System

Abstract

To provide a system which enables a user to pleasantly perform research by making an object itself answer through conversation.SOLUTION: The system includes a means for acquiring an image of an object by a camera, a communication means for transmitting the acquired image data, a generation means for generating a face of the object on the basis of the received image data, a conversation generation means for generating conversation contents by using the generated face, a transmission means for transmitting the generated face and conversation contents, and a display means for displaying the received face and conversation contents.SELECTED DRAWING: Figure 11

Description

[Technical Field]

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

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

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

[0004] While conventional research methods using web searches, AI chatbots, and image recognition tools are convenient, they can sometimes be unenjoyable. A fun way to conduct research is needed, especially for parents with children and general users who want to enjoy generative AI. The objective of this invention is to provide a system that allows users to conduct research in an enjoyable way by having the object itself answer questions through conversation. [Means for solving the problem]

[0005] The present invention is a system including means for acquiring an image of an object with a camera, communication means for transmitting the acquired image data, generation means for generating a face on the object based on received image data, conversation generation means for generating conversation content using the generated face, transmission means for transmitting the generated face and conversation content, and display means for displaying the received face and conversation content.The system also includes means for starting a conversation with the object, including greetings and self-introductions, and is characterized in that the user points the camera at the object and acquires an image of the object.This allows the user to enjoy researching.

[0006] A "camera" is a device for taking videos and still images.

[0007] The term "object" refers to an object or image that the user wants to examine.

[0008] "Image" refers to visual information captured by a camera and stored as digital data.

[0009] "Capture" refers to the act of collecting image data using a camera.

[0010] "Communication means" refers to the technology and protocols used to transmit image data to a server or other device.

[0011] "Generation means" refers to software or algorithms for generating new information or expressions based on image data.

[0012] "Face" refers to an image element with a human-like visual expression that is composited onto an object.

[0013] "Conversation generator" refers to algorithms or software that use natural language processing techniques to generate appropriate responses to mimic a conversation with a user.

[0014] "Transmission means" refers to the technology and protocols used to transmit the generated face and conversation content to the user's device.

[0015] "Display means" refers to hardware or software for visually displaying the received face and conversation content on the user's terminal.

[0016] "User" refers to a person who uses the system to examine an object.

[0017] A "greeting" is a simple word or phrase used to start a conversation.

[0018] "Self-introduction" refers to a short phrase or sentence that allows an object to briefly explain itself.

[0019] The term "server" refers to a computer system that receives and processes image data from a terminal.

[0020] "Terminal" refers to the device used by a user to operate this system, including, for example, a smartphone or tablet. [Brief explanation of the drawings]

[0021] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7]FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0022] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0023] First, the terms used in the following description will be explained.

[0024] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0025] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0026] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0027] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0028] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0029] [First embodiment]

[0030] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0031] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0032] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0033] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0034] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0035] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0036] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0037] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0038] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0039] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0040] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0041] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0042] This system allows a user to use a camera to capture an image of an object they wish to research, send that image to a server, generate a face for the object based on the image data received by the server, generate conversation content using the generated face, and send it back to the terminal to display to the user. This system provides an experience where the object itself answers questions, making research fun.

[0043] System Components

[0044] 1. Camera (device)

[0045] The device has a camera function and captures images of the object indicated by the user. The images captured by the camera are temporarily stored in the device's memory and used for subsequent processing.

[0046] 2. Communication method (terminal and server)

[0047] This includes communication technology for transmitting image data acquired by the device to a server. It is assumed that Wi-Fi or mobile data communication will be used as the means of communication.

[0048] 3. Receiving method and image processing (server)

[0049] The server receives the image data sent from the device and temporarily stores it. It then uses the stored image data to recognize the object and generate a face.

[0050] 4. Face generation means (server)

[0051] The server uses image processing algorithms to generate a face that matches the subject, often using generative models such as deep learning.

[0052] 5. Conversation Generation Method (Server)

[0053] After the server generates the face, it generates a conversation based on the face, using natural language processing technology to create a response to the user. For example, the generated face might say, "Hello! I'm this table. What do you want to know?"

[0054] 6. Transmission Method (Server)

[0055] The server sends the generated face and conversation content to the device, which includes image data and text data.

[0056] 7. Display means (terminal)

[0057] The device receives the face and conversation content from the server and displays it on the user interface, making it appear as if the object itself is speaking to the user.

[0058] Specific examples

[0059] For example, a user uses a smartphone camera to take a picture of a table. The smartphone sends the captured image to a server, which receives the image and generates a face on the table. The server then uses the generated face to generate a conversation, such as "Hello! I'm this table. What would you like to know?" The server then sends the generated face and conversation to the smartphone, which displays it to the user. The user can enjoy watching the table on the screen talk to them.

[0060] In this way, the system of the present invention allows users to enjoyably obtain information about objects, and is expected to be a particularly useful tool for parents and users who want to enjoy generative AI.

[0061] The processing flow will be explained below.

[0062] Step 1:

[0063] The user uses a camera to capture an image of the object they want to investigate. Using a device such as a smartphone or tablet, the user points the camera at the object and takes a photo.

[0064] Step 2:

[0065] The terminal sends the acquired image data to the server. The terminal uses a communication means to create an HTTP request to send the captured image to the server, and then sends it.

[0066] Step 3:

[0067] The server receives the image data sent from the terminal and temporarily stores the received image data in the server's storage.

[0068] Step 4:

[0069] The server analyzes the stored image data and generates a face that matches the subject. The server uses image processing algorithms to detect features in the image and generate facial elements that match the subject.

[0070] Step 5:

[0071] The server generates a conversation using the generated face. The server uses natural language processing technology to create an appropriate response for the user and determines the conversation based on the generated face.

[0072] Step 6:

[0073] The server sends the generated face and the conversation content to the device. The server then compiles the face image data and text data and creates a response to send to the device.

[0074] Step 7:

[0075] The device displays the face and conversation content received from the server. The device analyzes the received data and displays the generated face and conversation content on the user interface.

[0076] Examples:

[0077] For example, if a user points their smartphone camera at a table and takes a picture, the device sends the image to the server. The server analyzes the received image and generates a face for the image of the table. The server then generates a conversation such as "Hello! I'm this table. What would you like to know?" and sends it to the device. The device displays the received face and conversation on the user's interface, allowing the user to enjoy watching the table talk to them.

[0078] Example 1

[0079] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0080] Conventional image recognition systems have had issues with quickly recognizing objects and providing intuitive interaction with users. Furthermore, they lacked advanced natural language processing technology to generate appropriate responses for captured images, and methods for generating faces appropriate for the objects. As a result, it was difficult for users to acquire information about objects in an enjoyable and effective way.

[0081] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0082] In this invention, the server includes a recognition means, a generation means, and a conversation generation means, which enable the server to quickly and accurately recognize an object from image data and provide the user with natural conversation content linked to the generated face.

[0083] A "camera" is a device that optically captures an object and stores it as image data.

[0084] An "object" is an object that a user photographs with a camera to look up specific information.

[0085] "Image data" refers to visual information of an object captured by a camera and stored in digital format.

[0086] "Communication means" refers to the technology and protocols used to transmit image data from a device to a server, and examples include Wi-Fi and mobile data communications.

[0087] "Recognition means" refers to an algorithm or program for recognizing an object from received image data and analyzing its characteristics.

[0088] "Generation means" refers to algorithms or programs for generating appropriate faces and features for recognized objects, and specific examples include deep learning models.

[0089] "Conversation generation means" refers to natural language processing technology and algorithms for creating dialogue with the user based on the generated face.

[0090] "Transmission means" refers to the technology and protocols used to transmit the generated face and conversation content from the server to the terminal.

[0091] "Display means" refers to the technology or program for displaying the received face and conversation content on the terminal screen so that the user can visually confirm it.

[0092] The present invention is a system in which a user uses a camera to capture an image of an object they want to research, sends the image to a server, and the server generates a face for the object based on the received image data, generates conversation content using the generated face, and sends it back to the terminal to display to the user. This system provides the user with the experience of the object itself speaking to them, making research fun.

[0093] System Components

[0094] 1. Camera (device)

[0095] The device has a camera function and captures images of the object indicated by the user. The images captured by the camera are temporarily stored in the device's memory and used for subsequent processing.

[0096] 2. Communication method (terminal and server)

[0097] This includes communication technology for transmitting image data acquired by the device to a server. It is assumed that Wi-Fi or mobile data communication will be used as the means of communication.

[0098] 3. Receiving method and image processing (server)

[0099] The server receives the image data sent from the device and temporarily stores it. It then uses the stored image data to recognize the object.

[0100] 4. Face generation means (server)

[0101] The server uses image processing algorithms to generate a face suitable for the subject, using a deep learning model (e.g., generative artificial network (GAN)).

[0102] 5. Conversation Generation Method (Server)

[0103] The server generates conversational content based on the generated face, using natural language processing techniques (e.g., GPT model) to create responses to the user.

[0104] 6. Transmission Method (Server)

[0105] The server sends the generated face and conversation content to the device, which includes image data and text data.

[0106] 7. Display means (terminal)

[0107] The device displays the face and conversation content received from the server on the user interface, making it appear to the user as if the object itself is speaking to them.

[0108] Specific examples of operation

[0109] Consider a scenario where a user takes a picture of a table using a smartphone camera. The specific steps are as follows:

[0110] 1. Image acquisition: The user takes an image of the table using the smartphone camera.

[0111] 2. Image transmission: The smartphone sends the captured image to the server.

[0112] 3. Image processing: The server recognizes the received image data and generates a face that matches the table.

[0113] 4. Conversation generation: Based on the generated face, the server generates a conversation such as "Hello! I'm this table. What would you like to know?"

[0114] 5. Data transmission: The server sends the generated face and conversation content to the smartphone.

[0115] 6. Display: The smartphone displays the received data on the user interface, giving the user the impression that the table is talking to them.

[0116] Prompt Sentence Examples

[0117] An example prompt for input to a generative AI model is:

[0118] "Analyze an image of a table and generate a face that matches the table. Then generate a conversation for that face to say, 'Hello! I'm this table. What would you like to know?'"

[0119] Using this prompt, the generative AI model can perform the necessary processing.

[0120] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0121] Step 1:

[0122] Input: The user uses a device with a camera function to capture an image of the object to be examined (e.g., a table).

[0123] How it works: The user launches the camera app on their smartphone, points the camera at an object, and presses the shutter button.

[0124] Output: Image data stored on the device.

[0125] Step 2:

[0126] Input: Image data stored on the device.

[0127] How it works: The device sends image data to the server using Wi-Fi or mobile data.

[0128] Output: Image data sent to the server.

[0129] Step 3:

[0130] Input: Image data sent to the server.

[0131] Operation: The server receives the image data and temporarily stores it in storage. The server verifies the consistency of the received data.

[0132] Output: Image data stored on the server.

[0133] Step 4:

[0134] Input: Image data stored on the server.

[0135] How it works: The server uses image processing algorithms to recognize objects. Based on the recognition results, it uses a deep learning model (e.g., GAN) to generate a face that matches the object.

[0136] Output: The generated face image.

[0137] Step 5:

[0138] Input: Generated face image.

[0139] How it works: The server uses natural language processing techniques (e.g., GPT models) to generate a conversation based on the generated face, for example, creating a message like "Hello! I'm this table. What would you like to know?"

[0140] Output: The generated conversation.

[0141] Step 6:

[0142] Input: Generated face image and conversation content.

[0143] Operation: The server combines the facial image and conversation content into a single packet and sends it to the terminal.

[0144] Output: Facial image and conversation data sent to the device.

[0145] Step 7:

[0146] Input: Facial image and conversation data sent to the device.

[0147] Operation: The device analyzes the received data and displays it in the user interface. The device divides it into an image view and a text view and displays them on the screen.

[0148] Output: Facial image and conversation content displayed on the user's device.

[0149] (Application example 1)

[0150] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0151] In the past, there were limited ways for users to easily obtain product information in physical stores. Especially when there were many products on display, it was time-consuming to look up each product individually. Furthermore, there was a lack of systems that allowed users to obtain product information in an enjoyable way. This could result in a monotonous user experience and a decrease in motivation to purchase products.

[0152] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0153] In this invention, the server includes a generation means for generating a face on an object based on received image data, a conversation generation means for generating a conversation using a prompt sentence based on the generated face, and a means for transmitting the generated face and the conversation again via a communication means. This enables a system in which, when a user takes a photo of a product with a smartphone in a physical store, the product appears to be talking to them.

[0154] A "camera" is a photographing device that allows a user to capture an image of an object.

[0155] "Communication means" refers to the network means for transmitting acquired image data to the server and receiving data from the server, including Wi-Fi and mobile data communications.

[0156] The "server" is a computer system that processes image data received via a communication means, generates faces for objects, and generates conversation content.

[0157] The "generation means" refers to an algorithm or software for generating a face for an object based on image data received by the server.

[0158] A "prompt sentence" is an input sentence used to generate a conversation based on the face generated by the generation means.

[0159] The "conversation generation means" is a system that uses natural language processing technology with prompt sentences based on the generated face to generate conversation content from the target object.

[0160] The "transmission means" is a means for transmitting the face and conversation content generated by the server to the user terminal again via the communication means.

[0161] The "user interface" refers to a display device and software that allows the user to visually confirm the face and conversation content they receive.

[0162] A "portable device" is an electronic device that a user can carry with them, including a smartphone or tablet PC.

[0163] To implement this invention, a user first captures an image of an object using the camera of a portable device. A portable device refers to a portable electronic device such as a smartphone or tablet PC. These devices are equipped with a camera function and internet communication means.

[0164] When a user takes an image of an object, the image data is sent to a server via a communication means, such as Wi-Fi or mobile data communication.

[0165] The server is equipped with a generation means for generating a face on the target object based on the received image data. The generation means uses a deep learning-based generative model (e.g., Generative Adversarial Network (GAN)).

[0166] Next, the server generates a conversation using a prompt sentence based on the generated face. The prompt sentence used here utilizes a generative AI model and is processed by a conversation generation means that applies natural language processing technology. For example, a generative model from OpenAI (registered trademark) (e.g., GPT-3 (registered trademark)) can be used.

[0167] The generated face and conversation content are then sent to the user's portable device via a communication means, and the user's device displays the received face and conversation content on the user interface, allowing the user to feel as if the object is speaking to them.

[0168] As a concrete example, a user can take a picture of a snack in a physical store and the snack will speak to them, saying, "Hello! I'm potato chips. My calories are 500 kcal per 100 g. Is there anything else you'd like to know?"

[0169] Examples of prompts include:

[0170] "Generate one conversational phrase that corresponds to a smiling face:"

[0171] Through the above process, the system allows users to easily and enjoyably obtain detailed product information in a physical store. An important feature of the invention is that it provides a natural and engaging way to interact with a human through the generated faces and conversations.

[0172] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0173] Step 1:

[0174] The user captures an image of an object using the camera of a portable device. The input is the image of the object, and the output is image data temporarily stored in the internal memory. In this step, the camera is started and the image is taken by pressing the shutter.

[0175] Step 2:

[0176] The terminal sends the acquired image data to the server via a communication means. The input is the temporarily saved image data, and the output is the image data sent to the server. In this step, the image data is uploaded to the server using an HTTP POST request.

[0177] Step 3:

[0178] The server generates a face for the target based on the received image data. The input is the image data received by the server, and the output is the generated face image data. The server processes this data using a generative model (e.g., GAN) to generate a face that suits the target.

[0179] Step 4:

[0180] The server generates a conversation using a prompt sentence based on the generated face. The input is the generated face image data, and the output is the generated conversation. The server uses natural language processing technology and a generative AI model (e.g., GPT-3) to generate the conversation. An example of a prompt sentence here is, "Please generate one conversation corresponding to a smiling face."

[0181] Step 5:

[0182] The server again transmits the generated face and conversation content to the terminal via the communication means. The input is the generated face image data and conversation content, and the output is these data transmitted to the terminal. The server again transmits these data to the terminal using an HTTP response.

[0183] Step 6:

[0184] The device displays the received face and conversation content on the user interface. The input is the face image data and conversation content received from the server, and the output is the display content provided visually to the user. The device uses an image viewer and a text viewer to display the face and conversation content on the screen. This step allows the user to enjoy the experience of the object talking to them.

