System

Abstract

A system is provided.SOLUTION: A system comprising: means for collecting animal vocalizations and behavior; means for analyzing the collected vocalizations and behavior; means for translating the results of the analysis into human language; means for displaying the results of the translation to a user; and means for communicating the user's input back to the animal.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] Due to the impact of COVID-19 and rising prices, visitor numbers at zoos and pet-owning households have decreased, making it difficult to restore visitor numbers using traditional communication methods. Furthermore, there is a lack of technology that allows people to enjoy interacting with animals in new ways. Given this situation, there is a need to provide tools that allow people and animals to communicate in real time, thereby increasing visitor numbers, improving average customer spending, and providing new animal interaction experiences. [Means for solving the problem]

[0005] To solve the above problems, the present invention provides a system including a means for collecting animal sounds and behaviors, a means for analyzing the collected sounds and behaviors, a means for translating the analyzed results into human language, a means for displaying the translated results to the user, and a means for retransmitting the user's input to the animals. This system also includes a means for analyzing and translating the sounds and behaviors on the cloud, and a means for enabling translation in multiple languages, thereby realizing a new means of communication with animals for a diverse range of users.

[0006] "Animals" is a general term for living things such as pets, livestock, and wildlife, whose sounds and behavior can be analyzed by AI systems.

[0007] "Cry" refers to one of the means of communication that animals use sound, and has different characteristics depending on the species.

[0008] "Behavior" refers to the movements and gestures that animals show by moving their bodies, and is a way of expressing their intentions, emotions, etc.

[0009] "Collection means" refers to devices or software used to record or film animal sounds and behavior.

[0010] "Means of analysis" refers to algorithms and software that analyze the collected sounds and behaviors as digital data and extract their meaning and intentions.

[0011] "Translation means" refers to technology or software that converts the analyzed animal sounds and behavior data into human language.

[0012] "Display means" refers to devices or software for presenting the translation results to the user in the form of text, audio, or other formats.

[0013] The "cloud" is a network of computer resources and services provided over the Internet, and refers to a platform for online data analysis and translation.

[0014] "Multilingual support" refers to the ability to display or provide translation results in multiple languages, making them available to users who speak different languages. [Brief explanation of the drawings]

[0015] [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

[0016] 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.

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

[0018] 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).

[0019] 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.

[0020] 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.

[0021] 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.

[0022] 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."

[0023] [First embodiment]

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

[0025] 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.

[0026] 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).

[0027] 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.

[0028] 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.

[0029] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form 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.

[0030] 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.

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

[0032] 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.

[0033] 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.

[0034] 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.

[0035] 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."

[0036] This system collects and analyzes animal sounds and behaviors, translates them into human language, and displays them to the user, thereby realizing a new form of communication. Below, an embodiment of the present invention will be described in detail.

[0037] Overall system configuration

[0038] The system consists of three main components:

[0039] 1. Device (smartphone or tablet)

[0040] 2. Server

[0041] 3. Users

[0042] System Operation Overview

[0043] 1. Data collection by device

[0044] The user logs in to the device and starts the application. The application displays a screen for selecting the conversation mode with the animal.

[0045] Once the user selects a type of animal, the device will activate the camera and microphone to record the animal's sounds and movements in real time.

[0046] The collected data is temporarily stored on the device and then sent to a server for analysis.

[0047] 2. Data analysis and translation by the server

[0048] The server receives the audio and video data sent from the terminal. The received data is first decompressed.

[0049] The sound analysis engine analyzes the animal's cries, and as a result, the animal's emotions and intentions are extracted.

[0050] The video data is then run through motion analysis algorithms to extract more detailed information about the animal's behavior.

[0051] The extracted information is compared with a database and translated into the corresponding human language. These translation results are sent to the device as text data.

[0052] 3. Terminal display and user input

[0053] The device receives translation results from the server and displays them in real time, with the option to display them as text or speech synthesis.

[0054] The user sees the translation results and responds to the animal. Responses can be entered by text or voice.

[0055] 4. Analysis of user responses on the server

[0056] The user's response is sent from the terminal to the server.

[0057] The server analyzes the user's responses and converts the text data back into sounds and signs that the animals can understand.

[0058] The completed data is sent to a terminal, which outputs sounds and signs to the animal.

[0059] Explanation with concrete examples

[0060] Example: Conversation with your dog

[0061] User

[0062] 1. Launch the app on your smartphone and select "Conversation Mode."

[0063] 2. Select "Shiba Inu" as the type of dog.

[0064] Terminal

[0065] 1. Turn on the microphone and camera and record the Shiba Inu's barks and movements in real time.

[0066] 2. The collected data is compressed and sent to the server.

[0067] server

[0068] 1. The voice analysis engine analyzes the Shiba Inu's bark and extracts that the "woof woof" bark means "Welcome home! Let's play!"

[0069] 2. Using a video analysis algorithm, the Shiba Inu's movements are analyzed, and the tail-wagging behavior is also extracted as information that supports the above message.

[0070] 3. The analysis results are translated into human language and the text "Welcome back! Let's play!" is sent to the device.

[0071] Terminal

[0072] 1. The translation result is displayed to the user as text, and the speech synthesis feature plays back "Welcome back! Let's play!"

[0073] User

[0074] 1. Look at the message that appears and reply, "Wait a moment, I'll play with you soon." Use voice input to reply.

[0075] Terminal

[0076] 1. Analyze voice input, convert it into text, and send it to the server.

[0077] server

[0078] 1. Analyze the user's response and convert it into a sound or sign that the dog can understand, saying "Wait a moment, I'll play with you soon."

[0079] 2. Send the conversion result to the device.

[0080] Terminal

[0081] 1. Output the converted animal sounds and signs, and display whether the animal barks "woof woof."

[0082] The above is an embodiment of the present invention. This system allows users to enjoy communicating with animals in a new way.

[0083] The processing flow will be explained below.

[0084] Step 1:

[0085] User

[0086] Launch the app on your smartphone and log in.

[0087] Select "Conversation Mode" from the app's main screen.

[0088] Step 2:

[0089] Terminal

[0090] When a user selects conversation mode, the device requests access to GPS information, the camera, and the microphone.

[0091] The terminal displays a screen for selecting the type of animal, and the user selects the type of animal (e.g., dog).

[0092] Step 3:

[0093] Terminal

[0094] Activate the camera and microphone to record the sounds and movements of a selected animal (e.g., a dog) in real time.

[0095] The collected audio and video data is temporarily stored on the device.

[0096] Step 4:

[0097] Terminal

[0098] The collected data is compressed and a data packet is generated for transmission to the server.

[0099] The data packet is sent to the server using the HTTPS protocol or the like.

[0100] Step 5:

[0101] server

[0102] It receives data packets sent from the terminal and performs decompression processing.

[0103] The decompressed audio and video data is prepared for analysis.

[0104] Step 6:

[0105] server

[0106] The collected call data is analyzed using an audio analysis engine to extract call patterns.

[0107] Based on the extracted patterns, the animal's emotions and intentions are matched with a database to identify meaning.

[0108] Step 7:

[0109] server

[0110] The video data is run through a motion analysis algorithm to extract the animal's behavioral patterns.

[0111] The extracted behavioral patterns are also compared with a database to identify the animal's intentions.

[0112] Step 8:

[0113] server

[0114] The speech analysis data and motion analysis data are integrated to determine the final meaning.

[0115] The determined meaning is translated into human language using a natural language generation engine and prepared as text data.

[0116] Step 9:

[0117] server

[0118] The translated text data is packaged into data packets and sent to the terminal.

[0119] Step 10:

[0120] Terminal

[0121] The data packet received from the server is decompressed to obtain the text data of the translation result.

[0122] The translation results are displayed to the user (e.g., as text, or read aloud using speech synthesis).

[0123] Step 11:

[0124] User

[0125] Check the translation results and reply to the animal by text input or voice input.

[0126] Step 12:

[0127] Terminal

[0128] The user's response is saved as text or audio data and prepared for transmission to the server.

[0129] Step 13:

[0130] Terminal

[0131] The user's response data is converted into a data packet and sent to the server.

[0132] Step 14:

[0133] server

[0134] The user's response data is received and decompressed.

[0135] The decompressed audio data is analyzed and converted into text.

[0136] Step 15:

[0137] server

[0138] The user's response text is compared with a database to convert it into sounds and signs that animals can understand.

[0139] Step 16:

[0140] server

[0141] The generated data of sounds and signs is packaged into data packets and sent to the terminal.

[0142] Step 17:

[0143] Terminal

[0144] The data packets of sounds and signs received from the server are decompressed and the output data is obtained.

[0145] The output data is used to communicate with the animal through a speaker or display (e.g., playing a sound from a speaker or showing a sign on a display).

[0146] The above is a specific process flow for carrying out the invention, which allows users to enjoy real-time conversations with animals.

[0147] Example 1

[0148] 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."

[0149] Conventional methods of communication with animals have relied primarily on intuitive interpretation of animal behavior and calls, resulting in incomplete communication between humans and animals. Furthermore, many animal communication systems transmit analysis results in only one direction, making it difficult to establish two-way communication. Furthermore, it is difficult to complete analysis of animal calls and behavior within a device due to limitations in processing power. An efficient, two-way communication system that can solve these issues is needed.

[0150] 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.

[0151] In this invention, the server includes means for enabling a user to launch an application and log in, means for activating a camera and microphone to collect sounds and behaviors of animals based on the type of animal selected by the user, means for compressing the collected audio and video data and transmitting it to the server, means for decompressing and analyzing the data in the server and extracting the animal's emotions and intentions using a voice analysis engine and a motion analysis algorithm, means for comparing the extracted information with a database and translating it into human language, means for transmitting the translated text data to a terminal and displaying it to the user as text or synthesized speech, means for receiving the user's response as voice or text input and transmitting it again to the server, and means for converting the user's response in the server into sounds or signs that the animal can understand and transmitting them to the terminal for output, thereby enabling two-way and efficient communication between humans and animals.

[0152] A "user" is a person who uses the system to communicate with animals.

[0153] An "application" is a software program that runs on a smartphone or tablet and is used to collect and analyze animal sounds and behavior.

[0154] A "camera" is a photographic device used to capture the appearance and behavior of animals.

[0155] A "microphone" is an audio input device for recording animal sounds.

[0156] "Collected audio and video data" refers to data of animal sounds and movements recorded by the user's terminal.

[0157] "Compression" is a process for reducing the size of data.

[0158] A "server" is a remote computing device that receives, analyzes, and interprets collected data.

[0159] "Decompression" is the process of restoring compressed data to its original state.

[0160] A "voice analysis engine" is specialized software that analyzes animal sounds and extracts their emotions and intentions.

[0161] A "motion analysis algorithm" is a computational method for analyzing animal movements and extracting information from their behavior.

[0162] A "database" is an information collection device for storing and collating extracted information.

[0163] "Translation" is the process of converting animal emotions and intentions into human language.

[0164] "Text data" refers to data that expresses the translation results as text information.

[0165] "Speech synthesis" is a technology that converts text data into speech and plays it back.

[0166] A "response" refers to a response that the user makes to the animal.

[0167] "Calls and signs" are audio and visual cues that animals can understand.

[0168] This invention is a system that collects and analyzes animal sounds and behaviors, translates them into human language, and displays them to the user, thereby realizing a new form of communication. Specific embodiments of the invention are described below.

[0169] Overall system configuration

[0170] This system is mainly composed of three elements: "terminals," "servers," and "users."

[0171] Users launch applications and log in

[0172] The user launches the animal conversation application on their smartphone or tablet. Next, a login screen appears, and the user enters their account information to log in. After logging in, the user selects "Conversation Mode" and specifies the type of animal.

[0173] Data collection by terminal

[0174] When the user selects an animal, the device's camera and microphone are activated to capture the animal's sounds and behavior in real time. The collected audio and video data is temporarily stored in the device's memory, then compressed and sent to the server.

[0175] Data reception and decompression by the server

[0176] The server receives the audio and video data sent from the device. The received data is first decompressed. The audio data is sent to an audio analysis engine to analyze the animal's calls. The video data is sent to a motion analysis algorithm to analyze the animal's behavior.

[0177] Server-based parsing and translation

[0178] The voice analysis engine extracts emotions and intentions from the animal's cries. The motion analysis algorithm analyzes the animal's behavior from the video data and extracts more detailed information. The extracted information is compared with a database and the animal's emotions and intentions are translated into human language. The translation results are generated as text data and sent to the device.

[0179] Displaying translation results on your device

[0180] The device displays the translation results received from the server in real time. The display format can be selected as text or speech synthesis. The user can check the translation results on the device screen.

[0181] User response input

[0182] The user can then respond to the animal based on the translation results. Responses can be made by text input or voice input. For example, the user can say, "Just wait a moment, I'll play with you soon."

[0183] Response analysis and retranslation by the server

[0184] The server receives and analyzes the user's response data sent from the device. The analyzed text data is converted into sounds and signs that the animal can understand. The converted results are then sent back to the device.

[0185] Terminal response output

[0186] The device receives the converted data from the server and plays it back to the animal. The converted sound or sign is output to the animal. For example, it may respond to the user by barking "woof woof" or display a specific action.

[0187] Specific examples

[0188] Take the example of a conversation with your dog.

[0189] User

[0190] 1. Launch the app on your smartphone and select "Conversation Mode."

[0191] 2. Select "Shiba Inu" as the type of dog.

[0192] Terminal

[0193] 1. Turn on the camera and microphone to record the Shiba Inu's barks and movements in real time.

[0194] 2. The collected data is compressed and sent to the server.

[0195] server

[0196] 1. The voice analysis engine analyzes the Shiba Inu's bark and extracts that the "woof woof" bark means "Welcome home! Let's play!"

[0197] 2. The Shiba Inu's movements are analyzed using a video analysis algorithm, and the tail-wagging behavior is also extracted as information that supports the above message.

[0198] 3. The analysis results are translated into human language and the text "Welcome back! Let's play!" is sent to the device.

[0199] Terminal

[0200] 1. The translation result is displayed to the user as text, and the synthesized speech plays "Welcome back! Let's play!"

[0201] User

[0202] 1. Look at the message that appears and reply by voice, "Just wait a moment, I'll play with you soon."

[0203] Terminal

[0204] 1. Analyze voice input, convert it into text, and send it to the server.

[0205] server

[0206] 1. Analyze the user's response and convert it into a sound or sign that the dog can understand, saying "Wait a moment, I'll play with you soon."

[0207] 2. Send the conversion result to the device.

[0208] Terminal

[0209] 1. Output the converted animal sounds or signs, such as "woof woof" or displaying a specific action.

[0210] This system allows users to enjoy communicating with animals in a new way.

[0211] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0212] Step 1:

[0213] Users launch applications and log in

[0214] The user launches the animal conversation application on their smartphone or tablet and enters their account information on the login screen to log in. Based on the entered account information, the server performs authentication processing, and if authentication is successful, the conversation mode selection screen is displayed.

[0215] Input: Account information (username, password)

[0216] Output: Conversation mode selection screen

[0217] Step 2:

[0218] Selecting the animal species on the device and starting data collection

[0219] When a user selects an animal type on the application, the device's camera and microphone are activated and real-time recording of the animal's sounds and behavior begins. The collected audio and video data is temporarily stored in the device's memory.

[0220] Input: Animal species information

[0221] Output: Temporary storage of bird sounds and video data

[0222] Step 3:

[0223] Data compression by the terminal and transmission to the server

[0224] The device compresses the audio and video data it has temporarily stored and sends it to the server. Compression reduces the amount of data, making transmission more efficient.

[0225] Input: Stored audio and video data

[0226] Output: Compressed audio and video data

[0227] Step 4:

[0228] Server receives and decompresses data

[0229] The server receives the compressed audio and video data sent from the terminal, and then decompresses the data to restore it to its original state.

[0230] Input: Compressed audio and video data

[0231] Output: Uncompressed audio and video data

[0232] Step 5:

[0233] Server-based voice and behavior analysis

[0234] The server sends the decompressed audio data to an audio analysis engine that analyzes the animal's calls to extract emotions and intentions, while simultaneously sending the decompressed video data to a motion analysis algorithm that analyzes the animal's behavior to extract more detailed information.

[0235] Input: Uncompressed audio and video data

[0236] Output: Extracted emotions, intents, and analyzed behavioral information

[0237] Step 6:

[0238] Translation by the server and data transmission to the device

[0239] The server compares the extracted emotions, intentions, and behavioral information with a database and translates it into human language. The translation results are generated as text data and sent to the device.

[0240] Input: Extracted emotions, intentions, and behavioral information

[0241] Output: Translated text data

[0242] Step 7:

[0243] Displaying translation results on your device

[0244] The device displays the translation results received from the server in real time, and the user can check the translation results either as text or through voice synthesis.

[0245] Input: Translated text data

[0246] Output: Text display or speech synthesis

[0247] Step 8:

[0248] User response input

[0249] The user can then respond to the animal based on the translation results displayed. Responses can be made by text input or voice input, and the device will analyze the response and send it to the server.

[0250] Input: User response (text or voice)

[0251] Output: Response data sent to the server

[0252] Step 9:

[0253] Response analysis and retranslation by the server

[0254] The server analyzes the received user response data and converts the text data into sounds and signs that the animals can understand, then sends the converted results back to the device.

[0255] Input: User response data

[0256] Output: Animal-understandable sounds and signs

[0257] Step 10:

[0258] Response output to terminal

[0259] The terminal plays the response data received from the server to the animal and outputs the converted sounds and signs.

[0260] Input: Animal sounds and signs that animals can understand

[0261] Output: Animal sounds and signs

[0262] This allows for two-way communication between the user and the animal.

[0263] (Application example 1)

[0264] 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."

[0265] In the past, there were many barriers to communication between humans and animals, making it difficult to accurately understand what animals needed. Furthermore, there was a lack of means to understand an animal's dietary requirements and health status in real time and provide appropriate food, which made animal health management a challenge.

[0266] 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.

[0267] In this invention, the server includes means for collecting animal sounds and behavior, means for analyzing the collected sounds and behavior, means for translating the analysis results into human language, means for displaying the translated results to the user, means for transmitting the user's input back to the animal, and means for selecting and delivering food for the animal based on the analysis results, which makes it possible to accurately grasp the needs and health status of the animal and provide appropriate food in real time.

[0268] "Means for collecting animal sounds and behavior" refers to equipment and software that records animal sounds and behavior in real time.

[0269] "Means for analyzing calls and behavior" refers to algorithms and programs that analyze collected data on calls and behavior and understand their intentions and emotions.

[0270] The "means of translation into human language" is software that converts the analyzed animal sounds and behavior into the corresponding human language.

[0271] "Means for displaying to the user" refers to a device or application that presents the translated data to the user visually or audibly.

[0272] "Means for transmitting user input back to the animal" refers to technology for converting instructions and responses from the user into a format that the animal can understand and transmitting them to the animal.

[0273] "Means for selecting and delivering animal food based on analysis results" is a system for understanding the needs of animals, selecting the most suitable food for them, and delivering it.

[0274] A system for carrying out this invention includes technology for collecting and analyzing animal sounds and behavior, translating the results into human language, and displaying them. It also includes means for selecting and delivering animal food based on the analysis results. Specific embodiments for carrying out this invention are described below.

[0275] Overall system configuration

[0276] The system consists of four main components:

[0277] 1. Device (smartphone or tablet)

[0278] 2. Server

[0279] 3. Robot

[0280] 4. Users

[0281] System Operation Overview

[0282] Data collection by terminal

[0283] The user logs in to the device and starts the application. The application displays a screen for selecting the conversation mode with the animal.

[0284] Once the user selects an animal, the device activates its camera and microphone to record the animal's sounds and behavior in real time. The collected data is temporarily stored on the device and then sent to a server for analysis.

[0285] Data analysis and translation by server

[0286] The server receives the audio and video data sent from the device. The received data is first decompressed. An audio analysis engine (using, for example, PyAudio) analyzes the animal's sounds, and extracts the animal's emotions and intentions.

[0287] The video data is then run through a motion analysis algorithm (using OpenCV, for example) to extract more detailed information about the animal's behavior. The extracted information is then compared with a database and translated into the corresponding human language. These translations are then sent to the device as text data.

[0288] Terminal display and user input

[0289] The device displays the translation results received from the server in real time. The display format can be selected as text or speech synthesis. The user looks at the displayed translation results and responds to the animal. Responses can be entered as text or speech. The user's response is sent from the device to the server.

[0290] Analysis of user responses on the server

[0291] The server analyzes the user's response and converts the text data back into sounds and signs that the animal can understand. The completed data is sent to the terminal, which then outputs the sounds and signs to the animal.

[0292] Select and deliver animal food based on the analysis results

[0293] The server includes a means for selecting food for the animals based on the analysis results. The selected food is provided to the animals by a delivery agent (e.g., an automated delivery robot). This allows food to be supplied in real time according to the needs of the animals.

[0294] Specific examples

[0295] User

[0296] 1. Launch the app on your smartphone and select "Conversation Mode."

[0297] 2. Select "Shiba Inu" as the type of dog.

[0298] Terminal

[0299] 1. Turn on the microphone and camera and record the Shiba Inu's barks and movements in real time.

[0300] 2. The collected data is compressed and sent to the server.

[0301] server

[0302] 1. The voice analysis engine analyzes the Shiba Inu's bark and extracts that the "woof woof" bark means "Welcome home! Let's play!"

[0303] 2. Using a video analysis algorithm, the Shiba Inu's movements are analyzed, and the tail-wagging behavior is also extracted as information that supports the above message.

[0304] 3. The analysis results are translated into human language and the text "Welcome back! Let's play!" is sent to the device.

[0305] Terminal

[0306] 1. The translation result is displayed to the user as text, and the speech synthesis feature plays back "Welcome back! Let's play!"

[0307] User

[0308] 1. Look at the message that appears and reply, "Wait a moment, I'll play with you soon." Use voice input to reply.

[0309] server

[0310] 1. Analyze the user's response and convert it into a sound or sign that the dog can understand, saying "Wait a moment, I'll play with you soon."

[0311] 2. Send the conversion result to the device.

[0312] Terminal

[0313] 1. Output the converted animal sounds and signs, and display whether the animal barks "woof woof."

[0314] Prompt Sentence Examples

[0315] "Animal food delivery assistant system. Records animal sounds and behaviors and analyzes them using an audio analysis engine and video analysis algorithm. Based on the requests understood from these, the system selects and delivers the appropriate food. Assume the server API URL is example.com / api and the animal type is dog."

[0316] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0317] Step 1:

[0318] Data collection by terminal

[0319] The user logs in to the device and launches the application. After selecting the animal conversation mode and the type of animal, the device activates the camera and microphone. The camera and microphone are used to record and record the animal's cries and behavior in real time. This captures the animal's audio and video data, which is then temporarily stored on the device.

[0320] Input: User login information, animal type selection

[0321] Output: Recorded audio and video data

[0322] Step 2:

[0323] Sending data to the server

[0324] The device compresses the temporarily stored audio and video data and sends it to the server for analysis. Specifically, it uses a data compression algorithm to reduce the size of the audio data and video frames and uploads them to the server via an HTTP request.

[0325] Input: Recorded audio and video data

[0326] Output: Compressed audio and video data is sent to the server

[0327] Step 3:

[0328] Data analysis by server

[0329] The server receives the audio and video data sent from the device and performs the decompression process. The received data is analyzed by an audio analysis engine (e.g., PyAudio) to extract the animal's voice and its emotion and intention. In parallel, the video data is analyzed by a motion analysis algorithm (e.g., OpenCV) to extract detailed information about the animal's behavior.

