System

Abstract

A system is provided.SOLUTION: A system comprising: a means for collecting pet facial expressions, gestures, and sounds; a means for pre-processing the collected information; a means including a generative AI model for analyzing the pre-processed information and converting pet intentions into human language; a means for determining and displaying recommended products based on the analysis results; and a means for matching with candidate foster parents based on pet preferences and purchasing status.SELECTED DRAWING: Figure 1

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] Many pet owners find it difficult to understand their pets' true intentions. Accurately reading a pet's intentions and emotions from its behavior and vocalizations is a difficult task for many pet owners. The present invention aims to provide a system that eliminates the constraints on communication between pets and their owners and allows owners to clearly understand their pets' intentions. Furthermore, the present invention aims to enrich life with pets by suggesting optimal products and matching potential foster parents based on the pet's intentions. [Means for solving the problem]

[0005] The present invention solves the above problems by the following means. Specifically, it provides a means for collecting data on pets' facial expressions, gestures, and cries. It also includes a means for preprocessing the collected data and converting it into an appropriate format for analysis. It also includes a means for analyzing the preprocessed data and using a generative AI model to translate the pet's intentions into human language. It also provides a means for determining and displaying recommended products based on the analysis results, and a means for matching potential foster parents based on the pet's preferences and purchasing habits, thereby creating a system that enriches the relationship between pets and their owners.

[0006] "Data collection means" refers to a device or process for recording and collecting data on pet facial expressions, gestures, and sounds using a device such as a camera or microphone.

[0007] A "data pre-processing means" is a device or process for converting collected data into a form suitable for analysis.

[0008] A "generative AI model" is a machine learning algorithm or artificial intelligence technology that analyzes collected data and translates a pet's intentions into human language.

[0009] "Analysis means" refers to a device or process that uses a generative AI model based on pre-processed data to analyze the pet's intentions and convert them into language that humans can understand.

[0010] The "recommended product display means" is a device or process for selecting the most suitable product for the pet based on the analysis results and presenting it to the user.

[0011] A "matching means" is a device or process for finding suitable foster parent candidates and suggesting them to users based on pet preferences and purchasing habits.

[0012] "Notification means" refers to a device or process for notifying the user of analysis results, recommended products, and matching results.

[0013] "Real-time transmission means" refers to a device or process for transmitting collected data to a server in real time. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

[0022] [First embodiment]

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

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

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

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

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

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

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

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

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

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

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

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

[0035] This invention is a system that collects and analyzes data such as pet facial expressions, gestures, and cries, and converts the pet's intentions into human language. The purpose of this system is to help pet owners better understand their pets' feelings, purchase products that meet their pet's needs, and find suitable foster parents.

[0036] System configuration

[0037] 1. A device equipped with a camera and microphone that collects pet behavior data

[0038] 2. Software for preprocessing collected data

[0039] 3. A generative AI model on the server that analyzes the data and translates the pet's intentions into human language

[0040] 4. User interface that displays recommended products based on analysis results

[0041] 5. A server-based algorithm that matches potential adopters based on pet preferences and purchasing history

[0042] 6. Network protocol for notifying users of analysis results, product suggestions, and matching information

[0043] Explanation of the program processing flow

[0044] Data collection and preprocessing

[0045] The device uses a camera and microphone to record your pet's facial expressions, movements, and sounds in real time. This data is first preprocessed and converted into an appropriate format for analysis. This preprocessing includes noise removal and data format conversion.

[0046] Sending data to the server

[0047] The pre-processed data is then transmitted over the internet to a server in real time, designed to minimize data latency.

[0048] Data analysis

[0049] The server analyzes the received data using a generative AI model. This AI model infers the pet's intentions based on the pet's behavioral data and converts them into human language. For example, if the pet is excited, the analysis result may be "I want to play."

[0050] Recommended products

[0051] The server then selects the best product for your pet based on the analysis results. This selection takes into account the pet's current condition and past purchase history. The selected product is then notified to the user via their device.

[0052] Product purchase and preference data updates

[0053] The user views the recommended products and decides to purchase them. This purchase information is sent from the device to the server, and the pet's preference data is updated. This data is used to recommend future products and match potential adopters.

[0054] Matching potential foster parents

[0055] The server searches for suitable foster parent candidates based on pet preferences and purchasing history. When a suitable foster parent candidate is found, the information is notified to the user via the terminal.

[0056] Specific examples

[0057] For example, if a user's dog barks frequently, the device collects the barking data and sends it to the server. The server analyzes the data and determines that the dog is barking because it is bored. Based on the analysis results, the server recommends an educational toy for dogs and notifies the user. When the user purchases the toy, the purchase information is sent to the server, and the dog's preference data is updated. If the user is then looking for a foster parent, this preference data is referenced to present suitable foster parent candidates.

[0058] In this way, the present invention aims to facilitate communication between owners and pets and improve the quality of life for both by analyzing pet behavior, converting it into human language, and providing various services based on that.

[0059] The processing flow will be explained below.

[0060] Step 1: Collect data

[0061] The device records your pet's behavior in real time using a camera and microphone.

[0062] The device collects data such as your pet's facial expressions, gestures, and cries.

[0063] Step 2: Preprocessing the data

[0064] The data collected by the terminal is preprocessed, noise is removed, and data format conversion is performed.

[0065] The device converts the preprocessed data into a format suitable for analysis.

[0066] Step 3: Sending data

[0067] The terminal transmits the preprocessed data to a server via the Internet.

[0068] The device transmits data in real time to minimize delays.

[0069] Step 4: Receiving the data

[0070] The server receives the data sent from the terminal.

[0071] The server checks the integrity of the data received.

[0072] Step 5: Analyze the data

[0073] The server analyzes the received data using a generative AI model.

[0074] The server infers the pet's intentions based on the pet's behavioral data and converts them into human language.

[0075] Step 6: Notification of analysis results

[0076] The server generates data to notify the user of the analysis results.

[0077] The server sends the analysis results to the terminal via the Internet.

[0078] Step 7: Viewing the analysis results

[0079] The analysis results received by the terminal are displayed on the user interface.

[0080] The user checks the analysis results to understand the pet's intentions.

[0081] Step 8: Selecting recommended products

[0082] The server selects the best product for your pet based on the analysis results.

[0083] The server selects products taking into consideration past purchase history and pet preferences.

[0084] Step 9: Product Information Notification

[0085] The server generates data for notifying the user of recommended product information.

[0086] The server transmits product information to the terminal via the Internet.

[0087] Step 10: Display product information

[0088] The terminal displays the received product information on a user interface.

[0089] The user checks the displayed product information and makes a purchase decision.

[0090] Step 11: Submit your purchase information

[0091] The user performs a purchase operation and inputs the purchase information into the terminal.

[0092] The terminal sends the purchase information to the server.

[0093] Step 12: Maintain purchasing data

[0094] The server receives the purchase information and updates the pet's preference data.

[0095] The server stores the updated data and uses it for future recommendations and matches.

[0096] Step 13: Matching potential foster parents

[0097] The server searches for potential foster parents based on pet preferences and purchasing history.

[0098] The server generates data for notifying the user of the foster parent candidate information it has found.

[0099] Step 14: Notification of matching information

[0100] The server transmits information about potential foster parents to the terminal via the Internet.

[0101] The foster parent candidate information received by the terminal is displayed on a user interface.

[0102] Step 15: Check the matching results

[0103] The user checks the displayed foster parent candidate information and takes action if necessary.

[0104] Example 1

[0105] 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."

[0106] Understanding pets' intentions through data such as facial expressions, behavior, and vocalizations is a difficult task for pet owners. It is particularly important to accurately grasp pet stress and needs and respond appropriately accordingly. Proposing products based on pet needs and matching suitable foster parents are also important. However, existing systems lack the ability to analyze data in real time or accurately translate pets' intentions, and no technology has been established to increase owner satisfaction.

[0107] 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.

[0108] In this invention, the server includes means for collecting data on the pet's facial expressions, gestures, and cries, means for preprocessing the collected data, means for transmitting the preprocessed data to the server in real time, means including a generative AI model for analyzing the preprocessed data and converting the pet's intentions into human language, means for determining and displaying recommended products based on the analysis results, and means for matching with potential foster parents based on the pet's preferences and purchasing history. This makes it possible to accurately analyze the pet's intentions and quickly and effectively suggest appropriate products and match with potential foster parents.

[0109] "Pet facial expressions" are data that represent emotions and states shown by the movements and expressions of an animal's face.

[0110] "Gestures" are data that represent the movements and attitudes that animals show through specific actions and behaviors.

[0111] "Calls" are data that represent the sounds and sound patterns made by animals.

[0112] "Means for collecting data" refers to devices or methods that use cameras, microphones, or other devices to record a pet's facial expressions, gestures, and cries.

[0113] "Preprocessing means" refers to devices or methods that remove noise from collected data, convert the data format, and prepare it in a format suitable for analysis.

[0114] The "means for transmitting to the server" refers to a device or method for transferring the preprocessed data to the server using a communication network such as the Internet.

[0115] A "generative AI model" is a generative model that analyzes data based on a specific purpose and converts it into human language.

[0116] The "means for determining and displaying recommended products" refers to a device or method for selecting optimal products based on the analysis results and notifying the user of them.

[0117] "Preference data" is data that represents information about pet preferences and purchasing history.

[0118] A "means for matching with potential foster parents" is a device or method for selecting the most suitable potential foster parents based on pet preferences and purchasing habits.

[0119] The "means for notifying the user" refers to a device or method for notifying the user of the analysis results and recommended product information.

[0120] The "means for transmitting purchase information to the server" refers to a device or method for transmitting information about a product purchased by a user to the server and updating the pet's preference data.

[0121] This invention is a system that collects data such as pet facial expressions, gestures, and cries, analyzes the data, and converts the pet's intentions into human language. The system aims to help pet owners understand their pet's feelings, purchase products that meet their pet's needs, and find suitable foster parents.

[0122] The system is configured as follows: First, a device equipped with a camera and microphone is used to collect data on pet behavior. Specific hardware used includes an HD camera and a highly sensitive microphone. This device records the pet's behavior in real time and stores it as data.

[0123] The collected data is subjected to noise removal and format conversion using pre-processing software within the device, which prepares the data in a format suitable for analysis. After pre-processing is complete, the data is sent to a server via the Internet using an efficient, low-latency communication protocol (e.g., TCP / IP).

[0124] The server analyzes the received data using a generative AI model. This generative AI model infers the pet's intentions from its behavioral data and converts them into human language. Specifically, it uses advanced natural language processing techniques such as GPT-4 and BERT. Examples of prompts to be input to this AI model include the following:

[0125] Pet behavior data:

[0126] Expression: Smiling

[0127] Gesture: Jump

[0128] Sounds: Barking

[0129] Based on this behavioral data, infer your pet's intentions and translate them into human terms.

[0130] Based on the analysis results, the server selects the best product for the pet. This selection takes into account the pet's current condition and past purchase history. For example, an educational toy might be selected for a bored pet. This product information is notified to the user via their device. Notifications are sent via smartphone push notifications or email.

[0131] When a user purchases a product, the purchase information is sent from the device to the server, and the pet's preference data is updated. The updated data is used to suggest future products and match potential adopters. In this way, the system always reflects the pet's latest preferences and condition, allowing it to provide the most optimal service.

[0132] Furthermore, the server searches for suitable foster parent candidates based on pet preferences and purchasing history. When a suitable foster parent candidate is found, the information is notified to the user via the terminal, allowing the user to quickly find the best foster parent for their pet.

[0133] As described above, the present invention aims to facilitate communication with pet owners by analyzing their pet's behavior and converting it into human language, and to suggest products that meet the pet's needs and match them with potential foster parents.

[0134] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0135] Step 1: Data collection

[0136] The device uses a camera and microphone to record your pet's facial expressions, gestures, and sounds in real time. For example, an HD camera captures your pet's facial movements and overall movements, and a high-sensitivity microphone records its sounds. The input data is the pet's video and audio, and the output data is the recorded multimedia file.

[0137] Step 2: Data Preprocessing

[0138] The terminal performs noise removal and data format conversion on the collected data. For example, it filters out background noise from audio data and extracts the necessary frequency band. For image data, it cuts out unnecessary parts and adjusts the resolution. The input is the recorded multimedia file, and the output is a preprocessed data file.

[0139] Step 3: Send data to the server

[0140] The preprocessed data is sent to the server in real time using the Internet's TCP / IP protocol to minimize data transmission delays. The input is the preprocessed data file, and the output is the data sent to the server.

[0141] Step 4: Data analysis

[0142] The server analyzes the received data using a generative AI model. Specifically, a prompt sentence is input to the generative AI model (e.g., GPT-4 or BERT) to infer the intention from the pet's behavior data. The input is the preprocessed data and the prompt sentence, and the output is a natural language text that expresses the pet's intention.

[0143] Step 5: Selecting recommended products

[0144] The server selects the best product for your pet based on the analysis results. This selection takes into account the pet's current condition and past purchase history. For example, an educational toy might be selected for a bored pet. The input is the analysis results and purchase history data, and the output is information about the selected product.

[0145] Step 6: Notification of recommended products

[0146] The selected product information is notified to the user via the device. Notifications are sent via smartphone push notifications or email. The input is the selected product information, and the output is a notification message provided to the user. For example, a message such as "Your pet seems bored. Why not try a new educational toy?" is displayed.

[0147] Step 7: Purchase and update preference data

[0148] The user confirms the notification and purchases the product. The purchase information is sent from the terminal to the server, and the pet's preference data is updated. The input is the purchased product information, and the output is the updated preference data. For example, if the user purchases an educational toy, that information is added to the preference database.

[0149] Step 8: Matching potential foster parents

[0150] The server searches for suitable foster parent candidates based on the updated preference data. When a suitable foster parent candidate is found, the information is notified to the user via the terminal. The input is the updated preference data, and the output is information about the suitable foster parent candidate. For example, a notification such as "A suitable foster parent candidate has been found for your pet" may be sent.

[0151] Through these processing steps, the system can analyze the pet's intentions in real time and make optimal product recommendations and match potential adopters, helping owners better understand their pets' feelings and improve the quality of their pets' lives.

[0152] (Application example 1)

[0153] 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."

[0154] In recent years, the number of pets has increased, increasing the need for owners to understand their pets' feelings and provide appropriate care. However, it is difficult to read a pet's intentions from its facial expressions, gestures, and cries, and many owners are unable to accurately grasp their pets' needs. This can lead to stress for pets and problems such as not being able to provide appropriate products and services. Furthermore, choosing the right product from the large selection of products at pet supply stores can be difficult, especially for first-time pet owners. This invention aims to solve these problems and improve the quality of life for pets and their owners by accurately analyzing pets' feelings and recommending products to owners in real time.

[0155] 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.

[0156] In this invention, the server includes: means for collecting pet behavioral data; means for preprocessing the collected data; means including a generative AI model for analyzing the preprocessed data and converting the pet's intentions into human language; means for determining and displaying recommended products based on the analysis results; means for matching potential foster parents based on the pet's preferences and purchasing information; and means including a visual display capable of displaying the analysis results and product recommendations in real time. This enables accurate understanding of the pet's intentions and optimal product recommendations in real time. Furthermore, the system simultaneously matches potential foster parents using the pet's preference data, contributing to improving the quality of life for owners and their pets.

[0157] "Pet behavior data" refers to various physical and audio information displayed by a pet, such as facial expressions, gestures, and sounds.

[0158] "Preprocessing" refers to the process of removing noise from collected data and converting it into a format suitable for analysis, such as resizing and denoising image data.

[0159] A "generative AI model" is an artificial intelligence model that analyzes pet behavior data, infers their intentions, and converts them into human language. Specifically, it is built using machine learning and deep learning.

[0160] "Recommended products" refer to products that are deemed optimal based on your pet's current condition, preferences, and past purchasing history.

[0161] "Foster parent candidates" refer to the new owners who are best suited to a pet based on the pet's preferences and purchasing information.

[0162] "Visual display" refers to a visual display device for displaying analysis results and product suggestions in real time, including the displays of smart glasses.

[0163] A "cloud server" refers to a remote server that analyzes and stores data via the internet, allowing for efficient processing of large amounts of data.

[0164] "Real-time" refers to minimizing delays and processing and displaying data almost instantly.

[0165] "Analysis results" refers to the pet's intentions and recommended actions derived from the pet's behavioral data by the generative AI model.

[0166] "Preference data" refers to individual data about pets that is generated based on their preferences, past behavior, purchasing history, etc.

[0167] This invention is a system that collects and analyzes pet behavior data, converts the pet's intentions into human language, and presents recommended products. This helps pet owners understand their pet's needs more accurately and purchase appropriate products. The system is configured as follows:

[0168] Hardware Configuration

[0169] 1. Terminal

[0170] The device, which includes smart glasses and smartphones, is equipped with a camera and microphone to collect pet behavior data.

[0171] 2. Server

[0172] This is a cloud server for analyzing data. A generative AI model is installed on this server and analyzes pet behavior data.

[0173] 3. Visual Displays

[0174] A visual display device that displays analysis results and recommended products in real time, such as the display of smart glasses or the screen of a smartphone.

[0175] Software Configuration

[0176] 1. Data Collection and Preprocessing

[0177] The device uses a camera and microphone to record your pet's facial expressions, movements, and sounds in real time. This collected data is first preprocessed and converted into an appropriate format for analysis. Preprocessing includes noise removal and data format conversion. For example, the collected image data can be preprocessed using the Google Cloud Vision API.

[0178] 2. Sending data to the server

[0179] The pre-processed data is then sent over the internet to a cloud server, which is designed to minimize data latency and uses real-time communication technologies such as AWS IoT.

[0180] 3. Data Analysis

[0181] The server analyzes the received data using a generative AI model. This AI model infers the pet's intentions based on the pet's behavioral data and converts this into human language. For example, if the pet is excited, the analysis result may be "I want to play." GPT-3 and other models are used as generative AI models.

[0182] 4. Recommended products

[0183] Based on the analysis results, the server selects the most suitable product for the pet, taking into account the pet's current condition and past purchase history. The selected product is notified to the user via a visual display.

[0184] Example

[0185] An example of a brick-and-mortar application is a smart glasses application in a pet shop. When staff or customers wearing the smart glasses walk around the store with their pets, the glasses can read the pets' intentions from their facial expressions and cries, and make recommendations on products.

[0186] For example, when a user visits a pet shop and wears smart glasses, the glasses' built-in camera and microphone collect data on the pet's behavior in real time. The collected data is preprocessed and sent to a cloud server. A generative AI model on the server analyzes the data and, if it determines that the pet is bored, recommended products such as educational toys and treats are displayed on the smart glasses' display.

[0187] In this way, a system is realized that facilitates communication between pets and their owners and can make optimal and beneficial product suggestions.

[0188] Prompt Sentence Examples

[0189] I am currently building a system to analyze pet behavior. The image shows a dog. Please analyze the data on the dog's facial expressions, posture, and barks, and convert them into human words. Please tell me the emotions and intentions the dog is expressing.

[0190] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0191] Step 1:

[0192] Data collection

[0193] The device (smart glasses or smartphone) collects behavioral data such as facial expressions, gestures, and sounds of pets in real time using a camera and microphone. The input data is video and audio data, which is then stored directly in the device's memory.

[0194] Step 2:

[0195] Data Preprocessing

[0196] The device preprocesses the collected data. Specifically, image data is resized using OpenCV to remove unwanted noise, and audio data is converted into a clear format through noise filtering. The output of the preprocessing is image and audio data converted into a format suitable for analysis.

[0197] Step 3:

[0198] Data transmission

[0199] The terminal sends the preprocessed data to a cloud server. At this time, real-time communication technologies such as AWS IoT are used to instantly upload the data to the server. The input is the preprocessed data, and the output is the data stored on the server.

[0200] Step 4:

[0201] Data analysis

[0202] The server analyzes the collected data using a generative AI model on the cloud. The generative AI model analyzes the pet's behavior data and converts its intentions into human language. For example, "by analyzing the pet's facial expressions from image data and its cries from audio data, it determines that the pet is bored." The input is data stored on the cloud server, and the output is the text data of the analysis results.

[0203] Step 5:

[0204] Product presentation

[0205] The server selects the best products for pets based on the analysis results obtained from the generative AI model. It also takes into account past purchase history and preference data to determine recommended products. The input is the analysis results and purchase history data, and the output is a product list. This product information is sent to the terminal in real time.

[0206] Step 6:

[0207] Results display

[0208] The terminal displays the recommended product information sent from the server on a visual display. For example, "educational toys" or "snacks" are recommended on the display of smart glasses. The input is the recommended product information from the server, and the output is the product information displayed to the user's visual sense.

[0209] Step 7:

[0210] Sending purchasing information

[0211] If the user checks the recommended products and decides to purchase them, the purchase information is sent from the device to the server. The server updates the pet's preference data based on this purchase information. The input is the user's purchase action, and the output is the updated preference data.

[0212] Step 8:

[0213] Matching potential foster parents

[0214] If a user is looking for potential foster parents, the server searches for the most suitable foster parents based on pet preferences and purchase data, and notifies the terminal of the matching results. The input is pet preference data and purchase history, and the output is information on the matched foster parents.

[0215] In this way, a system is realized that facilitates communication between pets and their owners, and matches them with optimal and beneficial product suggestions and potential foster parents.

[0216] 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.