[0185] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0186] The present invention is a system in which a user uses a camera to capture an image of an object they want to examine, sends the image to a server, the server generates a face for the object based on the image data received, generates conversation content using the generated face, and sends it back to the terminal to display to the user. Furthermore, this system incorporates an emotion engine that recognizes the user's emotions, and can adjust the conversation content according to the user's emotions.

[0187] System Components

[0188] 1. Camera (device)

[0189] The device has a camera function and captures an image of the object indicated by the user. The captured image is temporarily stored in the device's memory.

[0190] 2. Communication method (terminal and server)

[0191] This includes communication technology for transmitting image data acquired by the device to a server. It is assumed that Wi-Fi or mobile data communication will be used as the means of communication.

[0192] 3. Receiving method and image processing (server)

[0193] The server receives the image data sent from the device and temporarily stores it. It then uses the stored image data to recognize the object and generate a face.

[0194] 4. Face generation means (server)

[0195] The server uses image processing algorithms to generate a face that matches the subject, often using generative models such as deep learning.

[0196] 5. Conversation Generation Method (Server)

[0197] After the server generates the face, it generates a conversation based on the face, using natural language processing technology to create an appropriate response for the user. For example, the generated face might say, "Hello! I'm this table. What do you want to know?"

[0198] 6. Transmission Method (Server)

[0199] The server sends the generated face and conversation content to the device, which includes image data and text data.

[0200] 7. Display means (terminal)

[0201] The device receives the face and conversation content from the server and displays it on the user interface, making it appear as if the object itself is speaking to the user.

[0202] 8. Emotion Engine (Server)

[0203] The server is equipped with an emotion engine that recognizes the user's emotions. This emotion engine analyzes the user's emotions from their facial expressions and voice, and adjusts the content of the conversation based on the results.

[0204] System Operation

[0205] 1. User Emotion Recognition

[0206] The emotion engine analyzes the user's facial expressions and tone of voice from camera footage and microphone audio to determine the user's current emotional state (e.g., joy, sadness, surprise, etc.).

[0207] 2. Adjust the conversation

[0208] The server adapts the conversation depending on the user's emotional state. For example, if the user is tired, the server might respond with, "You seem down, what's wrong?"

[0209] Specific examples

[0210] For example, a user uses a smartphone camera to take a picture of a table. The smartphone sends the captured image to a server, which receives the image and generates a face on the table. The server then uses an emotion engine to analyze the user's emotions. For example, if the server recognizes that the user is tired, it generates a conversation through the generated face, saying, "Hello! I'm this table. You look like you need a short break. Are you okay?" The server then sends the generated face and conversation to the smartphone, which displays it to the user. The user can enjoy watching the table on the screen talk to them according to their emotions.

[0211] In this way, the system of the present invention allows users to acquire information about objects in a fun and emotional way, and is expected to be a particularly useful tool for parents and users who want to enjoy generative AI.

[0212] The processing flow will be explained below.

[0213] Step 1:

[0214] The user uses a camera to capture an image of the object they want to investigate. Using a device such as a smartphone or tablet, the user points the camera at the object and takes a photo through the app.

[0215] Step 2:

[0216] The device sends the acquired image data to the server. The device sends the captured image file to the server using an HTTP request. The communication method is Wi-Fi or mobile data communication.

[0217] Step 3:

[0218] The server receives the image data sent from the terminal and stores it in the server's storage.

[0219] Step 4:

[0220] The server analyzes the image data and generates a face that matches the subject. The server uses a deep learning-based image processing algorithm to detect the contours and features of the subject in the image and generates a face based on this.

[0221] Step 5:

[0222] The server generates conversation content using the generated face. The server uses natural language processing technology (such as GPT-3) to determine the appropriate response to the user. A conversation text is also created along with the generated face image.

[0223] Step 6:

[0224] The server recognizes the user's emotions. Using an emotion engine, the server analyzes facial expressions and tone of voice from the user's camera footage and audio to identify their current emotion (happiness, sadness, surprise, etc.).

[0225] Step 7:

[0226] The server will adjust the conversation content based on the emotion recognition results. For example, if the user is tired, the conversation content will be adjusted to "Hello! I'm this table. You seem a little down. Are you okay?"

[0227] Step 8:

[0228] The server sends the generated facial image and adjusted conversation text to the terminal.

[0229] Step 9:

[0230] The device displays the face and conversation content received from the server. The device analyzes the received data and displays it on the user interface. This allows the user to experience the object on the screen as if it were speaking to them.

[0231] Examples:

[0232] For example, a user can take a picture of a table using the camera on their smartphone and send it to the server. The server receives the image and generates a face on the table. If the emotion engine later determines that the user is tired, the server generates a conversation such as, "Hello! I'm this table. You look a little down. Are you okay?" The server then sends the generated face and conversation to the smartphone, which displays it on the user interface. The user can enjoy watching the table on the screen talk to them according to their emotions.

[0233] Example 2

[0234] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0235] Conventional image recognition systems have difficulty recognizing a user's emotions and providing appropriate responses when interacting with an object. In particular, it has been difficult to automatically respond to a user's emotions, such as tiredness, happiness, or sadness. This can limit the user experience and reduce user satisfaction, as the information and responses provided by the system tend to be uniform.

[0236] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0237] In this invention, the server includes an emotion recognition means for analyzing the user's facial expression and tone of voice to determine their emotion, an emotion response means for adjusting the content of the conversation in accordance with the determined emotional state, and a generation means for receiving image data and generating a face on the target object, thereby enabling responses and dialogue that take the user's emotion into consideration.

[0238] A "camera" is a photographic device that a user uses to capture images.

[0239] "Communication means" refers to the technology used by the terminal to send the image data acquired by the terminal to the server, and includes Wi-Fi, mobile data communication, etc.

[0240] The "generation means" is a technology that performs processing to generate a face on the target object based on the image data received by the server.

[0241] The "conversation generation means" is a technology that allows the server to generate appropriate conversation content using the generated face, and includes natural language processing technology.

[0242] "Transmission means" refers to a technique by which the server transmits the generated face and conversation content to the terminal.

[0243] "Display means" refers to a technology for displaying the face and conversation content received by the terminal on the user interface.

[0244] "Emotion recognition means" is a technology that allows the server to analyze the user's facial expressions and tone of voice to determine their emotions.

[0245] "Emotion response means" is a technique that allows the server to adjust the content of conversation according to the user's emotional state.

[0246] The present invention is a system in which a user uses a camera to capture an image of an object they want to examine, sends the image to a server, the server generates a face for the object based on the image data received, generates conversation content using the generated face, and sends it back to the terminal to display to the user. Furthermore, this system has a built-in function to recognize the user's emotions and adjust the conversation content.

[0247] System Components

[0248] 1. Camera (device)

[0249] The device has a camera function and captures an image of the object indicated by the user. The captured image is temporarily stored in the device's memory. For example, a smartphone is used.

[0250] 2. Communication method (terminal and server)

[0251] This includes communication technology for transmitting image data acquired by the device to a server via Wi-Fi or mobile data communication.

[0252] 3. Receiving method and image processing (server)

[0253] The server receives the image data sent from the device, temporarily stores the data, and uses image processing algorithms (e.g., OpenCV or deep learning models) to recognize objects and generate faces.

[0254] 4. Face generation means (server)

[0255] The server uses a generative model (e.g., a deep learning-based generative adversarial network: GAN) to generate a face that suits the subject.

[0256] 5. Conversation Generation Method (Server)

[0257] The server uses natural language processing technology (e.g., GPT-3) to create appropriate conversation content based on the generated face. The generated face generates conversation content such as "Hello! I'm this table. What do you want to know?"

[0258] 6. Transmission Method (Server)

[0259] The server sends the generated face and conversation content to the device, which includes image data and text data.

[0260] 7. Display means (terminal)

[0261] The device receives the face and conversation content from the server and displays it on the user interface, making it appear as if the object is speaking to the user.

[0262] 8. Emotion Engine (Server)

[0263] The server is equipped with an emotion engine that analyzes the user's emotions from their facial expressions and voice, and adjusts the content of the conversation based on the analysis results.

[0264] Specific examples

[0265] For example, suppose a user uses a smartphone camera to take a picture of a table. The smartphone sends the captured image to a server, which receives the image and generates a face on the table. The server then uses an emotion engine to analyze the user's emotions. For example, if the server recognizes that the user is tired, it generates a conversation through the generated face, saying, "Hello! I'm this table. You look like you need a short break. Are you okay?" The server then sends the generated face and conversation to the smartphone, which displays it to the user. The user can enjoy watching the table on the screen talk to them according to their emotions.

[0266] Prompt Sentence Examples

[0267] By feeding the following sentences into the generative AI model, we can get prompts that replicate the system's functionality:

[0268] The user uses a camera to capture an image of the object they want to examine and sends the image to the server. The server processes the image and generates a face for the object. It then uses an emotion engine to analyze the user's emotions and generates corresponding conversation content. The generated face and conversation content are sent back to the device and displayed on the user interface. For example, if the user is tired, a response such as "Hello, it looks like you need a break" may be generated.

[0269] As described above, the system of the present invention allows users to acquire information about objects in a fun and emotional way. It is expected to be a particularly useful tool for parents and users who want to enjoy generative AI.

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

[0271] Step 1:

[0272] A user takes an image of an object using the device's camera. The user's input is to activate the camera and take a picture of the object. The device stores this image in memory. The input is the image of the object, and the output is the image data stored in memory. For example, a user takes a picture of a table with their smartphone.

[0273] Step 2:

[0274] The device sends the acquired image data to the server. The input of the device is the image data stored in its memory. The device uploads this data to the server using Wi-Fi or mobile data communication. The output is the image data to be transmitted. Specifically, the smartphone transmits the image data to the cloud server via the Internet.

[0275] Step 3:

[0276] The server receives image data sent from the terminal and temporarily stores it. The input to the server is the image data sent from the terminal. The server saves this received data in storage. The output is the saved image data. Specifically, the server saves the image data in a specific directory.

[0277] Step 4:

[0278] The server analyzes the image data and recognizes the object. Specifically, it identifies the object using an image processing algorithm (e.g., OpenCV). The input is the stored image data, and the output is the object recognition result. The server extracts the shape and features of the object.

[0279] Step 5:

[0280] The server generates an appropriate face for the recognized object. The face is generated using a generative model (e.g., deep learning model, GAN). The input is the object recognition result, and the output is image data of the generated face. Specifically, the server uses GAN to generate a face image of the table.

[0281] Step 6:

[0282] The server generates conversation content based on the generated face. It uses natural language processing technology (e.g., GPT-3) to create a response to the user. The input is image data of the generated face and information about the object, and the output is text data of the conversation content. Specifically, the server generates the conversation content, "Hello! I'm this table. What do you want to know?"

[0283] Step 7:

[0284] The server sends the generated face and the conversation content to the device. The server's input is image data of the generated face and text data of the conversation content. The server sends this data to the device. The output is the sent image data of the face and text data of the conversation content. Specifically, the cloud server sends this data to the smartphone.

[0285] Step 8:

[0286] The device receives the face and conversation data from the server and displays them on the user interface. The device inputs the received face and conversation data. The device displays these on the user interface. The output is the face and conversation data displayed on the user interface. Specifically, the face at the table and its conversation data are displayed on the smartphone screen.

[0287] Step 9:

[0288] The server's emotion engine analyzes the user's facial expressions and voice to determine their emotions. The input is the device's camera footage and microphone audio, and the output is the user's emotional state. The emotion engine analyzes the user's facial expression data and tone of voice data to determine their emotional state. For example, it determines whether the user is smiling.

[0289] Step 10:

[0290] The server adjusts the conversation content according to the determined emotional state. The input is the user's emotional state and the generated conversation content, and the output is the adjusted conversation content. The server re-creates appropriate conversation content according to the user's emotional state. For example, if the user is tired, the server responds with "Hello! I'm this table. It looks like you need a short break, is that okay?"

[0291] (Application example 2)

[0292] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0293] In recent years, there has been a growing emphasis on improving the customer experience in physical stores. In particular, there is a need for customers to interact with products and in-store displays and obtain enjoyable and useful information. However, conventional methods only provide customers with one-way information, making it difficult to provide services that respond to individual customers' emotions and needs. Another problem is that customers often cannot obtain sufficient information about in-store products and displays at first glance.

[0294] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0295] In this invention, the server includes means for capturing an image of an object with a camera, communication means for transmitting the captured image data, generation means for generating a face for the object based on the received image data, conversation generation means for generating conversation content using the generated face, means including an emotion engine for recognizing a user's emotion, means for adjusting the conversation content in accordance with the emotion, transmission means for transmitting the generated face and conversation content, and display means for displaying the received face and conversation content. This enables customers to interact with products and displays in the store in accordance with their emotions, and to obtain more personalized information in a fun and efficient manner.

[0296] A "camera" is a device for capturing images.

[0297] "Communication means" refers to the technology for transmitting acquired image data to a server.

[0298] "Generation means" refers to a technology for generating a face on an object based on received image data.

[0299] "Conversation generation means" refers to a technology for generating conversation content using the generated face.

[0300] "Emotion engine" refers to technology for analyzing a user's emotions.

[0301] "Transmission means" refers to the technology used to transmit the generated face and conversation content.

[0302] The "display means" is a device that has the function of displaying the received face and conversation content.

[0303] "Object" refers to the object whose image is captured by the camera.

[0304] "User" refers to a person who uses the system.

[0305] The present invention relates to a system for improving customer experience in physical stores. This system allows customers to take pictures of products and displays in the store with a camera, send the images to a server, generate faces for the objects, and then have conversations with them through the generated faces. Furthermore, the system has the ability to recognize the user's emotions and adjust the content of the conversation.

[0306] System Program

[0307] Using the camera

[0308] The device uses a camera to capture images of the object, which are temporarily stored in the device's memory.

[0309] Sending data

[0310] The acquired image data is sent to the server via a communication method such as Wi-Fi or mobile data communication. A specific technique that can be used is HTTP communication using requests.

[0311] Reception and image processing

[0312] The server receives image data sent from the device and temporarily stores it. It also generates a face for the object using OpenCV and the DALL-E model. This allows the object to have a face and be able to converse with the user.

[0313] Conversation Generation

[0314] Conversational content is generated based on the generated face, using large language models such as GPT-4 (registered trademark). The generated conversational content is designed to be user-friendly.

[0315] Emotion recognition and conversation regulation

[0316] The emotion engine on the server analyzes the user's emotions from the camera footage and microphone audio. Using Microsoft® Azure® emotion recognition APIs and other technologies, the system determines the user's emotional state and adjusts the conversation accordingly. For example, if the user is tired, the system will respond with something like, "You seem a little down. Are you okay?"

[0317] display

[0318] The generated face and conversation content are sent from the server to the terminal and displayed on the device, allowing customers to enjoy conversations with the products and displays in the store.

[0319] Examples and prompts

[0320] For example, you can use prompts like the following to generate responses that reflect the user's emotions:

[0321] "The user seems happy. Create a friendly dialogue for this emotion: Hello! I'm the product promoter. I have some great information for you!"

[0322] "The user seems tired. Create a dialogue that reflects this emotion: Hello! I have a great product. Please take a break and enjoy my explanation."

[0323] In this way, the system of the present invention allows customers to obtain information about objects in a fun and emotional way, and is particularly aimed at improving the customer experience in physical stores, by providing personalized services tailored to the needs of each individual customer.

[0324] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0325] Step 1:

[0326] The device uses a camera to capture an image of an object. The user points the camera of a smartphone or smart glasses at the object to take a picture, and the image data is temporarily stored in the device's memory. The input is the camera image, and the output is the image data stored in the memory.

[0327] Step 2:

[0328] The device uses a communication means to send the image data it has acquired. This is done by sending the image data to a server via Wi-Fi or mobile data communication. The input is the image data stored in the device memory, and the output is the image data sent to the server.

[0329] Step 3:

[0330] The server generates a face for the object based on the image data it receives. The server uses OpenCV and the DALL-E model to generate a face suitable for the object from the image data. The input is the image data sent to the server, and the output is the generated face image.

[0331] Step 4:

[0332] The server uses the generated face to generate conversational content for a conversation with the user. This process uses large language models such as GPT-4. The input is the generated face image and a prompt, and the output is the generated conversational content. An example of a specific prompt is, "The user looks happy. Please create a friendly dialogue for this emotion: Hello! I'm the introducer of this product. I have some information that's perfect for you!"

[0333] Step 5:

[0334] The server uses an emotion engine to recognize the user's emotions. The emotion engine in the server analyzes emotions from the user's camera footage and microphone audio. This uses Microsoft Azure's emotion recognition API, etc. The input is the user's facial expressions and voice data, and the output is the analyzed emotional state.

