system

Abstract

Provide a system. The present invention includes: a means for receiving presentation materials and extracting information about text, graphs, illustrations, layout, and color; A means for generating appropriate feedback by referring to attribute information of a listener; A means for submitting and allowing users to review said feedback; A system including:

Description

[Technical Field]

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

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

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

[0004] When creating presentation materials, there can be a gap between what the creator wants to convey and what the reader understands. This gap can lead to misunderstandings and errors in sales activities and internal communications, resulting in increased effort and man-hours. To avoid this, a system is needed to receive appropriate feedback during the material creation stage and improve the materials. [Means for solving the problem]

[0005] The system of the present invention includes the following means: First, it provides means for receiving presentation materials and extracting information about text, graphs, illustrations, layout, and color. Then, it provides means for referencing audience attribute information and generating appropriate feedback. The generated feedback is sent to the user and displayed so that the user can confirm it. It also includes means for analyzing the materials using a multimodal analysis engine and proposing slides tailored to specific audiences. This system customizes the materials based on the audience attributes and comprehension level, and visually displays the feedback content, allowing the user to efficiently improve the materials.

[0006] A "presentation" is a document in the form of slides used to convey information to an audience.

[0007] "Text" refers to information expressed as letters or sentences.

[0008] A "graph" is a diagram used to visually represent data or information, and includes formats such as bar graphs and pie charts.

[0009] An "illustration" is a drawn image or figure that conveys information visually.

[0010] "Layout" refers to the position and layout of text, graphs, and illustrations on a slide.

[0011] "Color" refers to the color combinations and color schemes used on the slides.

[0012] "Audience" refers to the people who view the presentation materials and receive the information.

[0013] "Attribute information" refers to data about the listener, such as their occupation, interests, and level of understanding.

[0014] "Feedback" refers to advice and evaluation, including suggestions and improvements to presentation materials.

[0015] A "multimodal analysis engine" is a system that analyzes a wide variety of information, including text, graphs, illustrations, layout, and color.

[0016] A "server" is a computer that performs processes such as receiving data, analyzing data, generating feedback, and transmitting data.

[0017] A "terminal" is a computer that allows users to upload presentation materials and receive feedback.

[0018] A "user" is a person who creates presentation materials and inputs and operates the system. [Brief explanation of the drawings]

[0019] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0020] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0021] First, the terms used in the following description will be explained.

[0022] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0023] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0024] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0025] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0026] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0027] [First embodiment]

[0028] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0029] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0030] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0031] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0032] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0033] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0034] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0035] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0036] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0037] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0038] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0039] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0040] MODE FOR CARRYING OUT THE INVENTION

[0041] The specific process flow and operation at each step of the embodiment of the present invention will be described below. Note that the roles of the server, terminal, and user as components of the system described below will be clearly defined.

[0042] Server-side processing

[0043] The server first receives the presentation material from the device, which contains information in different formats (text, graphs, illustrations, layout, color, etc.), and then invokes a multimodal analysis engine to extract the information.

[0044] The analysis engine analyzes the content of each slide in the presentation and extracts important text information. It also deciphers the graphical information in graphs and illustrations and understands their meaning. It also evaluates the layout and color usage of the slides and stores the results in a database.

[0045] The server then retrieves the listener's attribute information from a sales history database. This information includes the listener's occupation, interests, and level of understanding. The server generates appropriate feedback based on this data. For example, it might suggest adding detailed technical information to a listener in a technical job, or suggest improving the color layout for a listener who prioritizes visuals.

[0046] The generated feedback is sent to the terminal for the user to review, and the server also logs that the feedback transmission has been completed.

[0047] Terminal side processing

[0048] The terminal provides an interface for the user to upload presentation materials and check the generated feedback. First, the user uploads the presentation materials, and the terminal sends the materials to the server.

[0049] The user is also provided with a form through their device to input listener attributes and comprehension levels, which are then sent to the server and used to customize feedback.

[0050] After receiving the feedback from the server, the terminal visually displays the content, and the user can view the feedback through the terminal interface and specifically understand how to improve the presentation materials.

[0051] User-side processing

[0052] Users use the system to upload presentation materials and enter audience demographic information. They also check feedback sent from the server and make revisions to the materials. As a specific example, for materials created for technical audiences, users can incorporate feedback such as "add detailed technical information" or "simplify the use of colors in graphs" to further improve the materials.

[0053] Specific examples

[0054] Specifically, consider a scenario in which a user uploads a presentation document for "introducing a new product." This document includes the product's features and technical specifications. The user inputs that the audience is "technical," "high level of understanding," and "prefers simple design." The server analyzes this information and generates feedback such as "simplify the use of colors in graphs" or "add detailed technical information," and sends it to the device. The user then checks this feedback and improves the presentation document.

[0055] This system automatically analyzes presentation materials and provides appropriate feedback to the listener, thereby improving the quality of the materials. It also enables efficient communication by providing customized feedback based on the listener's attributes and level of understanding.

[0056] The processing flow will be explained below.

[0057] Step 1:

[0058] Users upload presentation materials to their devices, which then receive the materials and send them to the server.

[0059] Step 2:

[0060] The server receives the presentation materials sent from the terminal and stores them in a specific folder.

[0061] Step 3:

[0062] The server launches a multimodal analysis engine to extract information such as the document's text, graphs, illustrations, layout, and color.

[0063] Step 4:

[0064] The server uses an analytics engine to analyze the text content of each slide to extract key points and summaries, and to interpret information in graphs and illustrations to identify their meaning.

[0065] Step 5:

[0066] The server analyzes the layout and color of each slide and performs a visual evaluation, storing the results in a database.

[0067] Step 6:

[0068] The user inputs the listener's attributes and comprehension level using a form on the device, and the device sends this information to the server.

[0069] Step 7:

[0070] The server acquires attribute information of the corresponding listener from the business history database and combines the input information.

[0071] Step 8:

[0072] The server generates appropriate feedback based on the listener's attribute information, such as suggestions for adding detailed technical information or improving color usage.

[0073] Step 9:

[0074] The server sends the generated feedback to the device, and after the transmission is complete, it records the fact in a log.

[0075] Step 10:

[0076] The terminal visually displays the received feedback to the user, who then checks the feedback and modifies the presentation materials.

[0077] Step 11:

[0078] The user improves the material based on the feedback provided by the server, and if necessary, re-uploads the newly revised material and repeats the process.

[0079] This allows presentation materials to be efficiently improved and information to be conveyed to the audience more effectively.

[0080] Example 1

[0081] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0082] With conventional presentation systems, improving the quality of presentation materials requires a great deal of time and effort, and it is particularly difficult to customize presentation materials to suit the attributes and level of understanding of the audience. This places a heavy burden on the user when generating presentation materials, making it difficult to achieve efficient communication.

[0083] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0084] In this invention, the server includes means for receiving presentation materials and extracting information about text, graphs, illustrations, layout, and color, means for referencing listener attribute information and generating appropriate feedback, means for transmitting the feedback so that the user can confirm it, means for allowing listener attribute information to be input via a terminal, and means for recording the completion of feedback transmission in a log. This enables automatic analysis of presentation materials and provision of appropriate feedback tailored to the listener.

[0085] "Presentation materials" refers to slides and documents used for presentations and explanations, and include elements such as text, graphs, illustrations, layout, and color.

[0086] "Text" is an element that contains character information and is used as a sentence or keyword in presentation materials.

[0087] A "graph" is a diagram intended to visualize data, and is a means of visually representing numerical data.

[0088] "Illustrations" refer to diagrams and pictures in presentation materials, elements that visually explain concepts and processes.

[0089] "Layout" refers to the relative positions of each element in a presentation, and is related to visual balance and layout.

[0090] "Color" refers to the colors used in presentation materials, which are used to visually emphasize and enhance the impression.

[0091] "Listener attribute information" refers to information about the occupation, interests, and level of understanding of the recipient of the presentation materials.

[0092] "Feedback" refers to information including evaluations, improvements, and suggestions for presentation materials, which users use to revise their materials.

[0093] A "multimodal analysis engine" is an engine that has the ability to simultaneously analyze multiple types of data, such as text, images, and audio.

[0094] A "generative AI model" is an algorithm or program that uses artificial intelligence to automatically generate feedback from data.

[0095] The "sales history database" is a database that stores data on past sales activities and transactions, and is used to refer to the attribute information of listeners.

[0096] A "log" is a record of the history of operations and events within a system, and is used to track important actions such as sending feedback.

[0097] MODE FOR CARRYING OUT THE INVENTION

[0098] This invention is a system that automatically analyzes presentation materials and provides appropriate feedback to the audience. The specific process flow and operations at each step are explained below. The roles of the server, terminal, and user as system components are clearly described.

[0099] Server-side processing

[0100] The server first receives the presentation materials from the terminal. These materials include information such as text, graphs, illustrations, layout, and color. The server then launches a multimodal analysis engine to analyze this information. This engine includes image processing software (OpenCV), natural language processing software (NLTK, SpaCy), and data analysis software (NumPy, Pandas). The analysis engine analyzes each slide of the materials and uses Tesseract OCR to extract text information. It also analyzes graphs and illustrations to understand their meaning, and evaluates the layout and color usage of the slides. This data is then stored in a temporary database.

[0101] Next, the server retrieves the listener's attribute information from the sales history database. This information includes the listener's occupation, interests, and level of understanding. Based on this information, the server uses a generative AI model to automatically generate appropriate feedback. The generated feedback includes areas for improvement and specific suggestions.

[0102] Finally, the server sends the generated feedback to the device, and after the transmission is complete, it records the completion of the feedback transmission in a log.

[0103] Terminal side processing

[0104] The terminal provides the user with an interface for uploading presentation materials and checking the generated feedback. The user uploads presentation materials through the terminal, which then sends the materials to the server. The user also uses a form on the terminal to enter the audience's attributes and level of understanding. This information is also sent to the server.

[0105] After receiving the feedback from the server, the terminal will visually display the content, and the user can check the feedback through the terminal interface and specifically understand the improvements needed for the presentation materials.

[0106] User processing

[0107] Users upload presentation materials via their devices and enter audience demographic information. They can also review feedback sent from the server and make revisions to their materials. For example, for materials created for technical audiences, users can add detailed technical information or simply change the color scheme of graphs.

[0108] Specific examples

[0109] For example, consider a scenario in which a user uploads a presentation document for "introducing a new product." This document includes the product's features and technical specifications. The user inputs that the audience is "technical workers," has a "high level of understanding," and "prefers simple designs." The server analyzes this information and generates feedback such as "simplify the use of colors in graphs" or "add detailed technical information," and sends it to the device. The user can review this feedback and improve the presentation document.

[0110] Prompt Sentence Examples

[0111] Here is an example of a prompt that a user might enter into the system:

[0112] "I uploaded a presentation for new product X. The audience is technical and has a high level of understanding. They prefer simple designs."

[0113] This invention makes it possible to automatically analyze presentation materials and provide feedback tailored to the audience, thereby reducing the burden on users and realizing efficient communication.

[0114] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0115] Step 1:

[0116] The server receives presentation materials from the terminal. The input is a presentation material file (e.g., PDF or PPTX) uploaded by the user through the terminal. The server stores the material in data storage and prepares it for the next analysis process.

[0117] Step 2:

[0118] The server launches a multimodal analysis engine. This engine uses software such as OpenCV, NLTK, SpaCy, NumPy, and Pandas to extract each element of the presentation (text, graphs, illustrations, layout, and color). The input is the saved presentation. The output is the data for each extracted element. Specifically, it uses Tesseract OCR for text extraction and OpenCV for image and graph analysis.

[0119] Step 3:

[0120] The server analyzes the content of the presentation materials based on the extracted information. The input is the data extracted in step 2. The server analyzes this data using natural language processing to understand the content of the presentation. Specifically, it performs semantic analysis of the text using NLTK or SpaCy. The output is the analysis results.

[0121] Step 4:

[0122] The server retrieves the listener's attribute information from the sales history database. The input is a database query. The server executes an SQL query to extract information such as the listener's occupation, interests, and level of understanding. The output is the listener's attribute information.

[0123] Step 5:

[0124] The server uses the generative AI model to automatically generate feedback based on the analysis results and listener attribute information. The inputs are the analysis results from step 3 and the listener attribute information from step 4. The server inputs this data into the generative AI model and generates appropriate feedback. Specific operations include inputting data into the generative AI model and running the feedback generation algorithm. The generated feedback is obtained as the output.

[0125] Step 6:

[0126] The server sends the generated feedback to the device and logs the completion of the transmission. As input, there is the feedback generated in step 5. The server sends the feedback to the device via an HTTP request and logs the successful transmission. As output, the feedback transmission to the device is completed and the log is updated.

[0127] Step 7:

[0128] The terminal displays the feedback received from the server to the user. The input is the feedback received from the server. The terminal visually displays the feedback content so that the user can check it. The specific operation is to display the feedback on the interface. The output is a state in which the user can check the feedback.

[0129] Step 8:

[0130] The user modifies the presentation materials based on the feedback displayed on the device. The input is the feedback displayed on the device. The user checks the feedback and modifies the materials. Specifically, the user uses editing software to improve the materials. The output is a revised version of the presentation materials that reflects the feedback.

[0131] (Application example 1)

[0132] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0133] Currently, many autonomous vehicles lack the ability to grasp the driving situation in real time and provide appropriate feedback based on passenger attributes. This makes it difficult to ensure passenger safety and provide a comfortable driving experience in situations such as sudden braking or sudden changes in control. It is also difficult for users to obtain appropriate feedback in real time based on the audience when analyzing and improving presentation materials. A system that can solve these problems is needed.

[0134] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0135] In this invention, the server includes means for receiving presentation materials and extracting information about text, graphs, illustrations, layout, and color; means for referencing listener attribute information and generating appropriate feedback; and means for transmitting the feedback so that the user can confirm it. This improves the quality of the materials and enables efficient communication. The server also includes means for analyzing information about the driving situation and generating feedback in real time; means for providing visual and audio feedback to the driver and passengers; and means for customizing the feedback based on passenger attribute information. This enables appropriate feedback according to the driving situation in an autonomous vehicle and a safe and comfortable driving experience.

[0136] A "presentation material" is a document in slide format that is created by a user to convey specific information to others.

[0137] "Text" refers to character information, such as information expressed in the form of sentences or explanations.

[0138] A "graph" is a diagram such as a bar graph, line graph, or pie chart that visually represents data or information.

[0139] "Illustrations" are visual materials such as diagrams, pictures, and paintings that complement written or linguistic information.

[0140] "Layout" refers to how elements such as text and figures are laid out in presentation materials.

[0141] "Color" refers to color information that evokes visual sensations in presentation materials and display devices.

[0142] "Listener attribute information" refers to the occupation, interests, level of understanding, and other individual characteristic information of the people who will be listening to the presentation.

[0143] "Feedback" is return information that provides information such as analysis results and points for improvement to the user.

[0144] "Driving conditions" refers to information acquired by an autonomous vehicle based on its current driving state and surrounding environmental conditions.

[0145] "Real-time" means that information is processed simultaneously with the actual passage of time and is reflected immediately without delay.

[0146] A "driver" is a person who monitors an autonomous vehicle and takes manual control as needed.

[0147] A "passenger" is a person who is in an autonomous vehicle but who is not driving.

[0148] "Visual feedback" is information that is visually presented using text, diagrams, colors, etc.

[0149] "Audio feedback" refers to conveying information using sound, such as an audio message played over a speaker.

[0150] "Sensor information" refers to data acquired by sensors in autonomous vehicles, such as the vehicle's speed, the presence or absence of obstacles, and road conditions.

[0151] "Customization" means changing settings and content according to individual needs and attributes.

[0152] overview

[0153] This invention is a system that provides real-time feedback based on the driving situation in an autonomous vehicle and generates customized feedback according to passenger attribute information. It also has the function of analyzing the content of presentation materials from multiple angles and providing feedback tailored to the audience in order to improve the quality of presentation materials.

[0154] Hardware and software used

[0155] Hardware

[0156] Smart glasses, head-mounted displays

[0157] software

[0158] MultiModalAnalysisEngine

[0159] FeedbackGenerator (feedback generation engine)

[0160] Vehicle Sensors (Vehicle Sensor Modules)

[0161] FeedbackDisplay (Feedback Display Module)

[0162] System configuration and operation

[0163] 1. Server-side processing

[0164] The server first receives the presentation materials and extracts information about the text, graphs, illustrations, layout, and colors contained in the materials. During this process, the server uses the MultiModalAnalysisEngine to analyze the materials from multiple angles and saves the analysis results in a database. The server then obtains listener attribute information from the sales history database and uses the FeedbackGenerator to generate appropriate feedback based on that information. Information about the driving situation is obtained in real time from the VehicleSensors, and the driving situation is analyzed, providing visual and audio feedback to the driver and passengers.

[0165] 2. Terminal processing

[0166] The terminal provides an interface for the user, allowing them to upload presentation materials and input audience demographic information. Feedback received from the server is visually displayed on the terminal screen, allowing the user to review the feedback and improve the materials. The terminal in the autonomous vehicle also displays real-time feedback based on the driving situation and provides warning messages and other important information to the user and passengers.

[0167] 3. User-side processing

[0168] Users upload presentation materials and input audience demographic information via their devices. The materials are then revised and improved based on feedback received from the server. Additionally, the driver in the autonomous vehicle checks the feedback provided in real time and responds accordingly to the driving situation.

[0169] Specific examples

[0170] As a concrete example, consider a scenario in which a user uploads a presentation document for "introducing a new product for technical professionals." This document includes the product's features and technical specifications. The user inputs that the audience is "technical," "has a high level of understanding," and "prefers simple designs." The server analyzes this information and generates feedback such as "simplify the use of colors in graphs" or "add detailed technical information," and sends it to the device. The user then checks this feedback and improves the presentation document.

[0171] Prompt Sentence Examples

[0172] As a concrete example, the following prompt sentence is input into the generative AI model:

[0173] If the driving situation detects sudden braking, create a program that generates a safety warning message for passengers (especially children) and displays it on the dashboard and head-mounted display.

[0174] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0175] Step 1:

[0176] The server receives presentation materials from the terminal. These materials are slide-format documents created by users to convey specific information to others, and contain information such as text, graphs, illustrations, layout, and color. The input is the presentation materials, and the output is the document data to be analyzed.

[0177] Step 2:

[0178] The server launches the MultiModalAnalysisEngine and performs a multifaceted analysis of the presentation materials. During this analysis process, text information is extracted and graphical information from graphs and illustrations is deciphered. The layout and color usage of the slides are also evaluated. The input is the data of the materials to be analyzed, and the output is the analysis results data.

[0179] Step 3:

[0180] The server obtains the listener's attribute information from the sales history database, which includes the listener's occupation, interests, level of understanding, etc. The input is the listener's identification information, and the output is the listener's attribute information.

[0181] Step 4:

[0182] The server uses a FeedbackGenerator to generate appropriate feedback based on the acquired listener attribute information and analysis result data. For example, it might suggest adding detailed technical information to a listener in a technical job, or suggest improving the color layout for a listener who prioritizes visuals. The input is the listener attribute information and analysis result data, and the generated feedback data is obtained as output.

[0183] Step 5:

[0184] The server receives real-time information about the driving situation from VehicleSensors, including the vehicle speed, distance to obstacles, road conditions, etc. The input is sensor information, and the output is driving situation data.

[0185] Step 6:

[0186] The server analyzes the driving situation data and generates real-time feedback to the driver and passengers. For example, if sudden braking is detected, it generates an appropriate warning message. The input is driving situation data, and the output is real-time feedback.

