system

Abstract

We provide the system. [Solution] A means of generating text from audio data, A means of analyzing the generated text and improving the linguistic expression, A means for analyzing audio data to evaluate intonation and speed, A method for analyzing video data and evaluating facial expressions, A method for analyzing image data to evaluate appearance, A system that includes means for generating and displaying real-time feedback based on analysis results.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In modern society where there are concerns about the decline in face-to-face communication skills, especially among the younger generation, the opportunities for practice are limited. Therefore, there is room for improvement in aspects such as the inadequacy of language expression, intonation, speed, and other vocal aspects, as well as visual elements such as facial expressions and clothing. However, it is difficult to evaluate and improve these individually, and a tool for integrated support is required.

Means for Solving the Problems

[0005] This invention solves problems related to speech by providing means for generating text from speech data and analyzing linguistic expressions, as well as means for evaluating intonation and speed through speech analysis. Furthermore, by providing means for analyzing video data and evaluating facial expressions, and means for analyzing image data and evaluating appearance, the invention comprehensively evaluates visual elements as well, and provides a system that enables rapid and effective improvement of communication skills by generating and displaying real-time feedback based on the analysis results.

[0006] "Audio data" refers to information that represents audio signals in digital or analog format.

[0007] "Means for generating text" refers to a process or device that extracts linguistic information from audio data and converts it into textual information.

[0008] "Means of improving linguistic expression" refers to a function that analyzes generated text and modifies or suggests a format appropriate for dialogue.

[0009] "Means for evaluating intonation and speed" refers to a function that analyzes changes and speed in speech data to evaluate the nuances and tempo of the utterance.

[0010] "Video data" refers to information that represents moving images digitally or analogously.

[0011] "Methods for evaluating facial expressions" refer to functions that analyze facial movements and changes in facial expressions from video data to infer emotions and attitudes.

[0012] "Image data" refers to information that represents still images in digital or analog format.

[0013] "Means of evaluating appearance" refers to a process or device that analyzes image data, extracts characteristics related to an individual's appearance, and identifies areas for improvement.

[0014] "A means of generating and displaying feedback in real time" refers to a function that immediately forms evaluations and improvement proposals based on analysis results and presents them to the user in a visualized format. [Brief explanation of the drawing]

[0015] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14]It is a sequence diagram showing the processing flow of a data processing system in Application Example 2 when a sentiment engine is combined.

Embodiments for Carrying Out the Invention

[0016] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

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

[0018] In the following embodiments, a numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0019] In the following embodiments, a numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0020] In the following embodiments, a numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like.

[0021] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0022] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0023] [First Embodiment]

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

[0025] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0026] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0027] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0028] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0029] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0030] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0032] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0033] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0034] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0035] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0036] This invention includes a user terminal and a server system operating in the backend. The user uses the terminal to collect their own speech and actions and transmits this data to the server. The server performs analysis and evaluation based on this data to improve communication skills.

[0037] Specifically, audio data recorded on the device is sent to a server, which first uses speech recognition technology to convert the audio into text. The converted text is then analyzed using natural language processing algorithms to check for any omissions of respectful expressions or appropriate phrasing.

[0038] In parallel, the audio data itself is analyzed, and its intonation and speed are evaluated. If there is room for improvement in the tempo or tone of speech, the server marks those points and generates improvement suggestions.

[0039] Furthermore, users use their device's camera to capture their facial expressions. The video data is sent to a server, where facial recognition algorithms analyze things like the frequency of smiles and eye movements. Similarly, the image data is analyzed to evaluate appearances such as hairstyle and clothing, and feedback is generated based on that.

[0040] After all analysis is complete, the server generates comprehensive feedback from this individual data. This feedback is received by the terminal and displayed to the user in real time. For example, it may include specific comments such as, "Your use of polite language is inappropriate; you should rephrase it like this," or "You need to smile more."

[0041] Users can continuously improve their face-to-face communication skills by checking this feedback in real time. This process is designed to help users acquire effective communication methods.

[0042] The following describes the processing flow.

[0043] Step 1:

[0044] The user powers on the device, enables the camera and microphone, and starts a communication session. At this point, the device begins collecting audio and video data.

[0045] Step 2:

[0046] The device sends the collected audio data to the server. This data includes the user's speech. The server uses speech recognition technology to convert the audio data into text data.

[0047] Step 3:

[0048] The server receives text data and uses natural language processing algorithms to analyze the linguistic expressions. This process identifies inappropriate phrasing and errors in honorific language.

[0049] Step 4:

[0050] The server analyzes the intonation and speed of the audio data. It evaluates the tempo and tone, and generates improvement suggestions as needed.

[0051] Step 5:

[0052] The user's video data is sent from the terminal to the server. The server uses video recognition technology to analyze facial expressions and evaluate things like the frequency of smiles and how the user's gaze is directed.

[0053] Step 6:

[0054] The server analyzes the user's image data and evaluates their appearance, including hairstyle and clothing. It then prepares feedback based on this appearance.

[0055] Step 7:

[0056] The server integrates all analysis results and generates comprehensive feedback for the user. This feedback includes specific improvement measures and suggestions.

[0057] Step 8:

[0058] The device receives feedback sent from the server and displays it to the user in real time. The user can improve their communication skills by reviewing this feedback.

[0059] (Example 1)

[0060] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0061] There is a growing need to provide automated, real-time feedback to improve communication skills, comprehensively utilizing acoustic and visual data. However, achieving this requires effectively analyzing multiple elements and generating concrete and practical improvement suggestions for the user.

[0062] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0063] In this invention, the server includes means for generating text information from acoustic data, means for analyzing the generated text information and improving linguistic expression, means for analyzing acoustic data and evaluating intonation and speed, means for analyzing visual data and evaluating facial expressions, means for analyzing image data and evaluating appearance, and means for generating comprehensive feedback based on the analysis results and displaying it in real time on an information terminal. This enables users to quickly and comprehensively understand their weaknesses in communication skills and to make continuous improvements.

[0064] "Audio data" refers to data that represents information about sound, such as speech, in digital format.

[0065] "Textual information" refers to digital information, including text data converted from speech.

[0066] "Visual data" refers to digital data of images and videos acquired by imaging devices such as cameras.

[0067] "Image data" refers to data that represents still images or moving images in digital format.

[0068] "Feedback" refers to suggestions for improvement and points of concern provided to users based on analysis results, and is information that encourages users to improve their behavior and skills.

[0069] "Analysis" is the process of processing data based on calculations and logic to extract or evaluate information.

[0070] An "information terminal" is an electronic device used by a user to input or receive digital information.

[0071] This invention comprises an information terminal used by the user and a server system operating in the backend. Specifically, the user uses the information terminal to collect and input their own acoustic and visual data.

[0072] The user first launches an application on their information terminal to collect acoustic data. To do this, the terminal is equipped with a microphone. The terminal's camera is used to collect visual data, which includes information about the user's speech and facial expressions.

[0073] The terminal sends the collected acoustic data to the server. The server operates on a common cloud infrastructure and utilizes speech recognition technology (e.g., a commercial speech recognition API) to analyze the acoustic data and convert it into text. The converted text is further analyzed by natural language processing algorithms to provide appropriate linguistic representations.

[0074] Furthermore, the server analyzes the acoustic data itself, evaluating the intonation and speed of speech. This is designed to suggest improvements to pronunciation.

[0075] Simultaneously, the server analyzes the visual data sent from the terminal. This analysis is performed using facial recognition algorithms, which analyze changes in facial expressions and gaze to generate feedback regarding the user's emotional expression. In addition, the image data is analyzed to evaluate the user's appearance, specifically the characteristics of their clothing and hairstyle.

[0076] Based on these analysis results, the server generates comprehensive feedback. This feedback is transmitted to the information terminal in real time and presented to the user.

[0077] For example, by using a prompt such as, "I've recorded my presentation. Based on this data, please tell me how I can improve my communication skills. I'd especially like feedback on my speaking speed and facial expressions," the system can provide the user with customized feedback.

[0078] This allows users to quickly understand specific areas for improvement in their communication skills and to continuously work on improving them.

[0079] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0080] Step 1:

[0081] The user activates the information terminal and operates an application for collecting acoustic and visual data. When the user starts the recording / video recording function of their speech and actions, the terminal's microphone records the acoustic data as input, and the camera collects the visual data as input. The output is the acoustic and visual data, which is temporarily stored on the terminal.

[0082] Step 2:

[0083] The terminal sends the collected acoustic data to the server. The input to the acoustic data includes recorded audio files, which are transferred to the server via a data communication protocol. The server receives this acoustic data and provides it as output in an analyzable format.

[0084] Step 3:

[0085] The server processes the received audio data using a speech recognition engine. It uses the audio data as input and performs data processing to convert speech into text. The output is text generated by speech recognition. Specifically, by converting speech to text, the user's utterances are visualized.

[0086] Step 4:

[0087] The server analyzes the generated text information using a natural language processing algorithm. The text information is input, and data calculations are performed to extract appropriateness and areas for improvement in the linguistic expression. The output provides evaluation information of the linguistic expression for which improvements have been suggested. For example, this might include pointing out areas where honorific language is insufficient.

[0088] Step 5:

[0089] The server analyzes the acoustic data itself, evaluating intonation and speed. It uses acoustic data as input, and performs data calculations to analyze the waveform and speed of the speech. This results in suggestions for improving intonation and speed. Specifically, if the speaking tempo is too fast, it generates feedback suggesting "speak a little slower."

[0090] Step 6:

[0091] After the terminal transmits visual data, the server analyzes it. The visual data is input, and an expression recognition algorithm is used to analyze eye movements and changes in facial expressions. The output provides feedback information regarding facial features and emotional expression. For example, it can generate specific suggestions such as, "You should smile more."

[0092] Step 7:

[0093] The server analyzes image data and performs an appearance evaluation. The input includes image data, and the analysis algorithm extracts features such as how the clothing is arranged and the hairstyle. The output provides appearance-based feedback, including suggestions such as "You should dress a little more casually."

[0094] Step 8:

[0095] The server integrates the above analysis results and generates comprehensive feedback. Improvement suggestions and evaluation information obtained at each step are input, and data processing is performed to assemble the feedback content. The output provides comprehensive and specific feedback to the user.

[0096] Step 9:

[0097] The device receives feedback sent from the server and displays it to the user in real time. This allows the user to improve their communication skills based on the feedback. As output, feedback information that allows the user to immediately see suggestions for improvement is displayed on the device.

[0098] (Application Example 1)

[0099] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0100] In traditional customer service, individual staff members' communication skills rely on their personal experience and intuition, making it difficult to provide consistent service quality. New staff members, in particular, face challenges in improving their skills due to a lack of immediate feedback and concrete suggestions for improvement.

[0101] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0102] In this invention, the server includes means for generating coded symbols from speech information, means for analyzing the generated coded symbols and improving linguistic representation, and means for analyzing the speech information and evaluating intonation and speed. This enables staff in customer service to directly and in real time improve their communication skills.

[0103] "Audio information" refers to data containing the content of a communication, obtained by treating the waveform of a sound as a digital signal.

[0104] "Encoded symbols" are data obtained by analyzing audio information and converting it into text or symbolic formats.

[0105] "Analysis" is the process of breaking down data into smaller parts to reveal its content and characteristics.

[0106] "Linguistic expression" refers to sentences or phrases that convey meaning using a specific language.

[0107] "Intonation" refers to the changes in pitch and volume of sounds in speech.

[0108] "Speed" refers to the speed at which information is transmitted, and in the case of audio information, it specifically means the speed of speech.

[0109] "Visual information" refers to image and video data captured by cameras and other devices.

[0110] "Facial expression" refers to changes in emotions and will expressed through a person's face.

[0111] "Image information" refers to data captured through visual media such as photographs and illustrations.

[0112] To evaluate someone's "appearance" means to analyze their impression based on visual elements such as their hair and clothing.

[0113] A "portable display device" is a portable display device that allows users to instantly access and view information.

[0114] "Feedback" is the process of providing responses or reactions to a particular action or behavior.

[0115] "Customer service" refers to the work of providing product descriptions and services to customers in stores and other similar establishments.

[0116] In this application example, a system to support customer service is implemented using a portable display device, voice input device, and camera worn by the store clerk. The terminal collects voice and video information in real time and transmits it to a server. The server converts the voice information into coded characters using a speech recognition API (e.g., Google® Cloud Speech API). Next, it analyzes the linguistic representation of the generated coded characters using a natural language processing model (e.g., BERT) and provides appropriate improvement suggestions.

[0117] The intonation and speed of audio information are evaluated using acoustic analysis tools. Video information acquired by the camera mounted on the terminal is analyzed for changes in facial expressions using a face recognition library (e.g., OpenCV, Dlib). Based on this information, the server generates feedback in real time and displays it on a portable display device.

[0118] For example, feedback such as "Smiling more will create an even better impression" might be provided. Users can review this feedback during customer service and use it to optimize their communication.

[0119] By using generative AI models, it is possible to further personalize user feedback and promote effective improvement. An example of a prompt to the generative AI model might be, "What kind of real-time feedback should be given to increase the frequency of smiles during customer service?" Based on this prompt, the generative AI model will perform the necessary processing to provide the optimal feedback.

