system

Abstract

We provide the system. [Solution] Means of acquiring images, Means for sending the image to the server, The server performs image analysis and provides means for identifying objects. Means for retrieving information related to an identified object, A means for summarizing the information and transmitting it to a terminal, A means of displaying the transmitted information, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] There is a problem that it is difficult for a user to quickly and efficiently obtain detailed information about many objects that the user encounters in daily life. In particular, when the name and characteristics of an object are unknown, it is difficult to quickly obtain information by conventional information search methods. In addition, there is a lack of means for providing information in a form that is easy for users to understand while ensuring the reliability of the information.

Means for Solving the Problems

[0005] This invention proposes a system in which a user acquires an image of an object using an image acquisition means and transmits the image to a server. The server identifies the object using an image analysis means and retrieves information related to the identified object. Furthermore, it summarizes this information and transmits it to a terminal, displaying the information to the user. This enables rapid and efficient information provision regarding unknown objects, allowing users to gain useful insights.

[0006] "Means of acquiring images" refers to a function that allows a user to take an image of any object and input it into the system as digital data.

[0007] "Means of sending to the server" refers to communication functions for sending acquired image data to a server via a network such as the internet.

[0008] "Means for performing image analysis and identifying objects" refers to the process of using artificial intelligence technology to identify what an object in an image is based on the received image data.

[0009] "Means of searching for relevant information" refers to the process of searching for and obtaining information related to an identified object from databases on the internet or from reliable sources.

[0010] "Means of summarizing information and sending it to a device" refers to a function that concisely summarizes acquired information in a way that is easy for the user to understand and then transfers it to the device.

[0011] "Means of displaying information" refers to an interface for visually showing summarized information on a terminal's display. [Brief explanation of the drawing]

[0012] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] It shows an emotion map to which multiple emotions are mapped. [Figure 10] It shows an emotion map to which multiple emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [[ID=3*]] [[ID=**]] [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when an emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when an emotion engine is combined.

Mode for Carrying Out the Invention

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

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

[0015] It should be noted that there are some errors in the original text tags in the content you provided. For example, in the "[[ID=3*]]" and "[[ID=**]]" parts, the asterisks should be numbers. I have translated it according to the correct understanding. If you have any other questions, please feel free to let me know.In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

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

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

[0018] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor and an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark), and the like.

[0019] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0020] [First Embodiment]

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

[0022] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0023] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0024] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0025] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0026] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0027] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0029] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0030] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0031] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0032] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0033] This invention is a system that allows users to instantly obtain detailed information about objects around them. First, the user takes an image of an object of interest using the camera on their device. The device converts the obtained image into a predetermined format and transmits it to a server via the network.

[0034] The server processes the received images using image analysis algorithms to quickly and accurately identify objects. This process may utilize machine learning techniques such as convolutional neural networks (CNNs). Identified objects are then assigned relevant labels and tags.

[0035] The server then uses the identified labels to search the internet for relevant information. The search targets reliable sources such as encyclopedias, specialized websites, and review articles. The collected information is then summarized by AI to generate concise and easy-to-understand text.

[0036] The generated information is sent from the server to the terminal, which then displays the information on its screen. This allows the user to visually confirm detailed information about an object.

[0037] As a concrete example, if a user finds a plant they've never seen before, they point their device's camera at it and take a picture. The server identifies the type of plant and recognizes it as "rosemary." Next, it compiles information about the characteristics, uses, and health benefits of rosemary and sends it to the device. This allows the user to gain the necessary knowledge about the plant.

[0038] This system allows users to instantly deepen their understanding of objects and gain new knowledge.

[0039] The following describes the processing flow.

[0040] Step 1:

[0041] The user launches a dedicated app on their device and points the camera at an object to take a picture. At this time, the camera is set to capture the details of the object in high resolution.

[0042] Step 2:

[0043] The device compresses the captured image as digital data and sends it to the server via the network. During this process, the data is encrypted to maintain security.

[0044] Step 3:

[0045] The server adds the received image data to a queue for analysis. Here, the image data is converted back into a format that allows for high-speed processing.

[0046] Step 4:

[0047] An image analysis engine on the server processes the images and applies artificial intelligence algorithms to recognize objects. Appropriate labels and tags are then assigned to the recognized objects.

[0048] Step 5:

[0049] The server uses identified labels to search the internet for relevant information. Information sources are limited to those that have been verified for reliability and timeliness.

[0050] Step 6:

[0051] The server inputs the information obtained from the search into a natural language processing engine and generates a summarized text. This text is structured to be easily understood by the user.

[0052] Step 7:

[0053] The server sends the generated summary information to the terminal. The data is encrypted again before transmission, ensuring privacy and security.

[0054] Step 8:

[0055] The device decodes the received information and displays it on the screen for the user. The information may also be presented with visual elements such as diagrams and graphs.

[0056] Step 9:

[0057] The user can refer to the displayed information and request further information if necessary. In this case, if it is determined that additional information is needed, the process repeats from step 5 onward.

[0058] (Example 1)

[0059] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0060] In modern times, people are required to obtain information quickly and accurately about unfamiliar objects and phenomena they encounter in their daily lives. However, conventional information retrieval systems require users to individually input search terms and sift through reliable information, making information acquisition time-consuming and laborious. Furthermore, the reliability of the information and the ease of understanding the summarized information were also challenges.

[0061] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0062] In this invention, the server includes means for performing image analysis on a computing device to identify an object, means for searching for data related to the identified object, and means for summarizing the data using a generative artificial intelligence model. This enables users to quickly and reliably obtain detailed information about unknown objects and understand it intuitively.

[0063] "Means for acquiring images" refers to the function that allows the user to capture surrounding objects using a camera.

[0064] "Means of transmitting to a computing device via a communication infrastructure" refers to a function for delivering image data to a computing device via a network.

[0065] A "computer device" is a computer system that analyzes received data and performs information processing.

[0066] "Means for performing image analysis and identifying an object" refers to the process by which a computing device analyzes image data and identifies the captured object.

[0067] "Means of searching for data" refers to a function that allows a computing device to collect additional information about an identified object from the internet.

[0068] "Methods for summarizing data using generative artificial intelligence models" refer to artificial intelligence technologies used to analyze large amounts of information and summarize it in a form that is easy for users to understand.

[0069] A "display device" is a display device that allows users to visually confirm information.

[0070] "Means of return" refers to the function of sending information from a computing device to a display device.

[0071] "Means of collecting analyzed data from reliable sources" refers to the function of obtaining information from databases and websites that are of high quality and reliability.

[0072] This invention is a system that allows users to instantly obtain detailed information about objects around them. First, when a user discovers an object of interest, they use the camera on their device to take a picture of that object. The device can be a smartphone or tablet equipped with a high-performance camera. The captured image is converted to a predetermined format such as JPEG or PNG within the device. Then, this image data is transmitted to a server via the internet through a communication infrastructure. Here, it is common for the image to be sent as a POST request using the HTTP protocol.

[0073] On the server, received image data is first preprocessed and adjusted to an appropriate size and format. The server then runs a convolutional neural network (CNN) using open-source libraries such as TENSORFLOW® and PyTorch to analyze the image. Through this analysis, objects within the image are identified and associated labels and tags are assigned. Based on these identification results, the server searches for relevant information from reliable sources. This search typically utilizes APIs from Wikipedia or specific specialized websites, and web scraping is also performed as needed.

[0074] Next, the server uses a generative AI model to summarize the acquired information and generate concise text. For example, OpenAI's GPT model may be used. The summarized information is sent back to the terminal as an HTTP response, usually in JSON format.

[0075] The device analyzes the received information and displays it to the user in a visually easy-to-understand format. Text size, color, and font are adjusted, and related images are displayed on the screen, allowing the user to visually confirm the information.

[0076] As a concrete example, consider a scenario where a user discovers an unfamiliar plant. When the user takes a picture of this plant and inputs it into the system, the server identifies the plant as "rosemary." Next, information about rosemary, such as its characteristics, uses, and health benefits, is summarized and sent to the user's device. The user can then visually review this information and gain knowledge.

[0077] An example of a prompt message would be, "Identify the plant in this image and provide any relevant information."

[0078] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0079] Step 1:

[0080] The user uses the device's camera to take a picture of an object of interest. The captured image is saved to the device's internal memory. Here, the input is the image information acquired through the camera, and the output is the image file stored on the device.

[0081] Step 2:

[0082] The device converts the acquired image into a predetermined format. For example, it converts a RAW image to JPEG or PNG format. During this process, the image resolution and color depth are optimized. The input is RAW image data, and the output is the converted compressed image data.

[0083] Step 3:

[0084] The terminal sends the converted image data to the server via the network. The HTTP protocol is used, and the image data is uploaded as a POST request. The input is the converted image data, and the output is the data sent to the server.

[0085] Step 4:

[0086] The server first performs preprocessing on the received image data in order to analyze it. This preprocessing includes adjusting the image size and removing noise. The input is the image data sent to the server, and the output is image data that has been prepared in a format that can be analyzed.

[0087] Step 5:

[0088] The server performs image analysis by running a convolutional neural network (CNN). This process uses a pre-trained model to identify objects in the image. The input is pre-processed image data, and the output is label information about the identified objects.

[0089] Step 6:

[0090] The server searches for relevant information based on the identified labels. Here, it uses an API to retrieve information from trusted databases on the internet. The input is the labels of the identified objects, and the output is a dataset containing the relevant information.

[0091] Step 7:

[0092] The server summarizes the acquired information using a generative AI model. In this process, the key points of the acquired information are extracted, and text in a user-friendly format is generated. The input is a dataset of relevant information, and the output is the summarized text.

[0093] Step 8:

[0094] The server returns the summarized information to the terminal. The data is returned in JSON format as an HTTP response. The input is the summarized text, and the output is the JSON data sent to the terminal.

[0095] Step 9:

[0096] The terminal analyzes the received information and displays it visually on its screen. The input here is JSON data received from the server, and the output is detailed information displayed on the terminal's screen. This allows the user to intuitively understand detailed information about an object.

[0097] (Application Example 1)

[0098] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0099] Conventional object recognition systems are required not only to identify objects but also to provide information about them immediately and display it in a way that is directly useful to the user's life. However, existing systems often lack reliability and user-friendliness in their presentation, and they lack the flexibility to provide information in response to additional user requests. The challenge is to solve these problems and provide information that users can intuitively understand and that improves their quality of life.

[0100] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0101] In this invention, the server includes means for analyzing image data to identify an object, means for acquiring information related to the identified object, and means for summarizing the acquired information and transmitting it to an information terminal. This enables users to instantly acquire and understand the latest and most reliable information about objects around them.

[0102] "Image data" refers to visual information captured by cameras or sensors, in a format that can be processed by a computer.

[0103] An "information processing device" refers to a computer system that receives and analyzes image data, and has the function of processing and managing data.

[0104] "Target" refers to an object or item identified through image data analysis, and represents the object from which the user wishes to obtain information.

[0105] "Generative AI technology" refers to technologies that use artificial intelligence to automatically generate, analyze, and summarize data, and includes deep learning models.

[0106] An "information terminal" refers to a device used by a user to receive and display information, and includes smartphones, tablets, and displays.

[0107] An "information provision system" refers to a group of devices that include a series of processes such as analysis, information collection, summarization, and display based on image data, and is a system that provides users with the necessary information.

[0108] The system for implementing this invention first equips an information terminal, such as a smartphone or consumer robot, with a camera to acquire images of objects of interest to the user. The terminal uses this camera to capture image data and transmits this data to a specific information processing device using generative AI technology.

[0109] A server is used as the information processing device, and the server uses a deep learning framework such as TensorFlow to analyze the received image data. The server processes the image data using a convolutional neural network (CNN) to accurately and quickly identify the target. Once the target is identified, the server collects relevant information about that target from reliable databases and online resources, such as Wikipedia or reliable expert websites.

[0110] Next, the server utilizes generative AI models such as GPT-3® in the process of summarizing the collected information. This converts vast amounts of information into a concise and clear text format, making it easy for users to understand. The server then transmits this summarized information to an information terminal, where it is displayed visually. Users can then review the information through their terminal and deepen their practical understanding.

[0111] For example, a knowledge support robot in a home kitchen might use the following prompt to point its camera at a new cooking utensil and acquire information about it: "Please tell me how to use this cooking utensil and what its features are." Based on this, the robot can provide appropriate guidance and additional information in real time.

[0112] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0113] Step 1:

[0114] The user selects an object of interest using an information terminal and acquires image data using the camera. Input is obtained when the user points the camera at the object and presses the shutter button, and the camera module saves this image data to local memory.

