system

Abstract

The system according to the embodiment aims to analyze the sounds made by animals and understand their intentions and emotions. [Solution] A system according to an embodiment includes a collection unit, an analysis unit, and a generation unit. The collection unit collects animal sounds. The analysis unit analyzes the sounds collected by the collection unit to analyze the animal's intentions and emotions. The generation unit generates a response based on the analysis results obtained by the analysis unit.

Description

[Technical Field]

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

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

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

[0004] Conventional technology does not adequately analyze animal sounds to understand their intentions and emotions, and there is room for improvement.

[0005] The system according to the embodiment aims to analyze the sounds made by animals and understand their intentions and emotions. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, and a generation unit. The collection unit collects animal sounds. The analysis unit analyzes the sounds collected by the collection unit to analyze the intentions and emotions of the animals. The generation unit generates a response based on the analysis results obtained by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can analyze the sounds of animals and understand their intentions and emotions. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

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

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

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

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

[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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

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

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

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

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

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0027] Note that a device other than the data processing device 12 may 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 a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) An animal conversation system according to an embodiment of the present invention uses AI to analyze animal sounds and enable conversations with animals. This animal conversation system collects animal sounds and uses AI to analyze them. Next, it understands the animal's intentions and emotions based on the analysis results and generates a response accordingly. This allows users to converse with animals. For example, if a dog barks, the AI ​​analyzes the sound, understands that the dog wants to play, and can inform the user that "your dog wants to play." First, animal sounds are collected using a device that collects animal sounds. Next, the AI ​​analyzes the collected sounds and understands the animal's intentions and emotions. Finally, a response is generated based on the analysis results and provided to the user. This allows the animal conversation system to converse with animals by collecting, analyzing, and generating responses.

[0029] The animal conversation system according to the embodiment includes a collection unit, an analysis unit, and a generation unit. The collection unit collects animal sounds. The collection unit can collect animal sounds using, for example, a microphone or a sensor. The collection unit collects animal sounds in real time and stores them as digital data. For example, the collection unit can collect animal sounds using a high-sensitivity microphone and remove noise to obtain clear audio data. The collection unit can also detect animal sounds using a sensor and preferentially collect sounds in a specific frequency band. The analysis unit uses AI to analyze the sounds collected by the collection unit and understand the animal's intentions and emotions. The analysis unit can perform audio waveform analysis and frequency analysis using, for example, deep learning or a neural network. The analysis unit learns patterns of animal sounds and identifies the intentions and emotions indicated by specific sounds. For example, the analysis unit can analyze a dog's bark and understand that the dog wants to play. The analysis unit can also analyze the cat's meows and understand that the cat is "hungry." The generation unit generates a response based on the analysis result obtained by the analysis unit and provides it to the user. The generation unit can generate a response according to the animal's intentions and emotions using, for example, speech synthesis technology. The generation unit outputs the animal's intentions and emotions as text data and converts it into speech using speech synthesis technology. For example, if a dog wants to "play," the generation unit can tell the user, "The dog wants to play." If a cat feels "hungry," the generation unit can tell the user, "The cat is hungry." Thus, the animal conversation system according to the embodiment can collect and analyze animal meows and generate responses, allowing for conversations with animals.

[0030] The collection unit includes a device that collects animal sounds. The collection unit includes, for example, a device that collects animal sounds using a microphone or a sensor. The collection unit collects animal sounds in real time and stores them as digital data. For example, the collection unit can collect animal sounds using a high-sensitivity microphone and remove noise to obtain clear audio data. The collection unit can also detect animal sounds using a sensor and preferentially collect sounds in a specific frequency band. Thus, by including a device that collects animal sounds, collection accuracy is improved.

[0031] The analysis unit uses AI to analyze the collected cries and understand the animal's intentions and emotions. The analysis unit can perform audio waveform analysis and frequency analysis using, for example, deep learning and neural networks. The analysis unit learns the patterns of animal cries and identifies the intentions and emotions that specific cries indicate. For example, the analysis unit can analyze a dog's bark and understand that the dog wants to play. The analysis unit can also analyze a cat's meow and understand that the cat is hungry. This makes it possible to accurately understand an animal's intentions and emotions through AI analysis.

[0032] The generation unit can generate a response based on the analysis results and provide it to the user. The generation unit can generate a response based on the animal's intentions and emotions using, for example, speech synthesis technology. The generation unit outputs the animal's intentions and emotions as text data and converts it into speech using speech synthesis technology. For example, if a dog wants to play, the generation unit can tell the user, "The dog wants to play." Also, if a cat feels hungry, the generation unit can tell the user, "The cat is hungry." In this way, by generating a response based on the analysis results, the user can converse with the animals.