[0120] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0121] Step 1:

[0122] The device collects the user's voice information through the microphone and converts it into a digital format. The input is the user's raw voice, and the output is a digitized audio file. This digitized audio file is then sent to a server for subsequent speech recognition processing.

[0123] Step 2:

[0124] The server converts the received audio information into coded text using a speech recognition API. The input for this step is a digital audio file, and the output is coded text. The server then further processes this coded text for analysis.

[0125] Step 3:

[0126] The server uses a generative AI model to analyze coded text using natural language processing algorithms. The input for this step is coded text, and the output is an evaluation of the linguistic expression and suggestions for improvement. During this process, the server checks for vocabulary richness and grammatical appropriateness.

[0127] Step 4:

[0128] The server uses an acoustic analysis tool to evaluate the intonation and tempo of separately collected audio information. The input is a digital audio file, and the output is evaluation data of these characteristics. This identifies areas for improvement in tempo and tone.

[0129] Step 5:

[0130] The terminal transmits video information acquired using its camera to the server. The input is the user's real-time video, and the output is video data for analysis. This video data serves as preparation for facial expression analysis.

[0131] Step 6:

[0132] The server analyzes video data using a facial recognition library. The input is real-time video data, and the output is the result of facial expression evaluation. This allows the user's facial expression changes to be measured and evaluated.

[0133] Step 7:

[0134] The server generates specific feedback for customer service based on the analysis results of each of the steps described above. The input is the evaluation data from the past, and the output is the feedback message displayed to the user.

[0135] Step 8:

[0136] The terminal displays the generated feedback in real time on a portable display device. The input is feedback messages from the server, and the output is a screen display of the feedback that the user can see. This allows the user to immediately work on improving their customer service skills.

[0137] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0138] This invention includes a user-operated terminal and a server system that works in conjunction with it, in order to improve the user's face-to-face communication skills. In addition, it incorporates an emotion engine for recognizing and analyzing the user's emotions. The user uses the terminal to collect voice, video, and emotion data and transmits them to the server. Based on the diverse data, the server performs the necessary analysis and evaluation to support the improvement of communication skills.

[0139] The user records audio and captures video using their device. Simultaneously, an emotion engine extracts emotions from the user's audio and video data. The audio data is sent to a server and converted into text using speech recognition technology. The server analyzes the text data using natural language processing algorithms to identify areas for improvement in linguistic expression.

[0140] In the analysis of audio data, intonation and speed are evaluated to determine whether an appropriate tempo and tone are maintained. Furthermore, by analyzing video data, facial expressions and gaze are evaluated to confirm how the user's emotions are expressed. Using an emotion engine, the server captures the user's emotional changes at crucial moments and determines whether a particular statement and facial expression emotionally match.

[0141] The analysis results are sent to the user's device in the form of real-time feedback. Users can review this feedback and understand areas for improvement. For example, specific advice may be provided such as, "You had a sad expression when you said a certain word. It would be more effective if you conveyed it with a more positive expression."

[0142] Thus, the present invention enables the construction of a system that integrates voice analysis, facial recognition, and emotion analysis to provide users with specific and comprehensive feedback in real time. This system allows users to effectively improve their face-to-face communication skills.

[0143] The following describes the processing flow.

[0144] Step 1:

[0145] The user starts the device and enables the camera and microphone functions to begin a communication session. The device then activates its collection function and proceeds to collect audio and video data in real time.

[0146] Step 2:

[0147] The device sends the collected audio data to the server. The server uses speech recognition technology to convert the audio data into text. This converted text is used as basic data for analyzing the user's language expression.

[0148] Step 3:

[0149] The server analyzes the text using natural language processing algorithms to evaluate the appropriateness of honorifics and the accuracy of phrasing. Any deficiencies found will be used to develop further corrections and improvements.

[0150] Step 4:

[0151] The server analyzes the audio data and evaluates the intonation and speed of the user's speech. In particular, it checks whether the speech tempo and tone are appropriate and prepares to provide feedback if there are any inappropriate aspects.

[0152] Step 5:

[0153] The user's video data is sent from the terminal to the server. The server uses video recognition technology to analyze facial expressions and evaluate the user's expressions, including smiles and eye movements.

[0154] Step 6:

[0155] The server uses an emotion engine to extract user emotions from audio and video data. It evaluates how changes in the user's emotions are represented and identifies emotional consistency and inconsistencies.

[0156] Step 7:

[0157] The server generates comprehensive feedback from all analysis results. This feedback includes specific suggestions for improvement regarding language expression, facial expressions, speech speed, and emotional consistency.

[0158] Step 8:

[0159] The device displays feedback received from the server to the user in real time. Based on this feedback, the user can take concrete actions to improve their communication skills.

[0160] (Example 2)

[0161] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0162] Improving face-to-face communication skills is a crucial challenge in modern society. However, many people have limited opportunities to receive feedback on their vocal and nonverbal expressions. Therefore, objectively evaluating and improving one's own communication skills is difficult. Furthermore, recognizing the consistency of expressions with emotions is not easy. An effective system is needed to address this situation.

[0163] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0164] In this invention, the server includes means for generating text information from audio information, means for analyzing the generated text information and improving linguistic expression, means for analyzing audio information and evaluating intonation and speed, means for extracting emotions from audio and video information using an emotion analysis engine, and means for determining whether a particular statement and facial expression are emotionally consistent. This enables users to objectively and in real time evaluate their communication skills and quickly identify specific areas for improvement.

[0165] "Audio information" refers to data expressed through sound, and is information collected through recordings and other means.

[0166] "Textual information" refers to data in text format obtained as a result of converting audio information.

[0167] A "server" is a computing device that receives, processes, and transmits data over a network.

[0168] "Means of generation" refers to methods or techniques for producing specific data or results.

[0169] "Means of improvement" refer to methods and techniques for changing existing things into a better state.

[0170] "Means of evaluation" refers to methods and techniques for determining the state or value of an object.

[0171] An "emotion analysis engine" is a technology or software that recognizes emotions from audio and video information and analyzes their characteristics.

[0172] "Means of judgment" refers to methods and techniques for drawing conclusions based on information.

[0173] A "specific statement" refers to a linguistic expression that is clearly identifiable within a conversation or audio recording.

[0174] "Facial expression" refers to the visual expression of emotion related to the movement of facial muscles.

[0175] This invention is a system for improving a user's face-to-face communication skills. The user records audio and video using a terminal, which acts as an input device. This terminal is equipped with an emotion analysis engine, enabling the extraction of the user's emotion data in real time. The audio and video data recorded by the user are transmitted to a server via the internet.

[0176] The server converts the received audio data into text using speech recognition technology. A specific example would be using a speech recognition tool like the Google Speech-to-Text API. The converted text is then analyzed by a natural language processing algorithm to improve it into the most optimal linguistic representation.

[0177] Meanwhile, the server also analyzes the video data. Specifically, it uses common libraries such as OpenCV and Dlib to evaluate facial expressions and gaze for facial recognition. This process evaluates the user's emotional expression and prepares specific feedback for improvement as needed.

[0178] The emotion analysis engine evaluates the user's emotional state in detail based on audio and video information. This includes determining whether specific statements and facial expressions match.

[0179] Ultimately, the server provides real-time feedback to the user based on the analyzed data. This feedback is sent to the user's device and displayed as specific advice. For example, it might say, "You're speaking too fast; you should speak a little slower."

[0180] An example of a prompt message would be, "Please provide feedback to help the user improve their communication skills when giving an important presentation."

[0181] This system allows users to check and improve their communication style in real time, thereby effectively enhancing their face-to-face communication skills.

[0182] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0183] Step 1:

[0184] The user uses a device to record audio and video simultaneously. This process collects audio and video data using a microphone and camera. The input is the user's audio and video, and the output is the recorded audio and video files.

[0185] Step 2:

[0186] The device inputs recorded audio and video data into an emotion analysis engine in real time. This engine analyzes the tone of voice, the content of speech, and changes in facial expressions in the video to extract the user's emotions. The input consists of audio and video files, and the output is the user's emotion data.

[0187] Step 3:

[0188] The device transmits collected audio, video, and emotion data to the server. This data is protected by a secure protocol. Inputs are audio, video, and emotion data, while output is data transfer to the server.

[0189] Step 4:

[0190] The server inputs the received audio data into a speech recognition system, which then converts it into text. As a specific example, a speech recognition API is used to generate linguistic text from an audio waveform. The input is audio data, and the output is text data.

[0191] Step 5:

[0192] The server analyzes textual information using natural language processing algorithms to evaluate the validity and potential for improvement of the expression. Here, a generative AI model is used to analyze the text syntax and prepare appropriate feedback. The input is text data, and the output is analytical data regarding areas for improvement.

[0193] Step 6:

[0194] The server analyzes video data and evaluates non-verbal elements such as facial expressions and gaze. This process uses a video analysis library to detect facial features and quantify the user's facial expression state. The input is video data, and the output is facial expression analysis data.

[0195] Step 7:

[0196] The server uses emotional data to determine if a specific statement matches a facial expression. This allows it to assess the appropriateness of the communication and formulate necessary feedback. Inputs include emotional data, voice analysis data, and facial expression analysis data, while outputs include evaluation data and feedback information.

[0197] Step 8:

[0198] The server generates real-time feedback based on the analysis results and sends it to the user's terminal. For example, it creates specific advice regarding language and facial expressions based on the analysis data. The input is feedback information, and the output is the feedback displayed on the user's terminal.

[0199] This processing flow allows users to quickly receive comprehensive feedback on their communication skills.

[0200] (Application Example 2)

[0201] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0202] In face-to-face interactions, individual staff members face challenges in using appropriate intonation, speed, and facial expressions when communicating with customers, and in not being able to receive real-time feedback to effectively improve these skills.

[0203] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0204] In this invention, the server includes means for generating text from audio data, means for analyzing the generated text and improving linguistic expression, means for analyzing the audio data and evaluating intonation and speed, means for displaying on a visual device and generating real-time feedback based on the analyzed intonation, speed, and facial expressions, and means for playing back video recordings based on messages presented on the visual device to support the practice of corresponding skills. As a result, staff can receive timely and appropriate feedback on their communication skills, enabling them to improve their skills in face-to-face work.

[0205] "Voice data" refers to information that electronically captures and records a user's voice and can be converted into text information.

[0206] "Means for generating text" refer to functional elements for analyzing audio data and generating corresponding linguistic expressions.

[0207] "Means for improving linguistic expression" refers to functions that analyze linguistic errors and effects contained in generated text and optimize it to enable more effective communication.

[0208] "Means for evaluating intonation and speed" refers to processing functions that analyze the tone and speech speed within audio data and determine their appropriateness.

[0209] "Visual devices" are devices used by users to receive information visually, and include smart glasses and display devices.

[0210] "Means for generating real-time feedback" refers to a function that immediately provides users with feedback based on the results of the analysis of collected data.

[0211] "A means of playing back video recordings to support the practice of corresponding skills" refers to a support function that recreates past communications for practice, allowing users to improve their skills while identifying areas for improvement.

[0212] To implement this invention, a system is constructed in which a server and a terminal work together. The user collects video and audio via the terminal. The terminal is a smart glasses or similar device equipped with a camera and microphone. Data obtained by the user during conversation is transmitted to the server. The server generates text from the audio data and analyzes and improves the linguistic expression using natural language processing. The intonation and speed of the speech are also evaluated, and facial expressions are analyzed from the video data. This data is provided to the user's visual device as real-time feedback.

[0213] The specific devices used include OpenCV for data processing, SoundDevice for speech recognition, and a natural language processing library for text analysis. Feedback generation utilizes analysis results obtained from the data based on a pre-configured algorithm.

[0214] For example, when a user interacts with a customer, the smart glasses display real-time feedback such as, "Your voice tone sounds a little low. Try raising your energy level slightly." This feedback immediately suggests areas for improvement in communication, assisting in the smooth execution of face-to-face interactions. Furthermore, users can later review their performance and improve their skills using the provided recordings and analysis results.

[0215] An example of a prompt for a generating AI model is: "Generate communication improvement tips from the following user voice and facial expression data. Voice data: 'Hello, this is the new products corner.' Facial expression data: Not smiling enough. Please rate it." In this way, users can effectively improve their communication skills.

[0216] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0217] Step 1:

[0218] The user puts on smart glasses and begins a conversation with a customer. The device uses its built-in camera to capture the user's facial expressions as video data and its microphone to record audio data. This data is then prepared for transmission to a server.

[0219] Step 2:

[0220] Audio data transmitted from the terminal is received by the server. The server applies speech recognition technology to convert the audio data into text. The input is audio data, and the output is the converted text. This process involves speech analysis and text conversion.

[0221] Step 3:

[0222] The server analyzes the converted text using a natural language processing engine. It receives text data as input and identifies areas for improvement in linguistic expression as output. At this stage, grammatical errors and more appropriate expressions are identified, and the text is optimized.

[0223] Step 4:

[0224] The server analyzes the intonation and speed of the audio data. The input is the original audio data, and the output is evaluation information of the tone and tempo of the speech. This identifies the characteristics of the user's speaking style and clarifies which elements need improvement.

