system

Abstract

The system according to this embodiment aims to accurately understand the feelings and desires of animals and to facilitate effective communication between humans and animals. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a translation unit, a provision unit, and an output unit. The collection unit collects data on animal sounds, movements, and facial expressions. The analysis unit analyzes the data collected by the collection unit and determines the animal's feelings and desires. The translation unit translates the animal's feelings and desires determined by the analysis unit into human language. The provision unit provides the human language translated by the translation unit to the user. The output unit converts the human language provided by the provision unit back into animal language and outputs it through a speaker.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, the method including receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that it is difficult to accurately understand the feelings and desires of animals and to achieve effective communication between humans and animals.

[0005] The system according to the embodiment aims to accurately understand the feelings and desires of animals and to achieve effective communication between humans and animals.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, an analysis unit, a translation unit, a provision unit, and an output unit. The collection unit collects data on animal sounds, movements, and facial expressions. The analysis unit analyzes the data collected by the collection unit to determine the animal's feelings and desires. The translation unit translates the animal's feelings and desires determined by the analysis unit into human language. The provision unit provides the human language translated by the translation unit to the user. The output unit converts the human language provided by the provision unit back into animal language and outputs it through a speaker. [Effects of the Invention]

[0007] The system according to this embodiment can accurately understand the feelings and desires of animals and facilitate effective communication between humans and animals. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

[0011] In the following embodiments, the signed processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Furthermore, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include CPU (Central Processing Unit), GPU (Graphics Processing Unit), GPGPU (General-Purpose computing on Graphics Processing Units), APU (Accelerated Processing Unit), or TPU (Tensor Processing Unit).

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

[0013] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0014] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F controls communication between a plurality of computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

[0016] [First Embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] As shown in FIG. 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. 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. Also, the database 24 and the communication I / F 26 are 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).

[0019] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0023] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0025] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The conversation tool according to an embodiment of the present invention is a system that enables human-animal communication using AI. This system collects data such as animal sounds, movements, and facial expressions, and the AI ​​analyzes this data. Based on the analysis results, it translates the animal's feelings and desires into human language and provides it to the user. Furthermore, when the user speaks to an animal, those words are expressed through a speaker as the animal's language. This mechanism allows us to understand the thoughts and feelings of animals more deeply and to communicate with them. For example, it becomes possible to directly hear the feelings and desires of one's own pet. It also enables more effective information sharing and care provision in interactions with wild animals and in animal conservation activities. For example, the conversation tool collects data such as animal sounds, movements, and facial expressions. In this case, a smartphone or tablet device is used to acquire the data. For example, the sounds and movements of a pet are recorded or filmed on a smartphone, and that data is input into the AI. Next, the AI ​​analyzes the collected data. Based on the data such as animal sounds, movements, and facial expressions, the AI ​​determines the animal's feelings and desires. For example, if a dog is barking, the system analyzes the pattern, volume, and frequency of the bark to determine what the dog is trying to communicate. Based on the analysis, it translates the animal's feelings and desires into human language. For instance, if a dog wants to communicate that it is hungry, the system translates that feeling into human language such as "I'm hungry" and provides it to the user. This can be displayed on a smartphone or tablet screen or output as audio. Furthermore, when a user speaks to an animal, the system expresses those words as the animal's language through a speaker. For example, if a user says, "Let's go for a walk," the system converts those words into a dog bark and outputs it through the speaker. This makes it easier for animals to understand human language. This system allows us to understand the thoughts and feelings of animals more deeply and to communicate with them more effectively. For example, by understanding the feelings of pets, we can provide more appropriate care. It also allows us to understand the condition of animals and take appropriate action in interactions with wild animals and animal protection activities.This allows the communication tool to collect, analyze, translate, provide, and output data on animal sounds, movements, and facial expressions, thereby facilitating communication between humans and animals.

[0029] The conversation tool according to the embodiment comprises a collection unit, an analysis unit, a translation unit, a provision unit, and an output unit. The collection unit collects data on animal sounds, movements, and facial expressions. For example, the collection unit records animal sounds with a microphone, photographs movements with a camera, and acquires facial expressions as images. The collection unit can also collect data using a smartphone or tablet device, for example. For example, the collection unit records and videos pet sounds and movements with a smartphone and inputs that data into the AI. For example, the collection unit records animal sounds with a high-sensitivity microphone, photographs movements with a high-resolution camera, and acquires facial expressions with a high-precision image sensor. The analysis unit analyzes the data collected by the collection unit to determine the animal's feelings and desires. For example, the analysis unit analyzes the pattern, volume, and frequency of animal sounds to determine the animal's feelings and desires. For example, the analysis unit uses AI to analyze the sound waveform of animal sounds to determine the animal's feelings and desires. The analysis unit analyzes, for example, the patterns and speed of an animal's movements to determine its feelings and desires. The analysis unit analyzes, for example, changes in an animal's facial expressions to determine its feelings and desires. The translation unit translates the feelings and desires of the animal determined by the analysis unit into human language. The translation unit translates the feelings and desires of the animal using natural language processing technology, for example, AI. The translation unit translates the feelings and desires of the animal based on predefined phrases, for example. The translation unit translates the feelings and desires of the animal using speech synthesis technology, for example. The provision unit provides the human language translated by the translation unit to the user. The provision unit displays the translated human language on the screen of a smartphone or tablet, for example. The provision unit outputs the translated human language as audio, for example. The provision unit sends the translated human language as a text message, for example. The output unit converts the human language provided by the provision unit into animal language and outputs it through a speaker. The output unit converts the user's words into animal sounds and outputs them through a speaker, for example. The output unit converts the user's words into animal actions and communicates them to the animal. The output unit converts the user's words into animal facial expressions and communicates them to the animal. Thus, the conversation tool according to this embodiment can realize human-animal communication by collecting, analyzing, translating, providing, and outputting data on animal sounds, actions, and facial expressions.

[0030] The data collection unit collects data on animal sounds, movements, and facial expressions. For example, the unit records animal sounds with a microphone, captures movements with a camera, and acquires facial expressions as images. Specifically, the unit uses a high-sensitivity microphone to clearly record animal sounds and employs noise-canceling technology to remove ambient noise. For movement collection, a high-resolution camera is used to record animal movements in detail, capturing even subtle changes in movement. For facial expression collection, a high-precision image sensor is used to capture subtle changes in the animal's facial expressions. As a result, the data collection unit can collect data on animal sounds, movements, and facial expressions with high accuracy and detail. Furthermore, the data collection unit can also collect data using smartphones and tablets. For example, pet sounds and movements can be recorded and filmed on a smartphone, and that data can be input into the AI. Smartphones and tablets are easily portable, providing pet owners with the convenience of collecting data anytime, anywhere. The data collection unit records animal sounds with a high-sensitivity microphone, captures their movements with a high-resolution camera, and acquires their facial expressions with a high-precision image sensor. This allows the data collection unit to efficiently collect data on animal sounds, movements, and facial expressions using a variety of devices, and transmit it to the analysis unit in real time. The data collection unit can flexibly adapt to specific situations and conditions by adjusting the data collection frequency and accuracy. For example, if an animal is excited or exhibiting a specific behavior, the collection frequency can be increased to acquire more detailed data. This allows the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0031] The analysis unit analyzes the data collected by the collection unit to determine the animal's feelings and needs. For example, the analysis unit analyzes the patterns, volume, and frequency of animal vocalizations to determine the animal's feelings and needs. Specifically, it uses AI to analyze the sound waveform of animal vocalizations to determine the animal's feelings and needs. The AI ​​utilizes speech recognition technology to extract characteristics of vocalizations and compares them with past data to identify the animal's emotions and requests. It also analyzes the patterns and speed of animal movements to determine the animal's feelings and needs. For movement analysis, machine learning algorithms are used to learn the characteristics of animal movements and determine the meaning of specific movements. Furthermore, it analyzes changes in the animal's facial expressions to determine the animal's feelings and needs. For facial expression analysis, image recognition technology is used to capture subtle changes in the animal's facial expressions and infer emotions. As a result, the analysis unit can quickly and accurately analyze the collected data and understand the animal's feelings and needs in real time. In addition, the analysis unit can utilize past data and statistical information to analyze long-term animal behavior patterns and emotional fluctuations. For example, based on past vocalization data, the system can predict fluctuations in an animal's emotions during specific times and situations, thereby predicting future behavior. Furthermore, the analysis unit can use anomaly detection algorithms to detect unusual patterns and abnormal data, issuing early warnings. This allows the analysis unit to not only monitor the situation in real time but also to handle long-term animal behavior management and anomaly detection, improving the overall reliability and safety of the system.

[0032] The translation unit translates the feelings and desires of animals, as determined by the analysis unit, into human language. For example, the translation unit uses AI to translate animal feelings and desires using natural language processing technology. Specifically, the AI ​​analyzes data representing animal emotions and desires and converts it into language that humans can understand. It utilizes natural language processing technology to translate animal emotions and desires into appropriate words and phrases. For example, if an animal feels "hungry," the translation unit translates that emotion into human language such as "I'm hungry." It can also translate animal feelings and desires based on predefined phrases. These predefined phrases are set in advance based on patterns of animal behavior and vocalizations, and the appropriate phrase is selected according to the analysis results. Furthermore, the translation unit can also translate animal feelings and desires using speech synthesis technology. Using speech synthesis technology, the translated words are output as audio and provided to the user. This allows the translation unit to accurately and quickly translate animal feelings and desires into human language and provide it to the user. In addition, the translation unit can continuously improve translation accuracy based on user feedback. For example, users can provide ratings and comments on the translation results, allowing the AI ​​to learn from this feedback and improve translation accuracy. Furthermore, the translation unit is multilingual, accommodating users who speak different languages. This enables the unit to translate animal feelings and requests into multiple languages, providing appropriate information to a global audience.