[0115] Step 2:

[0116] The terminal converts the acquired image data into a predetermined format and transmits it to the server via the network. The image data is compressed, for example, into JPEG format, and then packetized according to the transmission protocol. The input is the image data, and the output is the data packets sent to the server.

[0117] Step 3:

[0118] The server decodes the received packets and reconstructs the image data. Then, it analyzes the image data using TensorFlow and identifies the target using a convolutional neural network (CNN). The input is the image data, and the output is the label of the identified target.

[0119] Step 4:

[0120] The server retrieves relevant information from reliable sources based on identified labels. The server uses APIs over the internet to query, for example, online encyclopedias and specialized websites. Input is label information, and output is document data.

[0121] Step 5:

[0122] The server summarizes the acquired information using a generative AI model, such as GPT-3. The summarization is based on natural language processing, converting vast amounts of information into short, easy-to-understand sentences. The input is document data, and the output is the summarized text.

[0123] Step 6:

[0124] The terminal displays the summarized text received from the server on the user interface. The user visually confirms and understands this information. The input is the summarized text, and the output is the visually represented information.

[0125] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0126] This invention is a system that enhances the user experience by combining an emotion engine with an object recognition and information provision system. First, the user takes a picture of an object of interest using the device's camera. The device compresses the image and sends it to the server. This communication is secure, and the data is encrypted to maintain security.

[0127] The server analyzes the received image and recognizes the object. Artificial intelligence technology is used for image analysis. After analysis, it searches the internet for information related to the identified object and selects reliable information. The acquired information is summarized using natural language processing and transmitted to the terminal in a clear and concise format.

[0128] The emotion engine uses the device's microphone and camera to recognize the user's emotions. This engine determines the user's emotions from their voice tone and facial expressions. For example, if the user appears surprised, it adjusts the content to provide more detailed information and reassure the user.

[0129] The terminal receives summary information from the server, adjusts it based on the sentiment engine's judgment, and displays it on the user's screen. This display changes the way information is presented and the order it is ordered according to the user's state, helping to facilitate better understanding.

[0130] For example, if a user shows a joyful expression upon seeing a new type of musical instrument, the emotion engine recognizes this and highlights information focusing on the instrument's tone and the enjoyment of playing it. This allows the user to instantly absorb new knowledge and become even more interested in the object.

[0131] This system allows users to deepen their understanding of objects and enjoy personalized information presentations that respond to their emotions.

[0132] The following describes the processing flow.

[0133] Step 1:

[0134] The user points the device's camera at an object and takes a picture. The device acquires a high-resolution image and saves it in a format suitable for analysis.

[0135] Step 2:

[0136] The device compresses the images and sends them to the server over the network. During this process, data encryption is performed to ensure privacy and security.

[0137] Step 3:

[0138] The server inputs the received image data into an analysis engine. Here, machine learning algorithms are applied to identify objects quickly and accurately.

[0139] Step 4:

[0140] The server searches the internet for information related to the identified object and collects reliable information. The information is limited to comprehensive and important information.

[0141] Step 5:

[0142] The server summarizes the collected information using natural language processing and generates a concise text format. This text is clearly and concisely structured.

[0143] Step 6:

[0144] The device activates an emotion engine that analyzes the user's facial expressions and tone of voice to determine their current emotions. For example, it identifies feelings such as joy, surprise, or anxiety.

[0145] Step 7:

[0146] Based on the analysis results of the emotion engine, the device adjusts the order and detail of the information it displays. For example, it might display additional information for positive emotions.

[0147] Step 8:

[0148] The device displays customized information to the user. Information is highlighted according to the user's interests, with the most relevant information presented first.

[0149] Step 9:

[0150] The user can review the displayed information and request additional questions or information. In this case, it is possible to repeat the previous process and update the information.

[0151] (Example 2)

[0152] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0153] Currently, there are limitations to systems that enable users to quickly and appropriately obtain information about objects. In particular, there is a lack of information provision that takes into account the user's emotional state, and the inability to provide a personalized experience is a challenge. Improvements are also needed in the selection of information from reliable sources and the speed of response.

[0154] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0155] In this invention, the server includes means for performing image analysis to identify objects, means for searching and summarizing information related to the objects on an information network, and means for recognizing the user's emotional state and adjusting the method of presenting information. This enables personalized information provision based on the user's emotions.

[0156] "Means of acquiring images" refers to devices or sensors used by a user to photograph objects of interest, and in this case, includes the process of generating image data using a camera.

[0157] "Means for transmitting images to a data processing device" refers to protocols and technical means for compressing and encrypting acquired images and securely transmitting them to a server over a network.

[0158] "Means of performing image analysis to identify objects" refers to algorithms and software that use artificial intelligence technology executed on a server to analyze images and recognize objects within them.

[0159] "Means of searching for information related to an object on an information network" refers to the process of using search engines or APIs to collect information on the internet based on an identified object.

[0160] "Methods of summarization" refers to techniques that use natural language processing to organize and simplify information obtained through search, making it easier for users to understand.

[0161] "Means for recognizing a user's emotional state" refers to technologies and algorithms for determining a user's current emotional state based on voice tone and facial expression analysis.

[0162] "Means of adjusting the method of information presentation" refers to systems and software functions that enable personalized information delivery by changing the displayed content and emphasis of information according to the user's emotional state.

[0163] "Means of displaying information" refers to devices or interfaces for providing adjusted information to a user visually or audibly, and in this case includes displays and audio output devices.

[0164] The object recognition and information provision system of the present invention integrates an emotion engine to improve the user experience. This system primarily functions as a server, terminal, and user. Specific embodiments are described in detail below.

[0165] The user takes a picture of an object of interest using the camera built into the device. The captured image is compressed and AES encrypted in real time within the device. For secure data transmission, the device uses the SSL / TLS protocol to send the image data to the server.

[0166] The transmitted images are first decrypted on the server. Artificial intelligence models such as TensorFlow are used for image analysis, and the server utilizes these models to effectively identify objects within the images. This results in highly accurate object recognition.

[0167] After identification, the server searches the internet for information related to the object. Standard search APIs are used for the search, and relevant information is collected from a wide range of sources. Furthermore, the retrieved information is summarized within the server using a generative AI model. This model, such as GPT, is transformed into a concise and easy-to-understand format through natural language processing techniques.

[0168] When presenting received information to the user, the device uses an emotion recognition engine to determine the user's emotional state. This engine utilizes the device's microphone and camera to analyze emotions by analyzing voice tone and facial expressions. The device then adjusts the priority and display format of information according to the user's emotional state.

[0169] As a concrete example, consider a scenario where a user is delighted to see a new musical instrument. The device recognizes this emotion and highlights information related to the instrument, particularly information about its tone and the enjoyment of playing it. This process allows the user to acquire new knowledge in a pleasant way, further stimulating their interest.

[0170] An example of a prompt message would be: "Retrieve detailed information about this instrument from the image taken with the camera. Also, highlight and present detailed information about the tone if the user is pleased."

[0171] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0172] Step 1:

[0173] The user takes a picture of an object of interest using the device's camera. The input is still image data obtained from the camera. The device compresses this image and applies AES encryption. The output is encrypted compressed image data. This process allows the data to be transmitted to the server efficiently and securely.

[0174] Step 2:

[0175] The terminal sends encrypted, compressed image data to the server using the SSL / TLS protocol. The input is image data encrypted within the terminal. The output is encrypted data stored on the server. This enables secure transmission of image data over the internet.

[0176] Step 3:

[0177] The server decrypts the image data received from the terminal. The input is encrypted, compressed image data. The server decrypts the image and performs image analysis using an AI model (e.g., TensorFlow). The output is information about the identified objects. This analysis step ensures that specific objects are accurately recognized within the image.

[0178] Step 4:

[0179] The server searches the internet for relevant information based on the identified object. The input is attribute data of the identified object. The output is a list of information related to the object. The server uses an API to retrieve useful data from reliable sources.

[0180] Step 5:

[0181] The server summarizes the acquired information using a generative AI model (e.g., GPT). The input is raw data related to an object. The output is a user-friendly, organized summary. This summary allows the user to obtain easily understandable information in a short amount of time.

[0182] Step 6:

[0183] After receiving summary information, the device analyzes the user's emotions using an emotion recognition engine. The input consists of summary information and the user's voice and facial expression data. The output is user emotional state evaluation data. The device determines the user's current emotions by analyzing voice tone and facial expressions.

[0184] Step 7:

[0185] The device adjusts the information it provides to the user based on the results of emotion recognition. Inputs are summary information and the user's emotional state evaluation data. Output is an adapted information display. For example, if a positive emotion is detected, information about the tone and enjoyment of a musical instrument is emphasized. This allows the user to receive personalized information in the most optimal way.

[0186] (Application Example 2)

[0187] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0188] Object recognition systems often lack personalization based on the user's emotions and interests when providing information related to identified objects. This can limit the user experience and diminish its usefulness, so there is a need to provide information that responds to the user's emotions and deliver a richer user experience.

[0189] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0190] In this invention, the server includes means for acquiring an image, means for transmitting the image to the server, means for performing image analysis on the server and identifying an object, and means for analyzing image data and audio data to recognize the user's emotions and adjusting the acquired information according to the emotions. This makes it possible to dynamically change the information display considering the user's emotional state and provide information that is tailored to the user's interests and emotions.

[0191] "Means for acquiring images" refers to a technological device used to capture visual information of an object of interest to a user using an electronic terminal.

[0192] "Means for transmitting the image to the server" refers to communication technology for transferring acquired image data to a specific server via a communication network such as the Internet.

[0193] "A means of performing image analysis on a server to identify objects" refers to a process that processes image data received on a server and uses AI technology to identify objects within the image.

[0194] "Means for searching for information related to an identified object" refers to methods for obtaining information associated with an identified object from databases or the internet.

[0195] "Means for summarizing the information and sending it to the terminal" refers to a procedure for concisely summarizing the acquired information and then sending the data back to the user's terminal.

[0196] "Means for displaying transmitted information" refers to a technical mechanism for visually presenting information received on the user's device.

[0197] "A means of analyzing image and audio data to recognize user emotions and adjusting acquired information according to those emotions" refers to a system that analyzes the user's emotional state using the device's camera and microphone to analyze voice and facial expressions, and then customizes the information provided based on the results.

[0198] A specific system for carrying out the present invention consists of a user terminal, a server, and an emotion recognition engine. The user terminal is a portable device such as a smartphone or smart glasses equipped with a camera and microphone. When a user takes a picture of a specific object using the terminal's camera, the terminal encrypts the image data and transmits it to the server via the internet.

[0199] The server implements image recognition software using AI technology, and analyzes received image data and identifies objects using frameworks such as TensorFlow and PyTorch. Information related to the identified objects is obtained from reliable sources on the internet. Furthermore, the information is summarized using a natural language processing library and sent to the terminal.

[0200] The user's device is equipped with an emotion recognition engine using technologies such as OpenCV and DeepFace. It analyzes the user's facial expressions and voice using the camera and microphone to determine the user's emotional state. As a result, the information sent from the server is reconstructed according to the user's emotions, and personalized content tailored to the user's interests is displayed on the screen.

[0201] For example, if a user photographs a new cooking utensil and shows an expression of interest, the system will provide detailed information on its use and recipes. An example of a prompt message to send to the generating AI model might be:

[0202] "Please provide effective usage instructions and related recipes for cooking utensils that users are interested in."

[0203] This allows users to quickly obtain information and deepen their understanding of and interest in the object. This format enables dynamic information delivery that responds to the user's emotions.

[0204] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0205] Step 1:

[0206] The user uses the device's camera to photograph an object of interest. The input is the camera feed from the user, and the device acquires this image data. The output is image data generated for transmission to the next process.

[0207] Step 2:

[0208] The terminal encrypts the acquired image data and sends it to the server. An encryption algorithm is used to securely protect the data and transmit it over the communication network. The input is the image data acquired in step 1, and the output is the encrypted image data.

[0209] Step 3:

[0210] The server decrypts the received image data and performs identification processing using image analysis software. It uses an AI model (e.g., TensorFlow) to perform data calculations to identify objects. The input is encrypted image data, and the output is the object identification result.

[0211] Step 4:

[0212] The server searches for information related to the identified object. It retrieves data from various sources via the internet and selects the most reliable information. The input is information about the identified object, and the output is a list of related information.

[0213] Step 5:

[0214] The server summarizes the acquired information using natural language processing techniques and sends it to the terminal. A data summarization algorithm (e.g., spaCy) organizes the content concisely. The input is a list of related information, and the output is the summarized information data.