[0225] Step 5:

[0226] The server analyzes video data to evaluate facial expressions. It receives video data as input and generates a detailed evaluation of facial expressions as output. By applying a facial expression recognition algorithm, the user's emotions and changes in their face are analyzed.

[0227] Step 6:

[0228] Based on the analysis results, the server generates real-time feedback. This feedback is sent to a visual device and displayed. This allows users to immediately identify areas for improvement and obtain information useful for communicating with customers.

[0229] Step 7:

[0230] After the user ends the conversation, the server comprehensively evaluates the overall performance and provides the user with detailed feedback. This includes the consistency between voice and facial expressions at specific moments, areas where language was optimized, and areas for improvement. Based on this information, the user can prepare for their next conversation.

[0231] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0232] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0233] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0234] [Second Embodiment]

[0235] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0236] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0237] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0238] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0239] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0240] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0241] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0242] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0243] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0244] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0245] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0246] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0247] This invention includes a user terminal and a server system operating in the backend. The user uses the terminal to collect their own speech and actions and transmits this data to the server. The server performs analysis and evaluation based on this data to improve communication skills.

[0248] Specifically, audio data recorded on the device is sent to a server, which first uses speech recognition technology to convert the audio into text. The converted text is then analyzed using natural language processing algorithms to check for any omissions of respectful expressions or appropriate phrasing.

[0249] In parallel, the audio data itself is analyzed, and its intonation and speed are evaluated. If there is room for improvement in the tempo or tone of speech, the server marks those points and generates improvement suggestions.

[0250] Furthermore, users use their device's camera to capture their facial expressions. The video data is sent to a server, where facial recognition algorithms analyze things like the frequency of smiles and eye movements. Similarly, the image data is analyzed to evaluate appearances such as hairstyle and clothing, and feedback is generated based on that.

[0251] After all analysis is complete, the server generates comprehensive feedback from this individual data. This feedback is received by the terminal and displayed to the user in real time. For example, it may include specific comments such as, "Your use of polite language is inappropriate; you should rephrase it like this," or "You need to smile more."

[0252] Users can continuously improve their face-to-face communication skills by checking this feedback in real time. This process is designed to help users acquire effective communication methods.

[0253] The following describes the processing flow.

[0254] Step 1:

[0255] The user powers on the device, enables the camera and microphone, and starts a communication session. At this point, the device begins collecting audio and video data.

[0256] Step 2:

[0257] The device sends the collected audio data to the server. This data includes the user's speech. The server uses speech recognition technology to convert the audio data into text data.

[0258] Step 3:

[0259] The server receives text data and uses natural language processing algorithms to analyze the linguistic expressions. This process identifies inappropriate phrasing and errors in honorific language.

[0260] Step 4:

[0261] The server analyzes the intonation and speed of the audio data. It evaluates the tempo and tone, and generates improvement suggestions as needed.

[0262] Step 5:

[0263] The user's video data is sent from the terminal to the server. The server uses video recognition technology to analyze facial expressions and evaluate things like the frequency of smiles and how the user's gaze is directed.

[0264] Step 6:

[0265] The server analyzes the user's image data and evaluates their appearance, including hairstyle and clothing. It then prepares feedback based on this appearance.

[0266] Step 7:

[0267] The server integrates all analysis results and generates comprehensive feedback for the user. This feedback includes specific improvement measures and suggestions.

[0268] Step 8:

[0269] The device receives feedback sent from the server and displays it to the user in real time. The user can improve their communication skills by reviewing this feedback.

[0270] (Example 1)

[0271] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0272] There is a growing need to provide automated, real-time feedback to improve communication skills, comprehensively utilizing acoustic and visual data. However, achieving this requires effectively analyzing multiple elements and generating concrete and practical improvement suggestions for the user.

[0273] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0274] In this invention, the server includes means for generating text information from acoustic data, means for analyzing the generated text information and improving linguistic expression, means for analyzing acoustic data and evaluating intonation and speed, means for analyzing visual data and evaluating facial expressions, means for analyzing image data and evaluating appearance, and means for generating comprehensive feedback based on the analysis results and displaying it in real time on an information terminal. This enables users to quickly and comprehensively understand their weaknesses in communication skills and to make continuous improvements.

[0275] "Audio data" refers to data that represents information about sound, such as speech, in digital format.

[0276] "Textual information" refers to digital information, including text data converted from speech.

[0277] "Visual data" refers to digital data of images and videos acquired by imaging devices such as cameras.

[0278] "Image data" refers to data that represents still images or moving images in digital format.

[0279] "Feedback" refers to improvement suggestions and points for attention provided to the user based on the analysis results, and is information that promotes the improvement of the user's behavior and skills.

[0280] "Analysis" is the process of processing data based on calculations and logic to extract or evaluate information.

[0281] "Information terminal" is an electronic device used by the user to input or receive digital information.

[0282] This invention has a configuration including an information terminal used by the user and a server system operating in the backend. Specifically, the user uses the information terminal to collect and input their own acoustic data and visual data.

[0283] The user first launches an application on the information terminal to collect acoustic data. To do this, the information terminal is equipped with a microphone. For the collection of visual data, the terminal's camera is used. As a result, information on the user's speech and expressions is collected.

[0284] The terminal sends the collected acoustic data to the server. The server operates on a general cloud infrastructure to analyze the acoustic data and utilizes speech recognition technology (e.g., commercial speech recognition APIs) to convert the acoustic data into character information. The converted character information is further analyzed by natural language processing algorithms to provide appropriate language expressions.

[0285] In addition, the server analyzes the acoustic data itself and evaluates the intonation and speed of speech. As a result, it is designed to propose improvements in pronunciation.

[0286] At the same time, the server analyzes the visual data sent from the terminal. The analysis of the visual data is performed by an expression recognition algorithm, which analyzes changes in expressions and lines of sight and generates feedback regarding the user's emotional expression. Also, the appearance, specifically the characteristics of clothes and hairstyles, is evaluated through the analysis of image data.

[0287] From these analysis results, the server generates comprehensive feedback. This feedback is transmitted to the information terminal in real time and presented to the user.

[0288] As a specific example, when the user uses a prompt sentence such as "I have recorded my presentation. Based on this data, please tell me the areas for improving my communication skills. In particular, I would like feedback regarding the speed of my speaking and my expressions.", the system can provide customized feedback to the user.

[0289] Thereby, the user can quickly understand the specific areas for improving their communication skills and can make continuous improvements.

[0290] The flow of the specific process in Example 1 will be described with reference to FIG. 11.

[0291] Step 1:

[0292] The user activates the information terminal and operates an application for collecting acoustic data and visual data. By starting the recording / recording function of the user's speech and actions, the microphone of the terminal records the acoustic data as input, and the camera collects the visual data as input. As output, acoustic and visual data temporarily stored in the terminal are obtained.

[0293] Step 2:

[0294] The terminal transmits the collected acoustic data to the server. The input of the acoustic data includes the recorded audio file, which is transferred to the server via the data communication protocol. The server receives this acoustic data and obtains acoustic data prepared in an analyzable form as output.

[0295] Step 3:

[0296] The server processes the received audio data using a speech recognition engine. It uses the audio data as input and performs data processing to convert speech into text. The output is text generated by speech recognition. Specifically, by converting speech to text, the user's utterances are visualized.

[0297] Step 4:

[0298] The server analyzes the generated text information using a natural language processing algorithm. The text information is input, and data calculations are performed to extract appropriateness and areas for improvement in the linguistic expression. The output provides evaluation information of the linguistic expression for which improvements have been suggested. For example, this might include pointing out areas where honorific language is insufficient.

[0299] Step 5:

[0300] The server analyzes the acoustic data itself, evaluating intonation and speed. It uses acoustic data as input, and performs data calculations to analyze the waveform and speed of the speech. This results in suggestions for improving intonation and speed. Specifically, if the speaking tempo is too fast, it generates feedback suggesting "speak a little slower."

[0301] Step 6:

[0302] After the terminal transmits visual data, the server analyzes it. The visual data is input, and an expression recognition algorithm is used to analyze eye movements and changes in facial expressions. The output provides feedback information regarding facial features and emotional expression. For example, it can generate specific suggestions such as, "You should smile more."

[0303] Step 7:

[0304] The server analyzes the image data and conducts an evaluation regarding appearance. The input includes the image data, and the clothing arrangement and hairstyle features are extracted by the analysis algorithm. As output, feedback based on appearance is obtained, including suggestions such as "the clothing should be made a bit more casual".

[0305] Step 8:

[0306] The server integrates the above analysis results and generates comprehensive feedback. The improvement plans and evaluation information obtained in each step are input, and data processing for assembling the content of the feedback is performed. As output, comprehensive and specific feedback for the user is obtained.

[0307] Step 9:

[0308] The terminal receives the feedback sent from the server and displays it to the user in real time. Thereby, the user can improve their communication skills based on the feedback. As output, feedback information that allows the user to immediately confirm the improvement plan is displayed on the terminal.

[0309] (Application Example 1)

[0310] Next, Application Example 1 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0311] In conventional customer service operations, the communication skills of individual staff members rely on personal experience and intuition, making it difficult to provide a uniform service quality. Especially for new staff, there are few means to obtain immediate feedback and specific improvement plans, and there is a problem that it takes time to improve skills.

[0312] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0313] In this invention, the server includes means for generating coded symbols from speech information, means for analyzing the generated coded symbols and improving linguistic representation, and means for analyzing the speech information and evaluating intonation and speed. This enables staff in customer service to directly and in real time improve their communication skills.

[0314] "Audio information" refers to data containing the content of a communication, obtained by treating the waveform of a sound as a digital signal.

[0315] "Encoded symbols" are data obtained by analyzing audio information and converting it into text or symbolic formats.

[0316] "Analysis" is the process of breaking down data into smaller parts to reveal its content and characteristics.

[0317] "Linguistic expression" refers to sentences or phrases that convey meaning using a specific language.

[0318] "Intonation" refers to the changes in pitch and volume of sounds in speech.

[0319] "Speed" refers to the speed at which information is transmitted, and in the case of audio information, it specifically means the speed of speech.

[0320] "Visual information" refers to image and video data captured by cameras and other devices.

[0321] "Facial expression" refers to changes in emotions and will expressed through a person's face.

[0322] "Image information" refers to data captured through visual media such as photographs and illustrations.

[0323] To evaluate someone's "appearance" means to analyze their impression based on visual elements such as their hair and clothing.

[0324] A "portable display device" is a portable display device that allows users to instantly access and view information.

[0325] "Feedback" is the process of providing responses or reactions to a particular action or behavior.

[0326] "Customer service" refers to the work of providing product descriptions and services to customers in stores and other similar establishments.

[0327] In this application example, a system to support customer service is implemented using a portable display device, voice input device, and camera worn by the store clerk. The terminal collects voice and video information in real time and transmits it to a server. The server converts the voice information into coded characters using a speech recognition API (e.g., Google Cloud Speech API). Next, it analyzes the linguistic representation of the generated coded characters using a natural language processing model (e.g., BERT) and provides appropriate improvement suggestions.

[0328] The intonation and speed of audio information are evaluated using acoustic analysis tools. Video information acquired by the camera mounted on the terminal is analyzed for changes in facial expressions using a face recognition library (e.g., OpenCV, Dlib). Based on this information, the server generates feedback in real time and displays it on a portable display device.

[0329] For example, feedback such as "Smiling more will create an even better impression" might be provided. Users can review this feedback during customer service and use it to optimize their communication.

[0330] By using generative AI models, it is possible to further personalize user feedback and promote effective improvement. An example of a prompt to the generative AI model might be, "What kind of real-time feedback should be given to increase the frequency of smiles during customer service?" Based on this prompt, the generative AI model will perform the necessary processing to provide the optimal feedback.

[0331] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0332] Step 1:

[0333] The device collects the user's voice information through the microphone and converts it into a digital format. The input is the user's raw voice, and the output is a digitized audio file. This digitized audio file is then sent to a server for subsequent speech recognition processing.

[0334] Step 2:

[0335] The server converts the received audio information into coded text using a speech recognition API. The input for this step is a digital audio file, and the output is coded text. The server then further processes this coded text for analysis.

[0336] Step 3:

[0337] The server uses a generative AI model to analyze coded text using natural language processing algorithms. The input for this step is coded text, and the output is an evaluation of the linguistic expression and suggestions for improvement. During this process, the server checks for vocabulary richness and grammatical appropriateness.

[0338] Step 4:

[0339] The server uses an acoustic analysis tool to evaluate the intonation and tempo of separately collected audio information. The input is a digital audio file, and the output is evaluation data of these characteristics. This identifies areas for improvement in tempo and tone.

[0340] Step 5:

[0341] The terminal transmits video information acquired using its camera to the server. The input is the user's real-time video, and the output is video data for analysis. This video data serves as preparation for facial expression analysis.

[0342] Step 6:

[0343] The server analyzes video data using a facial recognition library. The input is real-time video data, and the output is the result of facial expression evaluation. This allows the user's facial expression changes to be measured and evaluated.

[0344] Step 7:

[0345] The server generates specific feedback for customer service based on the analysis results of each of the steps described above. The input is the evaluation data from the past, and the output is the feedback message displayed to the user.