[0217] This invention combines a system that collects and analyzes data on pets' facial expressions, gestures, and cries, and converts the pet's intentions into human language based on that data, with an emotion engine that recognizes the user's emotions. The system aims to help users understand their pets' feelings, purchase products that meet their pets' needs, and find suitable potential adopters.

[0218] System configuration

[0219] 1. A device equipped with a camera and microphone that collects pet behavior data

[0220] 2. Software for preprocessing collected data

[0221] 3. A generative AI model on the server that analyzes the data and translates the pet's intentions into human language

[0222] 4. User interface that displays recommended products based on analysis results

[0223] 5. Emotion engine that recognizes user emotions

[0224] 6. A server-based algorithm that matches potential adopters based on pet preferences and purchasing history

[0225] 7. Network protocol for notifying users of analysis results, product suggestions, and matching information

[0226] Explanation of the program processing flow

[0227] Data collection and preprocessing

[0228] The device uses a camera and microphone to record your pet's facial expressions, movements, and cries in real time. The recorded data is pre-processed to remove noise and convert the data format. The pre-processed data is then converted into a format suitable for analysis.

[0229] Sending data to the server

[0230] The pre-processed data is then sent over the internet to a server, which is designed to transmit the data in real time and with minimal data latency.

[0231] Data analysis

[0232] The server analyzes the received data using a generative AI model. This AI model infers the pet's intentions based on the pet's behavioral data and translates this into human language. For example, if the pet is nervous, the analysis result may be "feeling anxious."

[0233] Recognizing user emotions with an emotion engine

[0234] The device or server captures the user's facial expressions and voice and analyzes them with an emotion engine. This allows the user's emotional state to be grasped in real time. For example, if the user is feeling stressed, it will be recognized as a "stressed state."

[0235] Notification of analysis results

[0236] The server generates data to notify the user of the analysis results and sends it to the terminal via the Internet. The terminal displays the received analysis results on a user interface, allowing the user to understand the pet's intentions.

[0237] Selection and display of recommended products

[0238] The server selects the best product for your pet based on the analysis results and the user's emotional state. It takes into consideration past purchase history, the pet's preferences, and the user's current emotional state. The selected product information is notified to the user via the device, which then displays it.

[0239] Product purchase and preference data updates

[0240] The user views the recommended products and decides to purchase them. The purchase information is sent from the device to the server, and the pet's preference data is updated. This data is used for future recommendations and matching.

[0241] Matching potential foster parents

[0242] The server searches for suitable foster parent candidates based on the user's pet preferences, purchasing history, and emotional state. When a suitable foster parent candidate is found, the information is notified to the user via the terminal.

[0243] Specific examples

[0244] For example, if a user's cat frequently licks itself, the device collects this behavioral data and sends it to the server. The server analyzes the data and determines that the cat is "stressed." The emotion engine then recognizes that the user is stressed. Based on this, the server recommends a cat toy that is effective in relieving stress and notifies the user. When the user purchases the toy, the purchase information is sent to the server, and the cat's preference data is updated. If the user is then looking for a foster parent, the updated preference data is referenced to present suitable foster parent candidates.

[0245] In this way, the present invention aims to facilitate communication between owners and pets and improve the quality of life for both by analyzing pet behavior and user emotions and providing various services based on that information.

[0246] The processing flow will be explained below.

[0247] Step 1: Collect data

[0248] The device uses a camera and microphone to record your pet's facial expressions, gestures, and cries in real time.

[0249] The device collects data on your pet's behavior.

[0250] Step 2: Preprocessing the data

[0251] The data collected by the device is pre-processed, including noise removal and format conversion.

[0252] The terminal converts the preprocessed data into a format suitable for analysis.

[0253] Step 3: Send data to the server

[0254] The terminal transmits the pre-processed data to the server in real time.

[0255] The terminal monitors the data transmission status, and if a transmission error is detected, retransmission is performed.

[0256] Step 4: Receiving and storing data

[0257] The server receives the data sent from the terminal.

[0258] The server stores the received data and prepares it for analysis.

[0259] Step 5: Analyze pet data

[0260] The server analyzes the pet's behavioral data using the generated AI model.

[0261] The server infers the pet's intentions and translates them into human language.

[0262] Step 6: Submit and view analysis results

[0263] The server generates the analysis results as data and sends them to the terminal.

[0264] The terminal receives the analysis results and displays them on the user interface.

[0265] The user checks the displayed analysis results and understands the pet's intentions.

[0266] Step 7: Recognizing user emotions with the emotion engine

[0267] The device uses a camera and microphone to record the user's facial expressions and voice.

[0268] The terminal or server analyzes the user's emotions using an emotion engine.

[0269] The server stores the analyzed user emotion information.

[0270] Step 8: Selecting recommended products

[0271] The server selects recommended products based on the pet analysis results and the user's emotional information.

[0272] The server decides on the product taking into consideration past purchase history and pet preference data.

[0273] Step 9: Notification of recommended products

[0274] The server sends information about recommended products to the terminal.

[0275] The terminal receives the recommended product information and displays it on the user interface.

[0276] Step 10: Purchase the product and submit your purchase information

[0277] The user checks the recommended products and decides to purchase them.

[0278] The terminal transmits the user's purchasing information to the server.

[0279] Step 11: Maintain purchasing data

[0280] The server receives the user's purchasing information and updates the pet preference data.

[0281] The server stores the updated data for future analysis.

[0282] Step 12: Matching potential foster parents

[0283] The server searches for potential foster parents based on pet preference data, purchasing history, and user emotional information.

[0284] The server sends information about suitable foster parent candidates to the terminal.

[0285] Step 13: Notification of matching information

[0286] The terminal receives the foster parent candidate information and displays it on a user interface.

[0287] The user checks the displayed foster parent candidate information and takes action if necessary.

[0288] In this way, the present invention realizes a system that analyzes pet behavior and user emotions and provides various services based on that information, facilitating communication between owners and pets and improving the quality of life for both.

[0289] Example 2

[0290] 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."

[0291] It is difficult to accurately understand a pet's intentions based on its facial expressions, gestures, and cries. It is also difficult to select appropriate products based on a pet's feelings and needs, or to find suitable potential adopters. Furthermore, there is a need to understand the user's emotional state in real time and utilize that information to provide more appropriate responses. However, few existing systems meet these requirements, and there is a lack of appropriate means to improve communication and the quality of life between owners and pets.

[0292] 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.

[0293] In this invention, the server includes means for collecting data on the pet's facial expressions, gestures, and cries, means for preprocessing the collected data, means for transmitting the preprocessed data to the server, means including a model for analyzing the transmitted data and converting the pet's intentions into human language, means for recognizing the user's emotions based on the analysis results, means for determining and displaying recommended products based on the analysis results and the user's emotional state, and means for matching with potential foster parents based on the pet's preferences and purchasing habits. This makes it possible to accurately understand the pet's behavior and intentions, propose appropriate products that also take the user's emotional state into consideration, and better match with potential foster parents.

[0294] "Data on pet's facial expressions, gestures, and sounds" refers to data that includes pet's facial expressions, body movements, and sounds.

[0295] "Means of collection" refers to devices such as cameras and microphones used to capture pets' facial expressions, gestures, and sounds, as well as the software that controls them.

[0296] "Preprocessing means" refers to software or algorithms used to remove noise from collected data and convert it into a form suitable for analysis.

[0297] "Means for transmitting to a server" refers to a communication protocol or network interface for transmitting the pre-processed data to a server via the Internet in real time.

[0298] "A model for analyzing and converting pet intentions into human language" refers to a generative AI model that infers intentions based on pet behavior data and converts them into human language.

[0299] "Means for recognizing user emotions" refers to emotion recognition engines or software that analyze the user's facial expressions and voice data to grasp the user's emotional state in real time.

[0300] "Means for determining and displaying recommended products" refers to software or devices that select appropriate products based on the analysis results and the user's emotional state, and display that information in a user interface.

[0301] "Means of matching potential foster parents based on pet preferences and purchasing history" refers to algorithms and systems that search for and match suitable foster parent candidates based on the pet's past preference data and purchasing history.

[0302] This system collects data on pets' facial expressions, gestures, and cries, analyzes their intentions based on the collected data, and notifies the user. Furthermore, it can recognize the user's emotional state in real time and recommend products based on that information. It can also match pets with suitable potential adopters based on their preferences and purchasing habits.

[0303] Hardware Configuration

[0304] This system uses the following hardware:

[0305] Device: A device that includes a camera (e.g., a high-resolution camera), a microphone, and a user interface (e.g., a smartphone or tablet).

[0306] Server: High-performance analysis server

[0307] Software Configuration

[0308] The following software is used:

[0309] Pre-processing software: OpenCV, ffmpeg, librosa, etc.

[0310] Generative AI models: AI models built using TensorFlow or PyTorch

[0311] Emotion recognition engine: Microsoft Azure Emotion API, etc.

[0312] Communication protocols: WebSocket, HTTP

[0313] Program processing flow

[0314] 1. Data Collection

[0315] The device uses a camera and microphone to record your pet's facial expressions, movements, and sounds in real time, and the captured data is temporarily stored in the device's storage.

[0316] 2. Pretreatment

[0317] The device performs preprocessing on the collected data, such as noise removal and data format conversion. Specifically, it uses OpenCV and ffmpeg to convert the video data format, and librosa to remove noise from the audio data.

[0318] 3. Data Transmission

[0319] The preprocessed data is sent in real time to the server using WebSocket or HTTP.

[0320] 4. Data Analysis

[0321] The server analyzes the received data using a generative AI model (using TensorFlow and PyTorch), which translates the pet's behavior and intentions into human terms, such as "feeling anxious" or "wanting to play."

[0322] 5. User Emotion Recognition

[0323] The device or server collects the user's facial and voice data and analyzes it using an emotion recognition engine. This allows the user's emotional state to be understood in real time. For example, it uses Microsoft Azure's Emotion API to determine whether the user is "stressed" or "relaxed."

[0324] 6. Notification of analysis results

[0325] The server generates data to notify the user of the pet's analysis results and sends it to the terminal via the Internet. The terminal displays the received analysis results in a GUI, allowing the user to understand the pet's intentions.

[0326] 7. Selection and display of recommended products

[0327] The server selects recommended products based on the pet analysis results and the user's emotional state. The information is then sent to the device, which displays the recommendations to the user. Past purchase history and pet preferences are also taken into consideration.

[0328] 8. Purchase and Preference Data Updates

[0329] When a user purchases a recommended product, the purchase information is sent from the device to the server, and the pet's preference data is updated. This information is used for future recommendations and matching with potential adopters.

[0330] 9. Matching potential foster parents

[0331] The server searches for suitable foster parents based on the user's pet preferences, purchasing history, and emotional state, and notifies the device of the information. If the user confirms the details and determines that the candidate is suitable, they can proceed to the next step.

[0332] Specific examples

[0333] For example, if a pet dog barks frequently, the device collects this behavioral data, preprocesses it, and sends it to the server. The server then analyzes it using a generative AI model and determines that the dog is feeling anxious. At the same time, an emotion recognition engine recognizes the user's stress level. Based on this, the server recommends a dog toy for stress relief and notifies the user. If the user purchases the toy, the preference data is updated. If the user is looking for a foster parent, suitable foster parent candidates are presented based on the updated data.

[0334] Examples of prompts include "Please tell me the analysis results of my dog's frequent barking behavior" and "Please give me the user's current emotional state and recommend products."

[0335] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0336] Step 1:

[0337] The device uses a camera and microphone to record the pet's facial expressions, behaviors, and sounds in real time. The input is video data captured by the camera and audio data collected by the microphone. The output is the collected raw data, which is temporarily stored in the device's storage.

[0338] Step 2:

[0339] The device performs preprocessing on the collected data. Specifically, it uses OpenCV for format conversion of video data and librosa for noise reduction of audio data. The input is the raw data collected in step 1. The output is data that has been denoised and converted into a format suitable for analysis.

[0340] Step 3:

[0341] The terminal sends the preprocessed data to the server in real time over the Internet. This communication uses WebSocket. The input is the preprocessed data. The output is the data that arrives at the server ready for analysis.

[0342] Step 4:

[0343] The server analyzes the received data using a generative AI model. Specifically, it uses TensorFlow to analyze the pet's behavioral patterns. The input is the data that arrives at the server. The output is the analysis results, which translate the pet's intentions into human words such as "I feel anxious" or "I want to play."

[0344] Step 5:

[0345] The device or server collects the user's facial and voice data and analyzes it using an emotion recognition engine. Specifically, the analysis is performed using Microsoft Azure's Emotion API. The input is the user's facial and voice data. The output is data indicating the user's emotional state, such as "stressed" or "relaxed."

[0346] Step 6:

[0347] The server generates data to notify the user based on the pet analysis results and sends it to the terminal via the Internet. The input is the analysis results regarding the pet's intentions and the user's emotional state. The output is data to notify the user, which is displayed on the user interface.

[0348] Step 7:

[0349] The server selects recommended products based on the pet analysis results and the user's emotional state. The selection also takes into account past purchase history and the pet's preferences. The input is data such as the analysis results, the user's emotional state, and past purchase history. The output is a list of selected recommended products, which is sent to the terminal via the Internet.

[0350] Step 8:

[0351] The user looks at the recommended products and decides whether to purchase them. If a purchase decision is made, the device sends the information to the server. The input is the user's purchase decision information. The output is updated pet preference data, which is saved on the server.

[0352] Step 9:

[0353] The server searches for suitable foster parent candidates based on pet preferences, purchase data, and the user's emotional state, and notifies the terminal of this information. The input is updated pet preference data, purchase history, and the user's emotional state. The output is information on suitable foster parent candidates, which is notified to the terminal.

[0354] (Application example 2)

[0355] 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."

[0356] In modern society, there is a demand for smoother communication between pets and their owners, improving the quality of life for both. In particular, there is a challenge in accurately understanding pet behavior and intentions and responding appropriately. Furthermore, there is a lack of services that reflect the user's emotional state. This makes it difficult to select products that meet the pet's needs and match them with suitable foster parents.

[0357] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for collecting data on the pet's facial expressions, gestures, and cries; means for preprocessing the collected data; means including a generative AI model for analyzing the preprocessed data and converting the pet's intentions into human language; means for determining and displaying recommended products based on the analysis results; means for matching with potential foster parents based on the pet's preferences and purchasing habits; means including an emotion engine for recognizing the user's emotions; means for suggesting recommended products based on the results of analysis by the generative AI model and the emotion engine and making the products available for purchase in a virtual store; means for displaying the analysis results in a user interface in real time; and means for notifying the user of the analysis results. This facilitates communication between pets and their owners, enabling accurate understanding of the pet's intentions, appropriate product suggestions and purchases, and effective matching with potential foster parents.

[0358] "Data on pets' facial expressions, gestures, and sounds" refers to recordings of the facial expressions, body movements, and sounds that pets make.

[0359] "Means of collection" refers to devices or methods for acquiring data on pets' facial expressions, gestures, and cries using devices such as cameras and microphones.

[0360] "Preprocessing means" refers to methods or processes that remove noise from collected data and convert it into a form suitable for analysis.

[0361] A "generative AI model" is an artificial intelligence model trained to analyze collected data, infer a pet's intentions, and translate them into human language.

[0362] "Means for determining and displaying recommended products" refers to a device or method that selects the most suitable products for pets based on the analysis results of the generative AI model and displays them on a user interface.

[0363] "Methods of matching potential foster parents based on pet preferences and purchasing history" refers to algorithms and processes that take into account the pet's past behavior and purchasing history to find the most suitable foster parent candidates.

[0364] The "emotion engine" is a software module that analyzes the user's facial expressions and voice to recognize the user's emotional state.

[0365] "Means for enabling purchase of products in a virtual store" refers to a device or method that provides a function that allows a user to check product information in a virtual space and purchase the product on the spot.

[0366] "Means for displaying analysis results on a user interface in real time" refers to technology or devices for displaying analyzed data on a user's display without delay.

[0367] The "means for notifying the user of the analysis results" refers to a communication technology or mechanism for communicating the analysis results to the user.

[0368] MODE FOR CARRYING OUT THE INVENTION

[0369] This invention combines a system that collects and analyzes data on pets' facial expressions, gestures, and cries, and converts the pet's intentions into human language based on that data, with an emotion engine that recognizes the user's emotions. The system aims to help users understand their pets' feelings, purchase products that meet their needs, and find suitable foster parents.

[0370] System configuration

[0371] 1. Data collection device: Cameras and microphones installed in smartphones or head-mounted displays (HMDs) collect data on pets' facial expressions, gestures, and cries in real time. The collected data undergoes pre-processing such as noise removal and data format conversion.

[0372] 2. Data transmission to the server: The preprocessed data is transmitted to the server via the Internet. Transmission is performed as close to real time as possible, and is designed to minimize data delays.

[0373] 3. Data Analysis Server: The server analyzes the received data using a generative AI model. This AI model infers the pet's intentions based on its behavioral data and converts them into human language. It also captures the user's facial expressions and voice and analyzes them with an emotion engine to understand the user's emotional state in real time.

[0374] 4. User Interface: The results of the analysis by the server are sent to the terminal via the Internet and displayed on the user interface, making it easier for the user to understand the intentions of their pet.

[0375] 5. Product Recommendation System: The server selects the most suitable pet product based on the analysis results and the user's emotional state. Specifically, it takes into account past purchase history, pet preferences, and the user's current emotional state. The selected product information is notified to the user via their terminal, and the user can view and purchase the product in the virtual store.

[0376] 6. Preference data and adoption matching: Information about purchased items is sent to the server to update the pet's preference data. This data is also used to match potential adopters, searching for and presenting suitable adopter candidates.

[0377] Hardware and software used

[0378] Hardware: Smartphone, smart glasses, head-mounted display (HMD), camera, microphone

[0379] Software: OpenCV (for image processing), EmotionAnalytics (emotion recognition module), PetBehaviorModel (pet behavior analysis model), ProductRecommender (product recommendation system)

[0380] Specific examples

[0381] For example, if a user's cat frequently licks itself, the device collects this behavioral data and sends it to the server. The server analyzes the collected data and determines that the cat is "stressed." The emotion engine simultaneously recognizes that the user is stressed. Based on this analysis, the server recommends a cat toy that is effective in relieving stress and notifies the user. When the user purchases the toy in the virtual store, the purchase information is sent to the server, and the cat's preference data is updated. Furthermore, if the user is looking for a foster parent, the updated preference data is referenced to present suitable foster parent candidates.

[0382] Prompt Sentence Examples

[0383] "Analyze footage of a cat frequently licking itself and infer the pet's intentions. Also, recognize the user's emotional state from facial expression data and suggest products that are effective in relieving stress in cats."

[0384] In this way, the present invention aims to facilitate communication between owners and pets and improve the quality of life for both by analyzing pet behavior and user emotions and providing various services based on that information.

[0385] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0386] Step 1:

[0387] Collecting pet data

[0388] Input: Pet's facial expressions, gestures, and cries

[0389] The device uses a camera and microphone installed in a smartphone or head-mounted display (HMD) to collect data on your pet's facial expressions, gestures, and cries in real time.

[0390] Output: Collected data (video data, audio data)

[0391] Step 2:

[0392] Data Preprocessing

[0393] Input: Collected data (video data, audio data)

[0394] The device pre-processes the collected data, which includes noise removal, grayscale conversion of images, and filtering of audio data.

[0395] Output: Pre-processed data (processed video data, audio data)

[0396] Step 3:

[0397] Sending data to the server

[0398] Input: Preprocessed data (processed video data, audio data)

[0399] The terminals then transmit the pre-processed data to a server via the internet, with the transmission being designed to occur as quickly as possible in real time, minimizing data latency.

[0400] Output: Data sent to the server

[0401] Step 4:

[0402] Data analysis

[0403] Input: Data sent to the server (processed video data, audio data)

[0404] The server analyzes the received data using a generative AI model, which infers the pet's intentions based on the behavioral data and translates them into human language.

[0405] Output: Analysis result that indicates the pet's intention (e.g. "I feel stressed")

[0406] Step 5:

[0407] User Emotion Recognition

[0408] Input: User's facial expressions and voice

[0409] The device captures the user's facial expressions and voice and analyzes them with an emotion engine, allowing the device to grasp the user's emotional state in real time.

[0410] Output: Analysis results showing the user's emotions (e.g., "stressed state")

[0411] Step 6:

[0412] Notification of analysis results

[0413] Input: Analysis results showing the pet's intentions, analysis results showing the user's emotions

[0414] The server generates data for notifying the user interface of the analysis results and transmits the data to the terminal via the Internet.

[0415] Output: Analysis results sent to the device

[0416] Step 7:

[0417] Display in the user interface

[0418] Input: Analysis results sent to the device

[0419] The terminal displays the received analysis results on a user interface, allowing the user to understand the pet's intentions.

[0420] Output: Analysis results displayed on the user interface

[0421] Step 8:

[0422] Product suggestion

[0423] Input: Analysis results showing the pet's intentions, analysis results showing the user's emotions

[0424] The server selects the most suitable pet products based on the analysis results and the user's emotional state, taking into account past purchase history, pet preferences, and the user's current emotional state.

[0425] Output: Selected recommended products

[0426] Step 9:

[0427] Buying products in a virtual store

[0428] Input: Selected recommended products

[0429] The user checks the product information displayed in the virtual store and purchases the product.