[0330] Input: Compressed audio and video data

[0331] Output: Analyzed sentiment, intent, and behavior details

[0332] Step 4:

[0333] Server-based translation

[0334] The server translates the analyzed audio and video data into human language. The extracted information is compared with a database and converted into the corresponding human language. The translation result is sent to the device in text format.

[0335] Input: Analyzed sentiment, intent, and behavior details

[0336] Output: Text data translated into human language

[0337] Step 5:

[0338] Display by terminal

[0339] The device receives translation results from the server and displays them in real time. Users can choose to display them as text or speech synthesis, allowing them to intuitively understand the animal's message.

[0340] Input: Text data translated into human language

[0341] Output: Text or audio displayed to the user

[0342] Step 6:

[0343] User response

[0344] The user sees the translation results and responds to the animal using text input or voice input, and the input data is sent from the device to the server.

[0345] Input: User's text or voice response

[0346] Output: User response data sent to the server

[0347] Step 7:

[0348] Server response analysis

[0349] The server analyzes the user's response and converts the text data back into sounds and signs that the animal can understand, and the results are sent to the device.

[0350] Input: User response data

[0351] Output: Animal-understandable sounds and signs

[0352] Step 8:

[0353] Terminal Output

[0354] The device outputs the converted sounds and signs to the animal, thereby communicating the user's intentions to the animal.

[0355] Input: Animal-understandable sounds and signs

[0356] Output: Actual sounds and signs

[0357] Step 9:

[0358] Food delivery based on analysis results

[0359] The server selects food for the animal based on the analysis results. The selected food is provided to the animal by a delivery agent (e.g., an automated delivery robot). The user can confirm the selection results and finalize the order.

[0360] Input: Analyzed animal needs data

[0361] Output: Selected food order and delivery

[0362] 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.

[0363] This system realizes a new form of communication by collecting and analyzing animal sounds and behaviors, translating them into human language, and displaying them to the user. The present invention further improves the quality of communication by combining it with an emotion engine that analyzes the user's emotions.

[0364] Overall system configuration

[0365] The system consists of four main components:

[0366] 1. Device (smartphone or tablet)

[0367] 2. Server

[0368] 3. Users

[0369] 4. Emotion Engine

[0370] System Operation Overview

[0371] 1. Data collection by device

[0372] The user logs in to the device and starts the application. The application displays a screen for selecting the conversation mode with the animal.

[0373] When the user selects an animal type, the device activates the camera and microphone to record the animal's sounds and movements, as well as the user's voice and facial expressions, in real time.

[0374] The collected data is temporarily stored on the device and then sent to a server for analysis.

[0375] 2. Data analysis and translation by the server

[0376] The server receives the audio and video data sent from the terminal. The received data is first decompressed.

[0377] The voice analysis engine analyzes the animal's cries and extracts the animal's emotions and intentions.

[0378] The video data is then run through motion analysis algorithms to extract more detailed information about the animal's behavior.

[0379] The emotion engine analyzes the user's voice and facial expressions to detect the user's emotional state.

[0380] The extracted information is compared with a database and translated into the corresponding human language. These translation results and the user's emotional state are sent to the device as text data.

[0381] 3. Terminal display and user input

[0382] The device displays the translation results received from the server and the user's emotional state in real time, and can be displayed in text or speech synthesis.

[0383] The user sees the translation results and responds to the animal. Responses can be entered by text or voice.

[0384] 4. Analysis of user responses on the server

[0385] The user's response is sent from the terminal to the server.

[0386] The server analyzes the user's responses and converts the text data back into sounds and signs that the animals can understand.

[0387] The completed data is sent to a terminal, which outputs sounds and signs to the animal.

[0388] Explanation with concrete examples

[0389] Example: Conversation with your dog

[0390] User

[0391] 1. Launch the app on your smartphone and select "Conversation Mode."

[0392] 2. Select "Shiba Inu" as the type of dog.

[0393] Terminal

[0394] 1. The microphone and camera are activated to record and record the Shiba Inu's barks and movements in real time, and also collect the user's voice and facial expressions.

[0395] 2. The collected data is compressed and sent to the server.

[0396] server

[0397] 1. The voice analysis engine analyzes the Shiba Inu's bark and extracts that the "woof woof" bark means "Welcome home! Let's play!"

[0398] 2. Using a video analysis algorithm, the Shiba Inu's movements are analyzed, and the tail-wagging behavior is also extracted as information that supports the above message.

[0399] 3. The emotion engine analyzes the user's voice and facial expressions to detect when the user is in an emotional state of "joy."

[0400] 4. The analysis results and emotional state are translated into human language, and the text "Welcome back! Let's play!" and the information "The user is happy" are sent to the device.

[0401] Terminal

[0402] 1. The translation result and emotional state are displayed to the user as text, and the synthesized voice phrase "Welcome back! Let's play!" is played.

[0403] User

[0404] 1. Look at the message that appears and reply, "Wait a moment, I'll play with you soon." Use voice input to reply.

[0405] Terminal

[0406] 1. Analyze voice input, convert it to text, and send it to the server.

[0407] server

[0408] 1. Analyze the user's response and convert it into a sound or sign that the dog can understand, saying "Wait a moment, I'll play with you soon."

[0409] 2. Send the conversion result to the device.

[0410] Terminal

[0411] 1. Output the converted animal sounds and signs, and display whether the animal barks "woof woof."

[0412] The above is a specific example of how to implement the present invention by incorporating an emotion engine. This process allows users to enjoy real-time conversations with animals, and the use of the emotion engine further improves the quality of communication.

[0413] The processing flow will be explained below.

[0414] Step 1:

[0415] User

[0416] Launch the app on your smartphone and log in.

[0417] Select "Conversation Mode" from the main screen.

[0418] Step 2:

[0419] Terminal

[0420] When a user selects conversation mode, the device requests access to GPS information, the camera, and the microphone.

[0421] The terminal displays a screen for selecting the type of animal, and the user selects the type of animal (e.g., dog).

[0422] Step 3:

[0423] Terminal

[0424] Activate the camera and microphone to record the sounds and movements of a selected animal (e.g., a dog) in real time.

[0425] At the same time, the user's facial expressions and voice are also collected.

[0426] The collected audio data, video data, and user voice and facial expression data are temporarily stored on the terminal.

[0427] Step 4:

[0428] Terminal

[0429] The collected data is compressed and a data packet is generated for transmission to the server.

[0430] The data packet is sent to the server using the HTTPS protocol or the like.

[0431] Step 5:

[0432] server

[0433] It receives data packets sent from the terminal and performs decompression processing.

[0434] The decompressed audio data, video data, and user voice and facial expression data are prepared for analysis.

[0435] Step 6:

[0436] server

[0437] The collected call data is analyzed using an audio analysis engine to extract call patterns.

[0438] Based on the extracted patterns, the animal's emotions and intentions are matched with a database to identify meaning.

[0439] Step 7:

[0440] server

[0441] The video data is run through a motion analysis algorithm to extract the animal's behavioral patterns.

[0442] The extracted behavioral patterns are also compared with a database to identify the animal's intentions.

[0443] Step 8:

[0444] server

[0445] The emotion engine is used to analyze the user's voice and facial expressions to detect the user's emotional state.

[0446] The speech analysis data, movement analysis data, and emotion analysis data are integrated to determine the final meaning.

[0447] Step 9:

[0448] server

[0449] The determined meaning is translated into human language using a natural language generation engine and prepared as text data.

[0450] The translated text data and emotional state data are packaged into a data packet and transmitted to the terminal.

[0451] Step 10:

[0452] Terminal

[0453] The data packet received from the server is decompressed to obtain the translation result text data and emotional state data.

[0454] Display the translation results and emotional state to the user (e.g., text display, speech synthesis).

[0455] Step 11:

[0456] User

[0457] Check the displayed translation results and the animal's emotional state, and reply to it using text or voice input.

[0458] Step 12:

[0459] Terminal

[0460] The user's response is saved as text or audio data and prepared for transmission to the server.

[0461] Step 13:

[0462] Terminal

[0463] The user's response data is converted into a data packet and sent to the server.

[0464] Step 14:

[0465] server

[0466] The user's response data is received and decompressed.

[0467] The decompressed audio data is analyzed and converted into text.

[0468] Step 15:

[0469] server

[0470] The user's response text is compared with a database to convert it into sounds and signs that animals can understand.

[0471] It also takes into account the user's emotional state to generate optimal sounds and signs.

[0472] Step 16:

[0473] server

[0474] The generated data of sounds and signs is packaged into data packets and sent to the terminal.

[0475] Step 17:

[0476] Terminal

[0477] The data packets of sounds and signs received from the server are decompressed and the output data is obtained.

[0478] The output data is used to communicate with the animal through a speaker or display (e.g., playing a sound from a speaker or showing a sign on a display).

[0479] The above is a concrete process flow for implementing the present invention in combination with an emotion engine. This process allows users to enjoy real-time conversations with animals, and the use of the emotion engine further improves the quality of communication.

[0480] Example 2

[0481] 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."

[0482] Conventional methods of communicating with animals have had difficulty analyzing and understanding the meaning of animal sounds and behavior. Furthermore, they have not been able to realize two-way communication that takes the user's emotions into account, making it difficult to communicate effectively with animals. This has made it difficult to correctly understand the animal's emotions and intentions and respond appropriately.

[0483] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes means for executing an audio analysis engine, a video analysis algorithm, and an emotion analysis engine on the cloud, means for analyzing collected data and extracting the emotions and intentions of animals and the emotional state of people, and means for translating the analysis results into human language. This enables accurate analysis of animal sounds and behaviors and two-way communication that takes the user's emotions into consideration.

[0484] "Terminal" refers to a device used to collect animal sounds and behavior, as well as the user's voice and facial expressions, such as a smartphone or tablet.

[0485] "Server" refers to a remote computer system that receives, analyzes, and translates data sent from a terminal.

[0486] A "voice analysis engine" refers to a program or algorithm that analyzes animal sounds and extracts emotions and intentions.

[0487] "Video analysis algorithms" refer to programs and algorithms that analyze animal behavior and movements and extract detailed information.

[0488] An "emotion analysis engine" refers to a program or algorithm that analyzes a user's voice and facial expressions to detect the user's emotional state.

[0489] "Collection" refers to obtaining data from animals and users in real time using sensors such as cameras and microphones.

[0490] "Analysis" refers to the processing performed on collected data, including detailed analysis of animal sounds and behavior, as well as people's emotional states.

[0491] "Translation" refers to converting an animal's emotions and intentions into human language based on the analysis results.

[0492] "Display" refers to conveying the translation results and the user's emotional state to the user on the device screen or using voice synthesis.

[0493] "Input" refers to an operation by the user to respond to the animal using text or voice.

[0494] This invention is a system that collects and analyzes animal sounds and behaviors, translates them into human language, and displays them to the user. Furthermore, by combining it with an emotion engine that analyzes the user's emotions, the quality of communication can be improved.

[0495] Overall system configuration

[0496] The system consists of four main components:

[0497] 1. Device (smartphone or tablet)

[0498] 2. Server

[0499] 3. Users

[0500] 4. Emotion Engine

[0501] System Operation Overview

[0502] The process begins when a user logs in to their device and launches the application. The application displays a screen where users can select a conversation mode with the animal. Once the user selects the type of animal, the device activates its camera and microphone, and collects the animal's sounds and behavior, as well as the user's voice and facial expressions, in real time. The collected data is temporarily stored on the device and then sent to a server.

[0503] The server receives the data sent from the device and analyzes the animal's cries and behavior using a voice analysis engine and video analysis algorithm. Specifically, IBM Watson (registered trademark) is commonly used as the voice analysis engine, and OpenCV is used as the video analysis algorithm. The Microsoft (registered trademark) Azure (registered trademark) Emotion API is used as the emotion analysis engine. The analysis results are compared with a database and translated into human language. These translation results and the user's emotional state are sent to the device as text data.

[0504] The device displays the translation results received from the server and the user's emotional state in real time. The user looks at the displayed results and responds to the animal. Responses can be entered as text or voice, and the input data is sent back to the server. The server analyzes the user's response and converts it into sounds or signs that the animal can understand. The converted data is sent to the device, which then outputs it to the animal.

[0505] Explanation with concrete examples

[0506] Example: Conversation with your dog

[0507] User

[0508] 1. Launch the app on your smartphone and select "Conversation Mode."

[0509] 2. Select "Shiba Inu" as the type of dog.

[0510] Terminal

[0511] 1. The microphone and camera are activated to record and record the Shiba Inu's barks and movements in real time, and also collect the user's voice and facial expressions.

[0512] 2. The collected data is compressed and sent to the server.

[0513] server

[0514] 1. Analyze the Shiba Inu's bark using a voice analysis engine (for example, IBM Watson) and extract that the bark "woof woof" means "Welcome home! Let's play!"

[0515] 2. Analyze the Shiba Inu's movements using a video analysis algorithm (e.g., OpenCV) and analyze the tail-wagging behavior.

[0516] 3. An emotion engine (for example, Microsoft Azure Emotion API) analyzes the user's voice and facial expressions to detect whether the user is feeling "joy."

[0517] 4. The analysis results and emotional state are translated into human language and the results are sent to the device.

[0518] Terminal

[0519] 1. The translated result and the user's emotional state are displayed as text, and the synthesized voice phrase "Welcome back! Let's play!" is played.

[0520] User

[0521] 1. Look at the message that appears and reply, "Wait a moment, I'll play with you soon." Use voice input to reply.

[0522] Terminal

[0523] 1. Analyze voice input, convert it to text, and send it to the server.

[0524] server

[0525] 1. Analyze the user's response and convert it into a sound or sign that the dog can understand, saying "Wait a moment, I'll play with you soon."

[0526] 2. Send the conversion result to the device.

[0527] Terminal

[0528] 1. Output the converted animal sounds and signs, and display whether the animal barks "woof woof."

[0529] Prompt Sentence Examples

[0530] Prompt: Simulate a conversation with your Shiba Inu dog. The user launches the app on their smartphone and selects conversation mode. Create an example where the Shiba Inu's bark "woof woof" means "Welcome home! Let's play!" and the user responds "Just wait a moment, I'll play with you soon." Include an emotion engine.

[0531] The above is a specific embodiment for carrying out the invention. This process allows users to enjoy real-time conversations with animals, and the quality of communication is improved by utilizing the emotion engine.

[0532] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0533] Step 1:

[0534] Device: The user logs in to the device and launches the application. A button for selecting "Animal Conversation Mode" appears on the screen. The user taps the button to select the conversation mode.

[0535] Input: User action to select the type of animal.

[0536] Output: The device prepares to activate the camera and microphone.

[0537] Step 2:

[0538] User: Select an animal type from the list on the screen, for example, "Shiba Inu."

[0539] Input: Select the type of animal (e.g. Shiba Inu).

[0540] Output: The device is ready to activate the camera and microphone.

[0541] Step 3:

[0542] Device: The camera and microphone are activated to collect animal sounds and behavior, as well as the user's voice and facial expressions in real time. The collected data is temporarily stored on the device.

[0543] Input: Animal sounds, actions, user voice, facial expressions.

[0544] Output: Collected data (audio data, video data).

[0545] Step 4:

[0546] Terminal: Compresses the collected data and sends it to the server.

[0547] Input: Collected data.

[0548] Output: Sending compressed data to the server.

[0549] Step 5:

[0550] Server: Receives compressed data sent from the terminal and decompresses it, dividing it into audio data and video data.

[0551] Input: Compressed data.

[0552] Output: Decompressed audio and video data.

[0553] Step 6:

[0554] Server: Using a voice analysis engine, analyze the voice data and extract the animal's emotions and intentions.

[0555] Input: Audio data.

[0556] Output: The animal's emotion or intention (e.g., "Welcome home! Let's play!").

[0557] Specific behavior: Performs analysis using a voice analysis engine (e.g. IBM Watson).

[0558] Step 7:

[0559] Server: Using video analysis algorithms, the video data is analyzed and animal behavior is analyzed in detail.

[0560] Input: Video data.

[0561] Output: Detailed information about the animal's behavior (e.g., tail wagging).

[0562] Specific operation: Perform analysis using a video analysis algorithm (e.g., OpenCV).

[0563] Step 8:

[0564] Server: Using an emotion analysis engine, analyzes the user's voice and facial expressions to detect the user's emotional state.

[0565] Input: User's audio and video data.

[0566] Output: The user's emotional state (e.g., emotion "joy").

[0567] Specific behavior: Performs analysis using a sentiment analysis engine (e.g., Microsoft Azure Emotion API).

[0568] Step 9:

[0569] Server: Based on the results of the animal analysis and the user's emotional state, the results are translated into human language and sent to the device as text data.

[0570] Input: Animal emotions and intentions, user emotional state.

[0571] Output: Translated text data (e.g. "Welcome back! Let's play!") and the user's emotional state.

[0572] Step 10:

[0573] Terminal: Displays the translation results received from the server and the user's emotional state in real time. The user can then respond to the animal based on the results.

[0574] Input: Translation result, user's emotional state.

[0575] Output: The text or audio to be displayed (e.g., "Welcome back! Let's play!").

[0576] Specific operation: The translation result is displayed as text on the screen and played back using speech synthesis if necessary.

[0577] Step 11:

[0578] User: Sees the displayed message and responds to the animal using text input or voice input.

[0579] Input: User response (e.g. "Just wait a moment, I'll play with you soon").

[0580] Output: Response data.

[0581] Step 12:

[0582] Terminal: Converts the user's voice input into text and sends it to the server.

[0583] Input: User response data.

[0584] Output: Sent to the server as text data.

[0585] Step 13:

[0586] Server: Receives the user's response data and begins analysis. The analysis results are converted into sounds and signs that animals can understand.

[0587] Input: User response data.

[0588] Output: A sound or signal that the animal can understand (e.g., a "wait a moment" sound or ear movement).

[0589] Step 14:

[0590] Server: Transmits the converted sound and sign data to the device.

[0591] Input: Converted call and sign data.

[0592] Output: Data sent to the terminal.

[0593] Step 15:

[0594] Terminal: Outputs the sounds and signs received from the server to the animals.

[0595] Input: Converted call and sign data.

[0596] Output: Animal sound or display (e.g. "woof woof").

[0597] Specific actions: Use speakers and displays to play sounds and show signs.

[0598] (Application example 2)

[0599] 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."

[0600] With conventional pet communication tools, it was difficult to understand a pet's cries and behaviors and grasp their emotions and intentions. Furthermore, there were no product recommendation services based on pet needs, making it difficult for users to select appropriate products that matched their pet's preferences. Furthermore, improving the quality of communication by taking into account the user's emotional state was also a challenge.

[0601] 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 and analyzing the pet's cries and behavior, means for translating the analysis results of the collected cries and behavior into human language, means for analyzing the user's emotional state, and means for displaying recommended products based on the pet's cries and behavior. This makes it possible to understand the pet's cries and behavior and suggest products to the user based on them. It also makes it possible to realize high-quality communication that takes the user's emotional state into consideration.

[0602] "Means for collecting animal sounds and behavior" refers to devices and software for capturing animal sounds and body movements as audio and video data.

[0603] "Means for analyzing" refers to computer programs or algorithms that analyze the collected audio and video data and extract specific patterns or meanings.

[0604] "Means of translation into human language" refers to technology that converts the results of analyzed animal sounds and behavior into text data or audio data that humans can understand.

[0605] "Displaying means" refers to a display device, speaker, or application software for visually or audibly presenting the translated text or speech to the user.

[0606] The "means for transmitting user input back to the animal" refers to a device or software that converts the instructions and responses given by the user to the pet into a format that the animal can understand and transmits them to the animal.

[0607] "Means for analyzing the user's emotional state" refers to technology or software that analyzes data such as the user's voice and facial expressions and identifies their emotional state.

[0608] "Means for displaying recommended products" refers to technology or devices for presenting appropriate products to users based on the animal's condition and analysis results.

[0609] The system for realizing this invention mainly consists of the following components: It functions as a system that promotes new forms of communication with animals and recommends products based on pet needs.

[0610] Hardware and software used

[0611] 1. Device (smartphone or tablet):

[0612] A device for collecting animal sounds and behavior in real time using a camera and microphone.

[0613] Application software (compatible with iOS or ANDROID (registered trademark)).

[0614] 2. Server:

[0615] Use cloud platforms for data analysis and translation (e.g., AWS®, GCP).

[0616] A speech analysis engine (e.g., Google® Cloud Speech-to-Text).

[0617] Video analysis algorithms (e.g., OpenCV, TENSORFLOW®).

[0618] Sentiment analysis engines (e.g., Emotion API, Microsoft Azure).

[0619] A database (e.g., MySQL, MongoDB).

[0620] Recommendation systems (e.g., Amazon Personalize).

[0621] 3. User:

[0622] Operate applications on your smartphone or tablet.

[0623] Communicate with animals and check and order recommended products.

[0624] Explanation of program processing

[0625] 1. Data collection by device:

[0626] The user launches the application, which uses the device's camera and microphone to collect real-time data on their pet's meows and movements, which is then compressed and sent to a cloud server.

[0627] 2. Data analysis and translation by the server:

[0628] Speech analysis: Using Google Cloud Speech-to-Text, pet cries are converted into text data and the intent is analyzed.

[0629] Video Analysis: Analyze your pet's behavior and identify its emotional state using OpenCV and TensorFlow.

[0630] Emotion Analysis: The Emotion API analyzes the user's voice and facial expressions to detect their emotional state.

[0631] Translation: Based on the analysis results, the pet's cries and actions are translated into human language.

[0632] 3. Recommendation Systems:

[0633] Using Amazon Personalize, we generate product recommendations based on pet sounds and behavior, and create a list of recommended products that also takes into account the user's emotional state.

[0634] 4. Terminal display and user input:

[0635] The translation results and recommended product list received from the server are displayed on the smartphone or tablet, allowing the user to check the displayed information and select a product.

[0636] Specific examples

[0637] For example, if a pet dog barks "woof woof," the sound is collected and analyzed on the server, which determines that the dog is hungry. Amazon Personalize then recommends a high-protein dry food, which is then displayed on the user's smartphone. The user can then easily order the dry food through the application.

[0638] Example prompts for generative AI models

[0639] An example of a prompt is:

[0640] Convert your pet's behavior and vocalizations into text data and analyze their emotions and intentions. For example, if your dog barks "woof woof," analyze whether it indicates hunger and provide a list of recommended foods based on that information.

[0641] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0642] Step 1:

[0643] Data collection by terminal

[0644] The device uses the smartphone or tablet's camera and microphone to collect the pet's sounds and behavior in real time. The input data is video of the pet's sounds and behavior, and the output is a compressed file of this data. Specifically, the device temporarily stores the data through an application and prepares it for transfer to a cloud server.

[0645] Step 2:

[0646] Data reception and decompression by the server

[0647] The server receives the compressed data sent from the terminal and decompresses it. The input data is the compressed bird sounds and video data, and the output is the decompressed raw data. Specifically, the server uses the receiving module to pass the data to the decompression program.

[0648] Step 3:

[0649] Audio analysis

[0650] The server analyzes the bird's calls using a speech analysis engine such as Google Cloud Speech-to-Text. The input data is the bird's call audio data, and the output is the text corresponding to the audio and an interpretation of the bird's intent. Specifically, the server sends the audio data to the analysis engine, which interprets the intent and converts it into text data.

[0651] Step 4:

[0652] Video Analysis

[0653] The server analyzes the pet's behavior using OpenCV and TensorFlow. The input data is video data, and the output is the results of the behavior analysis. In operation, the server breaks down the video data into frames and identifies behavior using a machine learning model.