[0033] The service provider delivers human words translated by the translation service provider to the user. For example, the service provider displays the translated human words on the screen of a smartphone or tablet. Specifically, it displays the translated words as text, allowing the user to visually confirm them. The service provider can also output the translated human words as audio. Using speech synthesis technology, it plays the translated words in a natural-sounding voice and delivers them to the user. Furthermore, the service provider can send the translated human words as text messages. For example, even if the user is in a distant location, they can communicate the feelings and needs of their animal through text messages. This allows the service provider to quickly and reliably provide users with appropriate information and support communication with animals. In addition, the service provider can collect user feedback and continuously improve the accuracy and effectiveness of its offerings. For example, by providing evaluations and comments on the information provided, the system learns from that feedback and improves its offerings. The service provider can also reliably transmit information using multiple communication methods. For example, it can reliably deliver important information using not only smartphone notifications but also voice calls, SMS, and email. This allows the service provider to deliver information to users quickly and reliably, and to facilitate communication with animals.

[0034] The output unit converts human speech provided by the provider unit into animal speech and outputs it through the speaker. For example, the output unit can convert user speech into animal sounds and output it through the speaker. Specifically, it uses speech synthesis technology to convert user speech into sounds that animals can understand and plays them back through the speaker. This makes it easier for animals to understand the user's intentions. The output unit can also convert user speech into animal actions and communicate them to the animal. For example, if a user says "sit," the output unit converts that speech into an action command that the animal can understand and communicates it to the animal. Furthermore, the output unit can convert user speech into animal facial expressions and communicate them to the animal. For example, if a user says "happy," the output unit converts that speech into a facial expression that the animal can understand and communicates it to the animal. In this way, the output unit converts user speech into a form that animals can easily understand, facilitating smooth communication with animals. In addition, the output unit can monitor the animal's response in real time and adjust the output content as needed. For example, if an animal does not follow instructions or exhibits a different response, the output unit analyzes the situation and modifies the output content accordingly. Furthermore, the output unit can customize the output content according to the animal species and individual differences. This allows the output unit to provide the optimal communication method for each animal, thereby building a trusting relationship with them.

[0035] The data collection unit can collect data on animal sounds, movements, and facial expressions using smartphones or tablet devices. For example, the data collection unit can record animal sounds, videotape movements, and photograph facial expressions using a smartphone. The data collection unit can also record animal sounds, videotape movements, and photograph facial expressions using a tablet device. The data collection unit can also collect data on animal sounds, movements, and facial expressions in real time using smartphones or tablet devices. This makes it easy to collect animal data using smartphones or tablet devices. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input animal sound data acquired with a smartphone into a generating AI and have the generating AI perform analysis of the sound data.

[0036] The analysis unit can analyze the patterns, volume, and frequency of animal sounds to determine the animal's feelings and desires. For example, the analysis unit can analyze the sound waveform of the animal's sound and extract the sound pattern. The analysis unit can also analyze the volume of the animal's sound and determine its intensity. The analysis unit can also analyze the frequency of the animal's sound and determine its pitch. By analyzing the patterns, volume, and frequency of the animal's sound, the analysis unit can accurately determine the animal's feelings and desires. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input animal sound data into a generating AI and have the generating AI perform the analysis of the sound data.

[0037] The translation unit can translate the determined feelings and desires of animals into human language. The translation unit can, for example, use AI to translate the feelings and desires of animals using natural language processing technology. The translation unit can also, for example, translate the feelings and desires of animals based on predefined phrases. The translation unit can also, for example, translate the feelings and desires of animals using speech synthesis technology. This allows users to understand the thoughts and desires of animals by translating them into human language. Some or all of the above-described processes in the translation unit may be performed using AI, for example, or without AI. For example, the translation unit can input data on the feelings and desires of animals into a generating AI and have the generating AI perform the translation.

[0038] The service provider can display the translated human words on a smartphone or tablet screen, or output them as audio. For example, the service provider can display the translated human words as text on a smartphone screen. For example, the service provider can also display the translated human words as text on a tablet screen. For example, the service provider can also output the translated human words as audio. This allows users to visually or audibly understand the feelings and desires of animals by displaying the translated human words on a screen or outputting them as audio. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the data of the translated human words into a generating AI and have the generating AI perform the display or audio output.

[0039] The output unit can convert the user's words into animal sounds and output them through a speaker. For example, the output unit can convert the user's words into animal sounds and output them through a speaker. The output unit can also convert the user's words into animal actions and communicate them to animals. For example, the output unit can convert the user's words into animal facial expressions and communicate them to animals. By converting the user's words into animal sounds and outputting them through a speaker, animals can more easily understand human language. Some or all of the above processing in the output unit may be performed using AI, for example, or without AI. For example, the output unit can input user's word data into a generating AI and have the generating AI perform the conversion into animal sounds.

[0040] The data collection unit can analyze the animal's past behavioral history and select the optimal data collection method. For example, if the animal was active during a specific time period in the past, the data collection unit will collect data during that time period. For example, if the animal exhibited a lot of activity in a specific location in the past, the data collection unit can also collect data at that location. For example, if the animal exhibited a specific behavioral pattern in the past, the data collection unit can also collect data based on that pattern. In this way, the optimal data collection method can be selected by analyzing the animal's past behavioral history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the animal's past behavioral data into a generating AI and have the generating AI perform an analysis of the behavioral history.

[0041] The data collection unit can filter data based on the animal's current health status and environment during data collection. For example, if the animal is healthy, the data collection unit will perform normal data collection. For example, if the animal is sick, the data collection unit can prioritize the collection of specific data. For example, if the animal is in a new environment, the data collection unit can collect data to help it adapt to that environment. This allows for the collection of more accurate data by filtering the data based on the animal's health status and environment. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input animal health status and environmental data into a generating AI and have the generating AI perform the filtering.

[0042] The data collection unit can prioritize the collection of highly relevant data by considering the geographical location of the animals during data collection. For example, if an animal is in a specific region, the data collection unit can prioritize the collection of data related to that region. For example, if an animal is on the move, the data collection unit can prioritize the collection of data related to its travel route. For example, if an animal is in a specific environment, the data collection unit can prioritize the collection of data related to that environment. In this way, by considering the geographical location of the animals, highly relevant data can be prioritized. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the geographical location data of the animals into a generating AI and have the generating AI select highly relevant data.

[0043] The data collection unit can analyze the animals' social media activities and collect relevant data during data collection. For example, if an animal is getting a lot of attention on social media, the data collection unit can collect data related to that activity. For example, if an animal is showing interest in a particular topic, the data collection unit can also collect data related to that topic. For example, if an animal is interacting with other animals, the data collection unit can also collect data related to that interaction. In this way, relevant data can be collected by analyzing the animals' social media activities. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the animals' social media activity data into a generating AI and have the generating AI perform the collection of relevant data.

[0044] The analysis unit can adjust the level of detail of the analysis based on the importance of animal sounds and actions during the analysis. For example, if animal sounds occur frequently, the analysis unit will perform a detailed analysis of those sounds. For example, if animal actions contain important information, the analysis unit can also perform a detailed analysis of those actions. For example, if animal sounds and actions are linked, the analysis unit can also analyze the linkage between them. This allows for detailed analysis of important information by adjusting the level of detail of the analysis based on the importance of animal sounds and actions. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input animal sound and action data into a generating AI and have the generating AI perform a detailed analysis.

[0045] The analysis unit can apply different analysis algorithms depending on the type of animal and individual differences during analysis. For example, the analysis unit uses different algorithms when analyzing the sounds of dogs and cats. The analysis unit can also adjust the analysis algorithm according to individual differences, even for animals of the same species. For example, the analysis unit can apply different algorithms to the analysis of wild animals and pets. By applying different analysis algorithms according to the type of animal and individual differences, more accurate analysis results can be provided. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input data on the type of animal and individual differences into a generating AI and have the generating AI select the analysis algorithm.

[0046] The analysis unit can determine the priority of analysis based on the animal's behavioral history during the analysis. For example, the analysis unit may prioritize analyzing behaviors that the animal has frequently performed in the past. The analysis unit may also prioritize analyzing behaviors that the animal performed during a specific time period. The analysis unit may also prioritize analyzing behaviors that the animal performed in a specific location. By determining the priority of analysis based on the animal's behavioral history, important behaviors can be prioritized for analysis. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input animal behavioral history data into a generating AI and have the generating AI perform the priority determination.