[0033] The collection unit can estimate the emotion of the animal and adjust the timing of collecting animal cries based on the estimated emotion of the animal. For example, if the animal is excited, the collection unit can adjust the collection device to collect animal cries frequently. Also, if the animal is relaxed, the collection unit can adjust the collection device to collect animal cries at intervals. Also, if the animal is stressed, the collection unit can adjust the collection device to collect animal cries immediately. In this way, by adjusting the collection timing based on the emotion of the animal, more appropriate animal cries can be collected.

[0034] The collection unit can select an appropriate collection method depending on the type of animal and individual differences. For example, in the case of dogs, the collection unit preferentially collects cries in a specific frequency band. In the case of cats, the collection unit can also analyze the pattern of the cries and select the optimal collection method. In the case of birds, the collection unit can also adjust the collection method based on the volume and rhythm of the cries. In this way, by selecting a collection method depending on the type of animal and individual differences, collection accuracy is improved.

[0035] The collection unit can filter out environmental and background sounds of the animals when collecting the animal cries to remove noise. For example, the collection unit can filter out surrounding wind sounds when collecting the animal cries to remove noise. The collection unit can also filter out sounds of other animals when collecting the animal cries to remove noise. The collection unit can also filter out traffic sounds and human voices when collecting the animal cries to remove noise. In this way, by filtering out environmental and background sounds, animal cries with less noise can be collected.

[0036] The collection unit can estimate the emotion of the animal and determine the priority of the cries to be collected based on the estimated emotion of the animal. For example, if the animal is excited, the collection unit can collect the cries of the animal with priority. Also, if the animal is relaxed, the collection unit can collect the cries of the animal later. Also, if the animal is feeling stressed, the collection unit can collect the cries of the animal with the highest priority. In this way, by determining the priority of the cries to be collected based on the emotion of the animal, important cries can be collected with priority.

[0037] When collecting animal cries, the collection unit can prioritize collecting highly relevant cries by taking into account the location information of the animals. For example, if the animal is in a specific location, the collection unit prioritizes collecting cries from that location. Furthermore, if the animal is moving, the collection unit can also prioritize collecting cries from the destination. Furthermore, if the animal is in a specific area, the collection unit can also prioritize collecting cries from that area. In this way, by taking into account the location information of the animals, highly relevant cries can be prioritized.

[0038] When collecting animal sounds, the collection unit can analyze the animal's behavioral patterns and collect sounds related to specific behaviors. For example, when an animal is playing, the collection unit collects sounds related to that behavior. Furthermore, when an animal is eating, the collection unit can also collect sounds related to that behavior. Furthermore, when an animal is resting, the collection unit can also collect sounds related to that behavior. In this way, by analyzing the animal's behavioral patterns, sounds related to specific behaviors can be collected.

[0039] The analysis unit can estimate the emotion of the animal and adjust the analysis algorithm based on the estimated emotion of the animal. For example, if the animal is excited, the analysis unit speeds up the analysis algorithm to quickly capture changes in emotion. Also, if the animal is relaxed, the analysis unit can adjust the algorithm to perform a detailed analysis. Also, if the animal is stressed, the analysis unit can refine the analysis algorithm to capture subtle changes in emotion. In this way, by adjusting the analysis algorithm based on the emotion of the animal, the accuracy of the analysis is improved.

[0040] During analysis, the analysis unit can improve the accuracy of the analysis by referring to past animal cry data. For example, the analysis unit analyzes the current cry pattern based on the past cry data. Furthermore, by referring to the past cry data, the analysis unit can more accurately capture changes in emotions. Furthermore, the analysis unit can also improve the accuracy of the analysis algorithm by using the past cry data. In this way, by referring to the past cry data, the analysis accuracy is improved.

[0041] During analysis, the analysis unit can apply different analysis methods depending on the type of animal and individual differences. For example, in the case of a dog, the analysis unit applies a method of analyzing the cries of a specific frequency band. In the case of a cat, the analysis unit can also apply a method of analyzing the cries' patterns. In the case of a bird, the analysis unit can also apply a method of analyzing the volume and rhythm of the cries. In this way, by applying an analysis method depending on the type of animal and individual differences, the analysis accuracy is improved.

[0042] The analysis unit can estimate the emotion of the animal and adjust the display method of the analysis results based on the estimated emotion of the animal. For example, if the animal is excited, the analysis unit can visually emphasize the change in emotion when displaying it. Furthermore, if the animal is relaxed, the analysis unit can display detailed analysis results. Furthermore, if the animal is feeling stressed, the analysis unit can visually display subtle changes in emotion. In this way, adjusting the display method of the analysis results based on the emotion of the animal makes the analysis results easier to understand.

[0043] The analysis unit can perform the analysis while taking into account the health condition and stress level of the animal. For example, the analysis unit adjusts the analysis algorithm while taking into account the health condition of the animal. The analysis unit can also analyze changes in emotions while taking into account the stress level of the animal. The analysis unit can also display the analysis results based on the health condition and stress level of the animal. This allows for more accurate analysis by taking into account the health condition and stress level of the animal.