[0430] Output: Purchase information

[0431] Step 10:

[0432] Preference data updates and foster parent matching

[0433] Input: Purchase information

[0434] The device sends the purchase information to the server, which updates the pet's preference data. Based on this, the server searches for suitable potential adopters.

[0435] Output: Updated preference data, suitable foster parents

[0436] 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.

[0437] 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.

[0438] 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.

[0439] [Second embodiment]

[0440] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0441] 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.

[0442] 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).

[0443] 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.

[0444] 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.

[0445] 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).

[0446] 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.

[0447] 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.

[0448] 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.

[0449] 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.

[0450] In the smart glasses 214, 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.

[0451] 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."

[0452] This invention is a system that collects and analyzes data such as pet facial expressions, gestures, and cries, and converts the pet's intentions into human language. The purpose of this system is to help pet owners better understand their pets' feelings, purchase products that meet their pet's needs, and find suitable foster parents.

[0453] System configuration

[0454] 1. A device equipped with a camera and microphone that collects pet behavior data

[0455] 2. Software for preprocessing collected data

[0456] 3. A generative AI model on the server that analyzes the data and translates the pet's intentions into human language

[0457] 4. User interface that displays recommended products based on analysis results

[0458] 5. A server-based algorithm that matches potential adopters based on pet preferences and purchasing history

[0459] 6. Network protocol for notifying users of analysis results, product suggestions, and matching information

[0460] Explanation of the program processing flow

[0461] Data collection and preprocessing

[0462] The device uses a camera and microphone to record your pet's facial expressions, movements, and sounds in real time. This data is first preprocessed and converted into an appropriate format for analysis. This preprocessing includes noise removal and data format conversion.

[0463] Sending data to the server

[0464] The pre-processed data is then transmitted over the internet to a server in real time, designed to minimize data latency.

[0465] Data analysis

[0466] The server analyzes the received data using a generative AI model. This AI model infers the pet's intentions based on the pet's behavioral data and converts them into human language. For example, if the pet is excited, the analysis result may be "I want to play."

[0467] Recommended products

[0468] The server then selects the best product for your pet based on the analysis results. This selection takes into account the pet's current condition and past purchase history. The selected product is then notified to the user via their device.

[0469] Product purchase and preference data updates

[0470] The user views the recommended products and decides to purchase them. This purchase information is sent from the device to the server, and the pet's preference data is updated. This data is used to recommend future products and match potential adopters.

[0471] Matching potential foster parents

[0472] The server searches for suitable foster parent candidates based on pet preferences and purchasing history. When a suitable foster parent candidate is found, the information is notified to the user via the terminal.

[0473] Specific examples

[0474] For example, if a user's dog barks frequently, the device collects the barking data and sends it to the server. The server analyzes the data and determines that the dog is barking because it is bored. Based on the analysis results, the server recommends an educational toy for dogs and notifies the user. When the user purchases the toy, the purchase information is sent to the server, and the dog's preference data is updated. If the user is then looking for a foster parent, this preference data is referenced to present suitable foster parent candidates.

[0475] In this way, the present invention aims to facilitate communication between owners and pets and improve the quality of life for both by analyzing pet behavior, converting it into human language, and providing various services based on that.

[0476] The processing flow will be explained below.

[0477] Step 1: Collect data

[0478] The device records your pet's behavior in real time using a camera and microphone.

[0479] The device collects data such as your pet's facial expressions, gestures, and cries.

[0480] Step 2: Preprocessing the data

[0481] The data collected by the terminal is preprocessed, noise is removed, and data format conversion is performed.

[0482] The device converts the preprocessed data into a format suitable for analysis.

[0483] Step 3: Sending data

[0484] The terminal transmits the preprocessed data to a server via the Internet.

[0485] The device transmits data in real time to minimize delays.

[0486] Step 4: Receiving the data

[0487] The server receives the data sent from the terminal.

[0488] The server checks the integrity of the data received.

[0489] Step 5: Analyze the data

[0490] The server analyzes the received data using a generative AI model.

[0491] The server infers the pet's intentions based on the pet's behavioral data and converts them into human language.

[0492] Step 6: Notification of analysis results

[0493] The server generates data to notify the user of the analysis results.

[0494] The server sends the analysis results to the terminal via the Internet.

[0495] Step 7: Viewing the analysis results

[0496] The analysis results received by the terminal are displayed on the user interface.

[0497] The user checks the analysis results to understand the pet's intentions.

[0498] Step 8: Selecting recommended products

[0499] The server selects the best product for your pet based on the analysis results.

[0500] The server selects products taking into consideration past purchase history and pet preferences.

[0501] Step 9: Product Information Notification

[0502] The server generates data for notifying the user of recommended product information.

[0503] The server transmits product information to the terminal via the Internet.

[0504] Step 10: Display product information

[0505] The terminal displays the received product information on a user interface.

[0506] The user checks the displayed product information and makes a purchase decision.

[0507] Step 11: Submit your purchase information

[0508] The user performs a purchase operation and inputs the purchase information into the terminal.

[0509] The terminal sends the purchase information to the server.

[0510] Step 12: Maintain purchasing data

[0511] The server receives the purchase information and updates the pet's preference data.

[0512] The server stores the updated data and uses it for future recommendations and matches.

[0513] Step 13: Matching potential foster parents

[0514] The server searches for potential foster parents based on pet preferences and purchasing history.

[0515] The server generates data for notifying the user of the foster parent candidate information it has found.

[0516] Step 14: Notification of matching information

[0517] The server transmits information about potential foster parents to the terminal via the Internet.

[0518] The foster parent candidate information received by the terminal is displayed on a user interface.

[0519] Step 15: Check the matching results

[0520] The user checks the displayed foster parent candidate information and takes action if necessary.

[0521] Example 1

[0522] 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."

[0523] Understanding pets' intentions through data such as facial expressions, behavior, and vocalizations is a difficult task for pet owners. It is particularly important to accurately grasp pet stress and needs and respond appropriately accordingly. Proposing products based on pet needs and matching suitable foster parents are also important. However, existing systems lack the ability to analyze data in real time or accurately translate pets' intentions, and no technology has been established to increase owner satisfaction.

[0524] 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.

[0525] In this invention, the server includes means for collecting data on the pet's facial expressions, gestures, and cries, means for preprocessing the collected data, means for transmitting the preprocessed data to the server in real time, means including a generative AI model for analyzing the preprocessed data and converting the pet's intentions into human language, means for determining and displaying recommended products based on the analysis results, and means for matching with potential foster parents based on the pet's preferences and purchasing history. This makes it possible to accurately analyze the pet's intentions and quickly and effectively suggest appropriate products and match with potential foster parents.

[0526] "Pet facial expressions" are data that represent emotions and states shown by the movements and expressions of an animal's face.

[0527] "Gestures" are data that represent the movements and attitudes that animals show through specific actions and behaviors.

[0528] "Calls" are data that represent the sounds and sound patterns made by animals.

[0529] "Means for collecting data" refers to devices or methods that use cameras, microphones, or other devices to record a pet's facial expressions, gestures, and cries.

[0530] "Preprocessing means" refers to devices or methods that remove noise from collected data, convert the data format, and prepare it in a format suitable for analysis.

[0531] The "means for transmitting to the server" refers to a device or method for transferring the preprocessed data to the server using a communication network such as the Internet.

[0532] A "generative AI model" is a generative model that analyzes data based on a specific purpose and converts it into human language.

[0533] The "means for determining and displaying recommended products" refers to a device or method for selecting optimal products based on the analysis results and notifying the user of them.

[0534] "Preference data" is data that represents information about pet preferences and purchasing history.

[0535] A "means for matching with potential foster parents" is a device or method for selecting the most suitable potential foster parents based on pet preferences and purchasing habits.

[0536] The "means for notifying the user" refers to a device or method for notifying the user of the analysis results and recommended product information.

[0537] The "means for transmitting purchase information to the server" refers to a device or method for transmitting information about a product purchased by a user to the server and updating the pet's preference data.

[0538] This invention is a system that collects data such as pet facial expressions, gestures, and cries, analyzes the data, and converts the pet's intentions into human language. The system aims to help pet owners understand their pet's feelings, purchase products that meet their pet's needs, and find suitable foster parents.

[0539] The system is configured as follows: First, a device equipped with a camera and microphone is used to collect data on pet behavior. Specific hardware used includes an HD camera and a highly sensitive microphone. This device records the pet's behavior in real time and stores it as data.

[0540] The collected data is subjected to noise removal and format conversion using pre-processing software within the device, which prepares the data in a format suitable for analysis. After pre-processing is complete, the data is sent to a server via the Internet using an efficient, low-latency communication protocol (e.g., TCP / IP).

[0541] The server analyzes the received data using a generative AI model. This generative AI model infers the pet's intentions from its behavioral data and converts them into human language. Specifically, it uses advanced natural language processing techniques such as GPT-4 and BERT. Examples of prompts to be input to this AI model include the following:

[0542] Pet behavior data:

[0543] Expression: Smiling

[0544] Gesture: Jump

[0545] Sounds: Barking

[0546] Based on this behavioral data, infer your pet's intentions and translate them into human terms.

[0547] Based on the analysis results, the server selects the best product for the pet. This selection takes into account the pet's current condition and past purchase history. For example, an educational toy might be selected for a bored pet. This product information is notified to the user via their device. Notifications are sent via smartphone push notifications or email.

[0548] When a user purchases a product, the purchase information is sent from the device to the server, and the pet's preference data is updated. The updated data is used to suggest future products and match potential adopters. In this way, the system always reflects the pet's latest preferences and condition, allowing it to provide the most optimal service.

[0549] Furthermore, the server searches for suitable foster parent candidates based on pet preferences and purchasing history. When a suitable foster parent candidate is found, the information is notified to the user via the terminal, allowing the user to quickly find the best foster parent for their pet.

[0550] As described above, the present invention aims to facilitate communication with pet owners by analyzing their pet's behavior and converting it into human language, and to suggest products that meet the pet's needs and match them with potential foster parents.

[0551] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0552] Step 1: Data collection

[0553] The device uses a camera and microphone to record your pet's facial expressions, gestures, and sounds in real time. For example, an HD camera captures your pet's facial movements and overall movements, and a high-sensitivity microphone records its sounds. The input data is the pet's video and audio, and the output data is the recorded multimedia file.

[0554] Step 2: Data Preprocessing

[0555] The terminal performs noise removal and data format conversion on the collected data. For example, it filters out background noise from audio data and extracts the necessary frequency band. For image data, it cuts out unnecessary parts and adjusts the resolution. The input is the recorded multimedia file, and the output is a preprocessed data file.

[0556] Step 3: Send data to the server

[0557] The preprocessed data is sent to the server in real time using the Internet's TCP / IP protocol to minimize data transmission delays. The input is the preprocessed data file, and the output is the data sent to the server.

[0558] Step 4: Data analysis

[0559] The server analyzes the received data using a generative AI model. Specifically, a prompt sentence is input to the generative AI model (e.g., GPT-4 or BERT) to infer the intention from the pet's behavior data. The input is the preprocessed data and the prompt sentence, and the output is a natural language text that expresses the pet's intention.

[0560] Step 5: Selecting recommended products

[0561] The server selects the best product for your pet based on the analysis results. This selection takes into account the pet's current condition and past purchase history. For example, an educational toy might be selected for a bored pet. The input is the analysis results and purchase history data, and the output is information about the selected product.

[0562] Step 6: Notification of recommended products

[0563] The selected product information is notified to the user via the device. Notifications are sent via smartphone push notifications or email. The input is the selected product information, and the output is a notification message provided to the user. For example, a message such as "Your pet seems bored. Why not try a new educational toy?" is displayed.

[0564] Step 7: Purchase and update preference data

[0565] The user confirms the notification and purchases the product. The purchase information is sent from the terminal to the server, and the pet's preference data is updated. The input is the purchased product information, and the output is the updated preference data. For example, if the user purchases an educational toy, that information is added to the preference database.

[0566] Step 8: Matching potential foster parents

[0567] The server searches for suitable foster parent candidates based on the updated preference data. When a suitable foster parent candidate is found, the information is notified to the user via the terminal. The input is the updated preference data, and the output is information about the suitable foster parent candidate. For example, a notification such as "A suitable foster parent candidate has been found for your pet" may be sent.

[0568] Through these processing steps, the system can analyze the pet's intentions in real time and make optimal product recommendations and match potential adopters, helping owners better understand their pets' feelings and improve the quality of their pets' lives.

[0569] (Application example 1)

[0570] 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."

[0571] In recent years, the number of pets has increased, increasing the need for owners to understand their pets' feelings and provide appropriate care. However, it is difficult to read a pet's intentions from its facial expressions, gestures, and cries, and many owners are unable to accurately grasp their pets' needs. This can lead to stress for pets and problems such as not being able to provide appropriate products and services. Furthermore, choosing the right product from the large selection of products at pet supply stores can be difficult, especially for first-time pet owners. This invention aims to solve these problems and improve the quality of life for pets and their owners by accurately analyzing pets' feelings and recommending products to owners in real time.

[0572] 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.

[0573] In this invention, the server includes: means for collecting pet behavioral data; means for preprocessing the collected data; means including a generative AI model for analyzing the preprocessed data and converting the pet's intentions into human language; means for determining and displaying recommended products based on the analysis results; means for matching potential foster parents based on the pet's preferences and purchasing information; and means including a visual display capable of displaying the analysis results and product recommendations in real time. This enables accurate understanding of the pet's intentions and optimal product recommendations in real time. Furthermore, the system simultaneously matches potential foster parents using the pet's preference data, contributing to improving the quality of life for owners and their pets.

[0574] "Pet behavior data" refers to various physical and audio information displayed by a pet, such as facial expressions, gestures, and sounds.

[0575] "Preprocessing" refers to the process of removing noise from collected data and converting it into a format suitable for analysis, such as resizing and denoising image data.

[0576] A "generative AI model" is an artificial intelligence model that analyzes pet behavior data, infers their intentions, and converts them into human language. Specifically, it is built using machine learning and deep learning.

[0577] "Recommended products" refer to products that are deemed optimal based on your pet's current condition, preferences, and past purchasing history.

[0578] "Foster parent candidates" refer to the new owners who are best suited to a pet based on the pet's preferences and purchasing information.

[0579] "Visual display" refers to a visual display device for displaying analysis results and product suggestions in real time, including the displays of smart glasses.

[0580] A "cloud server" refers to a remote server that analyzes and stores data via the internet, allowing for efficient processing of large amounts of data.

[0581] "Real-time" refers to minimizing delays and processing and displaying data almost instantly.

[0582] "Analysis results" refers to the pet's intentions and recommended actions derived from the pet's behavioral data by the generative AI model.

[0583] "Preference data" refers to individual data about pets that is generated based on their preferences, past behavior, purchasing history, etc.

[0584] This invention is a system that collects and analyzes pet behavior data, converts the pet's intentions into human language, and presents recommended products. This helps pet owners understand their pet's needs more accurately and purchase appropriate products. The system is configured as follows:

[0585] Hardware Configuration

[0586] 1. Terminal

[0587] The device, which includes smart glasses and smartphones, is equipped with a camera and microphone to collect pet behavior data.

[0588] 2. Server

[0589] This is a cloud server for analyzing data. A generative AI model is installed on this server and analyzes pet behavior data.

[0590] 3. Visual Displays

[0591] A visual display device that displays analysis results and recommended products in real time, such as the display of smart glasses or the screen of a smartphone.

[0592] Software Configuration

[0593] 1. Data Collection and Preprocessing

[0594] The device uses a camera and microphone to record your pet's facial expressions, movements, and sounds in real time. This collected data is first preprocessed and converted into an appropriate format for analysis. Preprocessing includes noise removal and data format conversion. For example, the collected image data can be preprocessed using the Google Cloud Vision API.

[0595] 2. Sending data to the server

[0596] The pre-processed data is then sent over the internet to a cloud server, which is designed to minimize data latency and uses real-time communication technologies such as AWS IoT.

[0597] 3. Data Analysis

[0598] The server analyzes the received data using a generative AI model. This AI model infers the pet's intentions based on the pet's behavioral data and converts this into human language. For example, if the pet is excited, the analysis result may be "I want to play." GPT-3 and other models are used as generative AI models.

[0599] 4. Recommended products

[0600] Based on the analysis results, the server selects the most suitable product for the pet, taking into account the pet's current condition and past purchase history. The selected product is notified to the user via a visual display.

[0601] Example

[0602] An example of a brick-and-mortar application is a smart glasses application in a pet shop. When staff or customers wearing the smart glasses walk around the store with their pets, the glasses can read the pets' intentions from their facial expressions and cries, and make recommendations on products.

[0603] For example, when a user visits a pet shop and wears smart glasses, the glasses' built-in camera and microphone collect data on the pet's behavior in real time. The collected data is preprocessed and sent to a cloud server. A generative AI model on the server analyzes the data and, if it determines that the pet is bored, recommended products such as educational toys and treats are displayed on the smart glasses' display.

[0604] In this way, a system is realized that facilitates communication between pets and their owners and can make optimal and beneficial product suggestions.

[0605] Prompt Sentence Examples

[0606] I am currently building a system to analyze pet behavior. The image shows a dog. Please analyze the data on the dog's facial expressions, posture, and barks, and convert them into human words. Please tell me the emotions and intentions the dog is expressing.

[0607] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0608] Step 1:

[0609] Data collection

[0610] The device (smart glasses or smartphone) collects behavioral data such as facial expressions, gestures, and sounds of pets in real time using a camera and microphone. The input data is video and audio data, which is then stored directly in the device's memory.

[0611] Step 2:

[0612] Data Preprocessing

[0613] The device preprocesses the collected data. Specifically, image data is resized using OpenCV to remove unwanted noise, and audio data is converted into a clear format through noise filtering. The output of the preprocessing is image and audio data converted into a format suitable for analysis.

[0614] Step 3:

[0615] Data transmission

[0616] The terminal sends the preprocessed data to a cloud server. At this time, real-time communication technologies such as AWS IoT are used to instantly upload the data to the server. The input is the preprocessed data, and the output is the data stored on the server.

[0617] Step 4:

[0618] Data analysis

[0619] The server analyzes the collected data using a generative AI model on the cloud. The generative AI model analyzes the pet's behavior data and converts its intentions into human language. For example, "by analyzing the pet's facial expressions from image data and its cries from audio data, it determines that the pet is bored." The input is data stored on the cloud server, and the output is the text data of the analysis results.

[0620] Step 5:

[0621] Product presentation

[0622] The server selects the best products for pets based on the analysis results obtained from the generative AI model. It also takes into account past purchase history and preference data to determine recommended products. The input is the analysis results and purchase history data, and the output is a product list. This product information is sent to the terminal in real time.

[0623] Step 6:

[0624] Results display

[0625] The terminal displays the recommended product information sent from the server on a visual display. For example, "educational toys" or "snacks" are recommended on the display of smart glasses. The input is the recommended product information from the server, and the output is the product information displayed to the user's visual sense.

[0626] Step 7:

[0627] Sending purchasing information

[0628] If the user checks the recommended products and decides to purchase them, the purchase information is sent from the device to the server. The server updates the pet's preference data based on this purchase information. The input is the user's purchase action, and the output is the updated preference data.

[0629] Step 8:

[0630] Matching potential foster parents

[0631] If a user is looking for potential foster parents, the server searches for the most suitable foster parents based on pet preferences and purchase data, and notifies the terminal of the matching results. The input is pet preference data and purchase history, and the output is information on the matched foster parents.

[0632] In this way, a system is realized that facilitates communication between pets and their owners, and matches them with optimal and beneficial product suggestions and potential foster parents.

[0633] 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.

[0634] This invention combines a system that collects and analyzes data on pets' facial expressions, gestures, and cries, and converts the pet's intentions into human language based on that data, with an emotion engine that recognizes the user's emotions. The system aims to help users understand their pets' feelings, purchase products that meet their pets' needs, and find suitable potential adopters.

[0635] System configuration

[0636] 1. A device equipped with a camera and microphone that collects pet behavior data

[0637] 2. Software for preprocessing collected data

[0638] 3. A generative AI model on the server that analyzes the data and translates the pet's intentions into human language

[0639] 4. User interface that displays recommended products based on analysis results

[0640] 5. Emotion engine that recognizes user emotions

[0641] 6. A server-based algorithm that matches potential adopters based on pet preferences and purchasing history

[0642] 7. Network protocol for notifying users of analysis results, product suggestions, and matching information

[0643] Explanation of the program processing flow

[0644] Data collection and preprocessing

[0645] The device uses a camera and microphone to record your pet's facial expressions, movements, and cries in real time. The recorded data is pre-processed to remove noise and convert the data format. The pre-processed data is then converted into a format suitable for analysis.

[0646] Sending data to the server

[0647] The pre-processed data is then sent over the internet to a server, which is designed to transmit the data in real time and with minimal data latency.

[0648] Data analysis

[0649] The server analyzes the received data using a generative AI model. This AI model infers the pet's intentions based on the pet's behavioral data and translates this into human language. For example, if the pet is nervous, the analysis result may be "feeling anxious."

[0650] Recognizing user emotions with an emotion engine

[0651] The device or server captures the user's facial expressions and voice and analyzes them with an emotion engine. This allows the user's emotional state to be grasped in real time. For example, if the user is feeling stressed, it will be recognized as a "stressed state."