[0346] Step 8:

[0347] The terminal displays the generated feedback in real time on a portable display device. The input is feedback messages from the server, and the output is a screen display of the feedback that the user can see. This allows the user to immediately work on improving their customer service skills.

[0348] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0349] This invention includes a user-operated terminal and a server system that works in conjunction with it, in order to improve the user's face-to-face communication skills. In addition, it incorporates an emotion engine for recognizing and analyzing the user's emotions. The user uses the terminal to collect voice, video, and emotion data and transmits them to the server. Based on the diverse data, the server performs the necessary analysis and evaluation to support the improvement of communication skills.

[0350] The user records audio and captures video using their device. Simultaneously, an emotion engine extracts emotions from the user's audio and video data. The audio data is sent to a server and converted into text using speech recognition technology. The server analyzes the text data using natural language processing algorithms to identify areas for improvement in linguistic expression.

[0351] In the analysis of audio data, intonation and speed are evaluated to determine whether an appropriate tempo and tone are maintained. Furthermore, by analyzing video data, facial expressions and gaze are evaluated to confirm how the user's emotions are expressed. Using an emotion engine, the server captures the user's emotional changes at crucial moments and determines whether a particular statement and facial expression emotionally match.

[0352] The analysis results are sent to the user's device in the form of real-time feedback. Users can review this feedback and understand areas for improvement. For example, specific advice may be provided such as, "You had a sad expression when you said a certain word. It would be more effective if you conveyed it with a more positive expression."

[0353] Thus, the present invention enables the construction of a system that integrates voice analysis, facial recognition, and emotion analysis to provide users with specific and comprehensive feedback in real time. This system allows users to effectively improve their face-to-face communication skills.

[0354] The following describes the processing flow.

[0355] Step 1:

[0356] The user starts the device and enables the camera and microphone functions to begin a communication session. The device then activates its collection function and proceeds to collect audio and video data in real time.

[0357] Step 2:

[0358] The device sends the collected audio data to the server. The server uses speech recognition technology to convert the audio data into text. This converted text is used as basic data for analyzing the user's language expression.

[0359] Step 3:

[0360] The server analyzes the text using natural language processing algorithms to evaluate the appropriateness of honorifics and the accuracy of phrasing. Any deficiencies found will be used to develop further corrections and improvements.

[0361] Step 4:

[0362] The server analyzes the audio data and evaluates the intonation and speed of the user's speech. In particular, it checks whether the speech tempo and tone are appropriate and prepares to provide feedback if there are any inappropriate aspects.

[0363] Step 5:

[0364] The user's video data is sent from the terminal to the server. The server uses video recognition technology to analyze facial expressions and evaluate the user's expressions, including smiles and eye movements.

[0365] Step 6:

[0366] The server uses an emotion engine to extract user emotions from audio and video data. It evaluates how changes in the user's emotions are represented and identifies emotional consistency and inconsistencies.

[0367] Step 7:

[0368] The server generates comprehensive feedback from all analysis results. This feedback includes specific suggestions for improvement regarding language expression, facial expressions, speech speed, and emotional consistency.

[0369] Step 8:

[0370] The device displays feedback received from the server to the user in real time. Based on this feedback, the user can take concrete actions to improve their communication skills.

[0371] (Example 2)

[0372] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0373] Improving face-to-face communication skills is a crucial challenge in modern society. However, many people have limited opportunities to receive feedback on their vocal and nonverbal expressions. Therefore, objectively evaluating and improving one's own communication skills is difficult. Furthermore, recognizing the consistency of expressions with emotions is not easy. An effective system is needed to address this situation.

[0374] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0375] In this invention, the server includes means for generating text information from audio information, means for analyzing the generated text information and improving linguistic expression, means for analyzing audio information and evaluating intonation and speed, means for extracting emotions from audio and video information using an emotion analysis engine, and means for determining whether a particular statement and facial expression are emotionally consistent. This enables users to objectively and in real time evaluate their communication skills and quickly identify specific areas for improvement.

[0376] "Audio information" refers to data expressed through sound, and is information collected through recordings and other means.

[0377] "Textual information" refers to data in text format obtained as a result of converting audio information.

[0378] A "server" is a computing device that receives, processes, and transmits data over a network.

[0379] "Means of generation" refers to methods or techniques for producing specific data or results.

[0380] "Means of improvement" refer to methods and techniques for changing existing things into a better state.

[0381] "Means of evaluation" refers to methods and techniques for determining the state or value of an object.

[0382] An "emotion analysis engine" is a technology or software that recognizes emotions from audio and video information and analyzes their characteristics.

[0383] "Means of judgment" refers to methods and techniques for drawing conclusions based on information.

[0384] A "specific statement" refers to a linguistic expression that is clearly identifiable within a conversation or audio recording.

[0385] "Facial expression" refers to the visual expression of emotion related to the movement of facial muscles.

[0386] This invention is a system for improving a user's face-to-face communication skills. The user records audio and video using a terminal, which acts as an input device. This terminal is equipped with an emotion analysis engine, enabling the extraction of the user's emotion data in real time. The audio and video data recorded by the user are transmitted to a server via the internet.

[0387] The server converts the received audio data into text using speech recognition technology. A specific example would be using a speech recognition tool like the Google Speech-to-Text API. The converted text is then analyzed by a natural language processing algorithm to improve it into the most optimal linguistic representation.

[0388] Meanwhile, the server also analyzes the video data. Specifically, it uses common libraries such as OpenCV and Dlib to evaluate facial expressions and gaze for facial recognition. This process evaluates the user's emotional expression and prepares specific feedback for improvement as needed.

[0389] The emotion analysis engine evaluates the user's emotional state in detail based on audio and video information. This includes determining whether specific statements and facial expressions match.

[0390] Ultimately, the server provides real-time feedback to the user based on the analyzed data. This feedback is sent to the user's device and displayed as specific advice. For example, it might say, "You're speaking too fast; you should speak a little slower."

[0391] An example of a prompt message would be, "Please provide feedback to help the user improve their communication skills when giving an important presentation."

[0392] This system allows users to check and improve their communication style in real time, thereby effectively enhancing their face-to-face communication skills.

[0393] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0394] Step 1:

[0395] The user uses a device to record audio and video simultaneously. This process collects audio and video data using a microphone and camera. The input is the user's audio and video, and the output is the recorded audio and video files.

[0396] Step 2:

[0397] The device inputs recorded audio and video data into an emotion analysis engine in real time. This engine analyzes the tone of voice, the content of speech, and changes in facial expressions in the video to extract the user's emotions. The input consists of audio and video files, and the output is the user's emotion data.

[0398] Step 3:

[0399] The device transmits collected audio, video, and emotion data to the server. This data is protected by a secure protocol. Inputs are audio, video, and emotion data, while output is data transfer to the server.

[0400] Step 4:

[0401] The server inputs the received audio data into a speech recognition system, which then converts it into text. As a specific example, a speech recognition API is used to generate linguistic text from an audio waveform. The input is audio data, and the output is text data.

[0402] Step 5:

[0403] The server analyzes textual information using natural language processing algorithms to evaluate the validity and potential for improvement of the expression. Here, a generative AI model is used to analyze the text syntax and prepare appropriate feedback. The input is text data, and the output is analytical data regarding areas for improvement.

[0404] Step 6:

[0405] The server analyzes video data and evaluates non-verbal elements such as facial expressions and gaze. This process uses a video analysis library to detect facial features and quantify the user's facial expression state. The input is video data, and the output is facial expression analysis data.

[0406] Step 7:

[0407] The server uses emotional data to determine if a specific statement matches a facial expression. This allows it to assess the appropriateness of the communication and formulate necessary feedback. Inputs include emotional data, voice analysis data, and facial expression analysis data, while outputs include evaluation data and feedback information.

[0408] Step 8:

[0409] The server generates real-time feedback based on the analysis results and sends it to the user's terminal. For example, it creates specific advice regarding language and facial expressions based on the analysis data. The input is feedback information, and the output is the feedback displayed on the user's terminal.

[0410] This processing flow allows users to quickly receive comprehensive feedback on their communication skills.

[0411] (Application Example 2)

[0412] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0413] In face-to-face interactions, individual staff members face challenges in using appropriate intonation, speed, and facial expressions when communicating with customers, and in not being able to receive real-time feedback to effectively improve these skills.

[0414] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0415] In this invention, the server includes means for generating text from audio data, means for analyzing the generated text and improving linguistic expression, means for analyzing the audio data and evaluating intonation and speed, means for displaying on a visual device and generating real-time feedback based on the analyzed intonation, speed, and facial expressions, and means for playing back video recordings based on messages presented on the visual device to support the practice of corresponding skills. As a result, staff can receive timely and appropriate feedback on their communication skills, enabling them to improve their skills in face-to-face work.

[0416] "Voice data" refers to information that electronically captures and records a user's voice and can be converted into text information.

[0417] "Means for generating text" refer to functional elements for analyzing audio data and generating corresponding linguistic expressions.

[0418] "Means for improving linguistic expression" refers to functions that analyze linguistic errors and effects contained in generated text and optimize it to enable more effective communication.

[0419] "Means for evaluating intonation and speed" refers to processing functions that analyze the tone and speech speed within audio data and determine their appropriateness.

[0420] "Visual devices" are devices used by users to receive information visually, and include smart glasses and display devices.

[0421] "Means for generating real-time feedback" refers to a function that immediately provides users with feedback based on the results of the analysis of collected data.

[0422] "A means of playing back video recordings to support the practice of corresponding skills" refers to a support function that recreates past communications for practice, allowing users to improve their skills while identifying areas for improvement.

[0423] To implement this invention, a system is constructed in which a server and a terminal work together. The user collects video and audio via the terminal. The terminal is a smart glasses or similar device equipped with a camera and microphone. Data obtained by the user during conversation is transmitted to the server. The server generates text from the audio data and analyzes and improves the linguistic expression using natural language processing. The intonation and speed of the speech are also evaluated, and facial expressions are analyzed from the video data. This data is provided to the user's visual device as real-time feedback.

[0424] The specific devices used include OpenCV for data processing, SoundDevice for speech recognition, and a natural language processing library for text analysis. Feedback generation utilizes analysis results obtained from the data based on a pre-configured algorithm.

[0425] For example, when a user interacts with a customer, the smart glasses display real-time feedback such as, "Your voice tone sounds a little low. Try raising your energy level slightly." This feedback immediately suggests areas for improvement in communication, assisting in the smooth execution of face-to-face interactions. Furthermore, users can later review their performance and improve their skills using the provided recordings and analysis results.

[0426] An example of a prompt for a generating AI model is: "Generate communication improvement tips from the following user voice and facial expression data. Voice data: 'Hello, this is the new products corner.' Facial expression data: Not smiling enough. Please rate it." In this way, users can effectively improve their communication skills.

[0427] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0428] Step 1:

[0429] The user puts on smart glasses and begins a conversation with a customer. The device uses its built-in camera to capture the user's facial expressions as video data and its microphone to record audio data. This data is then prepared for transmission to a server.

[0430] Step 2:

[0431] Audio data transmitted from the terminal is received by the server. The server applies speech recognition technology to convert the audio data into text. The input is audio data, and the output is the converted text. This process involves speech analysis and text conversion.

[0432] Step 3:

[0433] The server analyzes the converted text using a natural language processing engine. It receives text data as input and identifies areas for improvement in linguistic expression as output. At this stage, grammatical errors and more appropriate expressions are identified, and the text is optimized.

[0434] Step 4:

[0435] The server analyzes the intonation and speed of the audio data. The input is the original audio data, and the output is evaluation information of the tone and tempo of the speech. This identifies the characteristics of the user's speaking style and clarifies which elements need improvement.

[0436] Step 5:

[0437] The server analyzes video data to evaluate facial expressions. It receives video data as input and generates a detailed evaluation of facial expressions as output. By applying a facial expression recognition algorithm, the user's emotions and changes in their face are analyzed.

[0438] Step 6:

[0439] Based on the analysis results, the server generates real-time feedback. This feedback is sent to a visual device and displayed. This allows users to immediately identify areas for improvement and obtain information useful for communicating with customers.

[0440] Step 7:

[0441] After the user ends the conversation, the server comprehensively evaluates the overall performance and provides the user with detailed feedback. This includes the consistency between voice and facial expressions at specific moments, areas where language was optimized, and areas for improvement. Based on this information, the user can prepare for their next conversation.

[0442] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0443] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0444] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0445] [Third Embodiment]

[0446] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0447] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0448] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0449] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0450] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0451] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0452] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0453] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0454] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0455] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0456] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0457] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0458] This invention includes a user terminal and a server system operating in the backend. The user uses the terminal to collect their own speech and actions and transmits this data to the server. The server performs analysis and evaluation based on this data to improve communication skills.

[0459] Specifically, audio data recorded on the device is sent to a server, which first uses speech recognition technology to convert the audio into text. The converted text is then analyzed using natural language processing algorithms to check for any omissions of respectful expressions or appropriate phrasing.

[0460] In parallel, the audio data itself is analyzed, and its intonation and speed are evaluated. If there is room for improvement in the tempo or tone of speech, the server marks those points and generates improvement suggestions.

[0461] Furthermore, users use their device's camera to capture their facial expressions. The video data is sent to a server, where facial recognition algorithms analyze things like the frequency of smiles and eye movements. Similarly, the image data is analyzed to evaluate appearances such as hairstyle and clothing, and feedback is generated based on that.