[0044] During the analysis, the analysis unit can analyze the frequency and volume of the animal's cry and provide a detailed analysis result. For example, the analysis unit can analyze the frequency of the cry to capture changes in emotion. The analysis unit can also analyze the volume of the cry to evaluate the strength of the emotion. The analysis unit can also combine the frequency and volume of the cry to provide a detailed analysis result. In this way, by analyzing the frequency and volume of the cry, a detailed analysis result can be provided.

[0045] The generation unit can estimate the emotion of the animal and adjust the way in which the response is expressed based on the estimated emotion of the animal. For example, if the animal is excited, the generation unit expresses the response quickly and clearly. Also, if the animal is relaxed, the generation unit can express the response calmly. Also, if the animal is stressed, the generation unit can express the response gently. In this way, by adjusting the way in which the response is expressed based on the emotion of the animal, a more appropriate response can be generated.

[0046] When generating a response, the generation unit can adjust the level of detail of the response based on the animal's intentions and emotions. For example, if the animal wants to play, the generation unit generates a response that includes a specific suggestion for play. Furthermore, if the animal wants to eat, the generation unit can generate a detailed response regarding the timing and type of meal. Furthermore, if the animal wants to rest, the generation unit can also generate a response that includes a suggestion for rest. In this way, by adjusting the level of detail of the response based on the animal's intentions and emotions, a more appropriate response can be generated.

[0047] When generating a response, the generation unit can apply different response algorithms depending on the type of animal and individual differences. For example, in the case of a dog, the generation unit applies a response algorithm based on a specific behavior. In addition, in the case of a cat, the generation unit can apply a response algorithm based on the pattern of the animal's cry. In addition, in the case of a bird, the generation unit can also apply a response algorithm based on the volume and rhythm of the animal's cry. In this way, by applying a response algorithm depending on the type of animal and individual differences, a more appropriate response can be generated.

[0048] The generation unit can estimate the emotion of the animal and determine the priority of responses based on the estimated emotion of the animal. For example, if the animal is excited, the generation unit generates that response as a top priority. Also, if the animal is relaxed, the generation unit can generate that response later. Also, if the animal is feeling stressed, the generation unit can generate that response as a priority. In this way, by determining the priority of responses based on the emotion of the animal, important responses can be generated with priority.

[0049] When generating a response, the generation unit can provide an optimal response by referring to the animal's behavioral pattern and past response history. The generation unit generates a response that is optimal for the animal's behavioral pattern based on, for example, the past response history. The generation unit can also analyze the animal's behavioral pattern to provide an optimal response. The generation unit can also generate a response that is optimal for the animal's intention by referring to the past response history. In this way, the optimal response can be provided by referring to the animal's behavioral pattern and past response history.

[0050] When generating a response, the generation unit can customize the content of the response according to the environment and situation of the animal. For example, when the animal is outdoors, the generation unit generates a response appropriate for the environment. Furthermore, when the animal is indoors, the generation unit can generate a response appropriate for the situation. Furthermore, when the animal is in a specific situation, the generation unit can also generate a response appropriate for the situation. In this way, by customizing the content of the response according to the environment and situation of the animal, a more appropriate response can be generated.

[0051] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0052] The animal conversation system may further include a translation unit that translates animal sounds in real time. The translation unit instantly analyzes the sound data provided by the collection unit and provides the user with the translation results in real time. For example, the moment a dog barks, it can be translated as "I want to play" and the user can be instantly notified that "The dog wants to play." Similarly, the moment a cat meows, it can be translated as "I'm hungry" and the user can be instantly notified that "The cat is hungry." This allows the user to understand the intentions and emotions of animals in real time and respond quickly.

[0053] The animal conversation system may further include a recording and playback unit that records animal sounds and plays them back later. The recording and playback unit stores the sound data provided by the collection unit and allows the user to play them back as needed. For example, if a dog barks while the user is away, the unit can record the sound and play it back later for the user to check. Also, if a cat barks at a specific time, the unit can record the sound and play it back later for the user to check. This allows the user to record animal sounds and play them back later.

[0054] The animal conversation system may further include a learning unit that analyzes animal cries and learns their cries patterns. The learning unit analyzes the cries data provided by the collection unit and learns the animal cries patterns. For example, if a dog makes a specific cry in a specific situation, that pattern can be learned and used for future analyses. Also, if a cat makes a cry that expresses a specific emotion, that pattern can be learned and used for future analyses. In this way, the patterns of animal cries can be learned and the analysis accuracy can be improved.

[0055] The animal conversation system may further include a classifier that classifies animal sounds and generates different responses for different sounds. The classifier analyzes the sound data provided by the collector and classifies different sounds. For example, the classifier can classify dog ​​barks and meows and generate different responses for each. It can also classify cat meows and growls and generate different responses for each. This allows the system to classify animal sounds and generate more appropriate responses.