[0047] The analysis unit can adjust the order of analysis based on the relationships between animals during the analysis. For example, if an animal is interacting with other animals, the analysis unit may prioritize the analysis of that interaction. For example, if an animal is in a specific environment, the analysis unit may also prioritize the analysis related to that environment. For example, if an animal is exhibiting a specific behavior, the analysis unit may also prioritize the analysis related to that behavior. In this way, by adjusting the order of analysis based on the relationships between animals, highly relevant information can be analyzed preferentially. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input animal relationship data into a generating AI and have the generating AI execute the analysis in the correct order.

[0048] The translation unit can adjust the level of detail in the translation based on the importance of the animal's feelings and requests. For example, if the animal's request is urgent, the translation unit will provide a detailed translation. For example, if the animal's feelings are important, the translation unit can also translate those feelings in detail. For example, if the animal's request is general, the translation unit can provide a concise translation. This allows for detailed translation of important information by adjusting the level of detail based on the importance of the animal's feelings and requests. Some or all of the above processing in the translation unit may be performed using AI, for example, or not using AI. For example, the translation unit can input data on the animal's feelings and requests into a generating AI and have the generating AI perform the level of detail adjustment.

[0049] The translation unit can apply different translation algorithms depending on the type of animal and individual differences during translation. For example, the translation unit may use different algorithms for translating dogs and cats. The translation unit can also adjust the translation algorithm according to individual differences even within the same type of animal. For example, the translation unit may apply different algorithms for translating wild animals and pets. By applying different translation algorithms according to the type of animal and individual differences, more accurate translation results can be provided. Some or all of the above processes in the translation unit may be performed using AI, for example, or without AI. For example, the translation unit can input data on the type of animal and individual differences into a generating AI and have the generating AI select a translation algorithm.

[0050] The translation unit can determine translation priorities based on the animal's behavioral history during translation. For example, the translation unit may prioritize translating behaviors that the animal has frequently performed in the past. It may also prioritize translating behaviors that the animal performed during a specific time period. It may also prioritize translating behaviors that the animal performed in a specific location. This allows important behaviors to be prioritized by determining translation priorities based on the animal's behavioral history. Some or all of the above processing in the translation unit may be performed using AI, for example, or without AI. For example, the translation unit can input animal behavioral history data into a generating AI and have the generating AI perform the priority determination.

[0051] The translation unit can adjust the order of translations based on the relevance of the animals during translation. For example, if an animal is interacting with other animals, the translation unit may prioritize translations of those interactions. For example, if an animal is in a specific environment, the translation unit may also prioritize translations related to that environment. For example, if an animal is exhibiting a specific behavior, the translation unit may also prioritize translations related to that behavior. This allows for the prioritization of highly relevant information by adjusting the order of translations based on the relevance of the animals. Some or all of the above processing in the translation unit may be performed using AI, for example, or not using AI. For example, the translation unit can input animal relevance data into a generating AI and have the generating AI execute the translation order.

[0052] The service provider can adjust the level of detail provided based on the importance of the translated words at the time of provision. For example, if the translated words are urgent, the service provider will provide detailed information. For example, if the translated words are important, the service provider may also provide detailed information about those words. For example, if the translated words are general, the service provider may also provide concise information. This allows important information to be provided in detail by adjusting the level of detail based on the importance of the translated words. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the data of the translated words into a generating AI and have the generating AI perform the adjustment of the level of detail.

[0053] The service delivery unit can select the optimal delivery method by referring to the user's past operation history at the time of delivery. For example, the service delivery unit may prioritize selecting a delivery method that the user has preferred to use in the past. For example, the service delivery unit may also analyze patterns of delivery methods that the user has used in the past and propose the optimal delivery method. For example, the service delivery unit may select a delivery method appropriate to a specific situation based on the user's past operation history. In this way, the optimal delivery method can be selected by referring to the user's past operation history. Some or all of the above processing in the service delivery unit may be performed using AI, for example, or without using AI. For example, the service delivery unit may input the user's operation history data into a generating AI and have the generating AI perform the selection of the optimal delivery method.

[0054] The delivery unit can select the optimal delivery method by considering the user's device information at the time of delivery. For example, if the user is using a smartphone, the delivery unit can provide a delivery method that matches the screen size. For example, if the user is using a tablet, the delivery unit can also provide a delivery method optimized for a larger screen. For example, if the user is using a smartwatch, the delivery unit can also provide a concise and highly visible delivery method. In this way, the optimal delivery method can be selected by considering the user's device information. Some or all of the above processing in the delivery unit may be performed using AI, for example, or without AI. For example, the delivery unit can input user device information data into a generating AI and have the generating AI select the optimal delivery method.

[0055] The information provider can prioritize providing highly relevant information by considering the user's geographical location at the time of delivery. For example, if the user is in a specific region, the information provider can prioritize providing information related to that region. For example, if the user is on the move, the information provider can also prioritize providing information related to the travel route. For example, if the user is in a specific environment, the information provider can also prioritize providing information related to that environment. In this way, by considering the user's geographical location, highly relevant information can be prioritized. Some or all of the above processing in the information provider may be performed using AI, for example, or without using AI. For example, the information provider can input the user's geographical location data into a generating AI and have the generating AI select highly relevant information.

[0056] The output unit can adjust the level of detail in the output based on the importance of the user's words. For example, if the user's words are urgent, the output unit will output detailed information. For example, if the user's words are important, the output unit can also output those words in detail. For example, if the user's words are general, the output unit can also output concise information. This allows important information to be output in detail by adjusting the level of detail based on the importance of the user's words. Some or all of the above processing in the output unit may be performed using AI, for example, or without AI. For example, the output unit can input the user's word data into a generating AI and have the generating AI perform the level of detail adjustment.

[0057] The output unit can apply different output algorithms depending on the type of animal and individual differences during output. For example, the output unit may use different algorithms for dogs and cats. The output unit can also adjust the output algorithm according to individual differences, even for animals of the same species. For example, the output unit may apply different algorithms for wild animals and pets. By applying different output algorithms according to the type of animal and individual differences, more accurate information can be output. Some or all of the above processing in the output unit may be performed using AI, for example, or without AI. For example, the output unit can input data on the type of animal and individual differences into a generating AI and have the generating AI select the output algorithm.

[0058] The output unit can determine output priority based on the user's behavior history at the time of output. For example, the output unit may prioritize outputting actions that the user has frequently performed in the past. The output unit may also prioritize outputting actions that the user performed during a specific time period. The output unit may also prioritize outputting actions that the user performed in a specific location. By determining output priority based on the user's behavior history, important information can be prioritized for output. Some or all of the above processing in the output unit may be performed using AI, for example, or without AI. For example, the output unit can input user behavior history data into a generating AI and have the generating AI perform the priority determination.

[0059] The output unit can adjust the order of outputs based on the relationships between animals during output. For example, if an animal is interacting with other animals, the output unit may prioritize the output of that interaction. For example, if an animal is in a specific environment, the output unit may also prioritize the output related to that environment. For example, if an animal is exhibiting a specific behavior, the output unit may also prioritize the output related to that behavior. By adjusting the order of outputs based on the relationships between animals, highly relevant information can be prioritized for output. Some or all of the above processing in the output unit may be performed using AI, for example, or without AI. For example, the output unit can input animal relationship data into a generating AI and have the generating AI execute the output order.

[0060] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0061] The communication tool can be enhanced with features to monitor the animal's health. For example, the data collection unit can collect biometric data such as the animal's body temperature, heart rate, and respiratory rate. This allows for real-time monitoring of the animal's health and notification to the user if an abnormality is detected. The analysis unit can analyze the collected biometric data and evaluate the animal's health. For example, a high body temperature may indicate a fever, and an abnormally high heart rate may indicate stress or excitement. Furthermore, the information provision unit can provide the user with advice on the animal's health based on the analysis results. For example, if the animal has a fever, it can recommend cooling methods or consultation with a veterinarian. This allows for more effective animal health management.

[0062] The conversational tool can be enhanced with the ability to learn and predict animal behavior patterns. For example, the data collection unit can collect animal behavior data over a long period and accumulate behavior patterns. Next, the analysis unit can analyze the collected behavior data and learn the animal's behavior patterns. For example, if an animal tends to repeat a specific behavior at a particular time of day, that pattern can be recognized. Furthermore, the delivery unit can predict the animal's future behavior based on the learned behavior patterns and notify the user. For example, if an animal requests food at the same time every day, the user can be notified as that time approaches. This allows the user to anticipate the animal's needs and enables smoother communication.

[0063] The conversation tool can be enhanced with features to analyze the social behavior of animals and evaluate their compatibility with other animals. For example, the data collection unit can collect behavioral data when animals interact with other animals. Next, the analysis unit can analyze the collected data and evaluate the animals' social behavior. For example, if an animal exhibits aggressive behavior towards another animal, the analysis unit can suggest that the animals are incompatible. Furthermore, the service provider can evaluate the compatibility between animals based on the analysis results and provide advice to the user. For example, when introducing a new pet, the service provider can evaluate its compatibility with existing pets and provide appropriate advice. This can prevent conflicts between animals and enable smooth coexistence.

[0064] The conversational tool can be enhanced with features to support animal diet management. For example, the data collection unit can collect animal dietary data, recording the amount and frequency of meals. Next, the analysis unit can analyze the collected dietary data and evaluate the animal's eating patterns. For instance, if an animal is overeating, the analysis unit can recognize this pattern and suggest an appropriate amount of food. Furthermore, the provision unit can provide the user with a diet plan tailored to the animal's health condition based on the analysis results. For example, if an animal is prone to obesity, a calorie-restricted diet plan can be suggested to support health management. This makes animal diet management more effective.