[0462] After all analysis is complete, the server generates comprehensive feedback from this individual data. This feedback is received by the terminal and displayed to the user in real time. For example, it may include specific comments such as, "Your use of polite language is inappropriate; you should rephrase it like this," or "You need to smile more."

[0463] Users can continuously improve their face-to-face communication skills by checking this feedback in real time. This process is designed to help users acquire effective communication methods.

[0464] The following describes the processing flow.

[0465] Step 1:

[0466] The user powers on the device, enables the camera and microphone, and starts a communication session. At this point, the device begins collecting audio and video data.

[0467] Step 2:

[0468] The device sends the collected audio data to the server. This data includes the user's speech. The server uses speech recognition technology to convert the audio data into text data.

[0469] Step 3:

[0470] The server receives text data and uses natural language processing algorithms to analyze the linguistic expressions. This process identifies inappropriate phrasing and errors in honorific language.

[0471] Step 4:

[0472] The server analyzes the intonation and speed of the audio data. It evaluates the tempo and tone, and generates improvement suggestions as needed.

[0473] Step 5:

[0474] The user's video data is sent from the terminal to the server. The server uses video recognition technology to analyze facial expressions and evaluate things like the frequency of smiles and how the user's gaze is directed.

[0475] Step 6:

[0476] The server analyzes the user's image data and evaluates their appearance, including hairstyle and clothing. It then prepares feedback based on this appearance.

[0477] Step 7:

[0478] The server integrates all analysis results and generates comprehensive feedback for the user. This feedback includes specific improvement measures and suggestions.

[0479] Step 8:

[0480] The device receives feedback sent from the server and displays it to the user in real time. The user can improve their communication skills by reviewing this feedback.

[0481] (Example 1)

[0482] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0483] There is a growing need to provide automated, real-time feedback to improve communication skills, comprehensively utilizing acoustic and visual data. However, achieving this requires effectively analyzing multiple elements and generating concrete and practical improvement suggestions for the user.

[0484] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0485] In this invention, the server includes means for generating text information from acoustic data, means for analyzing the generated text information and improving linguistic expression, means for analyzing acoustic data and evaluating intonation and speed, means for analyzing visual data and evaluating facial expressions, means for analyzing image data and evaluating appearance, and means for generating comprehensive feedback based on the analysis results and displaying it in real time on an information terminal. This enables users to quickly and comprehensively understand their weaknesses in communication skills and to make continuous improvements.

[0486] "Audio data" refers to data that represents information about sound, such as speech, in digital format.

[0487] "Textual information" refers to digital information, including text data converted from speech.

[0488] "Visual data" refers to digital data of images and videos acquired by imaging devices such as cameras.

[0489] "Image data" refers to data that represents still images or moving images in digital format.

[0490] "Feedback" refers to suggestions for improvement and points of concern provided to users based on analysis results, and is information that encourages users to improve their behavior and skills.

[0491] "Analysis" is the process of processing data based on calculations and logic to extract or evaluate information.

[0492] An "information terminal" is an electronic device used by a user to input or receive digital information.

[0493] This invention comprises an information terminal used by the user and a server system operating in the backend. Specifically, the user uses the information terminal to collect and input their own acoustic and visual data.

[0494] The user first launches an application on their information terminal to collect acoustic data. To do this, the terminal is equipped with a microphone. The terminal's camera is used to collect visual data, which includes information about the user's speech and facial expressions.

[0495] The terminal sends the collected acoustic data to the server. The server operates on a common cloud infrastructure and utilizes speech recognition technology (e.g., a commercial speech recognition API) to analyze the acoustic data and convert it into text. The converted text is further analyzed by natural language processing algorithms to provide appropriate linguistic representations.

[0496] Furthermore, the server analyzes the acoustic data itself, evaluating the intonation and speed of speech. This is designed to suggest improvements to pronunciation.

[0497] Simultaneously, the server analyzes the visual data sent from the terminal. This analysis is performed using facial recognition algorithms, which analyze changes in facial expressions and gaze to generate feedback regarding the user's emotional expression. In addition, the image data is analyzed to evaluate the user's appearance, specifically the characteristics of their clothing and hairstyle.

[0498] Based on these analysis results, the server generates comprehensive feedback. This feedback is transmitted to the information terminal in real time and presented to the user.

[0499] For example, by using a prompt such as, "I've recorded my presentation. Based on this data, please tell me how I can improve my communication skills. I'd especially like feedback on my speaking speed and facial expressions," the system can provide the user with customized feedback.

[0500] This allows users to quickly understand specific areas for improvement in their communication skills and to continuously work on improving them.

[0501] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0502] Step 1:

[0503] The user activates the information terminal and operates an application for collecting acoustic and visual data. When the user starts the recording / video recording function of their speech and actions, the terminal's microphone records the acoustic data as input, and the camera collects the visual data as input. The output is the acoustic and visual data, which is temporarily stored on the terminal.

[0504] Step 2:

[0505] The terminal sends the collected acoustic data to the server. The input to the acoustic data includes recorded audio files, which are transferred to the server via a data communication protocol. The server receives this acoustic data and provides it as output in an analyzable format.

[0506] Step 3:

[0507] The server processes the received audio data using a speech recognition engine. It uses the audio data as input and performs data processing to convert speech into text. The output is text generated by speech recognition. Specifically, by converting speech to text, the user's utterances are visualized.

[0508] Step 4:

[0509] The server analyzes the generated text information using a natural language processing algorithm. The text information is input, and data calculations are performed to extract appropriateness and areas for improvement in the linguistic expression. The output provides evaluation information of the linguistic expression for which improvements have been suggested. For example, this might include pointing out areas where honorific language is insufficient.

[0510] Step 5:

[0511] The server analyzes the acoustic data itself, evaluating intonation and speed. It uses acoustic data as input, and performs data calculations to analyze the waveform and speed of the speech. This results in suggestions for improving intonation and speed. Specifically, if the speaking tempo is too fast, it generates feedback suggesting "speak a little slower."

[0512] Step 6:

[0513] After the terminal transmits visual data, the server analyzes it. The visual data is input, and an expression recognition algorithm is used to analyze eye movements and changes in facial expressions. The output provides feedback information regarding facial features and emotional expression. For example, it can generate specific suggestions such as, "You should smile more."

[0514] Step 7:

[0515] The server analyzes image data and performs an appearance evaluation. The input includes image data, and the analysis algorithm extracts features such as how the clothing is arranged and the hairstyle. The output provides appearance-based feedback, including suggestions such as "You should dress a little more casually."

[0516] Step 8:

[0517] The server integrates the above analysis results and generates comprehensive feedback. Improvement suggestions and evaluation information obtained at each step are input, and data processing is performed to assemble the feedback content. The output provides comprehensive and specific feedback to the user.

[0518] Step 9:

[0519] The device receives feedback sent from the server and displays it to the user in real time. This allows the user to improve their communication skills based on the feedback. As output, feedback information that allows the user to immediately see suggestions for improvement is displayed on the device.

[0520] (Application Example 1)

[0521] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0522] In traditional customer service, individual staff members' communication skills rely on their personal experience and intuition, making it difficult to provide consistent service quality. New staff members, in particular, face challenges in improving their skills due to a lack of immediate feedback and concrete suggestions for improvement.

[0523] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0524] In this invention, the server includes means for generating coded symbols from speech information, means for analyzing the generated coded symbols and improving linguistic representation, and means for analyzing the speech information and evaluating intonation and speed. This enables staff in customer service to directly and in real time improve their communication skills.

[0525] "Audio information" refers to data containing the content of a communication, obtained by treating the waveform of a sound as a digital signal.

[0526] "Encoded symbols" are data obtained by analyzing audio information and converting it into text or symbolic formats.

[0527] "Analysis" is the process of breaking down data into smaller parts to reveal its content and characteristics.

[0528] "Linguistic expression" refers to sentences or phrases that convey meaning using a specific language.

[0529] "Intonation" refers to the changes in pitch and volume of sounds in speech.

[0530] "Speed" refers to the speed at which information is transmitted, and in the case of audio information, it specifically means the speed of speech.

[0531] "Visual information" refers to image and video data captured by cameras and other devices.

[0532] "Facial expression" refers to changes in emotions and will expressed through a person's face.

[0533] "Image information" refers to data captured through visual media such as photographs and illustrations.

[0534] To evaluate someone's "appearance" means to analyze their impression based on visual elements such as their hair and clothing.

[0535] A "portable display device" is a portable display device that allows users to instantly access and view information.

[0536] "Feedback" is the process of providing responses or reactions to a particular action or behavior.

[0537] "Customer service" refers to the work of providing product descriptions and services to customers in stores and other similar establishments.

[0538] In this application example, a system to support customer service is implemented using a portable display device, voice input device, and camera worn by the store clerk. The terminal collects voice and video information in real time and transmits it to a server. The server converts the voice information into coded characters using a speech recognition API (e.g., Google Cloud Speech API). Next, it analyzes the linguistic representation of the generated coded characters using a natural language processing model (e.g., BERT) and provides appropriate improvement suggestions.

[0539] The intonation and speed of audio information are evaluated using acoustic analysis tools. Video information acquired by the camera mounted on the terminal is analyzed for changes in facial expressions using a face recognition library (e.g., OpenCV, Dlib). Based on this information, the server generates feedback in real time and displays it on a portable display device.

[0540] For example, feedback such as "Smiling more will create an even better impression" might be provided. Users can review this feedback during customer service and use it to optimize their communication.

[0541] By using generative AI models, it is possible to further personalize user feedback and promote effective improvement. An example of a prompt to the generative AI model might be, "What kind of real-time feedback should be given to increase the frequency of smiles during customer service?" Based on this prompt, the generative AI model will perform the necessary processing to provide the optimal feedback.

[0542] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0543] Step 1:

[0544] The device collects the user's voice information through the microphone and converts it into a digital format. The input is the user's raw voice, and the output is a digitized audio file. This digitized audio file is then sent to a server for subsequent speech recognition processing.

[0545] Step 2:

[0546] The server converts the received audio information into coded text using a speech recognition API. The input for this step is a digital audio file, and the output is coded text. The server then further processes this coded text for analysis.

[0547] Step 3:

[0548] The server uses a generative AI model to analyze coded text using natural language processing algorithms. The input for this step is coded text, and the output is an evaluation of the linguistic expression and suggestions for improvement. During this process, the server checks for vocabulary richness and grammatical appropriateness.

[0549] Step 4:

[0550] The server uses an acoustic analysis tool to evaluate the intonation and tempo of separately collected audio information. The input is a digital audio file, and the output is evaluation data of these characteristics. This identifies areas for improvement in tempo and tone.

[0551] Step 5:

[0552] The terminal transmits video information acquired using its camera to the server. The input is the user's real-time video, and the output is video data for analysis. This video data serves as preparation for facial expression analysis.

[0553] Step 6:

[0554] The server analyzes video data using a facial recognition library. The input is real-time video data, and the output is the result of facial expression evaluation. This allows the user's facial expression changes to be measured and evaluated.

[0555] Step 7:

[0556] The server generates specific feedback for customer service based on the analysis results of each of the steps described above. The input is the evaluation data from the past, and the output is the feedback message displayed to the user.

[0557] Step 8:

[0558] The terminal displays the generated feedback in real time on a portable display device. The input is feedback messages from the server, and the output is a screen display of the feedback that the user can see. This allows the user to immediately work on improving their customer service skills.

[0559] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0560] This invention includes a user-operated terminal and a server system that works in conjunction with it, in order to improve the user's face-to-face communication skills. In addition, it incorporates an emotion engine for recognizing and analyzing the user's emotions. The user uses the terminal to collect voice, video, and emotion data and transmits them to the server. Based on the diverse data, the server performs the necessary analysis and evaluation to support the improvement of communication skills.

[0561] The user records audio and captures video using their device. Simultaneously, an emotion engine extracts emotions from the user's audio and video data. The audio data is sent to a server and converted into text using speech recognition technology. The server analyzes the text data using natural language processing algorithms to identify areas for improvement in linguistic expression.

[0562] In the analysis of audio data, intonation and speed are evaluated to determine whether an appropriate tempo and tone are maintained. Furthermore, by analyzing video data, facial expressions and gaze are evaluated to confirm how the user's emotions are expressed. Using an emotion engine, the server captures the user's emotional changes at crucial moments and determines whether a particular statement and facial expression emotionally match.

[0563] The analysis results are sent to the user's device in the form of real-time feedback. Users can review this feedback and understand areas for improvement. For example, specific advice may be provided such as, "You had a sad expression when you said a certain word. It would be more effective if you conveyed it with a more positive expression."

[0564] Thus, the present invention enables the construction of a system that integrates voice analysis, facial recognition, and emotion analysis to provide users with specific and comprehensive feedback in real time. This system allows users to effectively improve their face-to-face communication skills.

[0565] The following describes the processing flow.

[0566] Step 1:

[0567] The user starts the device and enables the camera and microphone functions to begin a communication session. The device then activates its collection function and proceeds to collect audio and video data in real time.

[0568] Step 2:

[0569] The device sends the collected audio data to the server. The server uses speech recognition technology to convert the audio data into text. This converted text is used as basic data for analyzing the user's language expression.