[0652] Notification of analysis results

[0653] The server generates data to notify the user of the analysis results and sends it to the terminal via the Internet. The terminal displays the received analysis results on a user interface, allowing the user to understand the pet's intentions.

[0654] Selection and display of recommended products

[0655] The server selects the best product for your pet based on the analysis results and the user's emotional state. It takes into consideration past purchase history, the pet's preferences, and the user's current emotional state. The selected product information is notified to the user via the device, which then displays it.

[0656] Product purchase and preference data updates

[0657] The user views the recommended products and decides to purchase them. The purchase information is sent from the device to the server, and the pet's preference data is updated. This data is used for future recommendations and matching.

[0658] Matching potential foster parents

[0659] The server searches for suitable foster parent candidates based on the user's pet preferences, purchasing history, and emotional state. When a suitable foster parent candidate is found, the information is notified to the user via the terminal.

[0660] Specific examples

[0661] For example, if a user's cat frequently licks itself, the device collects this behavioral data and sends it to the server. The server analyzes the data and determines that the cat is "stressed." The emotion engine then recognizes that the user is stressed. Based on this, the server recommends a cat toy that is effective in relieving stress and notifies the user. When the user purchases the toy, the purchase information is sent to the server, and the cat's preference data is updated. If the user is then looking for a foster parent, the updated preference data is referenced to present suitable foster parent candidates.

[0662] In this way, the present invention aims to facilitate communication between owners and pets and improve the quality of life for both by analyzing pet behavior and user emotions and providing various services based on that information.

[0663] The processing flow will be explained below.

[0664] Step 1: Collect data

[0665] The device uses a camera and microphone to record your pet's facial expressions, gestures, and cries in real time.

[0666] The device collects data on your pet's behavior.

[0667] Step 2: Preprocessing the data

[0668] The data collected by the device is pre-processed, including noise removal and format conversion.

[0669] The terminal converts the preprocessed data into a format suitable for analysis.

[0670] Step 3: Send data to the server

[0671] The terminal transmits the pre-processed data to the server in real time.

[0672] The terminal monitors the data transmission status, and if a transmission error is detected, retransmission is performed.

[0673] Step 4: Receiving and storing data

[0674] The server receives the data sent from the terminal.

[0675] The server stores the received data and prepares it for analysis.

[0676] Step 5: Analyze pet data

[0677] The server analyzes the pet's behavioral data using the generated AI model.

[0678] The server infers the pet's intentions and translates them into human language.

[0679] Step 6: Submit and view analysis results

[0680] The server generates the analysis results as data and sends them to the terminal.

[0681] The terminal receives the analysis results and displays them on the user interface.

[0682] The user checks the displayed analysis results and understands the pet's intentions.

[0683] Step 7: Recognizing user emotions with the emotion engine

[0684] The device uses a camera and microphone to record the user's facial expressions and voice.

[0685] The terminal or server analyzes the user's emotions using an emotion engine.

[0686] The server stores the analyzed user emotion information.

[0687] Step 8: Selecting recommended products

[0688] The server selects recommended products based on the pet analysis results and the user's emotional information.

[0689] The server decides on the product taking into consideration past purchase history and pet preference data.

[0690] Step 9: Notification of recommended products

[0691] The server sends information about recommended products to the terminal.

[0692] The terminal receives the recommended product information and displays it on the user interface.

[0693] Step 10: Purchase the product and submit your purchase information

[0694] The user checks the recommended products and decides to purchase them.

[0695] The terminal transmits the user's purchasing information to the server.

[0696] Step 11: Maintain purchasing data

[0697] The server receives the user's purchasing information and updates the pet preference data.

[0698] The server stores the updated data for future analysis.

[0699] Step 12: Matching potential foster parents

[0700] The server searches for potential foster parents based on pet preference data, purchasing history, and user emotional information.

[0701] The server sends information about suitable foster parent candidates to the terminal.

[0702] Step 13: Notification of matching information

[0703] The terminal receives the foster parent candidate information and displays it on a user interface.

[0704] The user checks the displayed foster parent candidate information and takes action if necessary.

[0705] In this way, the present invention realizes a system that analyzes pet behavior and user emotions and provides various services based on that information, facilitating communication between owners and pets and improving the quality of life for both.

[0706] Example 2

[0707] 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."

[0708] It is difficult to accurately understand a pet's intentions based on its facial expressions, gestures, and cries. It is also difficult to select appropriate products based on a pet's feelings and needs, or to find suitable potential adopters. Furthermore, there is a need to understand the user's emotional state in real time and utilize that information to provide more appropriate responses. However, few existing systems meet these requirements, and there is a lack of appropriate means to improve communication and the quality of life between owners and pets.

[0709] 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.

[0710] In this invention, the server includes means for collecting data on the pet's facial expressions, gestures, and cries, means for preprocessing the collected data, means for transmitting the preprocessed data to the server, means including a model for analyzing the transmitted data and converting the pet's intentions into human language, means for recognizing the user's emotions based on the analysis results, means for determining and displaying recommended products based on the analysis results and the user's emotional state, and means for matching with potential foster parents based on the pet's preferences and purchasing habits. This makes it possible to accurately understand the pet's behavior and intentions, propose appropriate products that also take the user's emotional state into consideration, and better match with potential foster parents.

[0711] "Data on pet's facial expressions, gestures, and sounds" refers to data that includes pet's facial expressions, body movements, and sounds.

[0712] "Means of collection" refers to devices such as cameras and microphones used to capture pets' facial expressions, gestures, and sounds, as well as the software that controls them.

[0713] "Preprocessing means" refers to software or algorithms used to remove noise from collected data and convert it into a form suitable for analysis.

[0714] "Means for transmitting to a server" refers to a communication protocol or network interface for transmitting the pre-processed data to a server via the Internet in real time.

[0715] "A model for analyzing and converting pet intentions into human language" refers to a generative AI model that infers intentions based on pet behavior data and converts them into human language.

[0716] "Means for recognizing user emotions" refers to emotion recognition engines or software that analyze the user's facial expressions and voice data to grasp the user's emotional state in real time.

[0717] "Means for determining and displaying recommended products" refers to software or devices that select appropriate products based on the analysis results and the user's emotional state, and display that information in a user interface.

[0718] "Means of matching potential foster parents based on pet preferences and purchasing history" refers to algorithms and systems that search for and match suitable foster parent candidates based on the pet's past preference data and purchasing history.

[0719] This system collects data on pets' facial expressions, gestures, and cries, analyzes their intentions based on the collected data, and notifies the user. Furthermore, it can recognize the user's emotional state in real time and recommend products based on that information. It can also match pets with suitable potential adopters based on their preferences and purchasing habits.

[0720] Hardware Configuration

[0721] This system uses the following hardware:

[0722] Device: A device that includes a camera (e.g., a high-resolution camera), a microphone, and a user interface (e.g., a smartphone or tablet).

[0723] Server: High-performance analysis server

[0724] Software Configuration

[0725] The following software is used:

[0726] Pre-processing software: OpenCV, ffmpeg, librosa, etc.

[0727] Generative AI models: AI models built using TensorFlow or PyTorch

[0728] Emotion recognition engine: Microsoft Azure Emotion API, etc.

[0729] Communication protocols: WebSocket, HTTP

[0730] Program processing flow

[0731] 1. Data Collection

[0732] The device uses a camera and microphone to record your pet's facial expressions, movements, and sounds in real time, and the captured data is temporarily stored in the device's storage.

[0733] 2. Pretreatment

[0734] The device performs preprocessing on the collected data, such as noise removal and data format conversion. Specifically, it uses OpenCV and ffmpeg to convert the video data format, and librosa to remove noise from the audio data.

[0735] 3. Data Transmission

[0736] The preprocessed data is sent in real time to the server using WebSocket or HTTP.

[0737] 4. Data Analysis

[0738] The server analyzes the received data using a generative AI model (using TensorFlow and PyTorch), which translates the pet's behavior and intentions into human terms, such as "feeling anxious" or "wanting to play."

[0739] 5. User Emotion Recognition

[0740] The device or server collects the user's facial and voice data and analyzes it using an emotion recognition engine. This allows the user's emotional state to be understood in real time. For example, it uses Microsoft Azure's Emotion API to determine whether the user is "stressed" or "relaxed."

[0741] 6. Notification of analysis results

[0742] The server generates data to notify the user of the pet's analysis results and sends it to the terminal via the Internet. The terminal displays the received analysis results in a GUI, allowing the user to understand the pet's intentions.

[0743] 7. Selection and display of recommended products

[0744] The server selects recommended products based on the pet analysis results and the user's emotional state. The information is then sent to the device, which displays the recommendations to the user. Past purchase history and pet preferences are also taken into consideration.

[0745] 8. Purchase and Preference Data Updates

[0746] When a user purchases a recommended product, the purchase information is sent from the device to the server, and the pet's preference data is updated. This information is used for future recommendations and matching with potential adopters.

[0747] 9. Matching potential foster parents

[0748] The server searches for suitable foster parents based on the user's pet preferences, purchasing history, and emotional state, and notifies the device of the information. If the user confirms the details and determines that the candidate is suitable, they can proceed to the next step.

[0749] Specific examples

[0750] For example, if a pet dog barks frequently, the device collects this behavioral data, preprocesses it, and sends it to the server. The server then analyzes it using a generative AI model and determines that the dog is feeling anxious. At the same time, an emotion recognition engine recognizes the user's stress level. Based on this, the server recommends a dog toy for stress relief and notifies the user. If the user purchases the toy, the preference data is updated. If the user is looking for a foster parent, suitable foster parent candidates are presented based on the updated data.

[0751] Examples of prompts include "Please tell me the analysis results of my dog's frequent barking behavior" and "Please give me the user's current emotional state and recommend products."

[0752] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0753] Step 1:

[0754] The device uses a camera and microphone to record the pet's facial expressions, behaviors, and sounds in real time. The input is video data captured by the camera and audio data collected by the microphone. The output is the collected raw data, which is temporarily stored in the device's storage.

[0755] Step 2:

[0756] The device performs preprocessing on the collected data. Specifically, it uses OpenCV for format conversion of video data and librosa for noise reduction of audio data. The input is the raw data collected in step 1. The output is data that has been denoised and converted into a format suitable for analysis.

[0757] Step 3:

[0758] The terminal sends the preprocessed data to the server in real time over the Internet. This communication uses WebSocket. The input is the preprocessed data. The output is the data that arrives at the server ready for analysis.

[0759] Step 4:

[0760] The server analyzes the received data using a generative AI model. Specifically, it uses TensorFlow to analyze the pet's behavioral patterns. The input is the data that arrives at the server. The output is the analysis results, which translate the pet's intentions into human words such as "I feel anxious" or "I want to play."

[0761] Step 5:

[0762] The device or server collects the user's facial and voice data and analyzes it using an emotion recognition engine. Specifically, the analysis is performed using Microsoft Azure's Emotion API. The input is the user's facial and voice data. The output is data indicating the user's emotional state, such as "stressed" or "relaxed."

[0763] Step 6:

[0764] The server generates data to notify the user based on the pet analysis results and sends it to the terminal via the Internet. The input is the analysis results regarding the pet's intentions and the user's emotional state. The output is data to notify the user, which is displayed on the user interface.

[0765] Step 7:

[0766] The server selects recommended products based on the pet analysis results and the user's emotional state. The selection also takes into account past purchase history and the pet's preferences. The input is data such as the analysis results, the user's emotional state, and past purchase history. The output is a list of selected recommended products, which is sent to the terminal via the Internet.

[0767] Step 8:

[0768] The user looks at the recommended products and decides whether to purchase them. If a purchase decision is made, the device sends the information to the server. The input is the user's purchase decision information. The output is updated pet preference data, which is saved on the server.

[0769] Step 9:

[0770] The server searches for suitable foster parent candidates based on pet preferences, purchase data, and the user's emotional state, and notifies the terminal of this information. The input is updated pet preference data, purchase history, and the user's emotional state. The output is information on suitable foster parent candidates, which is notified to the terminal.

[0771] (Application example 2)

[0772] 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."

[0773] In modern society, there is a demand for smoother communication between pets and their owners, improving the quality of life for both. In particular, there is a challenge in accurately understanding pet behavior and intentions and responding appropriately. Furthermore, there is a lack of services that reflect the user's emotional state. This makes it difficult to select products that meet the pet's needs and match them with suitable foster parents.

[0774] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for collecting data on the pet's facial expressions, gestures, and cries; means for preprocessing the collected data; means including a generative AI model for analyzing the preprocessed data and converting the pet's intentions into human language; means for determining and displaying recommended products based on the analysis results; means for matching with potential foster parents based on the pet's preferences and purchasing habits; means including an emotion engine for recognizing the user's emotions; means for suggesting recommended products based on the results of analysis by the generative AI model and the emotion engine and making the products available for purchase in a virtual store; means for displaying the analysis results in a user interface in real time; and means for notifying the user of the analysis results. This facilitates communication between pets and their owners, enabling accurate understanding of the pet's intentions, appropriate product suggestions and purchases, and effective matching with potential foster parents.

[0775] "Data on pets' facial expressions, gestures, and sounds" refers to recordings of the facial expressions, body movements, and sounds that pets make.

[0776] "Means of collection" refers to devices or methods for acquiring data on pets' facial expressions, gestures, and cries using devices such as cameras and microphones.

[0777] "Preprocessing means" refers to methods or processes that remove noise from collected data and convert it into a form suitable for analysis.

[0778] A "generative AI model" is an artificial intelligence model trained to analyze collected data, infer a pet's intentions, and translate them into human language.

[0779] "Means for determining and displaying recommended products" refers to a device or method that selects the most suitable products for pets based on the analysis results of the generative AI model and displays them on a user interface.

[0780] "Methods of matching potential foster parents based on pet preferences and purchasing history" refers to algorithms and processes that take into account the pet's past behavior and purchasing history to find the most suitable foster parent candidates.

[0781] The "emotion engine" is a software module that analyzes the user's facial expressions and voice to recognize the user's emotional state.

[0782] "Means for enabling purchase of products in a virtual store" refers to a device or method that provides a function that allows a user to check product information in a virtual space and purchase the product on the spot.

[0783] "Means for displaying analysis results on a user interface in real time" refers to technology or devices for displaying analyzed data on a user's display without delay.

[0784] The "means for notifying the user of the analysis results" refers to a communication technology or mechanism for communicating the analysis results to the user.

[0785] MODE FOR CARRYING OUT THE INVENTION

[0786] This invention combines a system that collects and analyzes data on pets' facial expressions, gestures, and cries, and converts the pet's intentions into human language based on that data, with an emotion engine that recognizes the user's emotions. The system aims to help users understand their pets' feelings, purchase products that meet their needs, and find suitable foster parents.

[0787] System configuration

[0788] 1. Data collection device: Cameras and microphones installed in smartphones or head-mounted displays (HMDs) collect data on pets' facial expressions, gestures, and cries in real time. The collected data undergoes pre-processing such as noise removal and data format conversion.

[0789] 2. Data transmission to the server: The preprocessed data is transmitted to the server via the Internet. Transmission is performed as close to real time as possible, and is designed to minimize data delays.

[0790] 3. Data Analysis Server: The server analyzes the received data using a generative AI model. This AI model infers the pet's intentions based on its behavioral data and converts them into human language. It also captures the user's facial expressions and voice and analyzes them with an emotion engine to understand the user's emotional state in real time.

[0791] 4. User Interface: The results of the analysis by the server are sent to the terminal via the Internet and displayed on the user interface, making it easier for the user to understand the intentions of their pet.

[0792] 5. Product Recommendation System: The server selects the most suitable pet product based on the analysis results and the user's emotional state. Specifically, it takes into account past purchase history, pet preferences, and the user's current emotional state. The selected product information is notified to the user via their terminal, and the user can view and purchase the product in the virtual store.

[0793] 6. Preference data and adoption matching: Information about purchased items is sent to the server to update the pet's preference data. This data is also used to match potential adopters, searching for and presenting suitable adopter candidates.

[0794] Hardware and software used

[0795] Hardware: Smartphone, smart glasses, head-mounted display (HMD), camera, microphone

[0796] Software: OpenCV (for image processing), EmotionAnalytics (emotion recognition module), PetBehaviorModel (pet behavior analysis model), ProductRecommender (product recommendation system)

[0797] Specific examples

[0798] For example, if a user's cat frequently licks itself, the device collects this behavioral data and sends it to the server. The server analyzes the collected data and determines that the cat is "stressed." The emotion engine simultaneously recognizes that the user is stressed. Based on this analysis, the server recommends a cat toy that is effective in relieving stress and notifies the user. When the user purchases the toy in the virtual store, the purchase information is sent to the server, and the cat's preference data is updated. Furthermore, if the user is looking for a foster parent, the updated preference data is referenced to present suitable foster parent candidates.

[0799] Prompt Sentence Examples

[0800] "Analyze footage of a cat frequently licking itself and infer the pet's intentions. Also, recognize the user's emotional state from facial expression data and suggest products that are effective in relieving stress in cats."

[0801] In this way, the present invention aims to facilitate communication between owners and pets and improve the quality of life for both by analyzing pet behavior and user emotions and providing various services based on that information.

[0802] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0803] Step 1:

[0804] Collecting pet data

[0805] Input: Pet's facial expressions, gestures, and cries

[0806] The device uses a camera and microphone installed in a smartphone or head-mounted display (HMD) to collect data on your pet's facial expressions, gestures, and cries in real time.

[0807] Output: Collected data (video data, audio data)

[0808] Step 2:

[0809] Data Preprocessing

[0810] Input: Collected data (video data, audio data)

[0811] The device pre-processes the collected data, which includes noise removal, grayscale conversion of images, and filtering of audio data.

[0812] Output: Pre-processed data (processed video data, audio data)

[0813] Step 3:

[0814] Sending data to the server

[0815] Input: Preprocessed data (processed video data, audio data)

[0816] The terminals then transmit the pre-processed data to a server via the internet, with the transmission being designed to occur as quickly as possible in real time, minimizing data latency.

[0817] Output: Data sent to the server

[0818] Step 4:

[0819] Data analysis

[0820] Input: Data sent to the server (processed video data, audio data)

[0821] The server analyzes the received data using a generative AI model, which infers the pet's intentions based on the behavioral data and translates them into human language.

[0822] Output: Analysis result that indicates the pet's intention (e.g. "I feel stressed")

[0823] Step 5:

[0824] User Emotion Recognition

[0825] Input: User's facial expressions and voice

[0826] The device captures the user's facial expressions and voice and analyzes them with an emotion engine, allowing the device to grasp the user's emotional state in real time.

[0827] Output: Analysis results showing the user's emotions (e.g., "stressed state")

[0828] Step 6:

[0829] Notification of analysis results

[0830] Input: Analysis results showing the pet's intentions, analysis results showing the user's emotions

[0831] The server generates data for notifying the user interface of the analysis results and transmits the data to the terminal via the Internet.

[0832] Output: Analysis results sent to the device

[0833] Step 7:

[0834] Display in the user interface

[0835] Input: Analysis results sent to the device

[0836] The terminal displays the received analysis results on a user interface, allowing the user to understand the pet's intentions.

[0837] Output: Analysis results displayed on the user interface

[0838] Step 8:

[0839] Product suggestion

[0840] Input: Analysis results showing the pet's intentions, analysis results showing the user's emotions

[0841] The server selects the most suitable pet products based on the analysis results and the user's emotional state, taking into account past purchase history, pet preferences, and the user's current emotional state.

[0842] Output: Selected recommended products

[0843] Step 9:

[0844] Buying products in a virtual store

[0845] Input: Selected recommended products

[0846] The user checks the product information displayed in the virtual store and purchases the product.

[0847] Output: Purchase information

[0848] Step 10:

[0849] Preference data updates and foster parent matching

[0850] Input: Purchase information

[0851] The device sends the purchase information to the server, which updates the pet's preference data. Based on this, the server searches for suitable potential adopters.

[0852] Output: Updated preference data, suitable foster parents

[0853] 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.

[0854] 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.

[0855] 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.

[0856] [Third embodiment]

[0857] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0858] 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.

[0859] 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).

[0860] 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.

[0861] 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.

[0862] 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).

[0863] 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.

[0864] 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.

[0865] 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.

[0866] 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.

[0867] 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.

[0868] 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."

[0869] This invention is a system that collects and analyzes data such as pet facial expressions, gestures, and cries, and converts the pet's intentions into human language. The purpose of this system is to help pet owners better understand their pets' feelings, purchase products that meet their pet's needs, and find suitable foster parents.

[0870] System configuration

[0871] 1. A device equipped with a camera and microphone that collects pet behavior data

[0872] 2. Software for preprocessing collected data

[0873] 3. A generative AI model on the server that analyzes the data and translates the pet's intentions into human language

[0874] 4. User interface that displays recommended products based on analysis results

[0875] 5. A server-based algorithm that matches potential adopters based on pet preferences and purchasing history

[0876] 6. Network protocol for notifying users of analysis results, product suggestions, and matching information

[0877] Explanation of the program processing flow

[0878] Data collection and preprocessing

[0879] The device uses a camera and microphone to record your pet's facial expressions, movements, and sounds in real time. This data is first preprocessed and converted into an appropriate format for analysis. This preprocessing includes noise removal and data format conversion.

[0880] Sending data to the server

[0881] The pre-processed data is then transmitted over the internet to a server in real time, designed to minimize data latency.