[0065] The conversational tool can be enhanced with features to support animal training. For example, the data collection unit can collect animal training data and record training progress. Next, the analysis unit can analyze the collected training data and evaluate the effectiveness of the animal training. For example, it can evaluate how well the animal responds to specific commands and understand the training progress. Furthermore, the delivery unit can suggest improvements to the training or new training methods to the user based on the analysis results. For example, if the animal is slow to respond to a particular command, it can suggest changing the training method. This makes animal training more effective.

[0066] The conversational tool can be enhanced with features to monitor animal stress levels and provide advice for stress reduction. For example, the data collection unit can collect data related to animal stress and assess the stress level. Next, the analysis unit can analyze the collected data and determine the animal's stress level. For example, it can analyze the animal's heart rate, respiratory rate, and behavioral patterns to detect signs of stress. Furthermore, the provision unit can provide the user with advice for stress reduction based on the analysis results. For example, if the animal is stressed, it can suggest ways to create a relaxing environment or exercise methods to relieve stress. This allows for more effective stress management of animals.

[0067] The following briefly describes the processing flow for example form 1.

[0068] Step 1: The data collection unit collects data on animal sounds, movements, and facial expressions. For example, it may record animal sounds with a microphone, capture movements with a camera, and acquire facial expressions as images. The data collection unit can also collect data using a smartphone or tablet device. Step 2: The analysis unit analyzes the data collected by the collection unit to determine the animal's feelings and desires. For example, it analyzes the patterns, volume, and frequency of the animal's vocalizations to determine the animal's feelings and desires. It uses AI to analyze the sound waveform of the animal's vocalizations to determine the animal's feelings and desires. It analyzes the patterns and speed of the animal's movements to determine the animal's feelings and desires. It analyzes changes in the animal's facial expressions to determine the animal's feelings and desires. Step 3: The translation unit translates the animal's feelings and desires, as determined by the analysis unit, into human language. For example, it may use AI to translate the animal's feelings and desires using natural language processing technology, translate the animal's feelings and desires based on predefined phrases, or translate them using speech synthesis technology. Step 4: The delivery unit provides the user with the human words translated by the translation unit. For example, it displays the translated human words on the screen of a smartphone or tablet. It outputs the translated human words as audio. It sends the translated human words as a text message. Step 5: The output unit converts the human speech provided by the supply unit into animal speech and outputs it through the speaker. For example, it converts the user's speech into animal sounds and outputs them through the speaker. It converts the user's speech into animal actions and communicates them to the animals. It converts the user's speech into animal facial expressions and communicates them to the animals.

[0069] (Example of form 2) The conversation tool according to an embodiment of the present invention is a system that enables human-animal communication using AI. This system collects data such as animal sounds, movements, and facial expressions, and the AI ​​analyzes this data. Based on the analysis results, it translates the animal's feelings and desires into human language and provides it to the user. Furthermore, when the user speaks to an animal, those words are expressed through a speaker as the animal's language. This mechanism allows us to understand the thoughts and feelings of animals more deeply and to communicate with them. For example, it becomes possible to directly hear the feelings and desires of one's own pet. It also enables more effective information sharing and care provision in interactions with wild animals and in animal conservation activities. For example, the conversation tool collects data such as animal sounds, movements, and facial expressions. In this case, a smartphone or tablet device is used to acquire the data. For example, the sounds and movements of a pet are recorded or filmed on a smartphone, and that data is input into the AI. Next, the AI ​​analyzes the collected data. Based on the data such as animal sounds, movements, and facial expressions, the AI ​​determines the animal's feelings and desires. For example, if a dog is barking, the system analyzes the pattern, volume, and frequency of the bark to determine what the dog is trying to communicate. Based on the analysis, it translates the animal's feelings and desires into human language. For instance, if a dog wants to communicate that it is hungry, the system translates that feeling into human language such as "I'm hungry" and provides it to the user. This can be displayed on a smartphone or tablet screen or output as audio. Furthermore, when a user speaks to an animal, the system expresses those words as the animal's language through a speaker. For example, if a user says, "Let's go for a walk," the system converts those words into a dog bark and outputs it through the speaker. This makes it easier for animals to understand human language. This system allows us to understand the thoughts and feelings of animals more deeply and to communicate with them more effectively. For example, by understanding the feelings of pets, we can provide more appropriate care. It also allows us to understand the condition of animals and take appropriate action in interactions with wild animals and animal protection activities.This allows the communication tool to collect, analyze, translate, provide, and output data on animal sounds, movements, and facial expressions, thereby facilitating communication between humans and animals.

[0070] The conversation tool according to the embodiment comprises a collection unit, an analysis unit, a translation unit, a provision unit, and an output unit. The collection unit collects data on animal sounds, movements, and facial expressions. For example, the collection unit records animal sounds with a microphone, photographs movements with a camera, and acquires facial expressions as images. The collection unit can also collect data using a smartphone or tablet device, for example. For example, the collection unit records and videos pet sounds and movements with a smartphone and inputs that data into the AI. For example, the collection unit records animal sounds with a high-sensitivity microphone, photographs movements with a high-resolution camera, and acquires facial expressions with a high-precision image sensor. The analysis unit analyzes the data collected by the collection unit to determine the animal's feelings and desires. For example, the analysis unit analyzes the pattern, volume, and frequency of animal sounds to determine the animal's feelings and desires. For example, the analysis unit uses AI to analyze the sound waveform of animal sounds to determine the animal's feelings and desires. The analysis unit analyzes, for example, the patterns and speed of an animal's movements to determine its feelings and desires. The analysis unit analyzes, for example, changes in an animal's facial expressions to determine its feelings and desires. The translation unit translates the feelings and desires of the animal determined by the analysis unit into human language. The translation unit translates the feelings and desires of the animal using natural language processing technology, for example, AI. The translation unit translates the feelings and desires of the animal based on predefined phrases, for example. The translation unit translates the feelings and desires of the animal using speech synthesis technology, for example. The provision unit provides the human language translated by the translation unit to the user. The provision unit displays the translated human language on the screen of a smartphone or tablet, for example. The provision unit outputs the translated human language as audio, for example. The provision unit sends the translated human language as a text message, for example. The output unit converts the human language provided by the provision unit into animal language and outputs it through a speaker. The output unit converts the user's words into animal sounds and outputs them through a speaker, for example. The output unit converts the user's words into animal actions and communicates them to the animal. The output unit converts the user's words into animal facial expressions and communicates them to the animal. Thus, the conversation tool according to this embodiment can realize human-animal communication by collecting, analyzing, translating, providing, and outputting data on animal sounds, actions, and facial expressions.

[0071] The data collection unit collects data on animal sounds, movements, and facial expressions. For example, the unit records animal sounds with a microphone, captures movements with a camera, and acquires facial expressions as images. Specifically, the unit uses a high-sensitivity microphone to clearly record animal sounds and employs noise-canceling technology to remove ambient noise. For movement collection, a high-resolution camera is used to record animal movements in detail, capturing even subtle changes in movement. For facial expression collection, a high-precision image sensor is used to capture subtle changes in the animal's facial expressions. As a result, the data collection unit can collect data on animal sounds, movements, and facial expressions with high accuracy and detail. Furthermore, the data collection unit can also collect data using smartphones and tablets. For example, pet sounds and movements can be recorded and filmed on a smartphone, and that data can be input into the AI. Smartphones and tablets are easily portable, providing pet owners with the convenience of collecting data anytime, anywhere. The data collection unit records animal sounds with a high-sensitivity microphone, captures their movements with a high-resolution camera, and acquires their facial expressions with a high-precision image sensor. This allows the data collection unit to efficiently collect data on animal sounds, movements, and facial expressions using a variety of devices, and transmit it to the analysis unit in real time. The data collection unit can flexibly adapt to specific situations and conditions by adjusting the data collection frequency and accuracy. For example, if an animal is excited or exhibiting a specific behavior, the collection frequency can be increased to acquire more detailed data. This allows the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0072] The analysis unit analyzes the data collected by the collection unit to determine the animal's feelings and needs. For example, the analysis unit analyzes the patterns, volume, and frequency of animal vocalizations to determine the animal's feelings and needs. Specifically, it uses AI to analyze the sound waveform of animal vocalizations to determine the animal's feelings and needs. The AI ​​utilizes speech recognition technology to extract characteristics of vocalizations and compares them with past data to identify the animal's emotions and requests. It also analyzes the patterns and speed of animal movements to determine the animal's feelings and needs. For movement analysis, machine learning algorithms are used to learn the characteristics of animal movements and determine the meaning of specific movements. Furthermore, it analyzes changes in the animal's facial expressions to determine the animal's feelings and needs. For facial expression analysis, image recognition technology is used to capture subtle changes in the animal's facial expressions and infer emotions. As a result, the analysis unit can quickly and accurately analyze the collected data and understand the animal's feelings and needs in real time. In addition, the analysis unit can utilize past data and statistical information to analyze long-term animal behavior patterns and emotional fluctuations. For example, based on past vocalization data, the system can predict fluctuations in an animal's emotions during specific times and situations, thereby predicting future behavior. Furthermore, the analysis unit can use anomaly detection algorithms to detect unusual patterns and abnormal data, issuing early warnings. This allows the analysis unit to not only monitor the situation in real time but also to handle long-term animal behavior management and anomaly detection, improving the overall reliability and safety of the system.

