System

Abstract

The system according to the embodiment aims to translate the cry of the pet so that the user can understand the intention of the pet.SOLUTION: A system according to an embodiment includes a registration unit, an analysis unit, and a translation unit. The registration unit registers the user's pet's bark and the situation. The analysis unit analyzes the information registered by the registration unit. The translation unit translates the cry of the pet based on the information analyzed by the analysis unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, it is difficult to understand what a pet's cries mean, and there is room for improvement.

[0005] The system according to the embodiment aims to translate the cries of pets so that users can understand their pets' intentions. [Means for solving the problem]

[0006] The system according to the embodiment includes a registration unit, an analysis unit, and a translation unit. The registration unit allows a user to register a pet's cry and a situation. The analysis unit analyzes the information registered by the registration unit. The translation unit translates the pet's cry based on the information analyzed by the analysis unit. [Effects of the Invention]

[0007] An embodiment of the system can translate pet sounds and allow users to understand their pet's intentions. [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) A pet voice translation system according to an embodiment of the present invention is a system in which a user registers a pet's bark and a situation, and a generation AI analyzes the information to translate the pet's bark. In the pet voice translation system, a user registers a pet's bark and a situation, and a generation AI analyzes the information to translate the pet's bark. For example, a user records a pet's bark and registers the situation in which the bark occurred. The generation AI then learns the bark information and situation information registered by the user. The generation AI analyzes the bark information and situation information collected from multiple users and identifies the pet's bark pattern. The generation AI translates the pet's bark based on the learned bark pattern. For example, when a user records a pet's bark and inputs the bark into the generation AI, the generation AI analyzes the bark and infers what the pet is saying. This allows the pet cry translation system to make it easier for users to understand their pet's cry, improving communication with their pet. This allows the pet cry translation system to make it easier for users to understand their pet's cry, improving communication with their pet. For example, by accurately understanding what your pet wants, you can respond appropriately. Furthermore, by utilizing the cry information registered by other users, more accurate translations are possible.

[0029] A pet bark translation system according to an embodiment includes a registration unit, an analysis unit, and a translation unit. The registration unit allows a user to register a pet's bark and a situation. For example, the user may record the pet's bark when begging for food or when begging to play, and register each situation. This information is input to the generation AI. The analysis unit uses the generation AI to analyze the information registered by the registration unit. The generation AI learns the bark information and situation information registered by the user and identifies the pet's bark pattern. For example, if a specific bark means a begging for food, the generation AI learns that bark pattern. The translation unit uses the generation AI to translate the pet's bark based on the information analyzed by the analysis unit. The generation AI translates the pet's bark based on the learned bark pattern. For example, if a user records a pet's cry and inputs the cry into the generation AI, the generation AI will analyze the cry and infer what the pet is saying. This makes it easier for users to understand their pet's cry, improving communication with their pet.

[0030] The pet bark translation system includes a collection unit that collects bark information registered by other users. The collection unit collects the bark information registered by other users. The bark information includes, but is not limited to, audio data and text data, for example. The collection unit, for example, automatically collects the bark information registered by other users. The collection unit can also manually collect bark information provided by other users. For example, the collection unit collects audio data uploaded by other users and stores it in a database. By collecting the bark information of other users, the accuracy of the translation is improved. Some or all of the above-described processing in the collection unit may be performed, for example, using AI, or may be performed without using AI.

[0031] The pet bark translation system includes a learning unit that learns the collected information. The learning unit learns the collected information using a generation AI. The generation AI learns the collected data using technologies such as machine learning and deep learning. For example, the learning unit analyzes the collected bark information and situation information to identify the pet bark pattern. The learning unit can also update the generation AI model based on the collected data. For example, the learning unit retrains the generation AI model using newly collected data to improve translation accuracy. In this way, the accuracy of the system is improved by learning the collected information. Some or all of the above-described processing in the learning unit may be performed using AI, for example, or without AI.

[0032] The pet bark translation system includes a providing unit that provides the translation result to the user. The providing unit provides the translation result to the user. The providing unit provides the translation result by, for example, a method such as text display or audio output. For example, the providing unit displays the translation result produced by the generation AI as text. The providing unit can also output the translation result as audio. For example, the providing unit outputs the translation result as audio using speech synthesis technology. In this way, providing the translation result to the user makes it easier for the user to understand the pet's intentions. Some or all of the above-described processing in the providing unit may be performed, for example, using AI, or may be performed without using AI.

[0033] The analysis unit can extract features of the audio waveform. The analysis unit, for example, extracts features of the audio waveform. Features of the audio waveform include, but are not limited to, frequency components and amplitude. The analysis unit, for example, analyzes the frequency components of the audio waveform to extract features. The analysis unit can also analyze the amplitude of the audio waveform to extract features. For example, the analysis unit analyzes the peak values ​​of the audio waveform to extract features. By extracting features of the audio waveform in this way, the accuracy of the analysis is improved. Some or all of the above-described processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI.