[0882] Data analysis

[0883] The server analyzes the received data using a generative AI model. This AI model infers the pet's intentions based on the pet's behavioral data and converts them into human language. For example, if the pet is excited, the analysis result may be "I want to play."

[0884] Recommended products

[0885] The server then selects the best product for your pet based on the analysis results. This selection takes into account the pet's current condition and past purchase history. The selected product is then notified to the user via their device.

[0886] Product purchase and preference data updates

[0887] The user views the recommended products and decides to purchase them. This purchase information is sent from the device to the server, and the pet's preference data is updated. This data is used to recommend future products and match potential adopters.

[0888] Matching potential foster parents

[0889] The server searches for suitable foster parent candidates based on pet preferences and purchasing history. When a suitable foster parent candidate is found, the information is notified to the user via the terminal.

[0890] Specific examples

[0891] For example, if a user's dog barks frequently, the device collects the barking data and sends it to the server. The server analyzes the data and determines that the dog is barking because it is bored. Based on the analysis results, the server recommends an educational toy for dogs and notifies the user. When the user purchases the toy, the purchase information is sent to the server, and the dog's preference data is updated. If the user is then looking for a foster parent, this preference data is referenced to present suitable foster parent candidates.

[0892] In this way, the present invention aims to facilitate communication between owners and pets and improve the quality of life for both by analyzing pet behavior, converting it into human language, and providing various services based on that.

[0893] The processing flow will be explained below.

[0894] Step 1: Collect data

[0895] The device records your pet's behavior in real time using a camera and microphone.

[0896] The device collects data such as your pet's facial expressions, gestures, and cries.

[0897] Step 2: Preprocessing the data

[0898] The data collected by the terminal is preprocessed, noise is removed, and data format conversion is performed.

[0899] The device converts the preprocessed data into a format suitable for analysis.

[0900] Step 3: Sending data

[0901] The terminal transmits the preprocessed data to a server via the Internet.

[0902] The device transmits data in real time to minimize delays.

[0903] Step 4: Receiving the data

[0904] The server receives the data sent from the terminal.

[0905] The server checks the integrity of the data received.

[0906] Step 5: Analyze the data

[0907] The server analyzes the received data using a generative AI model.

[0908] The server infers the pet's intentions based on the pet's behavioral data and converts them into human language.

[0909] Step 6: Notification of analysis results

[0910] The server generates data to notify the user of the analysis results.

[0911] The server sends the analysis results to the terminal via the Internet.

[0912] Step 7: Viewing the analysis results

[0913] The analysis results received by the terminal are displayed on the user interface.

[0914] The user checks the analysis results to understand the pet's intentions.

[0915] Step 8: Selecting recommended products

[0916] The server selects the best product for your pet based on the analysis results.

[0917] The server selects products taking into consideration past purchase history and pet preferences.

[0918] Step 9: Product Information Notification

[0919] The server generates data for notifying the user of recommended product information.

[0920] The server transmits product information to the terminal via the Internet.

[0921] Step 10: Display product information

[0922] The terminal displays the received product information on a user interface.

[0923] The user checks the displayed product information and makes a purchase decision.

[0924] Step 11: Submit your purchase information

[0925] The user performs a purchase operation and inputs the purchase information into the terminal.

[0926] The terminal sends the purchase information to the server.

[0927] Step 12: Maintain purchasing data

[0928] The server receives the purchase information and updates the pet's preference data.

[0929] The server stores the updated data and uses it for future recommendations and matches.

[0930] Step 13: Matching potential foster parents

[0931] The server searches for potential foster parents based on pet preferences and purchasing history.

[0932] The server generates data for notifying the user of the foster parent candidate information it has found.

[0933] Step 14: Notification of matching information

[0934] The server transmits information about potential foster parents to the terminal via the Internet.

[0935] The foster parent candidate information received by the terminal is displayed on a user interface.

[0936] Step 15: Check the matching results

[0937] The user checks the displayed foster parent candidate information and takes action if necessary.

[0938] Example 1

[0939] 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."

[0940] Understanding pets' intentions through data such as facial expressions, behavior, and vocalizations is a difficult task for pet owners. It is particularly important to accurately grasp pet stress and needs and respond appropriately accordingly. Proposing products based on pet needs and matching suitable foster parents are also important. However, existing systems lack the ability to analyze data in real time or accurately translate pets' intentions, and no technology has been established to increase owner satisfaction.

[0941] 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.

[0942] In this invention, the server includes means for collecting data on the pet's facial expressions, gestures, and cries, means for preprocessing the collected data, means for transmitting the preprocessed data to the server in real time, means including a generative AI model for analyzing the preprocessed data and converting the pet's intentions into human language, means for determining and displaying recommended products based on the analysis results, and means for matching with potential foster parents based on the pet's preferences and purchasing history. This makes it possible to accurately analyze the pet's intentions and quickly and effectively suggest appropriate products and match with potential foster parents.

[0943] "Pet facial expressions" are data that represent emotions and states shown by the movements and expressions of an animal's face.

[0944] "Gestures" are data that represent the movements and attitudes that animals show through specific actions and behaviors.

[0945] "Calls" are data that represent the sounds and sound patterns made by animals.

[0946] "Means for collecting data" refers to devices or methods that use cameras, microphones, or other devices to record a pet's facial expressions, gestures, and cries.

[0947] "Preprocessing means" refers to devices or methods that remove noise from collected data, convert the data format, and prepare it in a format suitable for analysis.

[0948] The "means for transmitting to the server" refers to a device or method for transferring the preprocessed data to the server using a communication network such as the Internet.

[0949] A "generative AI model" is a generative model that analyzes data based on a specific purpose and converts it into human language.

[0950] The "means for determining and displaying recommended products" refers to a device or method for selecting optimal products based on the analysis results and notifying the user of them.

[0951] "Preference data" is data that represents information about pet preferences and purchasing history.

[0952] A "means for matching with potential foster parents" is a device or method for selecting the most suitable potential foster parents based on pet preferences and purchasing habits.

[0953] The "means for notifying the user" refers to a device or method for notifying the user of the analysis results and recommended product information.

[0954] The "means for transmitting purchase information to the server" refers to a device or method for transmitting information about a product purchased by a user to the server and updating the pet's preference data.

[0955] This invention is a system that collects data such as pet facial expressions, gestures, and cries, analyzes the data, and converts the pet's intentions into human language. The system aims to help pet owners understand their pet's feelings, purchase products that meet their pet's needs, and find suitable foster parents.

[0956] The system is configured as follows: First, a device equipped with a camera and microphone is used to collect data on pet behavior. Specific hardware used includes an HD camera and a highly sensitive microphone. This device records the pet's behavior in real time and stores it as data.

[0957] The collected data is subjected to noise removal and format conversion using pre-processing software within the device, which prepares the data in a format suitable for analysis. After pre-processing is complete, the data is sent to a server via the Internet using an efficient, low-latency communication protocol (e.g., TCP / IP).

[0958] The server analyzes the received data using a generative AI model. This generative AI model infers the pet's intentions from its behavioral data and converts them into human language. Specifically, it uses advanced natural language processing techniques such as GPT-4 and BERT. Examples of prompts to be input to this AI model include the following:

[0959] Pet behavior data:

[0960] Expression: Smiling

[0961] Gesture: Jump

[0962] Sounds: Barking

[0963] Based on this behavioral data, infer your pet's intentions and translate them into human terms.

[0964] Based on the analysis results, the server selects the best product for the pet. This selection takes into account the pet's current condition and past purchase history. For example, an educational toy might be selected for a bored pet. This product information is notified to the user via their device. Notifications are sent via smartphone push notifications or email.

[0965] When a user purchases a product, the purchase information is sent from the device to the server, and the pet's preference data is updated. The updated data is used to suggest future products and match potential adopters. In this way, the system always reflects the pet's latest preferences and condition, allowing it to provide the most optimal service.

[0966] Furthermore, the server searches for suitable foster parent candidates based on pet preferences and purchasing history. When a suitable foster parent candidate is found, the information is notified to the user via the terminal, allowing the user to quickly find the best foster parent for their pet.

[0967] As described above, the present invention aims to facilitate communication with pet owners by analyzing their pet's behavior and converting it into human language, and to suggest products that meet the pet's needs and match them with potential foster parents.

[0968] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0969] Step 1: Data collection

[0970] The device uses a camera and microphone to record your pet's facial expressions, gestures, and sounds in real time. For example, an HD camera captures your pet's facial movements and overall movements, and a high-sensitivity microphone records its sounds. The input data is the pet's video and audio, and the output data is the recorded multimedia file.

[0971] Step 2: Data Preprocessing

[0972] The terminal performs noise removal and data format conversion on the collected data. For example, it filters out background noise from audio data and extracts the necessary frequency band. For image data, it cuts out unnecessary parts and adjusts the resolution. The input is the recorded multimedia file, and the output is a preprocessed data file.

[0973] Step 3: Send data to the server

[0974] The preprocessed data is sent to the server in real time using the Internet's TCP / IP protocol to minimize data transmission delays. The input is the preprocessed data file, and the output is the data sent to the server.

[0975] Step 4: Data analysis

[0976] The server analyzes the received data using a generative AI model. Specifically, a prompt sentence is input to the generative AI model (e.g., GPT-4 or BERT) to infer the intention from the pet's behavior data. The input is the preprocessed data and the prompt sentence, and the output is a natural language text that expresses the pet's intention.

[0977] Step 5: Selecting recommended products

[0978] The server selects the best product for your pet based on the analysis results. This selection takes into account the pet's current condition and past purchase history. For example, an educational toy might be selected for a bored pet. The input is the analysis results and purchase history data, and the output is information about the selected product.

[0979] Step 6: Notification of recommended products

[0980] The selected product information is notified to the user via the device. Notifications are sent via smartphone push notifications or email. The input is the selected product information, and the output is a notification message provided to the user. For example, a message such as "Your pet seems bored. Why not try a new educational toy?" is displayed.

[0981] Step 7: Purchase and update preference data

[0982] The user confirms the notification and purchases the product. The purchase information is sent from the terminal to the server, and the pet's preference data is updated. The input is the purchased product information, and the output is the updated preference data. For example, if the user purchases an educational toy, that information is added to the preference database.

[0983] Step 8: Matching potential foster parents

[0984] The server searches for suitable foster parent candidates based on the updated preference data. When a suitable foster parent candidate is found, the information is notified to the user via the terminal. The input is the updated preference data, and the output is information about the suitable foster parent candidate. For example, a notification such as "A suitable foster parent candidate has been found for your pet" may be sent.

[0985] Through these processing steps, the system can analyze the pet's intentions in real time and make optimal product recommendations and match potential adopters, helping owners better understand their pets' feelings and improve the quality of their pets' lives.

[0986] (Application example 1)

[0987] 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."

[0988] In recent years, the number of pets has increased, increasing the need for owners to understand their pets' feelings and provide appropriate care. However, it is difficult to read a pet's intentions from its facial expressions, gestures, and cries, and many owners are unable to accurately grasp their pets' needs. This can lead to stress for pets and problems such as not being able to provide appropriate products and services. Furthermore, choosing the right product from the large selection of products at pet supply stores can be difficult, especially for first-time pet owners. This invention aims to solve these problems and improve the quality of life for pets and their owners by accurately analyzing pets' feelings and recommending products to owners in real time.

[0989] 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.

[0990] In this invention, the server includes: means for collecting pet behavioral data; means for preprocessing the collected data; means including a generative AI model for analyzing the preprocessed data and converting the pet's intentions into human language; means for determining and displaying recommended products based on the analysis results; means for matching potential foster parents based on the pet's preferences and purchasing information; and means including a visual display capable of displaying the analysis results and product recommendations in real time. This enables accurate understanding of the pet's intentions and optimal product recommendations in real time. Furthermore, the system simultaneously matches potential foster parents using the pet's preference data, contributing to improving the quality of life for owners and their pets.

[0991] "Pet behavior data" refers to various physical and audio information displayed by a pet, such as facial expressions, gestures, and sounds.

[0992] "Preprocessing" refers to the process of removing noise from collected data and converting it into a format suitable for analysis, such as resizing and denoising image data.

[0993] A "generative AI model" is an artificial intelligence model that analyzes pet behavior data, infers their intentions, and converts them into human language. Specifically, it is built using machine learning and deep learning.

[0994] "Recommended products" refer to products that are deemed optimal based on your pet's current condition, preferences, and past purchasing history.

[0995] "Foster parent candidates" refer to the new owners who are best suited to a pet based on the pet's preferences and purchasing information.

[0996] "Visual display" refers to a visual display device for displaying analysis results and product suggestions in real time, including the displays of smart glasses.

[0997] A "cloud server" refers to a remote server that analyzes and stores data via the internet, allowing for efficient processing of large amounts of data.

[0998] "Real-time" refers to minimizing delays and processing and displaying data almost instantly.

[0999] "Analysis results" refers to the pet's intentions and recommended actions derived from the pet's behavioral data by the generative AI model.

[1000] "Preference data" refers to individual data about pets that is generated based on their preferences, past behavior, purchasing history, etc.

[1001] This invention is a system that collects and analyzes pet behavior data, converts the pet's intentions into human language, and presents recommended products. This helps pet owners understand their pet's needs more accurately and purchase appropriate products. The system is configured as follows:

[1002] Hardware Configuration

[1003] 1. Terminal

[1004] The device, which includes smart glasses and smartphones, is equipped with a camera and microphone to collect pet behavior data.

[1005] 2. Server

[1006] This is a cloud server for analyzing data. A generative AI model is installed on this server and analyzes pet behavior data.

[1007] 3. Visual Displays

[1008] A visual display device that displays analysis results and recommended products in real time, such as the display of smart glasses or the screen of a smartphone.

[1009] Software Configuration

[1010] 1. Data Collection and Preprocessing

[1011] The device uses a camera and microphone to record your pet's facial expressions, movements, and sounds in real time. This collected data is first preprocessed and converted into an appropriate format for analysis. Preprocessing includes noise removal and data format conversion. For example, the collected image data can be preprocessed using the Google Cloud Vision API.

[1012] 2. Sending data to the server

[1013] The pre-processed data is then sent over the internet to a cloud server, which is designed to minimize data latency and uses real-time communication technologies such as AWS IoT.

[1014] 3. Data Analysis

[1015] The server analyzes the received data using a generative AI model. This AI model infers the pet's intentions based on the pet's behavioral data and converts this into human language. For example, if the pet is excited, the analysis result may be "I want to play." GPT-3 and other models are used as generative AI models.

[1016] 4. Recommended products

[1017] Based on the analysis results, the server selects the most suitable product for the pet, taking into account the pet's current condition and past purchase history. The selected product is notified to the user via a visual display.

[1018] Example

[1019] An example of a brick-and-mortar application is a smart glasses application in a pet shop. When staff or customers wearing the smart glasses walk around the store with their pets, the glasses can read the pets' intentions from their facial expressions and cries, and make recommendations on products.

[1020] For example, when a user visits a pet shop and wears smart glasses, the glasses' built-in camera and microphone collect data on the pet's behavior in real time. The collected data is preprocessed and sent to a cloud server. A generative AI model on the server analyzes the data and, if it determines that the pet is bored, recommended products such as educational toys and treats are displayed on the smart glasses' display.

[1021] In this way, a system is realized that facilitates communication between pets and their owners and can make optimal and beneficial product suggestions.

[1022] Prompt Sentence Examples

[1023] I am currently building a system to analyze pet behavior. The image shows a dog. Please analyze the data on the dog's facial expressions, posture, and barks, and convert them into human words. Please tell me the emotions and intentions the dog is expressing.

[1024] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1025] Step 1:

[1026] Data collection

[1027] The device (smart glasses or smartphone) collects behavioral data such as facial expressions, gestures, and sounds of pets in real time using a camera and microphone. The input data is video and audio data, which is then stored directly in the device's memory.

[1028] Step 2:

[1029] Data Preprocessing

[1030] The device preprocesses the collected data. Specifically, image data is resized using OpenCV to remove unwanted noise, and audio data is converted into a clear format through noise filtering. The output of the preprocessing is image and audio data converted into a format suitable for analysis.

[1031] Step 3:

[1032] Data transmission

[1033] The terminal sends the preprocessed data to a cloud server. At this time, real-time communication technologies such as AWS IoT are used to instantly upload the data to the server. The input is the preprocessed data, and the output is the data stored on the server.

[1034] Step 4:

[1035] Data analysis

[1036] The server analyzes the collected data using a generative AI model on the cloud. The generative AI model analyzes the pet's behavior data and converts its intentions into human language. For example, "by analyzing the pet's facial expressions from image data and its cries from audio data, it determines that the pet is bored." The input is data stored on the cloud server, and the output is the text data of the analysis results.

[1037] Step 5:

[1038] Product presentation

[1039] The server selects the best products for pets based on the analysis results obtained from the generative AI model. It also takes into account past purchase history and preference data to determine recommended products. The input is the analysis results and purchase history data, and the output is a product list. This product information is sent to the terminal in real time.

[1040] Step 6:

[1041] Results display

[1042] The terminal displays the recommended product information sent from the server on a visual display. For example, "educational toys" or "snacks" are recommended on the display of smart glasses. The input is the recommended product information from the server, and the output is the product information displayed to the user's visual sense.

[1043] Step 7:

[1044] Sending purchasing information

[1045] If the user checks the recommended products and decides to purchase them, the purchase information is sent from the device to the server. The server updates the pet's preference data based on this purchase information. The input is the user's purchase action, and the output is the updated preference data.

[1046] Step 8:

[1047] Matching potential foster parents

[1048] If a user is looking for potential foster parents, the server searches for the most suitable foster parents based on pet preferences and purchase data, and notifies the terminal of the matching results. The input is pet preference data and purchase history, and the output is information on the matched foster parents.

[1049] In this way, a system is realized that facilitates communication between pets and their owners, and matches them with optimal and beneficial product suggestions and potential foster parents.

[1050] 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.

[1051] This invention combines a system that collects and analyzes data on pets' facial expressions, gestures, and cries, and converts the pet's intentions into human language based on that data, with an emotion engine that recognizes the user's emotions. The system aims to help users understand their pets' feelings, purchase products that meet their pets' needs, and find suitable potential adopters.

[1052] System configuration

[1053] 1. A device equipped with a camera and microphone that collects pet behavior data

[1054] 2. Software for preprocessing collected data

[1055] 3. A generative AI model on the server that analyzes the data and translates the pet's intentions into human language

[1056] 4. User interface that displays recommended products based on analysis results

[1057] 5. Emotion engine that recognizes user emotions

[1058] 6. A server-based algorithm that matches potential adopters based on pet preferences and purchasing history

[1059] 7. Network protocol for notifying users of analysis results, product suggestions, and matching information

[1060] Explanation of the program processing flow

[1061] Data collection and preprocessing

[1062] The device uses a camera and microphone to record your pet's facial expressions, movements, and cries in real time. The recorded data is pre-processed to remove noise and convert the data format. The pre-processed data is then converted into a format suitable for analysis.

[1063] Sending data to the server

[1064] The pre-processed data is then sent over the internet to a server, which is designed to transmit the data in real time and with minimal data latency.

[1065] Data analysis

[1066] The server analyzes the received data using a generative AI model. This AI model infers the pet's intentions based on the pet's behavioral data and translates this into human language. For example, if the pet is nervous, the analysis result may be "feeling anxious."

[1067] Recognizing user emotions with an emotion engine

[1068] The device or server captures the user's facial expressions and voice and analyzes them with an emotion engine. This allows the user's emotional state to be grasped in real time. For example, if the user is feeling stressed, it will be recognized as a "stressed state."

[1069] Notification of analysis results

[1070] The server generates data to notify the user of the analysis results and sends it to the terminal via the Internet. The terminal displays the received analysis results on a user interface, allowing the user to understand the pet's intentions.

[1071] Selection and display of recommended products

[1072] The server selects the best product for your pet based on the analysis results and the user's emotional state. It takes into consideration past purchase history, the pet's preferences, and the user's current emotional state. The selected product information is notified to the user via the device, which then displays it.

[1073] Product purchase and preference data updates

[1074] The user views the recommended products and decides to purchase them. The purchase information is sent from the device to the server, and the pet's preference data is updated. This data is used for future recommendations and matching.

[1075] Matching potential foster parents

[1076] The server searches for suitable foster parent candidates based on the user's pet preferences, purchasing history, and emotional state. When a suitable foster parent candidate is found, the information is notified to the user via the terminal.

[1077] Specific examples

[1078] For example, if a user's cat frequently licks itself, the device collects this behavioral data and sends it to the server. The server analyzes the data and determines that the cat is "stressed." The emotion engine then recognizes that the user is stressed. Based on this, the server recommends a cat toy that is effective in relieving stress and notifies the user. When the user purchases the toy, the purchase information is sent to the server, and the cat's preference data is updated. If the user is then looking for a foster parent, the updated preference data is referenced to present suitable foster parent candidates.

[1079] In this way, the present invention aims to facilitate communication between owners and pets and improve the quality of life for both by analyzing pet behavior and user emotions and providing various services based on that information.

[1080] The processing flow will be explained below.

[1081] Step 1: Collect data

[1082] The device uses a camera and microphone to record your pet's facial expressions, gestures, and cries in real time.

[1083] The device collects data on your pet's behavior.

[1084] Step 2: Preprocessing the data

[1085] The data collected by the device is pre-processed, including noise removal and format conversion.