[0335] Step 6:

[0336] The server adjusts the conversation content based on the recognized emotion. If the user is recognized as tired, a response such as "You seem tired, what's wrong?" is generated. The input is the emotional state and the generated conversation content, and the output is the adjusted conversation content.

[0337] Step 7:

[0338] The server sends the generated face and adjusted conversation content to the terminal. The generated data is again sent to the terminal using a communication means. The input is the face image and conversation content, and the output is the data sent to the terminal.

[0339] Step 8:

[0340] The device displays the face and conversation content received from the server. This allows the user to see the object on the device screen speaking to them according to their emotions. The input is the face image and conversation content sent to the device, and the output is the data displayed on the user interface.

[0341] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0342] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0343] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0344] [Second embodiment]

[0345] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0346] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0347] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0348] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0349] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0350] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0351] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0352] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0353] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0354] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0355] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0356] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0357] This system allows a user to use a camera to capture an image of an object they wish to research, send that image to a server, generate a face for the object based on the image data received by the server, generate conversation content using the generated face, and send it back to the terminal to display to the user. This system provides an experience where the object itself answers questions, making research fun.

[0358] System Components

[0359] 1. Camera (device)

[0360] The device has a camera function and captures images of the object indicated by the user. The images captured by the camera are temporarily stored in the device's memory and used for subsequent processing.

[0361] 2. Communication method (terminal and server)

[0362] This includes communication technology for transmitting image data acquired by the device to a server. It is assumed that Wi-Fi or mobile data communication will be used as the means of communication.

[0363] 3. Receiving method and image processing (server)

[0364] The server receives the image data sent from the device and temporarily stores it. It then uses the stored image data to recognize the object and generate a face.

[0365] 4. Face generation means (server)

[0366] The server uses image processing algorithms to generate a face that matches the subject, often using generative models such as deep learning.

[0367] 5. Conversation Generation Method (Server)

[0368] After the server generates the face, it generates a conversation based on the face, using natural language processing technology to create a response to the user. For example, the generated face might say, "Hello! I'm this table. What do you want to know?"

[0369] 6. Transmission Method (Server)

[0370] The server sends the generated face and conversation content to the device, which includes image data and text data.

[0371] 7. Display means (terminal)

[0372] The device receives the face and conversation content from the server and displays it on the user interface, making it appear as if the object itself is speaking to the user.

[0373] Specific examples

[0374] For example, a user uses a smartphone camera to take a picture of a table. The smartphone sends the captured image to a server, which receives the image and generates a face on the table. The server then uses the generated face to generate a conversation, such as "Hello! I'm this table. What would you like to know?" The server then sends the generated face and conversation to the smartphone, which displays it to the user. The user can enjoy watching the table on the screen talk to them.

[0375] In this way, the system of the present invention allows users to enjoyably obtain information about objects, and is expected to be a particularly useful tool for parents and users who want to enjoy generative AI.

[0376] The processing flow will be explained below.

[0377] Step 1:

[0378] The user uses a camera to capture an image of the object they want to investigate. Using a device such as a smartphone or tablet, the user points the camera at the object and takes a photo.

[0379] Step 2:

[0380] The terminal sends the acquired image data to the server. The terminal uses a communication means to create an HTTP request to send the captured image to the server, and then sends it.

[0381] Step 3:

[0382] The server receives the image data sent from the terminal and temporarily stores the received image data in the server's storage.

[0383] Step 4:

[0384] The server analyzes the stored image data and generates a face that matches the subject. The server uses image processing algorithms to detect features in the image and generate facial elements that match the subject.

[0385] Step 5:

[0386] The server generates a conversation using the generated face. The server uses natural language processing technology to create an appropriate response for the user and determines the conversation based on the generated face.

[0387] Step 6:

[0388] The server sends the generated face and the conversation content to the device. The server then compiles the face image data and text data and creates a response to send to the device.

[0389] Step 7:

[0390] The device displays the face and conversation content received from the server. The device analyzes the received data and displays the generated face and conversation content on the user interface.

[0391] Examples:

[0392] For example, if a user points their smartphone camera at a table and takes a picture, the device sends the image to the server. The server analyzes the received image and generates a face for the image of the table. The server then generates a conversation such as "Hello! I'm this table. What would you like to know?" and sends it to the device. The device displays the received face and conversation on the user's interface, allowing the user to enjoy watching the table talk to them.

[0393] Example 1

[0394] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0395] Conventional image recognition systems have had issues with quickly recognizing objects and providing intuitive interaction with users. Furthermore, they lacked advanced natural language processing technology to generate appropriate responses for captured images, and methods for generating faces appropriate for the objects. As a result, it was difficult for users to acquire information about objects in an enjoyable and effective way.

[0396] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0397] In this invention, the server includes a recognition means, a generation means, and a conversation generation means, which enable the server to quickly and accurately recognize an object from image data and provide the user with natural conversation content linked to the generated face.

[0398] A "camera" is a device that optically captures an object and stores it as image data.

[0399] An "object" is an object that a user photographs with a camera to look up specific information.

[0400] "Image data" refers to visual information of an object captured by a camera and stored in digital format.

[0401] "Communication means" refers to the technology and protocols used to transmit image data from a device to a server, and examples include Wi-Fi and mobile data communications.

[0402] "Recognition means" refers to an algorithm or program for recognizing an object from received image data and analyzing its characteristics.

[0403] "Generation means" refers to algorithms or programs for generating appropriate faces and features for recognized objects, and specific examples include deep learning models.

[0404] "Conversation generation means" refers to natural language processing technology and algorithms for creating dialogue with the user based on the generated face.

[0405] "Transmission means" refers to the technology and protocols used to transmit the generated face and conversation content from the server to the terminal.

[0406] "Display means" refers to the technology or program for displaying the received face and conversation content on the terminal screen so that the user can visually confirm it.

[0407] The present invention is a system in which a user uses a camera to capture an image of an object they want to research, sends the image to a server, and the server generates a face for the object based on the received image data, generates conversation content using the generated face, and sends it back to the terminal to display to the user. This system provides the user with the experience of the object itself speaking to them, making research fun.

[0408] System Components

[0409] 1. Camera (device)

[0410] The device has a camera function and captures images of the object indicated by the user. The images captured by the camera are temporarily stored in the device's memory and used for subsequent processing.

[0411] 2. Communication method (terminal and server)

[0412] This includes communication technology for transmitting image data acquired by the device to a server. It is assumed that Wi-Fi or mobile data communication will be used as the means of communication.

[0413] 3. Receiving method and image processing (server)

[0414] The server receives the image data sent from the device and temporarily stores it. It then uses the stored image data to recognize the object.

[0415] 4. Face generation means (server)

[0416] The server uses image processing algorithms to generate a face suitable for the subject, using a deep learning model (e.g., generative artificial network (GAN)).

[0417] 5. Conversation Generation Method (Server)

[0418] The server generates conversational content based on the generated face, using natural language processing techniques (e.g., GPT model) to create responses to the user.

[0419] 6. Transmission Method (Server)

[0420] The server sends the generated face and conversation content to the device, which includes image data and text data.

[0421] 7. Display means (terminal)

[0422] The device displays the face and conversation content received from the server on the user interface, making it appear to the user as if the object itself is speaking to them.

[0423] Specific examples of operation

[0424] Consider a scenario where a user takes a picture of a table using a smartphone camera. The specific steps are as follows:

[0425] 1. Image acquisition: The user takes an image of the table using the smartphone camera.

[0426] 2. Image transmission: The smartphone sends the captured image to the server.

[0427] 3. Image processing: The server recognizes the received image data and generates a face that matches the table.

[0428] 4. Conversation generation: Based on the generated face, the server generates a conversation such as "Hello! I'm this table. What would you like to know?"

[0429] 5. Data transmission: The server sends the generated face and conversation content to the smartphone.

[0430] 6. Display: The smartphone displays the received data on the user interface, giving the user the impression that the table is talking to them.

[0431] Prompt Sentence Examples

[0432] An example prompt for input to a generative AI model is:

[0433] "Analyze an image of a table and generate a face that matches the table. Then generate a conversation for that face to say, 'Hello! I'm this table. What would you like to know?'"

[0434] Using this prompt, the generative AI model can perform the necessary processing.

[0435] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0436] Step 1:

[0437] Input: The user uses a device with a camera function to capture an image of the object to be examined (e.g., a table).

[0438] How it works: The user launches the camera app on their smartphone, points the camera at an object, and presses the shutter button.

[0439] Output: Image data stored on the device.

[0440] Step 2:

[0441] Input: Image data stored on the device.

[0442] How it works: The device sends image data to the server using Wi-Fi or mobile data.

[0443] Output: Image data sent to the server.

[0444] Step 3:

[0445] Input: Image data sent to the server.

[0446] Operation: The server receives the image data and temporarily stores it in storage. The server verifies the consistency of the received data.

[0447] Output: Image data stored on the server.

[0448] Step 4:

[0449] Input: Image data stored on the server.

[0450] How it works: The server uses image processing algorithms to recognize objects. Based on the recognition results, it uses a deep learning model (e.g., GAN) to generate a face that matches the object.

[0451] Output: The generated face image.

[0452] Step 5:

[0453] Input: Generated face image.

[0454] How it works: The server uses natural language processing techniques (e.g., GPT models) to generate a conversation based on the generated face, for example, creating a message like "Hello! I'm this table. What would you like to know?"

[0455] Output: The generated conversation.

[0456] Step 6:

[0457] Input: Generated face image and conversation content.

[0458] Operation: The server combines the facial image and conversation content into a single packet and sends it to the terminal.

[0459] Output: Facial image and conversation data sent to the device.

[0460] Step 7:

[0461] Input: Facial image and conversation data sent to the device.

[0462] Operation: The device analyzes the received data and displays it in the user interface. The device divides it into an image view and a text view and displays them on the screen.

[0463] Output: Facial image and conversation content displayed on the user's device.

[0464] (Application example 1)

[0465] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0466] In the past, there were limited ways for users to easily obtain product information in physical stores. Especially when there were many products on display, it was time-consuming to look up each product individually. Furthermore, there was a lack of systems that allowed users to obtain product information in an enjoyable way. This could result in a monotonous user experience and a decrease in motivation to purchase products.

[0467] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0468] In this invention, the server includes a generation means for generating a face on an object based on received image data, a conversation generation means for generating a conversation using a prompt sentence based on the generated face, and a means for transmitting the generated face and the conversation again via a communication means. This enables a system in which, when a user takes a photo of a product with a smartphone in a physical store, the product appears to be talking to them.

[0469] A "camera" is a photographing device that allows a user to capture an image of an object.

[0470] "Communication means" refers to the network means for transmitting acquired image data to the server and receiving data from the server, including Wi-Fi and mobile data communications.

[0471] The "server" is a computer system that processes image data received via a communication means, generates faces for objects, and generates conversation content.

[0472] The "generation means" refers to an algorithm or software for generating a face for an object based on image data received by the server.

[0473] A "prompt sentence" is an input sentence used to generate a conversation based on the face generated by the generation means.

[0474] The "conversation generation means" is a system that uses natural language processing technology with prompt sentences based on the generated face to generate conversation content from the target object.

[0475] The "transmission means" is a means for transmitting the face and conversation content generated by the server to the user terminal again via the communication means.

[0476] The "user interface" refers to a display device and software that allows the user to visually confirm the face and conversation content they receive.

[0477] A "portable device" is an electronic device that a user can carry with them, including a smartphone or tablet PC.

[0478] To implement this invention, a user first captures an image of an object using the camera of a portable device. A portable device refers to a portable electronic device such as a smartphone or tablet PC. These devices are equipped with a camera function and internet communication means.

[0479] When a user takes an image of an object, the image data is sent to a server via a communication means, such as Wi-Fi or mobile data communication.

[0480] The server is equipped with a generation means for generating a face on the target object based on the received image data. The generation means uses a deep learning-based generative model (e.g., Generative Adversarial Network (GAN)).

[0481] Next, the server generates a conversation using prompt sentences based on the generated face. The prompt sentences used here utilize a generative AI model and are processed by a conversation generation means that applies natural language processing technology. For example, OpenAI's generative model (e.g., GPT-3) can be used.

[0482] The generated face and conversation content are then sent to the user's portable device via a communication means, and the user's device displays the received face and conversation content on the user interface, allowing the user to feel as if the object is speaking to them.

[0483] As a concrete example, a user can take a picture of a snack in a physical store and the snack will speak to them, saying, "Hello! I'm potato chips. My calories are 500 kcal per 100 g. Is there anything else you'd like to know?"

[0484] Examples of prompts include:

[0485] "Generate one conversational phrase that corresponds to a smiling face:"

[0486] Through the above process, the system allows users to easily and enjoyably obtain detailed product information in a physical store. An important feature of the invention is that it provides a natural and engaging way to interact with a human through the generated faces and conversations.

[0487] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0488] Step 1:

[0489] The user captures an image of an object using the camera of a portable device. The input is the image of the object, and the output is image data temporarily stored in the internal memory. In this step, the camera is started and the image is taken by pressing the shutter.

[0490] Step 2:

[0491] The terminal sends the acquired image data to the server via a communication means. The input is the temporarily saved image data, and the output is the image data sent to the server. In this step, the image data is uploaded to the server using an HTTP POST request.

[0492] Step 3:

[0493] The server generates a face for the target based on the received image data. The input is the image data received by the server, and the output is the generated face image data. The server processes this data using a generative model (e.g., GAN) to generate a face that suits the target.

[0494] Step 4:

[0495] The server generates a conversation using a prompt sentence based on the generated face. The input is the generated face image data, and the output is the generated conversation. The server uses natural language processing technology and a generative AI model (e.g., GPT-3) to generate the conversation. An example of a prompt sentence here is, "Please generate one conversation corresponding to a smiling face."

[0496] Step 5:

[0497] The server again transmits the generated face and conversation content to the terminal via the communication means. The input is the generated face image data and conversation content, and the output is these data transmitted to the terminal. The server again transmits these data to the terminal using an HTTP response.

[0498] Step 6:

[0499] The device displays the received face and conversation content on the user interface. The input is the face image data and conversation content received from the server, and the output is the display content provided visually to the user. The device uses an image viewer and a text viewer to display the face and conversation content on the screen. This step allows the user to enjoy the experience of the object talking to them.

[0500] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0501] The present invention is a system in which a user uses a camera to capture an image of an object they want to examine, sends the image to a server, the server generates a face for the object based on the image data received, generates conversation content using the generated face, and sends it back to the terminal to display to the user. Furthermore, this system incorporates an emotion engine that recognizes the user's emotions, and can adjust the conversation content according to the user's emotions.

[0502] System Components

[0503] 1. Camera (device)

[0504] The device has a camera function and captures an image of the object indicated by the user. The captured image is temporarily stored in the device's memory.

[0505] 2. Communication method (terminal and server)

[0506] This includes communication technology for transmitting image data acquired by the device to a server. It is assumed that Wi-Fi or mobile data communication will be used as the means of communication.

[0507] 3. Receiving method and image processing (server)

[0508] The server receives the image data sent from the device and temporarily stores it. It then uses the stored image data to recognize the object and generate a face.

[0509] 4. Face generation means (server)

[0510] The server uses image processing algorithms to generate a face that matches the subject, often using generative models such as deep learning.

[0511] 5. Conversation Generation Method (Server)

[0512] After the server generates the face, it generates a conversation based on the face, using natural language processing technology to create an appropriate response for the user. For example, the generated face might say, "Hello! I'm this table. What do you want to know?"

[0513] 6. Transmission Method (Server)

[0514] The server sends the generated face and conversation content to the device, which includes image data and text data.

[0515] 7. Display means (terminal)

[0516] The device receives the face and conversation content from the server and displays it on the user interface, making it appear as if the object itself is speaking to the user.

[0517] 8. Emotion Engine (Server)

[0518] The server is equipped with an emotion engine that recognizes the user's emotions. This emotion engine analyzes the user's emotions from their facial expressions and voice, and adjusts the content of the conversation based on the results.

[0519] System Operation

[0520] 1. User Emotion Recognition

[0521] The emotion engine analyzes the user's facial expressions and tone of voice from camera footage and microphone audio to determine the user's current emotional state (e.g., joy, sadness, surprise, etc.).