[0073] The translation unit translates the feelings and desires of animals, as determined by the analysis unit, into human language. For example, the translation unit uses AI to translate animal feelings and desires using natural language processing technology. Specifically, the AI ​​analyzes data representing animal emotions and desires and converts it into language that humans can understand. It utilizes natural language processing technology to translate animal emotions and desires into appropriate words and phrases. For example, if an animal feels "hungry," the translation unit translates that emotion into human language such as "I'm hungry." It can also translate animal feelings and desires based on predefined phrases. These predefined phrases are set in advance based on patterns of animal behavior and vocalizations, and the appropriate phrase is selected according to the analysis results. Furthermore, the translation unit can also translate animal feelings and desires using speech synthesis technology. Using speech synthesis technology, the translated words are output as audio and provided to the user. This allows the translation unit to accurately and quickly translate animal feelings and desires into human language and provide it to the user. In addition, the translation unit can continuously improve translation accuracy based on user feedback. For example, users can provide ratings and comments on the translation results, allowing the AI ​​to learn from this feedback and improve translation accuracy. Furthermore, the translation unit is multilingual, accommodating users who speak different languages. This enables the unit to translate animal feelings and requests into multiple languages, providing appropriate information to a global audience.

[0074] The service provider delivers human words translated by the translation service provider to the user. For example, the service provider displays the translated human words on the screen of a smartphone or tablet. Specifically, it displays the translated words as text, allowing the user to visually confirm them. The service provider can also output the translated human words as audio. Using speech synthesis technology, it plays the translated words in a natural-sounding voice and delivers them to the user. Furthermore, the service provider can send the translated human words as text messages. For example, even if the user is in a distant location, they can communicate the feelings and needs of their animal through text messages. This allows the service provider to quickly and reliably provide users with appropriate information and support communication with animals. In addition, the service provider can collect user feedback and continuously improve the accuracy and effectiveness of its offerings. For example, by providing evaluations and comments on the information provided, the system learns from that feedback and improves its offerings. The service provider can also reliably transmit information using multiple communication methods. For example, it can reliably deliver important information using not only smartphone notifications but also voice calls, SMS, and email. This allows the service provider to deliver information to users quickly and reliably, and to facilitate communication with animals.

[0075] The output unit converts human speech provided by the provider unit into animal speech and outputs it through the speaker. For example, the output unit can convert user speech into animal sounds and output it through the speaker. Specifically, it uses speech synthesis technology to convert user speech into sounds that animals can understand and plays them back through the speaker. This makes it easier for animals to understand the user's intentions. The output unit can also convert user speech into animal actions and communicate them to the animal. For example, if a user says "sit," the output unit converts that speech into an action command that the animal can understand and communicates it to the animal. Furthermore, the output unit can convert user speech into animal facial expressions and communicate them to the animal. For example, if a user says "happy," the output unit converts that speech into a facial expression that the animal can understand and communicates it to the animal. In this way, the output unit converts user speech into a form that animals can easily understand, facilitating smooth communication with animals. In addition, the output unit can monitor the animal's response in real time and adjust the output content as needed. For example, if an animal does not follow instructions or exhibits a different response, the output unit analyzes the situation and modifies the output content accordingly. Furthermore, the output unit can customize the output content according to the animal species and individual differences. This allows the output unit to provide the optimal communication method for each animal, thereby building a trusting relationship with them.

[0076] The data collection unit can collect data on animal sounds, movements, and facial expressions using smartphones or tablet devices. For example, the data collection unit can record animal sounds, videotape movements, and photograph facial expressions using a smartphone. The data collection unit can also record animal sounds, videotape movements, and photograph facial expressions using a tablet device. The data collection unit can also collect data on animal sounds, movements, and facial expressions in real time using smartphones or tablet devices. This makes it easy to collect animal data using smartphones or tablet devices. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input animal sound data acquired with a smartphone into a generating AI and have the generating AI perform analysis of the sound data.

[0077] The analysis unit can analyze the patterns, volume, and frequency of animal sounds to determine the animal's feelings and desires. For example, the analysis unit can analyze the sound waveform of the animal's sound and extract the sound pattern. The analysis unit can also analyze the volume of the animal's sound and determine its intensity. The analysis unit can also analyze the frequency of the animal's sound and determine its pitch. By analyzing the patterns, volume, and frequency of the animal's sound, the analysis unit can accurately determine the animal's feelings and desires. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input animal sound data into a generating AI and have the generating AI perform the analysis of the sound data.

[0078] The translation unit can translate the determined feelings and desires of animals into human language. The translation unit can, for example, use AI to translate the feelings and desires of animals using natural language processing technology. The translation unit can also, for example, translate the feelings and desires of animals based on predefined phrases. The translation unit can also, for example, translate the feelings and desires of animals using speech synthesis technology. This allows users to understand the thoughts and desires of animals by translating them into human language. Some or all of the above-described processes in the translation unit may be performed using AI, for example, or without AI. For example, the translation unit can input data on the feelings and desires of animals into a generating AI and have the generating AI perform the translation.

[0079] The service provider can display the translated human words on a smartphone or tablet screen, or output them as audio. For example, the service provider can display the translated human words as text on a smartphone screen. For example, the service provider can also display the translated human words as text on a tablet screen. For example, the service provider can also output the translated human words as audio. This allows users to visually or audibly understand the feelings and desires of animals by displaying the translated human words on a screen or outputting them as audio. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the data of the translated human words into a generating AI and have the generating AI perform the display or audio output.

[0080] The output unit can convert the user's words into animal sounds and output them through a speaker. For example, the output unit can convert the user's words into animal sounds and output them through a speaker. The output unit can also convert the user's words into animal actions and communicate them to animals. For example, the output unit can convert the user's words into animal facial expressions and communicate them to animals. By converting the user's words into animal sounds and outputting them through a speaker, animals can more easily understand human language. Some or all of the above processing in the output unit may be performed using AI, for example, or without AI. For example, the output unit can input user's word data into a generating AI and have the generating AI perform the conversion into animal sounds.

[0081] The data collection unit can estimate the animal's emotions and adjust the timing of data collection based on the estimated emotions. For example, the data collection unit can collect data when the animal is relaxed to avoid stress. The data collection unit can also collect data when the animal is excited to capture changes in emotions. The data collection unit can also collect data when the animal is sleeping to obtain data during rest. By adjusting the timing of data collection based on the animal's emotions, more appropriate data can be collected. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not using AI. For example, the data collection unit can input animal emotion data into a generative AI and have the generative AI perform emotion estimation.

[0082] The data collection unit can analyze the animal's past behavioral history and select the optimal data collection method. For example, if the animal was active during a specific time period in the past, the data collection unit will collect data during that time period. For example, if the animal exhibited a lot of activity in a specific location in the past, the data collection unit can also collect data at that location. For example, if the animal exhibited a specific behavioral pattern in the past, the data collection unit can also collect data based on that pattern. In this way, the optimal data collection method can be selected by analyzing the animal's past behavioral history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the animal's past behavioral data into a generating AI and have the generating AI perform an analysis of the behavioral history.

[0083] The data collection unit can filter data based on the animal's current health status and environment during data collection. For example, if the animal is healthy, the data collection unit will perform normal data collection. For example, if the animal is sick, the data collection unit can prioritize the collection of specific data. For example, if the animal is in a new environment, the data collection unit can collect data to help it adapt to that environment. This allows for the collection of more accurate data by filtering the data based on the animal's health status and environment. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input animal health status and environmental data into a generating AI and have the generating AI perform the filtering.

[0084] The data collection unit can estimate the animal's emotions and determine the priority of data to collect based on the estimated animal's emotions. For example, if the animal is feeling anxious, the data collection unit will prioritize collecting data related to anxiety. For example, if the animal is happy, the data collection unit may also prioritize collecting data related to joy. For example, if the animal is angry, the data collection unit may also prioritize collecting data related to anger. This allows for the priority collection of important data by prioritizing data based on the animal's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, or not using AI. For example, the data collection unit can input animal emotion data into a generative AI and have the generative AI perform emotion estimation.

[0085] The data collection unit can prioritize the collection of highly relevant data by considering the geographical location of the animals during data collection. For example, if an animal is in a specific region, the data collection unit can prioritize the collection of data related to that region. For example, if an animal is on the move, the data collection unit can prioritize the collection of data related to its travel route. For example, if an animal is in a specific environment, the data collection unit can prioritize the collection of data related to that environment. In this way, by considering the geographical location of the animals, highly relevant data can be prioritized. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the geographical location data of the animals into a generating AI and have the generating AI select highly relevant data.

[0086] The data collection unit can analyze the animals' social media activities and collect relevant data during data collection. For example, if an animal is getting a lot of attention on social media, the data collection unit can collect data related to that activity. For example, if an animal is showing interest in a particular topic, the data collection unit can also collect data related to that topic. For example, if an animal is interacting with other animals, the data collection unit can also collect data related to that interaction. In this way, relevant data can be collected by analyzing the animals' social media activities. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the animals' social media activity data into a generating AI and have the generating AI perform the collection of relevant data.