[0056] The animal conversation system may further include a frequency analysis unit that analyzes the animal's cries and generates a response based on the frequency and volume of the cries. The frequency analysis unit analyzes the cries data provided by the collection unit and evaluates the frequency and volume of the cries. For example, if a dog's cries have a high frequency, the system may determine that the dog is excited and notify the user, "The dog is excited. Please calm it down." Alternatively, if a cat's cries have a low volume, the system may determine that the cat is relaxed and notify the user, "The cat is relaxed." This allows the system to generate a response based on the frequency and volume of the cries, more accurately conveying the animal's state.

[0057] The processing flow of the first embodiment will be briefly explained below.

[0058] Step 1: The collection unit collects animal sounds. The collection unit can collect animal sounds using, for example, a microphone or a sensor. The collection unit collects animal sounds in real time and stores them as digital data. For example, the collection unit can collect animal sounds using a high-sensitivity microphone and remove noise to obtain clear audio data. The collection unit can also detect animal sounds using a sensor and preferentially collect sounds in a specific frequency band. Step 2: The analysis unit uses AI to analyze the cries collected by the collection unit and understand the animal's intentions and emotions. The analysis unit can perform audio waveform analysis and frequency analysis using, for example, deep learning or neural networks. The analysis unit learns the patterns of animal cries and identifies the intentions and emotions that specific cries indicate. For example, the analysis unit can analyze a dog's bark and understand that the dog wants to play. The analysis unit can also analyze a cat's meow and understand that the cat is hungry. Step 3: The generation unit generates a response based on the analysis results obtained by the analysis unit and provides it to the user. The generation unit can generate a response according to the animal's intentions and emotions using, for example, speech synthesis technology. The generation unit outputs the animal's intentions and emotions as text data and converts it into speech using speech synthesis technology. For example, if a dog wants to play, the generation unit can tell the user, "The dog wants to play." Also, if a cat feels hungry, the generation unit can tell the user, "The cat is hungry."

[0059] (Example 2) An animal conversation system according to an embodiment of the present invention uses AI to analyze animal sounds and enable conversations with animals. This animal conversation system collects animal sounds and uses AI to analyze them. Next, it understands the animal's intentions and emotions based on the analysis results and generates a response accordingly. This allows users to converse with animals. For example, if a dog barks, the AI ​​analyzes the sound, understands that the dog wants to play, and can inform the user that "your dog wants to play." First, animal sounds are collected using a device that collects animal sounds. Next, the AI ​​analyzes the collected sounds and understands the animal's intentions and emotions. Finally, a response is generated based on the analysis results and provided to the user. This allows the animal conversation system to converse with animals by collecting, analyzing, and generating responses.

[0060] The animal conversation system according to the embodiment includes a collection unit, an analysis unit, and a generation unit. The collection unit collects animal sounds. The collection unit can collect animal sounds using, for example, a microphone or a sensor. The collection unit collects animal sounds in real time and stores them as digital data. For example, the collection unit can collect animal sounds using a high-sensitivity microphone and remove noise to obtain clear audio data. The collection unit can also detect animal sounds using a sensor and preferentially collect sounds in a specific frequency band. The analysis unit uses AI to analyze the sounds collected by the collection unit and understand the animal's intentions and emotions. The analysis unit can perform audio waveform analysis and frequency analysis using, for example, deep learning or a neural network. The analysis unit learns patterns of animal sounds and identifies the intentions and emotions indicated by specific sounds. For example, the analysis unit can analyze a dog's bark and understand that the dog wants to play. The analysis unit can also analyze the cat's meows and understand that the cat is "hungry." The generation unit generates a response based on the analysis result obtained by the analysis unit and provides it to the user. The generation unit can generate a response according to the animal's intentions and emotions using, for example, speech synthesis technology. The generation unit outputs the animal's intentions and emotions as text data and converts it into speech using speech synthesis technology. For example, if a dog wants to "play," the generation unit can tell the user, "The dog wants to play." If a cat feels "hungry," the generation unit can tell the user, "The cat is hungry." Thus, the animal conversation system according to the embodiment can collect and analyze animal meows and generate responses, allowing for conversations with animals.

[0061] The collection unit includes a device that collects animal sounds. The collection unit includes, for example, a device that collects animal sounds using a microphone or a sensor. The collection unit collects animal sounds in real time and stores them as digital data. For example, the collection unit can collect animal sounds using a high-sensitivity microphone and remove noise to obtain clear audio data. The collection unit can also detect animal sounds using a sensor and preferentially collect sounds in a specific frequency band. Thus, by including a device that collects animal sounds, collection accuracy is improved.

[0062] The analysis unit uses AI to analyze the collected cries and understand the animal's intentions and emotions. The analysis unit can perform audio waveform analysis and frequency analysis using, for example, deep learning and neural networks. The analysis unit learns the patterns of animal cries and identifies the intentions and emotions that specific cries indicate. For example, the analysis unit can analyze a dog's bark and understand that the dog wants to play. The analysis unit can also analyze a cat's meow and understand that the cat is hungry. This makes it possible to accurately understand an animal's intentions and emotions through AI analysis.