[0187] Step 7:

[0188] The server customizes the feedback based on the passenger's attributes, for example, by providing a gentler tone of warning if a child is on board. The input is the passenger's attributes and real-time feedback, and the output is the customized feedback.

[0189] Step 8:

[0190] The server sends the generated feedback to the terminal, which displays the feedback visually and audibly for the user and passengers to review. The input is customized feedback data, and the output is feedback provided visually and audibly.

[0191] Step 9:

[0192] Users upload presentation materials via their terminals and input audience attribute information. They also revise and improve the materials based on feedback provided by the server. Feedback data from the server is input, and improved presentation materials are obtained as output.

[0193] This series of steps enables real-time feedback on the presentation materials and the dashboard interface of the autonomous vehicle.

[0194] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0195] MODE FOR CARRYING OUT THE INVENTION

[0196] This invention is a system for efficiently creating and improving presentation materials, and by combining it with an emotion engine that recognizes the user's emotions, it provides more personalized feedback. The specific processing flow and operation at each step are explained below.

[0197] Server-side processing

[0198] The server first receives the presentation materials from the terminal. It then launches a multimodal analysis engine to extract information from the materials, such as text, graphs, illustrations, layout, and color. The analysis engine analyzes the text content of the materials and extracts important points and summaries. At the same time, it interprets the graphs and illustrations and records that information in a database. It also evaluates the slide layout and color usage.

[0199] The server then retrieves the listener's attribute information from the sales history database. This information includes the listener's occupation, interests, and level of understanding. The server generates appropriate feedback based on this data and the analysis results. Furthermore, it uses an emotion engine that recognizes the user's emotions to adjust the feedback content according to the user's emotions. For example, if the user is confused, it adds a more detailed explanation, and if the user is satisfied, it provides feedback that includes words of praise.

[0200] The generated feedback is sent to the device for the user to review, and the server records the completion of the feedback transmission in a log.

[0201] Terminal side processing

[0202] The device provides a user-friendly interface for uploading presentation materials, inputting audience attribute information, and checking the generated feedback. First, the user uploads the presentation materials, and the device sends the materials to the server. Next, the user inputs the audience attributes and comprehension level, and sends the information to the server.

[0203] After receiving the feedback, the device visually displays it to the user. Furthermore, the emotion engine recognizes the user's emotions in real time and adjusts the feedback content as needed. By providing feedback on the presentation materials uploaded by the user, areas for improvement can be clearly identified, allowing for efficient revisions.

[0204] User-side processing

[0205] Users use the system to upload presentation materials and input audience demographic information. Their emotions are then recognized through the emotion engine, and they receive feedback based on this. As a concrete example, consider a user creating highly technical materials who sets a specific audience as "technical professionals" with a "high level of understanding." If the emotion engine recognizes the user as confused, feedback could include adding more detailed technical information or simplifying the use of colors.

[0206] This allows users to receive more personalized advice tailored to their emotional state, rather than simply receiving feedback based on data analysis, allowing them to create and improve presentation materials efficiently and effectively.

[0207] Specific examples

[0208] As a concrete example, consider a scenario in which a user uploads a presentation document for "introducing a new product." This document includes the product's features and technical specifications. The user inputs that the audience is "technical," "high level of understanding," and "prefers simple design." If the emotion engine recognizes that the user is nervous, the feedback will include specific improvements such as "add detailed technical information" or "simplify the coloring of graphs," and an encouraging message such as "please remain calm and proceed."

[0209] This system not only allows for the improvement of presentation materials to suit the audience, but also provides feedback that takes into account the user's emotions, thereby improving the quality of presentation materials and providing more effective support for the user's presentation itself.

[0210] The processing flow will be explained below.

[0211] Step 1:

[0212] Users upload presentation materials to their devices, which then receive the materials and send them to the server.

[0213] Step 2:

[0214] The server receives the presentation materials sent from the terminal and stores them in a specific folder.

[0215] Step 3:

[0216] The server launches a multimodal analysis engine to extract information such as the document's text, graphs, illustrations, layout, and color.

[0217] Step 4:

[0218] The server uses an analytics engine to analyze the text content of each slide to extract key points and summaries, and to interpret information in graphs and illustrations to identify their meaning.

[0219] Step 5:

[0220] The server analyzes the layout and color of each slide and performs a visual evaluation, storing the results in a database.

[0221] Step 6:

[0222] The user inputs the listener's attributes and comprehension level using a form on the device, and the device sends this information to the server.

[0223] Step 7:

[0224] The server acquires attribute information of the corresponding listener from the business history database and combines the input information.

[0225] Step 8:

[0226] The server generates appropriate feedback based on the listener's attribute information, such as suggestions for adding detailed technical information or improving color usage.

[0227] Step 9:

[0228] The user views the presentation materials through the terminal, and the terminal activates an emotion engine to recognize the user's emotions at that time.

[0229] Step 10:

[0230] The emotion engine analyzes the user's facial expressions, tone of voice, input actions, etc. in real time to collect user emotional data.

[0231] Step 11:

[0232] The server receives the emotion data from the emotion engine and reflects it in the feedback content, for example, adding detailed explanations if the user is confused.

[0233] Step 12:

[0234] The generated feedback is sent from the server to the device, which then visually displays it to the user, including emotional encouragement or advice as needed.

[0235] Step 13:

[0236] The user can review the feedback displayed on their device, revise their presentation materials, and, if necessary, re-upload the revised materials and repeat the process.

[0237] This series of steps allows users to individually customize their presentation materials and improve them efficiently while receiving emotional support.

[0238] Example 2

[0239] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0240] Conventional presentation material creation systems provide feedback based on content analysis of materials and audience attribute information, but they lack the ability to provide personalized feedback that takes into account the user's emotions. This makes it difficult to improve materials that are optimized for each user's level of understanding and emotional state.

[0241] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0242] In this invention, the server includes means for receiving presentation materials and extracting information about text, charts, drawings, layout, and color, means for referencing listener attribute information to generate appropriate feedback, and means for recognizing the user's emotions and adjusting the feedback content, thereby making it possible to provide feedback optimized for the user's emotional state and improve the quality of the presentation materials.

[0243] A "presentation" is a document or slides that a user uses to visually present specific information.

[0244] "Text" refers to the written information contained in the presentation materials.

[0245] A "diagram" is a graph or chart that visually displays statistical data or information contained in presentation materials.

[0246] "Drawings" refer to illustrations and pictures included in presentation materials.

[0247] "Layout" refers to the layout and position of each element (text, charts, drawings, etc.) in a presentation document.

[0248] "Color" refers to the color combinations and color schemes used in presentation materials.

[0249] "Audience attribute information" refers to information that indicates the characteristics of the person or group to whom the presentation is directed, and includes occupation, interests, level of understanding, and the like.

[0250] "Feedback" refers to advice and evaluation that provides users with improvements and supplementary information for their presentation materials.

[0251] "Emotion recognition" is a technology that determines a user's emotional state from their facial expressions and voice.

[0252] A "multimodal analysis engine" is an engine for integrated processing and analysis of multiple data formats, such as text analysis, chart analysis, and image analysis.

[0253] The "emotion analysis engine" is an engine for analyzing the user's emotional state, and has the functions of facial expression recognition and voice analysis.

[0254] A "sales history database" is a database that includes attribute information of customers and listeners, history of past sales activities, etc.

[0255] "Real-time" means processing and feedback in real time, synchronized with the current time.

[0256] The present invention is a system that analyzes presentation materials uploaded by a user and generates appropriate feedback based on the listener's attribute information and the user's emotional recognition. Specific embodiments are described below.

[0257] Server-side processing

[0258] The server first receives the presentation materials from the device. It then uses a multimodal analysis engine (such as an NLP engine or CV engine) to extract information from the materials, such as text, charts, drawings, layout, and color. The analysis engine analyzes the text in the materials and extracts important key points and summaries. At the same time, it interprets graphs and illustrations and stores them in a database. It also evaluates the slide layout and color usage.

[0259] Next, the server retrieves the listener's attribute information from the sales history database. This information includes the listener's occupation, interests, and level of understanding. Based on this attribute information and the analysis results, a generative AI model is used to generate appropriate feedback. The content of the feedback is adjusted according to the user's emotional state, so an emotion recognition engine (such as facial expression recognition and voice analysis) is also used.

[0260] The generated feedback is sent to the terminal so that the user can check it, and the server records the feedback sending status in a log.

[0261] Terminal side processing

[0262] The terminal provides a user interface that allows the user to easily upload presentation materials. Once the user uploads the materials, the terminal sends the materials to the server. Next, the user inputs the audience's demographic information (e.g., occupation, level of understanding, interests, etc.), and sends this information to the server as well.

[0263] The device that receives the feedback visually displays the content and uses an emotion recognition engine in real time to analyze the user's emotions and adjust the feedback content as needed. This process allows users to efficiently identify areas for improvement in the material and make optimal adjustments.

[0264] User-side processing

[0265] Users can upload presentation materials and input audience demographic information through the system interface, and receive more personalized feedback by utilizing an emotion engine that recognizes emotions through facial expressions and voice recognition.

[0266] As a concrete example, consider the case where a user uploads a presentation document introducing a new product. This document includes the product's features and technical specifications. The user inputs that the audience is technical, has a high level of understanding, and prefers simple designs. If the emotion engine recognizes that the user is nervous, the feedback will include specific improvements such as "add detailed technical information" or "simplify the coloring of graphs," and an encouraging message such as "please remain calm and proceed."

[0267] Example prompts for generative AI models

[0268] "I uploaded a presentation for a new product. My audience is technical, has a high level of understanding, and prefers simple designs. The current presentation includes product features and technical specifications. The emotion engine recognizes that I'm nervous. Please provide specific improvements and feedback."

[0269] In this way, through specific embodiments of the invention, users can improve the quality of their presentation materials.

[0270] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0271] Step 1: Upload your presentation materials

[0272] Input: The user selects presentation materials through the terminal interface.

[0273] Specific behavior: Displays a file selection dialog in the user interface and sends the file selected by the user to the server via an HTTP request.

[0274] Output: Presentation materials are sent to the server

[0275] Step 2: Conducting data analysis

[0276] Input: The server receives the presentation materials from the terminal.

[0277] Specific operation: Launches the multimodal analysis engine to extract and analyze information such as text, charts, drawings, layout, and color.

[0278] Data processing:

[0279] Text analysis: Extracting important key points from text using an NLP engine

[0280] Chart analysis: Analyzes chart types and data using the CV engine and records them in the database

[0281] Layout and color evaluation: Using specific algorithms to evaluate the layout and color of slides

[0282] Output: The analysis results are stored in a database and serve as input for generating feedback.

[0283] Step 3: Obtaining listener demographic information

[0284] Input: Sales history database

[0285] Specific operation: The server executes an SQL query to obtain attribute information such as the listener's occupation, interests, and level of understanding.

[0286] Output: The acquired attribute information is stored in memory and serves as input for generating feedback.

[0287] Step 4: Perform emotion recognition

[0288] Input: User's facial expression data or voice data

[0289] Specific behavior:

[0290] Facial expression recognition: Captures the user's facial expressions in real time using the device's camera and analyzes them with the emotion engine.

[0291] Voice analysis: Records the user's voice using the device's microphone and recognizes emotions using the voice analysis engine

[0292] Output: Data on the user's emotional state is obtained, which serves as the input for generating feedback.

[0293] Step 5: Generate feedback

[0294] Input: Data analysis results, listener attribute information, user emotional state

[0295] Specific operation: The server uses a generative AI model to generate optimal feedback based on the prompt sentence.

[0296] An example of a specific prompt: "I've uploaded a presentation for a new product. My audience is technical, has a high level of understanding, and prefers simple designs. The current presentation includes product features and technical specifications. The emotion engine recognizes that I'm nervous. Please provide specific improvements and feedback."

[0297] Output: The generated feedback is sent to the terminal.

[0298] Step 6: Submit your feedback

[0299] Input: Feedback generated by a generative AI model

[0300] Specific operation: The server converts the feedback data into a format such as JSON and sends it to the device as an HTTP response.

[0301] Output: Feedback is sent to the device

[0302] Step 7: Review and adjust feedback

[0303] Input: Feedback data sent to the device

[0304] Specific operation: The device visually displays the feedback content in the user interface. It also re-runs the emotion recognition engine in real time and adjusts the feedback content as needed.

[0305] Output: Users can see feedback and identify specific areas for improvement in the material

[0306] (Application example 2)

[0307] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0308] Creating presentation materials and providing feedback on them requires consideration of audience demographic information and real-time sentiment analysis. However, current systems are unable to effectively utilize this information, making it difficult to provide prompt feedback that specifically identifies areas for improvement in the materials. Furthermore, brick-and-mortar stores lack systems that can directly grasp customer reactions in real time and respond immediately. Therefore, an effective method is needed to improve the quality of presentations and increase customer satisfaction.

[0309] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0310] In this invention, the server includes means for receiving presentation materials and extracting information about text, graphs, illustrations, layout, and color, means for referencing listener attribute information and generating appropriate feedback, means for transmitting the feedback so that the user can confirm it, means for analyzing the customer's facial expressions and tone of voice to analyze their reactions in real time, and means for visually displaying appropriate feedback based on the analysis results. This enables efficient creation and improvement of presentation materials and real-time feedback based on customer reactions.

[0311] A "presentation material" is a collection of information that includes elements such as text, slides, graphs, illustrations, layout, and color to convey information in a visual format.

[0312] "Text" refers to information expressed using letters and symbols.

[0313] A "graph" is a graphic that visually represents numerical data, and includes bar graphs, line graphs, pie charts, etc.

[0314] "Illustration" refers to a picture or diagram that visually supplements information.

[0315] "Layout" is a concept that refers to the alignment and relative positions of elements such as text, graphs, illustrations, and colors in presentation materials.

[0316] "Color" refers to the elements that are perceived as visual information by the three primary colors of light and their combinations.

[0317] "Feedback" refers to the response or evaluation provided to a user's actions or input.

[0318] "Listener attribute information" refers to individual data such as the occupation, interests, and level of understanding of the people listening to the presentation.

[0319] A "multimodal analysis engine" refers to technology that simultaneously analyzes multiple data formats, such as text, images, and audio.

[0320] "Real-time" is a time concept that refers to producing immediate responses or results the moment data is collected or processed.

[0321] "Analyzing facial expressions and tone of voice" refers to technology that analyzes an individual's facial expressions, voice pitch, strength, rhythm, etc., to mechanically determine that person's emotions and reactions.

[0322] "Visually displaying" refers to showing information to a user using visual elements such as text and images.

[0323] To realize this invention, a system having the following elements is required.

[0324] 1. Overall structure

[0325] The system is mainly composed of three entities: the server, the terminal, and the user. Each entity has a specific role and function.

[0326] 2. Server-side processing

[0327] The server has the following functions:

[0328] Receiving and analyzing presentation materials

[0329] The server receives presentation materials from the user's device and uses a multimodal analysis engine to extract information about text, graphs, illustrations, layout, and color. Specifically, the server analyzes the materials using Google Cloud Vision API or similar.

[0330] Acquiring customer attribute information and generating feedback

[0331] The server references the listener's attribute information (such as occupation, interests, and level of understanding) and uses a generative AI model to generate appropriate feedback. This information is obtained from a database such as Firebase.

[0332] Analysis of customer facial expressions and tone of voice and real-time feedback

[0333] The server uses Microsoft® Azure® Emotion API and other tools to analyze the customer's facial expressions and tone of voice, and analyzes their reactions in real time. Based on the analysis results, the server sends the data to the device to visually display appropriate feedback.

[0334] 3. Terminal processing

[0335] The device has the following features:

[0336] Upload presentation materials

[0337] The terminal transmits the presentation materials created by the user to the server.

[0338] Entering customer attribute information

[0339] The terminal provides an interface through which the user can input attribute information of listeners and transmit the information to the server.

[0340] Receiving and viewing feedback

[0341] The terminal receives the feedback sent from the server and visually displays it. Using a device such as smart glasses, the feedback is displayed in the user's field of vision in real time.

[0342] 4. User-side processing

[0343] The user performs the following operations:

[0344] Creating and uploading presentation materials

[0345] Users create presentation materials using smart glasses or tablets and upload them to the server via their devices.

[0346] Entering customer attribute information

[0347] Users input listener attribute information through their terminals, which allows the server to generate more personalized feedback.

[0348] Review and improve feedback

[0349] Users can check the feedback displayed on their devices and improve their presentation materials, adjusting the content of their materials and presentations based on real-time customer responses.

[0350] Specific examples

[0351] Example 1

[0352] Consider a scenario where a user is uploading a presentation document introducing a new product for technical professionals. The user enters that the audience is technical, has a high level of understanding, and prefers simple designs. If the system recognizes that the user is nervous, it will provide feedback with specific suggestions for improvement, such as "add more detailed technical information" or "simplify the coloring of graphs," as well as encouraging messages such as "please remain calm and proceed."

[0353] Prompt Sentence Examples

[0354] "Presentation begins. Use material A-123. Please enter customer attributes."

[0355] "Presentation material B-456 uploaded. The client is a technical professional. High level of understanding."

[0356] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0357] Program processing flow and specific explanation

[0358] Step 1: Upload your presentation materials

[0359] Device: The user uploads the presentation materials they created using smart glasses or a tablet to the device. Specifically, the user clicks the "upload" button on the document creation app. The input is the presentation material file, and the output is the document data sent to the server.

[0360] Step 2: Receiving and analyzing data

[0361] Server: The server receives the presentation materials sent from the device. It analyzes the received materials using a multimodal analysis engine such as Google Cloud Vision API to extract text, graphs, illustrations, layout, and color. The input is the received material data, and the output is structured data resulting from the analysis. Specifically, the server inputs the material data into the analysis engine and extracts the data according to a specific format.

[0362] Step 3: Enter customer attribute information

[0363] Terminal: The user uses the terminal interface to input the listener's attribute information (e.g., occupation, level of understanding, interests). The input is the customer attribute information entered by the user, and the output is the attribute information data sent to the server. Specifically, the user enters information into a selection box or text field and clicks the send button.

[0364] Step 4: Obtain customer attribute information

[0365] Server: The server retrieves customer attribute information from a database such as Firebase and integrates it with the attribute information entered by the user. The input is the attribute information data sent from the device and existing customer information retrieved from the database, and the output is the integrated customer attribute information.

[0366] Step 5: Generate feedback

[0367] Server: The server uses a generative AI model to generate appropriate feedback based on the customer attribute information integrated with the analysis results. Specifically, it inputs the generated prompt sentence into the generative AI model and outputs the feedback content. The input is customer attribute information integrated with the analysis results, and the output is the generated feedback data.

[0368] Step 6: Facial Expression and Tone Analysis

[0369] Server: During the presentation, the server receives real-time video and audio data from the smart glasses' camera and microphone, and uses the Microsoft Azure Emotion API to analyze the customer's facial expressions and tone of voice. The input is real-time video and audio data, and the output is the analyzed emotional data. Specifically, the server inputs the acquired video and audio data into the emotion analysis engine, which then analyzes the emotional data.

[0370] Step 7: Generate real-time feedback

[0371] Server: Based on the results of facial expression and tone analysis, the server generates appropriate feedback in real time and sends it to the device. The input is the emotion data from the analysis, and the output is real-time feedback data.

[0372] Step 8: View your feedback

[0373] Terminal: The terminal receives feedback from the server and displays it in real time on the display of smart glasses or a tablet. The input is the feedback data sent from the server, and the output is the visually displayed feedback content. Specifically, the terminal performs a rendering process to display the received feedback data on the display.