[0034] The learning unit can learn the collected data. The learning unit, for example, learns the collected data. The collected data includes, for example, voice data and text data, but is not limited to these examples. The learning unit, for example, analyzes and learns the collected voice data. The learning unit can also analyze and learn the collected text data. For example, the learning unit learns by combining the collected voice data and text data. In this way, the accuracy of the system is improved by learning the collected data. Some or all of the above-mentioned processing in the learning unit may be performed, for example, using AI, or may be performed without using AI.

[0035] The registration unit can analyze the user's past registration history and select the optimal registration method. The registration unit, for example, analyzes the user's past registration history and selects the optimal registration method. The past registration history includes, for example, date and time, content, frequency, etc., but is not limited to these examples. The registration unit, for example, automatically displays situations that the user has frequently registered in the past as candidates. The registration unit can also preferentially suggest registration methods (voice, text, etc.) that the user has used in the past. For example, the registration unit predicts and suggests situations to be used in a specific time period based on the user's past registration history. In this way, by analyzing the past registration history, the optimal registration method can be provided to the user. Some or all of the above-mentioned processing in the registration unit may be performed, for example, using AI or without using AI.

[0036] The registration unit may filter the pet sounds and situations based on the user's current living situation and areas of interest when registering the pet sounds and situations. For example, the registration unit may filter the pet sounds and situations based on the user's current living situation and areas of interest when registering the pet sounds and situations. Examples of living situations include, but are not limited to, a home environment and a work situation. Examples of areas of interest include, but are not limited to, hobbies and topics of interest. For example, if the user is busy, the registration unit may provide a simple registration procedure and omit the need to enter detailed information. Furthermore, if the user is interested in the health of their pet, the registration unit may prioritize displaying health-related situations. For example, if the user is traveling, the registration unit may prioritize displaying travel-related situations. By filtering based on the user's living situation and areas of interest, more relevant information can be registered. Some or all of the above-described processing in the registration unit may be performed using, for example, AI, or may be performed without using AI.

[0037] The registration unit can select the optimal registration means depending on the user's input method when registering the bird cry and the situation. For example, the registration unit selects the optimal registration means depending on the user's input method (voice, text, image, etc.) when registering the bird cry and the situation. Input methods include, but are not limited to, voice input, text input, image input, etc. For example, when a user registers a bird cry using voice, the registration unit automatically converts the voice into text using voice recognition technology. Furthermore, when a user registers a situation using text, the registration unit can provide an input completion function to enable quick input. For example, when a user registers a situation using an image, the registration unit automatically identifies the situation using image recognition technology. This selects the optimal registration means depending on the user's input method, thereby streamlining the registration process. Some or all of the above-described processing in the registration unit may be performed using, for example, AI or without AI.

[0038] The registration unit can prioritize registering highly relevant information in consideration of the user's geographical location information when registering a cry and a situation. For example, the registration unit prioritizes registering highly relevant information in consideration of the user's geographical location information when registering a cry and a situation. Geographical location information includes, but is not limited to, GPS data and address information. For example, when the user is at home, the registration unit prioritizes registering home-related situations. Furthermore, when the user is at a park, the registration unit can also prioritize registering park-related situations. For example, when the user is at a veterinary clinic, the registration unit prioritizes registering health-related situations. In this way, highly relevant information can be prioritized by considering the user's geographical location information. Some or all of the above-described processing in the registration unit may be performed, for example, using AI or without using AI.

[0039] The registration unit can analyze the user's social media activity and register related information when registering the pet's cry and situation. For example, the registration unit analyzes the user's social media activity and registers related information when registering the pet's cry and situation. Social media activity includes, but is not limited to, the content of posts and the number of likes. For example, when a user posts a photo of their pet on social media, the registration unit automatically registers the situation. The registration unit can also analyze the content of the user's social media posts and suggest related situations. For example, the registration unit can suggest related situations based on the activities of the user's friends on social media. This allows related information to be efficiently registered by analyzing the user's social media activity. Some or all of the above-mentioned processing in the registration unit may be performed using, for example, AI, or may be performed without using AI.

[0040] The registration unit can customize the registration method by reflecting the user's past feedback when registering the bird's cry and the situation. For example, the registration unit customizes the registration method by reflecting the user's past feedback when registering the bird's cry and the situation. Past feedback includes, but is not limited to, user ratings and comments. For example, the registration unit suggests an optimal registration procedure based on feedback provided by the user in the past. The registration unit can also preferentially register specific situations based on the user's past feedback. For example, the registration unit analyzes the user's past feedback and customizes the registration interface. This allows the registration method to be optimized by reflecting the user's past feedback. Some or all of the above-described processing in the registration unit may be performed using, for example, AI, or may be performed without using AI.

