tracking the gaze of an object.

[1708] (Claim 3)

[1709] 2. The system according to claim 1, wherein the analyzing means includes means for determining emotions based on facial expressions of the target object.

[1710] "Application Example 1"

[1711] (Claim 1)

[1712] a sensor that captures the reaction of the object;

[1713] analysis means for analyzing the captured data;

[1714] a voice generating means for generating voice advice based on the analysis result;

[1715] an output means for outputting the generated voice advice;

[1716] A system including a system that provides real-time audio advice to staff within a commercial facility.

[1717] (Claim 2)

[1718] 10. The system of claim 1, wherein the sensor includes means for tracking the gaze of an object.

[1719] (Claim 3)

[1720] 2. The system according to claim 1, wherein the analyzing means includes means for determining emotions based on facial expressions of the target object.

[1721] "Example 2: Combining Emotion Engines"

[1722] (Claim 1)

[1723] a sensor that captures the reaction of the object;

[1724] analysis means for analyzing the captured data;

[1725] a voice generating means for generating voice advice based on the analysis result;

[1726] an output means for outputting the generated voice advice;

[1727] an emotion engine that analyzes the user's emotions;

[1728] A system that provides users with real-time voice advice during business negotiations and meetings.

[1729] (Claim 2)

[1730] 10. The system of claim 1, wherein the sensor includes means for tracking the gaze of an object.

[1731] (Claim 3)

[1732] 2. The system according to claim 1, wherein the analyzing means includes means for determining emotions based on facial expressions of the target object.

[1733] (Claim 4)

[1734] 10. The system of claim 1, wherein the emotion engine includes means for analyzing a user's facial expression data and vocal tone.

[1735] (Claim 5)

[1736] 2. The system according to claim 1, wherein the analyzing means includes means for analyzing a combination of facial feature point data, gaze data, and emotion data.

[1737] "Application example 2 when combining emotion engines"

[1738] (Claim 1)

[1739] a sensing device that captures the subject's response;

[1740] evaluation means for analyzing the captured data;

[1741] a voice synthesis means for generating voice advice based on the analysis result;

[1742] a presentation means for outputting the generated audio advice;

[1743] A system in which equipment used on the work site provides real-time audio advice.

[1744] (Claim 2)

[1745] 10. The system of claim 1, wherein the sensing device includes means for tracking the subject's gaze.

[1746] (Claim 3)

[1747] 2. The system according to claim 1, wherein the evaluation means includes means for determining the subject's emotion based on facial expression. [Explanation of symbols]

[1748] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. a sensor that captures the reaction of the object; analysis means for analyzing the captured data; a voice generating means for generating voice advice based on the analysis result; an output means for outputting the generated voice advice; A system that provides users with real-time voice advice during business negotiations and meetings.

2. The system of claim 1 , wherein the sensor includes means for tracking the gaze of an object.

3. 2. The system of claim 1, wherein the analyzing means includes means for determining emotions based on facial expressions of the subject.

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