[0374] Prompt Sentence Examples

[0375] "Presentation begins. Use material A-123. Please enter customer attributes."

[0376] "Presentation material B-456 uploaded. The client is a technical professional. High level of understanding."

[0377] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0378] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0379] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0380] [Second embodiment]

[0381] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0382] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0383] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0384] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0385] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0386] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0387] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0388] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0389] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0390] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0391] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0392] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0393] MODE FOR CARRYING OUT THE INVENTION

[0394] The specific process flow and operation at each step of the embodiment of the present invention will be described below. Note that the roles of the server, terminal, and user as components of the system described below will be clearly defined.

[0395] Server-side processing

[0396] The server first receives the presentation material from the device, which contains information in different formats (text, graphs, illustrations, layout, color, etc.), and then invokes a multimodal analysis engine to extract the information.

[0397] The analysis engine analyzes the content of each slide in the presentation and extracts important text information. It also deciphers the graphical information in graphs and illustrations and understands their meaning. It also evaluates the layout and color usage of the slides and stores the results in a database.

[0398] The server then retrieves the listener's attribute information from a sales history database. This information includes the listener's occupation, interests, and level of understanding. The server generates appropriate feedback based on this data. For example, it might suggest adding detailed technical information to a listener in a technical job, or suggest improving the color layout for a listener who prioritizes visuals.

[0399] The generated feedback is sent to the terminal for the user to review, and the server also logs that the feedback transmission has been completed.

[0400] Terminal side processing

[0401] The terminal provides an interface for the user to upload presentation materials and check the generated feedback. First, the user uploads the presentation materials, and the terminal sends the materials to the server.

[0402] The user is also provided with a form through their device to input listener attributes and comprehension levels, which are then sent to the server and used to customize feedback.

[0403] After receiving the feedback from the server, the terminal visually displays the content, and the user can view the feedback through the terminal interface and specifically understand how to improve the presentation materials.

[0404] User-side processing

[0405] Users use the system to upload presentation materials and enter audience demographic information. They also check feedback sent from the server and make revisions to the materials. As a specific example, for materials created for technical audiences, users can incorporate feedback such as "add detailed technical information" or "simplify the use of colors in graphs" to further improve the materials.

[0406] Specific examples

[0407] Specifically, consider a scenario in which a user uploads a presentation document for "introducing a new product." This document includes the product's features and technical specifications. The user inputs that the audience is "technical," "high level of understanding," and "prefers simple design." The server analyzes this information and generates feedback such as "simplify the use of colors in graphs" or "add detailed technical information," and sends it to the device. The user then checks this feedback and improves the presentation document.

[0408] This system automatically analyzes presentation materials and provides appropriate feedback to the listener, thereby improving the quality of the materials. It also enables efficient communication by providing customized feedback based on the listener's attributes and level of understanding.

[0409] The processing flow will be explained below.

[0410] Step 1:

[0411] Users upload presentation materials to their devices, which then receive the materials and send them to the server.

[0412] Step 2:

[0413] The server receives the presentation materials sent from the terminal and stores them in a specific folder.

[0414] Step 3:

[0415] The server launches a multimodal analysis engine to extract information such as the document's text, graphs, illustrations, layout, and color.

[0416] Step 4:

[0417] The server uses an analytics engine to analyze the text content of each slide to extract key points and summaries, and to interpret information in graphs and illustrations to identify their meaning.

[0418] Step 5:

[0419] The server analyzes the layout and color of each slide and performs a visual evaluation, storing the results in a database.

[0420] Step 6:

[0421] The user inputs the listener's attributes and comprehension level using a form on the device, and the device sends this information to the server.

[0422] Step 7:

[0423] The server acquires attribute information of the corresponding listener from the business history database and combines the input information.

[0424] Step 8:

[0425] The server generates appropriate feedback based on the listener's attribute information, such as suggestions for adding detailed technical information or improving color usage.

[0426] Step 9:

[0427] The server sends the generated feedback to the device, and after the transmission is complete, it records the fact in a log.

[0428] Step 10:

[0429] The terminal visually displays the received feedback to the user, who then checks the feedback and modifies the presentation materials.

[0430] Step 11:

[0431] The user improves the material based on the feedback provided by the server, and if necessary, re-uploads the newly revised material and repeats the process.

[0432] This allows presentation materials to be efficiently improved and information to be conveyed to the audience more effectively.

[0433] Example 1

[0434] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0435] With conventional presentation systems, improving the quality of presentation materials requires a great deal of time and effort, and it is particularly difficult to customize presentation materials to suit the attributes and level of understanding of the audience. This places a heavy burden on the user when generating presentation materials, making it difficult to achieve efficient communication.

[0436] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0437] In this invention, the server includes means for receiving presentation materials and extracting information about text, graphs, illustrations, layout, and color, means for referencing listener attribute information and generating appropriate feedback, means for transmitting the feedback so that the user can confirm it, means for allowing listener attribute information to be input via a terminal, and means for recording the completion of feedback transmission in a log. This enables automatic analysis of presentation materials and provision of appropriate feedback tailored to the listener.

[0438] "Presentation materials" refers to slides and documents used for presentations and explanations, and include elements such as text, graphs, illustrations, layout, and color.

[0439] "Text" is an element that contains character information and is used as a sentence or keyword in presentation materials.

[0440] A "graph" is a diagram intended to visualize data, and is a means of visually representing numerical data.

[0441] "Illustrations" refer to diagrams and pictures in presentation materials, elements that visually explain concepts and processes.

[0442] "Layout" refers to the relative positions of each element in a presentation, and is related to visual balance and layout.

[0443] "Color" refers to the colors used in presentation materials, which are used to visually emphasize and enhance the impression.

[0444] "Listener attribute information" refers to information about the occupation, interests, and level of understanding of the recipient of the presentation materials.

[0445] "Feedback" refers to information including evaluations, improvements, and suggestions for presentation materials, which users use to revise their materials.

[0446] A "multimodal analysis engine" is an engine that has the ability to simultaneously analyze multiple types of data, such as text, images, and audio.

[0447] A "generative AI model" is an algorithm or program that uses artificial intelligence to automatically generate feedback from data.

[0448] The "sales history database" is a database that stores data on past sales activities and transactions, and is used to refer to the attribute information of listeners.

[0449] A "log" is a record of the history of operations and events within a system, and is used to track important actions such as sending feedback.

[0450] MODE FOR CARRYING OUT THE INVENTION

[0451] This invention is a system that automatically analyzes presentation materials and provides appropriate feedback to the audience. The specific process flow and operations at each step are explained below. The roles of the server, terminal, and user as system components are clearly described.

[0452] Server-side processing

[0453] The server first receives the presentation materials from the terminal. These materials include information such as text, graphs, illustrations, layout, and color. The server then launches a multimodal analysis engine to analyze this information. This engine includes image processing software (OpenCV), natural language processing software (NLTK, SpaCy), and data analysis software (NumPy, Pandas). The analysis engine analyzes each slide of the materials and uses Tesseract OCR to extract text information. It also analyzes graphs and illustrations to understand their meaning, and evaluates the layout and color usage of the slides. This data is then stored in a temporary database.

[0454] Next, the server retrieves the listener's attribute information from the sales history database. This information includes the listener's occupation, interests, and level of understanding. Based on this information, the server uses a generative AI model to automatically generate appropriate feedback. The generated feedback includes areas for improvement and specific suggestions.

[0455] Finally, the server sends the generated feedback to the device, and after the transmission is complete, it records the completion of the feedback transmission in a log.

[0456] Terminal side processing

[0457] The terminal provides the user with an interface for uploading presentation materials and checking the generated feedback. The user uploads presentation materials through the terminal, which then sends the materials to the server. The user also uses a form on the terminal to enter the audience's attributes and level of understanding. This information is also sent to the server.

[0458] After receiving the feedback from the server, the terminal will visually display the content, and the user can check the feedback through the terminal interface and specifically understand the improvements needed for the presentation materials.

[0459] User processing

[0460] Users upload presentation materials via their devices and enter audience demographic information. They can also review feedback sent from the server and make revisions to their materials. For example, for materials created for technical audiences, users can add detailed technical information or simply change the color scheme of graphs.

[0461] Specific examples

[0462] For example, consider a scenario in which a user uploads a presentation document for "introducing a new product." This document includes the product's features and technical specifications. The user inputs that the audience is "technical workers," has a "high level of understanding," and "prefers simple designs." The server analyzes this information and generates feedback such as "simplify the use of colors in graphs" or "add detailed technical information," and sends it to the device. The user can review this feedback and improve the presentation document.

[0463] Prompt Sentence Examples

[0464] Here is an example of a prompt that a user might enter into the system:

[0465] "I uploaded a presentation for new product X. The audience is technical and has a high level of understanding. They prefer simple designs."

[0466] This invention makes it possible to automatically analyze presentation materials and provide feedback tailored to the audience, thereby reducing the burden on users and realizing efficient communication.

[0467] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0468] Step 1:

[0469] The server receives presentation materials from the terminal. The input is a presentation material file (e.g., PDF or PPTX) uploaded by the user through the terminal. The server stores the material in data storage and prepares it for the next analysis process.

[0470] Step 2:

[0471] The server launches a multimodal analysis engine. This engine uses software such as OpenCV, NLTK, SpaCy, NumPy, and Pandas to extract each element of the presentation (text, graphs, illustrations, layout, and color). The input is the saved presentation. The output is the data for each extracted element. Specifically, it uses Tesseract OCR for text extraction and OpenCV for image and graph analysis.

[0472] Step 3:

[0473] The server analyzes the content of the presentation materials based on the extracted information. The input is the data extracted in step 2. The server analyzes this data using natural language processing to understand the content of the presentation. Specifically, it performs semantic analysis of the text using NLTK or SpaCy. The output is the analysis results.

[0474] Step 4:

[0475] The server retrieves the listener's attribute information from the sales history database. The input is a database query. The server executes an SQL query to extract information such as the listener's occupation, interests, and level of understanding. The output is the listener's attribute information.

[0476] Step 5:

[0477] The server uses the generative AI model to automatically generate feedback based on the analysis results and listener attribute information. The inputs are the analysis results from step 3 and the listener attribute information from step 4. The server inputs this data into the generative AI model and generates appropriate feedback. Specific operations include inputting data into the generative AI model and running the feedback generation algorithm. The generated feedback is obtained as the output.

[0478] Step 6:

[0479] The server sends the generated feedback to the device and logs the completion of the transmission. As input, there is the feedback generated in step 5. The server sends the feedback to the device via an HTTP request and logs the successful transmission. As output, the feedback transmission to the device is completed and the log is updated.

[0480] Step 7:

[0481] The terminal displays the feedback received from the server to the user. The input is the feedback received from the server. The terminal visually displays the feedback content so that the user can check it. The specific operation is to display the feedback on the interface. The output is a state in which the user can check the feedback.

[0482] Step 8:

[0483] The user modifies the presentation materials based on the feedback displayed on the device. The input is the feedback displayed on the device. The user checks the feedback and modifies the materials. Specifically, the user uses editing software to improve the materials. The output is a revised version of the presentation materials that reflects the feedback.

[0484] (Application example 1)

[0485] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0486] Currently, many autonomous vehicles lack the ability to grasp the driving situation in real time and provide appropriate feedback based on passenger attributes. This makes it difficult to ensure passenger safety and provide a comfortable driving experience in situations such as sudden braking or sudden changes in control. It is also difficult for users to obtain appropriate feedback in real time based on the audience when analyzing and improving presentation materials. A system that can solve these problems is needed.

[0487] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0488] In this invention, the server includes means for receiving presentation materials and extracting information about text, graphs, illustrations, layout, and color; means for referencing listener attribute information and generating appropriate feedback; and means for transmitting the feedback so that the user can confirm it. This improves the quality of the materials and enables efficient communication. The server also includes means for analyzing information about the driving situation and generating feedback in real time; means for providing visual and audio feedback to the driver and passengers; and means for customizing the feedback based on passenger attribute information. This enables appropriate feedback according to the driving situation in an autonomous vehicle and a safe and comfortable driving experience.

[0489] A "presentation material" is a document in slide format that is created by a user to convey specific information to others.

[0490] "Text" refers to character information, such as information expressed in the form of sentences or explanations.

[0491] A "graph" is a diagram such as a bar graph, line graph, or pie chart that visually represents data or information.

[0492] "Illustrations" are visual materials such as diagrams, pictures, and paintings that complement written or linguistic information.

[0493] "Layout" refers to how elements such as text and figures are laid out in presentation materials.

[0494] "Color" refers to color information that evokes visual sensations in presentation materials and display devices.

[0495] "Listener attribute information" refers to the occupation, interests, level of understanding, and other individual characteristic information of the people who will be listening to the presentation.

[0496] "Feedback" is return information that provides information such as analysis results and points for improvement to the user.

[0497] "Driving conditions" refers to information acquired by an autonomous vehicle based on its current driving state and surrounding environmental conditions.

[0498] "Real-time" means that information is processed simultaneously with the actual passage of time and is reflected immediately without delay.

[0499] A "driver" is a person who monitors an autonomous vehicle and takes manual control as needed.

[0500] A "passenger" is a person who is in an autonomous vehicle but who is not driving.

[0501] "Visual feedback" is information that is visually presented using text, diagrams, colors, etc.

[0502] "Audio feedback" refers to conveying information using sound, such as an audio message played over a speaker.

[0503] "Sensor information" refers to data acquired by sensors in autonomous vehicles, such as the vehicle's speed, the presence or absence of obstacles, and road conditions.

[0504] "Customization" means changing settings and content according to individual needs and attributes.

[0505] overview

[0506] This invention is a system that provides real-time feedback based on the driving situation in an autonomous vehicle and generates customized feedback according to passenger attribute information. It also has the function of analyzing the content of presentation materials from multiple angles and providing feedback tailored to the audience in order to improve the quality of presentation materials.

[0507] Hardware and software used

[0508] Hardware

[0509] Smart glasses, head-mounted displays

[0510] software

[0511] MultiModalAnalysisEngine

[0512] FeedbackGenerator (feedback generation engine)

[0513] Vehicle Sensors (Vehicle Sensor Modules)

[0514] FeedbackDisplay (Feedback Display Module)

[0515] System configuration and operation

[0516] 1. Server-side processing

[0517] The server first receives the presentation materials and extracts information about the text, graphs, illustrations, layout, and colors contained in the materials. During this process, the server uses the MultiModalAnalysisEngine to analyze the materials from multiple angles and saves the analysis results in a database. The server then obtains listener attribute information from the sales history database and uses the FeedbackGenerator to generate appropriate feedback based on that information. Information about the driving situation is obtained in real time from the VehicleSensors, and the driving situation is analyzed, providing visual and audio feedback to the driver and passengers.

[0518] 2. Terminal processing

[0519] The terminal provides an interface for the user, allowing them to upload presentation materials and input audience demographic information. Feedback received from the server is visually displayed on the terminal screen, allowing the user to review the feedback and improve the materials. The terminal in the autonomous vehicle also displays real-time feedback based on the driving situation and provides warning messages and other important information to the user and passengers.

[0520] 3. User-side processing

[0521] Users upload presentation materials and input audience demographic information via their devices. The materials are then revised and improved based on feedback received from the server. Additionally, the driver in the autonomous vehicle checks the feedback provided in real time and responds accordingly to the driving situation.

[0522] Specific examples

[0523] As a concrete example, consider a scenario in which a user uploads a presentation document for "introducing a new product for technical professionals." This document includes the product's features and technical specifications. The user inputs that the audience is "technical," "has a high level of understanding," and "prefers simple designs." The server analyzes this information and generates feedback such as "simplify the use of colors in graphs" or "add detailed technical information," and sends it to the device. The user then checks this feedback and improves the presentation document.

[0524] Prompt Sentence Examples

[0525] As a concrete example, the following prompt sentence is input into the generative AI model:

[0526] If the driving situation detects sudden braking, create a program that generates a safety warning message for passengers (especially children) and displays it on the dashboard and head-mounted display.

[0527] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0528] Step 1:

[0529] The server receives presentation materials from the terminal. These materials are slide-format documents created by users to convey specific information to others, and contain information such as text, graphs, illustrations, layout, and color. The input is the presentation materials, and the output is the document data to be analyzed.

[0530] Step 2:

[0531] The server launches the MultiModalAnalysisEngine and performs a multifaceted analysis of the presentation materials. During this analysis process, text information is extracted and graphical information from graphs and illustrations is deciphered. The layout and color usage of the slides are also evaluated. The input is the data of the materials to be analyzed, and the output is the analysis results data.

[0532] Step 3:

[0533] The server obtains the listener's attribute information from the sales history database, which includes the listener's occupation, interests, level of understanding, etc. The input is the listener's identification information, and the output is the listener's attribute information.

[0534] Step 4:

[0535] The server uses a FeedbackGenerator to generate appropriate feedback based on the acquired listener attribute information and analysis result data. For example, it might suggest adding detailed technical information to a listener in a technical job, or suggest improving the color layout for a listener who prioritizes visuals. The input is the listener attribute information and analysis result data, and the generated feedback data is obtained as output.

[0536] Step 5:

[0537] The server receives real-time information about the driving situation from VehicleSensors, including the vehicle speed, distance to obstacles, road conditions, etc. The input is sensor information, and the output is driving situation data.

[0538] Step 6:

[0539] The server analyzes the driving situation data and generates real-time feedback to the driver and passengers. For example, if sudden braking is detected, it generates an appropriate warning message. The input is driving situation data, and the output is real-time feedback.

[0540] Step 7:

[0541] The server customizes the feedback based on the passenger's attributes, for example, by providing a gentler tone of warning if a child is on board. The input is the passenger's attributes and real-time feedback, and the output is the customized feedback.

[0542] Step 8:

[0543] The server sends the generated feedback to the terminal, which displays the feedback visually and audibly for the user and passengers to review. The input is customized feedback data, and the output is feedback provided visually and audibly.

[0544] Step 9:

[0545] Users upload presentation materials via their terminals and input audience attribute information. They also revise and improve the materials based on feedback provided by the server. Feedback data from the server is input, and improved presentation materials are obtained as output.

[0546] This series of steps enables real-time feedback on the presentation materials and the dashboard interface of the autonomous vehicle.

[0547] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0548] MODE FOR CARRYING OUT THE INVENTION

[0549] This invention is a system for efficiently creating and improving presentation materials, and by combining it with an emotion engine that recognizes the user's emotions, it provides more personalized feedback. The specific processing flow and operation at each step are explained below.

[0550] Server-side processing

[0551] The server first receives the presentation materials from the terminal. It then launches a multimodal analysis engine to extract information from the materials, such as text, graphs, illustrations, layout, and color. The analysis engine analyzes the text content of the materials and extracts important points and summaries. At the same time, it interprets the graphs and illustrations and records that information in a database. It also evaluates the slide layout and color usage.

[0552] The server then retrieves the listener's attribute information from the sales history database. This information includes the listener's occupation, interests, and level of understanding. The server generates appropriate feedback based on this data and the analysis results. Furthermore, it uses an emotion engine that recognizes the user's emotions to adjust the feedback content according to the user's emotions. For example, if the user is confused, it adds a more detailed explanation, and if the user is satisfied, it provides feedback that includes words of praise.

[0553] The generated feedback is sent to the device for the user to review, and the server records the completion of the feedback transmission in a log.