[0522] 2. Adjust the conversation

[0523] The server adapts the conversation depending on the user's emotional state. For example, if the user is tired, the server might respond with, "You seem down, what's wrong?"

[0524] Specific examples

[0525] For example, a user uses a smartphone camera to take a picture of a table. The smartphone sends the captured image to a server, which receives the image and generates a face on the table. The server then uses an emotion engine to analyze the user's emotions. For example, if the server recognizes that the user is tired, it generates a conversation through the generated face, saying, "Hello! I'm this table. You look like you need a short break. Are you okay?" The server then sends the generated face and conversation to the smartphone, which displays it to the user. The user can enjoy watching the table on the screen talk to them according to their emotions.

[0526] In this way, the system of the present invention allows users to acquire information about objects in a fun and emotional way, and is expected to be a particularly useful tool for parents and users who want to enjoy generative AI.

[0527] The processing flow will be explained below.

[0528] Step 1:

[0529] The user uses a camera to capture an image of the object they want to investigate. Using a device such as a smartphone or tablet, the user points the camera at the object and takes a photo through the app.

[0530] Step 2:

[0531] The device sends the acquired image data to the server. The device sends the captured image file to the server using an HTTP request. The communication method is Wi-Fi or mobile data communication.

[0532] Step 3:

[0533] The server receives the image data sent from the terminal and stores it in the server's storage.

[0534] Step 4:

[0535] The server analyzes the image data and generates a face that matches the subject. The server uses a deep learning-based image processing algorithm to detect the contours and features of the subject in the image and generates a face based on this.

[0536] Step 5:

[0537] The server generates conversation content using the generated face. The server uses natural language processing technology (such as GPT-3) to determine the appropriate response to the user. A conversation text is also created along with the generated face image.

[0538] Step 6:

[0539] The server recognizes the user's emotions. Using an emotion engine, the server analyzes facial expressions and tone of voice from the user's camera footage and audio to identify their current emotion (happiness, sadness, surprise, etc.).

[0540] Step 7:

[0541] The server will adjust the conversation content based on the emotion recognition results. For example, if the user is tired, the conversation content will be adjusted to "Hello! I'm this table. You seem a little down. Are you okay?"

[0542] Step 8:

[0543] The server sends the generated facial image and adjusted conversation text to the terminal.

[0544] Step 9:

[0545] The device displays the face and conversation content received from the server. The device analyzes the received data and displays it on the user interface. This allows the user to experience the object on the screen as if it were speaking to them.

[0546] Examples:

[0547] For example, a user can take a picture of a table using the camera on their smartphone and send it to the server. The server receives the image and generates a face on the table. If the emotion engine later determines that the user is tired, the server generates a conversation such as, "Hello! I'm this table. You look a little down. Are you okay?" The server then sends the generated face and conversation to the smartphone, which displays it on the user interface. The user can enjoy watching the table on the screen talk to them according to their emotions.

[0548] Example 2

[0549] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0550] Conventional image recognition systems have difficulty recognizing a user's emotions and providing appropriate responses when interacting with an object. In particular, it has been difficult to automatically respond to a user's emotions, such as tiredness, happiness, or sadness. This can limit the user experience and reduce user satisfaction, as the information and responses provided by the system tend to be uniform.

[0551] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0552] In this invention, the server includes an emotion recognition means for analyzing the user's facial expression and tone of voice to determine their emotion, an emotion response means for adjusting the content of the conversation in accordance with the determined emotional state, and a generation means for receiving image data and generating a face on the target object, thereby enabling responses and dialogue that take the user's emotion into consideration.

[0553] A "camera" is a photographic device that a user uses to capture images.

[0554] "Communication means" refers to the technology used by the terminal to send the image data acquired by the terminal to the server, and includes Wi-Fi, mobile data communication, etc.

[0555] The "generation means" is a technology that performs processing to generate a face on the target object based on the image data received by the server.

[0556] The "conversation generation means" is a technology that allows the server to generate appropriate conversation content using the generated face, and includes natural language processing technology.

[0557] "Transmission means" refers to a technique by which the server transmits the generated face and conversation content to the terminal.

[0558] "Display means" refers to a technology for displaying the face and conversation content received by the terminal on the user interface.

[0559] "Emotion recognition means" is a technology that allows the server to analyze the user's facial expressions and tone of voice to determine their emotions.

[0560] "Emotion response means" is a technique that allows the server to adjust the content of conversation according to the user's emotional state.

[0561] The present invention is a system in which a user uses a camera to capture an image of an object they want to examine, sends the image to a server, the server generates a face for the object based on the image data received, generates conversation content using the generated face, and sends it back to the terminal to display to the user. Furthermore, this system has a built-in function to recognize the user's emotions and adjust the conversation content.

[0562] System Components

[0563] 1. Camera (device)

[0564] The device has a camera function and captures an image of the object indicated by the user. The captured image is temporarily stored in the device's memory. For example, a smartphone is used.

[0565] 2. Communication method (terminal and server)

[0566] This includes communication technology for transmitting image data acquired by the device to a server via Wi-Fi or mobile data communication.

[0567] 3. Receiving method and image processing (server)

[0568] The server receives the image data sent from the device, temporarily stores the data, and uses image processing algorithms (e.g., OpenCV or deep learning models) to recognize objects and generate faces.

[0569] 4. Face generation means (server)

[0570] The server uses a generative model (e.g., a deep learning-based generative adversarial network: GAN) to generate a face that suits the subject.

[0571] 5. Conversation Generation Method (Server)

[0572] The server uses natural language processing technology (e.g., GPT-3) to create appropriate conversation content based on the generated face. The generated face generates conversation content such as "Hello! I'm this table. What do you want to know?"

[0573] 6. Transmission Method (Server)

[0574] The server sends the generated face and conversation content to the device, which includes image data and text data.

[0575] 7. Display means (terminal)

[0576] The device receives the face and conversation content from the server and displays it on the user interface, making it appear as if the object is speaking to the user.

[0577] 8. Emotion Engine (Server)

[0578] The server is equipped with an emotion engine that analyzes the user's emotions from their facial expressions and voice, and adjusts the content of the conversation based on the analysis results.

[0579] Specific examples

[0580] For example, suppose a user uses a smartphone camera to take a picture of a table. The smartphone sends the captured image to a server, which receives the image and generates a face on the table. The server then uses an emotion engine to analyze the user's emotions. For example, if the server recognizes that the user is tired, it generates a conversation through the generated face, saying, "Hello! I'm this table. You look like you need a short break. Are you okay?" The server then sends the generated face and conversation to the smartphone, which displays it to the user. The user can enjoy watching the table on the screen talk to them according to their emotions.

[0581] Prompt Sentence Examples

[0582] By feeding the following sentences into the generative AI model, we can get prompts that replicate the system's functionality:

[0583] The user uses a camera to capture an image of the object they want to examine and sends the image to the server. The server processes the image and generates a face for the object. It then uses an emotion engine to analyze the user's emotions and generates corresponding conversation content. The generated face and conversation content are sent back to the device and displayed on the user interface. For example, if the user is tired, a response such as "Hello, it looks like you need a break" may be generated.

[0584] As described above, the system of the present invention allows users to acquire information about objects in a fun and emotional way. It is expected to be a particularly useful tool for parents and users who want to enjoy generative AI.

[0585] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0586] Step 1:

[0587] A user takes an image of an object using the device's camera. The user's input is to activate the camera and take a picture of the object. The device stores this image in memory. The input is the image of the object, and the output is the image data stored in memory. For example, a user takes a picture of a table with their smartphone.

[0588] Step 2:

[0589] The device sends the acquired image data to the server. The input of the device is the image data stored in its memory. The device uploads this data to the server using Wi-Fi or mobile data communication. The output is the image data to be transmitted. Specifically, the smartphone transmits the image data to the cloud server via the Internet.

[0590] Step 3:

[0591] The server receives image data sent from the terminal and temporarily stores it. The input to the server is the image data sent from the terminal. The server saves this received data in storage. The output is the saved image data. Specifically, the server saves the image data in a specific directory.

[0592] Step 4:

[0593] The server analyzes the image data and recognizes the object. Specifically, it identifies the object using an image processing algorithm (e.g., OpenCV). The input is the stored image data, and the output is the object recognition result. The server extracts the shape and features of the object.

[0594] Step 5:

[0595] The server generates an appropriate face for the recognized object. The face is generated using a generative model (e.g., deep learning model, GAN). The input is the object recognition result, and the output is image data of the generated face. Specifically, the server uses GAN to generate a face image of the table.

[0596] Step 6:

[0597] The server generates conversation content based on the generated face. It uses natural language processing technology (e.g., GPT-3) to create a response to the user. The input is image data of the generated face and information about the object, and the output is text data of the conversation content. Specifically, the server generates the conversation content, "Hello! I'm this table. What do you want to know?"

[0598] Step 7:

[0599] The server sends the generated face and the conversation content to the device. The server's input is image data of the generated face and text data of the conversation content. The server sends this data to the device. The output is the sent image data of the face and text data of the conversation content. Specifically, the cloud server sends this data to the smartphone.

[0600] Step 8:

[0601] The device receives the face and conversation data from the server and displays them on the user interface. The device inputs the received face and conversation data. The device displays these on the user interface. The output is the face and conversation data displayed on the user interface. Specifically, the face at the table and its conversation data are displayed on the smartphone screen.

[0602] Step 9:

[0603] The server's emotion engine analyzes the user's facial expressions and voice to determine their emotions. The input is the device's camera footage and microphone audio, and the output is the user's emotional state. The emotion engine analyzes the user's facial expression data and tone of voice data to determine their emotional state. For example, it determines whether the user is smiling.

[0604] Step 10:

[0605] The server adjusts the conversation content according to the determined emotional state. The input is the user's emotional state and the generated conversation content, and the output is the adjusted conversation content. The server re-creates appropriate conversation content according to the user's emotional state. For example, if the user is tired, the server responds with "Hello! I'm this table. It looks like you need a short break, is that okay?"

[0606] (Application example 2)

[0607] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0608] In recent years, there has been a growing emphasis on improving the customer experience in physical stores. In particular, there is a need for customers to interact with products and in-store displays and obtain enjoyable and useful information. However, conventional methods only provide customers with one-way information, making it difficult to provide services that respond to individual customers' emotions and needs. Another problem is that customers often cannot obtain sufficient information about in-store products and displays at first glance.

[0609] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0610] In this invention, the server includes means for capturing an image of an object with a camera, communication means for transmitting the captured image data, generation means for generating a face for the object based on the received image data, conversation generation means for generating conversation content using the generated face, means including an emotion engine for recognizing a user's emotion, means for adjusting the conversation content in accordance with the emotion, transmission means for transmitting the generated face and conversation content, and display means for displaying the received face and conversation content. This enables customers to interact with products and displays in the store in accordance with their emotions, and to obtain more personalized information in a fun and efficient manner.

[0611] A "camera" is a device for capturing images.

[0612] "Communication means" refers to the technology for transmitting acquired image data to a server.

[0613] "Generation means" refers to a technology for generating a face on an object based on received image data.

[0614] "Conversation generation means" refers to a technology for generating conversation content using the generated face.

[0615] "Emotion engine" refers to technology for analyzing a user's emotions.

[0616] "Transmission means" refers to the technology used to transmit the generated face and conversation content.

[0617] The "display means" is a device that has the function of displaying the received face and conversation content.

[0618] "Object" refers to the object whose image is captured by the camera.

[0619] "User" refers to a person who uses the system.

[0620] The present invention relates to a system for improving customer experience in physical stores. This system allows customers to take pictures of products and displays in the store with a camera, send the images to a server, generate faces for the objects, and then have conversations with them through the generated faces. Furthermore, the system has the ability to recognize the user's emotions and adjust the content of the conversation.

[0621] System Program

[0622] Using the camera

[0623] The device uses a camera to capture images of the object, which are temporarily stored in the device's memory.

[0624] Sending data

[0625] The acquired image data is sent to the server via a communication method such as Wi-Fi or mobile data communication. A specific technique that can be used is HTTP communication using requests.

[0626] Reception and image processing

[0627] The server receives image data sent from the device and temporarily stores it. It also generates a face for the object using OpenCV and the DALL-E model. This allows the object to have a face and be able to converse with the user.

[0628] Conversation Generation

[0629] Conversational content is generated based on the generated face, using large language models such as GPT-4. The generated conversational content is designed to be user-friendly.

[0630] Emotion recognition and conversation regulation

[0631] The emotion engine on the server analyzes the user's emotions from the camera footage and microphone audio. Using Microsoft Azure's emotion recognition API, the system determines the user's emotional state and adjusts the conversation accordingly. For example, if the user is tired, the system will respond with something like, "You seem a little down. Are you okay?"

[0632] display

[0633] The generated face and conversation content are sent from the server to the terminal and displayed on the device, allowing customers to enjoy conversations with the products and displays in the store.

[0634] Examples and prompts

[0635] For example, you can use prompts like the following to generate responses that reflect the user's emotions:

[0636] "The user seems happy. Create a friendly dialogue for this emotion: Hello! I'm the product promoter. I have some great information for you!"

[0637] "The user seems tired. Create a dialogue that reflects this emotion: Hello! I have a great product. Please take a break and enjoy my explanation."

[0638] In this way, the system of the present invention allows customers to obtain information about objects in a fun and emotional way, and is particularly aimed at improving the customer experience in physical stores, by providing personalized services tailored to the needs of each individual customer.

[0639] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0640] Step 1:

[0641] The device uses a camera to capture an image of an object. The user points the camera of a smartphone or smart glasses at the object to take a picture, and the image data is temporarily stored in the device's memory. The input is the camera image, and the output is the image data stored in the memory.

[0642] Step 2:

[0643] The device uses a communication means to send the image data it has acquired. This is done by sending the image data to a server via Wi-Fi or mobile data communication. The input is the image data stored in the device memory, and the output is the image data sent to the server.

[0644] Step 3:

[0645] The server generates a face for the object based on the image data it receives. The server uses OpenCV and the DALL-E model to generate a face suitable for the object from the image data. The input is the image data sent to the server, and the output is the generated face image.

[0646] Step 4:

[0647] The server uses the generated face to generate conversational content for a conversation with the user. This process uses large language models such as GPT-4. The input is the generated face image and a prompt, and the output is the generated conversational content. An example of a specific prompt is, "The user looks happy. Please create a friendly dialogue for this emotion: Hello! I'm the introducer of this product. I have some information that's perfect for you!"

[0648] Step 5:

[0649] The server uses an emotion engine to recognize the user's emotions. The emotion engine in the server analyzes emotions from the user's camera footage and microphone audio. This uses Microsoft Azure's emotion recognition API, etc. The input is the user's facial expressions and voice data, and the output is the analyzed emotional state.

[0650] Step 6:

[0651] The server adjusts the conversation content based on the recognized emotion. If the user is recognized as tired, a response such as "You seem tired, what's wrong?" is generated. The input is the emotional state and the generated conversation content, and the output is the adjusted conversation content.

[0652] Step 7:

[0653] The server sends the generated face and adjusted conversation content to the terminal. The generated data is again sent to the terminal using a communication means. The input is the face image and conversation content, and the output is the data sent to the terminal.

[0654] Step 8:

[0655] The device displays the face and conversation content received from the server. This allows the user to see the object on the device screen speaking to them according to their emotions. The input is the face image and conversation content sent to the device, and the output is the data displayed on the user interface.

[0656] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0657] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0658] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0659] [Third embodiment]

[0660] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0661] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0662] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0663] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0664] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0665] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0666] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0667] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0668] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0669] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0670] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0671] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[0672] This system allows a user to use a camera to capture an image of an object they wish to research, send that image to a server, generate a face for the object based on the image data received by the server, generate conversation content using the generated face, and send it back to the terminal to display to the user. This system provides an experience where the object itself answers questions, making research fun.

[0673] System Components

[0674] 1. Camera (device)

[0675] The device has a camera function and captures images of the object indicated by the user. The images captured by the camera are temporarily stored in the device's memory and used for subsequent processing.

[0676] 2. Communication method (terminal and server)

[0677] This includes communication technology for transmitting image data acquired by the device to a server. It is assumed that Wi-Fi or mobile data communication will be used as the means of communication.

[0678] 3. Receiving method and image processing (server)

[0679] The server receives the image data sent from the device and temporarily stores it. It then uses the stored image data to recognize the object and generate a face.

[0680] 4. Face generation means (server)

[0681] The server uses image processing algorithms to generate a face that matches the subject, often using generative models such as deep learning.