[0215] Step 6:

[0216] The user uses the device's microphone and camera to recognize emotions from voice and facial expressions. The device uses OpenCV and DeepFace to analyze the emotional state. The input is the user's voice and image data, and the output is the emotion determination result.

[0217] Step 7:

[0218] The device reconstructs information based on the sentiment assessment results and presents it to the user. Based on sentiment data, it adjusts the content and displays it in a visually easy-to-understand manner. Input consists of summary information and sentiment assessment results, while output is customized information for display.

[0219] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0220] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0221] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0222] [Second Embodiment]

[0223] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0224] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0225] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0226] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0227] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0228] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0229] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0230] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0231] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0232] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0233] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0234] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0235] This invention is a system that allows users to instantly obtain detailed information about objects around them. First, the user takes an image of an object of interest using the camera on their device. The device converts the obtained image into a predetermined format and transmits it to a server via the network.

[0236] The server processes the received images using image analysis algorithms to quickly and accurately identify objects. This process may utilize machine learning techniques such as convolutional neural networks (CNNs). Identified objects are then assigned relevant labels and tags.

[0237] The server then uses the identified labels to search the internet for relevant information. The search targets reliable sources such as encyclopedias, specialized websites, and review articles. The collected information is then summarized by AI to generate concise and easy-to-understand text.

[0238] The generated information is sent from the server to the terminal, which then displays the information on its screen. This allows the user to visually confirm detailed information about an object.

[0239] As a concrete example, if a user finds a plant they've never seen before, they point their device's camera at it and take a picture. The server identifies the type of plant and recognizes it as "rosemary." Next, it compiles information about the characteristics, uses, and health benefits of rosemary and sends it to the device. This allows the user to gain the necessary knowledge about the plant.

[0240] This system allows users to instantly deepen their understanding of objects and gain new knowledge.

[0241] The following describes the processing flow.

[0242] Step 1:

[0243] The user launches a dedicated app on their device and points the camera at an object to take a picture. At this time, the camera is set to capture the details of the object in high resolution.

[0244] Step 2:

[0245] The device compresses the captured image as digital data and sends it to the server via the network. During this process, the data is encrypted to maintain security.

[0246] Step 3:

[0247] The server adds the received image data to a queue for analysis. Here, the image data is converted back into a format that allows for high-speed processing.

[0248] Step 4:

[0249] An image analysis engine on the server processes the images and applies artificial intelligence algorithms to recognize objects. Appropriate labels and tags are then assigned to the recognized objects.

[0250] Step 5:

[0251] The server uses identified labels to search the internet for relevant information. Information sources are limited to those that have been verified for reliability and timeliness.

[0252] Step 6:

[0253] The server inputs the information obtained from the search into a natural language processing engine and generates a summarized text. This text is structured to be easily understood by the user.

[0254] Step 7:

[0255] The server sends the generated summary information to the terminal. The data is encrypted again before transmission, ensuring privacy and security.

[0256] Step 8:

[0257] The device decodes the received information and displays it on the screen for the user. The information may also be presented with visual elements such as diagrams and graphs.

[0258] Step 9:

[0259] The user can refer to the displayed information and request further information if necessary. In this case, if it is determined that additional information is needed, the process repeats from step 5 onward.

[0260] (Example 1)

[0261] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0262] In modern times, people are required to obtain information quickly and accurately about unfamiliar objects and phenomena they encounter in their daily lives. However, conventional information retrieval systems require users to individually input search terms and sift through reliable information, making information acquisition time-consuming and laborious. Furthermore, the reliability of the information and the ease of understanding the summarized information were also challenges.

[0263] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0264] In this invention, the server includes means for performing image analysis on a computing device to identify an object, means for searching for data related to the identified object, and means for summarizing the data using a generative artificial intelligence model. This enables users to quickly and reliably obtain detailed information about unknown objects and understand it intuitively.

[0265] "Means for acquiring images" refers to the function that allows the user to capture surrounding objects using a camera.

[0266] "Means of transmitting to a computing device via a communication infrastructure" refers to a function for delivering image data to a computing device via a network.

[0267] A "computer device" is a computer system that analyzes received data and performs information processing.

[0268] "Means for performing image analysis and identifying an object" refers to the process by which a computing device analyzes image data and identifies the captured object.

[0269] "Means of searching for data" refers to a function that allows a computing device to collect additional information about an identified object from the internet.

[0270] "Methods for summarizing data using generative artificial intelligence models" refer to artificial intelligence technologies used to analyze large amounts of information and summarize it in a form that is easy for users to understand.

[0271] A "display device" is a display device that allows users to visually confirm information.

[0272] "Means of return" refers to the function of sending information from a computing device to a display device.

[0273] "Means of collecting analyzed data from reliable sources" refers to the function of obtaining information from databases and websites that are of high quality and reliability.

[0274] This invention is a system that allows users to instantly obtain detailed information about objects around them. First, when a user discovers an object of interest, they use the camera on their device to take a picture of that object. The device can be a smartphone or tablet equipped with a high-performance camera. The captured image is converted to a predetermined format such as JPEG or PNG within the device. Then, this image data is transmitted to a server via the internet through a communication infrastructure. Here, it is common for the image to be sent as a POST request using the HTTP protocol.

[0275] On the server, received image data is first preprocessed and adjusted to an appropriate size and format. The server then runs a convolutional neural network (CNN) using open-source libraries such as TensorFlow or PyTorch to analyze the image. Through this analysis, objects within the image are identified and associated labels and tags are assigned. Based on these identification results, the server searches for relevant information from reliable sources. This search typically utilizes APIs from Wikipedia or specific specialized websites, and web scraping is also performed as needed.

[0276] Next, the server uses a generative AI model to summarize the acquired information and generate concise text. For example, OpenAI's GPT model may be used. The summarized information is sent back to the terminal as an HTTP response, usually in JSON format.

[0277] The device analyzes the received information and displays it to the user in a visually easy-to-understand format. Text size, color, and font are adjusted, and related images are displayed on the screen, allowing the user to visually confirm the information.

[0278] As a concrete example, consider a scenario where a user discovers an unfamiliar plant. When the user takes a picture of this plant and inputs it into the system, the server identifies the plant as "rosemary." Next, information about rosemary, such as its characteristics, uses, and health benefits, is summarized and sent to the user's device. The user can then visually review this information and gain knowledge.

[0279] An example of a prompt message would be, "Identify the plant in this image and provide any relevant information."

[0280] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0281] Step 1:

[0282] The user uses the camera of the terminal to take a picture of an object of interest. The taken picture is saved in the internal memory of the terminal. Here, the input is the image information obtained through the camera, and the output is the image file saved in the terminal.

[0283] Step 2:

[0284] The terminal converts the acquired image into a predetermined format. For example, it converts an image in RAW format into JPEG or PNG format. In this process, the resolution and color depth of the image are optimized. The input is the RAW format image data, and the output is the compressed image data after conversion.

[0285] Step 3:

[0286] The terminal transmits the converted image data to the server using the network. Here, the HTTP protocol is used, and the image data is uploaded as a POST request. The input is the image data after conversion, and the output is the data transmitted to the server.

[0287] Step 4:

[0288] The server first performs preprocessing in order to analyze the received image data. In this preprocessing, image size adjustment and noise removal are performed. The input is the image data transmitted to the server, and the output is the image data arranged in an analyzable format.

[0289] Step 5:

[0290] The server performs image analysis by running a convolutional neural network (CNN). In this process, a pre-trained model is used to identify the objects in the image. The input is the preprocessed image data, and the output is the label information regarding the identified objects.

[0291] Step 6:

[0292] The server searches for relevant information based on the identified labels. Here, it uses an API to retrieve information from trusted databases on the internet. The input is the labels of the identified objects, and the output is a dataset containing the relevant information.

[0293] Step 7:

[0294] The server summarizes the acquired information using a generative AI model. In this process, the key points of the acquired information are extracted, and text in a user-friendly format is generated. The input is a dataset of relevant information, and the output is the summarized text.

[0295] Step 8:

[0296] The server returns the summarized information to the terminal. The data is returned in JSON format as an HTTP response. The input is the summarized text, and the output is the JSON data sent to the terminal.

[0297] Step 9:

[0298] The terminal analyzes the received information and displays it visually on its screen. The input here is JSON data received from the server, and the output is detailed information displayed on the terminal's screen. This allows the user to intuitively understand detailed information about an object.

[0299] (Application Example 1)

[0300] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0301] In conventional object recognition systems, it is required not only to identify objects but also to immediately provide the information and display it in a form that is directly useful for the user's life. However, in existing systems, there are often deficiencies in the reliability of the information and user-friendly presentation, and there is also a lack of flexibility in providing information in response to additional requests from users. Solving such problems and providing information that can be intuitively understood by users and improves the quality of life is the challenge.

[0302] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0303] In this invention, the server includes means for performing image data analysis to identify an object, means for acquiring information related to the identified object, and means for summarizing the acquired information and transmitting it to the information terminal. As a result, the user can immediately obtain and understand the latest and highly reliable information about the objects around them.

[0304] "Image data" refers to visual information captured by a camera or sensor and is in a form that can be processed by a computer.

[0305] "Information processing device" means a computer system that receives image data and performs analysis, and has a function of processing and managing data.

[0306] "Object" refers to an object or article identified by image data analysis and indicates the object for which the user wants to obtain information.

[0307] "Generative AI technology" is a technology that automatically generates, analyzes, and summarizes data using artificial intelligence, and includes deep learning models and the like.

[0308] "Information terminal" means a device used by a user to receive and display information, and includes smartphones, tablets, and displays.

[0309] An "information provision system" refers to a group of devices that include a series of processes such as analysis, information collection, summarization, and display based on image data, and is a system that provides users with the necessary information.

[0310] The system for implementing this invention first equips an information terminal, such as a smartphone or consumer robot, with a camera to acquire images of objects of interest to the user. The terminal uses this camera to capture image data and transmits this data to a specific information processing device using generative AI technology.

[0311] A server is used as the information processing device, and the server uses a deep learning framework such as TensorFlow to analyze the received image data. The server processes the image data using a convolutional neural network (CNN) to accurately and quickly identify the target. Once the target is identified, the server collects relevant information about that target from reliable databases and online resources, such as Wikipedia or reliable expert websites.

[0312] Next, the server utilizes generative AI models such as GPT-3 in the process of summarizing the collected information. This converts vast amounts of information into a concise and clear text format, making it easy for users to understand. The server then transmits this summarized information to an information terminal, where it is displayed visually. Users can then review the information through their terminal and deepen their practical understanding.

[0313] For example, a knowledge support robot in a home kitchen might use the following prompt to point its camera at a new cooking utensil and acquire information about it: "Please tell me how to use this cooking utensil and what its features are." Based on this, the robot can provide appropriate guidance and additional information in real time.

[0314] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0315] Step 1:

[0316] The user selects an object of interest using an information terminal and acquires image data using the camera. Input is obtained when the user points the camera at the object and presses the shutter button, and the camera module saves this image data to local memory.

[0317] Step 2:

[0318] The terminal converts the acquired image data into a predetermined format and transmits it to the server via the network. The image data is compressed, for example, into JPEG format, and then packetized according to the transmission protocol. The input is the image data, and the output is the data packets sent to the server.

[0319] Step 3:

[0320] The server decodes the received packets and reconstructs the image data. Then, it analyzes the image data using TensorFlow and identifies the target using a convolutional neural network (CNN). The input is the image data, and the output is the label of the identified target.

[0321] Step 4:

[0322] The server retrieves relevant information from reliable sources based on identified labels. The server uses APIs over the internet to query, for example, online encyclopedias and specialized websites. Input is label information, and output is document data.

[0323] Step 5:

[0324] The server summarizes the acquired information using a generative AI model, such as GPT-3. The summarization is based on natural language processing, converting vast amounts of information into short, easy-to-understand sentences. The input is document data, and the output is the summarized text.

[0325] Step 6:

[0326] The terminal displays the summarized text received from the server on the user interface. The user visually confirms and understands this information. The input is the summarized text, and the output is the visually represented information.

[0327] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0328] This invention is a system that enhances the user experience by combining an emotion engine with an object recognition and information provision system. First, the user takes a picture of an object of interest using the device's camera. The device compresses the image and sends it to the server. This communication is secure, and the data is encrypted to maintain security.

[0329] The server analyzes the received image and recognizes the object. Artificial intelligence technology is used for image analysis. After analysis, it searches the internet for information related to the identified object and selects reliable information. The acquired information is summarized using natural language processing and transmitted to the terminal in a clear and concise format.