[0554] Terminal side processing

[0555] The device provides a user-friendly interface for uploading presentation materials, inputting audience attribute information, and checking the generated feedback. First, the user uploads the presentation materials, and the device sends the materials to the server. Next, the user inputs the audience attributes and comprehension level, and sends the information to the server.

[0556] After receiving the feedback, the device visually displays it to the user. Furthermore, the emotion engine recognizes the user's emotions in real time and adjusts the feedback content as needed. By providing feedback on the presentation materials uploaded by the user, areas for improvement can be clearly identified, allowing for efficient revisions.

[0557] User-side processing

[0558] Users use the system to upload presentation materials and input audience demographic information. Their emotions are then recognized through the emotion engine, and they receive feedback based on this. As a concrete example, consider a user creating highly technical materials who sets a specific audience as "technical professionals" with a "high level of understanding." If the emotion engine recognizes the user as confused, feedback could include adding more detailed technical information or simplifying the use of colors.

[0559] This allows users to receive more personalized advice tailored to their emotional state, rather than simply receiving feedback based on data analysis, allowing them to create and improve presentation materials efficiently and effectively.

[0560] Specific examples

[0561] As a concrete example, consider a scenario in which a user uploads a presentation document for "introducing a new product." This document includes the product's features and technical specifications. The user inputs that the audience is "technical," "high level of understanding," and "prefers simple design." If the emotion engine recognizes that the user is nervous, the feedback will include specific improvements such as "add detailed technical information" or "simplify the coloring of graphs," and an encouraging message such as "please remain calm and proceed."

[0562] This system not only allows for the improvement of presentation materials to suit the audience, but also provides feedback that takes into account the user's emotions, thereby improving the quality of presentation materials and providing more effective support for the user's presentation itself.

[0563] The processing flow will be explained below.

[0564] Step 1:

[0565] Users upload presentation materials to their devices, which then receive the materials and send them to the server.

[0566] Step 2:

[0567] The server receives the presentation materials sent from the terminal and stores them in a specific folder.

[0568] Step 3:

[0569] The server launches a multimodal analysis engine to extract information such as the document's text, graphs, illustrations, layout, and color.

[0570] Step 4:

[0571] The server uses an analytics engine to analyze the text content of each slide to extract key points and summaries, and to interpret information in graphs and illustrations to identify their meaning.

[0572] Step 5:

[0573] The server analyzes the layout and color of each slide and performs a visual evaluation, storing the results in a database.

[0574] Step 6:

[0575] The user inputs the listener's attributes and comprehension level using a form on the device, and the device sends this information to the server.

[0576] Step 7:

[0577] The server acquires attribute information of the corresponding listener from the business history database and combines the input information.

[0578] Step 8:

[0579] The server generates appropriate feedback based on the listener's attribute information, such as suggestions for adding detailed technical information or improving color usage.

[0580] Step 9:

[0581] The user views the presentation materials through the terminal, and the terminal activates an emotion engine to recognize the user's emotions at that time.

[0582] Step 10:

[0583] The emotion engine analyzes the user's facial expressions, tone of voice, input actions, etc. in real time to collect user emotional data.

[0584] Step 11:

[0585] The server receives the emotion data from the emotion engine and reflects it in the feedback content, for example, adding detailed explanations if the user is confused.

[0586] Step 12:

[0587] The generated feedback is sent from the server to the device, which then visually displays it to the user, including emotional encouragement or advice as needed.

[0588] Step 13:

[0589] The user can review the feedback displayed on their device, revise their presentation materials, and, if necessary, re-upload the revised materials and repeat the process.

[0590] This series of steps allows users to individually customize their presentation materials and improve them efficiently while receiving emotional support.

[0591] Example 2

[0592] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0593] Conventional presentation material creation systems provide feedback based on content analysis of materials and audience attribute information, but they lack the ability to provide personalized feedback that takes into account the user's emotions. This makes it difficult to improve materials that are optimized for each user's level of understanding and emotional state.

[0594] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0595] In this invention, the server includes means for receiving presentation materials and extracting information about text, charts, drawings, layout, and color, means for referencing listener attribute information to generate appropriate feedback, and means for recognizing the user's emotions and adjusting the feedback content, thereby making it possible to provide feedback optimized for the user's emotional state and improve the quality of the presentation materials.

[0596] A "presentation" is a document or slides that a user uses to visually present specific information.

[0597] "Text" refers to the written information contained in the presentation materials.

[0598] A "diagram" is a graph or chart that visually displays statistical data or information contained in presentation materials.

[0599] "Drawings" refer to illustrations and pictures included in presentation materials.

[0600] "Layout" refers to the layout and position of each element (text, charts, drawings, etc.) in a presentation document.

[0601] "Color" refers to the color combinations and color schemes used in presentation materials.

[0602] "Audience attribute information" refers to information that indicates the characteristics of the person or group to whom the presentation is directed, and includes occupation, interests, level of understanding, and the like.

[0603] "Feedback" refers to advice and evaluation that provides users with improvements and supplementary information for their presentation materials.

[0604] "Emotion recognition" is a technology that determines a user's emotional state from their facial expressions and voice.

[0605] A "multimodal analysis engine" is an engine for integrated processing and analysis of multiple data formats, such as text analysis, chart analysis, and image analysis.

[0606] The "emotion analysis engine" is an engine for analyzing the user's emotional state, and has the functions of facial expression recognition and voice analysis.

[0607] A "sales history database" is a database that includes attribute information of customers and listeners, history of past sales activities, etc.

[0608] "Real-time" means processing and feedback in real time, synchronized with the current time.

[0609] The present invention is a system that analyzes presentation materials uploaded by a user and generates appropriate feedback based on the listener's attribute information and the user's emotional recognition. Specific embodiments are described below.

[0610] Server-side processing

[0611] The server first receives the presentation materials from the device. It then uses a multimodal analysis engine (such as an NLP engine or CV engine) to extract information from the materials, such as text, charts, drawings, layout, and color. The analysis engine analyzes the text in the materials and extracts important key points and summaries. At the same time, it interprets graphs and illustrations and stores them in a database. It also evaluates the slide layout and color usage.

[0612] Next, the server retrieves the listener's attribute information from the sales history database. This information includes the listener's occupation, interests, and level of understanding. Based on this attribute information and the analysis results, a generative AI model is used to generate appropriate feedback. The content of the feedback is adjusted according to the user's emotional state, so an emotion recognition engine (such as facial expression recognition and voice analysis) is also used.

[0613] The generated feedback is sent to the terminal so that the user can check it, and the server records the feedback sending status in a log.

[0614] Terminal side processing

[0615] The terminal provides a user interface that allows the user to easily upload presentation materials. Once the user uploads the materials, the terminal sends the materials to the server. Next, the user inputs the audience's demographic information (e.g., occupation, level of understanding, interests, etc.), and sends this information to the server as well.

[0616] The device that receives the feedback visually displays the content and uses an emotion recognition engine in real time to analyze the user's emotions and adjust the feedback content as needed. This process allows users to efficiently identify areas for improvement in the material and make optimal adjustments.

[0617] User-side processing

[0618] Users can upload presentation materials and input audience demographic information through the system interface, and receive more personalized feedback by utilizing an emotion engine that recognizes emotions through facial expressions and voice recognition.

[0619] As a concrete example, consider the case where a user uploads a presentation document introducing a new product. This document includes the product's features and technical specifications. The user inputs that the audience is technical, has a high level of understanding, and prefers simple designs. If the emotion engine recognizes that the user is nervous, the feedback will include specific improvements such as "add detailed technical information" or "simplify the coloring of graphs," and an encouraging message such as "please remain calm and proceed."

[0620] Example prompts for generative AI models

[0621] "I uploaded a presentation for a new product. My audience is technical, has a high level of understanding, and prefers simple designs. The current presentation includes product features and technical specifications. The emotion engine recognizes that I'm nervous. Please provide specific improvements and feedback."

[0622] In this way, through specific embodiments of the invention, users can improve the quality of their presentation materials.

[0623] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0624] Step 1: Upload your presentation materials

[0625] Input: The user selects presentation materials through the terminal interface.

[0626] Specific behavior: Displays a file selection dialog in the user interface and sends the file selected by the user to the server via an HTTP request.

[0627] Output: Presentation materials are sent to the server

[0628] Step 2: Conducting data analysis

[0629] Input: The server receives the presentation materials from the terminal.

[0630] Specific operation: Launches the multimodal analysis engine to extract and analyze information such as text, charts, drawings, layout, and color.

[0631] Data processing:

[0632] Text analysis: Extracting important key points from text using an NLP engine

[0633] Chart analysis: Analyzes chart types and data using the CV engine and records them in the database

[0634] Layout and color evaluation: Using specific algorithms to evaluate the layout and color of slides

[0635] Output: The analysis results are stored in a database and serve as input for generating feedback.

[0636] Step 3: Obtaining listener demographic information

[0637] Input: Sales history database

[0638] Specific operation: The server executes an SQL query to obtain attribute information such as the listener's occupation, interests, and level of understanding.

[0639] Output: The acquired attribute information is stored in memory and serves as input for generating feedback.

[0640] Step 4: Perform emotion recognition

[0641] Input: User's facial expression data or voice data

[0642] Specific behavior:

[0643] Facial expression recognition: Captures the user's facial expressions in real time using the device's camera and analyzes them with the emotion engine.

[0644] Voice analysis: Records the user's voice using the device's microphone and recognizes emotions using the voice analysis engine

[0645] Output: Data on the user's emotional state is obtained, which serves as the input for generating feedback.

[0646] Step 5: Generate feedback

[0647] Input: Data analysis results, listener attribute information, user emotional state

[0648] Specific operation: The server uses a generative AI model to generate optimal feedback based on the prompt sentence.

[0649] An example of a specific prompt: "I've uploaded a presentation for a new product. My audience is technical, has a high level of understanding, and prefers simple designs. The current presentation includes product features and technical specifications. The emotion engine recognizes that I'm nervous. Please provide specific improvements and feedback."

[0650] Output: The generated feedback is sent to the terminal.

[0651] Step 6: Submit your feedback

[0652] Input: Feedback generated by a generative AI model

[0653] Specific operation: The server converts the feedback data into a format such as JSON and sends it to the device as an HTTP response.

[0654] Output: Feedback is sent to the device

[0655] Step 7: Review and adjust feedback

[0656] Input: Feedback data sent to the device

[0657] Specific operation: The device visually displays the feedback content in the user interface. It also re-runs the emotion recognition engine in real time and adjusts the feedback content as needed.

[0658] Output: Users can see feedback and identify specific areas for improvement in the material

[0659] (Application example 2)

[0660] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0661] Creating presentation materials and providing feedback on them requires consideration of audience demographic information and real-time sentiment analysis. However, current systems are unable to effectively utilize this information, making it difficult to provide prompt feedback that specifically identifies areas for improvement in the materials. Furthermore, brick-and-mortar stores lack systems that can directly grasp customer reactions in real time and respond immediately. Therefore, an effective method is needed to improve the quality of presentations and increase customer satisfaction.

[0662] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0663] In this invention, the server includes means for receiving presentation materials and extracting information about text, graphs, illustrations, layout, and color, means for referencing listener attribute information and generating appropriate feedback, means for transmitting the feedback so that the user can confirm it, means for analyzing the customer's facial expressions and tone of voice to analyze their reactions in real time, and means for visually displaying appropriate feedback based on the analysis results. This enables efficient creation and improvement of presentation materials and real-time feedback based on customer reactions.

[0664] A "presentation material" is a collection of information that includes elements such as text, slides, graphs, illustrations, layout, and color to convey information in a visual format.

[0665] "Text" refers to information expressed using letters and symbols.

[0666] A "graph" is a graphic that visually represents numerical data, and includes bar graphs, line graphs, pie charts, etc.

[0667] "Illustration" refers to a picture or diagram that visually supplements information.

[0668] "Layout" is a concept that refers to the alignment and relative positions of elements such as text, graphs, illustrations, and colors in presentation materials.

[0669] "Color" refers to the elements that are perceived as visual information by the three primary colors of light and their combinations.

[0670] "Feedback" refers to the response or evaluation provided to a user's actions or input.

[0671] "Listener attribute information" refers to individual data such as the occupation, interests, and level of understanding of the people listening to the presentation.

[0672] A "multimodal analysis engine" refers to technology that simultaneously analyzes multiple data formats, such as text, images, and audio.

[0673] "Real-time" is a time concept that refers to producing immediate responses or results the moment data is collected or processed.

[0674] "Analyzing facial expressions and tone of voice" refers to technology that analyzes an individual's facial expressions, voice pitch, strength, rhythm, etc., to mechanically determine that person's emotions and reactions.

[0675] "Visually displaying" refers to showing information to a user using visual elements such as text and images.

[0676] To realize this invention, a system having the following elements is required.

[0677] 1. Overall structure

[0678] The system is mainly composed of three entities: the server, the terminal, and the user. Each entity has a specific role and function.

[0679] 2. Server-side processing

[0680] The server has the following functions:

[0681] Receiving and analyzing presentation materials

[0682] The server receives presentation materials from the user's device and uses a multimodal analysis engine to extract information about text, graphs, illustrations, layout, and color. Specifically, the server analyzes the materials using Google Cloud Vision API and other tools.

[0683] Acquiring customer attribute information and generating feedback

[0684] The server references the listener's attribute information (such as occupation, interests, and level of understanding) and uses a generative AI model to generate appropriate feedback. This information is obtained from a database such as Firebase.

[0685] Analysis of customer facial expressions and tone of voice and real-time feedback

[0686] The server uses the Microsoft Azure Emotion API to analyze the customer's facial expressions and tone of voice, and analyzes their reactions in real time. Based on the analysis results, the server sends the data to the device to visually display appropriate feedback.

[0687] 3. Terminal processing

[0688] The device has the following features:

[0689] Upload presentation materials

[0690] The terminal transmits the presentation materials created by the user to the server.

[0691] Entering customer attribute information

[0692] The terminal provides an interface through which the user can input attribute information of listeners and transmit the information to the server.

[0693] Receiving and viewing feedback

[0694] The terminal receives the feedback sent from the server and visually displays it. Using a device such as smart glasses, the feedback is displayed in the user's field of vision in real time.

[0695] 4. User-side processing

[0696] The user performs the following operations:

[0697] Creating and uploading presentation materials

[0698] Users create presentation materials using smart glasses or tablets and upload them to the server via their devices.

[0699] Entering customer attribute information

[0700] Users input listener attribute information through their terminals, which allows the server to generate more personalized feedback.

[0701] Review and improve feedback

[0702] Users can check the feedback displayed on their devices and improve their presentation materials, adjusting the content of their materials and presentations based on real-time customer responses.

[0703] Specific examples

[0704] Example 1

[0705] Consider a scenario where a user is uploading a presentation document introducing a new product for technical professionals. The user enters that the audience is technical, has a high level of understanding, and prefers simple designs. If the system recognizes that the user is nervous, it will provide feedback with specific suggestions for improvement, such as "add more detailed technical information" or "simplify the coloring of graphs," as well as encouraging messages such as "please remain calm and proceed."

[0706] Prompt Sentence Examples

[0707] "Presentation begins. Use material A-123. Please enter customer attributes."

[0708] "Presentation material B-456 uploaded. The client is a technical professional. High level of understanding."

[0709] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0710] Program processing flow and specific explanation

[0711] Step 1: Upload your presentation materials

[0712] Device: The user uploads the presentation materials they created using smart glasses or a tablet to the device. Specifically, the user clicks the "upload" button on the document creation app. The input is the presentation material file, and the output is the document data sent to the server.

[0713] Step 2: Receiving and analyzing data

[0714] Server: The server receives the presentation materials sent from the device. It analyzes the received materials using a multimodal analysis engine such as Google Cloud Vision API to extract text, graphs, illustrations, layout, and color. The input is the received material data, and the output is structured data resulting from the analysis. Specifically, the server inputs the material data into the analysis engine and extracts the data according to a specific format.

[0715] Step 3: Enter customer attribute information

[0716] Terminal: The user uses the terminal interface to input the listener's attribute information (e.g., occupation, level of understanding, interests). The input is the customer attribute information entered by the user, and the output is the attribute information data sent to the server. Specifically, the user enters information into a selection box or text field and clicks the send button.

[0717] Step 4: Obtain customer attribute information

[0718] Server: The server retrieves customer attribute information from a database such as Firebase and integrates it with the attribute information entered by the user. The input is the attribute information data sent from the device and existing customer information retrieved from the database, and the output is the integrated customer attribute information.

[0719] Step 5: Generate feedback

[0720] Server: The server uses a generative AI model to generate appropriate feedback based on the customer attribute information integrated with the analysis results. Specifically, it inputs the generated prompt sentence into the generative AI model and outputs the feedback content. The input is customer attribute information integrated with the analysis results, and the output is the generated feedback data.

[0721] Step 6: Facial Expression and Tone Analysis

[0722] Server: During the presentation, the server receives real-time video and audio data from the smart glasses' camera and microphone, and uses the Microsoft Azure Emotion API to analyze the customer's facial expressions and tone of voice. The input is real-time video and audio data, and the output is the analyzed emotional data. Specifically, the server inputs the acquired video and audio data into the emotion analysis engine, which then analyzes the emotional data.

[0723] Step 7: Generate real-time feedback

[0724] Server: Based on the results of facial expression and tone analysis, the server generates appropriate feedback in real time and sends it to the device. The input is the emotion data from the analysis, and the output is real-time feedback data.

[0725] Step 8: View your feedback

[0726] Terminal: The terminal receives feedback from the server and displays it in real time on the display of smart glasses or a tablet. The input is the feedback data sent from the server, and the output is the visually displayed feedback content. Specifically, the terminal performs a rendering process to display the received feedback data on the display.

[0727] Prompt Sentence Examples

[0728] "Presentation begins. Use material A-123. Please enter customer attributes."

[0729] "Presentation material B-456 uploaded. The client is a technical professional. High level of understanding."

[0730] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0731] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0732] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0733] [Third embodiment]

[0734] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0735] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0736] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0737] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0738] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0739] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0740] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0741] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0742] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0743] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0744] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0745] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[0746] MODE FOR CARRYING OUT THE INVENTION

[0747] The specific process flow and operation at each step of the embodiment of the present invention will be described below. Note that the roles of the server, terminal, and user as components of the system described below will be clearly defined.

[0748] Server-side processing

[0749] The server first receives the presentation material from the device, which contains information in different formats (text, graphs, illustrations, layout, color, etc.), and then invokes a multimodal analysis engine to extract the information.

[0750] The analysis engine analyzes the content of each slide in the presentation and extracts important text information. It also deciphers the graphical information in graphs and illustrations and understands their meaning. It also evaluates the layout and color usage of the slides and stores the results in a database.

[0751] The server then retrieves the listener's attribute information from a sales history database. This information includes the listener's occupation, interests, and level of understanding. The server generates appropriate feedback based on this data. For example, it might suggest adding detailed technical information to a listener in a technical job, or suggest improving the color layout for a listener who prioritizes visuals.

[0752] The generated feedback is sent to the terminal for the user to review, and the server also logs that the feedback transmission has been completed.

[0753] Terminal side processing

[0754] The terminal provides an interface for the user to upload presentation materials and check the generated feedback. First, the user uploads the presentation materials, and the terminal sends the materials to the server.

[0755] The user is also provided with a form through their device to input listener attributes and comprehension levels, which are then sent to the server and used to customize feedback.

[0756] After receiving the feedback from the server, the terminal visually displays the content, and the user can view the feedback through the terminal interface and specifically understand how to improve the presentation materials.