[1086] The terminal converts the preprocessed data into a format suitable for analysis.

[1087] Step 3: Send data to the server

[1088] The terminal transmits the pre-processed data to the server in real time.

[1089] The terminal monitors the data transmission status, and if a transmission error is detected, retransmission is performed.

[1090] Step 4: Receiving and storing data

[1091] The server receives the data sent from the terminal.

[1092] The server stores the received data and prepares it for analysis.

[1093] Step 5: Analyze pet data

[1094] The server analyzes the pet's behavioral data using the generated AI model.

[1095] The server infers the pet's intentions and translates them into human language.

[1096] Step 6: Submit and view analysis results

[1097] The server generates the analysis results as data and sends them to the terminal.

[1098] The terminal receives the analysis results and displays them on the user interface.

[1099] The user checks the displayed analysis results and understands the pet's intentions.

[1100] Step 7: Recognizing user emotions with the emotion engine

[1101] The device uses a camera and microphone to record the user's facial expressions and voice.

[1102] The terminal or server analyzes the user's emotions using an emotion engine.

[1103] The server stores the analyzed user emotion information.

[1104] Step 8: Selecting recommended products

[1105] The server selects recommended products based on the pet analysis results and the user's emotional information.

[1106] The server decides on the product taking into consideration past purchase history and pet preference data.

[1107] Step 9: Notification of recommended products

[1108] The server sends information about recommended products to the terminal.

[1109] The terminal receives the recommended product information and displays it on the user interface.

[1110] Step 10: Purchase the product and submit your purchase information

[1111] The user checks the recommended products and decides to purchase them.

[1112] The terminal transmits the user's purchasing information to the server.

[1113] Step 11: Maintain purchasing data

[1114] The server receives the user's purchasing information and updates the pet preference data.

[1115] The server stores the updated data for future analysis.

[1116] Step 12: Matching potential foster parents

[1117] The server searches for potential foster parents based on pet preference data, purchasing history, and user emotional information.

[1118] The server sends information about suitable foster parent candidates to the terminal.

[1119] Step 13: Notification of matching information

[1120] The terminal receives the foster parent candidate information and displays it on a user interface.

[1121] The user checks the displayed foster parent candidate information and takes action if necessary.

[1122] In this way, the present invention realizes a system that analyzes pet behavior and user emotions and provides various services based on that information, facilitating communication between owners and pets and improving the quality of life for both.

[1123] Example 2

[1124] 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."

[1125] It is difficult to accurately understand a pet's intentions based on its facial expressions, gestures, and cries. It is also difficult to select appropriate products based on a pet's feelings and needs, or to find suitable potential adopters. Furthermore, there is a need to understand the user's emotional state in real time and utilize that information to provide more appropriate responses. However, few existing systems meet these requirements, and there is a lack of appropriate means to improve communication and the quality of life between owners and pets.

[1126] 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.

[1127] In this invention, the server includes means for collecting data on the pet's facial expressions, gestures, and cries, means for preprocessing the collected data, means for transmitting the preprocessed data to the server, means including a model for analyzing the transmitted data and converting the pet's intentions into human language, means for recognizing the user's emotions based on the analysis results, means for determining and displaying recommended products based on the analysis results and the user's emotional state, and means for matching with potential foster parents based on the pet's preferences and purchasing habits. This makes it possible to accurately understand the pet's behavior and intentions, propose appropriate products that also take the user's emotional state into consideration, and better match with potential foster parents.

[1128] "Data on pet's facial expressions, gestures, and sounds" refers to data that includes pet's facial expressions, body movements, and sounds.

[1129] "Means of collection" refers to devices such as cameras and microphones used to capture pets' facial expressions, gestures, and sounds, as well as the software that controls them.

[1130] "Preprocessing means" refers to software or algorithms used to remove noise from collected data and convert it into a form suitable for analysis.

[1131] "Means for transmitting to a server" refers to a communication protocol or network interface for transmitting the pre-processed data to a server via the Internet in real time.

[1132] "A model for analyzing and converting pet intentions into human language" refers to a generative AI model that infers intentions based on pet behavior data and converts them into human language.

[1133] "Means for recognizing user emotions" refers to emotion recognition engines or software that analyze the user's facial expressions and voice data to grasp the user's emotional state in real time.

[1134] "Means for determining and displaying recommended products" refers to software or devices that select appropriate products based on the analysis results and the user's emotional state, and display that information in a user interface.

[1135] "Means of matching potential foster parents based on pet preferences and purchasing history" refers to algorithms and systems that search for and match suitable foster parent candidates based on the pet's past preference data and purchasing history.

[1136] This system collects data on pets' facial expressions, gestures, and cries, analyzes their intentions based on the collected data, and notifies the user. Furthermore, it can recognize the user's emotional state in real time and recommend products based on that information. It can also match pets with suitable potential adopters based on their preferences and purchasing habits.

[1137] Hardware Configuration

[1138] This system uses the following hardware:

[1139] Device: A device that includes a camera (e.g., a high-resolution camera), a microphone, and a user interface (e.g., a smartphone or tablet).

[1140] Server: High-performance analysis server

[1141] Software Configuration

[1142] The following software is used:

[1143] Pre-processing software: OpenCV, ffmpeg, librosa, etc.

[1144] Generative AI models: AI models built using TensorFlow or PyTorch

[1145] Emotion recognition engine: Microsoft Azure Emotion API, etc.

[1146] Communication protocols: WebSocket, HTTP

[1147] Program processing flow

[1148] 1. Data Collection

[1149] The device uses a camera and microphone to record your pet's facial expressions, movements, and sounds in real time, and the captured data is temporarily stored in the device's storage.

[1150] 2. Pretreatment

[1151] The device performs preprocessing on the collected data, such as noise removal and data format conversion. Specifically, it uses OpenCV and ffmpeg to convert the video data format, and librosa to remove noise from the audio data.

[1152] 3. Data Transmission

[1153] The preprocessed data is sent in real time to the server using WebSocket or HTTP.

[1154] 4. Data Analysis

[1155] The server analyzes the received data using a generative AI model (using TensorFlow and PyTorch), which translates the pet's behavior and intentions into human terms, such as "feeling anxious" or "wanting to play."

[1156] 5. User Emotion Recognition

[1157] The device or server collects the user's facial and voice data and analyzes it using an emotion recognition engine. This allows the user's emotional state to be understood in real time. For example, it uses Microsoft Azure's Emotion API to determine whether the user is "stressed" or "relaxed."

[1158] 6. Notification of analysis results

[1159] The server generates data to notify the user of the pet's analysis results and sends it to the terminal via the Internet. The terminal displays the received analysis results in a GUI, allowing the user to understand the pet's intentions.

[1160] 7. Selection and display of recommended products

[1161] The server selects recommended products based on the pet analysis results and the user's emotional state. The information is then sent to the device, which displays the recommendations to the user. Past purchase history and pet preferences are also taken into consideration.

[1162] 8. Purchase and Preference Data Updates

[1163] When a user purchases a recommended product, the purchase information is sent from the device to the server, and the pet's preference data is updated. This information is used for future recommendations and matching with potential adopters.

[1164] 9. Matching potential foster parents

[1165] The server searches for suitable foster parents based on the user's pet preferences, purchasing history, and emotional state, and notifies the device of the information. If the user confirms the details and determines that the candidate is suitable, they can proceed to the next step.

[1166] Specific examples

[1167] For example, if a pet dog barks frequently, the device collects this behavioral data, preprocesses it, and sends it to the server. The server then analyzes it using a generative AI model and determines that the dog is feeling anxious. At the same time, an emotion recognition engine recognizes the user's stress level. Based on this, the server recommends a dog toy for stress relief and notifies the user. If the user purchases the toy, the preference data is updated. If the user is looking for a foster parent, suitable foster parent candidates are presented based on the updated data.

[1168] Examples of prompts include "Please tell me the analysis results of my dog's frequent barking behavior" and "Please give me the user's current emotional state and recommend products."

[1169] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1170] Step 1:

[1171] The device uses a camera and microphone to record the pet's facial expressions, behaviors, and sounds in real time. The input is video data captured by the camera and audio data collected by the microphone. The output is the collected raw data, which is temporarily stored in the device's storage.

[1172] Step 2:

[1173] The device performs preprocessing on the collected data. Specifically, it uses OpenCV for format conversion of video data and librosa for noise reduction of audio data. The input is the raw data collected in step 1. The output is data that has been denoised and converted into a format suitable for analysis.

[1174] Step 3:

[1175] The terminal sends the preprocessed data to the server in real time over the Internet. This communication uses WebSocket. The input is the preprocessed data. The output is the data that arrives at the server ready for analysis.

[1176] Step 4:

[1177] The server analyzes the received data using a generative AI model. Specifically, it uses TensorFlow to analyze the pet's behavioral patterns. The input is the data that arrives at the server. The output is the analysis results, which translate the pet's intentions into human words such as "I feel anxious" or "I want to play."

[1178] Step 5:

[1179] The device or server collects the user's facial and voice data and analyzes it using an emotion recognition engine. Specifically, the analysis is performed using Microsoft Azure's Emotion API. The input is the user's facial and voice data. The output is data indicating the user's emotional state, such as "stressed" or "relaxed."

[1180] Step 6:

[1181] The server generates data to notify the user based on the pet analysis results and sends it to the terminal via the Internet. The input is the analysis results regarding the pet's intentions and the user's emotional state. The output is data to notify the user, which is displayed on the user interface.

[1182] Step 7:

[1183] The server selects recommended products based on the pet analysis results and the user's emotional state. The selection also takes into account past purchase history and the pet's preferences. The input is data such as the analysis results, the user's emotional state, and past purchase history. The output is a list of selected recommended products, which is sent to the terminal via the Internet.

[1184] Step 8:

[1185] The user looks at the recommended products and decides whether to purchase them. If a purchase decision is made, the device sends the information to the server. The input is the user's purchase decision information. The output is updated pet preference data, which is saved on the server.

[1186] Step 9:

[1187] The server searches for suitable foster parent candidates based on pet preferences, purchase data, and the user's emotional state, and notifies the terminal of this information. The input is updated pet preference data, purchase history, and the user's emotional state. The output is information on suitable foster parent candidates, which is notified to the terminal.

[1188] (Application example 2)

[1189] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1190] In modern society, there is a demand for smoother communication between pets and their owners, improving the quality of life for both. In particular, there is a challenge in accurately understanding pet behavior and intentions and responding appropriately. Furthermore, there is a lack of services that reflect the user's emotional state. This makes it difficult to select products that meet the pet's needs and match them with suitable foster parents.

[1191] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for collecting data on the pet's facial expressions, gestures, and cries; means for preprocessing the collected data; means including a generative AI model for analyzing the preprocessed data and converting the pet's intentions into human language; means for determining and displaying recommended products based on the analysis results; means for matching with potential foster parents based on the pet's preferences and purchasing habits; means including an emotion engine for recognizing the user's emotions; means for suggesting recommended products based on the results of analysis by the generative AI model and the emotion engine and making the products available for purchase in a virtual store; means for displaying the analysis results in a user interface in real time; and means for notifying the user of the analysis results. This facilitates communication between pets and their owners, enabling accurate understanding of the pet's intentions, appropriate product suggestions and purchases, and effective matching with potential foster parents.

[1192] "Data on pets' facial expressions, gestures, and sounds" refers to recordings of the facial expressions, body movements, and sounds that pets make.

[1193] "Means of collection" refers to devices or methods for acquiring data on pets' facial expressions, gestures, and cries using devices such as cameras and microphones.

[1194] "Preprocessing means" refers to methods or processes that remove noise from collected data and convert it into a form suitable for analysis.

[1195] A "generative AI model" is an artificial intelligence model trained to analyze collected data, infer a pet's intentions, and translate them into human language.

[1196] "Means for determining and displaying recommended products" refers to a device or method that selects the most suitable products for pets based on the analysis results of the generative AI model and displays them on a user interface.

[1197] "Methods of matching potential foster parents based on pet preferences and purchasing history" refers to algorithms and processes that take into account the pet's past behavior and purchasing history to find the most suitable foster parent candidates.

[1198] The "emotion engine" is a software module that analyzes the user's facial expressions and voice to recognize the user's emotional state.

[1199] "Means for enabling purchase of products in a virtual store" refers to a device or method that provides a function that allows a user to check product information in a virtual space and purchase the product on the spot.

[1200] "Means for displaying analysis results on a user interface in real time" refers to technology or devices for displaying analyzed data on a user's display without delay.

[1201] The "means for notifying the user of the analysis results" refers to a communication technology or mechanism for communicating the analysis results to the user.

[1202] MODE FOR CARRYING OUT THE INVENTION

[1203] This invention combines a system that collects and analyzes data on pets' facial expressions, gestures, and cries, and converts the pet's intentions into human language based on that data, with an emotion engine that recognizes the user's emotions. The system aims to help users understand their pets' feelings, purchase products that meet their needs, and find suitable foster parents.

[1204] System configuration

[1205] 1. Data collection device: Cameras and microphones installed in smartphones or head-mounted displays (HMDs) collect data on pets' facial expressions, gestures, and cries in real time. The collected data undergoes pre-processing such as noise removal and data format conversion.

[1206] 2. Data transmission to the server: The preprocessed data is transmitted to the server via the Internet. Transmission is performed as close to real time as possible, and is designed to minimize data delays.

[1207] 3. Data Analysis Server: The server analyzes the received data using a generative AI model. This AI model infers the pet's intentions based on its behavioral data and converts them into human language. It also captures the user's facial expressions and voice and analyzes them with an emotion engine to understand the user's emotional state in real time.

[1208] 4. User Interface: The results of the analysis by the server are sent to the terminal via the Internet and displayed on the user interface, making it easier for the user to understand the intentions of their pet.

[1209] 5. Product Recommendation System: The server selects the most suitable pet product based on the analysis results and the user's emotional state. Specifically, it takes into account past purchase history, pet preferences, and the user's current emotional state. The selected product information is notified to the user via their terminal, and the user can view and purchase the product in the virtual store.

[1210] 6. Preference data and adoption matching: Information about purchased items is sent to the server to update the pet's preference data. This data is also used to match potential adopters, searching for and presenting suitable adopter candidates.

[1211] Hardware and software used

[1212] Hardware: Smartphone, smart glasses, head-mounted display (HMD), camera, microphone

[1213] Software: OpenCV (for image processing), EmotionAnalytics (emotion recognition module), PetBehaviorModel (pet behavior analysis model), ProductRecommender (product recommendation system)

[1214] Specific examples

[1215] For example, if a user's cat frequently licks itself, the device collects this behavioral data and sends it to the server. The server analyzes the collected data and determines that the cat is "stressed." The emotion engine simultaneously recognizes that the user is stressed. Based on this analysis, the server recommends a cat toy that is effective in relieving stress and notifies the user. When the user purchases the toy in the virtual store, the purchase information is sent to the server, and the cat's preference data is updated. Furthermore, if the user is looking for a foster parent, the updated preference data is referenced to present suitable foster parent candidates.

[1216] Prompt Sentence Examples

[1217] "Analyze footage of a cat frequently licking itself and infer the pet's intentions. Also, recognize the user's emotional state from facial expression data and suggest products that are effective in relieving stress in cats."

[1218] In this way, the present invention aims to facilitate communication between owners and pets and improve the quality of life for both by analyzing pet behavior and user emotions and providing various services based on that information.

[1219] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1220] Step 1:

[1221] Collecting pet data

[1222] Input: Pet's facial expressions, gestures, and cries

[1223] The device uses a camera and microphone installed in a smartphone or head-mounted display (HMD) to collect data on your pet's facial expressions, gestures, and cries in real time.

[1224] Output: Collected data (video data, audio data)

[1225] Step 2:

[1226] Data Preprocessing

[1227] Input: Collected data (video data, audio data)

[1228] The device pre-processes the collected data, which includes noise removal, grayscale conversion of images, and filtering of audio data.

[1229] Output: Pre-processed data (processed video data, audio data)

[1230] Step 3:

[1231] Sending data to the server

[1232] Input: Preprocessed data (processed video data, audio data)

[1233] The terminals then transmit the pre-processed data to a server via the internet, with the transmission being designed to occur as quickly as possible in real time, minimizing data latency.

[1234] Output: Data sent to the server

[1235] Step 4:

[1236] Data analysis

[1237] Input: Data sent to the server (processed video data, audio data)

[1238] The server analyzes the received data using a generative AI model, which infers the pet's intentions based on the behavioral data and translates them into human language.

[1239] Output: Analysis result that indicates the pet's intention (e.g. "I feel stressed")

[1240] Step 5:

[1241] User Emotion Recognition

[1242] Input: User's facial expressions and voice

[1243] The device captures the user's facial expressions and voice and analyzes them with an emotion engine, allowing the device to grasp the user's emotional state in real time.

[1244] Output: Analysis results showing the user's emotions (e.g., "stressed state")

[1245] Step 6:

[1246] Notification of analysis results

[1247] Input: Analysis results showing the pet's intentions, analysis results showing the user's emotions

[1248] The server generates data for notifying the user interface of the analysis results and transmits the data to the terminal via the Internet.

[1249] Output: Analysis results sent to the device

[1250] Step 7:

[1251] Display in the user interface

[1252] Input: Analysis results sent to the device

[1253] The terminal displays the received analysis results on a user interface, allowing the user to understand the pet's intentions.

[1254] Output: Analysis results displayed on the user interface

[1255] Step 8:

[1256] Product suggestion

[1257] Input: Analysis results showing the pet's intentions, analysis results showing the user's emotions

[1258] The server selects the most suitable pet products based on the analysis results and the user's emotional state, taking into account past purchase history, pet preferences, and the user's current emotional state.

[1259] Output: Selected recommended products

[1260] Step 9:

[1261] Buying products in a virtual store

[1262] Input: Selected recommended products

[1263] The user checks the product information displayed in the virtual store and purchases the product.

[1264] Output: Purchase information

[1265] Step 10:

[1266] Preference data updates and foster parent matching

[1267] Input: Purchase information

[1268] The device sends the purchase information to the server, which updates the pet's preference data. Based on this, the server searches for suitable potential adopters.

[1269] Output: Updated preference data, suitable foster parents

[1270] 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.

[1271] 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.

[1272] 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.

[1273] [Fourth embodiment]

[1274] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1275] 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.

[1276] 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).

[1277] 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.

[1278] 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.

[1279] 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).

[1280] 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.

[1281] 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.

[1282] 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.

[1283] 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.

[1284] 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.

[1285] 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.

[1286] 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."

[1287] This invention is a system that collects and analyzes data such as pet facial expressions, gestures, and cries, and converts the pet's intentions into human language. The purpose of this system is to help pet owners better understand their pets' feelings, purchase products that meet their pet's needs, and find suitable foster parents.

[1288] System configuration

[1289] 1. A device equipped with a camera and microphone that collects pet behavior data

[1290] 2. Software for preprocessing collected data

[1291] 3. A generative AI model on the server that analyzes the data and translates the pet's intentions into human language

[1292] 4. User interface that displays recommended products based on analysis results

[1293] 5. A server-based algorithm that matches potential adopters based on pet preferences and purchasing history

[1294] 6. Network protocol for notifying users of analysis results, product suggestions, and matching information

[1295] Explanation of the program processing flow

[1296] Data collection and preprocessing

[1297] The device uses a camera and microphone to record your pet's facial expressions, movements, and sounds in real time. This data is first preprocessed and converted into an appropriate format for analysis. This preprocessing includes noise removal and data format conversion.

[1298] Sending data to the server

[1299] The pre-processed data is then transmitted over the internet to a server in real time, designed to minimize data latency.

[1300] Data analysis

[1301] The server analyzes the received data using a generative AI model. This AI model infers the pet's intentions based on the pet's behavioral data and converts them into human language. For example, if the pet is excited, the analysis result may be "I want to play."

[1302] Recommended products

[1303] The server then selects the best product for your pet based on the analysis results. This selection takes into account the pet's current condition and past purchase history. The selected product is then notified to the user via their device.

[1304] Product purchase and preference data updates

[1305] The user views the recommended products and decides to purchase them. This purchase information is sent from the device to the server, and the pet's preference data is updated. This data is used to recommend future products and match potential adopters.

[1306] Matching potential foster parents

[1307] The server searches for suitable foster parent candidates based on pet preferences and purchasing history. When a suitable foster parent candidate is found, the information is notified to the user via the terminal.

[1308] Specific examples

[1309] For example, if a user's dog barks frequently, the device collects the barking data and sends it to the server. The server analyzes the data and determines that the dog is barking because it is bored. Based on the analysis results, the server recommends an educational toy for dogs and notifies the user. When the user purchases the toy, the purchase information is sent to the server, and the dog's preference data is updated. If the user is then looking for a foster parent, this preference data is referenced to present suitable foster parent candidates.

[1310] In this way, the present invention aims to facilitate communication between owners and pets and improve the quality of life for both by analyzing pet behavior, converting it into human language, and providing various services based on that.

[1311] The processing flow will be explained below.

[1312] Step 1: Collect data

[1313] The device records your pet's behavior in real time using a camera and microphone.

[1314] The device collects data such as your pet's facial expressions, gestures, and cries.

[1315] Step 2: Preprocessing the data

[1316] The data collected by the terminal is preprocessed, noise is removed, and data format conversion is performed.

[1317] The device converts the preprocessed data into a format suitable for analysis.