[0570] Step 3:

[0571] The server analyzes the text using natural language processing algorithms to evaluate the appropriateness of honorifics and the accuracy of phrasing. Any deficiencies found will be used to develop further corrections and improvements.

[0572] Step 4:

[0573] The server analyzes the audio data and evaluates the intonation and speed of the user's speech. In particular, it checks whether the speech tempo and tone are appropriate and prepares to provide feedback if there are any inappropriate aspects.

[0574] Step 5:

[0575] The user's video data is sent from the terminal to the server. The server uses video recognition technology to analyze facial expressions and evaluate the user's expressions, including smiles and eye movements.

[0576] Step 6:

[0577] The server uses an emotion engine to extract user emotions from audio and video data. It evaluates how changes in the user's emotions are represented and identifies emotional consistency and inconsistencies.

[0578] Step 7:

[0579] The server generates comprehensive feedback from all analysis results. This feedback includes specific suggestions for improvement regarding language expression, facial expressions, speech speed, and emotional consistency.

[0580] Step 8:

[0581] The device displays feedback received from the server to the user in real time. Based on this feedback, the user can take concrete actions to improve their communication skills.

[0582] (Example 2)

[0583] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0584] Improving face-to-face communication skills is a crucial challenge in modern society. However, many people have limited opportunities to receive feedback on their vocal and nonverbal expressions. Therefore, objectively evaluating and improving one's own communication skills is difficult. Furthermore, recognizing the consistency of expressions with emotions is not easy. An effective system is needed to address this situation.

[0585] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0586] In this invention, the server includes means for generating text information from audio information, means for analyzing the generated text information and improving linguistic expression, means for analyzing audio information and evaluating intonation and speed, means for extracting emotions from audio and video information using an emotion analysis engine, and means for determining whether a particular statement and facial expression are emotionally consistent. This enables users to objectively and in real time evaluate their communication skills and quickly identify specific areas for improvement.

[0587] "Audio information" refers to data expressed through sound, and is information collected through recordings and other means.

[0588] "Textual information" refers to data in text format obtained as a result of converting audio information.

[0589] A "server" is a computing device that receives, processes, and transmits data over a network.

[0590] "Means of generation" refers to methods or techniques for producing specific data or results.

[0591] "Means of improvement" refer to methods and techniques for changing existing things into a better state.

[0592] "Means of evaluation" refers to methods and techniques for determining the state or value of an object.

[0593] An "emotion analysis engine" is a technology or software that recognizes emotions from audio and video information and analyzes their characteristics.

[0594] "Means of judgment" refers to methods and techniques for drawing conclusions based on information.

[0595] A "specific statement" refers to a linguistic expression that is clearly identifiable within a conversation or audio recording.

[0596] "Facial expression" refers to the visual expression of emotion related to the movement of facial muscles.

[0597] This invention is a system for improving a user's face-to-face communication skills. The user records audio and video using a terminal, which acts as an input device. This terminal is equipped with an emotion analysis engine, enabling the extraction of the user's emotion data in real time. The audio and video data recorded by the user are transmitted to a server via the internet.

[0598] The server converts the received audio data into text using speech recognition technology. A specific example would be using a speech recognition tool like the Google Speech-to-Text API. The converted text is then analyzed by a natural language processing algorithm to improve it into the most optimal linguistic representation.

[0599] Meanwhile, the server also analyzes the video data. Specifically, it uses common libraries such as OpenCV and Dlib to evaluate facial expressions and gaze for facial recognition. This process evaluates the user's emotional expression and prepares specific feedback for improvement as needed.

[0600] The emotion analysis engine evaluates the user's emotional state in detail based on audio and video information. This includes determining whether specific statements and facial expressions match.

[0601] Ultimately, the server provides real-time feedback to the user based on the analyzed data. This feedback is sent to the user's device and displayed as specific advice. For example, it might say, "You're speaking too fast; you should speak a little slower."

[0602] An example of a prompt message would be, "Please provide feedback to help the user improve their communication skills when giving an important presentation."

[0603] This system allows users to check and improve their communication style in real time, thereby effectively enhancing their face-to-face communication skills.

[0604] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0605] Step 1:

[0606] The user uses a device to record audio and video simultaneously. This process collects audio and video data using a microphone and camera. The input is the user's audio and video, and the output is the recorded audio and video files.

[0607] Step 2:

[0608] The device inputs recorded audio and video data into an emotion analysis engine in real time. This engine analyzes the tone of voice, the content of speech, and changes in facial expressions in the video to extract the user's emotions. The input consists of audio and video files, and the output is the user's emotion data.

[0609] Step 3:

[0610] The device transmits collected audio, video, and emotion data to the server. This data is protected by a secure protocol. Inputs are audio, video, and emotion data, while output is data transfer to the server.

[0611] Step 4:

[0612] The server inputs the received audio data into a speech recognition system, which then converts it into text. As a specific example, a speech recognition API is used to generate linguistic text from an audio waveform. The input is audio data, and the output is text data.

[0613] Step 5:

[0614] The server analyzes textual information using natural language processing algorithms to evaluate the validity and potential for improvement of the expression. Here, a generative AI model is used to analyze the text syntax and prepare appropriate feedback. The input is text data, and the output is analytical data regarding areas for improvement.

[0615] Step 6:

[0616] The server analyzes video data and evaluates non-verbal elements such as facial expressions and gaze. This process uses a video analysis library to detect facial features and quantify the user's facial expression state. The input is video data, and the output is facial expression analysis data.

[0617] Step 7:

[0618] The server uses emotional data to determine if a specific statement matches a facial expression. This allows it to assess the appropriateness of the communication and formulate necessary feedback. Inputs include emotional data, voice analysis data, and facial expression analysis data, while outputs include evaluation data and feedback information.

[0619] Step 8:

[0620] The server generates real-time feedback based on the analysis results and sends it to the user's terminal. For example, it creates specific advice regarding language and facial expressions based on the analysis data. The input is feedback information, and the output is the feedback displayed on the user's terminal.

[0621] This processing flow allows users to quickly receive comprehensive feedback on their communication skills.

[0622] (Application Example 2)

[0623] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0624] In face-to-face interactions, individual staff members face challenges in using appropriate intonation, speed, and facial expressions when communicating with customers, and in not being able to receive real-time feedback to effectively improve these skills.

[0625] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0626] In this invention, the server includes means for generating text from audio data, means for analyzing the generated text and improving linguistic expression, means for analyzing the audio data and evaluating intonation and speed, means for displaying on a visual device and generating real-time feedback based on the analyzed intonation, speed, and facial expressions, and means for playing back video recordings based on messages presented on the visual device to support the practice of corresponding skills. As a result, staff can receive timely and appropriate feedback on their communication skills, enabling them to improve their skills in face-to-face work.

[0627] "Voice data" refers to information that electronically captures and records a user's voice and can be converted into text information.

[0628] "Means for generating text" refer to functional elements for analyzing audio data and generating corresponding linguistic expressions.

[0629] "Means for improving linguistic expression" refers to functions that analyze linguistic errors and effects contained in generated text and optimize it to enable more effective communication.

[0630] "Means for evaluating intonation and speed" refers to processing functions that analyze the tone and speech speed within audio data and determine their appropriateness.

[0631] "Visual devices" are devices used by users to receive information visually, and include smart glasses and display devices.

[0632] "Means for generating real-time feedback" refers to a function that immediately provides users with feedback based on the results of the analysis of collected data.

[0633] "A means of playing back video recordings to support the practice of corresponding skills" refers to a support function that recreates past communications for practice, allowing users to improve their skills while identifying areas for improvement.

[0634] To implement this invention, a system is constructed in which a server and a terminal work together. The user collects video and audio via the terminal. The terminal is a smart glasses or similar device equipped with a camera and microphone. Data obtained by the user during conversation is transmitted to the server. The server generates text from the audio data and analyzes and improves the linguistic expression using natural language processing. The intonation and speed of the speech are also evaluated, and facial expressions are analyzed from the video data. This data is provided to the user's visual device as real-time feedback.

[0635] The specific devices used include OpenCV for data processing, SoundDevice for speech recognition, and a natural language processing library for text analysis. Feedback generation utilizes analysis results obtained from the data based on a pre-configured algorithm.

[0636] For example, when a user interacts with a customer, the smart glasses display real-time feedback such as, "Your voice tone sounds a little low. Try raising your energy level slightly." This feedback immediately suggests areas for improvement in communication, assisting in the smooth execution of face-to-face interactions. Furthermore, users can later review their performance and improve their skills using the provided recordings and analysis results.

[0637] An example of a prompt for a generating AI model is: "Generate communication improvement tips from the following user voice and facial expression data. Voice data: 'Hello, this is the new products corner.' Facial expression data: Not smiling enough. Please rate it." In this way, users can effectively improve their communication skills.

[0638] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0639] Step 1:

[0640] The user puts on smart glasses and begins a conversation with a customer. The device uses its built-in camera to capture the user's facial expressions as video data and its microphone to record audio data. This data is then prepared for transmission to a server.

[0641] Step 2:

[0642] Audio data transmitted from the terminal is received by the server. The server applies speech recognition technology to convert the audio data into text. The input is audio data, and the output is the converted text. This process involves speech analysis and text conversion.

[0643] Step 3:

[0644] The server analyzes the converted text using a natural language processing engine. It receives text data as input and identifies areas for improvement in linguistic expression as output. At this stage, grammatical errors and more appropriate expressions are identified, and the text is optimized.

[0645] Step 4:

[0646] The server analyzes the intonation and speed of the audio data. The input is the original audio data, and the output is evaluation information of the tone and tempo of the speech. This identifies the characteristics of the user's speaking style and clarifies which elements need improvement.

[0647] Step 5:

[0648] The server analyzes video data to evaluate facial expressions. It receives video data as input and generates a detailed evaluation of facial expressions as output. By applying a facial expression recognition algorithm, the user's emotions and changes in their face are analyzed.

[0649] Step 6:

[0650] Based on the analysis results, the server generates real-time feedback. This feedback is sent to a visual device and displayed. This allows users to immediately identify areas for improvement and obtain information useful for communicating with customers.

[0651] Step 7:

[0652] After the user ends the conversation, the server comprehensively evaluates the overall performance and provides the user with detailed feedback. This includes the consistency between voice and facial expressions at specific moments, areas where language was optimized, and areas for improvement. Based on this information, the user can prepare for their next conversation.

[0653] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0654] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0655] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0656] [Fourth Embodiment]

[0657] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0658] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0659] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0660] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0661] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0662] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0663] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0664] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0665] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0666] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0667] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0668] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0669] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0670] This invention includes a user terminal and a server system operating in the backend. The user uses the terminal to collect their own speech and actions and transmits this data to the server. The server performs analysis and evaluation based on this data to improve communication skills.

[0671] Specifically, audio data recorded on the device is sent to a server, which first uses speech recognition technology to convert the audio into text. The converted text is then analyzed using natural language processing algorithms to check for any omissions of respectful expressions or appropriate phrasing.

[0672] In parallel, the audio data itself is analyzed, and its intonation and speed are evaluated. If there is room for improvement in the tempo or tone of speech, the server marks those points and generates improvement suggestions.

[0673] Furthermore, users use their device's camera to capture their facial expressions. The video data is sent to a server, where facial recognition algorithms analyze things like the frequency of smiles and eye movements. Similarly, the image data is analyzed to evaluate appearances such as hairstyle and clothing, and feedback is generated based on that.

[0674] After all analysis is complete, the server generates comprehensive feedback from this individual data. This feedback is received by the terminal and displayed to the user in real time. For example, it may include specific comments such as, "Your use of polite language is inappropriate; you should rephrase it like this," or "You need to smile more."

[0675] Users can continuously improve their face-to-face communication skills by checking this feedback in real time. This process is designed to help users acquire effective communication methods.

[0676] The following describes the processing flow.

[0677] Step 1:

[0678] The user powers on the device, enables the camera and microphone, and starts a communication session. At this point, the device begins collecting audio and video data.

[0679] Step 2:

[0680] The device sends the collected audio data to the server. This data includes the user's speech. The server uses speech recognition technology to convert the audio data into text data.

[0681] Step 3:

[0682] The server receives text data and uses natural language processing algorithms to analyze the linguistic expressions. This process identifies inappropriate phrasing and errors in honorific language.

[0683] Step 4:

[0684] The server analyzes the intonation and speed of the audio data. It evaluates the tempo and tone, and generates improvement suggestions as needed.

[0685] Step 5:

[0686] The user's video data is sent from the terminal to the server. The server uses video recognition technology to analyze facial expressions and evaluate things like the frequency of smiles and how the user's gaze is directed.

[0687] Step 6:

[0688] The server analyzes the user's image data and evaluates their appearance, including hairstyle and clothing. It then prepares feedback based on this appearance.

[0689] Step 7:

[0690] The server integrates all analysis results and generates comprehensive feedback for the user. This feedback includes specific improvement measures and suggestions.

[0691] Step 8:

[0692] The device receives feedback sent from the server and displays it to the user in real time. The user can improve their communication skills by reviewing this feedback.