[0682] 5. Conversation Generation Method (Server)

[0683] After the server generates the face, it generates a conversation based on the face, using natural language processing technology to create a response to the user. For example, the generated face might say, "Hello! I'm this table. What do you want to know?"

[0684] 6. Transmission Method (Server)

[0685] The server sends the generated face and conversation content to the device, which includes image data and text data.

[0686] 7. Display means (terminal)

[0687] The device receives the face and conversation content from the server and displays it on the user interface, making it appear as if the object itself is speaking to the user.

[0688] Specific examples

[0689] For example, a user uses a smartphone camera to take a picture of a table. The smartphone sends the captured image to a server, which receives the image and generates a face on the table. The server then uses the generated face to generate a conversation, such as "Hello! I'm this table. What would you like to know?" The server then sends the generated face and conversation to the smartphone, which displays it to the user. The user can enjoy watching the table on the screen talk to them.

[0690] In this way, the system of the present invention allows users to enjoyably obtain information about objects, and is expected to be a particularly useful tool for parents and users who want to enjoy generative AI.

[0691] The processing flow will be explained below.

[0692] Step 1:

[0693] The user uses a camera to capture an image of the object they want to investigate. Using a device such as a smartphone or tablet, the user points the camera at the object and takes a photo.

[0694] Step 2:

[0695] The terminal sends the acquired image data to the server. The terminal uses a communication means to create an HTTP request to send the captured image to the server, and then sends it.

[0696] Step 3:

[0697] The server receives the image data sent from the terminal and temporarily stores the received image data in the server's storage.

[0698] Step 4:

[0699] The server analyzes the stored image data and generates a face that matches the subject. The server uses image processing algorithms to detect features in the image and generate facial elements that match the subject.

[0700] Step 5:

[0701] The server generates a conversation using the generated face. The server uses natural language processing technology to create an appropriate response for the user and determines the conversation based on the generated face.

[0702] Step 6:

[0703] The server sends the generated face and the conversation content to the device. The server then compiles the face image data and text data and creates a response to send to the device.

[0704] Step 7:

[0705] The device displays the face and conversation content received from the server. The device analyzes the received data and displays the generated face and conversation content on the user interface.

[0706] Examples:

[0707] For example, if a user points their smartphone camera at a table and takes a picture, the device sends the image to the server. The server analyzes the received image and generates a face for the image of the table. The server then generates a conversation such as "Hello! I'm this table. What would you like to know?" and sends it to the device. The device displays the received face and conversation on the user's interface, allowing the user to enjoy watching the table talk to them.

[0708] Example 1

[0709] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0710] Conventional image recognition systems have had issues with quickly recognizing objects and providing intuitive interaction with users. Furthermore, they lacked advanced natural language processing technology to generate appropriate responses for captured images, and methods for generating faces appropriate for the objects. As a result, it was difficult for users to acquire information about objects in an enjoyable and effective way.

[0711] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0712] In this invention, the server includes a recognition means, a generation means, and a conversation generation means, which enable the server to quickly and accurately recognize an object from image data and provide the user with natural conversation content linked to the generated face.

[0713] A "camera" is a device that optically captures an object and stores it as image data.

[0714] An "object" is an object that a user photographs with a camera to look up specific information.

[0715] "Image data" refers to visual information of an object captured by a camera and stored in digital format.

[0716] "Communication means" refers to the technology and protocols used to transmit image data from a device to a server, and examples include Wi-Fi and mobile data communications.

[0717] "Recognition means" refers to an algorithm or program for recognizing an object from received image data and analyzing its characteristics.

[0718] "Generation means" refers to algorithms or programs for generating appropriate faces and features for recognized objects, and specific examples include deep learning models.

[0719] "Conversation generation means" refers to natural language processing technology and algorithms for creating dialogue with the user based on the generated face.

[0720] "Transmission means" refers to the technology and protocols used to transmit the generated face and conversation content from the server to the terminal.

[0721] "Display means" refers to the technology or program for displaying the received face and conversation content on the terminal screen so that the user can visually confirm it.

[0722] The present invention is a system in which a user uses a camera to capture an image of an object they want to research, sends the image to a server, and the server generates a face for the object based on the received image data, generates conversation content using the generated face, and sends it back to the terminal to display to the user. This system provides the user with the experience of the object itself speaking to them, making research fun.

[0723] System Components

[0724] 1. Camera (device)

[0725] The device has a camera function and captures images of the object indicated by the user. The images captured by the camera are temporarily stored in the device's memory and used for subsequent processing.

[0726] 2. Communication method (terminal and server)

[0727] This includes communication technology for transmitting image data acquired by the device to a server. It is assumed that Wi-Fi or mobile data communication will be used as the means of communication.

[0728] 3. Receiving method and image processing (server)

[0729] The server receives the image data sent from the device and temporarily stores it. It then uses the stored image data to recognize the object.

[0730] 4. Face generation means (server)

[0731] The server uses image processing algorithms to generate a face suitable for the subject, using a deep learning model (e.g., generative artificial network (GAN)).

[0732] 5. Conversation Generation Method (Server)

[0733] The server generates conversational content based on the generated face, using natural language processing techniques (e.g., GPT model) to create responses to the user.

[0734] 6. Transmission Method (Server)

[0735] The server sends the generated face and conversation content to the device, which includes image data and text data.

[0736] 7. Display means (terminal)

[0737] The device displays the face and conversation content received from the server on the user interface, making it appear to the user as if the object itself is speaking to them.

[0738] Specific examples of operation

[0739] Consider a scenario where a user takes a picture of a table using a smartphone camera. The specific steps are as follows:

[0740] 1. Image acquisition: The user takes an image of the table using the smartphone camera.

[0741] 2. Image transmission: The smartphone sends the captured image to the server.

[0742] 3. Image processing: The server recognizes the received image data and generates a face that matches the table.

[0743] 4. Conversation generation: Based on the generated face, the server generates a conversation such as "Hello! I'm this table. What would you like to know?"

[0744] 5. Data transmission: The server sends the generated face and conversation content to the smartphone.

[0745] 6. Display: The smartphone displays the received data on the user interface, giving the user the impression that the table is talking to them.

[0746] Prompt Sentence Examples

[0747] An example prompt for input to a generative AI model is:

[0748] "Analyze an image of a table and generate a face that matches the table. Then generate a conversation for that face to say, 'Hello! I'm this table. What would you like to know?'"

[0749] Using this prompt, the generative AI model can perform the necessary processing.

[0750] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0751] Step 1:

[0752] Input: The user uses a device with a camera function to capture an image of the object to be examined (e.g., a table).

[0753] How it works: The user launches the camera app on their smartphone, points the camera at an object, and presses the shutter button.

[0754] Output: Image data stored on the device.

[0755] Step 2:

[0756] Input: Image data stored on the device.

[0757] How it works: The device sends image data to the server using Wi-Fi or mobile data.

[0758] Output: Image data sent to the server.

[0759] Step 3:

[0760] Input: Image data sent to the server.

[0761] Operation: The server receives the image data and temporarily stores it in storage. The server verifies the consistency of the received data.

[0762] Output: Image data stored on the server.

[0763] Step 4:

[0764] Input: Image data stored on the server.

[0765] How it works: The server uses image processing algorithms to recognize objects. Based on the recognition results, it uses a deep learning model (e.g., GAN) to generate a face that matches the object.

[0766] Output: The generated face image.

[0767] Step 5:

[0768] Input: Generated face image.

[0769] How it works: The server uses natural language processing techniques (e.g., GPT models) to generate a conversation based on the generated face, for example, creating a message like "Hello! I'm this table. What would you like to know?"

[0770] Output: The generated conversation.

[0771] Step 6:

[0772] Input: Generated face image and conversation content.

[0773] Operation: The server combines the facial image and conversation content into a single packet and sends it to the terminal.

[0774] Output: Facial image and conversation data sent to the device.

[0775] Step 7:

[0776] Input: Facial image and conversation data sent to the device.

[0777] Operation: The device analyzes the received data and displays it in the user interface. The device divides it into an image view and a text view and displays them on the screen.

[0778] Output: Facial image and conversation content displayed on the user's device.

[0779] (Application example 1)

[0780] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0781] In the past, there were limited ways for users to easily obtain product information in physical stores. Especially when there were many products on display, it was time-consuming to look up each product individually. Furthermore, there was a lack of systems that allowed users to obtain product information in an enjoyable way. This could result in a monotonous user experience and a decrease in motivation to purchase products.

[0782] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0783] In this invention, the server includes a generation means for generating a face on an object based on received image data, a conversation generation means for generating a conversation using a prompt sentence based on the generated face, and a means for transmitting the generated face and the conversation again via a communication means. This enables a system in which, when a user takes a photo of a product with a smartphone in a physical store, the product appears to be talking to them.

[0784] A "camera" is a photographing device that allows a user to capture an image of an object.

[0785] "Communication means" refers to the network means for transmitting acquired image data to the server and receiving data from the server, including Wi-Fi and mobile data communications.

[0786] The "server" is a computer system that processes image data received via a communication means, generates faces for objects, and generates conversation content.

[0787] The "generation means" refers to an algorithm or software for generating a face for an object based on image data received by the server.

[0788] A "prompt sentence" is an input sentence used to generate a conversation based on the face generated by the generation means.

[0789] The "conversation generation means" is a system that uses natural language processing technology with prompt sentences based on the generated face to generate conversation content from the target object.

[0790] The "transmission means" is a means for transmitting the face and conversation content generated by the server to the user terminal again via the communication means.

[0791] The "user interface" refers to a display device and software that allows the user to visually confirm the face and conversation content they receive.

[0792] A "portable device" is an electronic device that a user can carry with them, including a smartphone or tablet PC.

[0793] To implement this invention, a user first captures an image of an object using the camera of a portable device. A portable device refers to a portable electronic device such as a smartphone or tablet PC. These devices are equipped with a camera function and internet communication means.

[0794] When a user takes an image of an object, the image data is sent to a server via a communication means, such as Wi-Fi or mobile data communication.

[0795] The server is equipped with a generation means for generating a face on the target object based on the received image data. The generation means uses a deep learning-based generative model (e.g., Generative Adversarial Network (GAN)).

[0796] Next, the server generates a conversation using prompt sentences based on the generated face. The prompt sentences used here utilize a generative AI model and are processed by a conversation generation means that applies natural language processing technology. For example, OpenAI's generative model (e.g., GPT-3) can be used.

[0797] The generated face and conversation content are then sent to the user's portable device via a communication means, and the user's device displays the received face and conversation content on the user interface, allowing the user to feel as if the object is speaking to them.

[0798] As a concrete example, a user can take a picture of a snack in a physical store and the snack will speak to them, saying, "Hello! I'm potato chips. My calories are 500 kcal per 100 g. Is there anything else you'd like to know?"

[0799] Examples of prompts include:

[0800] "Generate one conversational phrase that corresponds to a smiling face:"

[0801] Through the above process, the system allows users to easily and enjoyably obtain detailed product information in a physical store. An important feature of the invention is that it provides a natural and engaging way to interact with a human through the generated faces and conversations.

[0802] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0803] Step 1:

[0804] The user captures an image of an object using the camera of a portable device. The input is the image of the object, and the output is image data temporarily stored in the internal memory. In this step, the camera is started and the image is taken by pressing the shutter.

[0805] Step 2:

[0806] The terminal sends the acquired image data to the server via a communication means. The input is the temporarily saved image data, and the output is the image data sent to the server. In this step, the image data is uploaded to the server using an HTTP POST request.

[0807] Step 3:

[0808] The server generates a face for the target based on the received image data. The input is the image data received by the server, and the output is the generated face image data. The server processes this data using a generative model (e.g., GAN) to generate a face that suits the target.

[0809] Step 4:

[0810] The server generates a conversation using a prompt sentence based on the generated face. The input is the generated face image data, and the output is the generated conversation. The server uses natural language processing technology and a generative AI model (e.g., GPT-3) to generate the conversation. An example of a prompt sentence here is, "Please generate one conversation corresponding to a smiling face."

[0811] Step 5:

[0812] The server again transmits the generated face and conversation content to the terminal via the communication means. The input is the generated face image data and conversation content, and the output is these data transmitted to the terminal. The server again transmits these data to the terminal using an HTTP response.

[0813] Step 6:

[0814] The device displays the received face and conversation content on the user interface. The input is the face image data and conversation content received from the server, and the output is the display content provided visually to the user. The device uses an image viewer and a text viewer to display the face and conversation content on the screen. This step allows the user to enjoy the experience of the object talking to them.

[0815] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0816] The present invention is a system in which a user uses a camera to capture an image of an object they want to examine, sends the image to a server, the server generates a face for the object based on the image data received, generates conversation content using the generated face, and sends it back to the terminal to display to the user. Furthermore, this system incorporates an emotion engine that recognizes the user's emotions, and can adjust the conversation content according to the user's emotions.

[0817] System Components

[0818] 1. Camera (device)

[0819] The device has a camera function and captures an image of the object indicated by the user. The captured image is temporarily stored in the device's memory.

[0820] 2. Communication method (terminal and server)

[0821] This includes communication technology for transmitting image data acquired by the device to a server. It is assumed that Wi-Fi or mobile data communication will be used as the means of communication.

[0822] 3. Receiving method and image processing (server)

[0823] The server receives the image data sent from the device and temporarily stores it. It then uses the stored image data to recognize the object and generate a face.

[0824] 4. Face generation means (server)

[0825] The server uses image processing algorithms to generate a face that matches the subject, often using generative models such as deep learning.

[0826] 5. Conversation Generation Method (Server)

[0827] After the server generates the face, it generates a conversation based on the face, using natural language processing technology to create an appropriate response for the user. For example, the generated face might say, "Hello! I'm this table. What do you want to know?"

[0828] 6. Transmission Method (Server)

[0829] The server sends the generated face and conversation content to the device, which includes image data and text data.

[0830] 7. Display means (terminal)

[0831] The device receives the face and conversation content from the server and displays it on the user interface, making it appear as if the object itself is speaking to the user.

[0832] 8. Emotion Engine (Server)

[0833] The server is equipped with an emotion engine that recognizes the user's emotions. This emotion engine analyzes the user's emotions from their facial expressions and voice, and adjusts the content of the conversation based on the results.

[0834] System Operation

[0835] 1. User Emotion Recognition

[0836] The emotion engine analyzes the user's facial expressions and tone of voice from camera footage and microphone audio to determine the user's current emotional state (e.g., joy, sadness, surprise, etc.).

[0837] 2. Adjust the conversation

[0838] The server adapts the conversation depending on the user's emotional state. For example, if the user is tired, the server might respond with, "You seem down, what's wrong?"

[0839] Specific examples

[0840] For example, a user uses a smartphone camera to take a picture of a table. The smartphone sends the captured image to a server, which receives the image and generates a face on the table. The server then uses an emotion engine to analyze the user's emotions. For example, if the server recognizes that the user is tired, it generates a conversation through the generated face, saying, "Hello! I'm this table. You look like you need a short break. Are you okay?" The server then sends the generated face and conversation to the smartphone, which displays it to the user. The user can enjoy watching the table on the screen talk to them according to their emotions.

[0841] In this way, the system of the present invention allows users to acquire information about objects in a fun and emotional way, and is expected to be a particularly useful tool for parents and users who want to enjoy generative AI.

[0842] The processing flow will be explained below.

[0843] Step 1:

[0844] The user uses a camera to capture an image of the object they want to investigate. Using a device such as a smartphone or tablet, the user points the camera at the object and takes a photo through the app.

[0845] Step 2:

[0846] The device sends the acquired image data to the server. The device sends the captured image file to the server using an HTTP request. The communication method is Wi-Fi or mobile data communication.

[0847] Step 3:

[0848] The server receives the image data sent from the terminal and stores it in the server's storage.

[0849] Step 4:

[0850] The server analyzes the image data and generates a face that matches the subject. The server uses a deep learning-based image processing algorithm to detect the contours and features of the subject in the image and generates a face based on this.

[0851] Step 5:

[0852] The server generates conversation content using the generated face. The server uses natural language processing technology (such as GPT-3) to determine the appropriate response to the user. A conversation text is also created along with the generated face image.

[0853] Step 6:

[0854] The server recognizes the user's emotions. Using an emotion engine, the server analyzes facial expressions and tone of voice from the user's camera footage and audio to identify their current emotion (happiness, sadness, surprise, etc.).