[1318] Step 3: Sending data

[1319] The terminal transmits the preprocessed data to a server via the Internet.

[1320] The device transmits data in real time to minimize delays.

[1321] Step 4: Receiving the data

[1322] The server receives the data sent from the terminal.

[1323] The server checks the integrity of the data received.

[1324] Step 5: Analyze the data

[1325] The server analyzes the received data using a generative AI model.

[1326] The server infers the pet's intentions based on the pet's behavioral data and converts them into human language.

[1327] Step 6: Notification of analysis results

[1328] The server generates data to notify the user of the analysis results.

[1329] The server sends the analysis results to the terminal via the Internet.

[1330] Step 7: Viewing the analysis results

[1331] The analysis results received by the terminal are displayed on the user interface.

[1332] The user checks the analysis results to understand the pet's intentions.

[1333] Step 8: Selecting recommended products

[1334] The server selects the best product for your pet based on the analysis results.

[1335] The server selects products taking into consideration past purchase history and pet preferences.

[1336] Step 9: Product Information Notification

[1337] The server generates data for notifying the user of recommended product information.

[1338] The server transmits product information to the terminal via the Internet.

[1339] Step 10: Display product information

[1340] The terminal displays the received product information on a user interface.

[1341] The user checks the displayed product information and makes a purchase decision.

[1342] Step 11: Submit your purchase information

[1343] The user performs a purchase operation and inputs the purchase information into the terminal.

[1344] The terminal sends the purchase information to the server.

[1345] Step 12: Maintain purchasing data

[1346] The server receives the purchase information and updates the pet's preference data.

[1347] The server stores the updated data and uses it for future recommendations and matches.

[1348] Step 13: Matching potential foster parents

[1349] The server searches for potential foster parents based on pet preferences and purchasing history.

[1350] The server generates data for notifying the user of the foster parent candidate information it has found.

[1351] Step 14: Notification of matching information

[1352] The server transmits information about potential foster parents to the terminal via the Internet.

[1353] The foster parent candidate information received by the terminal is displayed on a user interface.

[1354] Step 15: Check the matching results

[1355] The user checks the displayed foster parent candidate information and takes action if necessary.

[1356] Example 1

[1357] 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."

[1358] Understanding pets' intentions through data such as facial expressions, behavior, and vocalizations is a difficult task for pet owners. It is particularly important to accurately grasp pet stress and needs and respond appropriately accordingly. Proposing products based on pet needs and matching suitable foster parents are also important. However, existing systems lack the ability to analyze data in real time or accurately translate pets' intentions, and no technology has been established to increase owner satisfaction.

[1359] 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.

[1360] In this invention, the server includes means for collecting data on the pet's facial expressions, gestures, and cries, means for preprocessing the collected data, means for transmitting the preprocessed data to the server in real time, means including a generative AI model for analyzing the preprocessed data and converting the pet's intentions into human language, means for determining and displaying recommended products based on the analysis results, and means for matching with potential foster parents based on the pet's preferences and purchasing history. This makes it possible to accurately analyze the pet's intentions and quickly and effectively suggest appropriate products and match with potential foster parents.

[1361] "Pet facial expressions" are data that represent emotions and states shown by the movements and expressions of an animal's face.

[1362] "Gestures" are data that represent the movements and attitudes that animals show through specific actions and behaviors.

[1363] "Calls" are data that represent the sounds and sound patterns made by animals.

[1364] "Means for collecting data" refers to devices or methods that use cameras, microphones, or other devices to record a pet's facial expressions, gestures, and cries.

[1365] "Preprocessing means" refers to devices or methods that remove noise from collected data, convert the data format, and prepare it in a format suitable for analysis.

[1366] The "means for transmitting to the server" refers to a device or method for transferring the preprocessed data to the server using a communication network such as the Internet.

[1367] A "generative AI model" is a generative model that analyzes data based on a specific purpose and converts it into human language.

[1368] The "means for determining and displaying recommended products" refers to a device or method for selecting optimal products based on the analysis results and notifying the user of them.

[1369] "Preference data" is data that represents information about pet preferences and purchasing history.

[1370] A "means for matching with potential foster parents" is a device or method for selecting the most suitable potential foster parents based on pet preferences and purchasing habits.

[1371] The "means for notifying the user" refers to a device or method for notifying the user of the analysis results and recommended product information.

[1372] The "means for transmitting purchase information to the server" refers to a device or method for transmitting information about a product purchased by a user to the server and updating the pet's preference data.

[1373] This invention is a system that collects data such as pet facial expressions, gestures, and cries, analyzes the data, and converts the pet's intentions into human language. The system aims to help pet owners understand their pet's feelings, purchase products that meet their pet's needs, and find suitable foster parents.

[1374] The system is configured as follows: First, a device equipped with a camera and microphone is used to collect data on pet behavior. Specific hardware used includes an HD camera and a highly sensitive microphone. This device records the pet's behavior in real time and stores it as data.

[1375] The collected data is subjected to noise removal and format conversion using pre-processing software within the device, which prepares the data in a format suitable for analysis. After pre-processing is complete, the data is sent to a server via the Internet using an efficient, low-latency communication protocol (e.g., TCP / IP).

[1376] The server analyzes the received data using a generative AI model. This generative AI model infers the pet's intentions from its behavioral data and converts them into human language. Specifically, it uses advanced natural language processing techniques such as GPT-4 and BERT. Examples of prompts to be input to this AI model include the following:

[1377] Pet behavior data:

[1378] Expression: Smiling

[1379] Gesture: Jump

[1380] Sounds: Barking

[1381] Based on this behavioral data, infer your pet's intentions and translate them into human terms.

[1382] Based on the analysis results, the server selects the best product for the pet. This selection takes into account the pet's current condition and past purchase history. For example, an educational toy might be selected for a bored pet. This product information is notified to the user via their device. Notifications are sent via smartphone push notifications or email.

[1383] When a user purchases a product, the purchase information is sent from the device to the server, and the pet's preference data is updated. The updated data is used to suggest future products and match potential adopters. In this way, the system always reflects the pet's latest preferences and condition, allowing it to provide the most optimal service.

[1384] Furthermore, the server searches for suitable foster parent candidates based on pet preferences and purchasing history. When a suitable foster parent candidate is found, the information is notified to the user via the terminal, allowing the user to quickly find the best foster parent for their pet.

[1385] As described above, the present invention aims to facilitate communication with pet owners by analyzing their pet's behavior and converting it into human language, and to suggest products that meet the pet's needs and match them with potential foster parents.

[1386] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1387] Step 1: Data collection

[1388] The device uses a camera and microphone to record your pet's facial expressions, gestures, and sounds in real time. For example, an HD camera captures your pet's facial movements and overall movements, and a high-sensitivity microphone records its sounds. The input data is the pet's video and audio, and the output data is the recorded multimedia file.

[1389] Step 2: Data Preprocessing

[1390] The terminal performs noise removal and data format conversion on the collected data. For example, it filters out background noise from audio data and extracts the necessary frequency band. For image data, it cuts out unnecessary parts and adjusts the resolution. The input is the recorded multimedia file, and the output is a preprocessed data file.

[1391] Step 3: Send data to the server

[1392] The preprocessed data is sent to the server in real time using the Internet's TCP / IP protocol to minimize data transmission delays. The input is the preprocessed data file, and the output is the data sent to the server.

[1393] Step 4: Data analysis

[1394] The server analyzes the received data using a generative AI model. Specifically, a prompt sentence is input to the generative AI model (e.g., GPT-4 or BERT) to infer the intention from the pet's behavior data. The input is the preprocessed data and the prompt sentence, and the output is a natural language text that expresses the pet's intention.

[1395] Step 5: Selecting recommended products

[1396] The server selects the best product for your pet based on the analysis results. This selection takes into account the pet's current condition and past purchase history. For example, an educational toy might be selected for a bored pet. The input is the analysis results and purchase history data, and the output is information about the selected product.

[1397] Step 6: Notification of recommended products

[1398] The selected product information is notified to the user via the device. Notifications are sent via smartphone push notifications or email. The input is the selected product information, and the output is a notification message provided to the user. For example, a message such as "Your pet seems bored. Why not try a new educational toy?" is displayed.

[1399] Step 7: Purchase and update preference data

[1400] The user confirms the notification and purchases the product. The purchase information is sent from the terminal to the server, and the pet's preference data is updated. The input is the purchased product information, and the output is the updated preference data. For example, if the user purchases an educational toy, that information is added to the preference database.

[1401] Step 8: Matching potential foster parents

[1402] The server searches for suitable foster parent candidates based on the updated preference data. When a suitable foster parent candidate is found, the information is notified to the user via the terminal. The input is the updated preference data, and the output is information about the suitable foster parent candidate. For example, a notification such as "A suitable foster parent candidate has been found for your pet" may be sent.

[1403] Through these processing steps, the system can analyze the pet's intentions in real time and make optimal product recommendations and match potential adopters, helping owners better understand their pets' feelings and improve the quality of their pets' lives.

[1404] (Application example 1)

[1405] 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."

[1406] In recent years, the number of pets has increased, increasing the need for owners to understand their pets' feelings and provide appropriate care. However, it is difficult to read a pet's intentions from its facial expressions, gestures, and cries, and many owners are unable to accurately grasp their pets' needs. This can lead to stress for pets and problems such as not being able to provide appropriate products and services. Furthermore, choosing the right product from the large selection of products at pet supply stores can be difficult, especially for first-time pet owners. This invention aims to solve these problems and improve the quality of life for pets and their owners by accurately analyzing pets' feelings and recommending products to owners in real time.

[1407] 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.

[1408] In this invention, the server includes: means for collecting pet behavioral data; means for preprocessing the collected data; means including a generative AI model for analyzing the preprocessed data and converting the pet's intentions into human language; means for determining and displaying recommended products based on the analysis results; means for matching potential foster parents based on the pet's preferences and purchasing information; and means including a visual display capable of displaying the analysis results and product recommendations in real time. This enables accurate understanding of the pet's intentions and optimal product recommendations in real time. Furthermore, the system simultaneously matches potential foster parents using the pet's preference data, contributing to improving the quality of life for owners and their pets.

[1409] "Pet behavior data" refers to various physical and audio information displayed by a pet, such as facial expressions, gestures, and sounds.

[1410] "Preprocessing" refers to the process of removing noise from collected data and converting it into a format suitable for analysis, such as resizing and denoising image data.

[1411] A "generative AI model" is an artificial intelligence model that analyzes pet behavior data, infers their intentions, and converts them into human language. Specifically, it is built using machine learning and deep learning.

[1412] "Recommended products" refer to products that are deemed optimal based on your pet's current condition, preferences, and past purchasing history.

[1413] "Foster parent candidates" refer to the new owners who are best suited to a pet based on the pet's preferences and purchasing information.

[1414] "Visual display" refers to a visual display device for displaying analysis results and product suggestions in real time, including the displays of smart glasses.

[1415] A "cloud server" refers to a remote server that analyzes and stores data via the internet, allowing for efficient processing of large amounts of data.

[1416] "Real-time" refers to minimizing delays and processing and displaying data almost instantly.

[1417] "Analysis results" refers to the pet's intentions and recommended actions derived from the pet's behavioral data by the generative AI model.

[1418] "Preference data" refers to individual data about pets that is generated based on their preferences, past behavior, purchasing history, etc.

[1419] This invention is a system that collects and analyzes pet behavior data, converts the pet's intentions into human language, and presents recommended products. This helps pet owners understand their pet's needs more accurately and purchase appropriate products. The system is configured as follows:

[1420] Hardware Configuration

[1421] 1. Terminal

[1422] The device, which includes smart glasses and smartphones, is equipped with a camera and microphone to collect pet behavior data.

[1423] 2. Server

[1424] This is a cloud server for analyzing data. A generative AI model is installed on this server and analyzes pet behavior data.

[1425] 3. Visual Displays

[1426] A visual display device that displays analysis results and recommended products in real time, such as the display of smart glasses or the screen of a smartphone.

[1427] Software Configuration

[1428] 1. Data Collection and Preprocessing

[1429] The device uses a camera and microphone to record your pet's facial expressions, movements, and sounds in real time. This collected data is first preprocessed and converted into an appropriate format for analysis. Preprocessing includes noise removal and data format conversion. For example, the collected image data can be preprocessed using the Google Cloud Vision API.

[1430] 2. Sending data to the server

[1431] The pre-processed data is then sent over the internet to a cloud server, which is designed to minimize data latency and uses real-time communication technologies such as AWS IoT.

[1432] 3. Data Analysis

[1433] The server analyzes the received data using a generative AI model. This AI model infers the pet's intentions based on the pet's behavioral data and converts this into human language. For example, if the pet is excited, the analysis result may be "I want to play." GPT-3 and other models are used as generative AI models.

[1434] 4. Recommended products

[1435] Based on the analysis results, the server selects the most suitable product for the pet, taking into account the pet's current condition and past purchase history. The selected product is notified to the user via a visual display.

[1436] Example

[1437] An example of a brick-and-mortar application is a smart glasses application in a pet shop. When staff or customers wearing the smart glasses walk around the store with their pets, the glasses can read the pets' intentions from their facial expressions and cries, and make recommendations on products.

[1438] For example, when a user visits a pet shop and wears smart glasses, the glasses' built-in camera and microphone collect data on the pet's behavior in real time. The collected data is preprocessed and sent to a cloud server. A generative AI model on the server analyzes the data and, if it determines that the pet is bored, recommended products such as educational toys and treats are displayed on the smart glasses' display.

[1439] In this way, a system is realized that facilitates communication between pets and their owners and can make optimal and beneficial product suggestions.

[1440] Prompt Sentence Examples

[1441] I am currently building a system to analyze pet behavior. The image shows a dog. Please analyze the data on the dog's facial expressions, posture, and barks, and convert them into human words. Please tell me the emotions and intentions the dog is expressing.

[1442] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1443] Step 1:

[1444] Data collection

[1445] The device (smart glasses or smartphone) collects behavioral data such as facial expressions, gestures, and sounds of pets in real time using a camera and microphone. The input data is video and audio data, which is then stored directly in the device's memory.

[1446] Step 2:

[1447] Data Preprocessing

[1448] The device preprocesses the collected data. Specifically, image data is resized using OpenCV to remove unwanted noise, and audio data is converted into a clear format through noise filtering. The output of the preprocessing is image and audio data converted into a format suitable for analysis.

[1449] Step 3:

[1450] Data transmission

[1451] The terminal sends the preprocessed data to a cloud server. At this time, real-time communication technologies such as AWS IoT are used to instantly upload the data to the server. The input is the preprocessed data, and the output is the data stored on the server.

[1452] Step 4:

[1453] Data analysis

[1454] The server analyzes the collected data using a generative AI model on the cloud. The generative AI model analyzes the pet's behavior data and converts its intentions into human language. For example, "by analyzing the pet's facial expressions from image data and its cries from audio data, it determines that the pet is bored." The input is data stored on the cloud server, and the output is the text data of the analysis results.

[1455] Step 5:

[1456] Product presentation

[1457] The server selects the best products for pets based on the analysis results obtained from the generative AI model. It also takes into account past purchase history and preference data to determine recommended products. The input is the analysis results and purchase history data, and the output is a product list. This product information is sent to the terminal in real time.

[1458] Step 6:

[1459] Results display

[1460] The terminal displays the recommended product information sent from the server on a visual display. For example, "educational toys" or "snacks" are recommended on the display of smart glasses. The input is the recommended product information from the server, and the output is the product information displayed to the user's visual sense.

[1461] Step 7:

[1462] Sending purchasing information

[1463] If the user checks the recommended products and decides to purchase them, the purchase information is sent from the device to the server. The server updates the pet's preference data based on this purchase information. The input is the user's purchase action, and the output is the updated preference data.

[1464] Step 8:

[1465] Matching potential foster parents

[1466] If a user is looking for potential foster parents, the server searches for the most suitable foster parents based on pet preferences and purchase data, and notifies the terminal of the matching results. The input is pet preference data and purchase history, and the output is information on the matched foster parents.

[1467] In this way, a system is realized that facilitates communication between pets and their owners, and matches them with optimal and beneficial product suggestions and potential foster parents.

[1468] 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.

[1469] This invention combines a system that collects and analyzes data on pets' facial expressions, gestures, and cries, and converts the pet's intentions into human language based on that data, with an emotion engine that recognizes the user's emotions. The system aims to help users understand their pets' feelings, purchase products that meet their pets' needs, and find suitable potential adopters.

[1470] System configuration

[1471] 1. A device equipped with a camera and microphone that collects pet behavior data

[1472] 2. Software for preprocessing collected data

[1473] 3. A generative AI model on the server that analyzes the data and translates the pet's intentions into human language

[1474] 4. User interface that displays recommended products based on analysis results

[1475] 5. Emotion engine that recognizes user emotions

[1476] 6. A server-based algorithm that matches potential adopters based on pet preferences and purchasing history

[1477] 7. Network protocol for notifying users of analysis results, product suggestions, and matching information

[1478] Explanation of the program processing flow

[1479] Data collection and preprocessing

[1480] The device uses a camera and microphone to record your pet's facial expressions, movements, and cries in real time. The recorded data is pre-processed to remove noise and convert the data format. The pre-processed data is then converted into a format suitable for analysis.

[1481] Sending data to the server

[1482] The pre-processed data is then sent over the internet to a server, which is designed to transmit the data in real time and with minimal data latency.

[1483] Data analysis

[1484] The server analyzes the received data using a generative AI model. This AI model infers the pet's intentions based on the pet's behavioral data and translates this into human language. For example, if the pet is nervous, the analysis result may be "feeling anxious."

[1485] Recognizing user emotions with an emotion engine

[1486] The device or server captures the user's facial expressions and voice and analyzes them with an emotion engine. This allows the user's emotional state to be grasped in real time. For example, if the user is feeling stressed, it will be recognized as a "stressed state."

[1487] Notification of analysis results

[1488] The server generates data to notify the user of the analysis results and sends it to the terminal via the Internet. The terminal displays the received analysis results on a user interface, allowing the user to understand the pet's intentions.

[1489] Selection and display of recommended products

[1490] The server selects the best product for your pet based on the analysis results and the user's emotional state. It takes into consideration past purchase history, the pet's preferences, and the user's current emotional state. The selected product information is notified to the user via the device, which then displays it.

[1491] Product purchase and preference data updates

[1492] The user views the recommended products and decides to purchase them. The purchase information is sent from the device to the server, and the pet's preference data is updated. This data is used for future recommendations and matching.

[1493] Matching potential foster parents

[1494] The server searches for suitable foster parent candidates based on the user's pet preferences, purchasing history, and emotional state. When a suitable foster parent candidate is found, the information is notified to the user via the terminal.

[1495] Specific examples

[1496] For example, if a user's cat frequently licks itself, the device collects this behavioral data and sends it to the server. The server analyzes the data and determines that the cat is "stressed." The emotion engine then recognizes that the user is stressed. Based on this, the server recommends a cat toy that is effective in relieving stress and notifies the user. When the user purchases the toy, the purchase information is sent to the server, and the cat's preference data is updated. If the user is then looking for a foster parent, the updated preference data is referenced to present suitable foster parent candidates.

[1497] In this way, the present invention aims to facilitate communication between owners and pets and improve the quality of life for both by analyzing pet behavior and user emotions and providing various services based on that information.

[1498] The processing flow will be explained below.

[1499] Step 1: Collect data

[1500] The device uses a camera and microphone to record your pet's facial expressions, gestures, and cries in real time.

[1501] The device collects data on your pet's behavior.

[1502] Step 2: Preprocessing the data

[1503] The data collected by the device is pre-processed, including noise removal and format conversion.

[1504] The terminal converts the preprocessed data into a format suitable for analysis.

[1505] Step 3: Send data to the server

[1506] The terminal transmits the pre-processed data to the server in real time.

[1507] The terminal monitors the data transmission status, and if a transmission error is detected, retransmission is performed.

[1508] Step 4: Receiving and storing data

[1509] The server receives the data sent from the terminal.

[1510] The server stores the received data and prepares it for analysis.

[1511] Step 5: Analyze pet data

[1512] The server analyzes the pet's behavioral data using the generated AI model.

[1513] The server infers the pet's intentions and translates them into human language.

[1514] Step 6: Submit and view analysis results

[1515] The server generates the analysis results as data and sends them to the terminal.

[1516] The terminal receives the analysis results and displays them on the user interface.

[1517] The user checks the displayed analysis results and understands the pet's intentions.

[1518] Step 7: Recognizing user emotions with the emotion engine

[1519] The device uses a camera and microphone to record the user's facial expressions and voice.

[1520] The terminal or server analyzes the user's emotions using an emotion engine.

[1521] The server stores the analyzed user emotion information.

[1522] Step 8: Selecting recommended products

[1523] The server selects recommended products based on the pet analysis results and the user's emotional information.

[1524] The server decides on the product taking into consideration past purchase history and pet preference data.

[1525] Step 9: Notification of recommended products

[1526] The server sends information about recommended products to the terminal.

[1527] The terminal receives the recommended product information and displays it on the user interface.

[1528] Step 10: Purchase the product and submit your purchase information

[1529] The user checks the recommended products and decides to purchase them.

[1530] The terminal transmits the user's purchasing information to the server.

[1531] Step 11: Maintain purchasing data

[1532] The server receives the user's purchasing information and updates the pet preference data.