[0654] Step 5:

[0655] Emotion analysis

[0656] The server uses the Emotion API to analyze the user's emotions. The input data is the user's voice and facial expression data, and the output is their emotional state. Specifically, the server sends the voice and video data to the emotion analysis engine and integrates the results.

[0657] Step 6:

[0658] translation

[0659] The server translates the animal's intentions into human language based on the analysis of its calls and behavior. The input data is the analysis results, and the output is translated text data. Specifically, the server compares it with an existing database and generates the corresponding text.

[0660] Step 7:

[0661] Recommendation Systems

[0662] The server uses Amazon Personalize to generate recommended products. The input data is the pet analysis results and the user's emotional state, and the output is a list of recommended products. Specifically, the server sends the data to the recommendation engine to create the optimal product list.

[0663] Step 8:

[0664] Display by terminal

[0665] The device receives the translation results and recommended product list from the server and displays them to the user. The input data is the translation results and recommended product list, and the output is visual and auditory feedback to the user. Specifically, the device updates the UI and displays related information.

[0666] Step 9:

[0667] User operation

[0668] The user checks the displayed information and selects a product. The input data is recommended product information from the system, and the output is order information. Specifically, the user makes the selection and orders using a touch screen or voice input.

[0669] Step 10:

[0670] Order processing and delivery

[0671] The server processes the user's order and notifies the delivery company. The input data is the user's order information, and the output is notification information to the delivery company. Specifically, the server links the order data to the delivery system and carries out shipping procedures.

[0672] 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.

[0673] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0674] 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.

[0675] [Second embodiment]

[0676] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0677] 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.

[0678] 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).

[0679] 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.

[0680] 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.

[0681] 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).

[0682] 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.

[0683] 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.

[0684] 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.

[0685] 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.

[0686] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0687] 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."

[0688] This system collects and analyzes animal sounds and behaviors, translates them into human language, and displays them to the user, thereby realizing a new form of communication. Below, an embodiment of the present invention will be described in detail.

[0689] Overall system configuration

[0690] The system consists of three main components:

[0691] 1. Device (smartphone or tablet)

[0692] 2. Server

[0693] 3. Users

[0694] System Operation Overview

[0695] 1. Data collection by device

[0696] The user logs in to the device and starts the application. The application displays a screen for selecting the conversation mode with the animal.

[0697] Once the user selects a type of animal, the device will activate the camera and microphone to record the animal's sounds and movements in real time.

[0698] The collected data is temporarily stored on the device and then sent to a server for analysis.

[0699] 2. Data analysis and translation by the server

[0700] The server receives the audio and video data sent from the terminal. The received data is first decompressed.

[0701] The sound analysis engine analyzes the animal's cries, and as a result, the animal's emotions and intentions are extracted.

[0702] The video data is then run through motion analysis algorithms to extract more detailed information about the animal's behavior.

[0703] The extracted information is compared with a database and translated into the corresponding human language. These translation results are sent to the device as text data.

[0704] 3. Terminal display and user input

[0705] The device receives translation results from the server and displays them in real time, with the option to display them as text or speech synthesis.

[0706] The user sees the translation results and responds to the animal. Responses can be entered by text or voice.

[0707] 4. Analysis of user responses on the server

[0708] The user's response is sent from the terminal to the server.

[0709] The server analyzes the user's responses and converts the text data back into sounds and signs that the animals can understand.

[0710] The completed data is sent to a terminal, which outputs sounds and signs to the animal.

[0711] Explanation with concrete examples

[0712] Example: Conversation with your dog

[0713] User

[0714] 1. Launch the app on your smartphone and select "Conversation Mode."

[0715] 2. Select "Shiba Inu" as the type of dog.

[0716] Terminal

[0717] 1. Turn on the microphone and camera and record the Shiba Inu's barks and movements in real time.

[0718] 2. The collected data is compressed and sent to the server.

[0719] server

[0720] 1. The voice analysis engine analyzes the Shiba Inu's bark and extracts that the "woof woof" bark means "Welcome home! Let's play!"

[0721] 2. Using a video analysis algorithm, the Shiba Inu's movements are analyzed, and the tail-wagging behavior is also extracted as information that supports the above message.

[0722] 3. The analysis results are translated into human language and the text "Welcome back! Let's play!" is sent to the device.

[0723] Terminal

[0724] 1. The translation result is displayed to the user as text, and the speech synthesis feature plays back "Welcome back! Let's play!"

[0725] User

[0726] 1. Look at the message that appears and reply, "Wait a moment, I'll play with you soon." Use voice input to reply.

[0727] Terminal

[0728] 1. Analyze voice input, convert it into text, and send it to the server.

[0729] server

[0730] 1. Analyze the user's response and convert it into a sound or sign that the dog can understand, saying "Wait a moment, I'll play with you soon."

[0731] 2. Send the conversion result to the device.

[0732] Terminal

[0733] 1. Output the converted animal sounds and signs, and display whether the animal barks "woof woof."

[0734] The above is an embodiment of the present invention. This system allows users to enjoy communicating with animals in a new way.

[0735] The processing flow will be explained below.

[0736] Step 1:

[0737] User

[0738] Launch the app on your smartphone and log in.

[0739] Select "Conversation Mode" from the app's main screen.

[0740] Step 2:

[0741] Terminal

[0742] When a user selects conversation mode, the device requests access to GPS information, the camera, and the microphone.

[0743] The terminal displays a screen for selecting the type of animal, and the user selects the type of animal (e.g., dog).

[0744] Step 3:

[0745] Terminal

[0746] Activate the camera and microphone to record the sounds and movements of a selected animal (e.g., a dog) in real time.

[0747] The collected audio and video data is temporarily stored on the device.

[0748] Step 4:

[0749] Terminal

[0750] The collected data is compressed and a data packet is generated for transmission to the server.

[0751] The data packet is sent to the server using the HTTPS protocol or the like.

[0752] Step 5:

[0753] server

[0754] It receives data packets sent from the terminal and performs decompression processing.

[0755] The decompressed audio and video data is prepared for analysis.

[0756] Step 6:

[0757] server

[0758] The collected call data is analyzed using an audio analysis engine to extract call patterns.

[0759] Based on the extracted patterns, the animal's emotions and intentions are matched with a database to identify meaning.

[0760] Step 7:

[0761] server

[0762] The video data is run through a motion analysis algorithm to extract the animal's behavioral patterns.

[0763] The extracted behavioral patterns are also compared with a database to identify the animal's intentions.

[0764] Step 8:

[0765] server

[0766] The speech analysis data and motion analysis data are integrated to determine the final meaning.

[0767] The determined meaning is translated into human language using a natural language generation engine and prepared as text data.

[0768] Step 9:

[0769] server

[0770] The translated text data is packaged into data packets and sent to the terminal.

[0771] Step 10:

[0772] Terminal

[0773] The data packet received from the server is decompressed to obtain the text data of the translation result.

[0774] The translation results are displayed to the user (e.g., as text, or read aloud using speech synthesis).

[0775] Step 11:

[0776] User

[0777] Check the translation results and reply to the animal by text input or voice input.

[0778] Step 12:

[0779] Terminal

[0780] The user's response is saved as text or audio data and prepared for transmission to the server.

[0781] Step 13:

[0782] Terminal

[0783] The user's response data is converted into a data packet and sent to the server.

[0784] Step 14:

[0785] server

[0786] The user's response data is received and decompressed.

[0787] The decompressed audio data is analyzed and converted into text.

[0788] Step 15:

[0789] server

[0790] The user's response text is compared with a database to convert it into sounds and signs that animals can understand.

[0791] Step 16:

[0792] server

[0793] The generated data of sounds and signs is packaged into data packets and sent to the terminal.

[0794] Step 17:

[0795] Terminal

[0796] The data packets of sounds and signs received from the server are decompressed and the output data is obtained.

[0797] The output data is used to communicate with the animal through a speaker or display (e.g., playing a sound from a speaker or showing a sign on a display).

[0798] The above is a specific process flow for carrying out the invention, which allows users to enjoy real-time conversations with animals.

[0799] Example 1

[0800] 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."

[0801] Conventional methods of communication with animals have relied primarily on intuitive interpretation of animal behavior and calls, resulting in incomplete communication between humans and animals. Furthermore, many animal communication systems transmit analysis results in only one direction, making it difficult to establish two-way communication. Furthermore, it is difficult to complete analysis of animal calls and behavior within a device due to limitations in processing power. An efficient, two-way communication system that can solve these issues is needed.

[0802] 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.

[0803] In this invention, the server includes means for enabling a user to launch an application and log in, means for activating a camera and microphone to collect sounds and behaviors of animals based on the type of animal selected by the user, means for compressing the collected audio and video data and transmitting it to the server, means for decompressing and analyzing the data in the server and extracting the animal's emotions and intentions using a voice analysis engine and a motion analysis algorithm, means for comparing the extracted information with a database and translating it into human language, means for transmitting the translated text data to a terminal and displaying it to the user as text or synthesized speech, means for receiving the user's response as voice or text input and transmitting it again to the server, and means for converting the user's response in the server into sounds or signs that the animal can understand and transmitting them to the terminal for output, thereby enabling two-way and efficient communication between humans and animals.

[0804] A "user" is a person who uses the system to communicate with animals.

[0805] An "application" is a software program that runs on a smartphone or tablet and is used to collect and analyze animal sounds and behavior.

[0806] A "camera" is a photographic device used to capture the appearance and behavior of animals.

[0807] A "microphone" is an audio input device for recording animal sounds.

[0808] "Collected audio and video data" refers to data of animal sounds and movements recorded by the user's terminal.

[0809] "Compression" is a process for reducing the size of data.

[0810] A "server" is a remote computing device that receives, analyzes, and interprets collected data.

[0811] "Decompression" is the process of restoring compressed data to its original state.

[0812] A "voice analysis engine" is specialized software that analyzes animal sounds and extracts their emotions and intentions.

[0813] A "motion analysis algorithm" is a computational method for analyzing animal movements and extracting information from their behavior.

[0814] A "database" is an information collection device for storing and collating extracted information.

[0815] "Translation" is the process of converting animal emotions and intentions into human language.

[0816] "Text data" refers to data that expresses the translation results as text information.

[0817] "Speech synthesis" is a technology that converts text data into speech and plays it back.

[0818] A "response" refers to a response that the user makes to the animal.

[0819] "Calls and signs" are audio and visual cues that animals can understand.

[0820] This invention is a system that collects and analyzes animal sounds and behaviors, translates them into human language, and displays them to the user, thereby realizing a new form of communication. Specific embodiments of the invention are described below.

[0821] Overall system configuration

[0822] This system is mainly composed of three elements: "terminals," "servers," and "users."

[0823] Users launch applications and log in

[0824] The user launches the animal conversation application on their smartphone or tablet. Next, a login screen appears, and the user enters their account information to log in. After logging in, the user selects "Conversation Mode" and specifies the type of animal.

[0825] Data collection by terminal

[0826] When the user selects an animal, the device's camera and microphone are activated to capture the animal's sounds and behavior in real time. The collected audio and video data is temporarily stored in the device's memory, then compressed and sent to the server.

[0827] Data reception and decompression by the server

[0828] The server receives the audio and video data sent from the device. The received data is first decompressed. The audio data is sent to an audio analysis engine to analyze the animal's calls. The video data is sent to a motion analysis algorithm to analyze the animal's behavior.

[0829] Server-based parsing and translation

[0830] The voice analysis engine extracts emotions and intentions from the animal's cries. The motion analysis algorithm analyzes the animal's behavior from the video data and extracts more detailed information. The extracted information is compared with a database and the animal's emotions and intentions are translated into human language. The translation results are generated as text data and sent to the device.

[0831] Displaying translation results on your device

[0832] The device displays the translation results received from the server in real time. The display format can be selected as text or speech synthesis. The user can check the translation results on the device screen.

[0833] User response input

[0834] The user can then respond to the animal based on the translation results. Responses can be made by text input or voice input. For example, the user can say, "Just wait a moment, I'll play with you soon."

[0835] Response analysis and retranslation by the server

[0836] The server receives and analyzes the user's response data sent from the device. The analyzed text data is converted into sounds and signs that the animal can understand. The converted results are then sent back to the device.

[0837] Terminal response output

[0838] The device receives the converted data from the server and plays it back to the animal. The converted sound or sign is output to the animal. For example, it may respond to the user by barking "woof woof" or display a specific action.

[0839] Specific examples

[0840] Take the example of a conversation with your dog.

[0841] User

[0842] 1. Launch the app on your smartphone and select "Conversation Mode."

[0843] 2. Select "Shiba Inu" as the type of dog.

[0844] Terminal

[0845] 1. Turn on the camera and microphone to record the Shiba Inu's barks and movements in real time.

[0846] 2. The collected data is compressed and sent to the server.

[0847] server

[0848] 1. The voice analysis engine analyzes the Shiba Inu's bark and extracts that the "woof woof" bark means "Welcome home! Let's play!"

[0849] 2. The Shiba Inu's movements are analyzed using a video analysis algorithm, and the tail-wagging behavior is also extracted as information that supports the above message.

[0850] 3. The analysis results are translated into human language and the text "Welcome back! Let's play!" is sent to the device.

[0851] Terminal

[0852] 1. The translation result is displayed to the user as text, and the synthesized speech plays "Welcome back! Let's play!"

[0853] User

[0854] 1. Look at the message that appears and reply by voice, "Just wait a moment, I'll play with you soon."

[0855] Terminal

[0856] 1. Analyze voice input, convert it into text, and send it to the server.

[0857] server

[0858] 1. Analyze the user's response and convert it into a sound or sign that the dog can understand, saying "Wait a moment, I'll play with you soon."

[0859] 2. Send the conversion result to the device.

[0860] Terminal

[0861] 1. Output the converted animal sounds or signs, such as "woof woof" or displaying a specific action.

[0862] This system allows users to enjoy communicating with animals in a new way.

[0863] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0864] Step 1:

[0865] Users launch applications and log in

[0866] The user launches the animal conversation application on their smartphone or tablet and enters their account information on the login screen to log in. Based on the entered account information, the server performs authentication processing, and if authentication is successful, the conversation mode selection screen is displayed.

[0867] Input: Account information (username, password)

[0868] Output: Conversation mode selection screen

[0869] Step 2:

[0870] Selecting the animal species on the device and starting data collection

[0871] When a user selects an animal type on the application, the device's camera and microphone are activated and real-time recording of the animal's sounds and behavior begins. The collected audio and video data is temporarily stored in the device's memory.

[0872] Input: Animal species information

[0873] Output: Temporary storage of bird sounds and video data

[0874] Step 3:

[0875] Data compression by the terminal and transmission to the server

[0876] The device compresses the audio and video data it has temporarily stored and sends it to the server. Compression reduces the amount of data, making transmission more efficient.

[0877] Input: Stored audio and video data

[0878] Output: Compressed audio and video data

[0879] Step 4:

[0880] Server receives and decompresses data

[0881] The server receives the compressed audio and video data sent from the terminal, and then decompresses the data to restore it to its original state.

[0882] Input: Compressed audio and video data

[0883] Output: Uncompressed audio and video data

[0884] Step 5:

[0885] Server-based voice and behavior analysis

[0886] The server sends the decompressed audio data to an audio analysis engine that analyzes the animal's calls to extract emotions and intentions, while simultaneously sending the decompressed video data to a motion analysis algorithm that analyzes the animal's behavior to extract more detailed information.

[0887] Input: Uncompressed audio and video data

[0888] Output: Extracted emotions, intents, and analyzed behavioral information

[0889] Step 6:

[0890] Translation by the server and data transmission to the device

[0891] The server compares the extracted emotions, intentions, and behavioral information with a database and translates it into human language. The translation results are generated as text data and sent to the device.

[0892] Input: Extracted emotions, intentions, and behavioral information

[0893] Output: Translated text data

[0894] Step 7:

[0895] Displaying translation results on your device

[0896] The device displays the translation results received from the server in real time, and the user can check the translation results either as text or through voice synthesis.

[0897] Input: Translated text data

[0898] Output: Text display or speech synthesis

[0899] Step 8:

[0900] User response input

[0901] The user can then respond to the animal based on the translation results displayed. Responses can be made by text input or voice input, and the device will analyze the response and send it to the server.

[0902] Input: User response (text or voice)

[0903] Output: Response data sent to the server

[0904] Step 9:

[0905] Response analysis and retranslation by the server

[0906] The server analyzes the received user response data and converts the text data into sounds and signs that the animals can understand, then sends the converted results back to the device.

[0907] Input: User response data

[0908] Output: Animal-understandable sounds and signs

[0909] Step 10:

[0910] Response output to terminal

[0911] The terminal plays the response data received from the server to the animal and outputs the converted sounds and signs.

[0912] Input: Animal sounds and signs that animals can understand

[0913] Output: Animal sounds and signs

[0914] This allows for two-way communication between the user and the animal.

[0915] (Application example 1)

[0916] 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."

[0917] In the past, there were many barriers to communication between humans and animals, making it difficult to accurately understand what animals needed. Furthermore, there was a lack of means to understand an animal's dietary requirements and health status in real time and provide appropriate food, which made animal health management a challenge.

[0918] 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.

[0919] In this invention, the server includes means for collecting animal sounds and behavior, means for analyzing the collected sounds and behavior, means for translating the analysis results into human language, means for displaying the translated results to the user, means for transmitting the user's input back to the animal, and means for selecting and delivering food for the animal based on the analysis results, which makes it possible to accurately grasp the needs and health status of the animal and provide appropriate food in real time.

[0920] "Means for collecting animal sounds and behavior" refers to equipment and software that records animal sounds and behavior in real time.

[0921] "Means for analyzing calls and behavior" refers to algorithms and programs that analyze collected data on calls and behavior and understand their intentions and emotions.

[0922] The "means of translation into human language" is software that converts the analyzed animal sounds and behavior into the corresponding human language.

[0923] "Means for displaying to the user" refers to a device or application that presents the translated data to the user visually or audibly.

[0924] "Means for transmitting user input back to the animal" refers to technology for converting instructions and responses from the user into a format that the animal can understand and transmitting them to the animal.

[0925] "Means for selecting and delivering animal food based on analysis results" is a system for understanding the needs of animals, selecting the most suitable food for them, and delivering it.

[0926] A system for carrying out this invention includes technology for collecting and analyzing animal sounds and behavior, translating the results into human language, and displaying them. It also includes means for selecting and delivering animal food based on the analysis results. Specific embodiments for carrying out this invention are described below.

[0927] Overall system configuration

[0928] The system consists of four main components:

[0929] 1. Device (smartphone or tablet)

[0930] 2. Server

[0931] 3. Robot

[0932] 4. Users

[0933] System Operation Overview

[0934] Data collection by terminal

[0935] The user logs in to the device and starts the application. The application displays a screen for selecting the conversation mode with the animal.

[0936] Once the user selects an animal, the device activates its camera and microphone to record the animal's sounds and behavior in real time. The collected data is temporarily stored on the device and then sent to a server for analysis.

[0937] Data analysis and translation by server

[0938] The server receives the audio and video data sent from the device. The received data is first decompressed. An audio analysis engine (using, for example, PyAudio) analyzes the animal's sounds, and extracts the animal's emotions and intentions.

[0939] The video data is then run through a motion analysis algorithm (using OpenCV, for example) to extract more detailed information about the animal's behavior. The extracted information is then compared with a database and translated into the corresponding human language. These translations are then sent to the device as text data.

[0940] Terminal display and user input

[0941] The device displays the translation results received from the server in real time. The display format can be selected as text or speech synthesis. The user looks at the displayed translation results and responds to the animal. Responses can be entered as text or speech. The user's response is sent from the device to the server.

[0942] Analysis of user responses on the server

[0943] The server analyzes the user's response and converts the text data back into sounds and signs that the animal can understand. The completed data is sent to the terminal, which then outputs the sounds and signs to the animal.

[0944] Select and deliver animal food based on the analysis results

[0945] The server includes a means for selecting food for the animals based on the analysis results. The selected food is provided to the animals by a delivery agent (e.g., an automated delivery robot). This allows food to be supplied in real time according to the needs of the animals.

[0946] Specific examples

[0947] User

[0948] 1. Launch the app on your smartphone and select "Conversation Mode."

[0949] 2. Select "Shiba Inu" as the type of dog.

[0950] Terminal

[0951] 1. Turn on the microphone and camera and record the Shiba Inu's barks and movements in real time.

[0952] 2. The collected data is compressed and sent to the server.

[0953] server

[0954] 1. The voice analysis engine analyzes the Shiba Inu's bark and extracts that the "woof woof" bark means "Welcome home! Let's play!"

[0955] 2. Using a video analysis algorithm, the Shiba Inu's movements are analyzed, and the tail-wagging behavior is also extracted as information that supports the above message.

[0956] 3. The analysis results are translated into human language and the text "Welcome back! Let's play!" is sent to the device.

[0957] Terminal

[0958] 1. The translation result is displayed to the user as text, and the speech synthesis feature plays back "Welcome back! Let's play!"

[0959] User

[0960] 1. Look at the message that appears and reply, "Wait a moment, I'll play with you soon." Use voice input to reply.

[0961] server

[0962] 1. Analyze the user's response and convert it into a sound or sign that the dog can understand, saying "Wait a moment, I'll play with you soon."

[0963] 2. Send the conversion result to the device.

[0964] Terminal

[0965] 1. Output the converted animal sounds and signs, and display whether the animal barks "woof woof."

[0966] Prompt Sentence Examples

[0967] "Animal food delivery assistant system. Records animal sounds and behaviors and analyzes them using an audio analysis engine and video analysis algorithm. Based on the requests understood from these, the system selects and delivers the appropriate food. Assume the server API URL is example.com / api and the animal type is dog."

[0968] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0969] Step 1:

[0970] Data collection by terminal

[0971] The user logs in to the device and launches the application. After selecting the animal conversation mode and the type of animal, the device activates the camera and microphone. The camera and microphone are used to record and record the animal's cries and behavior in real time. This captures the animal's audio and video data, which is then temporarily stored on the device.

[0972] Input: User login information, animal type selection

[0973] Output: Recorded audio and video data

[0974] Step 2:

[0975] Sending data to the server

[0976] The device compresses the temporarily stored audio and video data and sends it to the server for analysis. Specifically, it uses a data compression algorithm to reduce the size of the audio data and video frames and uploads them to the server via an HTTP request.

[0977] Input: Recorded audio and video data

[0978] Output: Compressed audio and video data is sent to the server

[0979] Step 3:

[0980] Data analysis by server

[0981] The server receives the audio and video data sent from the device and performs the decompression process. The received data is analyzed by an audio analysis engine (e.g., PyAudio) to extract the animal's voice and its emotion and intention. In parallel, the video data is analyzed by a motion analysis algorithm (e.g., OpenCV) to extract detailed information about the animal's behavior.

[0982] Input: Compressed audio and video data

[0983] Output: Analyzed sentiment, intent, and behavior details

[0984] Step 4:

[0985] Server-based translation

[0986] The server translates the analyzed audio and video data into human language. The extracted information is compared with a database and converted into the corresponding human language. The translation result is sent to the device in text format.

[0987] Input: Analyzed sentiment, intent, and behavior details

[0988] Output: Text data translated into human language

[0989] Step 5:

[0990] Display by terminal

[0991] The device receives translation results from the server and displays them in real time. Users can choose to display them as text or speech synthesis, allowing them to intuitively understand the animal's message.