[0855] Step 7:

[0856] The server will adjust the conversation content based on the emotion recognition results. For example, if the user is tired, the conversation content will be adjusted to "Hello! I'm this table. You seem a little down. Are you okay?"

[0857] Step 8:

[0858] The server sends the generated facial image and adjusted conversation text to the terminal.

[0859] Step 9:

[0860] The device displays the face and conversation content received from the server. The device analyzes the received data and displays it on the user interface. This allows the user to experience the object on the screen as if it were speaking to them.

[0861] Examples:

[0862] For example, a user can take a picture of a table using the camera on their smartphone and send it to the server. The server receives the image and generates a face on the table. If the emotion engine later determines that the user is tired, the server generates a conversation such as, "Hello! I'm this table. You look a little down. Are you okay?" The server then sends the generated face and conversation to the smartphone, which displays it on the user interface. The user can enjoy watching the table on the screen talk to them according to their emotions.

[0863] Example 2

[0864] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0865] Conventional image recognition systems have difficulty recognizing a user's emotions and providing appropriate responses when interacting with an object. In particular, it has been difficult to automatically respond to a user's emotions, such as tiredness, happiness, or sadness. This can limit the user experience and reduce user satisfaction, as the information and responses provided by the system tend to be uniform.

[0866] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0867] In this invention, the server includes an emotion recognition means for analyzing the user's facial expression and tone of voice to determine their emotion, an emotion response means for adjusting the content of the conversation in accordance with the determined emotional state, and a generation means for receiving image data and generating a face on the target object, thereby enabling responses and dialogue that take the user's emotion into consideration.

[0868] A "camera" is a photographic device that a user uses to capture images.

[0869] "Communication means" refers to the technology used by the terminal to send the image data acquired by the terminal to the server, and includes Wi-Fi, mobile data communication, etc.

[0870] The "generation means" is a technology that performs processing to generate a face on the target object based on the image data received by the server.

[0871] The "conversation generation means" is a technology that allows the server to generate appropriate conversation content using the generated face, and includes natural language processing technology.

[0872] "Transmission means" refers to a technique by which the server transmits the generated face and conversation content to the terminal.

[0873] "Display means" refers to a technology for displaying the face and conversation content received by the terminal on the user interface.

[0874] "Emotion recognition means" is a technology that allows the server to analyze the user's facial expressions and tone of voice to determine their emotions.

[0875] "Emotion response means" is a technique that allows the server to adjust the content of conversation according to the user's emotional state.

[0876] The present invention is a system in which a user uses a camera to capture an image of an object they want to examine, sends the image to a server, the server generates a face for the object based on the image data received, generates conversation content using the generated face, and sends it back to the terminal to display to the user. Furthermore, this system has a built-in function to recognize the user's emotions and adjust the conversation content.

[0877] System Components

[0878] 1. Camera (device)

[0879] The device has a camera function and captures an image of the object indicated by the user. The captured image is temporarily stored in the device's memory. For example, a smartphone is used.

[0880] 2. Communication method (terminal and server)

[0881] This includes communication technology for transmitting image data acquired by the device to a server via Wi-Fi or mobile data communication.

[0882] 3. Receiving method and image processing (server)

[0883] The server receives the image data sent from the device, temporarily stores the data, and uses image processing algorithms (e.g., OpenCV or deep learning models) to recognize objects and generate faces.

[0884] 4. Face generation means (server)

[0885] The server uses a generative model (e.g., a deep learning-based generative adversarial network: GAN) to generate a face that suits the subject.

[0886] 5. Conversation Generation Method (Server)

[0887] The server uses natural language processing technology (e.g., GPT-3) to create appropriate conversation content based on the generated face. The generated face generates conversation content such as "Hello! I'm this table. What do you want to know?"

[0888] 6. Transmission Method (Server)

[0889] The server sends the generated face and conversation content to the device, which includes image data and text data.

[0890] 7. Display means (terminal)

[0891] The device receives the face and conversation content from the server and displays it on the user interface, making it appear as if the object is speaking to the user.

[0892] 8. Emotion Engine (Server)

[0893] The server is equipped with an emotion engine that analyzes the user's emotions from their facial expressions and voice, and adjusts the content of the conversation based on the analysis results.

[0894] Specific examples

[0895] For example, suppose a user uses a smartphone camera to take a picture of a table. The smartphone sends the captured image to a server, which receives the image and generates a face on the table. The server then uses an emotion engine to analyze the user's emotions. For example, if the server recognizes that the user is tired, it generates a conversation through the generated face, saying, "Hello! I'm this table. You look like you need a short break. Are you okay?" The server then sends the generated face and conversation to the smartphone, which displays it to the user. The user can enjoy watching the table on the screen talk to them according to their emotions.

[0896] Prompt Sentence Examples

[0897] By feeding the following sentences into the generative AI model, we can get prompts that replicate the system's functionality:

[0898] The user uses a camera to capture an image of the object they want to examine and sends the image to the server. The server processes the image and generates a face for the object. It then uses an emotion engine to analyze the user's emotions and generates corresponding conversation content. The generated face and conversation content are sent back to the device and displayed on the user interface. For example, if the user is tired, a response such as "Hello, it looks like you need a break" may be generated.

[0899] As described above, the system of the present invention allows users to acquire information about objects in a fun and emotional way. It is expected to be a particularly useful tool for parents and users who want to enjoy generative AI.

[0900] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0901] Step 1:

[0902] A user takes an image of an object using the device's camera. The user's input is to activate the camera and take a picture of the object. The device stores this image in memory. The input is the image of the object, and the output is the image data stored in memory. For example, a user takes a picture of a table with their smartphone.

[0903] Step 2:

[0904] The device sends the acquired image data to the server. The input of the device is the image data stored in its memory. The device uploads this data to the server using Wi-Fi or mobile data communication. The output is the image data to be transmitted. Specifically, the smartphone transmits the image data to the cloud server via the Internet.

[0905] Step 3:

[0906] The server receives image data sent from the terminal and temporarily stores it. The input to the server is the image data sent from the terminal. The server saves this received data in storage. The output is the saved image data. Specifically, the server saves the image data in a specific directory.

[0907] Step 4:

[0908] The server analyzes the image data and recognizes the object. Specifically, it identifies the object using an image processing algorithm (e.g., OpenCV). The input is the stored image data, and the output is the object recognition result. The server extracts the shape and features of the object.

[0909] Step 5:

[0910] The server generates an appropriate face for the recognized object. The face is generated using a generative model (e.g., deep learning model, GAN). The input is the object recognition result, and the output is image data of the generated face. Specifically, the server uses GAN to generate a face image of the table.

[0911] Step 6:

[0912] The server generates conversation content based on the generated face. It uses natural language processing technology (e.g., GPT-3) to create a response to the user. The input is image data of the generated face and information about the object, and the output is text data of the conversation content. Specifically, the server generates the conversation content, "Hello! I'm this table. What do you want to know?"

[0913] Step 7:

[0914] The server sends the generated face and the conversation content to the device. The server's input is image data of the generated face and text data of the conversation content. The server sends this data to the device. The output is the sent image data of the face and text data of the conversation content. Specifically, the cloud server sends this data to the smartphone.

[0915] Step 8:

[0916] The device receives the face and conversation data from the server and displays them on the user interface. The device inputs the received face and conversation data. The device displays these on the user interface. The output is the face and conversation data displayed on the user interface. Specifically, the face at the table and its conversation data are displayed on the smartphone screen.

[0917] Step 9:

[0918] The server's emotion engine analyzes the user's facial expressions and voice to determine their emotions. The input is the device's camera footage and microphone audio, and the output is the user's emotional state. The emotion engine analyzes the user's facial expression data and tone of voice data to determine their emotional state. For example, it determines whether the user is smiling.

[0919] Step 10:

[0920] The server adjusts the conversation content according to the determined emotional state. The input is the user's emotional state and the generated conversation content, and the output is the adjusted conversation content. The server re-creates appropriate conversation content according to the user's emotional state. For example, if the user is tired, the server responds with "Hello! I'm this table. It looks like you need a short break, is that okay?"

[0921] (Application example 2)

[0922] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0923] In recent years, there has been a growing emphasis on improving the customer experience in physical stores. In particular, there is a need for customers to interact with products and in-store displays and obtain enjoyable and useful information. However, conventional methods only provide customers with one-way information, making it difficult to provide services that respond to individual customers' emotions and needs. Another problem is that customers often cannot obtain sufficient information about in-store products and displays at first glance.

[0924] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0925] In this invention, the server includes means for capturing an image of an object with a camera, communication means for transmitting the captured image data, generation means for generating a face for the object based on the received image data, conversation generation means for generating conversation content using the generated face, means including an emotion engine for recognizing a user's emotion, means for adjusting the conversation content in accordance with the emotion, transmission means for transmitting the generated face and conversation content, and display means for displaying the received face and conversation content. This enables customers to interact with products and displays in the store in accordance with their emotions, and to obtain more personalized information in a fun and efficient manner.

[0926] A "camera" is a device for capturing images.

[0927] "Communication means" refers to the technology for transmitting acquired image data to a server.

[0928] "Generation means" refers to a technology for generating a face on an object based on received image data.

[0929] "Conversation generation means" refers to a technology for generating conversation content using the generated face.

[0930] "Emotion engine" refers to technology for analyzing a user's emotions.

[0931] "Transmission means" refers to the technology used to transmit the generated face and conversation content.

[0932] The "display means" is a device that has the function of displaying the received face and conversation content.

[0933] "Object" refers to the object whose image is captured by the camera.

[0934] "User" refers to a person who uses the system.

[0935] The present invention relates to a system for improving customer experience in physical stores. This system allows customers to take pictures of products and displays in the store with a camera, send the images to a server, generate faces for the objects, and then have conversations with them through the generated faces. Furthermore, the system has the ability to recognize the user's emotions and adjust the content of the conversation.

[0936] System Program

[0937] Using the camera

[0938] The device uses a camera to capture images of the object, which are temporarily stored in the device's memory.

[0939] Sending data

[0940] The acquired image data is sent to the server via a communication method such as Wi-Fi or mobile data communication. A specific technique that can be used is HTTP communication using requests.

[0941] Reception and image processing

[0942] The server receives image data sent from the device and temporarily stores it. It also generates a face for the object using OpenCV and the DALL-E model. This allows the object to have a face and be able to converse with the user.

[0943] Conversation Generation

[0944] Conversational content is generated based on the generated face, using large language models such as GPT-4. The generated conversational content is designed to be user-friendly.

[0945] Emotion recognition and conversation regulation

[0946] The emotion engine on the server analyzes the user's emotions from the camera footage and microphone audio. Using Microsoft Azure's emotion recognition API, the system determines the user's emotional state and adjusts the conversation accordingly. For example, if the user is tired, the system will respond with something like, "You seem a little down. Are you okay?"

[0947] display

[0948] The generated face and conversation content are sent from the server to the terminal and displayed on the device, allowing customers to enjoy conversations with the products and displays in the store.

[0949] Examples and prompts

[0950] For example, you can use prompts like the following to generate responses that reflect the user's emotions:

[0951] "The user seems happy. Create a friendly dialogue for this emotion: Hello! I'm the product promoter. I have some great information for you!"

[0952] "The user seems tired. Create a dialogue that reflects this emotion: Hello! I have a great product. Please take a break and enjoy my explanation."

[0953] In this way, the system of the present invention allows customers to obtain information about objects in a fun and emotional way, and is particularly aimed at improving the customer experience in physical stores, by providing personalized services tailored to the needs of each individual customer.

[0954] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0955] Step 1:

[0956] The device uses a camera to capture an image of an object. The user points the camera of a smartphone or smart glasses at the object to take a picture, and the image data is temporarily stored in the device's memory. The input is the camera image, and the output is the image data stored in the memory.

[0957] Step 2:

[0958] The device uses a communication means to send the image data it has acquired. This is done by sending the image data to a server via Wi-Fi or mobile data communication. The input is the image data stored in the device memory, and the output is the image data sent to the server.

[0959] Step 3:

[0960] The server generates a face for the object based on the image data it receives. The server uses OpenCV and the DALL-E model to generate a face suitable for the object from the image data. The input is the image data sent to the server, and the output is the generated face image.

[0961] Step 4:

[0962] The server uses the generated face to generate conversational content for a conversation with the user. This process uses large language models such as GPT-4. The input is the generated face image and a prompt, and the output is the generated conversational content. An example of a specific prompt is, "The user looks happy. Please create a friendly dialogue for this emotion: Hello! I'm the introducer of this product. I have some information that's perfect for you!"

[0963] Step 5:

[0964] The server uses an emotion engine to recognize the user's emotions. The emotion engine in the server analyzes emotions from the user's camera footage and microphone audio. This uses Microsoft Azure's emotion recognition API, etc. The input is the user's facial expressions and voice data, and the output is the analyzed emotional state.

[0965] Step 6:

[0966] The server adjusts the conversation content based on the recognized emotion. If the user is recognized as tired, a response such as "You seem tired, what's wrong?" is generated. The input is the emotional state and the generated conversation content, and the output is the adjusted conversation content.

[0967] Step 7:

[0968] The server sends the generated face and adjusted conversation content to the terminal. The generated data is again sent to the terminal using a communication means. The input is the face image and conversation content, and the output is the data sent to the terminal.

[0969] Step 8:

[0970] The device displays the face and conversation content received from the server. This allows the user to see the object on the device screen speaking to them according to their emotions. The input is the face image and conversation content sent to the device, and the output is the data displayed on the user interface.

[0971] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0972] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0973] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[0974] [Fourth embodiment]

[0975] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0976] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0977] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0978] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0979] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0980] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0981] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0982] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0983] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0984] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0985] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0986] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0987] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[0988] This system allows a user to use a camera to capture an image of an object they wish to research, send that image to a server, generate a face for the object based on the image data received by the server, generate conversation content using the generated face, and send it back to the terminal to display to the user. This system provides an experience where the object itself answers questions, making research fun.

[0989] System Components

[0990] 1. Camera (device)

[0991] The device has a camera function and captures images of the object indicated by the user. The images captured by the camera are temporarily stored in the device's memory and used for subsequent processing.

[0992] 2. Communication method (terminal and server)

[0993] This includes communication technology for transmitting image data acquired by the device to a server. It is assumed that Wi-Fi or mobile data communication will be used as the means of communication.

[0994] 3. Receiving method and image processing (server)

[0995] The server receives the image data sent from the device and temporarily stores it. It then uses the stored image data to recognize the object and generate a face.

[0996] 4. Face generation means (server)

[0997] The server uses image processing algorithms to generate a face that matches the subject, often using generative models such as deep learning.

[0998] 5. Conversation Generation Method (Server)

[0999] After the server generates the face, it generates a conversation based on the face, using natural language processing technology to create a response to the user. For example, the generated face might say, "Hello! I'm this table. What do you want to know?"

[1000] 6. Transmission Method (Server)

[1001] The server sends the generated face and conversation content to the device, which includes image data and text data.

[1002] 7. Display means (terminal)

[1003] The device receives the face and conversation content from the server and displays it on the user interface, making it appear as if the object itself is speaking to the user.

[1004] Specific examples

[1005] For example, a user uses a smartphone camera to take a picture of a table. The smartphone sends the captured image to a server, which receives the image and generates a face on the table. The server then uses the generated face to generate a conversation, such as "Hello! I'm this table. What would you like to know?" The server then sends the generated face and conversation to the smartphone, which displays it to the user. The user can enjoy watching the table on the screen talk to them.

[1006] In this way, the system of the present invention allows users to enjoyably obtain information about objects, and is expected to be a particularly useful tool for parents and users who want to enjoy generative AI.

[1007] The processing flow will be explained below.

[1008] Step 1:

[1009] The user uses a camera to capture an image of the object they want to investigate. Using a device such as a smartphone or tablet, the user points the camera at the object and takes a photo.

[1010] Step 2:

[1011] The terminal sends the acquired image data to the server. The terminal uses a communication means to create an HTTP request to send the captured image to the server, and then sends it.

[1012] Step 3:

[1013] The server receives the image data sent from the terminal and temporarily stores the received image data in the server's storage.

[1014] Step 4:

[1015] The server analyzes the stored image data and generates a face that matches the subject. The server uses image processing algorithms to detect features in the image and generate facial elements that match the subject.

[1016] Step 5:

[1017] The server generates a conversation using the generated face. The server uses natural language processing technology to create an appropriate response for the user and determines the conversation based on the generated face.