[0757] User-side processing

[0758] Users use the system to upload presentation materials and enter audience demographic information. They also check feedback sent from the server and make revisions to the materials. As a specific example, for materials created for technical audiences, users can incorporate feedback such as "add detailed technical information" or "simplify the use of colors in graphs" to further improve the materials.

[0759] Specific examples

[0760] Specifically, consider a scenario in which a user uploads a presentation document for "introducing a new product." This document includes the product's features and technical specifications. The user inputs that the audience is "technical," "high level of understanding," and "prefers simple design." The server analyzes this information and generates feedback such as "simplify the use of colors in graphs" or "add detailed technical information," and sends it to the device. The user then checks this feedback and improves the presentation document.

[0761] This system automatically analyzes presentation materials and provides appropriate feedback to the listener, thereby improving the quality of the materials. It also enables efficient communication by providing customized feedback based on the listener's attributes and level of understanding.

[0762] The processing flow will be explained below.

[0763] Step 1:

[0764] Users upload presentation materials to their devices, which then receive the materials and send them to the server.

[0765] Step 2:

[0766] The server receives the presentation materials sent from the terminal and stores them in a specific folder.

[0767] Step 3:

[0768] The server launches a multimodal analysis engine to extract information such as the document's text, graphs, illustrations, layout, and color.

[0769] Step 4:

[0770] The server uses an analytics engine to analyze the text content of each slide to extract key points and summaries, and to interpret information in graphs and illustrations to identify their meaning.

[0771] Step 5:

[0772] The server analyzes the layout and color of each slide and performs a visual evaluation, storing the results in a database.

[0773] Step 6:

[0774] The user inputs the listener's attributes and comprehension level using a form on the device, and the device sends this information to the server.

[0775] Step 7:

[0776] The server acquires attribute information of the corresponding listener from the business history database and combines the input information.

[0777] Step 8:

[0778] The server generates appropriate feedback based on the listener's attribute information, such as suggestions for adding detailed technical information or improving color usage.

[0779] Step 9:

[0780] The server sends the generated feedback to the device, and after the transmission is complete, it records the fact in a log.

[0781] Step 10:

[0782] The terminal visually displays the received feedback to the user, who then checks the feedback and modifies the presentation materials.

[0783] Step 11:

[0784] The user improves the material based on the feedback provided by the server, and if necessary, re-uploads the newly revised material and repeats the process.

[0785] This allows presentation materials to be efficiently improved and information to be conveyed to the audience more effectively.

[0786] Example 1

[0787] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0788] With conventional presentation systems, improving the quality of presentation materials requires a great deal of time and effort, and it is particularly difficult to customize presentation materials to suit the attributes and level of understanding of the audience. This places a heavy burden on the user when generating presentation materials, making it difficult to achieve efficient communication.

[0789] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0790] In this invention, the server includes means for receiving presentation materials and extracting information about text, graphs, illustrations, layout, and color, means for referencing listener attribute information and generating appropriate feedback, means for transmitting the feedback so that the user can confirm it, means for allowing listener attribute information to be input via a terminal, and means for recording the completion of feedback transmission in a log. This enables automatic analysis of presentation materials and provision of appropriate feedback tailored to the listener.

[0791] "Presentation materials" refers to slides and documents used for presentations and explanations, and include elements such as text, graphs, illustrations, layout, and color.

[0792] "Text" is an element that contains character information and is used as a sentence or keyword in presentation materials.

[0793] A "graph" is a diagram intended to visualize data, and is a means of visually representing numerical data.

[0794] "Illustrations" refer to diagrams and pictures in presentation materials, elements that visually explain concepts and processes.

[0795] "Layout" refers to the relative positions of each element in a presentation, and is related to visual balance and layout.

[0796] "Color" refers to the colors used in presentation materials, which are used to visually emphasize and enhance the impression.

[0797] "Listener attribute information" refers to information about the occupation, interests, and level of understanding of the recipient of the presentation materials.

[0798] "Feedback" refers to information including evaluations, improvements, and suggestions for presentation materials, which users use to revise their materials.

[0799] A "multimodal analysis engine" is an engine that has the ability to simultaneously analyze multiple types of data, such as text, images, and audio.

[0800] A "generative AI model" is an algorithm or program that uses artificial intelligence to automatically generate feedback from data.

[0801] The "sales history database" is a database that stores data on past sales activities and transactions, and is used to refer to the attribute information of listeners.

[0802] A "log" is a record of the history of operations and events within a system, and is used to track important actions such as sending feedback.

[0803] MODE FOR CARRYING OUT THE INVENTION

[0804] This invention is a system that automatically analyzes presentation materials and provides appropriate feedback to the audience. The specific process flow and operations at each step are explained below. The roles of the server, terminal, and user as system components are clearly described.

[0805] Server-side processing

[0806] The server first receives the presentation materials from the terminal. These materials include information such as text, graphs, illustrations, layout, and color. The server then launches a multimodal analysis engine to analyze this information. This engine includes image processing software (OpenCV), natural language processing software (NLTK, SpaCy), and data analysis software (NumPy, Pandas). The analysis engine analyzes each slide of the materials and uses Tesseract OCR to extract text information. It also analyzes graphs and illustrations to understand their meaning, and evaluates the layout and color usage of the slides. This data is then stored in a temporary database.

[0807] Next, the server retrieves the listener's attribute information from the sales history database. This information includes the listener's occupation, interests, and level of understanding. Based on this information, the server uses a generative AI model to automatically generate appropriate feedback. The generated feedback includes areas for improvement and specific suggestions.

[0808] Finally, the server sends the generated feedback to the device, and after the transmission is complete, it records the completion of the feedback transmission in a log.

[0809] Terminal side processing

[0810] The terminal provides the user with an interface for uploading presentation materials and checking the generated feedback. The user uploads presentation materials through the terminal, which then sends the materials to the server. The user also uses a form on the terminal to enter the audience's attributes and level of understanding. This information is also sent to the server.

[0811] After receiving the feedback from the server, the terminal will visually display the content, and the user can check the feedback through the terminal interface and specifically understand the improvements needed for the presentation materials.

[0812] User processing

[0813] Users upload presentation materials via their devices and enter audience demographic information. They can also review feedback sent from the server and make revisions to their materials. For example, for materials created for technical audiences, users can add detailed technical information or simply change the color scheme of graphs.

[0814] Specific examples

[0815] For example, consider a scenario in which a user uploads a presentation document for "introducing a new product." This document includes the product's features and technical specifications. The user inputs that the audience is "technical workers," has a "high level of understanding," and "prefers simple designs." The server analyzes this information and generates feedback such as "simplify the use of colors in graphs" or "add detailed technical information," and sends it to the device. The user can review this feedback and improve the presentation document.

[0816] Prompt Sentence Examples

[0817] Here is an example of a prompt that a user might enter into the system:

[0818] "I uploaded a presentation for new product X. The audience is technical and has a high level of understanding. They prefer simple designs."

[0819] This invention makes it possible to automatically analyze presentation materials and provide feedback tailored to the audience, thereby reducing the burden on users and realizing efficient communication.

[0820] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0821] Step 1:

[0822] The server receives presentation materials from the terminal. The input is a presentation material file (e.g., PDF or PPTX) uploaded by the user through the terminal. The server stores the material in data storage and prepares it for the next analysis process.

[0823] Step 2:

[0824] The server launches a multimodal analysis engine. This engine uses software such as OpenCV, NLTK, SpaCy, NumPy, and Pandas to extract each element of the presentation (text, graphs, illustrations, layout, and color). The input is the saved presentation. The output is the data for each extracted element. Specifically, it uses Tesseract OCR for text extraction and OpenCV for image and graph analysis.

[0825] Step 3:

[0826] The server analyzes the content of the presentation materials based on the extracted information. The input is the data extracted in step 2. The server analyzes this data using natural language processing to understand the content of the presentation. Specifically, it performs semantic analysis of the text using NLTK or SpaCy. The output is the analysis results.

[0827] Step 4:

[0828] The server retrieves the listener's attribute information from the sales history database. The input is a database query. The server executes an SQL query to extract information such as the listener's occupation, interests, and level of understanding. The output is the listener's attribute information.

[0829] Step 5:

[0830] The server uses the generative AI model to automatically generate feedback based on the analysis results and listener attribute information. The inputs are the analysis results from step 3 and the listener attribute information from step 4. The server inputs this data into the generative AI model and generates appropriate feedback. Specific operations include inputting data into the generative AI model and running the feedback generation algorithm. The generated feedback is obtained as the output.

[0831] Step 6:

[0832] The server sends the generated feedback to the device and logs the completion of the transmission. As input, there is the feedback generated in step 5. The server sends the feedback to the device via an HTTP request and logs the successful transmission. As output, the feedback transmission to the device is completed and the log is updated.

[0833] Step 7:

[0834] The terminal displays the feedback received from the server to the user. The input is the feedback received from the server. The terminal visually displays the feedback content so that the user can check it. The specific operation is to display the feedback on the interface. The output is a state in which the user can check the feedback.

[0835] Step 8:

[0836] The user modifies the presentation materials based on the feedback displayed on the device. The input is the feedback displayed on the device. The user checks the feedback and modifies the materials. Specifically, the user uses editing software to improve the materials. The output is a revised version of the presentation materials that reflects the feedback.

[0837] (Application example 1)

[0838] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0839] Currently, many autonomous vehicles lack the ability to grasp the driving situation in real time and provide appropriate feedback based on passenger attributes. This makes it difficult to ensure passenger safety and provide a comfortable driving experience in situations such as sudden braking or sudden changes in control. It is also difficult for users to obtain appropriate feedback in real time based on the audience when analyzing and improving presentation materials. A system that can solve these problems is needed.

[0840] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0841] In this invention, the server includes means for receiving presentation materials and extracting information about text, graphs, illustrations, layout, and color; means for referencing listener attribute information and generating appropriate feedback; and means for transmitting the feedback so that the user can confirm it. This improves the quality of the materials and enables efficient communication. The server also includes means for analyzing information about the driving situation and generating feedback in real time; means for providing visual and audio feedback to the driver and passengers; and means for customizing the feedback based on passenger attribute information. This enables appropriate feedback according to the driving situation in an autonomous vehicle and a safe and comfortable driving experience.

[0842] A "presentation material" is a document in slide format that is created by a user to convey specific information to others.

[0843] "Text" refers to character information, such as information expressed in the form of sentences or explanations.

[0844] A "graph" is a diagram such as a bar graph, line graph, or pie chart that visually represents data or information.

[0845] "Illustrations" are visual materials such as diagrams, pictures, and paintings that complement written or linguistic information.

[0846] "Layout" refers to how elements such as text and figures are laid out in presentation materials.

[0847] "Color" refers to color information that evokes visual sensations in presentation materials and display devices.

[0848] "Listener attribute information" refers to the occupation, interests, level of understanding, and other individual characteristic information of the people who will be listening to the presentation.

[0849] "Feedback" is return information that provides information such as analysis results and points for improvement to the user.

[0850] "Driving conditions" refers to information acquired by an autonomous vehicle based on its current driving state and surrounding environmental conditions.

[0851] "Real-time" means that information is processed simultaneously with the actual passage of time and is reflected immediately without delay.

[0852] A "driver" is a person who monitors an autonomous vehicle and takes manual control as needed.

[0853] A "passenger" is a person who is in an autonomous vehicle but who is not driving.

[0854] "Visual feedback" is information that is visually presented using text, diagrams, colors, etc.

[0855] "Audio feedback" refers to conveying information using sound, such as an audio message played over a speaker.

[0856] "Sensor information" refers to data acquired by sensors in autonomous vehicles, such as the vehicle's speed, the presence or absence of obstacles, and road conditions.

[0857] "Customization" means changing settings and content according to individual needs and attributes.

[0858] overview

[0859] This invention is a system that provides real-time feedback based on the driving situation in an autonomous vehicle and generates customized feedback according to passenger attribute information. It also has the function of analyzing the content of presentation materials from multiple angles and providing feedback tailored to the audience in order to improve the quality of presentation materials.

[0860] Hardware and software used

[0861] Hardware

[0862] Smart glasses, head-mounted displays

[0863] software

[0864] MultiModalAnalysisEngine

[0865] FeedbackGenerator (feedback generation engine)

[0866] Vehicle Sensors (Vehicle Sensor Modules)

[0867] FeedbackDisplay (Feedback Display Module)

[0868] System configuration and operation

[0869] 1. Server-side processing

[0870] The server first receives the presentation materials and extracts information about the text, graphs, illustrations, layout, and colors contained in the materials. During this process, the server uses the MultiModalAnalysisEngine to analyze the materials from multiple angles and saves the analysis results in a database. The server then obtains listener attribute information from the sales history database and uses the FeedbackGenerator to generate appropriate feedback based on that information. Information about the driving situation is obtained in real time from the VehicleSensors, and the driving situation is analyzed, providing visual and audio feedback to the driver and passengers.

[0871] 2. Terminal processing

[0872] The terminal provides an interface for the user, allowing them to upload presentation materials and input audience demographic information. Feedback received from the server is visually displayed on the terminal screen, allowing the user to review the feedback and improve the materials. The terminal in the autonomous vehicle also displays real-time feedback based on the driving situation and provides warning messages and other important information to the user and passengers.

[0873] 3. User-side processing

[0874] Users upload presentation materials and input audience demographic information via their devices. The materials are then revised and improved based on feedback received from the server. Additionally, the driver in the autonomous vehicle checks the feedback provided in real time and responds accordingly to the driving situation.

[0875] Specific examples

[0876] As a concrete example, consider a scenario in which a user uploads a presentation document for "introducing a new product for technical professionals." This document includes the product's features and technical specifications. The user inputs that the audience is "technical," "has a high level of understanding," and "prefers simple designs." The server analyzes this information and generates feedback such as "simplify the use of colors in graphs" or "add detailed technical information," and sends it to the device. The user then checks this feedback and improves the presentation document.

[0877] Prompt Sentence Examples

[0878] As a concrete example, the following prompt sentence is input into the generative AI model:

[0879] If the driving situation detects sudden braking, create a program that generates a safety warning message for passengers (especially children) and displays it on the dashboard and head-mounted display.

[0880] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0881] Step 1:

[0882] The server receives presentation materials from the terminal. These materials are slide-format documents created by users to convey specific information to others, and contain information such as text, graphs, illustrations, layout, and color. The input is the presentation materials, and the output is the document data to be analyzed.

[0883] Step 2:

[0884] The server launches the MultiModalAnalysisEngine and performs a multifaceted analysis of the presentation materials. During this analysis process, text information is extracted and graphical information from graphs and illustrations is deciphered. The layout and color usage of the slides are also evaluated. The input is the data of the materials to be analyzed, and the output is the analysis results data.

[0885] Step 3:

[0886] The server obtains the listener's attribute information from the sales history database, which includes the listener's occupation, interests, level of understanding, etc. The input is the listener's identification information, and the output is the listener's attribute information.

[0887] Step 4:

[0888] The server uses a FeedbackGenerator to generate appropriate feedback based on the acquired listener attribute information and analysis result data. For example, it might suggest adding detailed technical information to a listener in a technical job, or suggest improving the color layout for a listener who prioritizes visuals. The input is the listener attribute information and analysis result data, and the generated feedback data is obtained as output.

[0889] Step 5:

[0890] The server receives real-time information about the driving situation from VehicleSensors, including the vehicle speed, distance to obstacles, road conditions, etc. The input is sensor information, and the output is driving situation data.

[0891] Step 6:

[0892] The server analyzes the driving situation data and generates real-time feedback to the driver and passengers. For example, if sudden braking is detected, it generates an appropriate warning message. The input is driving situation data, and the output is real-time feedback.

[0893] Step 7:

[0894] The server customizes the feedback based on the passenger's attributes, for example, by providing a gentler tone of warning if a child is on board. The input is the passenger's attributes and real-time feedback, and the output is the customized feedback.

[0895] Step 8:

[0896] The server sends the generated feedback to the terminal, which displays the feedback visually and audibly for the user and passengers to review. The input is customized feedback data, and the output is feedback provided visually and audibly.

[0897] Step 9:

[0898] Users upload presentation materials via their terminals and input audience attribute information. They also revise and improve the materials based on feedback provided by the server. Feedback data from the server is input, and improved presentation materials are obtained as output.

[0899] This series of steps enables real-time feedback on the presentation materials and the dashboard interface of the autonomous vehicle.

[0900] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0901] MODE FOR CARRYING OUT THE INVENTION

[0902] This invention is a system for efficiently creating and improving presentation materials, and by combining it with an emotion engine that recognizes the user's emotions, it provides more personalized feedback. The specific processing flow and operation at each step are explained below.

[0903] Server-side processing

[0904] The server first receives the presentation materials from the terminal. It then launches a multimodal analysis engine to extract information from the materials, such as text, graphs, illustrations, layout, and color. The analysis engine analyzes the text content of the materials and extracts important points and summaries. At the same time, it interprets the graphs and illustrations and records that information in a database. It also evaluates the slide layout and color usage.

[0905] The server then retrieves the listener's attribute information from the sales history database. This information includes the listener's occupation, interests, and level of understanding. The server generates appropriate feedback based on this data and the analysis results. Furthermore, it uses an emotion engine that recognizes the user's emotions to adjust the feedback content according to the user's emotions. For example, if the user is confused, it adds a more detailed explanation, and if the user is satisfied, it provides feedback that includes words of praise.

[0906] The generated feedback is sent to the device for the user to review, and the server records the completion of the feedback transmission in a log.

[0907] Terminal side processing

[0908] The device provides a user-friendly interface for uploading presentation materials, inputting audience attribute information, and checking the generated feedback. First, the user uploads the presentation materials, and the device sends the materials to the server. Next, the user inputs the audience attributes and comprehension level, and sends the information to the server.

[0909] After receiving the feedback, the device visually displays it to the user. Furthermore, the emotion engine recognizes the user's emotions in real time and adjusts the feedback content as needed. By providing feedback on the presentation materials uploaded by the user, areas for improvement can be clearly identified, allowing for efficient revisions.

[0910] User-side processing

[0911] Users use the system to upload presentation materials and input audience demographic information. Their emotions are then recognized through the emotion engine, and they receive feedback based on this. As a concrete example, consider a user creating highly technical materials who sets a specific audience as "technical professionals" with a "high level of understanding." If the emotion engine recognizes the user as confused, feedback could include adding more detailed technical information or simplifying the use of colors.

[0912] This allows users to receive more personalized advice tailored to their emotional state, rather than simply receiving feedback based on data analysis, allowing them to create and improve presentation materials efficiently and effectively.

[0913] Specific examples

[0914] As a concrete example, consider a scenario in which a user uploads a presentation document for "introducing a new product." This document includes the product's features and technical specifications. The user inputs that the audience is "technical," "high level of understanding," and "prefers simple design." If the emotion engine recognizes that the user is nervous, the feedback will include specific improvements such as "add detailed technical information" or "simplify the coloring of graphs," and an encouraging message such as "please remain calm and proceed."

[0915] This system not only allows for the improvement of presentation materials to suit the audience, but also provides feedback that takes into account the user's emotions, thereby improving the quality of presentation materials and providing more effective support for the user's presentation itself.

[0916] The processing flow will be explained below.

[0917] Step 1:

[0918] Users upload presentation materials to their devices, which then receive the materials and send them to the server.

[0919] Step 2:

[0920] The server receives the presentation materials sent from the terminal and stores them in a specific folder.

[0921] Step 3:

[0922] The server launches a multimodal analysis engine to extract information such as the document's text, graphs, illustrations, layout, and color.