[0330] The emotion engine uses the device's microphone and camera to recognize the user's emotions. This engine determines the user's emotions from their voice tone and facial expressions. For example, if the user appears surprised, it adjusts the content to provide more detailed information and reassure the user.

[0331] The terminal receives summary information from the server, adjusts it based on the sentiment engine's judgment, and displays it on the user's screen. This display changes the way information is presented and the order it is ordered according to the user's state, helping to facilitate better understanding.

[0332] For example, if a user shows a joyful expression upon seeing a new type of musical instrument, the emotion engine recognizes this and highlights information focusing on the instrument's tone and the enjoyment of playing it. This allows the user to instantly absorb new knowledge and become even more interested in the object.

[0333] This system allows users to deepen their understanding of objects and enjoy personalized information presentations that respond to their emotions.

[0334] The following describes the processing flow.

[0335] Step 1:

[0336] The user points the device's camera at an object and takes a picture. The device acquires a high-resolution image and saves it in a format suitable for analysis.

[0337] Step 2:

[0338] The device compresses the images and sends them to the server over the network. During this process, data encryption is performed to ensure privacy and security.

[0339] Step 3:

[0340] The server inputs the received image data into an analysis engine. Here, machine learning algorithms are applied to identify objects quickly and accurately.

[0341] Step 4:

[0342] The server searches the internet for information related to the identified object and collects reliable information. The information is limited to comprehensive and important information.

[0343] Step 5:

[0344] The server summarizes the collected information using natural language processing and generates a concise text format. This text is clearly and concisely structured.

[0345] Step 6:

[0346] The device activates an emotion engine that analyzes the user's facial expressions and tone of voice to determine their current emotions. For example, it identifies feelings such as joy, surprise, or anxiety.

[0347] Step 7:

[0348] Based on the analysis results of the emotion engine, the device adjusts the order and detail of the information it displays. For example, it might display additional information for positive emotions.

[0349] Step 8:

[0350] The device displays customized information to the user. Information is highlighted according to the user's interests, with the most relevant information presented first.

[0351] Step 9:

[0352] The user can review the displayed information and request additional questions or information. In this case, it is possible to repeat the previous process and update the information.

[0353] (Example 2)

[0354] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0355] Currently, there are limitations to systems that enable users to quickly and appropriately obtain information about objects. In particular, there is a lack of information provision that takes into account the user's emotional state, and the inability to provide a personalized experience is a challenge. Improvements are also needed in the selection of information from reliable sources and the speed of response.

[0356] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0357] In this invention, the server includes means for performing image analysis to identify objects, means for searching and summarizing information related to the objects on an information network, and means for recognizing the user's emotional state and adjusting the method of presenting information. This enables personalized information provision based on the user's emotions.

[0358] "Means of acquiring images" refers to devices or sensors used by a user to photograph objects of interest, and in this case, includes the process of generating image data using a camera.

[0359] "Means for transmitting images to a data processing device" refers to protocols and technical means for compressing and encrypting acquired images and securely transmitting them to a server over a network.

[0360] "Means of performing image analysis to identify objects" refers to algorithms and software that use artificial intelligence technology executed on a server to analyze images and recognize objects within them.

[0361] "Means of searching for information related to an object on an information network" refers to the process of using search engines or APIs to collect information on the internet based on an identified object.

[0362] "Methods of summarization" refers to techniques that use natural language processing to organize and simplify information obtained through search, making it easier for users to understand.

[0363] "Means for recognizing a user's emotional state" refers to technologies and algorithms for determining a user's current emotional state based on voice tone and facial expression analysis.

[0364] "Means of adjusting the method of information presentation" refers to systems and software functions that enable personalized information delivery by changing the displayed content and emphasis of information according to the user's emotional state.

[0365] "Means of displaying information" refers to devices or interfaces for providing adjusted information to a user visually or audibly, and in this case includes displays and audio output devices.

[0366] The object recognition and information provision system of the present invention integrates an emotion engine to improve the user experience. This system primarily functions as a server, terminal, and user. Specific embodiments are described in detail below.

[0367] The user takes a picture of an object of interest using the camera built into the device. The captured image is compressed and AES encrypted in real time within the device. For secure data transmission, the device uses the SSL / TLS protocol to send the image data to the server.

[0368] The transmitted images are first decrypted on the server. Artificial intelligence models such as TensorFlow are used for image analysis, and the server utilizes these models to effectively identify objects within the images. This results in highly accurate object recognition.

[0369] After identification, the server searches the internet for information related to the object. Standard search APIs are used for the search, and relevant information is collected from a wide range of sources. Furthermore, the retrieved information is summarized within the server using a generative AI model. This model, such as GPT, is transformed into a concise and easy-to-understand format through natural language processing techniques.

[0370] When presenting received information to the user, the device uses an emotion recognition engine to determine the user's emotional state. This engine utilizes the device's microphone and camera to analyze emotions by analyzing voice tone and facial expressions. The device then adjusts the priority and display format of information according to the user's emotional state.

[0371] As a concrete example, consider a scenario where a user is delighted to see a new musical instrument. The device recognizes this emotion and highlights information related to the instrument, particularly information about its tone and the enjoyment of playing it. This process allows the user to acquire new knowledge in a pleasant way, further stimulating their interest.

[0372] An example of a prompt message would be: "Retrieve detailed information about this instrument from the image taken with the camera. Also, highlight and present detailed information about the tone if the user is pleased."

[0373] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0374] Step 1:

[0375] The user takes a picture of an object of interest using the device's camera. The input is still image data obtained from the camera. The device compresses this image and applies AES encryption. The output is encrypted compressed image data. This process allows the data to be transmitted to the server efficiently and securely.

[0376] Step 2:

[0377] The terminal sends encrypted, compressed image data to the server using the SSL / TLS protocol. The input is image data encrypted within the terminal. The output is encrypted data stored on the server. This enables secure transmission of image data over the internet.

[0378] Step 3:

[0379] The server decrypts the image data received from the terminal. The input is encrypted, compressed image data. The server decrypts the image and performs image analysis using an AI model (e.g., TensorFlow). The output is information about the identified objects. This analysis step ensures that specific objects are accurately recognized within the image.

[0380] Step 4:

[0381] The server searches the internet for relevant information based on the identified object. The input is attribute data of the identified object. The output is a list of information related to the object. The server uses an API to retrieve useful data from reliable sources.

[0382] Step 5:

[0383] The server summarizes the acquired information using a generative AI model (e.g., GPT). The input is raw data related to an object. The output is a user-friendly, organized summary. This summary allows the user to obtain easily understandable information in a short amount of time.

[0384] Step 6:

[0385] After receiving summary information, the device analyzes the user's emotions using an emotion recognition engine. The input consists of summary information and the user's voice and facial expression data. The output is user emotional state evaluation data. The device determines the user's current emotions by analyzing voice tone and facial expressions.

[0386] Step 7:

[0387] The device adjusts the information it provides to the user based on the results of emotion recognition. Inputs are summary information and the user's emotional state evaluation data. Output is an adapted information display. For example, if a positive emotion is detected, information about the tone and enjoyment of a musical instrument is emphasized. This allows the user to receive personalized information in the most optimal way.

[0388] (Application Example 2)

[0389] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0390] Object recognition systems often lack personalization based on the user's emotions and interests when providing information related to identified objects. This can limit the user experience and diminish its usefulness, so there is a need to provide information that responds to the user's emotions and deliver a richer user experience.

[0391] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0392] In this invention, the server includes means for acquiring an image, means for transmitting the image to the server, means for performing image analysis on the server and identifying an object, and means for analyzing image data and audio data to recognize the user's emotions and adjusting the acquired information according to the emotions. This makes it possible to dynamically change the information display considering the user's emotional state and provide information that is tailored to the user's interests and emotions.

[0393] "Means for acquiring images" refers to a technological device used to capture visual information of an object of interest to a user using an electronic terminal.

[0394] "Means for transmitting the image to the server" refers to communication technology for transferring acquired image data to a specific server via a communication network such as the Internet.

[0395] "A means of performing image analysis on a server to identify objects" refers to a process that processes image data received on a server and uses AI technology to identify objects within the image.

[0396] "Means for searching for information related to an identified object" refers to methods for obtaining information associated with an identified object from databases or the internet.

[0397] "Means for summarizing the information and sending it to the terminal" refers to a procedure for concisely summarizing the acquired information and then sending the data back to the user's terminal.

[0398] "Means for displaying transmitted information" refers to a technical mechanism for visually presenting information received on the user's device.

[0399] "A means of analyzing image and audio data to recognize user emotions and adjusting acquired information according to those emotions" refers to a system that analyzes the user's emotional state using the device's camera and microphone to analyze voice and facial expressions, and then customizes the information provided based on the results.

[0400] A specific system for carrying out the present invention consists of a user terminal, a server, and an emotion recognition engine. The user terminal is a portable device such as a smartphone or smart glasses equipped with a camera and microphone. When a user takes a picture of a specific object using the terminal's camera, the terminal encrypts the image data and transmits it to the server via the internet.

[0401] The server implements image recognition software using AI technology, and analyzes received image data and identifies objects using frameworks such as TensorFlow and PyTorch. Information related to the identified objects is obtained from reliable sources on the internet. Furthermore, the information is summarized using a natural language processing library and sent to the terminal.

[0402] The user's device is equipped with an emotion recognition engine using technologies such as OpenCV and DeepFace. It analyzes the user's facial expressions and voice using the camera and microphone to determine the user's emotional state. As a result, the information sent from the server is reconstructed according to the user's emotions, and personalized content tailored to the user's interests is displayed on the screen.

[0403] For example, if a user photographs a new cooking utensil and shows an expression of interest, the system will provide detailed information on its use and recipes. An example of a prompt message to send to the generating AI model might be:

[0404] "Please provide effective usage instructions and related recipes for cooking utensils that users are interested in."

[0405] This allows users to quickly obtain information and deepen their understanding of and interest in the object. This format enables dynamic information delivery that responds to the user's emotions.

[0406] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0407] Step 1:

[0408] The user uses the device's camera to photograph an object of interest. The input is the camera feed from the user, and the device acquires this image data. The output is image data generated for transmission to the next process.

[0409] Step 2:

[0410] The terminal encrypts the acquired image data and sends it to the server. An encryption algorithm is used to securely protect the data and transmit it over the communication network. The input is the image data acquired in step 1, and the output is the encrypted image data.

[0411] Step 3:

[0412] The server decrypts the received image data and performs identification processing using image analysis software. It uses an AI model (e.g., TensorFlow) to perform data calculations to identify objects. The input is encrypted image data, and the output is the object identification result.

[0413] Step 4:

[0414] The server searches for information related to the identified object. It retrieves data from various sources via the internet and selects the most reliable information. The input is information about the identified object, and the output is a list of related information.

[0415] Step 5:

[0416] The server summarizes the acquired information using natural language processing techniques and sends it to the terminal. A data summarization algorithm (e.g., spaCy) organizes the content concisely. The input is a list of related information, and the output is the summarized information data.

[0417] Step 6:

[0418] The user uses the device's microphone and camera to recognize emotions from voice and facial expressions. The device uses OpenCV and DeepFace to analyze the emotional state. The input is the user's voice and image data, and the output is the emotion determination result.

[0419] Step 7:

[0420] The device reconstructs information based on the sentiment assessment results and presents it to the user. Based on sentiment data, it adjusts the content and displays it in a visually easy-to-understand manner. Input consists of summary information and sentiment assessment results, while output is customized information for display.

[0421] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0422] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0423] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0424] [Third Embodiment]

[0425] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0426] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0427] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0428] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0429] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0430] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0431] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0432] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0433] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0434] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0435] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0436] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0437] This invention is a system that allows users to instantly obtain detailed information about objects around them. First, the user takes an image of an object of interest using the camera on their device. The device converts the obtained image into a predetermined format and transmits it to a server via the network.

[0438] The server processes the received images using image analysis algorithms to quickly and accurately identify objects. This process may utilize machine learning techniques such as convolutional neural networks (CNNs). Identified objects are then assigned relevant labels and tags.

[0439] The server then uses the identified labels to search the internet for relevant information. The search targets reliable sources such as encyclopedias, specialized websites, and review articles. The collected information is then summarized by AI to generate concise and easy-to-understand text.

[0440] The generated information is sent from the server to the terminal, which then displays the information on its screen. This allows the user to visually confirm detailed information about an object.