[0063] The generation unit can generate a response based on the analysis results and provide it to the user. The generation unit can generate a response based on the animal's intentions and emotions using, for example, speech synthesis technology. The generation unit outputs the animal's intentions and emotions as text data and converts it into speech using speech synthesis technology. For example, if a dog wants to play, the generation unit can tell the user, "The dog wants to play." Also, if a cat feels hungry, the generation unit can tell the user, "The cat is hungry." In this way, by generating a response based on the analysis results, the user can converse with the animals.

[0064] The collection unit can estimate the emotion of the animal and adjust the timing of collecting animal cries based on the estimated emotion of the animal. For example, if the animal is excited, the collection unit can adjust the collection device to collect animal cries frequently. Also, if the animal is relaxed, the collection unit can adjust the collection device to collect animal cries at intervals. Also, if the animal is stressed, the collection unit can adjust the collection device to collect animal cries immediately. In this way, by adjusting the collection timing based on the emotion of the animal, more appropriate animal cries can be collected.

[0065] The collection unit can select an appropriate collection method depending on the type of animal and individual differences. For example, in the case of dogs, the collection unit preferentially collects cries in a specific frequency band. In the case of cats, the collection unit can also analyze the pattern of the cries and select the optimal collection method. In the case of birds, the collection unit can also adjust the collection method based on the volume and rhythm of the cries. In this way, by selecting a collection method depending on the type of animal and individual differences, collection accuracy is improved.

[0066] The collection unit can filter out environmental and background sounds of the animals when collecting the animal cries to remove noise. For example, the collection unit can filter out surrounding wind sounds when collecting the animal cries to remove noise. The collection unit can also filter out sounds of other animals when collecting the animal cries to remove noise. The collection unit can also filter out traffic sounds and human voices when collecting the animal cries to remove noise. In this way, by filtering out environmental and background sounds, animal cries with less noise can be collected.

[0067] The collection unit can estimate the emotion of the animal and determine the priority of the cries to be collected based on the estimated emotion of the animal. For example, if the animal is excited, the collection unit can collect the cries of the animal with priority. Also, if the animal is relaxed, the collection unit can collect the cries of the animal later. Also, if the animal is feeling stressed, the collection unit can collect the cries of the animal with the highest priority. In this way, by determining the priority of the cries to be collected based on the emotion of the animal, important cries can be collected with priority.

[0068] When collecting animal cries, the collection unit can prioritize collecting highly relevant cries by taking into account the location information of the animals. For example, if the animal is in a specific location, the collection unit prioritizes collecting cries from that location. Furthermore, if the animal is moving, the collection unit can also prioritize collecting cries from the destination. Furthermore, if the animal is in a specific area, the collection unit can also prioritize collecting cries from that area. In this way, by taking into account the location information of the animals, highly relevant cries can be prioritized.

[0069] When collecting animal sounds, the collection unit can analyze the animal's behavioral patterns and collect sounds related to specific behaviors. For example, when an animal is playing, the collection unit collects sounds related to that behavior. Furthermore, when an animal is eating, the collection unit can also collect sounds related to that behavior. Furthermore, when an animal is resting, the collection unit can also collect sounds related to that behavior. In this way, by analyzing the animal's behavioral patterns, sounds related to specific behaviors can be collected.

[0070] The analysis unit can estimate the emotion of the animal and adjust the analysis algorithm based on the estimated emotion of the animal. For example, if the animal is excited, the analysis unit speeds up the analysis algorithm to quickly capture changes in emotion. Also, if the animal is relaxed, the analysis unit can adjust the algorithm to perform a detailed analysis. Also, if the animal is stressed, the analysis unit can refine the analysis algorithm to capture subtle changes in emotion. In this way, by adjusting the analysis algorithm based on the emotion of the animal, the accuracy of the analysis is improved.

[0071] During analysis, the analysis unit can improve the accuracy of the analysis by referring to past animal cry data. For example, the analysis unit analyzes the current cry pattern based on the past cry data. Furthermore, by referring to the past cry data, the analysis unit can more accurately capture changes in emotions. Furthermore, the analysis unit can also improve the accuracy of the analysis algorithm by using the past cry data. In this way, by referring to the past cry data, the analysis accuracy is improved.

[0072] During analysis, the analysis unit can apply different analysis methods depending on the type of animal and individual differences. For example, in the case of a dog, the analysis unit applies a method of analyzing the cries of a specific frequency band. In the case of a cat, the analysis unit can also apply a method of analyzing the cries' patterns. In the case of a bird, the analysis unit can also apply a method of analyzing the volume and rhythm of the cries. In this way, by applying an analysis method depending on the type of animal and individual differences, the analysis accuracy is improved.