[0041] The analysis unit can adjust the level of detail of the analysis based on the importance of the call during analysis. For example, the analysis unit adjusts the level of detail of the analysis based on the importance of the call during analysis. The importance of the call includes, for example, frequency and volume, but is not limited to these examples. For example, the analysis unit performs a detailed analysis in the case of an important call. Furthermore, the analysis unit can also perform a concise analysis in the case of a general call. For example, in the case of a call related to a specific situation, an analysis specialized for that situation is performed. In this way, adjusting the level of detail of the analysis based on the importance of the call enables efficient analysis. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI.

[0042] The analysis unit can apply different analysis algorithms depending on the category of the cry during analysis. For example, the analysis unit applies different analysis algorithms depending on the category of the cry during analysis. Categories of cries include, for example, warning sounds and demanding sounds, but are not limited to these examples. For example, the analysis unit applies a specific algorithm to cries requesting food. The analysis unit can also apply a different algorithm to cries requesting play. For example, the analysis unit applies yet another algorithm to cries indicating a health condition. In this way, by applying different analysis algorithms depending on the category of the cry, the accuracy of the analysis is improved. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI.

[0043] The analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results during analysis. For example, the analysis unit improves the accuracy of the analysis by referring to the user's past analysis results during analysis. Past analysis results include, for example, analysis date and time and analysis content, but are not limited to these examples. For example, the analysis unit optimizes the analysis algorithm based on the user's past analysis results. The analysis unit can also learn specific patterns from the user's past analysis results and improve the analysis accuracy. For example, the analysis unit analyzes the user's past analysis results and improves the analysis method. By referring to the user's past analysis results, the accuracy of the analysis is improved. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI or without using AI.

[0044] The analysis unit can determine the priority of analysis based on the time of occurrence of the cries during analysis. For example, the analysis unit determines the priority of analysis based on the time of occurrence of the cries during analysis. The time of occurrence of the cries includes, but is not limited to, for example, the date, time, season, etc. The analysis unit, for example, prioritizes analysis of recent cries. The analysis unit can also prioritize analysis of cries occurring during a specific time period. For example, the analysis unit prioritizes analysis of cries occurring within a period specified by the user. This enables efficient analysis by determining the priority of analysis based on the time of occurrence of the cries. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI.

[0045] The analysis unit can adjust the order of analysis based on the relevance of the cries during analysis. For example, the analysis unit adjusts the order of analysis based on the relevance of the cries during analysis. The relevance of the cries includes, but is not limited to, for example, co-occurrence frequency and semantic relevance. For example, the analysis unit prioritizes analysis of highly relevant cries. The analysis unit can also prioritize analysis of cries related to a specific situation. For example, the analysis unit prioritizes analysis of highly relevant cries specified by the user. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the cries. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI.

[0046] The analysis unit can adjust the use of technical terminology during analysis according to the user's level of expertise. For example, the analysis unit adjusts the use of technical terminology during analysis according to the user's level of expertise. Expertise levels include, but are not limited to, beginner, intermediate, and advanced levels. For example, the analysis unit uses detailed technical terminology when the user has specialized knowledge. Furthermore, the analysis unit can also use concise terminology when the user has general knowledge. For example, if the user is a beginner, it uses easy-to-understand terminology. By adjusting the technical terminology during analysis according to the user's level of expertise, it is possible to provide analysis results that are easier to understand. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI.

[0047] The translation unit can adjust the level of detail of the translation based on the importance of the cry during translation. For example, the translation unit adjusts the level of detail of the translation based on the importance of the cry during translation. The importance of the cry includes, but is not limited to, for example, frequency and volume. For example, the translation unit provides a detailed translation for an important cry. The translation unit can also provide a concise translation for a general cry. For example, for a cry related to a specific situation, the translation unit provides a translation specialized for that situation. This enables efficient translation by adjusting the level of detail of the translation based on the importance of the cry. Some or all of the above-mentioned processing in the translation unit may be performed using, for example, AI, or may be performed without using AI.

[0048] The translation unit can apply different translation algorithms depending on the category of the cry during translation. For example, the translation unit applies different translation algorithms depending on the category of the cry during translation. Categories of cries include, but are not limited to, warning sounds and request sounds. For example, the translation unit applies a specific algorithm to cries that are asking for food. The translation unit can also apply a different algorithm to cries that are asking for play. For example, the translation unit applies yet another algorithm to cries that indicate a health condition. In this way, by applying different translation algorithms depending on the category of the cry, the accuracy of the translation is improved. Some or all of the above-mentioned processing in the translation unit may be performed using, for example, AI, or may be performed without using AI.