[0087] The analysis unit can estimate the animal's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the animal is relaxed, the analysis unit will display the analysis results in a calm manner. If the animal is excited, the analysis unit may also display detailed analysis results. If the animal is anxious, the analysis unit may also display the analysis results in a concise and reassuring manner. By adjusting the presentation of the analysis based on the animal's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input animal emotion data into the generative AI and have the generative AI perform emotion estimation.

[0088] The analysis unit can adjust the level of detail of the analysis based on the importance of animal sounds and actions during the analysis. For example, if animal sounds occur frequently, the analysis unit will perform a detailed analysis of those sounds. For example, if animal actions contain important information, the analysis unit can also perform a detailed analysis of those actions. For example, if animal sounds and actions are linked, the analysis unit can also analyze the linkage between them. This allows for detailed analysis of important information by adjusting the level of detail of the analysis based on the importance of animal sounds and actions. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input animal sound and action data into a generating AI and have the generating AI perform a detailed analysis.

[0089] The analysis unit can apply different analysis algorithms depending on the type of animal and individual differences during analysis. For example, the analysis unit uses different algorithms when analyzing the sounds of dogs and cats. The analysis unit can also adjust the analysis algorithm according to individual differences, even for animals of the same species. For example, the analysis unit can apply different algorithms to the analysis of wild animals and pets. By applying different analysis algorithms according to the type of animal and individual differences, more accurate analysis results can be provided. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input data on the type of animal and individual differences into a generating AI and have the generating AI select the analysis algorithm.

[0090] The analysis unit can estimate the animal's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the animal is relaxed, the analysis unit can perform a longer analysis. For example, if the animal is excited, the analysis unit can perform a shorter analysis. For example, if the animal is anxious, the analysis unit can perform a concise analysis. By adjusting the length of the analysis based on the animal's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input animal emotion data into a generative AI and have the generative AI perform emotion estimation.

[0091] The analysis unit can determine the priority of analysis based on the animal's behavioral history during the analysis. For example, the analysis unit may prioritize analyzing behaviors that the animal has frequently performed in the past. The analysis unit may also prioritize analyzing behaviors that the animal performed during a specific time period. The analysis unit may also prioritize analyzing behaviors that the animal performed in a specific location. By determining the priority of analysis based on the animal's behavioral history, important behaviors can be prioritized for analysis. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input animal behavioral history data into a generating AI and have the generating AI perform the priority determination.

[0092] The analysis unit can adjust the order of analysis based on the relationships between animals during the analysis. For example, if an animal is interacting with other animals, the analysis unit may prioritize the analysis of that interaction. For example, if an animal is in a specific environment, the analysis unit may also prioritize the analysis related to that environment. For example, if an animal is exhibiting a specific behavior, the analysis unit may also prioritize the analysis related to that behavior. In this way, by adjusting the order of analysis based on the relationships between animals, highly relevant information can be analyzed preferentially. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input animal relationship data into a generating AI and have the generating AI execute the analysis in the correct order.

[0093] The translation unit can estimate the animal's emotions and adjust the translation's expression based on the estimated emotions. For example, if the animal is relaxed, the translation unit will use a calm expression. If the animal is excited, the translation unit can also use a detailed expression. If the animal is anxious, the translation unit can also use a concise and reassuring expression. By adjusting the translation's expression based on the animal's emotions, a more appropriate translation result can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the translation unit may be performed using AI or not. For example, the translation unit can input animal emotion data into a generative AI and have the generative AI perform emotion estimation.

[0094] The translation unit can adjust the level of detail in the translation based on the importance of the animal's feelings and requests. For example, if the animal's request is urgent, the translation unit will provide a detailed translation. For example, if the animal's feelings are important, the translation unit can also translate those feelings in detail. For example, if the animal's request is general, the translation unit can provide a concise translation. This allows for detailed translation of important information by adjusting the level of detail based on the importance of the animal's feelings and requests. Some or all of the above processing in the translation unit may be performed using AI, for example, or not using AI. For example, the translation unit can input data on the animal's feelings and requests into a generating AI and have the generating AI perform the level of detail adjustment.

[0095] The translation unit can apply different translation algorithms depending on the type of animal and individual differences during translation. For example, the translation unit may use different algorithms for translating dogs and cats. The translation unit can also adjust the translation algorithm according to individual differences even within the same type of animal. For example, the translation unit may apply different algorithms for translating wild animals and pets. By applying different translation algorithms according to the type of animal and individual differences, more accurate translation results can be provided. Some or all of the above processes in the translation unit may be performed using AI, for example, or without AI. For example, the translation unit can input data on the type of animal and individual differences into a generating AI and have the generating AI select a translation algorithm.

[0096] The translation unit can estimate the animal's emotions and adjust the translation length based on the estimated emotions. For example, if the animal is relaxed, the translation unit may produce a longer translation. If the animal is excited, the translation unit may produce a shorter translation. If the animal is anxious, the translation unit may produce a more concise translation. By adjusting the translation length based on the animal's emotions, a more appropriate translation result can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the translation unit may be performed using AI, for example, or not using AI. For example, the translation unit can input animal emotion data into a generative AI and have the generative AI perform emotion estimation.

[0097] The translation unit can determine translation priorities based on the animal's behavioral history during translation. For example, the translation unit may prioritize translating behaviors that the animal has frequently performed in the past. It may also prioritize translating behaviors that the animal performed during a specific time period. It may also prioritize translating behaviors that the animal performed in a specific location. This allows important behaviors to be prioritized by determining translation priorities based on the animal's behavioral history. Some or all of the above processing in the translation unit may be performed using AI, for example, or without AI. For example, the translation unit can input animal behavioral history data into a generating AI and have the generating AI perform the priority determination.

[0098] The translation unit can adjust the order of translations based on the relevance of the animals during translation. For example, if an animal is interacting with other animals, the translation unit may prioritize translations of those interactions. For example, if an animal is in a specific environment, the translation unit may also prioritize translations related to that environment. For example, if an animal is exhibiting a specific behavior, the translation unit may also prioritize translations related to that behavior. This allows for the prioritization of highly relevant information by adjusting the order of translations based on the relevance of the animals. Some or all of the above processing in the translation unit may be performed using AI, for example, or not using AI. For example, the translation unit can input animal relevance data into a generating AI and have the generating AI execute the translation order.

[0099] The service provider can estimate the animal's emotions and adjust the presentation of the information based on the estimated emotions. For example, if the animal is relaxed, the service provider will present the information in a calm manner. If the animal is excited, the service provider may also provide detailed information. If the animal is anxious, the service provider may also present the information in a concise and reassuring manner. By adjusting the presentation of the information based on the animal's emotions, more appropriate information can be provided. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input animal emotion data into a generative AI and have the generative AI perform emotion estimation.

[0100] The service provider can adjust the level of detail provided based on the importance of the translated words at the time of provision. For example, if the translated words are urgent, the service provider will provide detailed information. For example, if the translated words are important, the service provider may also provide detailed information about those words. For example, if the translated words are general, the service provider may also provide concise information. This allows important information to be provided in detail by adjusting the level of detail based on the importance of the translated words. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the data of the translated words into a generating AI and have the generating AI perform the adjustment of the level of detail.

[0101] The service delivery unit can select the optimal delivery method by referring to the user's past operation history at the time of delivery. For example, the service delivery unit may prioritize selecting a delivery method that the user has preferred to use in the past. For example, the service delivery unit may also analyze patterns of delivery methods that the user has used in the past and propose the optimal delivery method. For example, the service delivery unit may select a delivery method appropriate to a specific situation based on the user's past operation history. In this way, the optimal delivery method can be selected by referring to the user's past operation history. Some or all of the above processing in the service delivery unit may be performed using AI, for example, or without using AI. For example, the service delivery unit may input the user's operation history data into a generating AI and have the generating AI perform the selection of the optimal delivery method.

[0102] The service provider can estimate the animal's emotions and adjust the length of the service based on the estimated emotions. For example, if the animal is relaxed, the service provider may provide a longer service. If the animal is excited, the service provider may provide a shorter service. If the animal is anxious, the service provider may provide a more concise service. By adjusting the length of the service based on the animal's emotions, more appropriate information can be provided. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the service provider may be performed using AI or not using AI. For example, the service provider can input animal emotion data into a generative AI and have the generative AI perform emotion estimation.

[0103] The delivery unit can select the optimal delivery method by considering the user's device information at the time of delivery. For example, if the user is using a smartphone, the delivery unit can provide a delivery method that matches the screen size. For example, if the user is using a tablet, the delivery unit can also provide a delivery method optimized for a larger screen. For example, if the user is using a smartwatch, the delivery unit can also provide a concise and highly visible delivery method. In this way, the optimal delivery method can be selected by considering the user's device information. Some or all of the above processing in the delivery unit may be performed using AI, for example, or without AI. For example, the delivery unit can input user device information data into a generating AI and have the generating AI select the optimal delivery method.