[0693] (Example 1)

[0694] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0695] There is a growing need to provide automated, real-time feedback to improve communication skills, comprehensively utilizing acoustic and visual data. However, achieving this requires effectively analyzing multiple elements and generating concrete and practical improvement suggestions for the user.

[0696] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0697] In this invention, the server includes means for generating text information from acoustic data, means for analyzing the generated text information and improving linguistic expression, means for analyzing acoustic data and evaluating intonation and speed, means for analyzing visual data and evaluating facial expressions, means for analyzing image data and evaluating appearance, and means for generating comprehensive feedback based on the analysis results and displaying it in real time on an information terminal. This enables users to quickly and comprehensively understand their weaknesses in communication skills and to make continuous improvements.

[0698] "Audio data" refers to data that represents information about sound, such as speech, in digital format.

[0699] "Textual information" refers to digital information, including text data converted from speech.

[0700] "Visual data" refers to digital data of images and videos acquired by imaging devices such as cameras.

[0701] "Image data" refers to data that represents still images or moving images in digital format.

[0702] "Feedback" refers to suggestions for improvement and points of concern provided to users based on analysis results, and is information that encourages users to improve their behavior and skills.

[0703] "Analysis" is the process of processing data based on calculations and logic to extract or evaluate information.

[0704] An "information terminal" is an electronic device used by a user to input or receive digital information.

[0705] This invention comprises an information terminal used by the user and a server system operating in the backend. Specifically, the user uses the information terminal to collect and input their own acoustic and visual data.

[0706] The user first launches an application on their information terminal to collect acoustic data. To do this, the terminal is equipped with a microphone. The terminal's camera is used to collect visual data, which includes information about the user's speech and facial expressions.

[0707] The terminal sends the collected acoustic data to the server. The server operates on a common cloud infrastructure and utilizes speech recognition technology (e.g., a commercial speech recognition API) to analyze the acoustic data and convert it into text. The converted text is further analyzed by natural language processing algorithms to provide appropriate linguistic representations.

[0708] Furthermore, the server analyzes the acoustic data itself, evaluating the intonation and speed of speech. This is designed to suggest improvements to pronunciation.

[0709] Simultaneously, the server analyzes the visual data sent from the terminal. This analysis is performed using facial recognition algorithms, which analyze changes in facial expressions and gaze to generate feedback regarding the user's emotional expression. In addition, the image data is analyzed to evaluate the user's appearance, specifically the characteristics of their clothing and hairstyle.

[0710] Based on these analysis results, the server generates comprehensive feedback. This feedback is transmitted to the information terminal in real time and presented to the user.

[0711] For example, by using a prompt such as, "I've recorded my presentation. Based on this data, please tell me how I can improve my communication skills. I'd especially like feedback on my speaking speed and facial expressions," the system can provide the user with customized feedback.

[0712] This allows users to quickly understand specific areas for improvement in their communication skills and to continuously work on improving them.

[0713] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0714] Step 1:

[0715] The user activates the information terminal and operates an application for collecting acoustic and visual data. When the user starts the recording / video recording function of their speech and actions, the terminal's microphone records the acoustic data as input, and the camera collects the visual data as input. The output is the acoustic and visual data, which is temporarily stored on the terminal.

[0716] Step 2:

[0717] The terminal sends the collected acoustic data to the server. The input to the acoustic data includes recorded audio files, which are transferred to the server via a data communication protocol. The server receives this acoustic data and provides it as output in an analyzable format.

[0718] Step 3:

[0719] The server processes the received audio data using a speech recognition engine. It uses the audio data as input and performs data processing to convert speech into text. The output is text generated by speech recognition. Specifically, by converting speech to text, the user's utterances are visualized.

[0720] Step 4:

[0721] The server analyzes the generated text information using a natural language processing algorithm. The text information is input, and data calculations are performed to extract appropriateness and areas for improvement in the linguistic expression. The output provides evaluation information of the linguistic expression for which improvements have been suggested. For example, this might include pointing out areas where honorific language is insufficient.

[0722] Step 5:

[0723] The server analyzes the acoustic data itself, evaluating intonation and speed. It uses acoustic data as input, and performs data calculations to analyze the waveform and speed of the speech. This results in suggestions for improving intonation and speed. Specifically, if the speaking tempo is too fast, it generates feedback suggesting "speak a little slower."

[0724] Step 6:

[0725] After the terminal transmits visual data, the server analyzes it. The visual data is input, and an expression recognition algorithm is used to analyze eye movements and changes in facial expressions. The output provides feedback information regarding facial features and emotional expression. For example, it can generate specific suggestions such as, "You should smile more."

[0726] Step 7:

[0727] The server analyzes image data and performs an appearance evaluation. The input includes image data, and the analysis algorithm extracts features such as how the clothing is arranged and the hairstyle. The output provides appearance-based feedback, including suggestions such as "You should dress a little more casually."

[0728] Step 8:

[0729] The server integrates the above analysis results and generates comprehensive feedback. Improvement suggestions and evaluation information obtained at each step are input, and data processing is performed to assemble the feedback content. The output provides comprehensive and specific feedback to the user.

[0730] Step 9:

[0731] The device receives feedback sent from the server and displays it to the user in real time. This allows the user to improve their communication skills based on the feedback. As output, feedback information that allows the user to immediately see suggestions for improvement is displayed on the device.

[0732] (Application Example 1)

[0733] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0734] In traditional customer service, individual staff members' communication skills rely on their personal experience and intuition, making it difficult to provide consistent service quality. New staff members, in particular, face challenges in improving their skills due to a lack of immediate feedback and concrete suggestions for improvement.

[0735] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0736] In this invention, the server includes means for generating coded symbols from speech information, means for analyzing the generated coded symbols and improving linguistic representation, and means for analyzing the speech information and evaluating intonation and speed. This enables staff in customer service to directly and in real time improve their communication skills.

[0737] "Audio information" refers to data containing the content of a communication, obtained by treating the waveform of a sound as a digital signal.

[0738] "Encoded symbols" are data obtained by analyzing audio information and converting it into text or symbolic formats.

[0739] "Analysis" is the process of breaking down data into smaller parts to reveal its content and characteristics.

[0740] "Linguistic expression" refers to sentences or phrases that convey meaning using a specific language.

[0741] "Intonation" refers to the changes in pitch and volume of sounds in speech.

[0742] "Speed" refers to the speed at which information is transmitted, and in the case of audio information, it specifically means the speed of speech.

[0743] "Visual information" refers to image and video data captured by cameras and other devices.

[0744] "Facial expression" refers to changes in emotions and will expressed through a person's face.

[0745] "Image information" refers to data captured through visual media such as photographs and illustrations.

[0746] To evaluate someone's "appearance" means to analyze their impression based on visual elements such as their hair and clothing.

[0747] A "portable display device" is a portable display device that allows users to instantly access and view information.

[0748] "Feedback" is the process of providing responses or reactions to a particular action or behavior.

[0749] "Customer service" refers to the work of providing product descriptions and services to customers in stores and other similar establishments.

[0750] In this application example, a system to support customer service is implemented using a portable display device, voice input device, and camera worn by the store clerk. The terminal collects voice and video information in real time and transmits it to a server. The server converts the voice information into coded characters using a speech recognition API (e.g., Google Cloud Speech API). Next, it analyzes the linguistic representation of the generated coded characters using a natural language processing model (e.g., BERT) and provides appropriate improvement suggestions.

[0751] The intonation and speed of audio information are evaluated using acoustic analysis tools. Video information acquired by the camera mounted on the terminal is analyzed for changes in facial expressions using a face recognition library (e.g., OpenCV, Dlib). Based on this information, the server generates feedback in real time and displays it on a portable display device.

[0752] For example, feedback such as "Smiling more will create an even better impression" might be provided. Users can review this feedback during customer service and use it to optimize their communication.

[0753] By using generative AI models, it is possible to further personalize user feedback and promote effective improvement. An example of a prompt to the generative AI model might be, "What kind of real-time feedback should be given to increase the frequency of smiles during customer service?" Based on this prompt, the generative AI model will perform the necessary processing to provide the optimal feedback.

[0754] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0755] Step 1:

[0756] The device collects the user's voice information through the microphone and converts it into a digital format. The input is the user's raw voice, and the output is a digitized audio file. This digitized audio file is then sent to a server for subsequent speech recognition processing.

[0757] Step 2:

[0758] The server converts the received audio information into coded text using a speech recognition API. The input for this step is a digital audio file, and the output is coded text. The server then further processes this coded text for analysis.

[0759] Step 3:

[0760] The server uses a generative AI model to analyze coded text using natural language processing algorithms. The input for this step is coded text, and the output is an evaluation of the linguistic expression and suggestions for improvement. During this process, the server checks for vocabulary richness and grammatical appropriateness.

[0761] Step 4:

[0762] The server uses an acoustic analysis tool to evaluate the intonation and tempo of separately collected audio information. The input is a digital audio file, and the output is evaluation data of these characteristics. This identifies areas for improvement in tempo and tone.

[0763] Step 5:

[0764] The terminal transmits video information acquired using its camera to the server. The input is the user's real-time video, and the output is video data for analysis. This video data serves as preparation for facial expression analysis.

[0765] Step 6:

[0766] The server analyzes video data using a facial recognition library. The input is real-time video data, and the output is the result of facial expression evaluation. This allows the user's facial expression changes to be measured and evaluated.

[0767] Step 7:

[0768] The server generates specific feedback for customer service based on the analysis results of each of the steps described above. The input is the evaluation data from the past, and the output is the feedback message displayed to the user.

[0769] Step 8:

[0770] The terminal displays the generated feedback in real time on a portable display device. The input is feedback messages from the server, and the output is a screen display of the feedback that the user can see. This allows the user to immediately work on improving their customer service skills.

[0771] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0772] This invention includes a user-operated terminal and a server system that works in conjunction with it, in order to improve the user's face-to-face communication skills. In addition, it incorporates an emotion engine for recognizing and analyzing the user's emotions. The user uses the terminal to collect voice, video, and emotion data and transmits them to the server. Based on the diverse data, the server performs the necessary analysis and evaluation to support the improvement of communication skills.

[0773] The user records audio and captures video using their device. Simultaneously, an emotion engine extracts emotions from the user's audio and video data. The audio data is sent to a server and converted into text using speech recognition technology. The server analyzes the text data using natural language processing algorithms to identify areas for improvement in linguistic expression.

[0774] In the analysis of audio data, intonation and speed are evaluated to determine whether an appropriate tempo and tone are maintained. Furthermore, by analyzing video data, facial expressions and gaze are evaluated to confirm how the user's emotions are expressed. Using an emotion engine, the server captures the user's emotional changes at crucial moments and determines whether a particular statement and facial expression emotionally match.

[0775] The analysis results are sent to the user's device in the form of real-time feedback. Users can review this feedback and understand areas for improvement. For example, specific advice may be provided such as, "You had a sad expression when you said a certain word. It would be more effective if you conveyed it with a more positive expression."

[0776] Thus, the present invention enables the construction of a system that integrates voice analysis, facial recognition, and emotion analysis to provide users with specific and comprehensive feedback in real time. This system allows users to effectively improve their face-to-face communication skills.

[0777] The following describes the processing flow.

[0778] Step 1:

[0779] The user starts the device and enables the camera and microphone functions to begin a communication session. The device then activates its collection function and proceeds to collect audio and video data in real time.

[0780] Step 2:

[0781] The device sends the collected audio data to the server. The server uses speech recognition technology to convert the audio data into text. This converted text is used as basic data for analyzing the user's language expression.

[0782] Step 3:

[0783] The server analyzes the text using natural language processing algorithms to evaluate the appropriateness of honorifics and the accuracy of phrasing. Any deficiencies found will be used to develop further corrections and improvements.

[0784] Step 4:

[0785] The server analyzes the audio data and evaluates the intonation and speed of the user's speech. In particular, it checks whether the speech tempo and tone are appropriate and prepares to provide feedback if there are any inappropriate aspects.

[0786] Step 5:

[0787] The user's video data is sent from the terminal to the server. The server uses video recognition technology to analyze facial expressions and evaluate the user's expressions, including smiles and eye movements.

[0788] Step 6:

[0789] The server uses an emotion engine to extract user emotions from audio and video data. It evaluates how changes in the user's emotions are represented and identifies emotional consistency and inconsistencies.

[0790] Step 7:

[0791] The server generates comprehensive feedback from all analysis results. This feedback includes specific suggestions for improvement regarding language expression, facial expressions, speech speed, and emotional consistency.

[0792] Step 8:

[0793] The device displays feedback received from the server to the user in real time. Based on this feedback, the user can take concrete actions to improve their communication skills.

[0794] (Example 2)

[0795] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0796] Improving face-to-face communication skills is a crucial challenge in modern society. However, many people have limited opportunities to receive feedback on their vocal and nonverbal expressions. Therefore, objectively evaluating and improving one's own communication skills is difficult. Furthermore, recognizing the consistency of expressions with emotions is not easy. An effective system is needed to address this situation.

[0797] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0798] In this invention, the server includes means for generating text information from audio information, means for analyzing the generated text information and improving linguistic expression, means for analyzing audio information and evaluating intonation and speed, means for extracting emotions from audio and video information using an emotion analysis engine, and means for determining whether a particular statement and facial expression are emotionally consistent. This enables users to objectively and in real time evaluate their communication skills and quickly identify specific areas for improvement.