[1533] The server stores the updated data for future analysis.

[1534] Step 12: Matching potential foster parents

[1535] The server searches for potential foster parents based on pet preference data, purchasing history, and user emotional information.

[1536] The server sends information about suitable foster parent candidates to the terminal.

[1537] Step 13: Notification of matching information

[1538] The terminal receives the foster parent candidate information and displays it on a user interface.

[1539] The user checks the displayed foster parent candidate information and takes action if necessary.

[1540] In this way, the present invention realizes a system that analyzes pet behavior and user emotions and provides various services based on that information, facilitating communication between owners and pets and improving the quality of life for both.

[1541] Example 2

[1542] 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."

[1543] It is difficult to accurately understand a pet's intentions based on its facial expressions, gestures, and cries. It is also difficult to select appropriate products based on a pet's feelings and needs, or to find suitable potential adopters. Furthermore, there is a need to understand the user's emotional state in real time and utilize that information to provide more appropriate responses. However, few existing systems meet these requirements, and there is a lack of appropriate means to improve communication and the quality of life between owners and pets.

[1544] 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.

[1545] In this invention, the server includes means for collecting data on the pet's facial expressions, gestures, and cries, means for preprocessing the collected data, means for transmitting the preprocessed data to the server, means including a model for analyzing the transmitted data and converting the pet's intentions into human language, means for recognizing the user's emotions based on the analysis results, means for determining and displaying recommended products based on the analysis results and the user's emotional state, and means for matching with potential foster parents based on the pet's preferences and purchasing habits. This makes it possible to accurately understand the pet's behavior and intentions, propose appropriate products that also take the user's emotional state into consideration, and better match with potential foster parents.

[1546] "Data on pet's facial expressions, gestures, and sounds" refers to data that includes pet's facial expressions, body movements, and sounds.

[1547] "Means of collection" refers to devices such as cameras and microphones used to capture pets' facial expressions, gestures, and sounds, as well as the software that controls them.

[1548] "Preprocessing means" refers to software or algorithms used to remove noise from collected data and convert it into a form suitable for analysis.

[1549] "Means for transmitting to a server" refers to a communication protocol or network interface for transmitting the pre-processed data to a server via the Internet in real time.

[1550] "A model for analyzing and converting pet intentions into human language" refers to a generative AI model that infers intentions based on pet behavior data and converts them into human language.

[1551] "Means for recognizing user emotions" refers to emotion recognition engines or software that analyze the user's facial expressions and voice data to grasp the user's emotional state in real time.

[1552] "Means for determining and displaying recommended products" refers to software or devices that select appropriate products based on the analysis results and the user's emotional state, and display that information in a user interface.

[1553] "Means of matching potential foster parents based on pet preferences and purchasing history" refers to algorithms and systems that search for and match suitable foster parent candidates based on the pet's past preference data and purchasing history.

[1554] This system collects data on pets' facial expressions, gestures, and cries, analyzes their intentions based on the collected data, and notifies the user. Furthermore, it can recognize the user's emotional state in real time and recommend products based on that information. It can also match pets with suitable potential adopters based on their preferences and purchasing habits.

[1555] Hardware Configuration

[1556] This system uses the following hardware:

[1557] Device: A device that includes a camera (e.g., a high-resolution camera), a microphone, and a user interface (e.g., a smartphone or tablet).

[1558] Server: High-performance analysis server

[1559] Software Configuration

[1560] The following software is used:

[1561] Pre-processing software: OpenCV, ffmpeg, librosa, etc.

[1562] Generative AI models: AI models built using TensorFlow or PyTorch

[1563] Emotion recognition engine: Microsoft Azure Emotion API, etc.

[1564] Communication protocols: WebSocket, HTTP

[1565] Program processing flow

[1566] 1. Data Collection

[1567] The device uses a camera and microphone to record your pet's facial expressions, movements, and sounds in real time, and the captured data is temporarily stored in the device's storage.

[1568] 2. Pretreatment

[1569] The device performs preprocessing on the collected data, such as noise removal and data format conversion. Specifically, it uses OpenCV and ffmpeg to convert the video data format, and librosa to remove noise from the audio data.

[1570] 3. Data Transmission

[1571] The preprocessed data is sent in real time to the server using WebSocket or HTTP.

[1572] 4. Data Analysis

[1573] The server analyzes the received data using a generative AI model (using TensorFlow and PyTorch), which translates the pet's behavior and intentions into human terms, such as "feeling anxious" or "wanting to play."

[1574] 5. User Emotion Recognition

[1575] The device or server collects the user's facial and voice data and analyzes it using an emotion recognition engine. This allows the user's emotional state to be understood in real time. For example, it uses Microsoft Azure's Emotion API to determine whether the user is "stressed" or "relaxed."

[1576] 6. Notification of analysis results

[1577] The server generates data to notify the user of the pet's analysis results and sends it to the terminal via the Internet. The terminal displays the received analysis results in a GUI, allowing the user to understand the pet's intentions.

[1578] 7. Selection and display of recommended products

[1579] The server selects recommended products based on the pet analysis results and the user's emotional state. The information is then sent to the device, which displays the recommendations to the user. Past purchase history and pet preferences are also taken into consideration.

[1580] 8. Purchase and Preference Data Updates

[1581] When a user purchases a recommended product, the purchase information is sent from the device to the server, and the pet's preference data is updated. This information is used for future recommendations and matching with potential adopters.

[1582] 9. Matching potential foster parents

[1583] The server searches for suitable foster parents based on the user's pet preferences, purchasing history, and emotional state, and notifies the device of the information. If the user confirms the details and determines that the candidate is suitable, they can proceed to the next step.

[1584] Specific examples

[1585] For example, if a pet dog barks frequently, the device collects this behavioral data, preprocesses it, and sends it to the server. The server then analyzes it using a generative AI model and determines that the dog is feeling anxious. At the same time, an emotion recognition engine recognizes the user's stress level. Based on this, the server recommends a dog toy for stress relief and notifies the user. If the user purchases the toy, the preference data is updated. If the user is looking for a foster parent, suitable foster parent candidates are presented based on the updated data.

[1586] Examples of prompts include "Please tell me the analysis results of my dog's frequent barking behavior" and "Please give me the user's current emotional state and recommend products."

[1587] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1588] Step 1:

[1589] The device uses a camera and microphone to record the pet's facial expressions, behaviors, and sounds in real time. The input is video data captured by the camera and audio data collected by the microphone. The output is the collected raw data, which is temporarily stored in the device's storage.

[1590] Step 2:

[1591] The device performs preprocessing on the collected data. Specifically, it uses OpenCV for format conversion of video data and librosa for noise reduction of audio data. The input is the raw data collected in step 1. The output is data that has been denoised and converted into a format suitable for analysis.

[1592] Step 3:

[1593] The terminal sends the preprocessed data to the server in real time over the Internet. This communication uses WebSocket. The input is the preprocessed data. The output is the data that arrives at the server ready for analysis.

[1594] Step 4:

[1595] The server analyzes the received data using a generative AI model. Specifically, it uses TensorFlow to analyze the pet's behavioral patterns. The input is the data that arrives at the server. The output is the analysis results, which translate the pet's intentions into human words such as "I feel anxious" or "I want to play."

[1596] Step 5:

[1597] The device or server collects the user's facial and voice data and analyzes it using an emotion recognition engine. Specifically, the analysis is performed using Microsoft Azure's Emotion API. The input is the user's facial and voice data. The output is data indicating the user's emotional state, such as "stressed" or "relaxed."

[1598] Step 6:

[1599] The server generates data to notify the user based on the pet analysis results and sends it to the terminal via the Internet. The input is the analysis results regarding the pet's intentions and the user's emotional state. The output is data to notify the user, which is displayed on the user interface.

[1600] Step 7:

[1601] The server selects recommended products based on the pet analysis results and the user's emotional state. The selection also takes into account past purchase history and the pet's preferences. The input is data such as the analysis results, the user's emotional state, and past purchase history. The output is a list of selected recommended products, which is sent to the terminal via the Internet.

[1602] Step 8:

[1603] The user looks at the recommended products and decides whether to purchase them. If a purchase decision is made, the device sends the information to the server. The input is the user's purchase decision information. The output is updated pet preference data, which is saved on the server.

[1604] Step 9:

[1605] The server searches for suitable foster parent candidates based on pet preferences, purchase data, and the user's emotional state, and notifies the terminal of this information. The input is updated pet preference data, purchase history, and the user's emotional state. The output is information on suitable foster parent candidates, which is notified to the terminal.

[1606] (Application example 2)

[1607] 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."

[1608] In modern society, there is a demand for smoother communication between pets and their owners, improving the quality of life for both. In particular, there is a challenge in accurately understanding pet behavior and intentions and responding appropriately. Furthermore, there is a lack of services that reflect the user's emotional state. This makes it difficult to select products that meet the pet's needs and match them with suitable foster parents.

[1609] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for collecting data on the pet's facial expressions, gestures, and cries; means for preprocessing the collected data; means including a generative AI model for analyzing the preprocessed data and converting the pet's intentions into human language; means for determining and displaying recommended products based on the analysis results; means for matching with potential foster parents based on the pet's preferences and purchasing habits; means including an emotion engine for recognizing the user's emotions; means for suggesting recommended products based on the results of analysis by the generative AI model and the emotion engine and making the products available for purchase in a virtual store; means for displaying the analysis results in a user interface in real time; and means for notifying the user of the analysis results. This facilitates communication between pets and their owners, enabling accurate understanding of the pet's intentions, appropriate product suggestions and purchases, and effective matching with potential foster parents.

[1610] "Data on pets' facial expressions, gestures, and sounds" refers to recordings of the facial expressions, body movements, and sounds that pets make.

[1611] "Means of collection" refers to devices or methods for acquiring data on pets' facial expressions, gestures, and cries using devices such as cameras and microphones.

[1612] "Preprocessing means" refers to methods or processes that remove noise from collected data and convert it into a form suitable for analysis.

[1613] A "generative AI model" is an artificial intelligence model trained to analyze collected data, infer a pet's intentions, and translate them into human language.

[1614] "Means for determining and displaying recommended products" refers to a device or method that selects the most suitable products for pets based on the analysis results of the generative AI model and displays them on a user interface.

[1615] "Methods of matching potential foster parents based on pet preferences and purchasing history" refers to algorithms and processes that take into account the pet's past behavior and purchasing history to find the most suitable foster parent candidates.

[1616] The "emotion engine" is a software module that analyzes the user's facial expressions and voice to recognize the user's emotional state.

[1617] "Means for enabling purchase of products in a virtual store" refers to a device or method that provides a function that allows a user to check product information in a virtual space and purchase the product on the spot.

[1618] "Means for displaying analysis results on a user interface in real time" refers to technology or devices for displaying analyzed data on a user's display without delay.

[1619] The "means for notifying the user of the analysis results" refers to a communication technology or mechanism for communicating the analysis results to the user.

[1620] MODE FOR CARRYING OUT THE INVENTION

[1621] This invention combines a system that collects and analyzes data on pets' facial expressions, gestures, and cries, and converts the pet's intentions into human language based on that data, with an emotion engine that recognizes the user's emotions. The system aims to help users understand their pets' feelings, purchase products that meet their needs, and find suitable foster parents.

[1622] System configuration

[1623] 1. Data collection device: Cameras and microphones installed in smartphones or head-mounted displays (HMDs) collect data on pets' facial expressions, gestures, and cries in real time. The collected data undergoes pre-processing such as noise removal and data format conversion.

[1624] 2. Data transmission to the server: The preprocessed data is transmitted to the server via the Internet. Transmission is performed as close to real time as possible, and is designed to minimize data delays.

[1625] 3. Data Analysis Server: The server analyzes the received data using a generative AI model. This AI model infers the pet's intentions based on its behavioral data and converts them into human language. It also captures the user's facial expressions and voice and analyzes them with an emotion engine to understand the user's emotional state in real time.

[1626] 4. User Interface: The results of the analysis by the server are sent to the terminal via the Internet and displayed on the user interface, making it easier for the user to understand the intentions of their pet.

[1627] 5. Product Recommendation System: The server selects the most suitable pet product based on the analysis results and the user's emotional state. Specifically, it takes into account past purchase history, pet preferences, and the user's current emotional state. The selected product information is notified to the user via their terminal, and the user can view and purchase the product in the virtual store.

[1628] 6. Preference data and adoption matching: Information about purchased items is sent to the server to update the pet's preference data. This data is also used to match potential adopters, searching for and presenting suitable adopter candidates.

[1629] Hardware and software used

[1630] Hardware: Smartphone, smart glasses, head-mounted display (HMD), camera, microphone

[1631] Software: OpenCV (for image processing), EmotionAnalytics (emotion recognition module), PetBehaviorModel (pet behavior analysis model), ProductRecommender (product recommendation system)

[1632] Specific examples

[1633] For example, if a user's cat frequently licks itself, the device collects this behavioral data and sends it to the server. The server analyzes the collected data and determines that the cat is "stressed." The emotion engine simultaneously recognizes that the user is stressed. Based on this analysis, the server recommends a cat toy that is effective in relieving stress and notifies the user. When the user purchases the toy in the virtual store, the purchase information is sent to the server, and the cat's preference data is updated. Furthermore, if the user is looking for a foster parent, the updated preference data is referenced to present suitable foster parent candidates.

[1634] Prompt Sentence Examples

[1635] "Analyze footage of a cat frequently licking itself and infer the pet's intentions. Also, recognize the user's emotional state from facial expression data and suggest products that are effective in relieving stress in cats."

[1636] In this way, the present invention aims to facilitate communication between owners and pets and improve the quality of life for both by analyzing pet behavior and user emotions and providing various services based on that information.

[1637] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1638] Step 1:

[1639] Collecting pet data

[1640] Input: Pet's facial expressions, gestures, and cries

[1641] The device uses a camera and microphone installed in a smartphone or head-mounted display (HMD) to collect data on your pet's facial expressions, gestures, and cries in real time.

[1642] Output: Collected data (video data, audio data)

[1643] Step 2:

[1644] Data Preprocessing

[1645] Input: Collected data (video data, audio data)

[1646] The device pre-processes the collected data, which includes noise removal, grayscale conversion of images, and filtering of audio data.

[1647] Output: Pre-processed data (processed video data, audio data)

[1648] Step 3:

[1649] Sending data to the server

[1650] Input: Preprocessed data (processed video data, audio data)

[1651] The terminals then transmit the pre-processed data to a server via the internet, with the transmission being designed to occur as quickly as possible in real time, minimizing data latency.

[1652] Output: Data sent to the server

[1653] Step 4:

[1654] Data analysis

[1655] Input: Data sent to the server (processed video data, audio data)

[1656] The server analyzes the received data using a generative AI model, which infers the pet's intentions based on the behavioral data and translates them into human language.

[1657] Output: Analysis result that indicates the pet's intention (e.g. "I feel stressed")

[1658] Step 5:

[1659] User Emotion Recognition

[1660] Input: User's facial expressions and voice

[1661] The device captures the user's facial expressions and voice and analyzes them with an emotion engine, allowing the device to grasp the user's emotional state in real time.

[1662] Output: Analysis results showing the user's emotions (e.g., "stressed state")

[1663] Step 6:

[1664] Notification of analysis results

[1665] Input: Analysis results showing the pet's intentions, analysis results showing the user's emotions

[1666] The server generates data for notifying the user interface of the analysis results and transmits the data to the terminal via the Internet.

[1667] Output: Analysis results sent to the device

[1668] Step 7:

[1669] Display in the user interface

[1670] Input: Analysis results sent to the device

[1671] The terminal displays the received analysis results on a user interface, allowing the user to understand the pet's intentions.

[1672] Output: Analysis results displayed on the user interface

[1673] Step 8:

[1674] Product suggestion

[1675] Input: Analysis results showing the pet's intentions, analysis results showing the user's emotions

[1676] The server selects the most suitable pet products based on the analysis results and the user's emotional state, taking into account past purchase history, pet preferences, and the user's current emotional state.

[1677] Output: Selected recommended products

[1678] Step 9:

[1679] Buying products in a virtual store

[1680] Input: Selected recommended products

[1681] The user checks the product information displayed in the virtual store and purchases the product.

[1682] Output: Purchase information

[1683] Step 10:

[1684] Preference data updates and foster parent matching

[1685] Input: Purchase information

[1686] The device sends the purchase information to the server, which updates the pet's preference data. Based on this, the server searches for suitable potential adopters.

[1687] Output: Updated preference data, suitable foster parents

[1688] 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.

[1689] 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.

[1690] 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.

[1691] 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.

[1692] 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.

[1693] 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.

[1694] 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).

[1695] 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.

[1696] 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."

[1697] 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.

[1698] 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).

[1699] 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.

[1700] 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.

[1701] 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.

[1702] 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.

[1703] 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.

[1704] 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.

[1705] 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.

[1706] 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.

[1707] 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.

[1708] 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.

[1709] The following is further disclosed regarding the above embodiment.

[1710] (Claim 1)

[1711] A means of collecting data on pets' facial expressions, gestures, and cries,

[1712] a means for pre-processing the collected data;

[1713] means for analyzing the pre-processed data and translating the pet's intent into human language, the means including a generative AI model;

[1714] A means for determining and displaying recommended products based on the analysis results;

[1715] The system includes a means of matching potential foster parents based on pet preferences and purchasing habits.

[1716] (Claim 2)

[1717] 10. The system according to claim 1, further comprising means for notifying a user of the analysis results.

[1718] (Claim 3)

[1719] 10. The system of claim 1, further comprising means for transmitting the collected data to a server in real time.

[1720] "Example 1"

[1721] (Claim 1)

[1722] A means of collecting data on pets' facial expressions, gestures, and cries,

[1723] a means for pre-processing the collected data;

[1724] means for transmitting the preprocessed data to a server in real time;

[1725] means for analyzing the pre-processed data and translating the pet's intent into human language, the means including a generative AI model;

[1726] A means for determining and displaying recommended products based on the analysis results;

[1727] The system includes a means of matching potential foster parents based on pet preferences and purchasing habits.

[1728] (Claim 2)

[1729] 10. The system according to claim 1, further comprising means for notifying a user of the analysis results.

[1730] (Claim 3)

[1731] 2. The system according to claim 1, further comprising means for, when the user decides to purchase a product, transmitting purchase information to the server and updating the pet preference data.

[1732] "Application Example 1"

[1733] (Claim 1)

[1734] A means for collecting pet behavior data;

[1735] a means for pre-processing the collected data;

[1736] means for analyzing the pre-processed data and translating the pet's intent into human language, the means including a generative AI model;

[1737] A means for determining and displaying recommended products based on the analysis results;

[1738] A method to match potential foster parents based on pet preferences and purchasing information,

[1739] The system includes means including a visual display capable of displaying analysis results and product suggestions in real time.

[1740] (Claim 2)

[1741] 10. The system of claim 1, further comprising means for visually notifying a user of the analysis results.

[1742] (Claim 3)

[1743] 10. The system of claim 1, further comprising means for transmitting the collected data to a cloud server in real time.

[1744] "Example 2: Combining Emotion Engines"

[1745] (Claim 1)

[1746] A means of collecting data on pets' facial expressions, gestures, and cries,

[1747] a means for pre-processing the collected data;

[1748] means for transmitting the preprocessed data to a server;

[1749] means for analyzing the transmitted data and translating the pet's intent into human language, the means including a model for translating the pet's intent into human language;

[1750] means for recognizing a user's emotion based on the analysis result;

[1751] A means for determining and displaying recommended products based on the analysis results and the user's emotional state;

[1752] The system includes a means of matching potential foster parents based on pet preferences and purchasing habits.

[1753] (Claim 2)

[1754] 10. The system according to claim 1, further comprising means for notifying a user of the analysis results.

[1755] (Claim 3)

[1756] 10. The system of claim 1, further comprising means for transmitting the collected data to a server in real time.

[1757] "Application example 2 when combining emotion engines"

[1758] (Claim 1)

[1759] A means of collecting data on pets' facial expressions, gestures, and cries,

[1760] a means for pre-processing the collected data;

[1761] means for analyzing the pre-processed data and translating the pet's intent into human language, the means including a generative AI model;

[1762] A means for determining and displaying recommended products based on the analysis results;

[1763] A method to match potential foster parents based on pet preferences and purchasing habits,

[1764] means including an emotion engine for recognizing an emotion of a user;

[1765] A means to recommend products based on the results of analysis by the generative AI model and emotion engine, and make the products available for purchase in a virtual store;

[1766] a means for displaying the analysis results in a user interface in real time;

[1767] Means for notifying the user of the analysis results

[1768] A system including:

[1769] (Claim 2)

[1770] 10. The system of claim 1, further comprising means for transmitting the collected data to a server in real time.

[1771] (Claim 3)

[1772] 10. The system according to claim 1, further comprising means for providing information about pet products in the virtual store and promoting purchases. [Explanation of symbols]

[1773] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. A means of collecting data on pets' facial expressions, gestures, and cries, a means for pre-processing the collected data; means for analyzing the pre-processed data and translating the pet's intent into human language, the means including a generative AI model; A means for determining and displaying recommended products based on the analysis results; The system includes a means of matching potential foster parents based on pet preferences and purchasing habits.

2. The system of claim 1 further comprising means for notifying a user of the analysis results.

3. 10. The system of claim 1, further comprising means for transmitting the collected data to a server in real time.

Images