[0104] The information provider can prioritize providing highly relevant information by considering the user's geographical location at the time of delivery. For example, if the user is in a specific region, the information provider can prioritize providing information related to that region. For example, if the user is on the move, the information provider can also prioritize providing information related to the travel route. For example, if the user is in a specific environment, the information provider can also prioritize providing information related to that environment. In this way, by considering the user's geographical location, highly relevant information can be prioritized. Some or all of the above processing in the information provider may be performed using AI, for example, or without using AI. For example, the information provider can input the user's geographical location data into a generating AI and have the generating AI select highly relevant information.

[0105] The output unit can estimate the animal's emotions and adjust the output's presentation based on the estimated emotions. For example, if the animal is relaxed, the output unit will use a calm presentation. If the animal is excited, the output unit can also output detailed information. If the animal is anxious, the output unit can also use a concise and reassuring presentation. By adjusting the output's presentation based on the animal's emotions, more appropriate information can be output. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the output unit may be performed using AI, or not using AI. For example, the output unit can input animal emotion data into a generative AI and have the generative AI perform emotion estimation.

[0106] The output unit can adjust the level of detail in the output based on the importance of the user's words. For example, if the user's words are urgent, the output unit will output detailed information. For example, if the user's words are important, the output unit can also output those words in detail. For example, if the user's words are general, the output unit can also output concise information. This allows important information to be output in detail by adjusting the level of detail based on the importance of the user's words. Some or all of the above processing in the output unit may be performed using AI, for example, or without AI. For example, the output unit can input the user's word data into a generating AI and have the generating AI perform the level of detail adjustment.

[0107] The output unit can apply different output algorithms depending on the type of animal and individual differences during output. For example, the output unit may use different algorithms for dogs and cats. The output unit can also adjust the output algorithm according to individual differences, even for animals of the same species. For example, the output unit may apply different algorithms for wild animals and pets. By applying different output algorithms according to the type of animal and individual differences, more accurate information can be output. Some or all of the above processing in the output unit may be performed using AI, for example, or without AI. For example, the output unit can input data on the type of animal and individual differences into a generating AI and have the generating AI select the output algorithm.

[0108] The output unit can estimate the animal's emotions and adjust the length of the output based on the estimated emotions. For example, the output unit may produce a longer output if the animal is relaxed. For example, the output unit may produce a shorter output if the animal is excited. For example, the output unit may produce a concise output if the animal is anxious. By adjusting the length of the output based on the animal's emotions, more appropriate information can be output. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the output unit may be performed using AI, for example, or without AI. For example, the output unit can input animal emotion data into a generative AI and have the generative AI perform emotion estimation.

[0109] The output unit can determine output priority based on the user's behavior history at the time of output. For example, the output unit may prioritize outputting actions that the user has frequently performed in the past. The output unit may also prioritize outputting actions that the user performed during a specific time period. The output unit may also prioritize outputting actions that the user performed in a specific location. By determining output priority based on the user's behavior history, important information can be prioritized for output. Some or all of the above processing in the output unit may be performed using AI, for example, or without AI. For example, the output unit can input user behavior history data into a generating AI and have the generating AI perform the priority determination.

[0110] The output unit can adjust the order of outputs based on the relationships between animals during output. For example, if an animal is interacting with other animals, the output unit may prioritize the output of that interaction. For example, if an animal is in a specific environment, the output unit may also prioritize the output related to that environment. For example, if an animal is exhibiting a specific behavior, the output unit may also prioritize the output related to that behavior. By adjusting the order of outputs based on the relationships between animals, highly relevant information can be prioritized for output. Some or all of the above processing in the output unit may be performed using AI, for example, or without AI. For example, the output unit can input animal relationship data into a generating AI and have the generating AI execute the output order.

[0111] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0112] The communication tool can be enhanced with features to monitor the animal's health. For example, the data collection unit can collect biometric data such as the animal's body temperature, heart rate, and respiratory rate. This allows for real-time monitoring of the animal's health and notification to the user if an abnormality is detected. The analysis unit can analyze the collected biometric data and evaluate the animal's health. For example, a high body temperature may indicate a fever, and an abnormally high heart rate may indicate stress or excitement. Furthermore, the information provision unit can provide the user with advice on the animal's health based on the analysis results. For example, if the animal has a fever, it can recommend cooling methods or consultation with a veterinarian. This allows for more effective animal health management.

[0113] The conversational tool can be enhanced with the ability to learn and predict animal behavior patterns. For example, the data collection unit can collect animal behavior data over a long period and accumulate behavior patterns. Next, the analysis unit can analyze the collected behavior data and learn the animal's behavior patterns. For example, if an animal tends to repeat a specific behavior at a particular time of day, that pattern can be recognized. Furthermore, the delivery unit can predict the animal's future behavior based on the learned behavior patterns and notify the user. For example, if an animal requests food at the same time every day, the user can be notified as that time approaches. This allows the user to anticipate the animal's needs and enables smoother communication.

[0114] The conversational tool can be enhanced with a function to estimate an animal's emotions and play appropriate music based on those emotions. For example, the analysis unit can analyze data such as animal sounds, movements, and facial expressions to estimate the animal's emotions. Then, the playback unit can select and play music that helps the animal relax or calms it down based on the estimated emotions. For example, if the animal is feeling anxious, calming music can be played to help it relax. If the animal is excited, calming music can be played to soothe it. By providing music that matches the animal's emotions, it is possible to reduce the animal's stress and provide a comfortable environment.

[0115] The conversation tool can be enhanced with features to analyze the social behavior of animals and evaluate their compatibility with other animals. For example, the data collection unit can collect behavioral data when animals interact with other animals. Next, the analysis unit can analyze the collected data and evaluate the animals' social behavior. For example, if an animal exhibits aggressive behavior towards another animal, the analysis unit can suggest that the animals are incompatible. Furthermore, the service provider can evaluate the compatibility between animals based on the analysis results and provide advice to the user. For example, when introducing a new pet, the service provider can evaluate its compatibility with existing pets and provide appropriate advice. This can prevent conflicts between animals and enable smooth coexistence.

[0116] The conversational tool can be enhanced with a function to estimate an animal's emotions and suggest appropriate play based on those emotions. For example, the analysis unit can analyze data such as animal sounds, movements, and facial expressions to estimate the animal's emotions. Then, the provision unit can suggest play that the animal will enjoy based on the estimated emotions. For example, if the animal is bored, a new toy or way of playing can be suggested to pique the animal's interest. Also, if the animal is stressed, a relaxing game can be suggested to reduce stress. In this way, by providing play that is tailored to the animal's emotions, the quality of life for the animal can be improved.

[0117] The conversational tool can be enhanced with features to support animal diet management. For example, the data collection unit can collect animal dietary data, recording the amount and frequency of meals. Next, the analysis unit can analyze the collected dietary data and evaluate the animal's eating patterns. For instance, if an animal is overeating, the analysis unit can recognize this pattern and suggest an appropriate amount of food. Furthermore, the provision unit can provide the user with a diet plan tailored to the animal's health condition based on the analysis results. For example, if an animal is prone to obesity, a calorie-restricted diet plan can be suggested to support health management. This makes animal diet management more effective.

[0118] The conversational tool can be enhanced with a function to estimate an animal's emotions and suggest appropriate care methods based on those estimated emotions. For example, the analysis unit can analyze data such as the animal's vocalizations, movements, and facial expressions to estimate the animal's emotions. Then, the provision unit can suggest the care methods the animal needs based on the estimated emotions. For example, if the animal is feeling anxious, it can suggest ways to create a relaxing environment to provide a sense of security. If the animal is excited, it can suggest ways to exercise to release energy. In this way, by providing care that is tailored to the animal's emotions, it is possible to support the animal's health and well-being.

[0119] The conversational tool can be enhanced with features to support animal training. For example, the data collection unit can collect animal training data and record training progress. Next, the analysis unit can analyze the collected training data and evaluate the effectiveness of the animal training. For example, it can evaluate how well the animal responds to specific commands and understand the training progress. Furthermore, the delivery unit can suggest improvements to the training or new training methods to the user based on the analysis results. For example, if the animal is slow to respond to a particular command, it can suggest changing the training method. This makes animal training more effective.

[0120] The conversational tool can be enhanced with a function to estimate an animal's emotions and make appropriate environmental adjustments based on those emotions. For example, the analysis unit can analyze data such as animal vocalizations, movements, and facial expressions to estimate the animal's emotions. Then, the provision unit can suggest an environment in which the animal can be comfortable based on the estimated emotions. For example, if the animal is feeling hot, the system can suggest a cooling method to maintain a comfortable temperature. If the animal is feeling cold, the system can suggest a heating method to provide a warm environment. In this way, the animal's comfort can be improved by adjusting the environment according to its emotions.

[0121] The conversational tool can be enhanced with features to monitor animal stress levels and provide advice for stress reduction. For example, the data collection unit can collect data related to animal stress and assess the stress level. Next, the analysis unit can analyze the collected data and determine the animal's stress level. For example, it can analyze the animal's heart rate, respiratory rate, and behavioral patterns to detect signs of stress. Furthermore, the provision unit can provide the user with advice for stress reduction based on the analysis results. For example, if the animal is stressed, it can suggest ways to create a relaxing environment or exercise methods to relieve stress. This allows for more effective stress management of animals.

[0122] The following briefly describes the processing flow for example form 2.