[0799] "Audio information" refers to data expressed through sound, and is information collected through recordings and other means.

[0800] "Textual information" refers to data in text format obtained as a result of converting audio information.

[0801] A "server" is a computing device that receives, processes, and transmits data over a network.

[0802] "Means of generation" refers to methods or techniques for producing specific data or results.

[0803] "Means of improvement" refer to methods and techniques for changing existing things into a better state.

[0804] "Means of evaluation" refers to methods and techniques for determining the state or value of an object.

[0805] An "emotion analysis engine" is a technology or software that recognizes emotions from audio and video information and analyzes their characteristics.

[0806] "Means of judgment" refers to methods and techniques for drawing conclusions based on information.

[0807] A "specific statement" refers to a linguistic expression that is clearly identifiable within a conversation or audio recording.

[0808] "Facial expression" refers to the visual expression of emotion related to the movement of facial muscles.

[0809] This invention is a system for improving a user's face-to-face communication skills. The user records audio and video using a terminal, which acts as an input device. This terminal is equipped with an emotion analysis engine, enabling the extraction of the user's emotion data in real time. The audio and video data recorded by the user are transmitted to a server via the internet.

[0810] The server converts the received audio data into text using speech recognition technology. A specific example would be using a speech recognition tool like the Google Speech-to-Text API. The converted text is then analyzed by a natural language processing algorithm to improve it into the most optimal linguistic representation.

[0811] Meanwhile, the server also analyzes the video data. Specifically, it uses common libraries such as OpenCV and Dlib to evaluate facial expressions and gaze for facial recognition. This process evaluates the user's emotional expression and prepares specific feedback for improvement as needed.

[0812] The emotion analysis engine evaluates the user's emotional state in detail based on audio and video information. This includes determining whether specific statements and facial expressions match.

[0813] Ultimately, the server provides real-time feedback to the user based on the analyzed data. This feedback is sent to the user's device and displayed as specific advice. For example, it might say, "You're speaking too fast; you should speak a little slower."

[0814] An example of a prompt message would be, "Please provide feedback to help the user improve their communication skills when giving an important presentation."

[0815] This system allows users to check and improve their communication style in real time, thereby effectively enhancing their face-to-face communication skills.

[0816] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0817] Step 1:

[0818] The user uses a device to record audio and video simultaneously. This process collects audio and video data using a microphone and camera. The input is the user's audio and video, and the output is the recorded audio and video files.

[0819] Step 2:

[0820] The device inputs recorded audio and video data into an emotion analysis engine in real time. This engine analyzes the tone of voice, the content of speech, and changes in facial expressions in the video to extract the user's emotions. The input consists of audio and video files, and the output is the user's emotion data.

[0821] Step 3:

[0822] The device transmits collected audio, video, and emotion data to the server. This data is protected by a secure protocol. Inputs are audio, video, and emotion data, while output is data transfer to the server.

[0823] Step 4:

[0824] The server inputs the received audio data into a speech recognition system, which then converts it into text. As a specific example, a speech recognition API is used to generate linguistic text from an audio waveform. The input is audio data, and the output is text data.

[0825] Step 5:

[0826] The server analyzes textual information using natural language processing algorithms to evaluate the validity and potential for improvement of the expression. Here, a generative AI model is used to analyze the text syntax and prepare appropriate feedback. The input is text data, and the output is analytical data regarding areas for improvement.

[0827] Step 6:

[0828] The server analyzes video data and evaluates non-verbal elements such as facial expressions and gaze. This process uses a video analysis library to detect facial features and quantify the user's facial expression state. The input is video data, and the output is facial expression analysis data.

[0829] Step 7:

[0830] The server uses emotional data to determine if a specific statement matches a facial expression. This allows it to assess the appropriateness of the communication and formulate necessary feedback. Inputs include emotional data, voice analysis data, and facial expression analysis data, while outputs include evaluation data and feedback information.

[0831] Step 8:

[0832] The server generates real-time feedback based on the analysis results and sends it to the user's terminal. For example, it creates specific advice regarding language and facial expressions based on the analysis data. The input is feedback information, and the output is the feedback displayed on the user's terminal.

[0833] This processing flow allows users to quickly receive comprehensive feedback on their communication skills.

[0834] (Application Example 2)

[0835] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0836] In face-to-face interactions, individual staff members face challenges in using appropriate intonation, speed, and facial expressions when communicating with customers, and in not being able to receive real-time feedback to effectively improve these skills.

[0837] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0838] In this invention, the server includes means for generating text from audio data, means for analyzing the generated text and improving linguistic expression, means for analyzing the audio data and evaluating intonation and speed, means for displaying on a visual device and generating real-time feedback based on the analyzed intonation, speed, and facial expressions, and means for playing back video recordings based on messages presented on the visual device to support the practice of corresponding skills. As a result, staff can receive timely and appropriate feedback on their communication skills, enabling them to improve their skills in face-to-face work.

[0839] "Voice data" refers to information that electronically captures and records a user's voice and can be converted into text information.

[0840] "Means for generating text" refer to functional elements for analyzing audio data and generating corresponding linguistic expressions.

[0841] "Means for improving linguistic expression" refers to functions that analyze linguistic errors and effects contained in generated text and optimize it to enable more effective communication.

[0842] "Means for evaluating intonation and speed" refers to processing functions that analyze the tone and speech speed within audio data and determine their appropriateness.

[0843] "Visual devices" are devices used by users to receive information visually, and include smart glasses and display devices.

[0844] "Means for generating real-time feedback" refers to a function that immediately provides users with feedback based on the results of the analysis of collected data.

[0845] "A means of playing back video recordings to support the practice of corresponding skills" refers to a support function that recreates past communications for practice, allowing users to improve their skills while identifying areas for improvement.

[0846] To implement this invention, a system is constructed in which a server and a terminal work together. The user collects video and audio via the terminal. The terminal is a smart glasses or similar device equipped with a camera and microphone. Data obtained by the user during conversation is transmitted to the server. The server generates text from the audio data and analyzes and improves the linguistic expression using natural language processing. The intonation and speed of the speech are also evaluated, and facial expressions are analyzed from the video data. This data is provided to the user's visual device as real-time feedback.

[0847] The specific devices used include OpenCV for data processing, SoundDevice for speech recognition, and a natural language processing library for text analysis. Feedback generation utilizes analysis results obtained from the data based on a pre-configured algorithm.

[0848] For example, when a user interacts with a customer, the smart glasses display real-time feedback such as, "Your voice tone sounds a little low. Try raising your energy level slightly." This feedback immediately suggests areas for improvement in communication, assisting in the smooth execution of face-to-face interactions. Furthermore, users can later review their performance and improve their skills using the provided recordings and analysis results.

[0849] An example of a prompt for a generating AI model is: "Generate communication improvement tips from the following user voice and facial expression data. Voice data: 'Hello, this is the new products corner.' Facial expression data: Not smiling enough. Please rate it." In this way, users can effectively improve their communication skills.

[0850] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0851] Step 1:

[0852] The user puts on smart glasses and begins a conversation with a customer. The device uses its built-in camera to capture the user's facial expressions as video data and its microphone to record audio data. This data is then prepared for transmission to a server.

[0853] Step 2:

[0854] Audio data transmitted from the terminal is received by the server. The server applies speech recognition technology to convert the audio data into text. The input is audio data, and the output is the converted text. This process involves speech analysis and text conversion.

[0855] Step 3:

[0856] The server analyzes the converted text using a natural language processing engine. It receives text data as input and identifies areas for improvement in linguistic expression as output. At this stage, grammatical errors and more appropriate expressions are identified, and the text is optimized.

[0857] Step 4:

[0858] The server analyzes the intonation and speed of the audio data. The input is the original audio data, and the output is evaluation information of the tone and tempo of the speech. This identifies the characteristics of the user's speaking style and clarifies which elements need improvement.

[0859] Step 5:

[0860] The server analyzes video data to evaluate facial expressions. It receives video data as input and generates a detailed evaluation of facial expressions as output. By applying a facial expression recognition algorithm, the user's emotions and changes in their face are analyzed.

[0861] Step 6:

[0862] Based on the analysis results, the server generates real-time feedback. This feedback is sent to a visual device and displayed. This allows users to immediately identify areas for improvement and obtain information useful for communicating with customers.

[0863] Step 7:

[0864] After the user ends the conversation, the server comprehensively evaluates the overall performance and provides the user with detailed feedback. This includes the consistency between voice and facial expressions at specific moments, areas where language was optimized, and areas for improvement. Based on this information, the user can prepare for their next conversation.

[0865] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0866] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0867] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0868] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0869] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0870] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0871] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0872] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0873] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0874] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0875] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0876] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0877] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0878] 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.

[0879] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0880] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0881] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0882] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0883] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0884] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0885] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0886] The following is further disclosed regarding the embodiments described above.

[0887] (Claim 1)

[0888] A means of generating text from audio data,

[0889] A means of analyzing the generated text and improving the linguistic expression,

[0890] A means for analyzing audio data to evaluate intonation and speed,

[0891] A method for analyzing video data and evaluating facial expressions,

[0892] A method for analyzing image data to evaluate appearance,

[0893] A system that includes means for generating and displaying real-time feedback based on analysis results.

[0894] (Claim 2)

[0895] The system according to claim 1, comprising means for analyzing hairstyle and clothing characteristics in order to evaluate appearance.

[0896] (Claim 3)

[0897] The system according to claim 1, comprising means for detecting changes in the face in order to evaluate facial expressions.

[0898] "Example 1"

[0899] (Claim 1)

[0900] A means of generating textual information from acoustic data,

[0901] A means of analyzing generated textual information and improving linguistic representation,

[0902] A means for analyzing acoustic data to evaluate intonation and speed,

[0903] A method for analyzing visual data and evaluating facial expressions,

[0904] A method for analyzing image data to evaluate appearance,

[0905] A system that generates comprehensive feedback based on analysis results and includes means for displaying it in real time on an information terminal.

[0906] (Claim 2)

[0907] The system according to claim 1, comprising means for analyzing hairstyle and clothing characteristics in order to evaluate appearance.

[0908] (Claim 3)

[0909] The system according to claim 1, comprising means for detecting and evaluating changes in facial emotion.

[0910] "Application Example 1"

[0911] (Claim 1)

[0912] A means for generating coded symbols from audio information,

[0913] A means for analyzing the generated coded symbols and improving the linguistic representation,

[0914] A means for analyzing audio information and evaluating intonation and speed,

[0915] A method for analyzing video information and evaluating facial expressions,

[0916] A means of analyzing image information to evaluate appearance,

[0917] A method using a portable display device that generates and displays feedback in real time based on the analysis results,

[0918] A system that includes means for using feedback to improve customer service interactions.

[0919] (Claim 2)

[0920] The system according to claim 1, comprising means for analyzing hair and clothing characteristics in order to evaluate appearance.

[0921] (Claim 3)

[0922] The system according to claim 1, comprising means for detecting changes in the face in order to evaluate facial expressions.

[0923] "Example 2 of combining an emotion engine"

[0924] (Claim 1)

[0925] A means for generating text information from audio information,

[0926] A means of analyzing generated textual information and improving linguistic representation,

[0927] A means for analyzing audio information to evaluate tone and speed,

[0928] A method for analyzing video information and evaluating facial expressions,

[0929] A means for extracting emotions from audio and video information using an emotion analysis engine,

[0930] A means of determining whether a particular statement and facial expression emotionally match,

[0931] A system that includes means for generating and displaying real-time feedback based on analysis results.

[0932] (Claim 2)

[0933] The system according to claim 1, comprising means for analyzing hair and clothing characteristics in order to evaluate appearance.

[0934] (Claim 3)

[0935] The system according to claim 1, comprising means for detecting changes in the face in order to evaluate facial expressions.

[0936] "Application example 2 when combining with an emotional engine"

[0937] (Claim 1)

[0938] A means of generating text from audio data,

[0939] A means of analyzing generated text and improving linguistic expression,

[0940] A means for analyzing audio data to evaluate intonation and speed,

[0941] A method for analyzing video data and evaluating facial expressions,

[0942] A means for generating real-time feedback and displaying it on a visual device based on analyzed intonation, speed, and facial expressions,

[0943] A means of supporting the practice of corresponding skills by playing back video recordings based on messages presented on a visual device,

[0944] A system that includes this.

[0945] (Claim 2)

[0946] The system according to claim 1, comprising means for analyzing hair and clothing characteristics in order to evaluate appearance.

[0947] (Claim 3)

[0948] The system according to claim 1, comprising means for detecting changes in the face in order to evaluate facial expressions. [Explanation of Symbols]

[0949] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means of generating text from audio data, A means of analyzing the generated text and improving the linguistic expression, A means for analyzing audio data to evaluate intonation and speed, A method for analyzing video data and evaluating facial expressions, A method for analyzing image data to evaluate appearance, A system that includes means for generating and displaying real-time feedback based on analysis results.

2. The system according to claim 1, comprising means for analyzing hairstyle and clothing characteristics in order to evaluate appearance.

3. The system according to claim 1, comprising means for detecting changes in the face in order to evaluate facial expressions.

Images