[0441] As a concrete example, if a user finds a plant they've never seen before, they point their device's camera at it and take a picture. The server identifies the type of plant and recognizes it as "rosemary." Next, it compiles information about the characteristics, uses, and health benefits of rosemary and sends it to the device. This allows the user to gain the necessary knowledge about the plant.

[0442] This system allows users to instantly deepen their understanding of objects and gain new knowledge.

[0443] The following describes the processing flow.

[0444] Step 1:

[0445] The user launches a dedicated app on their device and points the camera at an object to take a picture. At this time, the camera is set to capture the details of the object in high resolution.

[0446] Step 2:

[0447] The device compresses the captured image as digital data and sends it to the server via the network. During this process, the data is encrypted to maintain security.

[0448] Step 3:

[0449] The server adds the received image data to a queue for analysis. Here, the image data is converted back into a format that allows for high-speed processing.

[0450] Step 4:

[0451] An image analysis engine on the server processes the images and applies artificial intelligence algorithms to recognize objects. Appropriate labels and tags are then assigned to the recognized objects.

[0452] Step 5:

[0453] The server uses identified labels to search the internet for relevant information. Information sources are limited to those that have been verified for reliability and timeliness.

[0454] Step 6:

[0455] The server inputs the information obtained from the search into a natural language processing engine and generates a summarized text. This text is structured to be easily understood by the user.

[0456] Step 7:

[0457] The server sends the generated summary information to the terminal. The data is encrypted again before transmission, ensuring privacy and security.

[0458] Step 8:

[0459] The device decodes the received information and displays it on the screen for the user. The information may also be presented with visual elements such as diagrams and graphs.

[0460] Step 9:

[0461] The user can refer to the displayed information and request further information if necessary. In this case, if it is determined that additional information is needed, the process repeats from step 5 onward.

[0462] (Example 1)

[0463] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0464] In modern times, people are required to obtain information quickly and accurately about unfamiliar objects and phenomena they encounter in their daily lives. However, conventional information retrieval systems require users to individually input search terms and sift through reliable information, making information acquisition time-consuming and laborious. Furthermore, the reliability of the information and the ease of understanding the summarized information were also challenges.

[0465] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0466] In this invention, the server includes means for performing image analysis on a computing device to identify an object, means for searching for data related to the identified object, and means for summarizing the data using a generative artificial intelligence model. This enables users to quickly and reliably obtain detailed information about unknown objects and understand it intuitively.

[0467] "Means for acquiring images" refers to the function that allows the user to capture surrounding objects using a camera.

[0468] "Means of transmitting to a computing device via a communication infrastructure" refers to a function for delivering image data to a computing device via a network.

[0469] A "computer device" is a computer system that analyzes received data and performs information processing.

[0470] "Means for performing image analysis and identifying an object" refers to the process by which a computing device analyzes image data and identifies the captured object.

[0471] "Means of searching for data" refers to a function that allows a computing device to collect additional information about an identified object from the internet.

[0472] "Methods for summarizing data using generative artificial intelligence models" refer to artificial intelligence technologies used to analyze large amounts of information and summarize it in a form that is easy for users to understand.

[0473] A "display device" is a display device that allows users to visually confirm information.

[0474] "Means of return" refers to the function of sending information from a computing device to a display device.

[0475] "Means of collecting analyzed data from reliable sources" refers to the function of obtaining information from databases and websites that are of high quality and reliability.

[0476] This invention is a system that allows users to instantly obtain detailed information about objects around them. First, when a user discovers an object of interest, they use the camera on their device to take a picture of that object. The device can be a smartphone or tablet equipped with a high-performance camera. The captured image is converted to a predetermined format such as JPEG or PNG within the device. Then, this image data is transmitted to a server via the internet through a communication infrastructure. Here, it is common for the image to be sent as a POST request using the HTTP protocol.

[0477] On the server, received image data is first preprocessed and adjusted to an appropriate size and format. The server then runs a convolutional neural network (CNN) using open-source libraries such as TensorFlow or PyTorch to analyze the image. Through this analysis, objects within the image are identified and associated labels and tags are assigned. Based on these identification results, the server searches for relevant information from reliable sources. This search typically utilizes APIs from Wikipedia or specific specialized websites, and web scraping is also performed as needed.

[0478] Next, the server uses a generative AI model to summarize the acquired information and generate concise text. For example, OpenAI's GPT model may be used. The summarized information is sent back to the terminal as an HTTP response, usually in JSON format.

[0479] The device analyzes the received information and displays it to the user in a visually easy-to-understand format. Text size, color, and font are adjusted, and related images are displayed on the screen, allowing the user to visually confirm the information.

[0480] As a concrete example, consider a scenario where a user discovers an unfamiliar plant. When the user takes a picture of this plant and inputs it into the system, the server identifies the plant as "rosemary." Next, information about rosemary, such as its characteristics, uses, and health benefits, is summarized and sent to the user's device. The user can then visually review this information and gain knowledge.

[0481] An example of a prompt message would be, "Identify the plant in this image and provide any relevant information."

[0482] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0483] Step 1:

[0484] The user uses the device's camera to take a picture of an object of interest. The captured image is saved to the device's internal memory. Here, the input is the image information acquired through the camera, and the output is the image file stored on the device.

[0485] Step 2:

[0486] The device converts the acquired image into a predetermined format. For example, it converts a RAW image to JPEG or PNG format. During this process, the image resolution and color depth are optimized. The input is RAW image data, and the output is the converted compressed image data.

[0487] Step 3:

[0488] The terminal sends the converted image data to the server via the network. The HTTP protocol is used, and the image data is uploaded as a POST request. The input is the converted image data, and the output is the data sent to the server.

[0489] Step 4:

[0490] The server first performs preprocessing on the received image data in order to analyze it. This preprocessing includes adjusting the image size and removing noise. The input is the image data sent to the server, and the output is image data that has been prepared in a format that can be analyzed.

[0491] Step 5:

[0492] The server performs image analysis by running a convolutional neural network (CNN). This process uses a pre-trained model to identify objects in the image. The input is pre-processed image data, and the output is label information about the identified objects.

[0493] Step 6:

[0494] The server searches for relevant information based on the identified labels. Here, it uses an API to retrieve information from trusted databases on the internet. The input is the labels of the identified objects, and the output is a dataset containing the relevant information.

[0495] Step 7:

[0496] The server summarizes the acquired information using a generative AI model. In this process, the key points of the acquired information are extracted, and text in a user-friendly format is generated. The input is a dataset of relevant information, and the output is the summarized text.

[0497] Step 8:

[0498] The server returns the summarized information to the terminal. The data is returned in JSON format as an HTTP response. The input is the summarized text, and the output is the JSON data sent to the terminal.

[0499] Step 9:

[0500] The terminal analyzes the received information and displays it visually on its screen. The input here is JSON data received from the server, and the output is detailed information displayed on the terminal's screen. This allows the user to intuitively understand detailed information about an object.

[0501] (Application Example 1)

[0502] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0503] Conventional object recognition systems are required not only to identify objects but also to provide information about them immediately and display it in a way that is directly useful to the user's life. However, existing systems often lack reliability and user-friendliness in their presentation, and they lack the flexibility to provide information in response to additional user requests. The challenge is to solve these problems and provide information that users can intuitively understand and that improves their quality of life.

[0504] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0505] In this invention, the server includes means for analyzing image data to identify an object, means for acquiring information related to the identified object, and means for summarizing the acquired information and transmitting it to an information terminal. This enables users to instantly acquire and understand the latest and most reliable information about objects around them.

[0506] "Image data" refers to visual information captured by cameras or sensors, in a format that can be processed by a computer.

[0507] An "information processing device" refers to a computer system that receives and analyzes image data, and has the function of processing and managing data.

[0508] "Target" refers to an object or item identified through image data analysis, and represents the object from which the user wishes to obtain information.

[0509] "Generative AI technology" refers to technologies that use artificial intelligence to automatically generate, analyze, and summarize data, and includes deep learning models.

[0510] An "information terminal" refers to a device used by a user to receive and display information, and includes smartphones, tablets, and displays.

[0511] An "information provision system" refers to a group of devices that include a series of processes such as analysis, information collection, summarization, and display based on image data, and is a system that provides users with the necessary information.

[0512] The system for implementing this invention first equips an information terminal, such as a smartphone or consumer robot, with a camera to acquire images of objects of interest to the user. The terminal uses this camera to capture image data and transmits this data to a specific information processing device using generative AI technology.

[0513] A server is used as the information processing device, and the server uses a deep learning framework such as TensorFlow to analyze the received image data. The server processes the image data using a convolutional neural network (CNN) to accurately and quickly identify the target. Once the target is identified, the server collects relevant information about that target from reliable databases and online resources, such as Wikipedia or reliable expert websites.

[0514] Next, the server utilizes generative AI models such as GPT-3 in the process of summarizing the collected information. This converts vast amounts of information into a concise and clear text format, making it easy for users to understand. The server then transmits this summarized information to an information terminal, where it is displayed visually. Users can then review the information through their terminal and deepen their practical understanding.

[0515] For example, a knowledge support robot in a home kitchen might use the following prompt to point its camera at a new cooking utensil and acquire information about it: "Please tell me how to use this cooking utensil and what its features are." Based on this, the robot can provide appropriate guidance and additional information in real time.

[0516] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0517] Step 1:

[0518] The user selects an object of interest using an information terminal and acquires image data using the camera. Input is obtained when the user points the camera at the object and presses the shutter button, and the camera module saves this image data to local memory.

[0519] Step 2:

[0520] The terminal converts the acquired image data into a predetermined format and transmits it to the server via the network. The image data is compressed, for example, into JPEG format, and then packetized according to the transmission protocol. The input is the image data, and the output is the data packets sent to the server.

[0521] Step 3:

[0522] The server decodes the received packets and reconstructs the image data. Then, it analyzes the image data using TensorFlow and identifies the target using a convolutional neural network (CNN). The input is the image data, and the output is the label of the identified target.

[0523] Step 4:

[0524] The server retrieves relevant information from reliable sources based on identified labels. The server uses APIs over the internet to query, for example, online encyclopedias and specialized websites. Input is label information, and output is document data.

[0525] Step 5:

[0526] The server summarizes the acquired information using a generative AI model, such as GPT-3. The summarization is based on natural language processing, converting vast amounts of information into short, easy-to-understand sentences. The input is document data, and the output is the summarized text.

[0527] Step 6:

[0528] The terminal displays the summarized text received from the server on the user interface. The user visually confirms and understands this information. The input is the summarized text, and the output is the visually represented information.

[0529] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0530] This invention is a system that enhances the user experience by combining an emotion engine with an object recognition and information provision system. First, the user takes a picture of an object of interest using the device's camera. The device compresses the image and sends it to the server. This communication is secure, and the data is encrypted to maintain security.

[0531] The server analyzes the received image and recognizes the object. Artificial intelligence technology is used for image analysis. After analysis, it searches the internet for information related to the identified object and selects reliable information. The acquired information is summarized using natural language processing and transmitted to the terminal in a clear and concise format.

[0532] The emotion engine uses the device's microphone and camera to recognize the user's emotions. This engine determines the user's emotions from their voice tone and facial expressions. For example, if the user appears surprised, it adjusts the content to provide more detailed information and reassure the user.

[0533] The terminal receives summary information from the server, adjusts it based on the sentiment engine's judgment, and displays it on the user's screen. This display changes the way information is presented and the order it is ordered according to the user's state, helping to facilitate better understanding.

[0534] For example, if a user shows a joyful expression upon seeing a new type of musical instrument, the emotion engine recognizes this and highlights information focusing on the instrument's tone and the enjoyment of playing it. This allows the user to instantly absorb new knowledge and become even more interested in the object.

[0535] This system allows users to deepen their understanding of objects and enjoy personalized information presentations that respond to their emotions.

[0536] The following describes the processing flow.

[0537] Step 1:

[0538] The user points the device's camera at an object and takes a picture. The device acquires a high-resolution image and saves it in a format suitable for analysis.

[0539] Step 2:

[0540] The device compresses the images and sends them to the server over the network. During this process, data encryption is performed to ensure privacy and security.

[0541] Step 3:

[0542] The server inputs the received image data into an analysis engine. Here, machine learning algorithms are applied to identify objects quickly and accurately.

[0543] Step 4:

[0544] The server searches the internet for information related to the identified object and collects reliable information. The information is limited to comprehensive and important information.

[0545] Step 5:

[0546] The server summarizes the collected information using natural language processing and generates a concise text format. This text is clearly and concisely structured.

[0547] Step 6:

[0548] The device activates an emotion engine that analyzes the user's facial expressions and tone of voice to determine their current emotions. For example, it identifies feelings such as joy, surprise, or anxiety.