[0992] Input: Text data translated into human language

[0993] Output: Text or audio displayed to the user

[0994] Step 6:

[0995] User response

[0996] The user sees the translation results and responds to the animal using text input or voice input, and the input data is sent from the device to the server.

[0997] Input: User's text or voice response

[0998] Output: User response data sent to the server

[0999] Step 7:

[1000] Server response analysis

[1001] The server analyzes the user's response and converts the text data back into sounds and signs that the animal can understand, and the results are sent to the device.

[1002] Input: User response data

[1003] Output: Animal-understandable sounds and signs

[1004] Step 8:

[1005] Terminal Output

[1006] The device outputs the converted sounds and signs to the animal, thereby communicating the user's intentions to the animal.

[1007] Input: Animal-understandable sounds and signs

[1008] Output: Actual sounds and signs

[1009] Step 9:

[1010] Food delivery based on analysis results

[1011] The server selects food for the animal based on the analysis results. The selected food is provided to the animal by a delivery agent (e.g., an automated delivery robot). The user can confirm the selection results and finalize the order.

[1012] Input: Analyzed animal needs data

[1013] Output: Selected food order and delivery

[1014] 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.

[1015] This system realizes a new form of communication by collecting and analyzing animal sounds and behaviors, translating them into human language, and displaying them to the user. The present invention further improves the quality of communication by combining it with an emotion engine that analyzes the user's emotions.

[1016] Overall system configuration

[1017] The system consists of four main components:

[1018] 1. Device (smartphone or tablet)

[1019] 2. Server

[1020] 3. Users

[1021] 4. Emotion Engine

[1022] System Operation Overview

[1023] 1. Data collection by device

[1024] The user logs in to the device and starts the application. The application displays a screen for selecting the conversation mode with the animal.

[1025] When the user selects an animal type, the device activates the camera and microphone to record the animal's sounds and movements, as well as the user's voice and facial expressions, in real time.

[1026] The collected data is temporarily stored on the device and then sent to a server for analysis.

[1027] 2. Data analysis and translation by the server

[1028] The server receives the audio and video data sent from the terminal. The received data is first decompressed.

[1029] The voice analysis engine analyzes the animal's cries and extracts the animal's emotions and intentions.

[1030] The video data is then run through motion analysis algorithms to extract more detailed information about the animal's behavior.

[1031] The emotion engine analyzes the user's voice and facial expressions to detect the user's emotional state.

[1032] The extracted information is compared with a database and translated into the corresponding human language. These translation results and the user's emotional state are sent to the device as text data.

[1033] 3. Terminal display and user input

[1034] The device displays the translation results received from the server and the user's emotional state in real time, and can be displayed in text or speech synthesis.

[1035] The user sees the translation results and responds to the animal. Responses can be entered by text or voice.

[1036] 4. Analysis of user responses on the server

[1037] The user's response is sent from the terminal to the server.

[1038] The server analyzes the user's responses and converts the text data back into sounds and signs that the animals can understand.

[1039] The completed data is sent to a terminal, which outputs sounds and signs to the animal.

[1040] Explanation with concrete examples

[1041] Example: Conversation with your dog

[1042] User

[1043] 1. Launch the app on your smartphone and select "Conversation Mode."

[1044] 2. Select "Shiba Inu" as the type of dog.

[1045] Terminal

[1046] 1. The microphone and camera are activated to record and record the Shiba Inu's barks and movements in real time, and also collect the user's voice and facial expressions.

[1047] 2. The collected data is compressed and sent to the server.

[1048] server

[1049] 1. The voice analysis engine analyzes the Shiba Inu's bark and extracts that the "woof woof" bark means "Welcome home! Let's play!"

[1050] 2. Using a video analysis algorithm, the Shiba Inu's movements are analyzed, and the tail-wagging behavior is also extracted as information that supports the above message.

[1051] 3. The emotion engine analyzes the user's voice and facial expressions to detect when the user is in an emotional state of "joy."

[1052] 4. The analysis results and emotional state are translated into human language, and the text "Welcome back! Let's play!" and the information "The user is happy" are sent to the device.

[1053] Terminal

[1054] 1. The translation result and emotional state are displayed to the user as text, and the synthesized voice phrase "Welcome back! Let's play!" is played.

[1055] User

[1056] 1. Look at the message that appears and reply, "Wait a moment, I'll play with you soon." Use voice input to reply.

[1057] Terminal

[1058] 1. Analyze voice input, convert it to text, and send it to the server.

[1059] server

[1060] 1. Analyze the user's response and convert it into a sound or sign that the dog can understand, saying "Wait a moment, I'll play with you soon."

[1061] 2. Send the conversion result to the device.

[1062] Terminal

[1063] 1. Output the converted animal sounds and signs, and display whether the animal barks "woof woof."

[1064] The above is a specific example of how to implement the present invention by incorporating an emotion engine. This process allows users to enjoy real-time conversations with animals, and the use of the emotion engine further improves the quality of communication.

[1065] The processing flow will be explained below.

[1066] Step 1:

[1067] User

[1068] Launch the app on your smartphone and log in.

[1069] Select "Conversation Mode" from the main screen.

[1070] Step 2:

[1071] Terminal

[1072] When a user selects conversation mode, the device requests access to GPS information, the camera, and the microphone.

[1073] The terminal displays a screen for selecting the type of animal, and the user selects the type of animal (e.g., dog).

[1074] Step 3:

[1075] Terminal

[1076] Activate the camera and microphone to record the sounds and movements of a selected animal (e.g., a dog) in real time.

[1077] At the same time, the user's facial expressions and voice are also collected.

[1078] The collected audio data, video data, and user voice and facial expression data are temporarily stored on the terminal.

[1079] Step 4:

[1080] Terminal

[1081] The collected data is compressed and a data packet is generated for transmission to the server.

[1082] The data packet is sent to the server using the HTTPS protocol or the like.

[1083] Step 5:

[1084] server

[1085] It receives data packets sent from the terminal and performs decompression processing.

[1086] The decompressed audio data, video data, and user voice and facial expression data are prepared for analysis.

[1087] Step 6:

[1088] server

[1089] The collected call data is analyzed using an audio analysis engine to extract call patterns.

[1090] Based on the extracted patterns, the animal's emotions and intentions are matched with a database to identify meaning.

[1091] Step 7:

[1092] server

[1093] The video data is run through a motion analysis algorithm to extract the animal's behavioral patterns.

[1094] The extracted behavioral patterns are also compared with a database to identify the animal's intentions.

[1095] Step 8:

[1096] server

[1097] The emotion engine is used to analyze the user's voice and facial expressions to detect the user's emotional state.

[1098] The speech analysis data, movement analysis data, and emotion analysis data are integrated to determine the final meaning.

[1099] Step 9:

[1100] server

[1101] The determined meaning is translated into human language using a natural language generation engine and prepared as text data.

[1102] The translated text data and emotional state data are packaged into a data packet and transmitted to the terminal.

[1103] Step 10:

[1104] Terminal

[1105] The data packet received from the server is decompressed to obtain the translation result text data and emotional state data.

[1106] Display the translation results and emotional state to the user (e.g., text display, speech synthesis).

[1107] Step 11:

[1108] User

[1109] Check the displayed translation results and the animal's emotional state, and reply to it using text or voice input.

[1110] Step 12:

[1111] Terminal

[1112] The user's response is saved as text or audio data and prepared for transmission to the server.

[1113] Step 13:

[1114] Terminal

[1115] The user's response data is converted into a data packet and sent to the server.

[1116] Step 14:

[1117] server

[1118] The user's response data is received and decompressed.

[1119] The decompressed audio data is analyzed and converted into text.

[1120] Step 15:

[1121] server

[1122] The user's response text is compared with a database to convert it into sounds and signs that animals can understand.

[1123] It also takes into account the user's emotional state to generate optimal sounds and signs.

[1124] Step 16:

[1125] server

[1126] The generated data of sounds and signs is packaged into data packets and sent to the terminal.

[1127] Step 17:

[1128] Terminal

[1129] The data packets of sounds and signs received from the server are decompressed and the output data is obtained.

[1130] The output data is used to communicate with the animal through a speaker or display (e.g., playing a sound from a speaker or showing a sign on a display).

[1131] The above is a concrete process flow for implementing the present invention in combination with an emotion engine. This process allows users to enjoy real-time conversations with animals, and the use of the emotion engine further improves the quality of communication.

[1132] Example 2

[1133] 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."

[1134] Conventional methods of communicating with animals have had difficulty analyzing and understanding the meaning of animal sounds and behavior. Furthermore, they have not been able to realize two-way communication that takes the user's emotions into account, making it difficult to communicate effectively with animals. This has made it difficult to correctly understand the animal's emotions and intentions and respond appropriately.

[1135] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes means for executing an audio analysis engine, a video analysis algorithm, and an emotion analysis engine on the cloud, means for analyzing collected data and extracting the emotions and intentions of animals and the emotional state of people, and means for translating the analysis results into human language. This enables accurate analysis of animal sounds and behaviors and two-way communication that takes the user's emotions into consideration.

[1136] "Terminal" refers to a device used to collect animal sounds and behavior, as well as the user's voice and facial expressions, such as a smartphone or tablet.

[1137] "Server" refers to a remote computer system that receives, analyzes, and translates data sent from a terminal.

[1138] A "voice analysis engine" refers to a program or algorithm that analyzes animal sounds and extracts emotions and intentions.

[1139] "Video analysis algorithms" refer to programs and algorithms that analyze animal behavior and movements and extract detailed information.

[1140] An "emotion analysis engine" refers to a program or algorithm that analyzes a user's voice and facial expressions to detect the user's emotional state.

[1141] "Collection" refers to obtaining data from animals and users in real time using sensors such as cameras and microphones.

[1142] "Analysis" refers to the processing performed on collected data, including detailed analysis of animal sounds and behavior, as well as people's emotional states.

[1143] "Translation" refers to converting an animal's emotions and intentions into human language based on the analysis results.

[1144] "Display" refers to conveying the translation results and the user's emotional state to the user on the device screen or using voice synthesis.

[1145] "Input" refers to an operation by the user to respond to the animal using text or voice.

[1146] This invention is a system that collects and analyzes animal sounds and behaviors, translates them into human language, and displays them to the user. Furthermore, by combining it with an emotion engine that analyzes the user's emotions, the quality of communication can be improved.

[1147] Overall system configuration

[1148] The system consists of four main components:

[1149] 1. Device (smartphone or tablet)

[1150] 2. Server

[1151] 3. Users

[1152] 4. Emotion Engine

[1153] System Operation Overview

[1154] The process begins when a user logs in to their device and launches the application. The application displays a screen where users can select a conversation mode with the animal. Once the user selects the type of animal, the device activates its camera and microphone, and collects the animal's sounds and behavior, as well as the user's voice and facial expressions, in real time. The collected data is temporarily stored on the device and then sent to a server.

[1155] The server receives the data sent from the device and analyzes the animal's cries and behavior using a voice analysis engine and video analysis algorithm. Commonly used voice analysis engines include IBM Watson, and video analysis algorithms include OpenCV. Microsoft Azure Emotion API is used as the emotion analysis engine. The analysis results are compared with a database and translated into human language. These translation results and the user's emotional state are sent to the device as text data.

[1156] The device displays the translation results received from the server and the user's emotional state in real time. The user looks at the displayed results and responds to the animal. Responses can be entered as text or voice, and the input data is sent back to the server. The server analyzes the user's response and converts it into sounds or signs that the animal can understand. The converted data is sent to the device, which then outputs it to the animal.

[1157] Explanation with concrete examples

[1158] Example: Conversation with your dog

[1159] User

[1160] 1. Launch the app on your smartphone and select "Conversation Mode."

[1161] 2. Select "Shiba Inu" as the type of dog.

[1162] Terminal

[1163] 1. The microphone and camera are activated to record and record the Shiba Inu's barks and movements in real time, and also collect the user's voice and facial expressions.

[1164] 2. The collected data is compressed and sent to the server.

[1165] server

[1166] 1. Analyze the Shiba Inu's bark using a voice analysis engine (for example, IBM Watson) and extract that the bark "woof woof" means "Welcome home! Let's play!"

[1167] 2. Analyze the Shiba Inu's movements using a video analysis algorithm (e.g., OpenCV) and analyze the tail-wagging behavior.

[1168] 3. An emotion engine (for example, Microsoft Azure Emotion API) analyzes the user's voice and facial expressions to detect whether the user is feeling "joy."

[1169] 4. The analysis results and emotional state are translated into human language and the results are sent to the device.

[1170] Terminal

[1171] 1. The translated result and the user's emotional state are displayed as text, and the synthesized voice phrase "Welcome back! Let's play!" is played.

[1172] User

[1173] 1. Look at the message that appears and reply, "Wait a moment, I'll play with you soon." Use voice input to reply.

[1174] Terminal

[1175] 1. Analyze voice input, convert it to text, and send it to the server.

[1176] server

[1177] 1. Analyze the user's response and convert it into a sound or sign that the dog can understand, saying "Wait a moment, I'll play with you soon."

[1178] 2. Send the conversion result to the device.

[1179] Terminal

[1180] 1. Output the converted animal sounds and signs, and display whether the animal barks "woof woof."

[1181] Prompt Sentence Examples

[1182] Prompt: Simulate a conversation with your Shiba Inu dog. The user launches the app on their smartphone and selects conversation mode. Create an example where the Shiba Inu's bark "woof woof" means "Welcome home! Let's play!" and the user responds "Just wait a moment, I'll play with you soon." Include an emotion engine.

[1183] The above is a specific embodiment for carrying out the invention. This process allows users to enjoy real-time conversations with animals, and the quality of communication is improved by utilizing the emotion engine.

[1184] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1185] Step 1:

[1186] Device: The user logs in to the device and launches the application. A button for selecting "Animal Conversation Mode" appears on the screen. The user taps the button to select the conversation mode.

[1187] Input: User action to select the type of animal.

[1188] Output: The device prepares to activate the camera and microphone.

[1189] Step 2:

[1190] User: Select an animal type from the list on the screen, for example, "Shiba Inu."

[1191] Input: Select the type of animal (e.g. Shiba Inu).

[1192] Output: The device is ready to activate the camera and microphone.

[1193] Step 3:

[1194] Device: The camera and microphone are activated to collect animal sounds and behavior, as well as the user's voice and facial expressions in real time. The collected data is temporarily stored on the device.

[1195] Input: Animal sounds, actions, user voice, facial expressions.

[1196] Output: Collected data (audio data, video data).

[1197] Step 4:

[1198] Terminal: Compresses the collected data and sends it to the server.

[1199] Input: Collected data.

[1200] Output: Sending compressed data to the server.

[1201] Step 5:

[1202] Server: Receives compressed data sent from the terminal and decompresses it, dividing it into audio data and video data.

[1203] Input: Compressed data.

[1204] Output: Decompressed audio and video data.

[1205] Step 6:

[1206] Server: Using a voice analysis engine, analyze the voice data and extract the animal's emotions and intentions.

[1207] Input: Audio data.

[1208] Output: The animal's emotion or intention (e.g., "Welcome home! Let's play!").

[1209] Specific behavior: Performs analysis using a voice analysis engine (e.g. IBM Watson).

[1210] Step 7:

[1211] Server: Using video analysis algorithms, the video data is analyzed and animal behavior is analyzed in detail.

[1212] Input: Video data.

[1213] Output: Detailed information about the animal's behavior (e.g., tail wagging).

[1214] Specific operation: Perform analysis using a video analysis algorithm (e.g., OpenCV).

[1215] Step 8:

[1216] Server: Using an emotion analysis engine, analyzes the user's voice and facial expressions to detect the user's emotional state.

[1217] Input: User's audio and video data.

[1218] Output: The user's emotional state (e.g., emotion "joy").

[1219] Specific behavior: Performs analysis using a sentiment analysis engine (e.g., Microsoft Azure Emotion API).

[1220] Step 9:

[1221] Server: Based on the results of the animal analysis and the user's emotional state, the results are translated into human language and sent to the device as text data.

[1222] Input: Animal emotions and intentions, user emotional state.

[1223] Output: Translated text data (e.g. "Welcome back! Let's play!") and the user's emotional state.

[1224] Step 10:

[1225] Terminal: Displays the translation results received from the server and the user's emotional state in real time. The user can then respond to the animal based on the results.

[1226] Input: Translation result, user's emotional state.

[1227] Output: The text or audio to be displayed (e.g., "Welcome back! Let's play!").

[1228] Specific operation: The translation result is displayed as text on the screen and played back using speech synthesis if necessary.

[1229] Step 11:

[1230] User: Sees the displayed message and responds to the animal using text input or voice input.

[1231] Input: User response (e.g. "Just wait a moment, I'll play with you soon").

[1232] Output: Response data.

[1233] Step 12:

[1234] Terminal: Converts the user's voice input into text and sends it to the server.

[1235] Input: User response data.

[1236] Output: Sent to the server as text data.

[1237] Step 13:

[1238] Server: Receives the user's response data and begins analysis. The analysis results are converted into sounds and signs that animals can understand.

[1239] Input: User response data.

[1240] Output: A sound or signal that the animal can understand (e.g., a "wait a moment" sound or ear movement).

[1241] Step 14:

[1242] Server: Transmits the converted sound and sign data to the device.

[1243] Input: Converted call and sign data.

[1244] Output: Data sent to the terminal.

[1245] Step 15:

[1246] Terminal: Outputs the sounds and signs received from the server to the animals.

[1247] Input: Converted call and sign data.

[1248] Output: Animal sound or display (e.g. "woof woof").

[1249] Specific actions: Use speakers and displays to play sounds and show signs.

[1250] (Application example 2)

[1251] 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."

[1252] With conventional pet communication tools, it was difficult to understand a pet's cries and behaviors and grasp their emotions and intentions. Furthermore, there were no product recommendation services based on pet needs, making it difficult for users to select appropriate products that matched their pet's preferences. Furthermore, improving the quality of communication by taking into account the user's emotional state was also a challenge.

[1253] 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 and analyzing the pet's cries and behavior, means for translating the analysis results of the collected cries and behavior into human language, means for analyzing the user's emotional state, and means for displaying recommended products based on the pet's cries and behavior. This makes it possible to understand the pet's cries and behavior and suggest products to the user based on them. It also makes it possible to realize high-quality communication that takes the user's emotional state into consideration.

[1254] "Means for collecting animal sounds and behavior" refers to devices and software for capturing animal sounds and body movements as audio and video data.

[1255] "Means for analyzing" refers to computer programs or algorithms that analyze the collected audio and video data and extract specific patterns or meanings.

[1256] "Means of translation into human language" refers to technology that converts the results of analyzed animal sounds and behavior into text data or audio data that humans can understand.

[1257] "Displaying means" refers to a display device, speaker, or application software for visually or audibly presenting the translated text or speech to the user.

[1258] The "means for transmitting user input back to the animal" refers to a device or software that converts the instructions and responses given by the user to the pet into a format that the animal can understand and transmits them to the animal.

[1259] "Means for analyzing the user's emotional state" refers to technology or software that analyzes data such as the user's voice and facial expressions and identifies their emotional state.

[1260] "Means for displaying recommended products" refers to technology or devices for presenting appropriate products to users based on the animal's condition and analysis results.

[1261] The system for realizing this invention mainly consists of the following components: It functions as a system that promotes new forms of communication with animals and recommends products based on pet needs.

[1262] Hardware and software used

[1263] 1. Device (smartphone or tablet):

[1264] A device for collecting animal sounds and behavior in real time using a camera and microphone.

[1265] Application software (compatible with iOS or Android).

[1266] 2. Server:

[1267] Use cloud platforms for data analysis and translation (e.g., AWS, GCP).

[1268] A speech analysis engine (e.g., Google Cloud Speech-to-Text).

[1269] Video analysis algorithms (e.g., OpenCV, TensorFlow).

[1270] Sentiment analysis engines (e.g., Emotion API, Microsoft Azure).

[1271] A database (e.g., MySQL, MongoDB).

[1272] Recommendation systems (e.g., Amazon Personalize).

[1273] 3. User:

[1274] Operate applications on your smartphone or tablet.

[1275] Communicate with animals and check and order recommended products.

[1276] Explanation of program processing

[1277] 1. Data collection by device:

[1278] The user launches the application, which uses the device's camera and microphone to collect real-time data on their pet's meows and movements, which is then compressed and sent to a cloud server.

[1279] 2. Data analysis and translation by the server:

[1280] Speech analysis: Using Google Cloud Speech-to-Text, pet cries are converted into text data and the intent is analyzed.

[1281] Video Analysis: Analyze your pet's behavior and identify its emotional state using OpenCV and TensorFlow.

[1282] Emotion Analysis: The Emotion API analyzes the user's voice and facial expressions to detect their emotional state.

[1283] Translation: Based on the analysis results, the pet's cries and actions are translated into human language.

[1284] 3. Recommendation Systems:

[1285] Using Amazon Personalize, we generate product recommendations based on pet sounds and behavior, and create a list of recommended products that also takes into account the user's emotional state.

[1286] 4. Terminal display and user input:

[1287] The translation results and recommended product list received from the server are displayed on the smartphone or tablet, allowing the user to check the displayed information and select a product.

[1288] Specific examples

[1289] For example, if a pet dog barks "woof woof," the sound is collected and analyzed on the server, which determines that the dog is hungry. Amazon Personalize then recommends a high-protein dry food, which is then displayed on the user's smartphone. The user can then easily order the dry food through the application.

[1290] Example prompts for generative AI models

[1291] An example of a prompt is:

[1292] Convert your pet's behavior and vocalizations into text data and analyze their emotions and intentions. For example, if your dog barks "woof woof," analyze whether it indicates hunger and provide a list of recommended foods based on that information.

[1293] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1294] Step 1:

[1295] Data collection by terminal

[1296] The device uses the smartphone or tablet's camera and microphone to collect the pet's sounds and behavior in real time. The input data is video of the pet's sounds and behavior, and the output is a compressed file of this data. Specifically, the device temporarily stores the data through an application and prepares it for transfer to a cloud server.

[1297] Step 2:

[1298] Data reception and decompression by the server

[1299] The server receives the compressed data sent from the terminal and decompresses it. The input data is the compressed bird sounds and video data, and the output is the decompressed raw data. Specifically, the server uses the receiving module to pass the data to the decompression program.

[1300] Step 3:

[1301] Audio analysis

[1302] The server analyzes the bird's calls using a speech analysis engine such as Google Cloud Speech-to-Text. The input data is the bird's call audio data, and the output is the text corresponding to the audio and an interpretation of the bird's intent. Specifically, the server sends the audio data to the analysis engine, which interprets the intent and converts it into text data.

[1303] Step 4:

[1304] Video Analysis

[1305] The server analyzes the pet's behavior using OpenCV and TensorFlow. The input data is video data, and the output is the results of the behavior analysis. In operation, the server breaks down the video data into frames and identifies behavior using a machine learning model.

[1306] Step 5:

[1307] Emotion analysis

[1308] The server uses the Emotion API to analyze the user's emotions. The input data is the user's voice and facial expression data, and the output is their emotional state. Specifically, the server sends the voice and video data to the emotion analysis engine and integrates the results.

[1309] Step 6:

[1310] translation

[1311] The server translates the animal's intentions into human language based on the analysis of its calls and behavior. The input data is the analysis results, and the output is translated text data. Specifically, the server compares it with an existing database and generates the corresponding text.

[1312] Step 7:

[1313] Recommendation Systems

[1314] The server uses Amazon Personalize to generate recommended products. The input data is the pet analysis results and the user's emotional state, and the output is a list of recommended products. Specifically, the server sends the data to the recommendation engine to create the optimal product list.