[0073] The analysis unit can estimate the emotion of the animal and adjust the display method of the analysis results based on the estimated emotion of the animal. For example, if the animal is excited, the analysis unit can visually emphasize the change in emotion when displaying it. Furthermore, if the animal is relaxed, the analysis unit can display detailed analysis results. Furthermore, if the animal is feeling stressed, the analysis unit can visually display subtle changes in emotion. In this way, adjusting the display method of the analysis results based on the emotion of the animal makes the analysis results easier to understand.

[0074] The analysis unit can perform the analysis while taking into account the health condition and stress level of the animal. For example, the analysis unit adjusts the analysis algorithm while taking into account the health condition of the animal. The analysis unit can also analyze changes in emotions while taking into account the stress level of the animal. The analysis unit can also display the analysis results based on the health condition and stress level of the animal. This allows for more accurate analysis by taking into account the health condition and stress level of the animal.

[0075] During the analysis, the analysis unit can analyze the frequency and volume of the animal's cry and provide a detailed analysis result. For example, the analysis unit can analyze the frequency of the cry to capture changes in emotion. The analysis unit can also analyze the volume of the cry to evaluate the strength of the emotion. The analysis unit can also combine the frequency and volume of the cry to provide a detailed analysis result. In this way, by analyzing the frequency and volume of the cry, a detailed analysis result can be provided.

[0076] The generation unit can estimate the emotion of the animal and adjust the way in which the response is expressed based on the estimated emotion of the animal. For example, if the animal is excited, the generation unit expresses the response quickly and clearly. Also, if the animal is relaxed, the generation unit can express the response calmly. Also, if the animal is stressed, the generation unit can express the response gently. In this way, by adjusting the way in which the response is expressed based on the emotion of the animal, a more appropriate response can be generated.

[0077] When generating a response, the generation unit can adjust the level of detail of the response based on the animal's intentions and emotions. For example, if the animal wants to play, the generation unit generates a response that includes a specific suggestion for play. Furthermore, if the animal wants to eat, the generation unit can generate a detailed response regarding the timing and type of meal. Furthermore, if the animal wants to rest, the generation unit can also generate a response that includes a suggestion for rest. In this way, by adjusting the level of detail of the response based on the animal's intentions and emotions, a more appropriate response can be generated.

[0078] When generating a response, the generation unit can apply different response algorithms depending on the type of animal and individual differences. For example, in the case of a dog, the generation unit applies a response algorithm based on a specific behavior. In addition, in the case of a cat, the generation unit can apply a response algorithm based on the pattern of the animal's cry. In addition, in the case of a bird, the generation unit can also apply a response algorithm based on the volume and rhythm of the animal's cry. In this way, by applying a response algorithm depending on the type of animal and individual differences, a more appropriate response can be generated.

[0079] The generation unit can estimate the emotion of the animal and determine the priority of responses based on the estimated emotion of the animal. For example, if the animal is excited, the generation unit generates that response as a top priority. Also, if the animal is relaxed, the generation unit can generate that response later. Also, if the animal is feeling stressed, the generation unit can generate that response as a priority. In this way, by determining the priority of responses based on the emotion of the animal, important responses can be generated with priority.

[0080] When generating a response, the generation unit can provide an optimal response by referring to the animal's behavioral pattern and past response history. The generation unit generates a response that is optimal for the animal's behavioral pattern based on, for example, the past response history. The generation unit can also analyze the animal's behavioral pattern to provide an optimal response. The generation unit can also generate a response that is optimal for the animal's intention by referring to the past response history. In this way, the optimal response can be provided by referring to the animal's behavioral pattern and past response history.

[0081] When generating a response, the generation unit can customize the content of the response according to the environment and situation of the animal. For example, when the animal is outdoors, the generation unit generates a response appropriate for the environment. Furthermore, when the animal is indoors, the generation unit can generate a response appropriate for the situation. Furthermore, when the animal is in a specific situation, the generation unit can also generate a response appropriate for the situation. In this way, by customizing the content of the response according to the environment and situation of the animal, a more appropriate response can be generated. === Hard Collateral 1-1 === Each of the multiple elements including the collection unit, analysis unit, and generation unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects animal sounds using the microphone 38B or a sensor of the smart device 14, and the collected sounds are analyzed by the specific processing unit 290 of the data processing device 12. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and understands the intentions and emotions of the animal using deep learning or a neural network. The generation unit is realized, for example, by the control unit 46A of the smart device 14, and generates a response based on the analysis result and provides it to the user. === Hard Collateral 1-2 === Each of the multiple elements including the collection unit, analysis unit, and generation unit described above is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects animal sounds using the microphone 238 or a sensor of the smart glasses 214, and the collected sounds are analyzed by the specific processing unit 290 of the data processing device 12. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and understands the intentions and emotions of the animals using deep learning or a neural network. The generation unit is realized, for example, by the control unit 46A of the smart glasses 214, and generates a response based on the analysis result and provides it to the user. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned collection unit, analysis unit, and generation unit is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the collection unit collects animal sounds using the microphone 238 or sensor of the headset-type terminal 314, and the collected sounds are analyzed by the specific processing unit 290 of the data processing device 12. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and understands the intentions and emotions of the animal using deep learning or a neural network. The generation unit is realized, for example, by the control unit 46A of the headset-type terminal 314, and generates a response based on the analysis result and provides it to the user. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned collection unit, analysis unit, and generation unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects animal sounds using the microphone 238 or sensors of the robot 414, and the collected sounds are analyzed by the specific processing unit 290 of the data processing device 12. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and understands the intentions and emotions of the animal using deep learning or a neural network. The generation unit is realized, for example, by the control unit 46A of the robot 414, and generates a response based on the analysis result and provides it to the user.