[0549] Step 7:

[0550] Based on the analysis results of the emotion engine, the device adjusts the order and detail of the information it displays. For example, it might display additional information for positive emotions.

[0551] Step 8:

[0552] The device displays customized information to the user. Information is highlighted according to the user's interests, with the most relevant information presented first.

[0553] Step 9:

[0554] The user can review the displayed information and request additional questions or information. In this case, it is possible to repeat the previous process and update the information.

[0555] (Example 2)

[0556] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0557] Currently, there are limitations to systems that enable users to quickly and appropriately obtain information about objects. In particular, there is a lack of information provision that takes into account the user's emotional state, and the inability to provide a personalized experience is a challenge. Improvements are also needed in the selection of information from reliable sources and the speed of response.

[0558] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0559] In this invention, the server includes means for performing image analysis to identify objects, means for searching and summarizing information related to the objects on an information network, and means for recognizing the user's emotional state and adjusting the method of presenting information. This enables personalized information provision based on the user's emotions.

[0560] "Means of acquiring images" refers to devices or sensors used by a user to photograph objects of interest, and in this case, includes the process of generating image data using a camera.

[0561] "Means for transmitting images to a data processing device" refers to protocols and technical means for compressing and encrypting acquired images and securely transmitting them to a server over a network.

[0562] "Means of performing image analysis to identify objects" refers to algorithms and software that use artificial intelligence technology executed on a server to analyze images and recognize objects within them.

[0563] "Means of searching for information related to an object on an information network" refers to the process of using search engines or APIs to collect information on the internet based on an identified object.

[0564] "Methods of summarization" refers to techniques that use natural language processing to organize and simplify information obtained through search, making it easier for users to understand.

[0565] "Means for recognizing a user's emotional state" refers to technologies and algorithms for determining a user's current emotional state based on voice tone and facial expression analysis.

[0566] "Means of adjusting the method of information presentation" refers to systems and software functions that enable personalized information delivery by changing the displayed content and emphasis of information according to the user's emotional state.

[0567] "Means of displaying information" refers to devices or interfaces for providing adjusted information to a user visually or audibly, and in this case includes displays and audio output devices.

[0568] The object recognition and information provision system of the present invention integrates an emotion engine to improve the user experience. This system primarily functions as a server, terminal, and user. Specific embodiments are described in detail below.

[0569] The user takes a picture of an object of interest using the camera built into the device. The captured image is compressed and AES encrypted in real time within the device. For secure data transmission, the device uses the SSL / TLS protocol to send the image data to the server.

[0570] The transmitted images are first decrypted on the server. Artificial intelligence models such as TensorFlow are used for image analysis, and the server utilizes these models to effectively identify objects within the images. This results in highly accurate object recognition.

[0571] After identification, the server searches the internet for information related to the object. Standard search APIs are used for the search, and relevant information is collected from a wide range of sources. Furthermore, the retrieved information is summarized within the server using a generative AI model. This model, such as GPT, is transformed into a concise and easy-to-understand format through natural language processing techniques.

[0572] When presenting received information to the user, the device uses an emotion recognition engine to determine the user's emotional state. This engine utilizes the device's microphone and camera to analyze emotions by analyzing voice tone and facial expressions. The device then adjusts the priority and display format of information according to the user's emotional state.

[0573] As a concrete example, consider a scenario where a user is delighted to see a new musical instrument. The device recognizes this emotion and highlights information related to the instrument, particularly information about its tone and the enjoyment of playing it. This process allows the user to acquire new knowledge in a pleasant way, further stimulating their interest.

[0574] An example of a prompt message would be: "Retrieve detailed information about this instrument from the image taken with the camera. Also, highlight and present detailed information about the tone if the user is pleased."

[0575] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0576] Step 1:

[0577] The user takes a picture of an object of interest using the device's camera. The input is still image data obtained from the camera. The device compresses this image and applies AES encryption. The output is encrypted compressed image data. This process allows the data to be transmitted to the server efficiently and securely.

[0578] Step 2:

[0579] The terminal sends encrypted, compressed image data to the server using the SSL / TLS protocol. The input is image data encrypted within the terminal. The output is encrypted data stored on the server. This enables secure transmission of image data over the internet.

[0580] Step 3:

[0581] The server decrypts the image data received from the terminal. The input is encrypted, compressed image data. The server decrypts the image and performs image analysis using an AI model (e.g., TensorFlow). The output is information about the identified objects. This analysis step ensures that specific objects are accurately recognized within the image.

[0582] Step 4:

[0583] The server searches the internet for relevant information based on the identified object. The input is attribute data of the identified object. The output is a list of information related to the object. The server uses an API to retrieve useful data from reliable sources.

[0584] Step 5:

[0585] The server summarizes the acquired information using a generative AI model (e.g., GPT). The input is raw data related to an object. The output is a user-friendly, organized summary. This summary allows the user to obtain easily understandable information in a short amount of time.

[0586] Step 6:

[0587] After receiving summary information, the device analyzes the user's emotions using an emotion recognition engine. The input consists of summary information and the user's voice and facial expression data. The output is user emotional state evaluation data. The device determines the user's current emotions by analyzing voice tone and facial expressions.

[0588] Step 7:

[0589] The device adjusts the information it provides to the user based on the results of emotion recognition. Inputs are summary information and the user's emotional state evaluation data. Output is an adapted information display. For example, if a positive emotion is detected, information about the tone and enjoyment of a musical instrument is emphasized. This allows the user to receive personalized information in the most optimal way.

[0590] (Application Example 2)

[0591] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0592] Object recognition systems often lack personalization based on the user's emotions and interests when providing information related to identified objects. This can limit the user experience and diminish its usefulness, so there is a need to provide information that responds to the user's emotions and deliver a richer user experience.

[0593] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0594] In this invention, the server includes means for acquiring an image, means for transmitting the image to the server, means for performing image analysis on the server and identifying an object, and means for analyzing image data and audio data to recognize the user's emotions and adjusting the acquired information according to the emotions. This makes it possible to dynamically change the information display considering the user's emotional state and provide information that is tailored to the user's interests and emotions.

[0595] "Means for acquiring images" refers to a technological device used to capture visual information of an object of interest to a user using an electronic terminal.

[0596] "Means for transmitting the image to the server" refers to communication technology for transferring acquired image data to a specific server via a communication network such as the Internet.

[0597] "A means of performing image analysis on a server to identify objects" refers to a process that processes image data received on a server and uses AI technology to identify objects within the image.

[0598] "Means for searching for information related to an identified object" refers to methods for obtaining information associated with an identified object from databases or the internet.

[0599] "Means for summarizing the information and sending it to the terminal" refers to a procedure for concisely summarizing the acquired information and then sending the data back to the user's terminal.

[0600] "Means for displaying transmitted information" refers to a technical mechanism for visually presenting information received on the user's device.

[0601] "A means of analyzing image and audio data to recognize user emotions and adjusting acquired information according to those emotions" refers to a system that analyzes the user's emotional state using the device's camera and microphone to analyze voice and facial expressions, and then customizes the information provided based on the results.

[0602] A specific system for carrying out the present invention consists of a user terminal, a server, and an emotion recognition engine. The user terminal is a portable device such as a smartphone or smart glasses equipped with a camera and microphone. When a user takes a picture of a specific object using the terminal's camera, the terminal encrypts the image data and transmits it to the server via the internet.

[0603] The server implements image recognition software using AI technology, and analyzes received image data and identifies objects using frameworks such as TensorFlow and PyTorch. Information related to the identified objects is obtained from reliable sources on the internet. Furthermore, the information is summarized using a natural language processing library and sent to the terminal.

[0604] The user's device is equipped with an emotion recognition engine using technologies such as OpenCV and DeepFace. It analyzes the user's facial expressions and voice using the camera and microphone to determine the user's emotional state. As a result, the information sent from the server is reconstructed according to the user's emotions, and personalized content tailored to the user's interests is displayed on the screen.

[0605] For example, if a user photographs a new cooking utensil and shows an expression of interest, the system will provide detailed information on its use and recipes. An example of a prompt message to send to the generating AI model might be:

[0606] "Please provide effective usage instructions and related recipes for cooking utensils that users are interested in."

[0607] This allows users to quickly obtain information and deepen their understanding of and interest in the object. This format enables dynamic information delivery that responds to the user's emotions.

[0608] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0609] Step 1:

[0610] The user uses the device's camera to photograph an object of interest. The input is the camera feed from the user, and the device acquires this image data. The output is image data generated for transmission to the next process.

[0611] Step 2:

[0612] The terminal encrypts the acquired image data and sends it to the server. An encryption algorithm is used to securely protect the data and transmit it over the communication network. The input is the image data acquired in step 1, and the output is the encrypted image data.

[0613] Step 3:

[0614] The server decrypts the received image data and performs identification processing using image analysis software. It uses an AI model (e.g., TensorFlow) to perform data calculations to identify objects. The input is encrypted image data, and the output is the object identification result.

[0615] Step 4:

[0616] The server searches for information related to the identified object. It retrieves data from various sources via the internet and selects the most reliable information. The input is information about the identified object, and the output is a list of related information.

[0617] Step 5:

[0618] The server summarizes the acquired information using natural language processing techniques and sends it to the terminal. A data summarization algorithm (e.g., spaCy) organizes the content concisely. The input is a list of related information, and the output is the summarized information data.

[0619] Step 6:

[0620] The user uses the device's microphone and camera to recognize emotions from voice and facial expressions. The device uses OpenCV and DeepFace to analyze the emotional state. The input is the user's voice and image data, and the output is the emotion determination result.

[0621] Step 7:

[0622] The device reconstructs information based on the sentiment assessment results and presents it to the user. Based on sentiment data, it adjusts the content and displays it in a visually easy-to-understand manner. Input consists of summary information and sentiment assessment results, while output is customized information for display.

[0623] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0624] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0625] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0626] [Fourth Embodiment]

[0627] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0628] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0629] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0630] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0631] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0632] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0633] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0634] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0635] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0636] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0637] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0638] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0639] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0640] This invention is a system that allows users to instantly obtain detailed information about objects around them. First, the user takes an image of an object of interest using the camera on their device. The device converts the obtained image into a predetermined format and transmits it to a server via the network.

[0641] The server processes the received images using image analysis algorithms to quickly and accurately identify objects. This process may utilize machine learning techniques such as convolutional neural networks (CNNs). Identified objects are then assigned relevant labels and tags.

[0642] The server then uses the identified labels to search the internet for relevant information. The search targets reliable sources such as encyclopedias, specialized websites, and review articles. The collected information is then summarized by AI to generate concise and easy-to-understand text.

[0643] The generated information is sent from the server to the terminal, which then displays the information on its screen. This allows the user to visually confirm detailed information about an object.

[0644] As a concrete example, if a user finds a plant they've never seen before, they point their device's camera at it and take a picture. The server identifies the type of plant and recognizes it as "rosemary." Next, it compiles information about the characteristics, uses, and health benefits of rosemary and sends it to the device. This allows the user to gain the necessary knowledge about the plant.

[0645] This system allows users to instantly deepen their understanding of objects and gain new knowledge.

[0646] The following describes the processing flow.

[0647] Step 1:

[0648] The user launches a dedicated app on their device and points the camera at an object to take a picture. At this time, the camera is set to capture the details of the object in high resolution.

[0649] Step 2:

[0650] The device compresses the captured image as digital data and sends it to the server via the network. During this process, the data is encrypted to maintain security.

[0651] Step 3:

[0652] The server adds the received image data to a queue for analysis. Here, the image data is converted back into a format that allows for high-speed processing.

[0653] Step 4:

[0654] An image analysis engine on the server processes the images and applies artificial intelligence algorithms to recognize objects. Appropriate labels and tags are then assigned to the recognized objects.

[0655] Step 5:

[0656] The server uses identified labels to search the internet for relevant information. Information sources are limited to those that have been verified for reliability and timeliness.

[0657] Step 6:

[0658] The server inputs the information obtained from the search into a natural language processing engine and generates a summarized text. This text is structured to be easily understood by the user.

[0659] Step 7:

[0660] The server sends the generated summary information to the terminal. The data is encrypted again before transmission, ensuring privacy and security.

[0661] Step 8:

[0662] The device decodes the received information and displays it on the screen for the user. The information may also be presented with visual elements such as diagrams and graphs.

[0663] Step 9:

[0664] The user can refer to the displayed information and request further information if necessary. In this case, if it is determined that additional information is needed, the process repeats from step 5 onward.