[1315] Step 8:

[1316] Display by terminal

[1317] The device receives the translation results and recommended product list from the server and displays them to the user. The input data is the translation results and recommended product list, and the output is visual and auditory feedback to the user. Specifically, the device updates the UI and displays related information.

[1318] Step 9:

[1319] User operation

[1320] The user checks the displayed information and selects a product. The input data is recommended product information from the system, and the output is order information. Specifically, the user makes the selection and orders using a touch screen or voice input.

[1321] Step 10:

[1322] Order processing and delivery

[1323] The server processes the user's order and notifies the delivery company. The input data is the user's order information, and the output is notification information to the delivery company. Specifically, the server links the order data to the delivery system and carries out shipping procedures.

[1324] 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.

[1325] 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.

[1326] 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.

[1327] [Third embodiment]

[1328] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1329] 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.

[1330] 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).

[1331] 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.

[1332] 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.

[1333] 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).

[1334] 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.

[1335] 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.

[1336] 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.

[1337] 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.

[1338] 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.

[1339] 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."

[1340] This system collects and analyzes animal sounds and behaviors, translates them into human language, and displays them to the user, thereby realizing a new form of communication. Below, an embodiment of the present invention will be described in detail.

[1341] Overall system configuration

[1342] The system consists of three main components:

[1343] 1. Device (smartphone or tablet)

[1344] 2. Server

[1345] 3. Users

[1346] System Operation Overview

[1347] 1. Data collection by device

[1348] The user logs in to the device and starts the application. The application displays a screen for selecting the conversation mode with the animal.

[1349] Once the user selects a type of animal, the device will activate the camera and microphone to record the animal's sounds and movements in real time.

[1350] The collected data is temporarily stored on the device and then sent to a server for analysis.

[1351] 2. Data analysis and translation by the server

[1352] The server receives the audio and video data sent from the terminal. The received data is first decompressed.

[1353] The sound analysis engine analyzes the animal's cries, and as a result, the animal's emotions and intentions are extracted.

[1354] The video data is then run through motion analysis algorithms to extract more detailed information about the animal's behavior.

[1355] The extracted information is compared with a database and translated into the corresponding human language. These translation results are sent to the device as text data.

[1356] 3. Terminal display and user input

[1357] The device receives translation results from the server and displays them in real time, with the option to display them as text or speech synthesis.

[1358] The user sees the translation results and responds to the animal. Responses can be entered by text or voice.

[1359] 4. Analysis of user responses on the server

[1360] The user's response is sent from the terminal to the server.

[1361] The server analyzes the user's responses and converts the text data back into sounds and signs that the animals can understand.

[1362] The completed data is sent to a terminal, which outputs sounds and signs to the animal.

[1363] Explanation with concrete examples

[1364] Example: Conversation with your dog

[1365] User

[1366] 1. Launch the app on your smartphone and select "Conversation Mode."

[1367] 2. Select "Shiba Inu" as the type of dog.

[1368] Terminal

[1369] 1. Turn on the microphone and camera and record the Shiba Inu's barks and movements in real time.

[1370] 2. The collected data is compressed and sent to the server.

[1371] server

[1372] 1. The voice analysis engine analyzes the Shiba Inu's bark and extracts that the "woof woof" bark means "Welcome home! Let's play!"

[1373] 2. Using a video analysis algorithm, the Shiba Inu's movements are analyzed, and the tail-wagging behavior is also extracted as information that supports the above message.

[1374] 3. The analysis results are translated into human language and the text "Welcome back! Let's play!" is sent to the device.

[1375] Terminal

[1376] 1. The translation result is displayed to the user as text, and the speech synthesis feature plays back "Welcome back! Let's play!"

[1377] User

[1378] 1. Look at the message that appears and reply, "Wait a moment, I'll play with you soon." Use voice input to reply.

[1379] Terminal

[1380] 1. Analyze voice input, convert it into text, and send it to the server.

[1381] server

[1382] 1. Analyze the user's response and convert it into a sound or sign that the dog can understand, saying "Wait a moment, I'll play with you soon."

[1383] 2. Send the conversion result to the device.

[1384] Terminal

[1385] 1. Output the converted animal sounds and signs, and display whether the animal barks "woof woof."

[1386] The above is an embodiment of the present invention. This system allows users to enjoy communicating with animals in a new way.

[1387] The processing flow will be explained below.

[1388] Step 1:

[1389] User

[1390] Launch the app on your smartphone and log in.

[1391] Select "Conversation Mode" from the app's main screen.

[1392] Step 2:

[1393] Terminal

[1394] When a user selects conversation mode, the device requests access to GPS information, the camera, and the microphone.

[1395] The terminal displays a screen for selecting the type of animal, and the user selects the type of animal (e.g., dog).

[1396] Step 3:

[1397] Terminal

[1398] Activate the camera and microphone to record the sounds and movements of a selected animal (e.g., a dog) in real time.

[1399] The collected audio and video data is temporarily stored on the device.

[1400] Step 4:

[1401] Terminal

[1402] The collected data is compressed and a data packet is generated for transmission to the server.

[1403] The data packet is sent to the server using the HTTPS protocol or the like.

[1404] Step 5:

[1405] server

[1406] It receives data packets sent from the terminal and performs decompression processing.

[1407] The decompressed audio and video data is prepared for analysis.

[1408] Step 6:

[1409] server

[1410] The collected call data is analyzed using an audio analysis engine to extract call patterns.

[1411] Based on the extracted patterns, the animal's emotions and intentions are matched with a database to identify meaning.

[1412] Step 7:

[1413] server

[1414] The video data is run through a motion analysis algorithm to extract the animal's behavioral patterns.

[1415] The extracted behavioral patterns are also compared with a database to identify the animal's intentions.

[1416] Step 8:

[1417] server

[1418] The speech analysis data and motion analysis data are integrated to determine the final meaning.

[1419] The determined meaning is translated into human language using a natural language generation engine and prepared as text data.

[1420] Step 9:

[1421] server

[1422] The translated text data is packaged into data packets and sent to the terminal.

[1423] Step 10:

[1424] Terminal

[1425] The data packet received from the server is decompressed to obtain the text data of the translation result.

[1426] The translation results are displayed to the user (e.g., as text, or read aloud using speech synthesis).

[1427] Step 11:

[1428] User

[1429] Check the translation results and reply to the animal by text input or voice input.

[1430] Step 12:

[1431] Terminal

[1432] The user's response is saved as text or audio data and prepared for transmission to the server.

[1433] Step 13:

[1434] Terminal

[1435] The user's response data is converted into a data packet and sent to the server.

[1436] Step 14:

[1437] server

[1438] The user's response data is received and decompressed.

[1439] The decompressed audio data is analyzed and converted into text.

[1440] Step 15:

[1441] server

[1442] The user's response text is compared with a database to convert it into sounds and signs that animals can understand.

[1443] Step 16:

[1444] server

[1445] The generated data of sounds and signs is packaged into data packets and sent to the terminal.

[1446] Step 17:

[1447] Terminal

[1448] The data packets of sounds and signs received from the server are decompressed and the output data is obtained.

[1449] The output data is used to communicate with the animal through a speaker or display (e.g., playing a sound from a speaker or showing a sign on a display).

[1450] The above is a specific process flow for carrying out the invention, which allows users to enjoy real-time conversations with animals.

[1451] Example 1

[1452] 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."

[1453] Conventional methods of communication with animals have relied primarily on intuitive interpretation of animal behavior and calls, resulting in incomplete communication between humans and animals. Furthermore, many animal communication systems transmit analysis results in only one direction, making it difficult to establish two-way communication. Furthermore, it is difficult to complete analysis of animal calls and behavior within a device due to limitations in processing power. An efficient, two-way communication system that can solve these issues is needed.

[1454] 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.

[1455] In this invention, the server includes means for enabling a user to launch an application and log in, means for activating a camera and microphone to collect sounds and behaviors of animals based on the type of animal selected by the user, means for compressing the collected audio and video data and transmitting it to the server, means for decompressing and analyzing the data in the server and extracting the animal's emotions and intentions using a voice analysis engine and a motion analysis algorithm, means for comparing the extracted information with a database and translating it into human language, means for transmitting the translated text data to a terminal and displaying it to the user as text or synthesized speech, means for receiving the user's response as voice or text input and transmitting it again to the server, and means for converting the user's response in the server into sounds or signs that the animal can understand and transmitting them to the terminal for output, thereby enabling two-way and efficient communication between humans and animals.

[1456] A "user" is a person who uses the system to communicate with animals.

[1457] An "application" is a software program that runs on a smartphone or tablet and is used to collect and analyze animal sounds and behavior.

[1458] A "camera" is a photographic device used to capture the appearance and behavior of animals.

[1459] A "microphone" is an audio input device for recording animal sounds.

[1460] "Collected audio and video data" refers to data of animal sounds and movements recorded by the user's terminal.

[1461] "Compression" is a process for reducing the size of data.

[1462] A "server" is a remote computing device that receives, analyzes, and interprets collected data.

[1463] "Decompression" is the process of restoring compressed data to its original state.

[1464] A "voice analysis engine" is specialized software that analyzes animal sounds and extracts their emotions and intentions.

[1465] A "motion analysis algorithm" is a computational method for analyzing animal movements and extracting information from their behavior.

[1466] A "database" is an information collection device for storing and collating extracted information.

[1467] "Translation" is the process of converting animal emotions and intentions into human language.

[1468] "Text data" refers to data that expresses the translation results as text information.

[1469] "Speech synthesis" is a technology that converts text data into speech and plays it back.

[1470] A "response" refers to a response that the user makes to the animal.

[1471] "Calls and signs" are audio and visual cues that animals can understand.

[1472] This invention is a system that collects and analyzes animal sounds and behaviors, translates them into human language, and displays them to the user, thereby realizing a new form of communication. Specific embodiments of the invention are described below.

[1473] Overall system configuration

[1474] This system is mainly composed of three elements: "terminals," "servers," and "users."

[1475] Users launch applications and log in

[1476] The user launches the animal conversation application on their smartphone or tablet. Next, a login screen appears, and the user enters their account information to log in. After logging in, the user selects "Conversation Mode" and specifies the type of animal.

[1477] Data collection by terminal

[1478] When the user selects an animal, the device's camera and microphone are activated to capture the animal's sounds and behavior in real time. The collected audio and video data is temporarily stored in the device's memory, then compressed and sent to the server.

[1479] Data reception and decompression by the server

[1480] The server receives the audio and video data sent from the device. The received data is first decompressed. The audio data is sent to an audio analysis engine to analyze the animal's calls. The video data is sent to a motion analysis algorithm to analyze the animal's behavior.

[1481] Server-based parsing and translation

[1482] The voice analysis engine extracts emotions and intentions from the animal's cries. The motion analysis algorithm analyzes the animal's behavior from the video data and extracts more detailed information. The extracted information is compared with a database and the animal's emotions and intentions are translated into human language. The translation results are generated as text data and sent to the device.

[1483] Displaying translation results on your device

[1484] The device displays the translation results received from the server in real time. The display format can be selected as text or speech synthesis. The user can check the translation results on the device screen.

[1485] User response input

[1486] The user can then respond to the animal based on the translation results. Responses can be made by text input or voice input. For example, the user can say, "Just wait a moment, I'll play with you soon."

[1487] Response analysis and retranslation by the server

[1488] The server receives and analyzes the user's response data sent from the device. The analyzed text data is converted into sounds and signs that the animal can understand. The converted results are then sent back to the device.

[1489] Terminal response output

[1490] The device receives the converted data from the server and plays it back to the animal. The converted sound or sign is output to the animal. For example, it may respond to the user by barking "woof woof" or display a specific action.

[1491] Specific examples

[1492] Take the example of a conversation with your dog.

[1493] User

[1494] 1. Launch the app on your smartphone and select "Conversation Mode."

[1495] 2. Select "Shiba Inu" as the type of dog.

[1496] Terminal

[1497] 1. Turn on the camera and microphone to record the Shiba Inu's barks and movements in real time.

[1498] 2. The collected data is compressed and sent to the server.

[1499] server

[1500] 1. The voice analysis engine analyzes the Shiba Inu's bark and extracts that the "woof woof" bark means "Welcome home! Let's play!"

[1501] 2. The Shiba Inu's movements are analyzed using a video analysis algorithm, and the tail-wagging behavior is also extracted as information that supports the above message.

[1502] 3. The analysis results are translated into human language and the text "Welcome back! Let's play!" is sent to the device.

[1503] Terminal

[1504] 1. The translation result is displayed to the user as text, and the synthesized speech plays "Welcome back! Let's play!"

[1505] User

[1506] 1. Look at the message that appears and reply by voice, "Just wait a moment, I'll play with you soon."

[1507] Terminal

[1508] 1. Analyze voice input, convert it into text, and send it to the server.

[1509] server

[1510] 1. Analyze the user's response and convert it into a sound or sign that the dog can understand, saying "Wait a moment, I'll play with you soon."

[1511] 2. Send the conversion result to the device.

[1512] Terminal

[1513] 1. Output the converted animal sounds or signs, such as "woof woof" or displaying a specific action.

[1514] This system allows users to enjoy communicating with animals in a new way.

[1515] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1516] Step 1:

[1517] Users launch applications and log in

[1518] The user launches the animal conversation application on their smartphone or tablet and enters their account information on the login screen to log in. Based on the entered account information, the server performs authentication processing, and if authentication is successful, the conversation mode selection screen is displayed.

[1519] Input: Account information (username, password)

[1520] Output: Conversation mode selection screen

[1521] Step 2:

[1522] Selecting the animal species on the device and starting data collection

[1523] When a user selects an animal type on the application, the device's camera and microphone are activated and real-time recording of the animal's sounds and behavior begins. The collected audio and video data is temporarily stored in the device's memory.

[1524] Input: Animal species information

[1525] Output: Temporary storage of bird sounds and video data

[1526] Step 3:

[1527] Data compression by the terminal and transmission to the server

[1528] The device compresses the audio and video data it has temporarily stored and sends it to the server. Compression reduces the amount of data, making transmission more efficient.

[1529] Input: Stored audio and video data

[1530] Output: Compressed audio and video data

[1531] Step 4:

[1532] Server receives and decompresses data

[1533] The server receives the compressed audio and video data sent from the terminal, and then decompresses the data to restore it to its original state.

[1534] Input: Compressed audio and video data

[1535] Output: Uncompressed audio and video data

[1536] Step 5:

[1537] Server-based voice and behavior analysis

[1538] The server sends the decompressed audio data to an audio analysis engine that analyzes the animal's calls to extract emotions and intentions, while simultaneously sending the decompressed video data to a motion analysis algorithm that analyzes the animal's behavior to extract more detailed information.

[1539] Input: Uncompressed audio and video data

[1540] Output: Extracted emotions, intents, and analyzed behavioral information

[1541] Step 6:

[1542] Translation by the server and data transmission to the device

[1543] The server compares the extracted emotions, intentions, and behavioral information with a database and translates it into human language. The translation results are generated as text data and sent to the device.

[1544] Input: Extracted emotions, intentions, and behavioral information

[1545] Output: Translated text data

[1546] Step 7:

[1547] Displaying translation results on your device

[1548] The device displays the translation results received from the server in real time, and the user can check the translation results either as text or through voice synthesis.

[1549] Input: Translated text data

[1550] Output: Text display or speech synthesis

[1551] Step 8:

[1552] User response input

[1553] The user can then respond to the animal based on the translation results displayed. Responses can be made by text input or voice input, and the device will analyze the response and send it to the server.

[1554] Input: User response (text or voice)

[1555] Output: Response data sent to the server

[1556] Step 9:

[1557] Response analysis and retranslation by the server

[1558] The server analyzes the received user response data and converts the text data into sounds and signs that the animals can understand, then sends the converted results back to the device.

[1559] Input: User response data

[1560] Output: Animal-understandable sounds and signs

[1561] Step 10:

[1562] Response output to terminal

[1563] The terminal plays the response data received from the server to the animal and outputs the converted sounds and signs.

[1564] Input: Animal sounds and signs that animals can understand

[1565] Output: Animal sounds and signs

[1566] This allows for two-way communication between the user and the animal.

[1567] (Application example 1)

[1568] 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."

[1569] In the past, there were many barriers to communication between humans and animals, making it difficult to accurately understand what animals needed. Furthermore, there was a lack of means to understand an animal's dietary requirements and health status in real time and provide appropriate food, which made animal health management a challenge.

[1570] 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.

[1571] In this invention, the server includes means for collecting animal sounds and behavior, means for analyzing the collected sounds and behavior, means for translating the analysis results into human language, means for displaying the translated results to the user, means for transmitting the user's input back to the animal, and means for selecting and delivering food for the animal based on the analysis results, which makes it possible to accurately grasp the needs and health status of the animal and provide appropriate food in real time.

[1572] "Means for collecting animal sounds and behavior" refers to equipment and software that records animal sounds and behavior in real time.

[1573] "Means for analyzing calls and behavior" refers to algorithms and programs that analyze collected data on calls and behavior and understand their intentions and emotions.

[1574] The "means of translation into human language" is software that converts the analyzed animal sounds and behavior into the corresponding human language.

[1575] "Means for displaying to the user" refers to a device or application that presents the translated data to the user visually or audibly.

[1576] "Means for transmitting user input back to the animal" refers to technology for converting instructions and responses from the user into a format that the animal can understand and transmitting them to the animal.

[1577] "Means for selecting and delivering animal food based on analysis results" is a system for understanding the needs of animals, selecting the most suitable food for them, and delivering it.

[1578] A system for carrying out this invention includes technology for collecting and analyzing animal sounds and behavior, translating the results into human language, and displaying them. It also includes means for selecting and delivering animal food based on the analysis results. Specific embodiments for carrying out this invention are described below.

[1579] Overall system configuration

[1580] The system consists of four main components:

[1581] 1. Device (smartphone or tablet)

[1582] 2. Server

[1583] 3. Robot

[1584] 4. Users

[1585] System Operation Overview

[1586] Data collection by terminal

[1587] The user logs in to the device and starts the application. The application displays a screen for selecting the conversation mode with the animal.

[1588] Once the user selects an animal, the device activates its camera and microphone to record the animal's sounds and behavior in real time. The collected data is temporarily stored on the device and then sent to a server for analysis.

[1589] Data analysis and translation by server

[1590] The server receives the audio and video data sent from the device. The received data is first decompressed. An audio analysis engine (using, for example, PyAudio) analyzes the animal's sounds, and extracts the animal's emotions and intentions.

[1591] The video data is then run through a motion analysis algorithm (using OpenCV, for example) to extract more detailed information about the animal's behavior. The extracted information is then compared with a database and translated into the corresponding human language. These translations are then sent to the device as text data.

[1592] Terminal display and user input

[1593] The device displays the translation results received from the server in real time. The display format can be selected as text or speech synthesis. The user looks at the displayed translation results and responds to the animal. Responses can be entered as text or speech. The user's response is sent from the device to the server.

[1594] Analysis of user responses on the server

[1595] The server analyzes the user's response and converts the text data back into sounds and signs that the animal can understand. The completed data is sent to the terminal, which then outputs the sounds and signs to the animal.

[1596] Select and deliver animal food based on the analysis results

[1597] The server includes a means for selecting food for the animals based on the analysis results. The selected food is provided to the animals by a delivery agent (e.g., an automated delivery robot). This allows food to be supplied in real time according to the needs of the animals.

[1598] Specific examples

[1599] User

[1600] 1. Launch the app on your smartphone and select "Conversation Mode."

[1601] 2. Select "Shiba Inu" as the type of dog.

[1602] Terminal

[1603] 1. Turn on the microphone and camera and record the Shiba Inu's barks and movements in real time.

[1604] 2. The collected data is compressed and sent to the server.

[1605] server

[1606] 1. The voice analysis engine analyzes the Shiba Inu's bark and extracts that the "woof woof" bark means "Welcome home! Let's play!"

[1607] 2. Using a video analysis algorithm, the Shiba Inu's movements are analyzed, and the tail-wagging behavior is also extracted as information that supports the above message.

[1608] 3. The analysis results are translated into human language and the text "Welcome back! Let's play!" is sent to the device.

[1609] Terminal

[1610] 1. The translation result is displayed to the user as text, and the speech synthesis feature plays back "Welcome back! Let's play!"

[1611] User

[1612] 1. Look at the message that appears and reply, "Wait a moment, I'll play with you soon." Use voice input to reply.

[1613] server

[1614] 1. Analyze the user's response and convert it into a sound or sign that the dog can understand, saying "Wait a moment, I'll play with you soon."

[1615] 2. Send the conversion result to the device.

[1616] Terminal

[1617] 1. Output the converted animal sounds and signs, and display whether the animal barks "woof woof."

[1618] Prompt Sentence Examples

[1619] "Animal food delivery assistant system. Records animal sounds and behaviors and analyzes them using an audio analysis engine and video analysis algorithm. Based on the requests understood from these, the system selects and delivers the appropriate food. Assume the server API URL is example.com / api and the animal type is dog."

[1620] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1621] Step 1:

[1622] Data collection by terminal

[1623] The user logs in to the device and launches the application. After selecting the animal conversation mode and the type of animal, the device activates the camera and microphone. The camera and microphone are used to record and record the animal's cries and behavior in real time. This captures the animal's audio and video data, which is then temporarily stored on the device.

[1624] Input: User login information, animal type selection

[1625] Output: Recorded audio and video data

[1626] Step 2:

[1627] Sending data to the server

[1628] The device compresses the temporarily stored audio and video data and sends it to the server for analysis. Specifically, it uses a data compression algorithm to reduce the size of the audio data and video frames and uploads them to the server via an HTTP request.

[1629] Input: Recorded audio and video data

[1630] Output: Compressed audio and video data is sent to the server

[1631] Step 3:

[1632] Data analysis by server

[1633] The server receives the audio and video data sent from the device and performs the decompression process. The received data is analyzed by an audio analysis engine (e.g., PyAudio) to extract the animal's voice and its emotion and intention. In parallel, the video data is analyzed by a motion analysis algorithm (e.g., OpenCV) to extract detailed information about the animal's behavior.

[1634] Input: Compressed audio and video data

[1635] Output: Analyzed sentiment, intent, and behavior details

[1636] Step 4:

[1637] Server-based translation

[1638] The server translates the analyzed audio and video data into human language. The extracted information is compared with a database and converted into the corresponding human language. The translation result is sent to the device in text format.

[1639] Input: Analyzed sentiment, intent, and behavior details

[1640] Output: Text data translated into human language

[1641] Step 5:

[1642] Display by terminal

[1643] The device receives translation results from the server and displays them in real time. Users can choose to display them as text or speech synthesis, allowing them to intuitively understand the animal's message.

[1644] Input: Text data translated into human language

[1645] Output: Text or audio displayed to the user

[1646] Step 6:

[1647] User response

[1648] The user sees the translation results and responds to the animal using text input or voice input, and the input data is sent from the device to the server.

[1649] Input: User's text or voice response

[1650] Output: User response data sent to the server

[1651] Step 7:

[1652] Server response analysis

[1653] The server analyzes the user's response and converts the text data back into sounds and signs that the animal can understand, and the results are sent to the device.

[1654] Input: User response data

[1655] Output: Animal-understandable sounds and signs

[1656] Step 8:

[1657] Terminal Output

[1658] The device outputs the converted sounds and signs to the animal, thereby communicating the user's intentions to the animal.