[1018] Step 6:

[1019] The server sends the generated face and the conversation content to the device. The server then compiles the face image data and text data and creates a response to send to the device.

[1020] Step 7:

[1021] The device displays the face and conversation content received from the server. The device analyzes the received data and displays the generated face and conversation content on the user interface.

[1022] Examples:

[1023] For example, if a user points their smartphone camera at a table and takes a picture, the device sends the image to the server. The server analyzes the received image and generates a face for the image of the table. The server then generates a conversation such as "Hello! I'm this table. What would you like to know?" and sends it to the device. The device displays the received face and conversation on the user's interface, allowing the user to enjoy watching the table talk to them.

[1024] Example 1

[1025] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1026] Conventional image recognition systems have had issues with quickly recognizing objects and providing intuitive interaction with users. Furthermore, they lacked advanced natural language processing technology to generate appropriate responses for captured images, and methods for generating faces appropriate for the objects. As a result, it was difficult for users to acquire information about objects in an enjoyable and effective way.

[1027] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1028] In this invention, the server includes a recognition means, a generation means, and a conversation generation means, which enable the server to quickly and accurately recognize an object from image data and provide the user with natural conversation content linked to the generated face.

[1029] A "camera" is a device that optically captures an object and stores it as image data.

[1030] An "object" is an object that a user photographs with a camera to look up specific information.

[1031] "Image data" refers to visual information of an object captured by a camera and stored in digital format.

[1032] "Communication means" refers to the technology and protocols used to transmit image data from a device to a server, and examples include Wi-Fi and mobile data communications.

[1033] "Recognition means" refers to an algorithm or program for recognizing an object from received image data and analyzing its characteristics.

[1034] "Generation means" refers to algorithms or programs for generating appropriate faces and features for recognized objects, and specific examples include deep learning models.

[1035] "Conversation generation means" refers to natural language processing technology and algorithms for creating dialogue with the user based on the generated face.

[1036] "Transmission means" refers to the technology and protocols used to transmit the generated face and conversation content from the server to the terminal.

[1037] "Display means" refers to the technology or program for displaying the received face and conversation content on the terminal screen so that the user can visually confirm it.

[1038] The present invention is a system in which a user uses a camera to capture an image of an object they want to research, sends the image to a server, and the server generates a face for the object based on the received image data, generates conversation content using the generated face, and sends it back to the terminal to display to the user. This system provides the user with the experience of the object itself speaking to them, making research fun.

[1039] System Components

[1040] 1. Camera (device)

[1041] The device has a camera function and captures images of the object indicated by the user. The images captured by the camera are temporarily stored in the device's memory and used for subsequent processing.

[1042] 2. Communication method (terminal and server)

[1043] This includes communication technology for transmitting image data acquired by the device to a server. It is assumed that Wi-Fi or mobile data communication will be used as the means of communication.

[1044] 3. Receiving method and image processing (server)

[1045] The server receives the image data sent from the device and temporarily stores it. It then uses the stored image data to recognize the object.

[1046] 4. Face generation means (server)

[1047] The server uses image processing algorithms to generate a face suitable for the subject, using a deep learning model (e.g., generative artificial network (GAN)).

[1048] 5. Conversation Generation Method (Server)

[1049] The server generates conversational content based on the generated face, using natural language processing techniques (e.g., GPT model) to create responses to the user.

[1050] 6. Transmission Method (Server)

[1051] The server sends the generated face and conversation content to the device, which includes image data and text data.

[1052] 7. Display means (terminal)

[1053] The device displays the face and conversation content received from the server on the user interface, making it appear to the user as if the object itself is speaking to them.

[1054] Specific examples of operation

[1055] Consider a scenario where a user takes a picture of a table using a smartphone camera. The specific steps are as follows:

[1056] 1. Image acquisition: The user takes an image of the table using the smartphone camera.

[1057] 2. Image transmission: The smartphone sends the captured image to the server.

[1058] 3. Image processing: The server recognizes the received image data and generates a face that matches the table.

[1059] 4. Conversation generation: Based on the generated face, the server generates a conversation such as "Hello! I'm this table. What would you like to know?"

[1060] 5. Data transmission: The server sends the generated face and conversation content to the smartphone.

[1061] 6. Display: The smartphone displays the received data on the user interface, giving the user the impression that the table is talking to them.

[1062] Prompt Sentence Examples

[1063] An example prompt for input to a generative AI model is:

[1064] "Analyze an image of a table and generate a face that matches the table. Then generate a conversation for that face to say, 'Hello! I'm this table. What would you like to know?'"

[1065] Using this prompt, the generative AI model can perform the necessary processing.

[1066] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1067] Step 1:

[1068] Input: The user uses a device with a camera function to capture an image of the object to be examined (e.g., a table).

[1069] How it works: The user launches the camera app on their smartphone, points the camera at an object, and presses the shutter button.

[1070] Output: Image data stored on the device.

[1071] Step 2:

[1072] Input: Image data stored on the device.

[1073] How it works: The device sends image data to the server using Wi-Fi or mobile data.

[1074] Output: Image data sent to the server.

[1075] Step 3:

[1076] Input: Image data sent to the server.

[1077] Operation: The server receives the image data and temporarily stores it in storage. The server verifies the consistency of the received data.

[1078] Output: Image data stored on the server.

[1079] Step 4:

[1080] Input: Image data stored on the server.

[1081] How it works: The server uses image processing algorithms to recognize objects. Based on the recognition results, it uses a deep learning model (e.g., GAN) to generate a face that matches the object.

[1082] Output: The generated face image.

[1083] Step 5:

[1084] Input: Generated face image.

[1085] How it works: The server uses natural language processing techniques (e.g., GPT models) to generate a conversation based on the generated face, for example, creating a message like "Hello! I'm this table. What would you like to know?"

[1086] Output: The generated conversation.

[1087] Step 6:

[1088] Input: Generated face image and conversation content.

[1089] Operation: The server combines the facial image and conversation content into a single packet and sends it to the terminal.

[1090] Output: Facial image and conversation data sent to the device.

[1091] Step 7:

[1092] Input: Facial image and conversation data sent to the device.

[1093] Operation: The device analyzes the received data and displays it in the user interface. The device divides it into an image view and a text view and displays them on the screen.

[1094] Output: Facial image and conversation content displayed on the user's device.

[1095] (Application example 1)

[1096] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1097] In the past, there were limited ways for users to easily obtain product information in physical stores. Especially when there were many products on display, it was time-consuming to look up each product individually. Furthermore, there was a lack of systems that allowed users to obtain product information in an enjoyable way. This could result in a monotonous user experience and a decrease in motivation to purchase products.

[1098] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1099] In this invention, the server includes a generation means for generating a face on an object based on received image data, a conversation generation means for generating a conversation using a prompt sentence based on the generated face, and a means for transmitting the generated face and the conversation again via a communication means. This enables a system in which, when a user takes a photo of a product with a smartphone in a physical store, the product appears to be talking to them.

[1100] A "camera" is a photographing device that allows a user to capture an image of an object.

[1101] "Communication means" refers to the network means for transmitting acquired image data to the server and receiving data from the server, including Wi-Fi and mobile data communications.

[1102] The "server" is a computer system that processes image data received via a communication means, generates faces for objects, and generates conversation content.

[1103] The "generation means" refers to an algorithm or software for generating a face for an object based on image data received by the server.

[1104] A "prompt sentence" is an input sentence used to generate a conversation based on the face generated by the generation means.

[1105] The "conversation generation means" is a system that uses natural language processing technology with prompt sentences based on the generated face to generate conversation content from the target object.

[1106] The "transmission means" is a means for transmitting the face and conversation content generated by the server to the user terminal again via the communication means.

[1107] The "user interface" refers to a display device and software that allows the user to visually confirm the face and conversation content they receive.

[1108] A "portable device" is an electronic device that a user can carry with them, including a smartphone or tablet PC.

[1109] To implement this invention, a user first captures an image of an object using the camera of a portable device. A portable device refers to a portable electronic device such as a smartphone or tablet PC. These devices are equipped with a camera function and internet communication means.

[1110] When a user takes an image of an object, the image data is sent to a server via a communication means, such as Wi-Fi or mobile data communication.

[1111] The server is equipped with a generation means for generating a face on the target object based on the received image data. The generation means uses a deep learning-based generative model (e.g., Generative Adversarial Network (GAN)).

[1112] Next, the server generates a conversation using prompt sentences based on the generated face. The prompt sentences used here utilize a generative AI model and are processed by a conversation generation means that applies natural language processing technology. For example, OpenAI's generative model (e.g., GPT-3) can be used.

[1113] The generated face and conversation content are then sent to the user's portable device via a communication means, and the user's device displays the received face and conversation content on the user interface, allowing the user to feel as if the object is speaking to them.

[1114] As a concrete example, a user can take a picture of a snack in a physical store and the snack will speak to them, saying, "Hello! I'm potato chips. My calories are 500 kcal per 100 g. Is there anything else you'd like to know?"

[1115] Examples of prompts include:

[1116] "Generate one conversational phrase that corresponds to a smiling face:"

[1117] Through the above process, the system allows users to easily and enjoyably obtain detailed product information in a physical store. An important feature of the invention is that it provides a natural and engaging way to interact with a human through the generated faces and conversations.

[1118] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1119] Step 1:

[1120] The user captures an image of an object using the camera of a portable device. The input is the image of the object, and the output is image data temporarily stored in the internal memory. In this step, the camera is started and the image is taken by pressing the shutter.

[1121] Step 2:

[1122] The terminal sends the acquired image data to the server via a communication means. The input is the temporarily saved image data, and the output is the image data sent to the server. In this step, the image data is uploaded to the server using an HTTP POST request.

[1123] Step 3:

[1124] The server generates a face for the target based on the received image data. The input is the image data received by the server, and the output is the generated face image data. The server processes this data using a generative model (e.g., GAN) to generate a face that suits the target.

[1125] Step 4:

[1126] The server generates a conversation using a prompt sentence based on the generated face. The input is the generated face image data, and the output is the generated conversation. The server uses natural language processing technology and a generative AI model (e.g., GPT-3) to generate the conversation. An example of a prompt sentence here is, "Please generate one conversation corresponding to a smiling face."

[1127] Step 5:

[1128] The server again transmits the generated face and conversation content to the terminal via the communication means. The input is the generated face image data and conversation content, and the output is these data transmitted to the terminal. The server again transmits these data to the terminal using an HTTP response.

[1129] Step 6:

[1130] The device displays the received face and conversation content on the user interface. The input is the face image data and conversation content received from the server, and the output is the display content provided visually to the user. The device uses an image viewer and a text viewer to display the face and conversation content on the screen. This step allows the user to enjoy the experience of the object talking to them.

[1131] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1132] The present invention is a system in which a user uses a camera to capture an image of an object they want to examine, sends the image to a server, the server generates a face for the object based on the image data received, generates conversation content using the generated face, and sends it back to the terminal to display to the user. Furthermore, this system incorporates an emotion engine that recognizes the user's emotions, and can adjust the conversation content according to the user's emotions.

[1133] System Components

[1134] 1. Camera (device)

[1135] The device has a camera function and captures an image of the object indicated by the user. The captured image is temporarily stored in the device's memory.

[1136] 2. Communication method (terminal and server)

[1137] This includes communication technology for transmitting image data acquired by the device to a server. It is assumed that Wi-Fi or mobile data communication will be used as the means of communication.

[1138] 3. Receiving method and image processing (server)

[1139] The server receives the image data sent from the device and temporarily stores it. It then uses the stored image data to recognize the object and generate a face.

[1140] 4. Face generation means (server)

[1141] The server uses image processing algorithms to generate a face that matches the subject, often using generative models such as deep learning.

[1142] 5. Conversation Generation Method (Server)

[1143] After the server generates the face, it generates a conversation based on the face, using natural language processing technology to create an appropriate response for the user. For example, the generated face might say, "Hello! I'm this table. What do you want to know?"

[1144] 6. Transmission Method (Server)

[1145] The server sends the generated face and conversation content to the device, which includes image data and text data.

[1146] 7. Display means (terminal)

[1147] The device receives the face and conversation content from the server and displays it on the user interface, making it appear as if the object itself is speaking to the user.

[1148] 8. Emotion Engine (Server)

[1149] The server is equipped with an emotion engine that recognizes the user's emotions. This emotion engine analyzes the user's emotions from their facial expressions and voice, and adjusts the content of the conversation based on the results.

[1150] System Operation

[1151] 1. User Emotion Recognition

[1152] The emotion engine analyzes the user's facial expressions and tone of voice from camera footage and microphone audio to determine the user's current emotional state (e.g., joy, sadness, surprise, etc.).

[1153] 2. Adjust the conversation

[1154] The server adapts the conversation depending on the user's emotional state. For example, if the user is tired, the server might respond with, "You seem down, what's wrong?"

[1155] Specific examples

[1156] For example, a user uses a smartphone camera to take a picture of a table. The smartphone sends the captured image to a server, which receives the image and generates a face on the table. The server then uses an emotion engine to analyze the user's emotions. For example, if the server recognizes that the user is tired, it generates a conversation through the generated face, saying, "Hello! I'm this table. You look like you need a short break. Are you okay?" The server then sends the generated face and conversation to the smartphone, which displays it to the user. The user can enjoy watching the table on the screen talk to them according to their emotions.

[1157] In this way, the system of the present invention allows users to acquire information about objects in a fun and emotional way, and is expected to be a particularly useful tool for parents and users who want to enjoy generative AI.

[1158] The processing flow will be explained below.

[1159] Step 1:

[1160] The user uses a camera to capture an image of the object they want to investigate. Using a device such as a smartphone or tablet, the user points the camera at the object and takes a photo through the app.

[1161] Step 2:

[1162] The device sends the acquired image data to the server. The device sends the captured image file to the server using an HTTP request. The communication method is Wi-Fi or mobile data communication.

[1163] Step 3:

[1164] The server receives the image data sent from the terminal and stores it in the server's storage.

[1165] Step 4:

[1166] The server analyzes the image data and generates a face that matches the subject. The server uses a deep learning-based image processing algorithm to detect the contours and features of the subject in the image and generates a face based on this.

[1167] Step 5:

[1168] The server generates conversation content using the generated face. The server uses natural language processing technology (such as GPT-3) to determine the appropriate response to the user. A conversation text is also created along with the generated face image.

[1169] Step 6:

[1170] The server recognizes the user's emotions. Using an emotion engine, the server analyzes facial expressions and tone of voice from the user's camera footage and audio to identify their current emotion (happiness, sadness, surprise, etc.).

[1171] Step 7:

[1172] The server will adjust the conversation content based on the emotion recognition results. For example, if the user is tired, the conversation content will be adjusted to "Hello! I'm this table. You seem a little down. Are you okay?"

[1173] Step 8:

[1174] The server sends the generated facial image and adjusted conversation text to the terminal.

[1175] Step 9:

[1176] The device displays the face and conversation content received from the server. The device analyzes the received data and displays it on the user interface. This allows the user to experience the object on the screen as if it were speaking to them.

[1177] Examples:

[1178] For example, a user can take a picture of a table using the camera on their smartphone and send it to the server. The server receives the image and generates a face on the table. If the emotion engine later determines that the user is tired, the server generates a conversation such as, "Hello! I'm this table. You look a little down. Are you okay?" The server then sends the generated face and conversation to the smartphone, which displays it on the user interface. The user can enjoy watching the table on the screen talk to them according to their emotions.

[1179] Example 2

[1180] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1181] Conventional image recognition systems have difficulty recognizing a user's emotions and providing appropriate responses when interacting with an object. In particular, it has been difficult to automatically respond to a user's emotions, such as tiredness, happiness, or sadness. This can limit the user experience and reduce user satisfaction, as the information and responses provided by the system tend to be uniform.

[1182] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1183] In this invention, the server includes an emotion recognition means for analyzing the user's facial expression and tone of voice to determine their emotion, an emotion response means for adjusting the content of the conversation in accordance with the determined emotional state, and a generation means for receiving image data and generating a face on the target object, thereby enabling responses and dialogue that take the user's emotion into consideration.

[1184] A "camera" is a photographic device that a user uses to capture images.

[1185] "Communication means" refers to the technology used by the terminal to send the image data acquired by the terminal to the server, and includes Wi-Fi, mobile data communication, etc.

[1186] The "generation means" is a technology that performs processing to generate a face on the target object based on the image data received by the server.

[1187] The "conversation generation means" is a technology that allows the server to generate appropriate conversation content using the generated face, and includes natural language processing technology.