[0665] (Example 1)

[0666] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0667] In modern times, people are required to obtain information quickly and accurately about unfamiliar objects and phenomena they encounter in their daily lives. However, conventional information retrieval systems require users to individually input search terms and sift through reliable information, making information acquisition time-consuming and laborious. Furthermore, the reliability of the information and the ease of understanding the summarized information were also challenges.

[0668] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0669] In this invention, the server includes means for performing image analysis on a computing device to identify an object, means for searching for data related to the identified object, and means for summarizing the data using a generative artificial intelligence model. This enables users to quickly and reliably obtain detailed information about unknown objects and understand it intuitively.

[0670] "Means for acquiring images" refers to the function that allows the user to capture surrounding objects using a camera.

[0671] "Means of transmitting to a computing device via a communication infrastructure" refers to a function for delivering image data to a computing device via a network.

[0672] A "computer device" is a computer system that analyzes received data and performs information processing.

[0673] "Means for performing image analysis and identifying an object" refers to the process by which a computing device analyzes image data and identifies the captured object.

[0674] "Means of searching for data" refers to a function that allows a computing device to collect additional information about an identified object from the internet.

[0675] "Methods for summarizing data using generative artificial intelligence models" refer to artificial intelligence technologies used to analyze large amounts of information and summarize it in a form that is easy for users to understand.

[0676] A "display device" is a display device that allows users to visually confirm information.

[0677] "Means of return" refers to the function of sending information from a computing device to a display device.

[0678] "Means of collecting analyzed data from reliable sources" refers to the function of obtaining information from databases and websites that are of high quality and reliability.

[0679] This invention is a system that allows users to instantly obtain detailed information about objects around them. First, when a user discovers an object of interest, they use the camera on their device to take a picture of that object. The device can be a smartphone or tablet equipped with a high-performance camera. The captured image is converted to a predetermined format such as JPEG or PNG within the device. Then, this image data is transmitted to a server via the internet through a communication infrastructure. Here, it is common for the image to be sent as a POST request using the HTTP protocol.

[0680] On the server, received image data is first preprocessed and adjusted to an appropriate size and format. The server then runs a convolutional neural network (CNN) using open-source libraries such as TensorFlow or PyTorch to analyze the image. Through this analysis, objects within the image are identified and associated labels and tags are assigned. Based on these identification results, the server searches for relevant information from reliable sources. This search typically utilizes APIs from Wikipedia or specific specialized websites, and web scraping is also performed as needed.

[0681] Next, the server uses a generative AI model to summarize the acquired information and generate concise text. For example, OpenAI's GPT model may be used. The summarized information is sent back to the terminal as an HTTP response, usually in JSON format.

[0682] The device analyzes the received information and displays it to the user in a visually easy-to-understand format. Text size, color, and font are adjusted, and related images are displayed on the screen, allowing the user to visually confirm the information.

[0683] As a concrete example, consider a scenario where a user discovers an unfamiliar plant. When the user takes a picture of this plant and inputs it into the system, the server identifies the plant as "rosemary." Next, information about rosemary, such as its characteristics, uses, and health benefits, is summarized and sent to the user's device. The user can then visually review this information and gain knowledge.

[0684] An example of a prompt message would be, "Identify the plant in this image and provide any relevant information."

[0685] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0686] Step 1:

[0687] The user uses the device's camera to take a picture of an object of interest. The captured image is saved to the device's internal memory. Here, the input is the image information acquired through the camera, and the output is the image file stored on the device.

[0688] Step 2:

[0689] The device converts the acquired image into a predetermined format. For example, it converts a RAW image to JPEG or PNG format. During this process, the image resolution and color depth are optimized. The input is RAW image data, and the output is the converted compressed image data.

[0690] Step 3:

[0691] The terminal sends the converted image data to the server via the network. The HTTP protocol is used, and the image data is uploaded as a POST request. The input is the converted image data, and the output is the data sent to the server.

[0692] Step 4:

[0693] The server first performs preprocessing on the received image data in order to analyze it. This preprocessing includes adjusting the image size and removing noise. The input is the image data sent to the server, and the output is image data that has been prepared in a format that can be analyzed.

[0694] Step 5:

[0695] The server performs image analysis by running a convolutional neural network (CNN). This process uses a pre-trained model to identify objects in the image. The input is pre-processed image data, and the output is label information about the identified objects.

[0696] Step 6:

[0697] The server searches for relevant information based on the identified labels. Here, it uses an API to retrieve information from trusted databases on the internet. The input is the labels of the identified objects, and the output is a dataset containing the relevant information.

[0698] Step 7:

[0699] The server summarizes the acquired information using a generative AI model. In this process, the key points of the acquired information are extracted, and text in a user-friendly format is generated. The input is a dataset of relevant information, and the output is the summarized text.

[0700] Step 8:

[0701] The server returns the summarized information to the terminal. The data is returned in JSON format as an HTTP response. The input is the summarized text, and the output is the JSON data sent to the terminal.

[0702] Step 9:

[0703] The terminal analyzes the received information and displays it visually on its screen. The input here is JSON data received from the server, and the output is detailed information displayed on the terminal's screen. This allows the user to intuitively understand detailed information about an object.

[0704] (Application Example 1)

[0705] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0706] Conventional object recognition systems are required not only to identify objects but also to provide information about them immediately and display it in a way that is directly useful to the user's life. However, existing systems often lack reliability and user-friendliness in their presentation, and they lack the flexibility to provide information in response to additional user requests. The challenge is to solve these problems and provide information that users can intuitively understand and that improves their quality of life.

[0707] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0708] In this invention, the server includes means for analyzing image data to identify an object, means for acquiring information related to the identified object, and means for summarizing the acquired information and transmitting it to an information terminal. This enables users to instantly acquire and understand the latest and most reliable information about objects around them.

[0709] "Image data" refers to visual information captured by cameras or sensors, in a format that can be processed by a computer.

[0710] An "information processing device" refers to a computer system that receives and analyzes image data, and has the function of processing and managing data.

[0711] "Target" refers to an object or item identified through image data analysis, and represents the object from which the user wishes to obtain information.

[0712] "Generative AI technology" refers to technologies that use artificial intelligence to automatically generate, analyze, and summarize data, and includes deep learning models.

[0713] An "information terminal" refers to a device used by a user to receive and display information, and includes smartphones, tablets, and displays.

[0714] An "information provision system" refers to a group of devices that include a series of processes such as analysis, information collection, summarization, and display based on image data, and is a system that provides users with the necessary information.

[0715] The system for implementing this invention first equips an information terminal, such as a smartphone or consumer robot, with a camera to acquire images of objects of interest to the user. The terminal uses this camera to capture image data and transmits this data to a specific information processing device using generative AI technology.

[0716] A server is used as the information processing device, and the server uses a deep learning framework such as TensorFlow to analyze the received image data. The server processes the image data using a convolutional neural network (CNN) to accurately and quickly identify the target. Once the target is identified, the server collects relevant information about that target from reliable databases and online resources, such as Wikipedia or reliable expert websites.

[0717] Next, the server utilizes generative AI models such as GPT-3 in the process of summarizing the collected information. This converts vast amounts of information into a concise and clear text format, making it easy for users to understand. The server then transmits this summarized information to an information terminal, where it is displayed visually. Users can then review the information through their terminal and deepen their practical understanding.

[0718] For example, a knowledge support robot in a home kitchen might use the following prompt to point its camera at a new cooking utensil and acquire information about it: "Please tell me how to use this cooking utensil and what its features are." Based on this, the robot can provide appropriate guidance and additional information in real time.

[0719] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0720] Step 1:

[0721] The user selects an object of interest using an information terminal and acquires image data using the camera. Input is obtained when the user points the camera at the object and presses the shutter button, and the camera module saves this image data to local memory.

[0722] Step 2:

[0723] The terminal converts the acquired image data into a predetermined format and transmits it to the server via the network. The image data is compressed, for example, into JPEG format, and then packetized according to the transmission protocol. The input is the image data, and the output is the data packets sent to the server.

[0724] Step 3:

[0725] The server decodes the received packets and reconstructs the image data. Then, it analyzes the image data using TensorFlow and identifies the target using a convolutional neural network (CNN). The input is the image data, and the output is the label of the identified target.

[0726] Step 4:

[0727] The server retrieves relevant information from reliable sources based on identified labels. The server uses APIs over the internet to query, for example, online encyclopedias and specialized websites. Input is label information, and output is document data.

[0728] Step 5:

[0729] The server summarizes the acquired information using a generative AI model, such as GPT-3. The summarization is based on natural language processing, converting vast amounts of information into short, easy-to-understand sentences. The input is document data, and the output is the summarized text.

[0730] Step 6:

[0731] The terminal displays the summarized text received from the server on the user interface. The user visually confirms and understands this information. The input is the summarized text, and the output is the visually represented information.

[0732] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0733] This invention is a system that enhances the user experience by combining an emotion engine with an object recognition and information provision system. First, the user takes a picture of an object of interest using the device's camera. The device compresses the image and sends it to the server. This communication is secure, and the data is encrypted to maintain security.

[0734] The server analyzes the received image and recognizes the object. Artificial intelligence technology is used for image analysis. After analysis, it searches the internet for information related to the identified object and selects reliable information. The acquired information is summarized using natural language processing and transmitted to the terminal in a clear and concise format.

[0735] The emotion engine uses the device's microphone and camera to recognize the user's emotions. This engine determines the user's emotions from their voice tone and facial expressions. For example, if the user appears surprised, it adjusts the content to provide more detailed information and reassure the user.

[0736] The terminal receives summary information from the server, adjusts it based on the sentiment engine's judgment, and displays it on the user's screen. This display changes the way information is presented and the order it is ordered according to the user's state, helping to facilitate better understanding.

[0737] For example, if a user shows a joyful expression upon seeing a new type of musical instrument, the emotion engine recognizes this and highlights information focusing on the instrument's tone and the enjoyment of playing it. This allows the user to instantly absorb new knowledge and become even more interested in the object.

[0738] This system allows users to deepen their understanding of objects and enjoy personalized information presentations that respond to their emotions.

[0739] The following describes the processing flow.

[0740] Step 1:

[0741] The user points the device's camera at an object and takes a picture. The device acquires a high-resolution image and saves it in a format suitable for analysis.

[0742] Step 2:

[0743] The device compresses the images and sends them to the server over the network. During this process, data encryption is performed to ensure privacy and security.

[0744] Step 3:

[0745] The server inputs the received image data into an analysis engine. Here, machine learning algorithms are applied to identify objects quickly and accurately.

[0746] Step 4:

[0747] The server searches the internet for information related to the identified object and collects reliable information. The information is limited to comprehensive and important information.

[0748] Step 5:

[0749] The server summarizes the collected information using natural language processing and generates a concise text format. This text is clearly and concisely structured.

[0750] Step 6:

[0751] The device activates an emotion engine that analyzes the user's facial expressions and tone of voice to determine their current emotions. For example, it identifies feelings such as joy, surprise, or anxiety.

[0752] Step 7:

[0753] Based on the analysis results of the emotion engine, the device adjusts the order and detail of the information it displays. For example, it might display additional information for positive emotions.

[0754] Step 8:

[0755] The device displays customized information to the user. Information is highlighted according to the user's interests, with the most relevant information presented first.

[0756] Step 9:

[0757] The user can review the displayed information and request additional questions or information. In this case, it is possible to repeat the previous process and update the information.

[0758] (Example 2)

[0759] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0760] Currently, there are limitations to systems that enable users to quickly and appropriately obtain information about objects. In particular, there is a lack of information provision that takes into account the user's emotional state, and the inability to provide a personalized experience is a challenge. Improvements are also needed in the selection of information from reliable sources and the speed of response.

[0761] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0762] In this invention, the server includes means for performing image analysis to identify objects, means for searching and summarizing information related to the objects on an information network, and means for recognizing the user's emotional state and adjusting the method of presenting information. This enables personalized information provision based on the user's emotions.

[0763] "Means of acquiring images" refers to devices or sensors used by a user to photograph objects of interest, and in this case, includes the process of generating image data using a camera.

[0764] "Means for transmitting images to a data processing device" refers to protocols and technical means for compressing and encrypting acquired images and securely transmitting them to a server over a network.

[0765] "Means of performing image analysis to identify objects" refers to algorithms and software that use artificial intelligence technology executed on a server to analyze images and recognize objects within them.

[0766] "Means of searching for information related to an object on an information network" refers to the process of using search engines or APIs to collect information on the internet based on an identified object.