[1659] Input: Animal-understandable sounds and signs

[1660] Output: Actual sounds and signs

[1661] Step 9:

[1662] Food delivery based on analysis results

[1663] The server selects food for the animal based on the analysis results. The selected food is provided to the animal by a delivery agent (e.g., an automated delivery robot). The user can confirm the selection results and finalize the order.

[1664] Input: Analyzed animal needs data

[1665] Output: Selected food order and delivery

[1666] 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.

[1667] This system realizes a new form of communication by collecting and analyzing animal sounds and behaviors, translating them into human language, and displaying them to the user. The present invention further improves the quality of communication by combining it with an emotion engine that analyzes the user's emotions.

[1668] Overall system configuration

[1669] The system consists of four main components:

[1670] 1. Device (smartphone or tablet)

[1671] 2. Server

[1672] 3. Users

[1673] 4. Emotion Engine

[1674] System Operation Overview

[1675] 1. Data collection by device

[1676] The user logs in to the device and starts the application. The application displays a screen for selecting the conversation mode with the animal.

[1677] When the user selects an animal type, the device activates the camera and microphone to record the animal's sounds and movements, as well as the user's voice and facial expressions, in real time.

[1678] The collected data is temporarily stored on the device and then sent to a server for analysis.

[1679] 2. Data analysis and translation by the server

[1680] The server receives the audio and video data sent from the terminal. The received data is first decompressed.

[1681] The voice analysis engine analyzes the animal's cries and extracts the animal's emotions and intentions.

[1682] The video data is then run through motion analysis algorithms to extract more detailed information about the animal's behavior.

[1683] The emotion engine analyzes the user's voice and facial expressions to detect the user's emotional state.

[1684] The extracted information is compared with a database and translated into the corresponding human language. These translation results and the user's emotional state are sent to the device as text data.

[1685] 3. Terminal display and user input

[1686] The device displays the translation results received from the server and the user's emotional state in real time, and can be displayed in text or speech synthesis.

[1687] The user sees the translation results and responds to the animal. Responses can be entered by text or voice.

[1688] 4. Analysis of user responses on the server

[1689] The user's response is sent from the terminal to the server.

[1690] The server analyzes the user's responses and converts the text data back into sounds and signs that the animals can understand.

[1691] The completed data is sent to a terminal, which outputs sounds and signs to the animal.

[1692] Explanation with concrete examples

[1693] Example: Conversation with your dog

[1694] User

[1695] 1. Launch the app on your smartphone and select "Conversation Mode."

[1696] 2. Select "Shiba Inu" as the type of dog.

[1697] Terminal

[1698] 1. The microphone and camera are activated to record and record the Shiba Inu's barks and movements in real time, and also collect the user's voice and facial expressions.

[1699] 2. The collected data is compressed and sent to the server.

[1700] server

[1701] 1. The voice analysis engine analyzes the Shiba Inu's bark and extracts that the "woof woof" bark means "Welcome home! Let's play!"

[1702] 2. Using a video analysis algorithm, the Shiba Inu's movements are analyzed, and the tail-wagging behavior is also extracted as information that supports the above message.

[1703] 3. The emotion engine analyzes the user's voice and facial expressions to detect when the user is in an emotional state of "joy."

[1704] 4. The analysis results and emotional state are translated into human language, and the text "Welcome back! Let's play!" and the information "The user is happy" are sent to the device.

[1705] Terminal

[1706] 1. The translation result and emotional state are displayed to the user as text, and the synthesized voice phrase "Welcome back! Let's play!" is played.

[1707] User

[1708] 1. Look at the message that appears and reply, "Wait a moment, I'll play with you soon." Use voice input to reply.

[1709] Terminal

[1710] 1. Analyze voice input, convert it to text, and send it to the server.

[1711] server

[1712] 1. Analyze the user's response and convert it into a sound or sign that the dog can understand, saying "Wait a moment, I'll play with you soon."

[1713] 2. Send the conversion result to the device.

[1714] Terminal

[1715] 1. Output the converted animal sounds and signs, and display whether the animal barks "woof woof."

[1716] The above is a specific example of how to implement the present invention by incorporating an emotion engine. This process allows users to enjoy real-time conversations with animals, and the use of the emotion engine further improves the quality of communication.

[1717] The processing flow will be explained below.

[1718] Step 1:

[1719] User

[1720] Launch the app on your smartphone and log in.

[1721] Select "Conversation Mode" from the main screen.

[1722] Step 2:

[1723] Terminal

[1724] When a user selects conversation mode, the device requests access to GPS information, the camera, and the microphone.

[1725] The terminal displays a screen for selecting the type of animal, and the user selects the type of animal (e.g., dog).

[1726] Step 3:

[1727] Terminal

[1728] Activate the camera and microphone to record the sounds and movements of a selected animal (e.g., a dog) in real time.

[1729] At the same time, the user's facial expressions and voice are also collected.

[1730] The collected audio data, video data, and user voice and facial expression data are temporarily stored on the terminal.

[1731] Step 4:

[1732] Terminal

[1733] The collected data is compressed and a data packet is generated for transmission to the server.

[1734] The data packet is sent to the server using the HTTPS protocol or the like.

[1735] Step 5:

[1736] server

[1737] It receives data packets sent from the terminal and performs decompression processing.

[1738] The decompressed audio data, video data, and user voice and facial expression data are prepared for analysis.

[1739] Step 6:

[1740] server

[1741] The collected call data is analyzed using an audio analysis engine to extract call patterns.

[1742] Based on the extracted patterns, the animal's emotions and intentions are matched with a database to identify meaning.

[1743] Step 7:

[1744] server

[1745] The video data is run through a motion analysis algorithm to extract the animal's behavioral patterns.

[1746] The extracted behavioral patterns are also compared with a database to identify the animal's intentions.

[1747] Step 8:

[1748] server

[1749] The emotion engine is used to analyze the user's voice and facial expressions to detect the user's emotional state.

[1750] The speech analysis data, movement analysis data, and emotion analysis data are integrated to determine the final meaning.

[1751] Step 9:

[1752] server

[1753] The determined meaning is translated into human language using a natural language generation engine and prepared as text data.

[1754] The translated text data and emotional state data are packaged into a data packet and transmitted to the terminal.

[1755] Step 10:

[1756] Terminal

[1757] The data packet received from the server is decompressed to obtain the translation result text data and emotional state data.

[1758] Display the translation results and emotional state to the user (e.g., text display, speech synthesis).

[1759] Step 11:

[1760] User

[1761] Check the displayed translation results and the animal's emotional state, and reply to it using text or voice input.

[1762] Step 12:

[1763] Terminal

[1764] The user's response is saved as text or audio data and prepared for transmission to the server.

[1765] Step 13:

[1766] Terminal

[1767] The user's response data is converted into a data packet and sent to the server.

[1768] Step 14:

[1769] server

[1770] The user's response data is received and decompressed.

[1771] The decompressed audio data is analyzed and converted into text.

[1772] Step 15:

[1773] server

[1774] The user's response text is compared with a database to convert it into sounds and signs that animals can understand.

[1775] It also takes into account the user's emotional state to generate optimal sounds and signs.

[1776] Step 16:

[1777] server

[1778] The generated data of sounds and signs is packaged into data packets and sent to the terminal.

[1779] Step 17:

[1780] Terminal

[1781] The data packets of sounds and signs received from the server are decompressed and the output data is obtained.

[1782] The output data is used to communicate with the animal through a speaker or display (e.g., playing a sound from a speaker or showing a sign on a display).

[1783] The above is a concrete process flow for implementing the present invention in combination with an emotion engine. This process allows users to enjoy real-time conversations with animals, and the use of the emotion engine further improves the quality of communication.

[1784] Example 2

[1785] 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."

[1786] Conventional methods of communicating with animals have had difficulty analyzing and understanding the meaning of animal sounds and behavior. Furthermore, they have not been able to realize two-way communication that takes the user's emotions into account, making it difficult to communicate effectively with animals. This has made it difficult to correctly understand the animal's emotions and intentions and respond appropriately.

[1787] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes means for executing an audio analysis engine, a video analysis algorithm, and an emotion analysis engine on the cloud, means for analyzing collected data and extracting the emotions and intentions of animals and the emotional state of people, and means for translating the analysis results into human language. This enables accurate analysis of animal sounds and behaviors and two-way communication that takes the user's emotions into consideration.

[1788] "Terminal" refers to a device used to collect animal sounds and behavior, as well as the user's voice and facial expressions, such as a smartphone or tablet.

[1789] "Server" refers to a remote computer system that receives, analyzes, and translates data sent from a terminal.

[1790] A "voice analysis engine" refers to a program or algorithm that analyzes animal sounds and extracts emotions and intentions.

[1791] "Video analysis algorithms" refer to programs and algorithms that analyze animal behavior and movements and extract detailed information.

[1792] An "emotion analysis engine" refers to a program or algorithm that analyzes a user's voice and facial expressions to detect the user's emotional state.

[1793] "Collection" refers to obtaining data from animals and users in real time using sensors such as cameras and microphones.

[1794] "Analysis" refers to the processing performed on collected data, including detailed analysis of animal sounds and behavior, as well as people's emotional states.

[1795] "Translation" refers to converting an animal's emotions and intentions into human language based on the analysis results.

[1796] "Display" refers to conveying the translation results and the user's emotional state to the user on the device screen or using voice synthesis.

[1797] "Input" refers to an operation by the user to respond to the animal using text or voice.

[1798] This invention is a system that collects and analyzes animal sounds and behaviors, translates them into human language, and displays them to the user. Furthermore, by combining it with an emotion engine that analyzes the user's emotions, the quality of communication can be improved.

[1799] Overall system configuration

[1800] The system consists of four main components:

[1801] 1. Device (smartphone or tablet)

[1802] 2. Server

[1803] 3. Users

[1804] 4. Emotion Engine

[1805] System Operation Overview

[1806] The process begins when a user logs in to their device and launches the application. The application displays a screen where users can select a conversation mode with the animal. Once the user selects the type of animal, the device activates its camera and microphone, and collects the animal's sounds and behavior, as well as the user's voice and facial expressions, in real time. The collected data is temporarily stored on the device and then sent to a server.

[1807] The server receives the data sent from the device and analyzes the animal's cries and behavior using a voice analysis engine and video analysis algorithm. Commonly used voice analysis engines include IBM Watson, and video analysis algorithms include OpenCV. Microsoft Azure Emotion API is used as the emotion analysis engine. The analysis results are compared with a database and translated into human language. These translation results and the user's emotional state are sent to the device as text data.

[1808] The device displays the translation results received from the server and the user's emotional state in real time. The user looks at the displayed results and responds to the animal. Responses can be entered as text or voice, and the input data is sent back to the server. The server analyzes the user's response and converts it into sounds or signs that the animal can understand. The converted data is sent to the device, which then outputs it to the animal.

[1809] Explanation with concrete examples

[1810] Example: Conversation with your dog

[1811] User

[1812] 1. Launch the app on your smartphone and select "Conversation Mode."

[1813] 2. Select "Shiba Inu" as the type of dog.

[1814] Terminal

[1815] 1. The microphone and camera are activated to record and record the Shiba Inu's barks and movements in real time, and also collect the user's voice and facial expressions.

[1816] 2. The collected data is compressed and sent to the server.

[1817] server

[1818] 1. Analyze the Shiba Inu's bark using a voice analysis engine (for example, IBM Watson) and extract that the bark "woof woof" means "Welcome home! Let's play!"

[1819] 2. Analyze the Shiba Inu's movements using a video analysis algorithm (e.g., OpenCV) and analyze the tail-wagging behavior.

[1820] 3. An emotion engine (for example, Microsoft Azure Emotion API) analyzes the user's voice and facial expressions to detect whether the user is feeling "joy."

[1821] 4. The analysis results and emotional state are translated into human language and the results are sent to the device.

[1822] Terminal

[1823] 1. The translated result and the user's emotional state are displayed as text, and the synthesized voice phrase "Welcome back! Let's play!" is played.

[1824] User

[1825] 1. Look at the message that appears and reply, "Wait a moment, I'll play with you soon." Use voice input to reply.

[1826] Terminal

[1827] 1. Analyze voice input, convert it to text, and send it to the server.

[1828] server

[1829] 1. Analyze the user's response and convert it into a sound or sign that the dog can understand, saying "Wait a moment, I'll play with you soon."

[1830] 2. Send the conversion result to the device.

[1831] Terminal

[1832] 1. Output the converted animal sounds and signs, and display whether the animal barks "woof woof."

[1833] Prompt Sentence Examples

[1834] Prompt: Simulate a conversation with your Shiba Inu dog. The user launches the app on their smartphone and selects conversation mode. Create an example where the Shiba Inu's bark "woof woof" means "Welcome home! Let's play!" and the user responds "Just wait a moment, I'll play with you soon." Include an emotion engine.

[1835] The above is a specific embodiment for carrying out the invention. This process allows users to enjoy real-time conversations with animals, and the quality of communication is improved by utilizing the emotion engine.

[1836] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1837] Step 1:

[1838] Device: The user logs in to the device and launches the application. A button for selecting "Animal Conversation Mode" appears on the screen. The user taps the button to select the conversation mode.

[1839] Input: User action to select the type of animal.

[1840] Output: The device prepares to activate the camera and microphone.

[1841] Step 2:

[1842] User: Select an animal type from the list on the screen, for example, "Shiba Inu."

[1843] Input: Select the type of animal (e.g. Shiba Inu).

[1844] Output: The device is ready to activate the camera and microphone.

[1845] Step 3:

[1846] Device: The camera and microphone are activated to collect animal sounds and behavior, as well as the user's voice and facial expressions in real time. The collected data is temporarily stored on the device.

[1847] Input: Animal sounds, actions, user voice, facial expressions.

[1848] Output: Collected data (audio data, video data).

[1849] Step 4:

[1850] Terminal: Compresses the collected data and sends it to the server.

[1851] Input: Collected data.

[1852] Output: Sending compressed data to the server.

[1853] Step 5:

[1854] Server: Receives compressed data sent from the terminal and decompresses it, dividing it into audio data and video data.

[1855] Input: Compressed data.

[1856] Output: Decompressed audio and video data.

[1857] Step 6:

[1858] Server: Using a voice analysis engine, analyze the voice data and extract the animal's emotions and intentions.

[1859] Input: Audio data.

[1860] Output: The animal's emotion or intention (e.g., "Welcome home! Let's play!").

[1861] Specific behavior: Performs analysis using a voice analysis engine (e.g. IBM Watson).

[1862] Step 7:

[1863] Server: Using video analysis algorithms, the video data is analyzed and animal behavior is analyzed in detail.

[1864] Input: Video data.

[1865] Output: Detailed information about the animal's behavior (e.g., tail wagging).

[1866] Specific operation: Perform analysis using a video analysis algorithm (e.g., OpenCV).

[1867] Step 8:

[1868] Server: Using an emotion analysis engine, analyzes the user's voice and facial expressions to detect the user's emotional state.

[1869] Input: User's audio and video data.

[1870] Output: The user's emotional state (e.g., emotion "joy").

[1871] Specific behavior: Performs analysis using a sentiment analysis engine (e.g., Microsoft Azure Emotion API).

[1872] Step 9:

[1873] Server: Based on the results of the animal analysis and the user's emotional state, the results are translated into human language and sent to the device as text data.

[1874] Input: Animal emotions and intentions, user emotional state.

[1875] Output: Translated text data (e.g. "Welcome back! Let's play!") and the user's emotional state.

[1876] Step 10:

[1877] Terminal: Displays the translation results received from the server and the user's emotional state in real time. The user can then respond to the animal based on the results.

[1878] Input: Translation result, user's emotional state.

[1879] Output: The text or audio to be displayed (e.g., "Welcome back! Let's play!").

[1880] Specific operation: The translation result is displayed as text on the screen and played back using speech synthesis if necessary.

[1881] Step 11:

[1882] User: Sees the displayed message and responds to the animal using text input or voice input.

[1883] Input: User response (e.g. "Just wait a moment, I'll play with you soon").

[1884] Output: Response data.

[1885] Step 12:

[1886] Terminal: Converts the user's voice input into text and sends it to the server.

[1887] Input: User response data.

[1888] Output: Sent to the server as text data.

[1889] Step 13:

[1890] Server: Receives the user's response data and begins analysis. The analysis results are converted into sounds and signs that animals can understand.

[1891] Input: User response data.

[1892] Output: A sound or signal that the animal can understand (e.g., a "wait a moment" sound or ear movement).

[1893] Step 14:

[1894] Server: Transmits the converted sound and sign data to the device.

[1895] Input: Converted call and sign data.

[1896] Output: Data sent to the terminal.

[1897] Step 15:

[1898] Terminal: Outputs the sounds and signs received from the server to the animals.

[1899] Input: Converted call and sign data.

[1900] Output: Animal sound or display (e.g. "woof woof").

[1901] Specific actions: Use speakers and displays to play sounds and show signs.

[1902] (Application example 2)

[1903] 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."

[1904] With conventional pet communication tools, it was difficult to understand a pet's cries and behaviors and grasp their emotions and intentions. Furthermore, there were no product recommendation services based on pet needs, making it difficult for users to select appropriate products that matched their pet's preferences. Furthermore, improving the quality of communication by taking into account the user's emotional state was also a challenge.

[1905] 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 and analyzing the pet's cries and behavior, means for translating the analysis results of the collected cries and behavior into human language, means for analyzing the user's emotional state, and means for displaying recommended products based on the pet's cries and behavior. This makes it possible to understand the pet's cries and behavior and suggest products to the user based on them. It also makes it possible to realize high-quality communication that takes the user's emotional state into consideration.

[1906] "Means for collecting animal sounds and behavior" refers to devices and software for capturing animal sounds and body movements as audio and video data.

[1907] "Means for analyzing" refers to computer programs or algorithms that analyze the collected audio and video data and extract specific patterns or meanings.

[1908] "Means of translation into human language" refers to technology that converts the results of analyzed animal sounds and behavior into text data or audio data that humans can understand.

[1909] "Displaying means" refers to a display device, speaker, or application software for visually or audibly presenting the translated text or speech to the user.

[1910] The "means for transmitting user input back to the animal" refers to a device or software that converts the instructions and responses given by the user to the pet into a format that the animal can understand and transmits them to the animal.

[1911] "Means for analyzing the user's emotional state" refers to technology or software that analyzes data such as the user's voice and facial expressions and identifies their emotional state.

[1912] "Means for displaying recommended products" refers to technology or devices for presenting appropriate products to users based on the animal's condition and analysis results.

[1913] The system for realizing this invention mainly consists of the following components: It functions as a system that promotes new forms of communication with animals and recommends products based on pet needs.

[1914] Hardware and software used

[1915] 1. Device (smartphone or tablet):

[1916] A device for collecting animal sounds and behavior in real time using a camera and microphone.

[1917] Application software (compatible with iOS or Android).

[1918] 2. Server:

[1919] Use cloud platforms for data analysis and translation (e.g., AWS, GCP).

[1920] A speech analysis engine (e.g., Google Cloud Speech-to-Text).

[1921] Video analysis algorithms (e.g., OpenCV, TensorFlow).

[1922] Sentiment analysis engines (e.g., Emotion API, Microsoft Azure).

[1923] A database (e.g., MySQL, MongoDB).

[1924] Recommendation systems (e.g., Amazon Personalize).

[1925] 3. User:

[1926] Operate applications on your smartphone or tablet.

[1927] Communicate with animals and check and order recommended products.

[1928] Explanation of program processing

[1929] 1. Data collection by device:

[1930] The user launches the application, which uses the device's camera and microphone to collect real-time data on their pet's meows and movements, which is then compressed and sent to a cloud server.

[1931] 2. Data analysis and translation by the server:

[1932] Speech analysis: Using Google Cloud Speech-to-Text, pet cries are converted into text data and the intent is analyzed.

[1933] Video Analysis: Analyze your pet's behavior and identify its emotional state using OpenCV and TensorFlow.

[1934] Emotion Analysis: The Emotion API analyzes the user's voice and facial expressions to detect their emotional state.

[1935] Translation: Based on the analysis results, the pet's cries and actions are translated into human language.

[1936] 3. Recommendation Systems:

[1937] Using Amazon Personalize, we generate product recommendations based on pet sounds and behavior, and create a list of recommended products that also takes into account the user's emotional state.

[1938] 4. Terminal display and user input:

[1939] The translation results and recommended product list received from the server are displayed on the smartphone or tablet, allowing the user to check the displayed information and select a product.

[1940] Specific examples

[1941] For example, if a pet dog barks "woof woof," the sound is collected and analyzed on the server, which determines that the dog is hungry. Amazon Personalize then recommends a high-protein dry food, which is then displayed on the user's smartphone. The user can then easily order the dry food through the application.

[1942] Example prompts for generative AI models

[1943] An example of a prompt is:

[1944] Convert your pet's behavior and vocalizations into text data and analyze their emotions and intentions. For example, if your dog barks "woof woof," analyze whether it indicates hunger and provide a list of recommended foods based on that information.

[1945] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1946] Step 1:

[1947] Data collection by terminal

[1948] The device uses the smartphone or tablet's camera and microphone to collect the pet's sounds and behavior in real time. The input data is video of the pet's sounds and behavior, and the output is a compressed file of this data. Specifically, the device temporarily stores the data through an application and prepares it for transfer to a cloud server.

[1949] Step 2:

[1950] Data reception and decompression by the server

[1951] The server receives the compressed data sent from the terminal and decompresses it. The input data is the compressed bird sounds and video data, and the output is the decompressed raw data. Specifically, the server uses the receiving module to pass the data to the decompression program.

[1952] Step 3:

[1953] Audio analysis

[1954] The server analyzes the bird's calls using a speech analysis engine such as Google Cloud Speech-to-Text. The input data is the bird's call audio data, and the output is the text corresponding to the audio and an interpretation of the bird's intent. Specifically, the server sends the audio data to the analysis engine, which interprets the intent and converts it into text data.

[1955] Step 4:

[1956] Video Analysis

[1957] The server analyzes the pet's behavior using OpenCV and TensorFlow. The input data is video data, and the output is the results of the behavior analysis. In operation, the server breaks down the video data into frames and identifies behavior using a machine learning model.

[1958] Step 5:

[1959] Emotion analysis

[1960] The server uses the Emotion API to analyze the user's emotions. The input data is the user's voice and facial expression data, and the output is their emotional state. Specifically, the server sends the voice and video data to the emotion analysis engine and integrates the results.

[1961] Step 6:

[1962] translation

[1963] The server translates the animal's intentions into human language based on the analysis of its calls and behavior. The input data is the analysis results, and the output is translated text data. Specifically, the server compares it with an existing database and generates the corresponding text.

[1964] Step 7:

[1965] Recommendation Systems

[1966] The server uses Amazon Personalize to generate recommended products. The input data is the pet analysis results and the user's emotional state, and the output is a list of recommended products. Specifically, the server sends the data to the recommendation engine to create the optimal product list.

[1967] Step 8:

[1968] Display by terminal

[1969] The device receives the translation results and recommended product list from the server and displays them to the user. The input data is the translation results and recommended product list, and the output is visual and auditory feedback to the user. Specifically, the device updates the UI and displays related information.

[1970] Step 9:

[1971] User operation

[1972] The user checks the displayed information and selects a product. The input data is recommended product information from the system, and the output is order information. Specifically, the user makes the selection and orders using a touch screen or voice input.

[1973] Step 10:

[1974] Order processing and delivery

[1975] The server processes the user's order and notifies the delivery company. The input data is the user's order information, and the output is notification information to the delivery company. Specifically, the server links the order data to the delivery system and carries out shipping procedures.

[1976] 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.

[1977] 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.

[1978] 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.