[0082] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0083] The animal conversation system can further include a health management unit that monitors the health of the animal. The health management unit collects biological information such as the animal's body temperature, heart rate, and respiratory rate, and provides it to the analysis unit. For example, if a dog's body temperature is high, the analysis unit can determine that the dog may be suffering from heatstroke, and the generation unit can notify the user, "The dog may be suffering from heatstroke. Please move the dog to a cooler place." Similarly, if a cat's heart rate is abnormally high, the analysis unit can determine that the cat is feeling stressed, and the generation unit can notify the user, "The cat is feeling stressed. Please let the cat rest in a quiet place." This allows the health of animals to be monitored in real time and prompts appropriate action.

[0084] The animal conversation system can further include a behavior monitoring unit that monitors the behavior of animals. The behavior monitoring unit monitors the animal's movements using cameras and sensors and provides the information to the analysis unit. For example, if a dog frequently walks around the same place, the analysis unit can determine that the dog is feeling anxious, and the generation unit can notify the user, "The dog is feeling anxious. Please play with it to reassure it." Similarly, if a cat remains motionless for a long period of time, the analysis unit can determine that the cat may be unwell, and the generation unit can notify the user, "The cat may be unwell. Please consult a veterinarian." This allows for monitoring animal behavior and early detection of abnormalities.

[0085] The animal conversation system can further include a diet management unit that manages the animal's diet. The diet management unit records the amount and frequency of the animal's meals and provides the data to the analysis unit. For example, if a dog often leaves food on its plate, the analysis unit can determine that the dog has a poor appetite, and the generation unit can notify the user, "The dog has a poor appetite. Please reconsider the contents of its meals." Similarly, if a cat is overeating, the analysis unit can determine that the cat is stressed, and the generation unit can notify the user, "The cat is overeating. Please investigate the cause of the stress." This allows for the animal's diet to be managed and its health to be maintained.

[0086] The animal conversation system may further include a sleep monitoring unit that monitors the animal's sleep state. The sleep monitoring unit records the animal's sleep time and sleep quality and provides the data to the analysis unit. For example, if a dog wakes up frequently during the night, the analysis unit may determine that the dog is feeling anxious, and the generation unit may notify the user, "Your dog is waking up frequently during the night. Please sleep with your dog to reassure him." Similarly, if a cat sleeps for long periods during the day, the analysis unit may determine that the cat may be unwell, and the generation unit may notify the user, "Your cat is sleeping for long periods during the day. Please consult a veterinarian." This allows the animal's sleep state to be monitored and its health to be maintained.

[0087] The animal conversation system may further include a social behavior monitoring unit that monitors the social behavior of animals. The social behavior monitoring unit monitors how animals interact with other animals and humans and provides the information to the analysis unit. For example, if a dog avoids playing with other dogs, the analysis unit may determine that the dog is experiencing social stress, and the generation unit may notify the user, "The dog is avoiding playing with other dogs. Please play with it to reduce social stress." Similarly, if a cat exhibits aggressive behavior toward humans, the analysis unit may determine that the cat is experiencing anxiety, and the generation unit may notify the user, "The cat is exhibiting aggressive behavior. Please let it rest in a quiet place to reduce anxiety." This allows the system to monitor the social behavior of animals and encourage appropriate responses.

[0088] The animal conversation system may further include a translation unit that translates animal sounds in real time. The translation unit instantly analyzes the sound data provided by the collection unit and provides the user with the translation results in real time. For example, the moment a dog barks, it can be translated as "I want to play" and the user can be instantly notified that "The dog wants to play." Similarly, the moment a cat meows, it can be translated as "I'm hungry" and the user can be instantly notified that "The cat is hungry." This allows the user to understand the intentions and emotions of animals in real time and respond quickly.

[0089] The animal conversation system may further include a recording and playback unit that records animal sounds and plays them back later. The recording and playback unit stores the sound data provided by the collection unit and allows the user to play them back as needed. For example, if a dog barks while the user is away, the unit can record the sound and play it back later for the user to check. Also, if a cat barks at a specific time, the unit can record the sound and play it back later for the user to check. This allows the user to record animal sounds and play them back later.