[0923] Step 4:

[0924] The server uses an analytics engine to analyze the text content of each slide to extract key points and summaries, and to interpret information in graphs and illustrations to identify their meaning.

[0925] Step 5:

[0926] The server analyzes the layout and color of each slide and performs a visual evaluation, storing the results in a database.

[0927] Step 6:

[0928] The user inputs the listener's attributes and comprehension level using a form on the device, and the device sends this information to the server.

[0929] Step 7:

[0930] The server acquires attribute information of the corresponding listener from the business history database and combines the input information.

[0931] Step 8:

[0932] The server generates appropriate feedback based on the listener's attribute information, such as suggestions for adding detailed technical information or improving color usage.

[0933] Step 9:

[0934] The user views the presentation materials through the terminal, and the terminal activates an emotion engine to recognize the user's emotions at that time.

[0935] Step 10:

[0936] The emotion engine analyzes the user's facial expressions, tone of voice, input actions, etc. in real time to collect user emotional data.

[0937] Step 11:

[0938] The server receives the emotion data from the emotion engine and reflects it in the feedback content, for example, adding detailed explanations if the user is confused.

[0939] Step 12:

[0940] The generated feedback is sent from the server to the device, which then visually displays it to the user, including emotional encouragement or advice as needed.

[0941] Step 13:

[0942] The user can review the feedback displayed on their device, revise their presentation materials, and, if necessary, re-upload the revised materials and repeat the process.

[0943] This series of steps allows users to individually customize their presentation materials and improve them efficiently while receiving emotional support.

[0944] Example 2

[0945] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0946] Conventional presentation material creation systems provide feedback based on content analysis of materials and audience attribute information, but they lack the ability to provide personalized feedback that takes into account the user's emotions. This makes it difficult to improve materials that are optimized for each user's level of understanding and emotional state.

[0947] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0948] In this invention, the server includes means for receiving presentation materials and extracting information about text, charts, drawings, layout, and color, means for referencing listener attribute information to generate appropriate feedback, and means for recognizing the user's emotions and adjusting the feedback content, thereby making it possible to provide feedback optimized for the user's emotional state and improve the quality of the presentation materials.

[0949] A "presentation" is a document or slides that a user uses to visually present specific information.

[0950] "Text" refers to the written information contained in the presentation materials.

[0951] A "diagram" is a graph or chart that visually displays statistical data or information contained in presentation materials.

[0952] "Drawings" refer to illustrations and pictures included in presentation materials.

[0953] "Layout" refers to the layout and position of each element (text, charts, drawings, etc.) in a presentation document.

[0954] "Color" refers to the color combinations and color schemes used in presentation materials.

[0955] "Audience attribute information" refers to information that indicates the characteristics of the person or group to whom the presentation is directed, and includes occupation, interests, level of understanding, and the like.

[0956] "Feedback" refers to advice and evaluation that provides users with improvements and supplementary information for their presentation materials.

[0957] "Emotion recognition" is a technology that determines a user's emotional state from their facial expressions and voice.

[0958] A "multimodal analysis engine" is an engine for integrated processing and analysis of multiple data formats, such as text analysis, chart analysis, and image analysis.

[0959] The "emotion analysis engine" is an engine for analyzing the user's emotional state, and has the functions of facial expression recognition and voice analysis.

[0960] A "sales history database" is a database that includes attribute information of customers and listeners, history of past sales activities, etc.

[0961] "Real-time" means processing and feedback in real time, synchronized with the current time.

[0962] The present invention is a system that analyzes presentation materials uploaded by a user and generates appropriate feedback based on the listener's attribute information and the user's emotional recognition. Specific embodiments are described below.

[0963] Server-side processing

[0964] The server first receives the presentation materials from the device. It then uses a multimodal analysis engine (such as an NLP engine or CV engine) to extract information from the materials, such as text, charts, drawings, layout, and color. The analysis engine analyzes the text in the materials and extracts important key points and summaries. At the same time, it interprets graphs and illustrations and stores them in a database. It also evaluates the slide layout and color usage.

[0965] Next, the server retrieves the listener's attribute information from the sales history database. This information includes the listener's occupation, interests, and level of understanding. Based on this attribute information and the analysis results, a generative AI model is used to generate appropriate feedback. The content of the feedback is adjusted according to the user's emotional state, so an emotion recognition engine (such as facial expression recognition and voice analysis) is also used.

[0966] The generated feedback is sent to the terminal so that the user can check it, and the server records the feedback sending status in a log.

[0967] Terminal side processing

[0968] The terminal provides a user interface that allows the user to easily upload presentation materials. Once the user uploads the materials, the terminal sends the materials to the server. Next, the user inputs the audience's demographic information (e.g., occupation, level of understanding, interests, etc.), and sends this information to the server as well.

[0969] The device that receives the feedback visually displays the content and uses an emotion recognition engine in real time to analyze the user's emotions and adjust the feedback content as needed. This process allows users to efficiently identify areas for improvement in the material and make optimal adjustments.

[0970] User-side processing

[0971] Users can upload presentation materials and input audience demographic information through the system interface, and receive more personalized feedback by utilizing an emotion engine that recognizes emotions through facial expressions and voice recognition.

[0972] As a concrete example, consider the case where a user uploads a presentation document introducing a new product. This document includes the product's features and technical specifications. The user inputs that the audience is technical, has a high level of understanding, and prefers simple designs. If the emotion engine recognizes that the user is nervous, the feedback will include specific improvements such as "add detailed technical information" or "simplify the coloring of graphs," and an encouraging message such as "please remain calm and proceed."

[0973] Example prompts for generative AI models

[0974] "I uploaded a presentation for a new product. My audience is technical, has a high level of understanding, and prefers simple designs. The current presentation includes product features and technical specifications. The emotion engine recognizes that I'm nervous. Please provide specific improvements and feedback."

[0975] In this way, through specific embodiments of the invention, users can improve the quality of their presentation materials.

[0976] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0977] Step 1: Upload your presentation materials

[0978] Input: The user selects presentation materials through the terminal interface.

[0979] Specific behavior: Displays a file selection dialog in the user interface and sends the file selected by the user to the server via an HTTP request.

[0980] Output: Presentation materials are sent to the server

[0981] Step 2: Conducting data analysis

[0982] Input: The server receives the presentation materials from the terminal.

[0983] Specific operation: Launches the multimodal analysis engine to extract and analyze information such as text, charts, drawings, layout, and color.

[0984] Data processing:

[0985] Text analysis: Extracting important key points from text using an NLP engine

[0986] Chart analysis: Analyzes chart types and data using the CV engine and records them in the database

[0987] Layout and color evaluation: Using specific algorithms to evaluate the layout and color of slides

[0988] Output: The analysis results are stored in a database and serve as input for generating feedback.

[0989] Step 3: Obtaining listener demographic information

[0990] Input: Sales history database

[0991] Specific operation: The server executes an SQL query to obtain attribute information such as the listener's occupation, interests, and level of understanding.

[0992] Output: The acquired attribute information is stored in memory and serves as input for generating feedback.

[0993] Step 4: Perform emotion recognition

[0994] Input: User's facial expression data or voice data

[0995] Specific behavior:

[0996] Facial expression recognition: Captures the user's facial expressions in real time using the device's camera and analyzes them with the emotion engine.

[0997] Voice analysis: Records the user's voice using the device's microphone and recognizes emotions using the voice analysis engine

[0998] Output: Data on the user's emotional state is obtained, which serves as the input for generating feedback.

[0999] Step 5: Generate feedback

[1000] Input: Data analysis results, listener attribute information, user emotional state

[1001] Specific operation: The server uses a generative AI model to generate optimal feedback based on the prompt sentence.

[1002] An example of a specific prompt: "I've uploaded a presentation for a new product. My audience is technical, has a high level of understanding, and prefers simple designs. The current presentation includes product features and technical specifications. The emotion engine recognizes that I'm nervous. Please provide specific improvements and feedback."

[1003] Output: The generated feedback is sent to the terminal.

[1004] Step 6: Submit your feedback

[1005] Input: Feedback generated by a generative AI model

[1006] Specific operation: The server converts the feedback data into a format such as JSON and sends it to the device as an HTTP response.

[1007] Output: Feedback is sent to the device

[1008] Step 7: Review and adjust feedback

[1009] Input: Feedback data sent to the device

[1010] Specific operation: The device visually displays the feedback content in the user interface. It also re-runs the emotion recognition engine in real time and adjusts the feedback content as needed.

[1011] Output: Users can see feedback and identify specific areas for improvement in the material

[1012] (Application example 2)

[1013] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1014] Creating presentation materials and providing feedback on them requires consideration of audience demographic information and real-time sentiment analysis. However, current systems are unable to effectively utilize this information, making it difficult to provide prompt feedback that specifically identifies areas for improvement in the materials. Furthermore, brick-and-mortar stores lack systems that can directly grasp customer reactions in real time and respond immediately. Therefore, an effective method is needed to improve the quality of presentations and increase customer satisfaction.

[1015] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1016] In this invention, the server includes means for receiving presentation materials and extracting information about text, graphs, illustrations, layout, and color, means for referencing listener attribute information and generating appropriate feedback, means for transmitting the feedback so that the user can confirm it, means for analyzing the customer's facial expressions and tone of voice to analyze their reactions in real time, and means for visually displaying appropriate feedback based on the analysis results. This enables efficient creation and improvement of presentation materials and real-time feedback based on customer reactions.

[1017] A "presentation material" is a collection of information that includes elements such as text, slides, graphs, illustrations, layout, and color to convey information in a visual format.

[1018] "Text" refers to information expressed using letters and symbols.

[1019] A "graph" is a graphic that visually represents numerical data, and includes bar graphs, line graphs, pie charts, etc.

[1020] "Illustration" refers to a picture or diagram that visually supplements information.

[1021] "Layout" is a concept that refers to the alignment and relative positions of elements such as text, graphs, illustrations, and colors in presentation materials.

[1022] "Color" refers to the elements that are perceived as visual information by the three primary colors of light and their combinations.

[1023] "Feedback" refers to the response or evaluation provided to a user's actions or input.

[1024] "Listener attribute information" refers to individual data such as the occupation, interests, and level of understanding of the people listening to the presentation.

[1025] A "multimodal analysis engine" refers to technology that simultaneously analyzes multiple data formats, such as text, images, and audio.

[1026] "Real-time" is a time concept that refers to producing immediate responses or results the moment data is collected or processed.

[1027] "Analyzing facial expressions and tone of voice" refers to technology that analyzes an individual's facial expressions, voice pitch, strength, rhythm, etc., to mechanically determine that person's emotions and reactions.

[1028] "Visually displaying" refers to showing information to a user using visual elements such as text and images.

[1029] To realize this invention, a system having the following elements is required.

[1030] 1. Overall structure

[1031] The system is mainly composed of three entities: the server, the terminal, and the user. Each entity has a specific role and function.

[1032] 2. Server-side processing

[1033] The server has the following functions:

[1034] Receiving and analyzing presentation materials

[1035] The server receives presentation materials from the user's device and uses a multimodal analysis engine to extract information about text, graphs, illustrations, layout, and color. Specifically, the server analyzes the materials using Google Cloud Vision API and other tools.

[1036] Acquiring customer attribute information and generating feedback

[1037] The server references the listener's attribute information (such as occupation, interests, and level of understanding) and uses a generative AI model to generate appropriate feedback. This information is obtained from a database such as Firebase.

[1038] Analysis of customer facial expressions and tone of voice and real-time feedback

[1039] The server uses the Microsoft Azure Emotion API to analyze the customer's facial expressions and tone of voice, and analyzes their reactions in real time. Based on the analysis results, the server sends the data to the device to visually display appropriate feedback.

[1040] 3. Terminal processing

[1041] The device has the following features:

[1042] Upload presentation materials

[1043] The terminal transmits the presentation materials created by the user to the server.

[1044] Entering customer attribute information

[1045] The terminal provides an interface through which the user can input attribute information of listeners and transmit the information to the server.

[1046] Receiving and viewing feedback

[1047] The terminal receives the feedback sent from the server and visually displays it. Using a device such as smart glasses, the feedback is displayed in the user's field of vision in real time.

[1048] 4. User-side processing

[1049] The user performs the following operations:

[1050] Creating and uploading presentation materials

[1051] Users create presentation materials using smart glasses or tablets and upload them to the server via their devices.

[1052] Entering customer attribute information

[1053] Users input listener attribute information through their terminals, which allows the server to generate more personalized feedback.

[1054] Review and improve feedback

[1055] Users can check the feedback displayed on their devices and improve their presentation materials, adjusting the content of their materials and presentations based on real-time customer responses.

[1056] Specific examples

[1057] Example 1

[1058] Consider a scenario where a user is uploading a presentation document introducing a new product for technical professionals. The user enters that the audience is technical, has a high level of understanding, and prefers simple designs. If the system recognizes that the user is nervous, it will provide feedback with specific suggestions for improvement, such as "add more detailed technical information" or "simplify the coloring of graphs," as well as encouraging messages such as "please remain calm and proceed."

[1059] Prompt Sentence Examples

[1060] "Presentation begins. Use material A-123. Please enter customer attributes."

[1061] "Presentation material B-456 uploaded. The client is a technical professional. High level of understanding."

[1062] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1063] Program processing flow and specific explanation

[1064] Step 1: Upload your presentation materials

[1065] Device: The user uploads the presentation materials they created using smart glasses or a tablet to the device. Specifically, the user clicks the "upload" button on the document creation app. The input is the presentation material file, and the output is the document data sent to the server.

[1066] Step 2: Receiving and analyzing data

[1067] Server: The server receives the presentation materials sent from the device. It analyzes the received materials using a multimodal analysis engine such as Google Cloud Vision API to extract text, graphs, illustrations, layout, and color. The input is the received material data, and the output is structured data resulting from the analysis. Specifically, the server inputs the material data into the analysis engine and extracts the data according to a specific format.

[1068] Step 3: Enter customer attribute information

[1069] Terminal: The user uses the terminal interface to input the listener's attribute information (e.g., occupation, level of understanding, interests). The input is the customer attribute information entered by the user, and the output is the attribute information data sent to the server. Specifically, the user enters information into a selection box or text field and clicks the send button.

[1070] Step 4: Obtain customer attribute information

[1071] Server: The server retrieves customer attribute information from a database such as Firebase and integrates it with the attribute information entered by the user. The input is the attribute information data sent from the device and existing customer information retrieved from the database, and the output is the integrated customer attribute information.

[1072] Step 5: Generate feedback

[1073] Server: The server uses a generative AI model to generate appropriate feedback based on the customer attribute information integrated with the analysis results. Specifically, it inputs the generated prompt sentence into the generative AI model and outputs the feedback content. The input is customer attribute information integrated with the analysis results, and the output is the generated feedback data.

[1074] Step 6: Facial Expression and Tone Analysis

[1075] Server: During the presentation, the server receives real-time video and audio data from the smart glasses' camera and microphone, and uses the Microsoft Azure Emotion API to analyze the customer's facial expressions and tone of voice. The input is real-time video and audio data, and the output is the analyzed emotional data. Specifically, the server inputs the acquired video and audio data into the emotion analysis engine, which then analyzes the emotional data.

[1076] Step 7: Generate real-time feedback

[1077] Server: Based on the results of facial expression and tone analysis, the server generates appropriate feedback in real time and sends it to the device. The input is the emotion data from the analysis, and the output is real-time feedback data.

[1078] Step 8: View your feedback

[1079] Terminal: The terminal receives feedback from the server and displays it in real time on the display of smart glasses or a tablet. The input is the feedback data sent from the server, and the output is the visually displayed feedback content. Specifically, the terminal performs a rendering process to display the received feedback data on the display.

[1080] Prompt Sentence Examples

[1081] "Presentation begins. Use material A-123. Please enter customer attributes."

[1082] "Presentation material B-456 uploaded. The client is a technical professional. High level of understanding."

[1083] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1084] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1085] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1086] [Fourth embodiment]

[1087] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1088] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1089] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1090] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1091] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1092] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1093] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1094] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1095] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1096] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1097] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1098] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1099] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1100] MODE FOR CARRYING OUT THE INVENTION

[1101] The specific process flow and operation at each step of the embodiment of the present invention will be described below. Note that the roles of the server, terminal, and user as components of the system described below will be clearly defined.

[1102] Server-side processing

[1103] The server first receives the presentation material from the device, which contains information in different formats (text, graphs, illustrations, layout, color, etc.), and then invokes a multimodal analysis engine to extract the information.

[1104] The analysis engine analyzes the content of each slide in the presentation and extracts important text information. It also deciphers the graphical information in graphs and illustrations and understands their meaning. It also evaluates the layout and color usage of the slides and stores the results in a database.

[1105] The server then retrieves the listener's attribute information from a sales history database. This information includes the listener's occupation, interests, and level of understanding. The server generates appropriate feedback based on this data. For example, it might suggest adding detailed technical information to a listener in a technical job, or suggest improving the color layout for a listener who prioritizes visuals.

[1106] The generated feedback is sent to the terminal for the user to review, and the server also logs that the feedback transmission has been completed.

[1107] Terminal side processing

[1108] The terminal provides an interface for the user to upload presentation materials and check the generated feedback. First, the user uploads the presentation materials, and the terminal sends the materials to the server.

[1109] The user is also provided with a form through their device to input listener attributes and comprehension levels, which are then sent to the server and used to customize feedback.

[1110] After receiving the feedback from the server, the terminal visually displays the content, and the user can view the feedback through the terminal interface and specifically understand how to improve the presentation materials.

[1111] User-side processing

[1112] Users use the system to upload presentation materials and enter audience demographic information. They also check feedback sent from the server and make revisions to the materials. As a specific example, for materials created for technical audiences, users can incorporate feedback such as "add detailed technical information" or "simplify the use of colors in graphs" to further improve the materials.

[1113] Specific examples

[1114] Specifically, consider a scenario in which a user uploads a presentation document for "introducing a new product." This document includes the product's features and technical specifications. The user inputs that the audience is "technical," "high level of understanding," and "prefers simple design." The server analyzes this information and generates feedback such as "simplify the use of colors in graphs" or "add detailed technical information," and sends it to the device. The user then checks this feedback and improves the presentation document.

[1115] This system automatically analyzes presentation materials and provides appropriate feedback to the listener, thereby improving the quality of the materials. It also enables efficient communication by providing customized feedback based on the listener's attributes and level of understanding.

[1116] The processing flow will be explained below.

[1117] Step 1:

[1118] Users upload presentation materials to their devices, which then receive the materials and send them to the server.

[1119] Step 2:

[1120] The server receives the presentation materials sent from the terminal and stores them in a specific folder.

[1121] Step 3:

[1122] The server launches a multimodal analysis engine to extract information such as the document's text, graphs, illustrations, layout, and color.

[1123] Step 4:

[1124] The server uses an analytics engine to analyze the text content of each slide to extract key points and summaries, and to interpret information in graphs and illustrations to identify their meaning.

[1125] Step 5:

[1126] The server analyzes the layout and color of each slide and performs a visual evaluation, storing the results in a database.

[1127] Step 6:

[1128] The user inputs the listener's attributes and comprehension level using a form on the device, and the device sends this information to the server.

[1129] Step 7:

[1130] The server acquires attribute information of the corresponding listener from the business history database and combines the input information.

[1131] Step 8:

[1132] The server generates appropriate feedback based on the listener's attribute information, such as suggestions for adding detailed technical information or improving color usage.

[1133] Step 9:

[1134] The server sends the generated feedback to the device, and after the transmission is complete, it records the fact in a log.