[1979] [Fourth embodiment]

[1980] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1981] 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.

[1982] 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).

[1983] 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.

[1984] 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.

[1985] 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).

[1986] 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.

[1987] 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.

[1988] 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.

[1989] 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.

[1990] 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.

[1991] 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.

[1992] 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."

[1993] This system collects and analyzes animal sounds and behaviors, translates them into human language, and displays them to the user, thereby realizing a new form of communication. Below, an embodiment of the present invention will be described in detail.

[1994] Overall system configuration

[1995] The system consists of three main components:

[1996] 1. Device (smartphone or tablet)

[1997] 2. Server

[1998] 3. Users

[1999] System Operation Overview

[2000] 1. Data collection by device

[2001] The user logs in to the device and starts the application. The application displays a screen for selecting the conversation mode with the animal.

[2002] Once the user selects a type of animal, the device will activate the camera and microphone to record the animal's sounds and movements in real time.

[2003] The collected data is temporarily stored on the device and then sent to a server for analysis.

[2004] 2. Data analysis and translation by the server

[2005] The server receives the audio and video data sent from the terminal. The received data is first decompressed.

[2006] The sound analysis engine analyzes the animal's cries, and as a result, the animal's emotions and intentions are extracted.

[2007] The video data is then run through motion analysis algorithms to extract more detailed information about the animal's behavior.

[2008] The extracted information is compared with a database and translated into the corresponding human language. These translation results are sent to the device as text data.

[2009] 3. Terminal display and user input

[2010] The device receives translation results from the server and displays them in real time, with the option to display them as text or speech synthesis.

[2011] The user sees the translation results and responds to the animal. Responses can be entered by text or voice.

[2012] 4. Analysis of user responses on the server

[2013] The user's response is sent from the terminal to the server.

[2014] The server analyzes the user's responses and converts the text data back into sounds and signs that the animals can understand.

[2015] The completed data is sent to a terminal, which outputs sounds and signs to the animal.

[2016] Explanation with concrete examples

[2017] Example: Conversation with your dog

[2018] User

[2019] 1. Launch the app on your smartphone and select "Conversation Mode."

[2020] 2. Select "Shiba Inu" as the type of dog.

[2021] Terminal

[2022] 1. Turn on the microphone and camera and record the Shiba Inu's barks and movements in real time.

[2023] 2. The collected data is compressed and sent to the server.

[2024] server

[2025] 1. The voice analysis engine analyzes the Shiba Inu's bark and extracts that the "woof woof" bark means "Welcome home! Let's play!"

[2026] 2. Using a video analysis algorithm, the Shiba Inu's movements are analyzed, and the tail-wagging behavior is also extracted as information that supports the above message.

[2027] 3. The analysis results are translated into human language and the text "Welcome back! Let's play!" is sent to the device.

[2028] Terminal

[2029] 1. The translation result is displayed to the user as text, and the speech synthesis feature plays back "Welcome back! Let's play!"

[2030] User

[2031] 1. Look at the message that appears and reply, "Wait a moment, I'll play with you soon." Use voice input to reply.

[2032] Terminal

[2033] 1. Analyze voice input, convert it into text, and send it to the server.

[2034] server

[2035] 1. Analyze the user's response and convert it into a sound or sign that the dog can understand, saying "Wait a moment, I'll play with you soon."

[2036] 2. Send the conversion result to the device.

[2037] Terminal

[2038] 1. Output the converted animal sounds and signs, and display whether the animal barks "woof woof."

[2039] The above is an embodiment of the present invention. This system allows users to enjoy communicating with animals in a new way.

[2040] The processing flow will be explained below.

[2041] Step 1:

[2042] User

[2043] Launch the app on your smartphone and log in.

[2044] Select "Conversation Mode" from the app's main screen.

[2045] Step 2:

[2046] Terminal

[2047] When a user selects conversation mode, the device requests access to GPS information, the camera, and the microphone.

[2048] The terminal displays a screen for selecting the type of animal, and the user selects the type of animal (e.g., dog).

[2049] Step 3:

[2050] Terminal

[2051] Activate the camera and microphone to record the sounds and movements of a selected animal (e.g., a dog) in real time.

[2052] The collected audio and video data is temporarily stored on the device.

[2053] Step 4:

[2054] Terminal

[2055] The collected data is compressed and a data packet is generated for transmission to the server.

[2056] The data packet is sent to the server using the HTTPS protocol or the like.

[2057] Step 5:

[2058] server

[2059] It receives data packets sent from the terminal and performs decompression processing.

[2060] The decompressed audio and video data is prepared for analysis.

[2061] Step 6:

[2062] server

[2063] The collected call data is analyzed using an audio analysis engine to extract call patterns.

[2064] Based on the extracted patterns, the animal's emotions and intentions are matched with a database to identify meaning.

[2065] Step 7:

[2066] server

[2067] The video data is run through a motion analysis algorithm to extract the animal's behavioral patterns.

[2068] The extracted behavioral patterns are also compared with a database to identify the animal's intentions.

[2069] Step 8:

[2070] server

[2071] The speech analysis data and motion analysis data are integrated to determine the final meaning.

[2072] The determined meaning is translated into human language using a natural language generation engine and prepared as text data.

[2073] Step 9:

[2074] server

[2075] The translated text data is packaged into data packets and sent to the terminal.

[2076] Step 10:

[2077] Terminal

[2078] The data packet received from the server is decompressed to obtain the text data of the translation result.

[2079] The translation results are displayed to the user (e.g., as text, or read aloud using speech synthesis).

[2080] Step 11:

[2081] User

[2082] Check the translation results and reply to the animal by text input or voice input.

[2083] Step 12:

[2084] Terminal

[2085] The user's response is saved as text or audio data and prepared for transmission to the server.

[2086] Step 13:

[2087] Terminal

[2088] The user's response data is converted into a data packet and sent to the server.

[2089] Step 14:

[2090] server

[2091] The user's response data is received and decompressed.

[2092] The decompressed audio data is analyzed and converted into text.

[2093] Step 15:

[2094] server

[2095] The user's response text is compared with a database to convert it into sounds and signs that animals can understand.

[2096] Step 16:

[2097] server

[2098] The generated data of sounds and signs is packaged into data packets and sent to the terminal.

[2099] Step 17:

[2100] Terminal

[2101] The data packets of sounds and signs received from the server are decompressed and the output data is obtained.

[2102] The output data is used to communicate with the animal through a speaker or display (e.g., playing a sound from a speaker or showing a sign on a display).

[2103] The above is a specific process flow for carrying out the invention, which allows users to enjoy real-time conversations with animals.

[2104] Example 1

[2105] 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."

[2106] Conventional methods of communication with animals have relied primarily on intuitive interpretation of animal behavior and calls, resulting in incomplete communication between humans and animals. Furthermore, many animal communication systems transmit analysis results in only one direction, making it difficult to establish two-way communication. Furthermore, it is difficult to complete analysis of animal calls and behavior within a device due to limitations in processing power. An efficient, two-way communication system that can solve these issues is needed.

[2107] 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.

[2108] In this invention, the server includes means for enabling a user to launch an application and log in, means for activating a camera and microphone to collect sounds and behaviors of animals based on the type of animal selected by the user, means for compressing the collected audio and video data and transmitting it to the server, means for decompressing and analyzing the data in the server and extracting the animal's emotions and intentions using a voice analysis engine and a motion analysis algorithm, means for comparing the extracted information with a database and translating it into human language, means for transmitting the translated text data to a terminal and displaying it to the user as text or synthesized speech, means for receiving the user's response as voice or text input and transmitting it again to the server, and means for converting the user's response in the server into sounds or signs that the animal can understand and transmitting them to the terminal for output, thereby enabling two-way and efficient communication between humans and animals.

[2109] A "user" is a person who uses the system to communicate with animals.

[2110] An "application" is a software program that runs on a smartphone or tablet and is used to collect and analyze animal sounds and behavior.

[2111] A "camera" is a photographic device used to capture the appearance and behavior of animals.

[2112] A "microphone" is an audio input device for recording animal sounds.

[2113] "Collected audio and video data" refers to data of animal sounds and movements recorded by the user's terminal.

[2114] "Compression" is a process for reducing the size of data.

[2115] A "server" is a remote computing device that receives, analyzes, and interprets collected data.

[2116] "Decompression" is the process of restoring compressed data to its original state.

[2117] A "voice analysis engine" is specialized software that analyzes animal sounds and extracts their emotions and intentions.

[2118] A "motion analysis algorithm" is a computational method for analyzing animal movements and extracting information from their behavior.

[2119] A "database" is an information collection device for storing and collating extracted information.

[2120] "Translation" is the process of converting animal emotions and intentions into human language.

[2121] "Text data" refers to data that expresses the translation results as text information.

[2122] "Speech synthesis" is a technology that converts text data into speech and plays it back.

[2123] A "response" refers to a response that the user makes to the animal.

[2124] "Calls and signs" are audio and visual cues that animals can understand.

[2125] This invention is a system that collects and analyzes animal sounds and behaviors, translates them into human language, and displays them to the user, thereby realizing a new form of communication. Specific embodiments of the invention are described below.

[2126] Overall system configuration

[2127] This system is mainly composed of three elements: "terminals," "servers," and "users."

[2128] Users launch applications and log in

[2129] The user launches the animal conversation application on their smartphone or tablet. Next, a login screen appears, and the user enters their account information to log in. After logging in, the user selects "Conversation Mode" and specifies the type of animal.

[2130] Data collection by terminal

[2131] When the user selects an animal, the device's camera and microphone are activated to capture the animal's sounds and behavior in real time. The collected audio and video data is temporarily stored in the device's memory, then compressed and sent to the server.

[2132] Data reception and decompression by the server

[2133] The server receives the audio and video data sent from the device. The received data is first decompressed. The audio data is sent to an audio analysis engine to analyze the animal's calls. The video data is sent to a motion analysis algorithm to analyze the animal's behavior.

[2134] Server-based parsing and translation

[2135] The voice analysis engine extracts emotions and intentions from the animal's cries. The motion analysis algorithm analyzes the animal's behavior from the video data and extracts more detailed information. The extracted information is compared with a database and the animal's emotions and intentions are translated into human language. The translation results are generated as text data and sent to the device.

[2136] Displaying translation results on your device

[2137] The device displays the translation results received from the server in real time. The display format can be selected as text or speech synthesis. The user can check the translation results on the device screen.

[2138] User response input

[2139] The user can then respond to the animal based on the translation results. Responses can be made by text input or voice input. For example, the user can say, "Just wait a moment, I'll play with you soon."

[2140] Response analysis and retranslation by the server

[2141] The server receives and analyzes the user's response data sent from the device. The analyzed text data is converted into sounds and signs that the animal can understand. The converted results are then sent back to the device.

[2142] Terminal response output

[2143] The device receives the converted data from the server and plays it back to the animal. The converted sound or sign is output to the animal. For example, it may respond to the user by barking "woof woof" or display a specific action.

[2144] Specific examples

[2145] Take the example of a conversation with your dog.

[2146] User

[2147] 1. Launch the app on your smartphone and select "Conversation Mode."

[2148] 2. Select "Shiba Inu" as the type of dog.

[2149] Terminal

[2150] 1. Turn on the camera and microphone to record the Shiba Inu's barks and movements in real time.

[2151] 2. The collected data is compressed and sent to the server.

[2152] server

[2153] 1. The voice analysis engine analyzes the Shiba Inu's bark and extracts that the "woof woof" bark means "Welcome home! Let's play!"

[2154] 2. The Shiba Inu's movements are analyzed using a video analysis algorithm, and the tail-wagging behavior is also extracted as information that supports the above message.

[2155] 3. The analysis results are translated into human language and the text "Welcome back! Let's play!" is sent to the device.

[2156] Terminal

[2157] 1. The translation result is displayed to the user as text, and the synthesized speech plays "Welcome back! Let's play!"

[2158] User

[2159] 1. Look at the message that appears and reply by voice, "Just wait a moment, I'll play with you soon."

[2160] Terminal

[2161] 1. Analyze voice input, convert it into text, and send it to the server.

[2162] server

[2163] 1. Analyze the user's response and convert it into a sound or sign that the dog can understand, saying "Wait a moment, I'll play with you soon."

[2164] 2. Send the conversion result to the device.

[2165] Terminal

[2166] 1. Output the converted animal sounds or signs, such as "woof woof" or displaying a specific action.

[2167] This system allows users to enjoy communicating with animals in a new way.

[2168] The flow of the identification process in the first embodiment will be described with reference to FIG.

[2169] Step 1:

[2170] Users launch applications and log in

[2171] The user launches the animal conversation application on their smartphone or tablet and enters their account information on the login screen to log in. Based on the entered account information, the server performs authentication processing, and if authentication is successful, the conversation mode selection screen is displayed.

[2172] Input: Account information (username, password)

[2173] Output: Conversation mode selection screen

[2174] Step 2:

[2175] Selecting the animal species on the device and starting data collection

[2176] When a user selects an animal type on the application, the device's camera and microphone are activated and real-time recording of the animal's sounds and behavior begins. The collected audio and video data is temporarily stored in the device's memory.

[2177] Input: Animal species information

[2178] Output: Temporary storage of bird sounds and video data

[2179] Step 3:

[2180] Data compression by the terminal and transmission to the server

[2181] The device compresses the audio and video data it has temporarily stored and sends it to the server. Compression reduces the amount of data, making transmission more efficient.

[2182] Input: Stored audio and video data

[2183] Output: Compressed audio and video data

[2184] Step 4:

[2185] Server receives and decompresses data

[2186] The server receives the compressed audio and video data sent from the terminal, and then decompresses the data to restore it to its original state.

[2187] Input: Compressed audio and video data

[2188] Output: Uncompressed audio and video data

[2189] Step 5:

[2190] Server-based voice and behavior analysis

[2191] The server sends the decompressed audio data to an audio analysis engine that analyzes the animal's calls to extract emotions and intentions, while simultaneously sending the decompressed video data to a motion analysis algorithm that analyzes the animal's behavior to extract more detailed information.

[2192] Input: Uncompressed audio and video data

[2193] Output: Extracted emotions, intents, and analyzed behavioral information

[2194] Step 6:

[2195] Translation by the server and data transmission to the device

[2196] The server compares the extracted emotions, intentions, and behavioral information with a database and translates it into human language. The translation results are generated as text data and sent to the device.

[2197] Input: Extracted emotions, intentions, and behavioral information

[2198] Output: Translated text data

[2199] Step 7:

[2200] Displaying translation results on your device

[2201] The device displays the translation results received from the server in real time, and the user can check the translation results either as text or through voice synthesis.

[2202] Input: Translated text data

[2203] Output: Text display or speech synthesis

[2204] Step 8:

[2205] User response input

[2206] The user can then respond to the animal based on the translation results displayed. Responses can be made by text input or voice input, and the device will analyze the response and send it to the server.

[2207] Input: User response (text or voice)

[2208] Output: Response data sent to the server

[2209] Step 9:

[2210] Response analysis and retranslation by the server

[2211] The server analyzes the received user response data and converts the text data into sounds and signs that the animals can understand, then sends the converted results back to the device.

[2212] Input: User response data

[2213] Output: Animal-understandable sounds and signs

[2214] Step 10:

[2215] Response output to terminal

[2216] The terminal plays the response data received from the server to the animal and outputs the converted sounds and signs.

[2217] Input: Animal sounds and signs that animals can understand

[2218] Output: Animal sounds and signs

[2219] This allows for two-way communication between the user and the animal.

[2220] (Application example 1)

[2221] 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."

[2222] In the past, there were many barriers to communication between humans and animals, making it difficult to accurately understand what animals needed. Furthermore, there was a lack of means to understand an animal's dietary requirements and health status in real time and provide appropriate food, which made animal health management a challenge.

[2223] 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.

[2224] In this invention, the server includes means for collecting animal sounds and behavior, means for analyzing the collected sounds and behavior, means for translating the analysis results into human language, means for displaying the translated results to the user, means for transmitting the user's input back to the animal, and means for selecting and delivering food for the animal based on the analysis results, which makes it possible to accurately grasp the needs and health status of the animal and provide appropriate food in real time.

[2225] "Means for collecting animal sounds and behavior" refers to equipment and software that records animal sounds and behavior in real time.

[2226] "Means for analyzing calls and behavior" refers to algorithms and programs that analyze collected data on calls and behavior and understand their intentions and emotions.

[2227] The "means of translation into human language" is software that converts the analyzed animal sounds and behavior into the corresponding human language.

[2228] "Means for displaying to the user" refers to a device or application that presents the translated data to the user visually or audibly.

[2229] "Means for transmitting user input back to the animal" refers to technology for converting instructions and responses from the user into a format that the animal can understand and transmitting them to the animal.

[2230] "Means for selecting and delivering animal food based on analysis results" is a system for understanding the needs of animals, selecting the most suitable food for them, and delivering it.

[2231] A system for carrying out this invention includes technology for collecting and analyzing animal sounds and behavior, translating the results into human language, and displaying them. It also includes means for selecting and delivering animal food based on the analysis results. Specific embodiments for carrying out this invention are described below.

[2232] Overall system configuration

[2233] The system consists of four main components:

[2234] 1. Device (smartphone or tablet)

[2235] 2. Server

[2236] 3. Robot

[2237] 4. Users

[2238] System Operation Overview

[2239] Data collection by terminal

[2240] The user logs in to the device and starts the application. The application displays a screen for selecting the conversation mode with the animal.

[2241] Once the user selects an animal, the device activates its camera and microphone to record the animal's sounds and behavior in real time. The collected data is temporarily stored on the device and then sent to a server for analysis.

[2242] Data analysis and translation by server

[2243] The server receives the audio and video data sent from the device. The received data is first decompressed. An audio analysis engine (using, for example, PyAudio) analyzes the animal's sounds, and extracts the animal's emotions and intentions.

[2244] The video data is then run through a motion analysis algorithm (using OpenCV, for example) to extract more detailed information about the animal's behavior. The extracted information is then compared with a database and translated into the corresponding human language. These translations are then sent to the device as text data.

[2245] Terminal display and user input

[2246] The device displays the translation results received from the server in real time. The display format can be selected as text or speech synthesis. The user looks at the displayed translation results and responds to the animal. Responses can be entered as text or speech. The user's response is sent from the device to the server.

[2247] Analysis of user responses on the server

[2248] The server analyzes the user's response and converts the text data back into sounds and signs that the animal can understand. The completed data is sent to the terminal, which then outputs the sounds and signs to the animal.

[2249] Select and deliver animal food based on the analysis results

[2250] The server includes a means for selecting food for the animals based on the analysis results. The selected food is provided to the animals by a delivery agent (e.g., an automated delivery robot). This allows food to be supplied in real time according to the needs of the animals.

[2251] Specific examples

[2252] User

[2253] 1. Launch the app on your smartphone and select "Conversation Mode."

[2254] 2. Select "Shiba Inu" as the type of dog.

[2255] Terminal

[2256] 1. Turn on the microphone and camera and record the Shiba Inu's barks and movements in real time.

[2257] 2. The collected data is compressed and sent to the server.

[2258] server

[2259] 1. The voice analysis engine analyzes the Shiba Inu's bark and extracts that the "woof woof" bark means "Welcome home! Let's play!"

[2260] 2. Using a video analysis algorithm, the Shiba Inu's movements are analyzed, and the tail-wagging behavior is also extracted as information that supports the above message.

[2261] 3. The analysis results are translated into human language and the text "Welcome back! Let's play!" is sent to the device.

[2262] Terminal

[2263] 1. The translation result is displayed to the user as text, and the speech synthesis feature plays back "Welcome back! Let's play!"

[2264] User

[2265] 1. Look at the message that appears and reply, "Wait a moment, I'll play with you soon." Use voice input to reply.

[2266] server

[2267] 1. Analyze the user's response and convert it into a sound or sign that the dog can understand, saying "Wait a moment, I'll play with you soon."

[2268] 2. Send the conversion result to the device.

[2269] Terminal

[2270] 1. Output the converted animal sounds and signs, and display whether the animal barks "woof woof."

[2271] Prompt Sentence Examples

[2272] "Animal food delivery assistant system. Records animal sounds and behaviors and analyzes them using an audio analysis engine and video analysis algorithm. Based on the requests understood from these, the system selects and delivers the appropriate food. Assume the server API URL is example.com / api and the animal type is dog."

[2273] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[2274] Step 1:

[2275] Data collection by terminal

[2276] The user logs in to the device and launches the application. After selecting the animal conversation mode and the type of animal, the device activates the camera and microphone. The camera and microphone are used to record and record the animal's cries and behavior in real time. This captures the animal's audio and video data, which is then temporarily stored on the device.

[2277] Input: User login information, animal type selection

[2278] Output: Recorded audio and video data

[2279] Step 2:

[2280] Sending data to the server

[2281] The device compresses the temporarily stored audio and video data and sends it to the server for analysis. Specifically, it uses a data compression algorithm to reduce the size of the audio data and video frames and uploads them to the server via an HTTP request.

[2282] Input: Recorded audio and video data

[2283] Output: Compressed audio and video data is sent to the server

[2284] Step 3:

[2285] Data analysis by server

[2286] The server receives the audio and video data sent from the device and performs the decompression process. The received data is analyzed by an audio analysis engine (e.g., PyAudio) to extract the animal's voice and its emotion and intention. In parallel, the video data is analyzed by a motion analysis algorithm (e.g., OpenCV) to extract detailed information about the animal's behavior.

[2287] Input: Compressed audio and video data

[2288] Output: Analyzed sentiment, intent, and behavior details

[2289] Step 4:

[2290] Server-based translation

[2291] The server translates the analyzed audio and video data into human language. The extracted information is compared with a database and converted into the corresponding human language. The translation result is sent to the device in text format.

[2292] Input: Analyzed sentiment, intent, and behavior details

[2293] Output: Text data translated into human language

[2294] Step 5:

[2295] Display by terminal

[2296] The device receives translation results from the server and displays them in real time. Users can choose to display them as text or speech synthesis, allowing them to intuitively understand the animal's message.

[2297] Input: Text data translated into human language

[2298] Output: Text or audio displayed to the user

[2299] Step 6:

[2300] User response

[2301] The user sees the translation results and responds to the animal using text input or voice input, and the input data is sent from the device to the server.

[2302] Input: User's text or voice response

[2303] Output: User response data sent to the server

[2304] Step 7:

[2305] Server response analysis

[2306] The server analyzes the user's response and converts the text data back into sounds and signs that the animal can understand, and the results are sent to the device.

[2307] Input: User response data

[2308] Output: Animal-understandable sounds and signs

[2309] Step 8:

[2310] Terminal Output

[2311] The device outputs the converted sounds and signs to the animal, thereby communicating the user's intentions to the animal.

[2312] Input: Animal-understandable sounds and signs

[2313] Output: Actual sounds and signs

[2314] Step 9:

[2315] Food delivery based on analysis results

[2316] The server selects food for the animal based on the analysis results. The selected food is provided to the animal by a delivery agent (e.g., an automated delivery robot). The user can confirm the selection results and finalize the order.

[2317] Input: Analyzed animal needs data

[2318] Output: Selected food order and delivery

[2319] 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.

[2320] This system realizes a new form of communication by collecting and analyzing animal sounds and behaviors, translating them into human language, and displaying them to the user. The present invention further improves the quality of communication by combining it with an emotion engine that analyzes the user's emotions.

[2321] Overall system configuration

[2322] The system consists of four main components:

[2323] 1. Device (smartphone or tablet)

[2324] 2. Server

[2325] 3. Users

[2326] 4. Emotion Engine

[2327] System Operation Overview

[2328] 1. Data collection by device

[2329] The user logs in to the device and starts the application. The application displays a screen for selecting the conversation mode with the animal.