[0767] "Methods of summarization" refers to techniques that use natural language processing to organize and simplify information obtained through search, making it easier for users to understand.

[0768] "Means for recognizing a user's emotional state" refers to technologies and algorithms for determining a user's current emotional state based on voice tone and facial expression analysis.

[0769] "Means of adjusting the method of information presentation" refers to systems and software functions that enable personalized information delivery by changing the displayed content and emphasis of information according to the user's emotional state.

[0770] "Means of displaying information" refers to devices or interfaces for providing adjusted information to a user visually or audibly, and in this case includes displays and audio output devices.

[0771] The object recognition and information provision system of the present invention integrates an emotion engine to improve the user experience. This system primarily functions as a server, terminal, and user. Specific embodiments are described in detail below.

[0772] The user takes a picture of an object of interest using the camera built into the device. The captured image is compressed and AES encrypted in real time within the device. For secure data transmission, the device uses the SSL / TLS protocol to send the image data to the server.

[0773] The transmitted images are first decrypted on the server. Artificial intelligence models such as TensorFlow are used for image analysis, and the server utilizes these models to effectively identify objects within the images. This results in highly accurate object recognition.

[0774] After identification, the server searches the internet for information related to the object. Standard search APIs are used for the search, and relevant information is collected from a wide range of sources. Furthermore, the retrieved information is summarized within the server using a generative AI model. This model, such as GPT, is transformed into a concise and easy-to-understand format through natural language processing techniques.

[0775] When presenting received information to the user, the device uses an emotion recognition engine to determine the user's emotional state. This engine utilizes the device's microphone and camera to analyze emotions by analyzing voice tone and facial expressions. The device then adjusts the priority and display format of information according to the user's emotional state.

[0776] As a concrete example, consider a scenario where a user is delighted to see a new musical instrument. The device recognizes this emotion and highlights information related to the instrument, particularly information about its tone and the enjoyment of playing it. This process allows the user to acquire new knowledge in a pleasant way, further stimulating their interest.

[0777] An example of a prompt message would be: "Retrieve detailed information about this instrument from the image taken with the camera. Also, highlight and present detailed information about the tone if the user is pleased."

[0778] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0779] Step 1:

[0780] The user takes a picture of an object of interest using the device's camera. The input is still image data obtained from the camera. The device compresses this image and applies AES encryption. The output is encrypted compressed image data. This process allows the data to be transmitted to the server efficiently and securely.

[0781] Step 2:

[0782] The terminal sends encrypted, compressed image data to the server using the SSL / TLS protocol. The input is image data encrypted within the terminal. The output is encrypted data stored on the server. This enables secure transmission of image data over the internet.

[0783] Step 3:

[0784] The server decrypts the image data received from the terminal. The input is encrypted, compressed image data. The server decrypts the image and performs image analysis using an AI model (e.g., TensorFlow). The output is information about the identified objects. This analysis step ensures that specific objects are accurately recognized within the image.

[0785] Step 4:

[0786] The server searches the internet for relevant information based on the identified object. The input is attribute data of the identified object. The output is a list of information related to the object. The server uses an API to retrieve useful data from reliable sources.

[0787] Step 5:

[0788] The server summarizes the acquired information using a generative AI model (e.g., GPT). The input is raw data related to an object. The output is a user-friendly, organized summary. This summary allows the user to obtain easily understandable information in a short amount of time.

[0789] Step 6:

[0790] After receiving summary information, the device analyzes the user's emotions using an emotion recognition engine. The input consists of summary information and the user's voice and facial expression data. The output is user emotional state evaluation data. The device determines the user's current emotions by analyzing voice tone and facial expressions.

[0791] Step 7:

[0792] The device adjusts the information it provides to the user based on the results of emotion recognition. Inputs are summary information and the user's emotional state evaluation data. Output is an adapted information display. For example, if a positive emotion is detected, information about the tone and enjoyment of a musical instrument is emphasized. This allows the user to receive personalized information in the most optimal way.

[0793] (Application Example 2)

[0794] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0795] Object recognition systems often lack personalization based on the user's emotions and interests when providing information related to identified objects. This can limit the user experience and diminish its usefulness, so there is a need to provide information that responds to the user's emotions and deliver a richer user experience.

[0796] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0797] In this invention, the server includes means for acquiring an image, means for transmitting the image to the server, means for performing image analysis on the server and identifying an object, and means for analyzing image data and audio data to recognize the user's emotions and adjusting the acquired information according to the emotions. This makes it possible to dynamically change the information display considering the user's emotional state and provide information that is tailored to the user's interests and emotions.

[0798] "Means for acquiring images" refers to a technological device used to capture visual information of an object of interest to a user using an electronic terminal.

[0799] "Means for transmitting the image to the server" refers to communication technology for transferring acquired image data to a specific server via a communication network such as the Internet.

[0800] "A means of performing image analysis on a server to identify objects" refers to a process that processes image data received on a server and uses AI technology to identify objects within the image.

[0801] "Means for searching for information related to an identified object" refers to methods for obtaining information associated with an identified object from databases or the internet.

[0802] "Means for summarizing the information and sending it to the terminal" refers to a procedure for concisely summarizing the acquired information and then sending the data back to the user's terminal.

[0803] "Means for displaying transmitted information" refers to a technical mechanism for visually presenting information received on the user's device.

[0804] "A means of analyzing image and audio data to recognize user emotions and adjusting acquired information according to those emotions" refers to a system that analyzes the user's emotional state using the device's camera and microphone to analyze voice and facial expressions, and then customizes the information provided based on the results.

[0805] A specific system for carrying out the present invention consists of a user terminal, a server, and an emotion recognition engine. The user terminal is a portable device such as a smartphone or smart glasses equipped with a camera and microphone. When a user takes a picture of a specific object using the terminal's camera, the terminal encrypts the image data and transmits it to the server via the internet.

[0806] The server implements image recognition software using AI technology, and analyzes received image data and identifies objects using frameworks such as TensorFlow and PyTorch. Information related to the identified objects is obtained from reliable sources on the internet. Furthermore, the information is summarized using a natural language processing library and sent to the terminal.

[0807] The user's device is equipped with an emotion recognition engine using technologies such as OpenCV and DeepFace. It analyzes the user's facial expressions and voice using the camera and microphone to determine the user's emotional state. As a result, the information sent from the server is reconstructed according to the user's emotions, and personalized content tailored to the user's interests is displayed on the screen.

[0808] For example, if a user photographs a new cooking utensil and shows an expression of interest, the system will provide detailed information on its use and recipes. An example of a prompt message to send to the generating AI model might be:

[0809] "Please provide effective usage instructions and related recipes for cooking utensils that users are interested in."

[0810] This allows users to quickly obtain information and deepen their understanding of and interest in the object. This format enables dynamic information delivery that responds to the user's emotions.

[0811] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0812] Step 1:

[0813] The user uses the device's camera to photograph an object of interest. The input is the camera feed from the user, and the device acquires this image data. The output is image data generated for transmission to the next process.

[0814] Step 2:

[0815] The terminal encrypts the acquired image data and sends it to the server. An encryption algorithm is used to securely protect the data and transmit it over the communication network. The input is the image data acquired in step 1, and the output is the encrypted image data.

[0816] Step 3:

[0817] The server decrypts the received image data and performs identification processing using image analysis software. It uses an AI model (e.g., TensorFlow) to perform data calculations to identify objects. The input is encrypted image data, and the output is the object identification result.

[0818] Step 4:

[0819] The server searches for information related to the identified object. It retrieves data from various sources via the internet and selects the most reliable information. The input is information about the identified object, and the output is a list of related information.

[0820] Step 5:

[0821] The server summarizes the acquired information using natural language processing techniques and sends it to the terminal. A data summarization algorithm (e.g., spaCy) organizes the content concisely. The input is a list of related information, and the output is the summarized information data.

[0822] Step 6:

[0823] The user uses the device's microphone and camera to recognize emotions from voice and facial expressions. The device uses OpenCV and DeepFace to analyze the emotional state. The input is the user's voice and image data, and the output is the emotion determination result.

[0824] Step 7:

[0825] The device reconstructs information based on the sentiment assessment results and presents it to the user. Based on sentiment data, it adjusts the content and displays it in a visually easy-to-understand manner. Input consists of summary information and sentiment assessment results, while output is customized information for display.

[0826] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0827] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0828] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0829] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0830] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0831] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0832] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0833] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0834] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0835] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0836] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0837] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0838] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0839] 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.

[0840] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0841] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0842] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0843] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0844] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0845] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0846] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted as being incorporated by reference.

[0847] The following is further disclosed regarding the embodiments described above.

[0848] (Claim 1)

[0849] Means of acquiring images,

[0850] Means for sending the image to the server,

[0851] The server performs image analysis and provides means for identifying objects.

[0852] Means for retrieving information related to an identified object,

[0853] A means for summarizing the information and transmitting it to a terminal,

[0854] A means of displaying the transmitted information,

[0855] A system that includes this.

[0856] (Claim 2)

[0857] The system according to claim 1, further comprising means for performing information collection processing again when the user requests additional information.

[0858] (Claim 3)

[0859] The system according to claim 1, comprising means for collecting the analyzed information from reliable sources.

[0860] "Example 1"

[0861] (Claim 1)

[0862] Means of acquiring images,

[0863] Means for transmitting the image to a computing device via a communication infrastructure,

[0864] A means for performing image analysis on a computing device to identify an object,

[0865] A means for searching for data related to the identified object,

[0866] A means for summarizing the data and returning it to a display device,

[0867] A means for displaying the returned data,

[0868] A means of summarizing data using a generative artificial intelligence model,

[0869] A system that includes this.

[0870] (Claim 2)

[0871] The system according to claim 1, further comprising means for collecting data again when the user requests additional data.

[0872] (Claim 3)

[0873] The system according to claim 1, comprising means for collecting the analyzed data from a reliable source.

[0874] "Application Example 1"

[0875] (Claim 1)

[0876] A device for acquiring image data,

[0877] A device for transmitting the image data to an information processing device,

[0878] A device that performs image data analysis in an information processing system to identify a target,

[0879] A device for acquiring information related to a specified target,

[0880] A device that summarizes acquired information and transmits it to an information terminal,

[0881] A device that visually displays the transmitted information,

[0882] A device that uses generative AI technology to identify objects and summarize information,

[0883] An information provision system that includes this.

[0884] (Claim 2)

[0885] The information provision system according to claim 1, further comprising a device that performs a new re-acquisition process in response to a user's request.

[0886] (Claim 3)

[0887] The information provision system according to claim 1, comprising a device for selecting and collecting acquired information from highly reliable sources.

[0888] "Example 2 of combining an emotion engine"

[0889] (Claim 1)

[0890] Means of acquiring images,

[0891] Means for transmitting the image to a data processing device,

[0892] Image analysis is performed in the data processing device, and object identification means,

[0893] A means for searching for information related to an identified object on an information network,

[0894] A means for summarizing the information and transmitting it to an information terminal,

[0895] A means of recognizing the user's emotional state and adjusting the way information is presented,

[0896] A means of displaying the transmitted information,

[0897] An information provision system that includes this.

[0898] (Claim 2)

[0899] The information provision system according to claim 1, further comprising means for performing information collection processing again when a user requests additional information.

[0900] (Claim 3)

[0901] The information provision system according to claim 1, comprising means for collecting the analyzed information from reliable sources.

[0902] "Application example 2 when combining with an emotional engine"

[0903] (Claim 1)

[0904] Means of acquiring images,

[0905] Means for sending the image to the server,

[0906] The server performs image analysis and provides means for identifying objects.

[0907] Means for retrieving information related to an identified object,

[0908] A means for summarizing the information and transmitting it to a terminal,

[0909] A means of displaying the transmitted information,

[0910] A means for analyzing image and audio data to recognize user emotions and adjusting the acquired information according to those emotions,

[0911] A system that includes this.

[0912] (Claim 2)

[0913] The system according to claim 1, further comprising means for performing information collection processing again when the user requests additional information.

[0914] (Claim 3)

[0915] The system according to claim 1, comprising means for collecting analyzed information from reliable sources, and means for dynamically changing the information display in consideration of the user's emotional state. [Explanation of Symbols]

[0916] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. Means of acquiring images, Means for sending the image to the server, The server performs image analysis and provides means for identifying objects. Means for retrieving information related to an identified object, A means for summarizing the information and transmitting it to a terminal, A means of displaying the transmitted information, A system that includes this.

2. The system according to claim 1, further comprising means for performing information collection processing again when the user requests additional information.

3. The system according to claim 1, comprising means for collecting the analyzed information from reliable sources.

Images