[1135] Step 10:

[1136] The terminal visually displays the received feedback to the user, who then checks the feedback and modifies the presentation materials.

[1137] Step 11:

[1138] The user improves the material based on the feedback provided by the server, and if necessary, re-uploads the newly revised material and repeats the process.

[1139] This allows presentation materials to be efficiently improved and information to be conveyed to the audience more effectively.

[1140] Example 1

[1141] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1142] With conventional presentation systems, improving the quality of presentation materials requires a great deal of time and effort, and it is particularly difficult to customize presentation materials to suit the attributes and level of understanding of the audience. This places a heavy burden on the user when generating presentation materials, making it difficult to achieve efficient communication.

[1143] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1144] In this invention, the server includes means for receiving presentation materials and extracting information about text, graphs, illustrations, layout, and color, means for referencing listener attribute information and generating appropriate feedback, means for transmitting the feedback so that the user can confirm it, means for allowing listener attribute information to be input via a terminal, and means for recording the completion of feedback transmission in a log. This enables automatic analysis of presentation materials and provision of appropriate feedback tailored to the listener.

[1145] "Presentation materials" refers to slides and documents used for presentations and explanations, and include elements such as text, graphs, illustrations, layout, and color.

[1146] "Text" is an element that contains character information and is used as a sentence or keyword in presentation materials.

[1147] A "graph" is a diagram intended to visualize data, and is a means of visually representing numerical data.

[1148] "Illustrations" refer to diagrams and pictures in presentation materials, elements that visually explain concepts and processes.

[1149] "Layout" refers to the relative positions of each element in a presentation, and is related to visual balance and layout.

[1150] "Color" refers to the colors used in presentation materials, which are used to visually emphasize and enhance the impression.

[1151] "Listener attribute information" refers to information about the occupation, interests, and level of understanding of the recipient of the presentation materials.

[1152] "Feedback" refers to information including evaluations, improvements, and suggestions for presentation materials, which users use to revise their materials.

[1153] A "multimodal analysis engine" is an engine that has the ability to simultaneously analyze multiple types of data, such as text, images, and audio.

[1154] A "generative AI model" is an algorithm or program that uses artificial intelligence to automatically generate feedback from data.

[1155] The "sales history database" is a database that stores data on past sales activities and transactions, and is used to refer to the attribute information of listeners.

[1156] A "log" is a record of the history of operations and events within a system, and is used to track important actions such as sending feedback.

[1157] MODE FOR CARRYING OUT THE INVENTION

[1158] This invention is a system that automatically analyzes presentation materials and provides appropriate feedback to the audience. The specific process flow and operations at each step are explained below. The roles of the server, terminal, and user as system components are clearly described.

[1159] Server-side processing

[1160] The server first receives the presentation materials from the terminal. These materials include information such as text, graphs, illustrations, layout, and color. The server then launches a multimodal analysis engine to analyze this information. This engine includes image processing software (OpenCV), natural language processing software (NLTK, SpaCy), and data analysis software (NumPy, Pandas). The analysis engine analyzes each slide of the materials and uses Tesseract OCR to extract text information. It also analyzes graphs and illustrations to understand their meaning, and evaluates the layout and color usage of the slides. This data is then stored in a temporary database.

[1161] Next, the server retrieves the listener's attribute information from the sales history database. This information includes the listener's occupation, interests, and level of understanding. Based on this information, the server uses a generative AI model to automatically generate appropriate feedback. The generated feedback includes areas for improvement and specific suggestions.

[1162] Finally, the server sends the generated feedback to the device, and after the transmission is complete, it records the completion of the feedback transmission in a log.

[1163] Terminal side processing

[1164] The terminal provides the user with an interface for uploading presentation materials and checking the generated feedback. The user uploads presentation materials through the terminal, which then sends the materials to the server. The user also uses a form on the terminal to enter the audience's attributes and level of understanding. This information is also sent to the server.

[1165] After receiving the feedback from the server, the terminal will visually display the content, and the user can check the feedback through the terminal interface and specifically understand the improvements needed for the presentation materials.

[1166] User processing

[1167] Users upload presentation materials via their devices and enter audience demographic information. They can also review feedback sent from the server and make revisions to their materials. For example, for materials created for technical audiences, users can add detailed technical information or simply change the color scheme of graphs.

[1168] Specific examples

[1169] For example, consider a scenario in which a user uploads a presentation document for "introducing a new product." This document includes the product's features and technical specifications. The user inputs that the audience is "technical workers," has a "high level of understanding," and "prefers simple designs." The server analyzes this information and generates feedback such as "simplify the use of colors in graphs" or "add detailed technical information," and sends it to the device. The user can review this feedback and improve the presentation document.

[1170] Prompt Sentence Examples

[1171] Here is an example of a prompt that a user might enter into the system:

[1172] "I uploaded a presentation for new product X. The audience is technical and has a high level of understanding. They prefer simple designs."

[1173] This invention makes it possible to automatically analyze presentation materials and provide feedback tailored to the audience, thereby reducing the burden on users and realizing efficient communication.

[1174] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1175] Step 1:

[1176] The server receives presentation materials from the terminal. The input is a presentation material file (e.g., PDF or PPTX) uploaded by the user through the terminal. The server stores the material in data storage and prepares it for the next analysis process.

[1177] Step 2:

[1178] The server launches a multimodal analysis engine. This engine uses software such as OpenCV, NLTK, SpaCy, NumPy, and Pandas to extract each element of the presentation (text, graphs, illustrations, layout, and color). The input is the saved presentation. The output is the data for each extracted element. Specifically, it uses Tesseract OCR for text extraction and OpenCV for image and graph analysis.

[1179] Step 3:

[1180] The server analyzes the content of the presentation materials based on the extracted information. The input is the data extracted in step 2. The server analyzes this data using natural language processing to understand the content of the presentation. Specifically, it performs semantic analysis of the text using NLTK or SpaCy. The output is the analysis results.

[1181] Step 4:

[1182] The server retrieves the listener's attribute information from the sales history database. The input is a database query. The server executes an SQL query to extract information such as the listener's occupation, interests, and level of understanding. The output is the listener's attribute information.

[1183] Step 5:

[1184] The server uses the generative AI model to automatically generate feedback based on the analysis results and listener attribute information. The inputs are the analysis results from step 3 and the listener attribute information from step 4. The server inputs this data into the generative AI model and generates appropriate feedback. Specific operations include inputting data into the generative AI model and running the feedback generation algorithm. The generated feedback is obtained as the output.

[1185] Step 6:

[1186] The server sends the generated feedback to the device and logs the completion of the transmission. As input, there is the feedback generated in step 5. The server sends the feedback to the device via an HTTP request and logs the successful transmission. As output, the feedback transmission to the device is completed and the log is updated.

[1187] Step 7:

[1188] The terminal displays the feedback received from the server to the user. The input is the feedback received from the server. The terminal visually displays the feedback content so that the user can check it. The specific operation is to display the feedback on the interface. The output is a state in which the user can check the feedback.

[1189] Step 8:

[1190] The user modifies the presentation materials based on the feedback displayed on the device. The input is the feedback displayed on the device. The user checks the feedback and modifies the materials. Specifically, the user uses editing software to improve the materials. The output is a revised version of the presentation materials that reflects the feedback.

[1191] (Application example 1)

[1192] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1193] Currently, many autonomous vehicles lack the ability to grasp the driving situation in real time and provide appropriate feedback based on passenger attributes. This makes it difficult to ensure passenger safety and provide a comfortable driving experience in situations such as sudden braking or sudden changes in control. It is also difficult for users to obtain appropriate feedback in real time based on the audience when analyzing and improving presentation materials. A system that can solve these problems is needed.

[1194] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1195] In this invention, the server includes means for receiving presentation materials and extracting information about text, graphs, illustrations, layout, and color; means for referencing listener attribute information and generating appropriate feedback; and means for transmitting the feedback so that the user can confirm it. This improves the quality of the materials and enables efficient communication. The server also includes means for analyzing information about the driving situation and generating feedback in real time; means for providing visual and audio feedback to the driver and passengers; and means for customizing the feedback based on passenger attribute information. This enables appropriate feedback according to the driving situation in an autonomous vehicle and a safe and comfortable driving experience.

[1196] A "presentation material" is a document in slide format that is created by a user to convey specific information to others.

[1197] "Text" refers to character information, such as information expressed in the form of sentences or explanations.

[1198] A "graph" is a diagram such as a bar graph, line graph, or pie chart that visually represents data or information.

[1199] "Illustrations" are visual materials such as diagrams, pictures, and paintings that complement written or linguistic information.

[1200] "Layout" refers to how elements such as text and figures are laid out in presentation materials.

[1201] "Color" refers to color information that evokes visual sensations in presentation materials and display devices.

[1202] "Listener attribute information" refers to the occupation, interests, level of understanding, and other individual characteristic information of the people who will be listening to the presentation.

[1203] "Feedback" is return information that provides information such as analysis results and points for improvement to the user.

[1204] "Driving conditions" refers to information acquired by an autonomous vehicle based on its current driving state and surrounding environmental conditions.

[1205] "Real-time" means that information is processed simultaneously with the actual passage of time and is reflected immediately without delay.

[1206] A "driver" is a person who monitors an autonomous vehicle and takes manual control as needed.

[1207] A "passenger" is a person who is in an autonomous vehicle but who is not driving.

[1208] "Visual feedback" is information that is visually presented using text, diagrams, colors, etc.

[1209] "Audio feedback" refers to conveying information using sound, such as an audio message played over a speaker.

[1210] "Sensor information" refers to data acquired by sensors in autonomous vehicles, such as the vehicle's speed, the presence or absence of obstacles, and road conditions.

[1211] "Customization" means changing settings and content according to individual needs and attributes.

[1212] overview

[1213] This invention is a system that provides real-time feedback based on the driving situation in an autonomous vehicle and generates customized feedback according to passenger attribute information. It also has the function of analyzing the content of presentation materials from multiple angles and providing feedback tailored to the audience in order to improve the quality of presentation materials.

[1214] Hardware and software used

[1215] Hardware

[1216] Smart glasses, head-mounted displays

[1217] software

[1218] MultiModalAnalysisEngine

[1219] FeedbackGenerator (feedback generation engine)

[1220] Vehicle Sensors (Vehicle Sensor Modules)

[1221] FeedbackDisplay (Feedback Display Module)

[1222] System configuration and operation

[1223] 1. Server-side processing

[1224] The server first receives the presentation materials and extracts information about the text, graphs, illustrations, layout, and colors contained in the materials. During this process, the server uses the MultiModalAnalysisEngine to analyze the materials from multiple angles and saves the analysis results in a database. The server then obtains listener attribute information from the sales history database and uses the FeedbackGenerator to generate appropriate feedback based on that information. Information about the driving situation is obtained in real time from the VehicleSensors, and the driving situation is analyzed, providing visual and audio feedback to the driver and passengers.

[1225] 2. Terminal processing

[1226] The terminal provides an interface for the user, allowing them to upload presentation materials and input audience demographic information. Feedback received from the server is visually displayed on the terminal screen, allowing the user to review the feedback and improve the materials. The terminal in the autonomous vehicle also displays real-time feedback based on the driving situation and provides warning messages and other important information to the user and passengers.

[1227] 3. User-side processing

[1228] Users upload presentation materials and input audience demographic information via their devices. The materials are then revised and improved based on feedback received from the server. Additionally, the driver in the autonomous vehicle checks the feedback provided in real time and responds accordingly to the driving situation.

[1229] Specific examples

[1230] As a concrete example, consider a scenario in which a user uploads a presentation document for "introducing a new product for technical professionals." This document includes the product's features and technical specifications. The user inputs that the audience is "technical," "has a high level of understanding," and "prefers simple designs." The server analyzes this information and generates feedback such as "simplify the use of colors in graphs" or "add detailed technical information," and sends it to the device. The user then checks this feedback and improves the presentation document.

[1231] Prompt Sentence Examples

[1232] As a concrete example, the following prompt sentence is input into the generative AI model:

[1233] If the driving situation detects sudden braking, create a program that generates a safety warning message for passengers (especially children) and displays it on the dashboard and head-mounted display.

[1234] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1235] Step 1:

[1236] The server receives presentation materials from the terminal. These materials are slide-format documents created by users to convey specific information to others, and contain information such as text, graphs, illustrations, layout, and color. The input is the presentation materials, and the output is the document data to be analyzed.

[1237] Step 2:

[1238] The server launches the MultiModalAnalysisEngine and performs a multifaceted analysis of the presentation materials. During this analysis process, text information is extracted and graphical information from graphs and illustrations is deciphered. The layout and color usage of the slides are also evaluated. The input is the data of the materials to be analyzed, and the output is the analysis results data.

[1239] Step 3:

[1240] The server obtains the listener's attribute information from the sales history database, which includes the listener's occupation, interests, level of understanding, etc. The input is the listener's identification information, and the output is the listener's attribute information.

[1241] Step 4:

[1242] The server uses a FeedbackGenerator to generate appropriate feedback based on the acquired listener attribute information and analysis result data. For example, it might suggest adding detailed technical information to a listener in a technical job, or suggest improving the color layout for a listener who prioritizes visuals. The input is the listener attribute information and analysis result data, and the generated feedback data is obtained as output.

[1243] Step 5:

[1244] The server receives real-time information about the driving situation from VehicleSensors, including the vehicle speed, distance to obstacles, road conditions, etc. The input is sensor information, and the output is driving situation data.

[1245] Step 6:

[1246] The server analyzes the driving situation data and generates real-time feedback to the driver and passengers. For example, if sudden braking is detected, it generates an appropriate warning message. The input is driving situation data, and the output is real-time feedback.

[1247] Step 7:

[1248] The server customizes the feedback based on the passenger's attributes, for example, by providing a gentler tone of warning if a child is on board. The input is the passenger's attributes and real-time feedback, and the output is the customized feedback.

[1249] Step 8:

[1250] The server sends the generated feedback to the terminal, which displays the feedback visually and audibly for the user and passengers to review. The input is customized feedback data, and the output is feedback provided visually and audibly.

[1251] Step 9:

[1252] Users upload presentation materials via their terminals and input audience attribute information. They also revise and improve the materials based on feedback provided by the server. Feedback data from the server is input, and improved presentation materials are obtained as output.

[1253] This series of steps enables real-time feedback on the presentation materials and the dashboard interface of the autonomous vehicle.

[1254] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1255] MODE FOR CARRYING OUT THE INVENTION

[1256] This invention is a system for efficiently creating and improving presentation materials, and by combining it with an emotion engine that recognizes the user's emotions, it provides more personalized feedback. The specific processing flow and operation at each step are explained below.

[1257] Server-side processing

[1258] The server first receives the presentation materials from the terminal. It then launches a multimodal analysis engine to extract information from the materials, such as text, graphs, illustrations, layout, and color. The analysis engine analyzes the text content of the materials and extracts important points and summaries. At the same time, it interprets the graphs and illustrations and records that information in a database. It also evaluates the slide layout and color usage.

[1259] The server then retrieves the listener's attribute information from the sales history database. This information includes the listener's occupation, interests, and level of understanding. The server generates appropriate feedback based on this data and the analysis results. Furthermore, it uses an emotion engine that recognizes the user's emotions to adjust the feedback content according to the user's emotions. For example, if the user is confused, it adds a more detailed explanation, and if the user is satisfied, it provides feedback that includes words of praise.

[1260] The generated feedback is sent to the device for the user to review, and the server records the completion of the feedback transmission in a log.

[1261] Terminal side processing

[1262] The device provides a user-friendly interface for uploading presentation materials, inputting audience attribute information, and checking the generated feedback. First, the user uploads the presentation materials, and the device sends the materials to the server. Next, the user inputs the audience attributes and comprehension level, and sends the information to the server.

[1263] After receiving the feedback, the device visually displays it to the user. Furthermore, the emotion engine recognizes the user's emotions in real time and adjusts the feedback content as needed. By providing feedback on the presentation materials uploaded by the user, areas for improvement can be clearly identified, allowing for efficient revisions.

[1264] User-side processing

[1265] Users use the system to upload presentation materials and input audience demographic information. Their emotions are then recognized through the emotion engine, and they receive feedback based on this. As a concrete example, consider a user creating highly technical materials who sets a specific audience as "technical professionals" with a "high level of understanding." If the emotion engine recognizes the user as confused, feedback could include adding more detailed technical information or simplifying the use of colors.

[1266] This allows users to receive more personalized advice tailored to their emotional state, rather than simply receiving feedback based on data analysis, allowing them to create and improve presentation materials efficiently and effectively.

[1267] Specific examples

[1268] As a concrete example, consider a scenario in which a user uploads a presentation document for "introducing a new product." This document includes the product's features and technical specifications. The user inputs that the audience is "technical," "high level of understanding," and "prefers simple design." If the emotion engine recognizes that the user is nervous, the feedback will include specific improvements such as "add detailed technical information" or "simplify the coloring of graphs," and an encouraging message such as "please remain calm and proceed."

[1269] This system not only allows for the improvement of presentation materials to suit the audience, but also provides feedback that takes into account the user's emotions, thereby improving the quality of presentation materials and providing more effective support for the user's presentation itself.

[1270] The processing flow will be explained below.

[1271] Step 1:

[1272] Users upload presentation materials to their devices, which then receive the materials and send them to the server.

[1273] Step 2:

[1274] The server receives the presentation materials sent from the terminal and stores them in a specific folder.

[1275] Step 3:

[1276] The server launches a multimodal analysis engine to extract information such as the document's text, graphs, illustrations, layout, and color.

[1277] Step 4:

[1278] The server uses an analytics engine to analyze the text content of each slide to extract key points and summaries, and to interpret information in graphs and illustrations to identify their meaning.

[1279] Step 5:

[1280] The server analyzes the layout and color of each slide and performs a visual evaluation, storing the results in a database.

[1281] Step 6:

[1282] The user inputs the listener's attributes and comprehension level using a form on the device, and the device sends this information to the server.

[1283] Step 7:

[1284] The server acquires attribute information of the corresponding listener from the business history database and combines the input information.

[1285] Step 8:

[1286] The server generates appropriate feedback based on the listener's attribute information, such as suggestions for adding detailed technical information or improving color usage.

[1287] Step 9:

[1288] The user views the presentation materials through the terminal, and the terminal activates an emotion engine to recognize the user's emotions at that time.

[1289] Step 10:

[1290] The emotion engine analyzes the user's facial expressions, tone of voice, input actions, etc. in real time to collect user emotional data.

[1291] Step 11:

[1292] The server receives the emotion data from the emotion engine and reflects it in the feedback content, for example, adding detailed explanations if the user is confused.

[1293] Step 12:

[1294] The generated feedback is sent from the server to the device, which then visually displays it to the user, including emotional encouragement or advice as needed.

[1295] Step 13:

[1296] The user can review the feedback displayed on their device, revise their presentation materials, and, if necessary, re-upload the revised materials and repeat the process.

[1297] This series of steps allows users to individually customize their presentation materials and improve them efficiently while receiving emotional support.

[1298] Example 2

[1299] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1300] Conventional presentation material creation systems provide feedback based on content analysis of materials and audience attribute information, but they lack the ability to provide personalized feedback that takes into account the user's emotions. This makes it difficult to improve materials that are optimized for each user's level of understanding and emotional state.

[1301] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1302] In this invention, the server includes means for receiving presentation materials and extracting information about text, charts, drawings, layout, and color, means for referencing listener attribute information to generate appropriate feedback, and means for recognizing the user's emotions and adjusting the feedback content, thereby making it possible to provide feedback optimized for the user's emotional state and improve the quality of the presentation materials.