[1188] "Transmission means" refers to a technique by which the server transmits the generated face and conversation content to the terminal.

[1189] "Display means" refers to a technology for displaying the face and conversation content received by the terminal on the user interface.

[1190] "Emotion recognition means" is a technology that allows the server to analyze the user's facial expressions and tone of voice to determine their emotions.

[1191] "Emotion response means" is a technique that allows the server to adjust the content of conversation according to the user's emotional state.

[1192] The present invention is a system in which a user uses a camera to capture an image of an object they want to examine, sends the image to a server, the server generates a face for the object based on the image data received, generates conversation content using the generated face, and sends it back to the terminal to display to the user. Furthermore, this system has a built-in function to recognize the user's emotions and adjust the conversation content.

[1193] System Components

[1194] 1. Camera (device)

[1195] The device has a camera function and captures an image of the object indicated by the user. The captured image is temporarily stored in the device's memory. For example, a smartphone is used.

[1196] 2. Communication method (terminal and server)

[1197] This includes communication technology for transmitting image data acquired by the device to a server via Wi-Fi or mobile data communication.

[1198] 3. Receiving method and image processing (server)

[1199] The server receives the image data sent from the device, temporarily stores the data, and uses image processing algorithms (e.g., OpenCV or deep learning models) to recognize objects and generate faces.

[1200] 4. Face generation means (server)

[1201] The server uses a generative model (e.g., a deep learning-based generative adversarial network: GAN) to generate a face that suits the subject.

[1202] 5. Conversation Generation Method (Server)

[1203] The server uses natural language processing technology (e.g., GPT-3) to create appropriate conversation content based on the generated face. The generated face generates conversation content such as "Hello! I'm this table. What do you want to know?"

[1204] 6. Transmission Method (Server)

[1205] The server sends the generated face and conversation content to the device, which includes image data and text data.

[1206] 7. Display means (terminal)

[1207] The device receives the face and conversation content from the server and displays it on the user interface, making it appear as if the object is speaking to the user.

[1208] 8. Emotion Engine (Server)

[1209] The server is equipped with an emotion engine that analyzes the user's emotions from their facial expressions and voice, and adjusts the content of the conversation based on the analysis results.

[1210] Specific examples

[1211] For example, suppose a user uses a smartphone camera to take a picture of a table. The smartphone sends the captured image to a server, which receives the image and generates a face on the table. The server then uses an emotion engine to analyze the user's emotions. For example, if the server recognizes that the user is tired, it generates a conversation through the generated face, saying, "Hello! I'm this table. You look like you need a short break. Are you okay?" The server then sends the generated face and conversation to the smartphone, which displays it to the user. The user can enjoy watching the table on the screen talk to them according to their emotions.

[1212] Prompt Sentence Examples

[1213] By feeding the following sentences into the generative AI model, we can get prompts that replicate the system's functionality:

[1214] The user uses a camera to capture an image of the object they want to examine and sends the image to the server. The server processes the image and generates a face for the object. It then uses an emotion engine to analyze the user's emotions and generates corresponding conversation content. The generated face and conversation content are sent back to the device and displayed on the user interface. For example, if the user is tired, a response such as "Hello, it looks like you need a break" may be generated.

[1215] As described above, the system of the present invention allows users to acquire information about objects in a fun and emotional way. It is expected to be a particularly useful tool for parents and users who want to enjoy generative AI.

[1216] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1217] Step 1:

[1218] A user takes an image of an object using the device's camera. The user's input is to activate the camera and take a picture of the object. The device stores this image in memory. The input is the image of the object, and the output is the image data stored in memory. For example, a user takes a picture of a table with their smartphone.

[1219] Step 2:

[1220] The device sends the acquired image data to the server. The input of the device is the image data stored in its memory. The device uploads this data to the server using Wi-Fi or mobile data communication. The output is the image data to be transmitted. Specifically, the smartphone transmits the image data to the cloud server via the Internet.

[1221] Step 3:

[1222] The server receives image data sent from the terminal and temporarily stores it. The input to the server is the image data sent from the terminal. The server saves this received data in storage. The output is the saved image data. Specifically, the server saves the image data in a specific directory.

[1223] Step 4:

[1224] The server analyzes the image data and recognizes the object. Specifically, it identifies the object using an image processing algorithm (e.g., OpenCV). The input is the stored image data, and the output is the object recognition result. The server extracts the shape and features of the object.

[1225] Step 5:

[1226] The server generates an appropriate face for the recognized object. The face is generated using a generative model (e.g., deep learning model, GAN). The input is the object recognition result, and the output is image data of the generated face. Specifically, the server uses GAN to generate a face image of the table.

[1227] Step 6:

[1228] The server generates conversation content based on the generated face. It uses natural language processing technology (e.g., GPT-3) to create a response to the user. The input is image data of the generated face and information about the object, and the output is text data of the conversation content. Specifically, the server generates the conversation content, "Hello! I'm this table. What do you want to know?"

[1229] Step 7:

[1230] The server sends the generated face and the conversation content to the device. The server's input is image data of the generated face and text data of the conversation content. The server sends this data to the device. The output is the sent image data of the face and text data of the conversation content. Specifically, the cloud server sends this data to the smartphone.

[1231] Step 8:

[1232] The device receives the face and conversation data from the server and displays them on the user interface. The device inputs the received face and conversation data. The device displays these on the user interface. The output is the face and conversation data displayed on the user interface. Specifically, the face at the table and its conversation data are displayed on the smartphone screen.

[1233] Step 9:

[1234] The server's emotion engine analyzes the user's facial expressions and voice to determine their emotions. The input is the device's camera footage and microphone audio, and the output is the user's emotional state. The emotion engine analyzes the user's facial expression data and tone of voice data to determine their emotional state. For example, it determines whether the user is smiling.

[1235] Step 10:

[1236] The server adjusts the conversation content according to the determined emotional state. The input is the user's emotional state and the generated conversation content, and the output is the adjusted conversation content. The server re-creates appropriate conversation content according to the user's emotional state. For example, if the user is tired, the server responds with "Hello! I'm this table. It looks like you need a short break, is that okay?"

[1237] (Application example 2)

[1238] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1239] In recent years, there has been a growing emphasis on improving the customer experience in physical stores. In particular, there is a need for customers to interact with products and in-store displays and obtain enjoyable and useful information. However, conventional methods only provide customers with one-way information, making it difficult to provide services that respond to individual customers' emotions and needs. Another problem is that customers often cannot obtain sufficient information about in-store products and displays at first glance.

[1240] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1241] In this invention, the server includes means for capturing an image of an object with a camera, communication means for transmitting the captured image data, generation means for generating a face for the object based on the received image data, conversation generation means for generating conversation content using the generated face, means including an emotion engine for recognizing a user's emotion, means for adjusting the conversation content in accordance with the emotion, transmission means for transmitting the generated face and conversation content, and display means for displaying the received face and conversation content. This enables customers to interact with products and displays in the store in accordance with their emotions, and to obtain more personalized information in a fun and efficient manner.

[1242] A "camera" is a device for capturing images.

[1243] "Communication means" refers to the technology for transmitting acquired image data to a server.

[1244] "Generation means" refers to a technology for generating a face on an object based on received image data.

[1245] "Conversation generation means" refers to a technology for generating conversation content using the generated face.

[1246] "Emotion engine" refers to technology for analyzing a user's emotions.

[1247] "Transmission means" refers to the technology used to transmit the generated face and conversation content.

[1248] The "display means" is a device that has the function of displaying the received face and conversation content.

[1249] "Object" refers to the object whose image is captured by the camera.

[1250] "User" refers to a person who uses the system.

[1251] The present invention relates to a system for improving customer experience in physical stores. This system allows customers to take pictures of products and displays in the store with a camera, send the images to a server, generate faces for the objects, and then have conversations with them through the generated faces. Furthermore, the system has the ability to recognize the user's emotions and adjust the content of the conversation.

[1252] System Program

[1253] Using the camera

[1254] The device uses a camera to capture images of the object, which are temporarily stored in the device's memory.

[1255] Sending data

[1256] The acquired image data is sent to the server via a communication method such as Wi-Fi or mobile data communication. A specific technique that can be used is HTTP communication using requests.

[1257] Reception and image processing

[1258] The server receives image data sent from the device and temporarily stores it. It also generates a face for the object using OpenCV and the DALL-E model. This allows the object to have a face and be able to converse with the user.

[1259] Conversation Generation

[1260] Conversational content is generated based on the generated face, using large language models such as GPT-4. The generated conversational content is designed to be user-friendly.

[1261] Emotion recognition and conversation regulation

[1262] The emotion engine on the server analyzes the user's emotions from the camera footage and microphone audio. Using Microsoft Azure's emotion recognition API, the system determines the user's emotional state and adjusts the conversation accordingly. For example, if the user is tired, the system will respond with something like, "You seem a little down. Are you okay?"

[1263] display

[1264] The generated face and conversation content are sent from the server to the terminal and displayed on the device, allowing customers to enjoy conversations with the products and displays in the store.

[1265] Examples and prompts

[1266] For example, you can use prompts like the following to generate responses that reflect the user's emotions:

[1267] "The user seems happy. Create a friendly dialogue for this emotion: Hello! I'm the product promoter. I have some great information for you!"

[1268] "The user seems tired. Create a dialogue that reflects this emotion: Hello! I have a great product. Please take a break and enjoy my explanation."

[1269] In this way, the system of the present invention allows customers to obtain information about objects in a fun and emotional way, and is particularly aimed at improving the customer experience in physical stores, by providing personalized services tailored to the needs of each individual customer.

[1270] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1271] Step 1:

[1272] The device uses a camera to capture an image of an object. The user points the camera of a smartphone or smart glasses at the object to take a picture, and the image data is temporarily stored in the device's memory. The input is the camera image, and the output is the image data stored in the memory.

[1273] Step 2:

[1274] The device uses a communication means to send the image data it has acquired. This is done by sending the image data to a server via Wi-Fi or mobile data communication. The input is the image data stored in the device memory, and the output is the image data sent to the server.

[1275] Step 3:

[1276] The server generates a face for the object based on the image data it receives. The server uses OpenCV and the DALL-E model to generate a face suitable for the object from the image data. The input is the image data sent to the server, and the output is the generated face image.

[1277] Step 4:

[1278] The server uses the generated face to generate conversational content for a conversation with the user. This process uses large language models such as GPT-4. The input is the generated face image and a prompt, and the output is the generated conversational content. An example of a specific prompt is, "The user looks happy. Please create a friendly dialogue for this emotion: Hello! I'm the introducer of this product. I have some information that's perfect for you!"

[1279] Step 5:

[1280] The server uses an emotion engine to recognize the user's emotions. The emotion engine in the server analyzes emotions from the user's camera footage and microphone audio. This uses Microsoft Azure's emotion recognition API, etc. The input is the user's facial expressions and voice data, and the output is the analyzed emotional state.

[1281] Step 6:

[1282] The server adjusts the conversation content based on the recognized emotion. If the user is recognized as tired, a response such as "You seem tired, what's wrong?" is generated. The input is the emotional state and the generated conversation content, and the output is the adjusted conversation content.

[1283] Step 7:

[1284] The server sends the generated face and adjusted conversation content to the terminal. The generated data is again sent to the terminal using a communication means. The input is the face image and conversation content, and the output is the data sent to the terminal.

[1285] Step 8:

[1286] The device displays the face and conversation content received from the server. This allows the user to see the object on the device screen speaking to them according to their emotions. The input is the face image and conversation content sent to the device, and the output is the data displayed on the user interface.

[1287] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1288] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1289] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1290] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1291] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1292] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1293] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1294] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1295] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1296] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1297] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1298] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1299] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1300] 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.

[1301] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1302] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1303] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[1304] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1305] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1306] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1307] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1308] The following is further disclosed regarding the above embodiment.

[1309] (Claim 1)

[1310] means for acquiring an image of the object with a camera;

[1311] a communication means for transmitting the acquired image data;

[1312] a generation means for generating a face on the object based on the received image data;

[1313] a conversation generation means for generating conversation content using the generated face;

[1314] a transmitting means for transmitting the generated face and the conversation content;

[1315] a display means for displaying the received face and conversation content;

[1316] A system including:

[1317] (Claim 2)

[1318] 10. The system of claim 1, further comprising means for initiating a conversation with the object, including greeting and introducing oneself.

[1319] (Claim 3)

[1320] 2. The system according to claim 1, wherein a user points a camera at an object to capture an image of the object.

[1321] "Example 1"

[1322] (Claim 1)

[1323] means for acquiring an image of the object with a camera;

[1324] a communication means for transmitting the acquired image data;

[1325] recognition means for recognizing an object based on the received image data;

[1326] a generating means for generating a face suitable for the recognized object;

[1327] a conversation generation means for generating conversation content using the generated face;

[1328] a transmitting means for transmitting the generated face and the conversation content;

[1329] a display means for displaying the received face and conversation content;

[1330] A system including:

[1331] (Claim 2)

[1332] 10. The system of claim 1, further comprising means for initiating a conversation with the object, including greeting and introducing oneself.

[1333] (Claim 3)

[1334] The system according to claim 1, characterized in that the communication means uses Wi-Fi or mobile data communication.

[1335] (Claim 4)

[1336] 10. The system of claim 1, wherein the face is generated using a deep learning model.

[1337] (Claim 5)

[1338] 10. The system of claim 1, wherein the conversation content is generated using natural language processing techniques.

[1339] "Application Example 1"

[1340] (Claim 1)

[1341] means for acquiring an image of the object with a camera;

[1342] means for transmitting the acquired image data to a server via a communication means;

[1343] a generating means for generating a face on the target object based on image data received on the server side;

[1344] a conversation generation means for generating a conversation content using a prompt sentence based on the generated face;

[1345] a means for transmitting the generated face and the conversation content again via a communication means;

[1346] a display means for displaying the received face and conversation content on a user interface;

[1347] A system including:

[1348] (Claim 2)

[1349] 10. The system of claim 1, further comprising means for initiating a conversation with the object, including greeting and introducing oneself.

[1350] (Claim 3)

[1351] 2. The system according to claim 1, wherein a user uses a portable device to point a camera at an object to capture the image.

[1352] "Example 2: Combining Emotion Engines"

[1353] (Claim 1)

[1354] means for acquiring an image of the object with a camera;

[1355] a communication means for transmitting the acquired image data;

[1356] a generation means for generating a face on the object based on the received image data;

[1357] a conversation generation means for generating conversation content using the generated face;

[1358] a transmitting means for transmitting the generated face and the conversation content;

[1359] a display means for displaying the received face and conversation content;

[1360] emotion recognition means for analyzing a user's facial expression and tone of voice to determine the user's emotion;

[1361] an emotional response means for adjusting the content of the conversation in accordance with the determined emotional state;

[1362] A system including:

[1363] (Claim 2)

[1364] 10. The system of claim 1, further comprising means for initiating a conversation with the object, including greeting and introducing oneself.

[1365] (Claim 3)

[1366] 2. The system according to claim 1, wherein a user points a camera at an object to capture an image of the object.

[1367] "Application example 2 when combining emotion engines"

[1368] (Claim 1)

[1369] means for acquiring an image of the object with a camera;

[1370] a communication means for transmitting the acquired image data;

[1371] a generation means for generating a face on the object based on the received image data;

[1372] a conversation generation means for generating conversation content using the generated face;

[1373] means including an emotion engine for recognizing an emotion of a user;

[1374] A way to adjust the content of conversations depending on emotions,

[1375] a transmitting means for transmitting the generated face and the conversation content;

[1376] a display means for displaying the received face and conversation content;

[1377] A system including:

[1378] (Claim 2)

[1379] 10. The system of claim 1, further comprising means for initiating a conversation with the object, including greeting and introducing oneself.

[1380] (Claim 3)

[1381] 2. The system according to claim 1, wherein a user points a camera at an object to capture an image of the object. [Explanation of symbols]

[1382] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. means for acquiring an image of an object with a camera; a communication means for transmitting the acquired image data; a generation means for generating a face on the object based on the received image data; a conversation generation means for generating conversation content using the generated face; a transmitting means for transmitting the generated face and the conversation content; a display means for displaying the received face and conversation content; A system including:

2. The system of claim 1 , further comprising means for initiating a conversation with the object, including greetings and self-introductions.

3. 2. The system of claim 1, wherein a user points a camera at an object to capture an image of the object.

Images