[2330] When the user selects an animal type, the device activates the camera and microphone to record the animal's sounds and movements, as well as the user's voice and facial expressions, in real time.

[2331] The collected data is temporarily stored on the device and then sent to a server for analysis.

[2332] 2. Data analysis and translation by the server

[2333] The server receives the audio and video data sent from the terminal. The received data is first decompressed.

[2334] The voice analysis engine analyzes the animal's cries and extracts the animal's emotions and intentions.

[2335] The video data is then run through motion analysis algorithms to extract more detailed information about the animal's behavior.

[2336] The emotion engine analyzes the user's voice and facial expressions to detect the user's emotional state.

[2337] The extracted information is compared with a database and translated into the corresponding human language. These translation results and the user's emotional state are sent to the device as text data.

[2338] 3. Terminal display and user input

[2339] The device displays the translation results received from the server and the user's emotional state in real time, and can be displayed in text or speech synthesis.

[2340] The user sees the translation results and responds to the animal. Responses can be entered by text or voice.

[2341] 4. Analysis of user responses on the server

[2342] The user's response is sent from the terminal to the server.

[2343] The server analyzes the user's responses and converts the text data back into sounds and signs that the animals can understand.

[2344] The completed data is sent to a terminal, which outputs sounds and signs to the animal.

[2345] Explanation with concrete examples

[2346] Example: Conversation with your dog

[2347] User

[2348] 1. Launch the app on your smartphone and select "Conversation Mode."

[2349] 2. Select "Shiba Inu" as the type of dog.

[2350] Terminal

[2351] 1. The microphone and camera are activated to record and record the Shiba Inu's barks and movements in real time, and also collect the user's voice and facial expressions.

[2352] 2. The collected data is compressed and sent to the server.

[2353] server

[2354] 1. The voice analysis engine analyzes the Shiba Inu's bark and extracts that the "woof woof" bark means "Welcome home! Let's play!"

[2355] 2. Using a video analysis algorithm, the Shiba Inu's movements are analyzed, and the tail-wagging behavior is also extracted as information that supports the above message.

[2356] 3. The emotion engine analyzes the user's voice and facial expressions to detect when the user is in an emotional state of "joy."

[2357] 4. The analysis results and emotional state are translated into human language, and the text "Welcome back! Let's play!" and the information "The user is happy" are sent to the device.

[2358] Terminal

[2359] 1. The translation result and emotional state are displayed to the user as text, and the synthesized voice phrase "Welcome back! Let's play!" is played.

[2360] User

[2361] 1. Look at the message that appears and reply, "Wait a moment, I'll play with you soon." Use voice input to reply.

[2362] Terminal

[2363] 1. Analyze voice input, convert it to text, and send it to the server.

[2364] server

[2365] 1. Analyze the user's response and convert it into a sound or sign that the dog can understand, saying "Wait a moment, I'll play with you soon."

[2366] 2. Send the conversion result to the device.

[2367] Terminal

[2368] 1. Output the converted animal sounds and signs, and display whether the animal barks "woof woof."

[2369] The above is a specific example of how to implement the present invention by incorporating an emotion engine. This process allows users to enjoy real-time conversations with animals, and the use of the emotion engine further improves the quality of communication.

[2370] The processing flow will be explained below.

[2371] Step 1:

[2372] User

[2373] Launch the app on your smartphone and log in.

[2374] Select "Conversation Mode" from the main screen.

[2375] Step 2:

[2376] Terminal

[2377] When a user selects conversation mode, the device requests access to GPS information, the camera, and the microphone.

[2378] The terminal displays a screen for selecting the type of animal, and the user selects the type of animal (e.g., dog).

[2379] Step 3:

[2380] Terminal

[2381] Activate the camera and microphone to record the sounds and movements of a selected animal (e.g., a dog) in real time.

[2382] At the same time, the user's facial expressions and voice are also collected.

[2383] The collected audio data, video data, and user voice and facial expression data are temporarily stored on the terminal.

[2384] Step 4:

[2385] Terminal

[2386] The collected data is compressed and a data packet is generated for transmission to the server.

[2387] The data packet is sent to the server using the HTTPS protocol or the like.

[2388] Step 5:

[2389] server

[2390] It receives data packets sent from the terminal and performs decompression processing.

[2391] The decompressed audio data, video data, and user voice and facial expression data are prepared for analysis.

[2392] Step 6:

[2393] server

[2394] The collected call data is analyzed using an audio analysis engine to extract call patterns.

[2395] Based on the extracted patterns, the animal's emotions and intentions are matched with a database to identify meaning.

[2396] Step 7:

[2397] server

[2398] The video data is run through a motion analysis algorithm to extract the animal's behavioral patterns.

[2399] The extracted behavioral patterns are also compared with a database to identify the animal's intentions.

[2400] Step 8:

[2401] server

[2402] The emotion engine is used to analyze the user's voice and facial expressions to detect the user's emotional state.

[2403] The speech analysis data, movement analysis data, and emotion analysis data are integrated to determine the final meaning.

[2404] Step 9:

[2405] server

[2406] The determined meaning is translated into human language using a natural language generation engine and prepared as text data.

[2407] The translated text data and emotional state data are packaged into a data packet and transmitted to the terminal.

[2408] Step 10:

[2409] Terminal

[2410] The data packet received from the server is decompressed to obtain the translation result text data and emotional state data.

[2411] Display the translation results and emotional state to the user (e.g., text display, speech synthesis).

[2412] Step 11:

[2413] User

[2414] Check the displayed translation results and the animal's emotional state, and reply to it using text or voice input.

[2415] Step 12:

[2416] Terminal

[2417] The user's response is saved as text or audio data and prepared for transmission to the server.

[2418] Step 13:

[2419] Terminal

[2420] The user's response data is converted into a data packet and sent to the server.

[2421] Step 14:

[2422] server

[2423] The user's response data is received and decompressed.

[2424] The decompressed audio data is analyzed and converted into text.

[2425] Step 15:

[2426] server

[2427] The user's response text is compared with a database to convert it into sounds and signs that animals can understand.

[2428] It also takes into account the user's emotional state to generate optimal sounds and signs.

[2429] Step 16:

[2430] server

[2431] The generated data of sounds and signs is packaged into data packets and sent to the terminal.

[2432] Step 17:

[2433] Terminal

[2434] The data packets of sounds and signs received from the server are decompressed and the output data is obtained.

[2435] The output data is used to communicate with the animal through a speaker or display (e.g., playing a sound from a speaker or showing a sign on a display).

[2436] The above is a concrete process flow for implementing the present invention in combination with an emotion engine. This process allows users to enjoy real-time conversations with animals, and the use of the emotion engine further improves the quality of communication.

[2437] Example 2

[2438] 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."

[2439] Conventional methods of communicating with animals have had difficulty analyzing and understanding the meaning of animal sounds and behavior. Furthermore, they have not been able to realize two-way communication that takes the user's emotions into account, making it difficult to communicate effectively with animals. This has made it difficult to correctly understand the animal's emotions and intentions and respond appropriately.

[2440] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes means for executing an audio analysis engine, a video analysis algorithm, and an emotion analysis engine on the cloud, means for analyzing collected data and extracting the emotions and intentions of animals and the emotional state of people, and means for translating the analysis results into human language. This enables accurate analysis of animal sounds and behaviors and two-way communication that takes the user's emotions into consideration.

[2441] "Terminal" refers to a device used to collect animal sounds and behavior, as well as the user's voice and facial expressions, such as a smartphone or tablet.

[2442] "Server" refers to a remote computer system that receives, analyzes, and translates data sent from a terminal.

[2443] A "voice analysis engine" refers to a program or algorithm that analyzes animal sounds and extracts emotions and intentions.

[2444] "Video analysis algorithms" refer to programs and algorithms that analyze animal behavior and movements and extract detailed information.

[2445] An "emotion analysis engine" refers to a program or algorithm that analyzes a user's voice and facial expressions to detect the user's emotional state.

[2446] "Collection" refers to obtaining data from animals and users in real time using sensors such as cameras and microphones.

[2447] "Analysis" refers to the processing performed on collected data, including detailed analysis of animal sounds and behavior, as well as people's emotional states.

[2448] "Translation" refers to converting an animal's emotions and intentions into human language based on the analysis results.

[2449] "Display" refers to conveying the translation results and the user's emotional state to the user on the device screen or using voice synthesis.

[2450] "Input" refers to an operation by the user to respond to the animal using text or voice.

[2451] This invention is a system that collects and analyzes animal sounds and behaviors, translates them into human language, and displays them to the user. Furthermore, by combining it with an emotion engine that analyzes the user's emotions, the quality of communication can be improved.

[2452] Overall system configuration

[2453] The system consists of four main components:

[2454] 1. Device (smartphone or tablet)

[2455] 2. Server

[2456] 3. Users

[2457] 4. Emotion Engine

[2458] System Operation Overview

[2459] The process begins when a user logs in to their device and launches the application. The application displays a screen where users can select a conversation mode with the animal. Once the user selects the type of animal, the device activates its camera and microphone, and collects the animal's sounds and behavior, as well as the user's voice and facial expressions, in real time. The collected data is temporarily stored on the device and then sent to a server.

[2460] The server receives the data sent from the device and analyzes the animal's cries and behavior using a voice analysis engine and video analysis algorithm. Commonly used voice analysis engines include IBM Watson, and video analysis algorithms include OpenCV. Microsoft Azure Emotion API is used as the emotion analysis engine. The analysis results are compared with a database and translated into human language. These translation results and the user's emotional state are sent to the device as text data.

[2461] The device displays the translation results received from the server and the user's emotional state in real time. The user looks at the displayed results and responds to the animal. Responses can be entered as text or voice, and the input data is sent back to the server. The server analyzes the user's response and converts it into sounds or signs that the animal can understand. The converted data is sent to the device, which then outputs it to the animal.

[2462] Explanation with concrete examples

[2463] Example: Conversation with your dog

[2464] User

[2465] 1. Launch the app on your smartphone and select "Conversation Mode."

[2466] 2. Select "Shiba Inu" as the type of dog.

[2467] Terminal

[2468] 1. The microphone and camera are activated to record and record the Shiba Inu's barks and movements in real time, and also collect the user's voice and facial expressions.

[2469] 2. The collected data is compressed and sent to the server.

[2470] server

[2471] 1. Analyze the Shiba Inu's bark using a voice analysis engine (for example, IBM Watson) and extract that the bark "woof woof" means "Welcome home! Let's play!"

[2472] 2. Analyze the Shiba Inu's movements using a video analysis algorithm (e.g., OpenCV) and analyze the tail-wagging behavior.

[2473] 3. An emotion engine (for example, Microsoft Azure Emotion API) analyzes the user's voice and facial expressions to detect whether the user is feeling "joy."

[2474] 4. The analysis results and emotional state are translated into human language and the results are sent to the device.

[2475] Terminal

[2476] 1. The translated result and the user's emotional state are displayed as text, and the synthesized voice phrase "Welcome back! Let's play!" is played.

[2477] User

[2478] 1. Look at the message that appears and reply, "Wait a moment, I'll play with you soon." Use voice input to reply.

[2479] Terminal

[2480] 1. Analyze voice input, convert it to text, and send it to the server.

[2481] server

[2482] 1. Analyze the user's response and convert it into a sound or sign that the dog can understand, saying "Wait a moment, I'll play with you soon."

[2483] 2. Send the conversion result to the device.

[2484] Terminal

[2485] 1. Output the converted animal sounds and signs, and display whether the animal barks "woof woof."

[2486] Prompt Sentence Examples

[2487] Prompt: Simulate a conversation with your Shiba Inu dog. The user launches the app on their smartphone and selects conversation mode. Create an example where the Shiba Inu's bark "woof woof" means "Welcome home! Let's play!" and the user responds "Just wait a moment, I'll play with you soon." Include an emotion engine.

[2488] The above is a specific embodiment for carrying out the invention. This process allows users to enjoy real-time conversations with animals, and the quality of communication is improved by utilizing the emotion engine.

[2489] The flow of the identification process in the second embodiment will be described with reference to FIG.

[2490] Step 1:

[2491] Device: The user logs in to the device and launches the application. A button for selecting "Animal Conversation Mode" appears on the screen. The user taps the button to select the conversation mode.

[2492] Input: User action to select the type of animal.

[2493] Output: The device prepares to activate the camera and microphone.

[2494] Step 2:

[2495] User: Select an animal type from the list on the screen, for example, "Shiba Inu."

[2496] Input: Select the type of animal (e.g. Shiba Inu).

[2497] Output: The device is ready to activate the camera and microphone.

[2498] Step 3:

[2499] Device: The camera and microphone are activated to collect animal sounds and behavior, as well as the user's voice and facial expressions in real time. The collected data is temporarily stored on the device.

[2500] Input: Animal sounds, actions, user voice, facial expressions.

[2501] Output: Collected data (audio data, video data).

[2502] Step 4:

[2503] Terminal: Compresses the collected data and sends it to the server.

[2504] Input: Collected data.

[2505] Output: Sending compressed data to the server.

[2506] Step 5:

[2507] Server: Receives compressed data sent from the terminal and decompresses it, dividing it into audio data and video data.

[2508] Input: Compressed data.

[2509] Output: Decompressed audio and video data.

[2510] Step 6:

[2511] Server: Using a voice analysis engine, analyze the voice data and extract the animal's emotions and intentions.

[2512] Input: Audio data.

[2513] Output: The animal's emotion or intention (e.g., "Welcome home! Let's play!").

[2514] Specific behavior: Performs analysis using a voice analysis engine (e.g. IBM Watson).

[2515] Step 7:

[2516] Server: Using video analysis algorithms, the video data is analyzed and animal behavior is analyzed in detail.

[2517] Input: Video data.

[2518] Output: Detailed information about the animal's behavior (e.g., tail wagging).

[2519] Specific operation: Perform analysis using a video analysis algorithm (e.g., OpenCV).

[2520] Step 8:

[2521] Server: Using an emotion analysis engine, analyzes the user's voice and facial expressions to detect the user's emotional state.

[2522] Input: User's audio and video data.

[2523] Output: The user's emotional state (e.g., emotion "joy").

[2524] Specific behavior: Performs analysis using a sentiment analysis engine (e.g., Microsoft Azure Emotion API).

[2525] Step 9:

[2526] Server: Based on the results of the animal analysis and the user's emotional state, the results are translated into human language and sent to the device as text data.

[2527] Input: Animal emotions and intentions, user emotional state.

[2528] Output: Translated text data (e.g. "Welcome back! Let's play!") and the user's emotional state.

[2529] Step 10:

[2530] Terminal: Displays the translation results received from the server and the user's emotional state in real time. The user can then respond to the animal based on the results.

[2531] Input: Translation result, user's emotional state.

[2532] Output: The text or audio to be displayed (e.g., "Welcome back! Let's play!").

[2533] Specific operation: The translation result is displayed as text on the screen and played back using speech synthesis if necessary.

[2534] Step 11:

[2535] User: Sees the displayed message and responds to the animal using text input or voice input.

[2536] Input: User response (e.g. "Just wait a moment, I'll play with you soon").

[2537] Output: Response data.

[2538] Step 12:

[2539] Terminal: Converts the user's voice input into text and sends it to the server.

[2540] Input: User response data.

[2541] Output: Sent to the server as text data.

[2542] Step 13:

[2543] Server: Receives the user's response data and begins analysis. The analysis results are converted into sounds and signs that animals can understand.

[2544] Input: User response data.

[2545] Output: A sound or signal that the animal can understand (e.g., a "wait a moment" sound or ear movement).

[2546] Step 14:

[2547] Server: Transmits the converted sound and sign data to the device.

[2548] Input: Converted call and sign data.

[2549] Output: Data sent to the terminal.

[2550] Step 15:

[2551] Terminal: Outputs the sounds and signs received from the server to the animals.

[2552] Input: Converted call and sign data.

[2553] Output: Animal sound or display (e.g. "woof woof").

[2554] Specific actions: Use speakers and displays to play sounds and show signs.

[2555] (Application example 2)

[2556] 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."

[2557] With conventional pet communication tools, it was difficult to understand a pet's cries and behaviors and grasp their emotions and intentions. Furthermore, there were no product recommendation services based on pet needs, making it difficult for users to select appropriate products that matched their pet's preferences. Furthermore, improving the quality of communication by taking into account the user's emotional state was also a challenge.

[2558] 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 and analyzing the pet's cries and behavior, means for translating the analysis results of the collected cries and behavior into human language, means for analyzing the user's emotional state, and means for displaying recommended products based on the pet's cries and behavior. This makes it possible to understand the pet's cries and behavior and suggest products to the user based on them. It also makes it possible to realize high-quality communication that takes the user's emotional state into consideration.

[2559] "Means for collecting animal sounds and behavior" refers to devices and software for capturing animal sounds and body movements as audio and video data.

[2560] "Means for analyzing" refers to computer programs or algorithms that analyze the collected audio and video data and extract specific patterns or meanings.

[2561] "Means of translation into human language" refers to technology that converts the results of analyzed animal sounds and behavior into text data or audio data that humans can understand.

[2562] "Displaying means" refers to a display device, speaker, or application software for visually or audibly presenting the translated text or speech to the user.

[2563] The "means for transmitting user input back to the animal" refers to a device or software that converts the instructions and responses given by the user to the pet into a format that the animal can understand and transmits them to the animal.

[2564] "Means for analyzing the user's emotional state" refers to technology or software that analyzes data such as the user's voice and facial expressions and identifies their emotional state.

[2565] "Means for displaying recommended products" refers to technology or devices for presenting appropriate products to users based on the animal's condition and analysis results.

[2566] The system for realizing this invention mainly consists of the following components: It functions as a system that promotes new forms of communication with animals and recommends products based on pet needs.

[2567] Hardware and software used

[2568] 1. Device (smartphone or tablet):

[2569] A device for collecting animal sounds and behavior in real time using a camera and microphone.

[2570] Application software (compatible with iOS or Android).

[2571] 2. Server:

[2572] Use cloud platforms for data analysis and translation (e.g., AWS, GCP).

[2573] A speech analysis engine (e.g., Google Cloud Speech-to-Text).

[2574] Video analysis algorithms (e.g., OpenCV, TensorFlow).

[2575] Sentiment analysis engines (e.g., Emotion API, Microsoft Azure).

[2576] A database (e.g., MySQL, MongoDB).

[2577] Recommendation systems (e.g., Amazon Personalize).

[2578] 3. User:

[2579] Operate applications on your smartphone or tablet.

[2580] Communicate with animals and check and order recommended products.

[2581] Explanation of program processing

[2582] 1. Data collection by device:

[2583] The user launches the application, which uses the device's camera and microphone to collect real-time data on their pet's meows and movements, which is then compressed and sent to a cloud server.

[2584] 2. Data analysis and translation by the server:

[2585] Speech analysis: Using Google Cloud Speech-to-Text, pet cries are converted into text data and the intent is analyzed.

[2586] Video Analysis: Analyze your pet's behavior and identify its emotional state using OpenCV and TensorFlow.

[2587] Emotion Analysis: The Emotion API analyzes the user's voice and facial expressions to detect their emotional state.

[2588] Translation: Based on the analysis results, the pet's cries and actions are translated into human language.

[2589] 3. Recommendation Systems:

[2590] Using Amazon Personalize, we generate product recommendations based on pet sounds and behavior, and create a list of recommended products that also takes into account the user's emotional state.

[2591] 4. Terminal display and user input:

[2592] The translation results and recommended product list received from the server are displayed on the smartphone or tablet, allowing the user to check the displayed information and select a product.

[2593] Specific examples

[2594] For example, if a pet dog barks "woof woof," the sound is collected and analyzed on the server, which determines that the dog is hungry. Amazon Personalize then recommends a high-protein dry food, which is then displayed on the user's smartphone. The user can then easily order the dry food through the application.

[2595] Example prompts for generative AI models

[2596] An example of a prompt is:

[2597] Convert your pet's behavior and vocalizations into text data and analyze their emotions and intentions. For example, if your dog barks "woof woof," analyze whether it indicates hunger and provide a list of recommended foods based on that information.

[2598] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[2599] Step 1:

[2600] Data collection by terminal

[2601] The device uses the smartphone or tablet's camera and microphone to collect the pet's sounds and behavior in real time. The input data is video of the pet's sounds and behavior, and the output is a compressed file of this data. Specifically, the device temporarily stores the data through an application and prepares it for transfer to a cloud server.

[2602] Step 2:

[2603] Data reception and decompression by the server

[2604] The server receives the compressed data sent from the terminal and decompresses it. The input data is the compressed bird sounds and video data, and the output is the decompressed raw data. Specifically, the server uses the receiving module to pass the data to the decompression program.

[2605] Step 3:

[2606] Audio analysis

[2607] The server analyzes the bird's calls using a speech analysis engine such as Google Cloud Speech-to-Text. The input data is the bird's call audio data, and the output is the text corresponding to the audio and an interpretation of the bird's intent. Specifically, the server sends the audio data to the analysis engine, which interprets the intent and converts it into text data.

[2608] Step 4:

[2609] Video Analysis

[2610] The server analyzes the pet's behavior using OpenCV and TensorFlow. The input data is video data, and the output is the results of the behavior analysis. In operation, the server breaks down the video data into frames and identifies behavior using a machine learning model.

[2611] Step 5:

[2612] Emotion analysis

[2613] The server uses the Emotion API to analyze the user's emotions. The input data is the user's voice and facial expression data, and the output is their emotional state. Specifically, the server sends the voice and video data to the emotion analysis engine and integrates the results.

[2614] Step 6:

[2615] translation

[2616] The server translates the animal's intentions into human language based on the analysis of its calls and behavior. The input data is the analysis results, and the output is translated text data. Specifically, the server compares it with an existing database and generates the corresponding text.

[2617] Step 7:

[2618] Recommendation Systems

[2619] The server uses Amazon Personalize to generate recommended products. The input data is the pet analysis results and the user's emotional state, and the output is a list of recommended products. Specifically, the server sends the data to the recommendation engine to create the optimal product list.

[2620] Step 8:

[2621] Display by terminal

[2622] The device receives the translation results and recommended product list from the server and displays them to the user. The input data is the translation results and recommended product list, and the output is visual and auditory feedback to the user. Specifically, the device updates the UI and displays related information.

[2623] Step 9:

[2624] User operation

[2625] The user checks the displayed information and selects a product. The input data is recommended product information from the system, and the output is order information. Specifically, the user makes the selection and orders using a touch screen or voice input.

[2626] Step 10:

[2627] Order processing and delivery

[2628] The server processes the user's order and notifies the delivery company. The input data is the user's order information, and the output is notification information to the delivery company. Specifically, the server links the order data to the delivery system and carries out shipping procedures.

[2629] 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.

[2630] 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.

[2631] 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.

[2632] 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.

[2633] 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.

[2634] 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.

[2635] 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).

[2636] 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.

[2637] 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."

[2638] 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.

[2639] 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).

[2640] 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.

[2641] 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.

[2642] 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.

[2643] 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.

[2644] 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 ex...

Claims

1. A means of collecting animal sounds and behaviors; A means of analyzing the collected sounds and behaviors, A means of translating the analyzed results into human language; means for displaying the translated results to a user; The system includes a means for redirecting user input to the animal.

2. The system according to claim 1, further comprising a means for analyzing and translating animal sounds and behaviors on the cloud.

3. 10. The system of claim 1, further comprising means for enabling multilingual translation.

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