[0090] The animal conversation system may further include a learning unit that analyzes animal cries and learns their cries patterns. The learning unit analyzes the cries data provided by the collection unit and learns the animal cries patterns. For example, if a dog makes a specific cry in a specific situation, that pattern can be learned and used for future analyses. Also, if a cat makes a cry that expresses a specific emotion, that pattern can be learned and used for future analyses. In this way, the patterns of animal cries can be learned and the analysis accuracy can be improved.

[0091] The animal conversation system may further include a classifier that classifies animal sounds and generates different responses for different sounds. The classifier analyzes the sound data provided by the collector and classifies different sounds. For example, the classifier can classify dog ​​barks and meows and generate different responses for each. It can also classify cat meows and growls and generate different responses for each. This allows the system to classify animal sounds and generate more appropriate responses.

[0092] The animal conversation system may further include a frequency analysis unit that analyzes the animal's cries and generates a response based on the frequency and volume of the cries. The frequency analysis unit analyzes the cries data provided by the collection unit and evaluates the frequency and volume of the cries. For example, if a dog's cries have a high frequency, the system may determine that the dog is excited and notify the user, "The dog is excited. Please calm it down." Alternatively, if a cat's cries have a low volume, the system may determine that the cat is relaxed and notify the user, "The cat is relaxed." This allows the system to generate a response based on the frequency and volume of the cries, more accurately conveying the animal's state.

[0093] The processing flow of the second embodiment will be briefly explained below.

[0094] Step 1: The collection unit collects animal sounds. The collection unit can collect animal sounds using, for example, a microphone or a sensor. The collection unit collects animal sounds in real time and stores them as digital data. For example, the collection unit can collect animal sounds using a high-sensitivity microphone and remove noise to obtain clear audio data. The collection unit can also detect animal sounds using a sensor and preferentially collect sounds in a specific frequency band. Step 2: The analysis unit uses AI to analyze the cries collected by the collection unit and understand the animal's intentions and emotions. The analysis unit can perform audio waveform analysis and frequency analysis using, for example, deep learning or neural networks. The analysis unit learns the patterns of animal cries and identifies the intentions and emotions that specific cries indicate. For example, the analysis unit can analyze a dog's bark and understand that the dog wants to play. The analysis unit can also analyze a cat's meow and understand that the cat is hungry. Step 3: The generation unit generates a response based on the analysis results obtained by the analysis unit and provides it to the user. The generation unit can generate a response according to the animal's intentions and emotions using, for example, speech synthesis technology. The generation unit outputs the animal's intentions and emotions as text data and converts it into speech using speech synthesis technology. For example, if a dog wants to play, the generation unit can tell the user, "The dog wants to play." Also, if a cat feels hungry, the generation unit can tell the user, "The cat is hungry."

[0095] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0096] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0097] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, 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.

[0098] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0099] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0100] 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.

[0101] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0102] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0103] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0104] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0105] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0106] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0107] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0108] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0109] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0110] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0111] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0112] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0113] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0114] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0115] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0116] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0117] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0118] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0119] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0120] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0121] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0122] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0123] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0124] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0125] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0126] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0127] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0128] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0129] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0130] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0131] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0132] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0133] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0134] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0135] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0136] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0137] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0138] The control 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0139] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0140] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0141] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0142] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0143] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0144] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0145] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0146] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0147] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0148] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0149] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0150] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0151] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0152] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0153] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0154] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0155] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0156] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0157] 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.

[0158] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0159] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0160] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[0161] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0162] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0163] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0164] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0165] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0166] [Explanation of symbols]

[0167] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a collection unit that collects animal sounds; an analysis unit that analyzes the animal's cries collected by the collection unit and analyzes the animal's intentions and emotions; a generation unit that generates a response based on the analysis result obtained by the analysis unit. A system characterized by:

2. The collecting unit Equipped with a device that collects animal sounds 2. The system of claim 1.

3. The analysis unit Analyzing collected sounds using AI to understand the intentions and emotions of animals 2. The system of claim 1.

4. The generation unit Generate a response based on the analysis results and provide it to the user 2. The system of claim 1.

5. The collecting unit Estimate the emotion of the animal and adjust the timing of collecting animal sounds based on the estimated emotion.

2. The system of claim 1.

6. The collecting unit Select an appropriate collection method depending on the type of animal and individual differences.

2. The system of claim 1.

7. The collecting unit When collecting animal sounds, filter out the ambient and background noise to remove noise.

2. The system of claim 1.

8. The collecting unit Estimate the emotion of the animal and prioritize the sounds to be collected based on the estimated emotion.

2. The system of claim 1.

9. The collecting unit When collecting animal calls, prioritize collection of relevant calls based on animal location information.

2. The system of claim 1.

10. The collecting unit When collecting animal sounds, analyze animal behavior patterns and collect sounds related to specific behaviors.

2. The system of claim 1.

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