[1303] A "presentation" is a document or slides that a user uses to visually present specific information.

[1304] "Text" refers to the written information contained in the presentation materials.

[1305] A "diagram" is a graph or chart that visually displays statistical data or information contained in presentation materials.

[1306] "Drawings" refer to illustrations and pictures included in presentation materials.

[1307] "Layout" refers to the layout and position of each element (text, charts, drawings, etc.) in a presentation document.

[1308] "Color" refers to the color combinations and color schemes used in presentation materials.

[1309] "Audience attribute information" refers to information that indicates the characteristics of the person or group to whom the presentation is directed, and includes occupation, interests, level of understanding, and the like.

[1310] "Feedback" refers to advice and evaluation that provides users with improvements and supplementary information for their presentation materials.

[1311] "Emotion recognition" is a technology that determines a user's emotional state from their facial expressions and voice.

[1312] A "multimodal analysis engine" is an engine for integrated processing and analysis of multiple data formats, such as text analysis, chart analysis, and image analysis.

[1313] The "emotion analysis engine" is an engine for analyzing the user's emotional state, and has the functions of facial expression recognition and voice analysis.

[1314] A "sales history database" is a database that includes attribute information of customers and listeners, history of past sales activities, etc.

[1315] "Real-time" means processing and feedback in real time, synchronized with the current time.

[1316] The present invention is a system that analyzes presentation materials uploaded by a user and generates appropriate feedback based on the listener's attribute information and the user's emotional recognition. Specific embodiments are described below.

[1317] Server-side processing

[1318] The server first receives the presentation materials from the device. It then uses a multimodal analysis engine (such as an NLP engine or CV engine) to extract information from the materials, such as text, charts, drawings, layout, and color. The analysis engine analyzes the text in the materials and extracts important key points and summaries. At the same time, it interprets graphs and illustrations and stores them in a database. It also evaluates the slide layout and color usage.

[1319] Next, the server retrieves the listener's attribute information from the sales history database. This information includes the listener's occupation, interests, and level of understanding. Based on this attribute information and the analysis results, a generative AI model is used to generate appropriate feedback. The content of the feedback is adjusted according to the user's emotional state, so an emotion recognition engine (such as facial expression recognition and voice analysis) is also used.

[1320] The generated feedback is sent to the terminal so that the user can check it, and the server records the feedback sending status in a log.

[1321] Terminal side processing

[1322] The terminal provides a user interface that allows the user to easily upload presentation materials. Once the user uploads the materials, the terminal sends the materials to the server. Next, the user inputs the audience's demographic information (e.g., occupation, level of understanding, interests, etc.), and sends this information to the server as well.

[1323] The device that receives the feedback visually displays the content and uses an emotion recognition engine in real time to analyze the user's emotions and adjust the feedback content as needed. This process allows users to efficiently identify areas for improvement in the material and make optimal adjustments.

[1324] User-side processing

[1325] Users can upload presentation materials and input audience demographic information through the system interface, and receive more personalized feedback by utilizing an emotion engine that recognizes emotions through facial expressions and voice recognition.

[1326] As a concrete example, consider the case where a user uploads a presentation document introducing a new product. This document includes the product's features and technical specifications. The user inputs that the audience is technical, has a high level of understanding, and prefers simple designs. If the emotion engine recognizes that the user is nervous, the feedback will include specific improvements such as "add detailed technical information" or "simplify the coloring of graphs," and an encouraging message such as "please remain calm and proceed."

[1327] Example prompts for generative AI models

[1328] "I uploaded a presentation for a new product. My audience is technical, has a high level of understanding, and prefers simple designs. The current presentation includes product features and technical specifications. The emotion engine recognizes that I'm nervous. Please provide specific improvements and feedback."

[1329] In this way, through specific embodiments of the invention, users can improve the quality of their presentation materials.

[1330] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1331] Step 1: Upload your presentation materials

[1332] Input: The user selects presentation materials through the terminal interface.

[1333] Specific behavior: Displays a file selection dialog in the user interface and sends the file selected by the user to the server via an HTTP request.

[1334] Output: Presentation materials are sent to the server

[1335] Step 2: Conducting data analysis

[1336] Input: The server receives the presentation materials from the terminal.

[1337] Specific operation: Launches the multimodal analysis engine to extract and analyze information such as text, charts, drawings, layout, and color.

[1338] Data processing:

[1339] Text analysis: Extracting important key points from text using an NLP engine

[1340] Chart analysis: Analyzes chart types and data using the CV engine and records them in the database

[1341] Layout and color evaluation: Using specific algorithms to evaluate the layout and color of slides

[1342] Output: The analysis results are stored in a database and serve as input for generating feedback.

[1343] Step 3: Obtaining listener demographic information

[1344] Input: Sales history database

[1345] Specific operation: The server executes an SQL query to obtain attribute information such as the listener's occupation, interests, and level of understanding.

[1346] Output: The acquired attribute information is stored in memory and serves as input for generating feedback.

[1347] Step 4: Perform emotion recognition

[1348] Input: User's facial expression data or voice data

[1349] Specific behavior:

[1350] Facial expression recognition: Captures the user's facial expressions in real time using the device's camera and analyzes them with the emotion engine.

[1351] Voice analysis: Records the user's voice using the device's microphone and recognizes emotions using the voice analysis engine

[1352] Output: Data on the user's emotional state is obtained, which serves as the input for generating feedback.

[1353] Step 5: Generate feedback

[1354] Input: Data analysis results, listener attribute information, user emotional state

[1355] Specific operation: The server uses a generative AI model to generate optimal feedback based on the prompt sentence.

[1356] An example of a specific prompt: "I've uploaded a presentation for a new product. My audience is technical, has a high level of understanding, and prefers simple designs. The current presentation includes product features and technical specifications. The emotion engine recognizes that I'm nervous. Please provide specific improvements and feedback."

[1357] Output: The generated feedback is sent to the terminal.

[1358] Step 6: Submit your feedback

[1359] Input: Feedback generated by a generative AI model

[1360] Specific operation: The server converts the feedback data into a format such as JSON and sends it to the device as an HTTP response.

[1361] Output: Feedback is sent to the device

[1362] Step 7: Review and adjust feedback

[1363] Input: Feedback data sent to the device

[1364] Specific operation: The device visually displays the feedback content in the user interface. It also re-runs the emotion recognition engine in real time and adjusts the feedback content as needed.

[1365] Output: Users can see feedback and identify specific areas for improvement in the material

[1366] (Application example 2)

[1367] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1368] Creating presentation materials and providing feedback on them requires consideration of audience demographic information and real-time sentiment analysis. However, current systems are unable to effectively utilize this information, making it difficult to provide prompt feedback that specifically identifies areas for improvement in the materials. Furthermore, brick-and-mortar stores lack systems that can directly grasp customer reactions in real time and respond immediately. Therefore, an effective method is needed to improve the quality of presentations and increase customer satisfaction.

[1369] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1370] In this invention, the server includes means for receiving presentation materials and extracting information about text, graphs, illustrations, layout, and color, means for referencing listener attribute information and generating appropriate feedback, means for transmitting the feedback so that the user can confirm it, means for analyzing the customer's facial expressions and tone of voice to analyze their reactions in real time, and means for visually displaying appropriate feedback based on the analysis results. This enables efficient creation and improvement of presentation materials and real-time feedback based on customer reactions.

[1371] A "presentation material" is a collection of information that includes elements such as text, slides, graphs, illustrations, layout, and color to convey information in a visual format.

[1372] "Text" refers to information expressed using letters and symbols.

[1373] A "graph" is a graphic that visually represents numerical data, and includes bar graphs, line graphs, pie charts, etc.

[1374] "Illustration" refers to a picture or diagram that visually supplements information.

[1375] "Layout" is a concept that refers to the alignment and relative positions of elements such as text, graphs, illustrations, and colors in presentation materials.

[1376] "Color" refers to the elements that are perceived as visual information by the three primary colors of light and their combinations.

[1377] "Feedback" refers to the response or evaluation provided to a user's actions or input.

[1378] "Listener attribute information" refers to individual data such as the occupation, interests, and level of understanding of the people listening to the presentation.

[1379] A "multimodal analysis engine" refers to technology that simultaneously analyzes multiple data formats, such as text, images, and audio.

[1380] "Real-time" is a time concept that refers to producing immediate responses or results the moment data is collected or processed.

[1381] "Analyzing facial expressions and tone of voice" refers to technology that analyzes an individual's facial expressions, voice pitch, strength, rhythm, etc., to mechanically determine that person's emotions and reactions.

[1382] "Visually displaying" refers to showing information to a user using visual elements such as text and images.

[1383] To realize this invention, a system having the following elements is required.

[1384] 1. Overall structure

[1385] The system is mainly composed of three entities: the server, the terminal, and the user. Each entity has a specific role and function.

[1386] 2. Server-side processing

[1387] The server has the following functions:

[1388] Receiving and analyzing presentation materials

[1389] The server receives presentation materials from the user's device and uses a multimodal analysis engine to extract information about text, graphs, illustrations, layout, and color. Specifically, the server analyzes the materials using Google Cloud Vision API and other tools.

[1390] Acquiring customer attribute information and generating feedback

[1391] The server references the listener's attribute information (such as occupation, interests, and level of understanding) and uses a generative AI model to generate appropriate feedback. This information is obtained from a database such as Firebase.

[1392] Analysis of customer facial expressions and tone of voice and real-time feedback

[1393] The server uses the Microsoft Azure Emotion API to analyze the customer's facial expressions and tone of voice, and analyzes their reactions in real time. Based on the analysis results, the server sends the data to the device to visually display appropriate feedback.

[1394] 3. Terminal processing

[1395] The device has the following features:

[1396] Upload presentation materials

[1397] The terminal transmits the presentation materials created by the user to the server.

[1398] Entering customer attribute information

[1399] The terminal provides an interface through which the user can input attribute information of listeners and transmit the information to the server.

[1400] Receiving and viewing feedback

[1401] The terminal receives the feedback sent from the server and visually displays it. Using a device such as smart glasses, the feedback is displayed in the user's field of vision in real time.

[1402] 4. User-side processing

[1403] The user performs the following operations:

[1404] Creating and uploading presentation materials

[1405] Users create presentation materials using smart glasses or tablets and upload them to the server via their devices.

[1406] Entering customer attribute information

[1407] Users input listener attribute information through their terminals, which allows the server to generate more personalized feedback.

[1408] Review and improve feedback

[1409] Users can check the feedback displayed on their devices and improve their presentation materials, adjusting the content of their materials and presentations based on real-time customer responses.

[1410] Specific examples

[1411] Example 1

[1412] Consider a scenario where a user is uploading a presentation document introducing a new product for technical professionals. The user enters that the audience is technical, has a high level of understanding, and prefers simple designs. If the system recognizes that the user is nervous, it will provide feedback with specific suggestions for improvement, such as "add more detailed technical information" or "simplify the coloring of graphs," as well as encouraging messages such as "please remain calm and proceed."

[1413] Prompt Sentence Examples

[1414] "Presentation begins. Use material A-123. Please enter customer attributes."

[1415] "Presentation material B-456 uploaded. The client is a technical professional. High level of understanding."

[1416] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1417] Program processing flow and specific explanation

[1418] Step 1: Upload your presentation materials

[1419] Device: The user uploads the presentation materials they created using smart glasses or a tablet to the device. Specifically, the user clicks the "upload" button on the document creation app. The input is the presentation material file, and the output is the document data sent to the server.

[1420] Step 2: Receiving and analyzing data

[1421] Server: The server receives the presentation materials sent from the device. It analyzes the received materials using a multimodal analysis engine such as Google Cloud Vision API to extract text, graphs, illustrations, layout, and color. The input is the received material data, and the output is structured data resulting from the analysis. Specifically, the server inputs the material data into the analysis engine and extracts the data according to a specific format.

[1422] Step 3: Enter customer attribute information

[1423] Terminal: The user uses the terminal interface to input the listener's attribute information (e.g., occupation, level of understanding, interests). The input is the customer attribute information entered by the user, and the output is the attribute information data sent to the server. Specifically, the user enters information into a selection box or text field and clicks the send button.

[1424] Step 4: Obtain customer attribute information

[1425] Server: The server retrieves customer attribute information from a database such as Firebase and integrates it with the attribute information entered by the user. The input is the attribute information data sent from the device and existing customer information retrieved from the database, and the output is the integrated customer attribute information.

[1426] Step 5: Generate feedback

[1427] Server: The server uses a generative AI model to generate appropriate feedback based on the customer attribute information integrated with the analysis results. Specifically, it inputs the generated prompt sentence into the generative AI model and outputs the feedback content. The input is customer attribute information integrated with the analysis results, and the output is the generated feedback data.

[1428] Step 6: Facial Expression and Tone Analysis

[1429] Server: During the presentation, the server receives real-time video and audio data from the smart glasses' camera and microphone, and uses the Microsoft Azure Emotion API to analyze the customer's facial expressions and tone of voice. The input is real-time video and audio data, and the output is the analyzed emotional data. Specifically, the server inputs the acquired video and audio data into the emotion analysis engine, which then analyzes the emotional data.

[1430] Step 7: Generate real-time feedback

[1431] Server: Based on the results of facial expression and tone analysis, the server generates appropriate feedback in real time and sends it to the device. The input is the emotion data from the analysis, and the output is real-time feedback data.

[1432] Step 8: View your feedback

[1433] Terminal: The terminal receives feedback from the server and displays it in real time on the display of smart glasses or a tablet. The input is the feedback data sent from the server, and the output is the visually displayed feedback content. Specifically, the terminal performs a rendering process to display the received feedback data on the display.

[1434] Prompt Sentence Examples

[1435] "Presentation begins. Use material A-123. Please enter customer attributes."

[1436] "Presentation material B-456 uploaded. The client is a technical professional. High level of understanding."

[1437] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1438] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1439] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1440] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1441] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1442] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1443] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1444] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1445] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1446] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1447] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1448] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1449] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1450] 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.

[1451] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1452] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1453] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[1454] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1455] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1456] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1457] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1458] The following is further disclosed regarding the above embodiment.

[1459] (Claim 1)

[1460] means for receiving presentation materials and extracting information regarding text, graphs, illustrations, layout, and color;

[1461] A means for generating appropriate feedback by referring to attribute information of a listener;

[1462] A means for submitting and allowing users to review said feedback;

[1463] A system including:

[1464] (Claim 2)

[1465] A means of analyzing the material using a multimodal analysis engine;

[1466] A means to suggest slides tailored to specific audiences;

[1467] 10. The system of claim 1, comprising:

[1468] (Claim 3)

[1469] A means to refer to a sales history database and customize materials based on the listener's attributes and level of understanding,

[1470] a visual display of the feedback to allow users to improve the material; and

[1471] 10. The system of claim 1, comprising:

[1472] "Example 1"

[1473] (Claim 1)

[1474] means for receiving presentation materials and extracting information regarding text, graphs, illustrations, layout, and color;

[1475] A means for generating appropriate feedback by referring to attribute information of a listener;

[1476] A means for submitting and allowing users to review said feedback;

[1477] A means for inputting attribute information of a listener through a terminal;

[1478] A means to log the successful submission of feedback;

[1479] A system including:

[1480] (Claim 2)

[1481] A means of analyzing the material using a multimodal analysis engine;

[1482] A means to suggest slides tailored to specific audiences;

[1483] A means of automatically generating feedback using a generative AI model; and

[1484] 10. The system of claim 1, comprising:

[1485] (Claim 3)

[1486] A means to refer to a sales history database and customize materials based on the listener's attributes and level of understanding,

[1487] a visual display of the feedback to allow users to improve the material; and

[1488] a means for recording a history of feedback submissions in a log;

[1489] 10. The system of claim 1, comprising:

[1490] "Application Example 1"

[1491] (Claim 1)

[1492] means for receiving presentation materials and extracting information regarding text, graphs, illustrations, layout, and color;

[1493] A means for generating appropriate feedback by referring to attribute information of a listener;

[1494] A means for submitting and allowing users to review said feedback;

[1495] a means for analyzing information about the driving situation and generating feedback in real time;

[1496] a means of providing visual and audio feedback to the driver and passengers;

[1497] A means to customize feedback based on passenger demographic information; and

[1498] A system including:

[1499] (Claim 2)

[1500] A means of analyzing the material using a multimodal analysis engine;

[1501] A means to suggest slides tailored to specific audiences;

[1502] A means of acquiring and analyzing sensor information on driving conditions in real time,

[1503] A means to customize feedback based on passenger attributes; and

[1504] 10. The system of claim 1, comprising:

[1505] (Claim 3)

[1506] A means to refer to a sales history database and customize materials based on the listener's attributes and level of understanding,

[1507] a visual display of the feedback to allow users to improve the material; and

[1508] means for detecting changes in operating conditions and generating warning messages for passengers;

[1509] 10. The system of claim 1, comprising:

[1510] "Example 2: Combining Emotion Engines"

[1511] (Claim 1)

[1512] means for receiving the presentation material and extracting information regarding text, charts, drawings, layout, and color;

[1513] A means for generating appropriate feedback by referring to attribute information of a listener;

[1514] a means for recognizing a user's emotions and adjusting the feedback content;

[1515] A means for submitting and allowing users to review said feedback;

[1516] A system including:

[1517] (Claim 2)

[1518] A means of analyzing the material using a multimodal analysis engine;

[1519] A means to suggest slides tailored to specific audiences;

[1520] A means for recognizing user emotions using a sentiment analysis engine and adjusting the feedback content;

[1521] 10. The system of claim 1, comprising:

[1522] (Claim 3)

[1523] A means to refer to a sales history database and customize materials based on the listener's attributes and level of understanding,

[1524] a visual display of the feedback to allow users to improve the material; and

[1525] A means of recognizing user emotions in real time and adjusting feedback content;

[1526] 10. The system of claim 1, comprising:

[1527] "Application example 2 when combining emotion engines"

[1528] (Claim 1)

[1529] means for receiving presentation materials and extracting information regarding text, graphs, illustrations, layout, and color;

[1530] A means for generating appropriate feedback by referring to attribute information of a listener;

[1531] A means for submitting and allowing users to review said feedback;

[1532] A means of analyzing customer facial expressions and tone of voice to analyze reactions in real time,

[1533] a means for visually displaying appropriate feedback based on the analysis results;

[1534] A system including:

[1535] (Claim 2)

[1536] A means of analyzing the material using a multimodal analysis engine;

[1537] A means to suggest slides tailored to specific audiences;

[1538] A means of real-time facial recognition and voice analysis of customers,

[1539] 10. The system of claim 1, comprising:

[1540] (Claim 3)

[1541] A means to refer to a sales history database and customize materials based on the listener's attributes and level of understanding,

[1542] a visual display of the feedback to allow users to improve the material; and

[1543] A means of providing feedback based on customer responses in real time;

[1544] 10. The system of claim 1, comprising: [Explanation of symbols]

[1545] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. means for receiving presentation materials and extracting information regarding text, graphs, illustrations, layout, and color; A means for generating appropriate feedback by referring to attribute information of a listener; A means for submitting and allowing users to review said feedback; A system including:

2. A means of analyzing the material using a multimodal analysis engine; A means to suggest slides tailored to specific audiences; The system of claim 1 , comprising:

3. A means to refer to a sales history database and customize materials based on the listener's attributes and level of understanding, a visual display of the feedback to allow users to improve the material; and The system of claim 1 , comprising:

Images