[0123] Step 1: The data collection unit collects data on animal sounds, movements, and facial expressions. For example, it may record animal sounds with a microphone, capture movements with a camera, and acquire facial expressions as images. The data collection unit can also collect data using a smartphone or tablet device. Step 2: The analysis unit analyzes the data collected by the collection unit to determine the animal's feelings and desires. For example, it analyzes the patterns, volume, and frequency of the animal's vocalizations to determine the animal's feelings and desires. It uses AI to analyze the sound waveform of the animal's vocalizations to determine the animal's feelings and desires. It analyzes the patterns and speed of the animal's movements to determine the animal's feelings and desires. It analyzes changes in the animal's facial expressions to determine the animal's feelings and desires. Step 3: The translation unit translates the animal's feelings and desires, as determined by the analysis unit, into human language. For example, it may use AI to translate the animal's feelings and desires using natural language processing technology, translate the animal's feelings and desires based on predefined phrases, or translate them using speech synthesis technology. Step 4: The delivery unit provides the user with the human words translated by the translation unit. For example, it displays the translated human words on the screen of a smartphone or tablet. It outputs the translated human words as audio. It sends the translated human words as a text message. Step 5: The output unit converts the human speech provided by the supply unit into animal speech and outputs it through the speaker. For example, it converts the user's speech into animal sounds and outputs them through the speaker. It converts the user's speech into animal actions and communicates them to the animals. It converts the user's speech into animal facial expressions and communicates them to the animals.

[0124] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0125] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0126] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0127] Each of the multiple elements described above, including the collection unit, analysis unit, translation unit, provision unit, and output unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit collects animal sounds, movements, and facial expressions using the camera 42 and microphone 38B of the smart device 14. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. The translation unit is implemented in the specific processing unit 290 of the data processing unit 12 and translates the animal's feelings and desires into human language based on the analysis results. The provision unit is implemented in the control unit 46A of the smart device 14 and provides the translated words to the user. The output unit converts the user's words into animal language and outputs them using the speaker 40B of the smart device 14. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0128] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0129] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0130] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0131] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0132] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0133] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0134] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0135] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0136] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0137] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0138] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0139] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0140] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0141] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0142] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0143] Each of the multiple elements described above, including the collection unit, analysis unit, translation unit, provision unit, and output unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit collects animal sounds, movements, and facial expressions using the camera 42 and microphone 238 of the smart glasses 214. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. The translation unit is implemented in the specific processing unit 290 of the data processing unit 12 and translates the animal's feelings and desires into human language based on the analysis results. The provision unit is implemented in the control unit 46A of the smart glasses 214 and provides the translated words to the user. The output unit converts the user's words into animal language and outputs them using the speaker 240 of the smart glasses 214. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0144] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0145] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0146] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0147] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0148] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0149] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0150] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0151] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0152] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0153] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0154] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0155] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0156] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0157] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0158] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0159] Each of the multiple elements described above, including the collection unit, analysis unit, translation unit, provision unit, and output unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit collects animal sounds, movements, and facial expressions using the camera 42 and microphone 238 of the headset terminal 314. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. The translation unit is implemented in the specific processing unit 290 of the data processing unit 12 and translates the animal's feelings and desires into human language based on the analysis results. The provision unit is implemented in the control unit 46A of the headset terminal 314 and provides the translated words to the user. The output unit converts the user's words into animal language using the speaker 240 of the headset terminal 314 and outputs it. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0160] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0161] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0162] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0163] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0164] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0165] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0166] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0167] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0168] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0169] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0170] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0171] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0172] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0173] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0174] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0175] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0176] Each of the multiple elements described above, including the collection unit, analysis unit, translation unit, provision unit, and output unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the collection unit collects animal sounds, movements, and facial expressions using the camera 42 and microphone 238 of the robot 414. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. The translation unit is implemented in the specific processing unit 290 of the data processing unit 12 and translates the animal's feelings and requests into human language based on the analysis results. The provision unit is implemented in the control unit 46A of the robot 414 and provides the translated words to the user. The output unit converts the user's words into animal language using the speaker 240 of the robot 414 and outputs it. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0177] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0178] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0179] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0180] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0181] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0182] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0183] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0184] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0185] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0186] 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.

[0187] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0188] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0189] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0190] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0191] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0192] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0193] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0194] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0195] (Note 1) A data collection unit that collects data on animal sounds, movements, and facial expressions, An analysis unit analyzes the data collected by the aforementioned collection unit to determine the animal's feelings and desires, A translation unit that translates the feelings and desires of the animal, as determined by the aforementioned analysis unit, into human language, A providing unit that provides the user with the human words translated by the aforementioned translation unit, The system includes an output unit that converts human language provided by the providing unit into animal language and outputs it from a speaker. A system characterized by the following features. (Note 2) The aforementioned collection unit is Collect data on animal sounds, movements, and facial expressions using smartphones and tablet devices. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, By analyzing the patterns, volume, and frequency of animal sounds, we can determine the animal's feelings and desires. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned translation department, Translate the feelings and desires of the assessed animals into human language. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned supply unit is, The translated words of the person are displayed on the screen of a smartphone or tablet, or output as audio. The system described in Appendix 1, characterized by the features described herein. (Note 6) The output unit is, It converts the user's words into animal sounds and outputs them through a speaker. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is We estimate the emotions of animals and adjust the timing of data collection based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is Analyze the past behavioral history of animals and select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is When collecting data, filtering is performed based on the animal's current health status and environment. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is The system estimates the emotions of animals and prioritizes the data to collect based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When collecting data, the system prioritizes the collection of highly relevant data, taking into account the animals' geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is During data collection, analyze the animals' social media activity and collect relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, We estimate the emotions of animals and adjust the representation of the analysis based on the estimated emotions of the animals. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, the level of detail is adjusted based on the importance of animal sounds and movements. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the animal species and individual differences. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, The system estimates the animal's emotions and adjusts the length of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, the priority of analyses is determined based on the animal's behavioral history. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During analysis, the order of analysis is adjusted based on the relevance of the animals. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned translation department, It estimates the animal's emotions and adjusts the translation's expression based on the estimated animal's emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned translation department, During translation, adjust the level of detail based on the importance of the animal's feelings and needs. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned translation department, During translation, different translation algorithms are applied depending on the animal species and individual differences. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned translation department, It estimates the animal's emotions and adjusts the translation length based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned translation department, During translation, translation priorities are determined based on the animal's behavioral history. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned translation department, During translation, the order of translations is adjusted based on the relevance of the animals. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned supply unit is, The system estimates the animal's emotions and adjusts the presentation of the offering based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned supply unit is, When providing the translation, adjust the level of detail based on the importance of the translated words. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned supply unit is, When providing the service, the optimal delivery method is selected by referring to the user's past operation history. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned supply unit is, It estimates the animal's emotions and adjusts the length of the offering based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned supply unit is, When providing the service, the optimal delivery method will be selected, taking into account the user's device information. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned supply unit is, When providing information, we prioritize providing highly relevant information, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 31) The output unit is, It estimates the emotions of animals and adjusts the way the output is represented based on the estimated emotions of the animals. The system described in Appendix 1, characterized by the features described herein. (Note 32) The output unit is, When outputting, adjust the level of detail in the output based on the importance of the user's words. The system described in Appendix 1, characterized by the features described herein. (Note 33) The output unit is, When outputting, different output algorithms are applied depending on the animal species and individual differences. The system described in Appendix 1, characterized by the features described herein. (Note 34) The output unit is, It estimates the animal's emotions and adjusts the length of the output based on the estimated animal's emotions. The system described in Appendix 1, characterized by the features described herein. (Note 35) The output unit is, When outputting data, the output priority is determined based on the user's behavior history. The system described in Appendix 1, characterized by the features described herein. (Note 36) The output unit is, When outputting, adjust the order of the output based on the relevance of the animals. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0196] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A data collection unit that collects data on animal sounds, movements, and facial expressions, An analysis unit analyzes the data collected by the aforementioned collection unit to determine the animal's feelings and desires, A translation unit that translates the feelings and desires of the animal, as determined by the aforementioned analysis unit, into human language, A providing unit that provides the user with the human words translated by the aforementioned translation unit, The system includes an output unit that converts human language provided by the providing unit into animal language and outputs it from a speaker. A system characterized by the following features.

2. The aforementioned collection unit is Collect data on animal sounds, movements, and facial expressions using smartphones and tablet devices. The system according to feature 1.

3. The aforementioned analysis unit, By analyzing the patterns, volume, and frequency of animal sounds, we can determine the animal's feelings and desires. The system according to feature 1.

4. The aforementioned translation department, Translate the feelings and desires of the assessed animals into human language. The system according to feature 1.

5. The aforementioned supply unit is, The translated words of the person are displayed on the screen of a smartphone or tablet, or output as audio. The system according to feature 1.

6. The output unit is, It converts the user's words into animal sounds and outputs them through a speaker. The system according to feature 1.

7. The aforementioned collection unit is We estimate the emotions of animals and adjust the timing of data collection based on the estimated emotions. The system according to feature 1.

8. The aforementioned collection unit is Analyze the past behavioral history of animals and select the optimal data collection method. The system according to feature 1.

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