[0049] The translation unit can improve the accuracy of translation by referring to the user's past translation results during translation. For example, the translation unit improves the accuracy of translation by referring to the user's past translation results during translation. Past translation results include, but are not limited to, for example, the translation date and time and the translation content. For example, the translation unit optimizes the translation algorithm based on the user's past translation results. The translation unit can also learn specific patterns from the user's past translation results to improve the translation accuracy. For example, the translation unit analyzes the user's past translation results and improves the translation method. In this way, the accuracy of translation is improved by referring to the user's past translation results. Some or all of the above-mentioned processing in the translation unit may be performed, for example, using AI, or may be performed without using AI.

[0050] The translation unit can determine the priority of translation based on the time when the bird cries occurred during translation. For example, the translation unit determines the priority of translation based on the time when the bird cries occurred during translation. The time when the bird cries occurred includes, but is not limited to, for example, the date, time, or season. For example, the translation unit prioritizes translating recent bird cries. The translation unit can also prioritize translating bird cries that occurred during a specific time period. For example, the translation unit prioritizes translating bird cries that occurred within a period specified by the user. This enables efficient translation by determining the priority of translation based on the time when the bird cries occurred. Some or all of the above-described processing in the translation unit may be performed using AI, for example, or without using AI.

[0051] The translation unit can adjust the order of translation based on the relevance of the cries during translation. For example, the translation unit adjusts the order of translation based on the relevance of the cries during translation. The relevance of the cries includes, but is not limited to, co-occurrence frequency and semantic relevance. For example, the translation unit prioritizes translating highly relevant cries. The translation unit can also prioritize translating cries related to a specific situation. For example, the translation unit prioritizes translating highly relevant cries specified by the user. This enables efficient translation by adjusting the order of translation based on the relevance of the cries. Some or all of the above-described processing in the translation unit may be performed using AI, for example, or may be performed without using AI.

[0052] The translation unit can adjust the use of technical terminology in the translation according to the user's level of expertise during translation. For example, the translation unit adjusts the use of technical terminology in the translation according to the user's level of expertise during translation. Expertise levels include, but are not limited to, beginner, intermediate, and advanced. For example, the translation unit uses detailed technical terminology when the user has specialized knowledge. The translation unit can also use concise terminology when the user has general knowledge. For example, if the user is a beginner, it uses easy-to-understand terminology. In this way, by adjusting the technical terminology in the translation according to the user's level of expertise, it is possible to provide a translation result that is easier to understand. Some or all of the above-mentioned processing in the translation unit may be performed using, for example, AI, or may be performed without using AI.

[0053] The collection unit can analyze other users' past collection histories at the time of collection and select the optimal collection method. For example, the collection unit analyzes other users' past collection histories at the time of collection and selects the optimal collection method. Past collection history includes, for example, collection date and time and collection content, but is not limited to such examples. The collection unit proposes the optimal collection method, for example, based on bird call information frequently collected by other users. The collection unit can also predict and propose bird call information to be collected during a specific time period from other users' past collection histories. For example, the collection unit analyzes other users' past collection histories and optimizes the collection method. In this way, the optimal collection method can be provided by analyzing other users' past collection histories. Some or all of the above-described processing in the collection unit may be performed, for example, using AI or without using AI.

[0054] The collection unit may perform filtering based on the other user's current living situation and areas of interest at the time of collection. For example, the collection unit may perform filtering based on the other user's current living situation and areas of interest at the time of collection. Examples of living situations include, but are not limited to, home environment and work situation. Examples of areas of interest include, but are not limited to, hobbies and topics of interest. For example, if the other user is busy, the collection unit may provide a simple collection procedure and omit detailed information input. Furthermore, if the other user is interested in the health of their pet, the collection unit may prioritize collecting health-related bark information. For example, if the other user is traveling, the collection unit may prioritize collecting travel-related bark information. By performing filtering based on the other user's living situation and areas of interest, more relevant information can be collected. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI.

[0055] The collection unit can prioritize collecting highly relevant information by taking into account the geographical location information of other users when collecting information. For example, the collection unit prioritizes collecting highly relevant information by taking into account the geographical location information of other users when collecting information. Geographical location information includes, but is not limited to, GPS data and address information. For example, when another user is at home, the collection unit prioritizes collecting home-related cry information. Furthermore, when another user is at a park, the collection unit can also prioritize collecting park-related cry information. For example, when another user is at a veterinary clinic, the collection unit prioritizes collecting health-related cry information. In this way, highly relevant information can be prioritized by taking into account the geographical location information of other users. Some or all of the above-described processing by the collection unit may be performed using AI, for example, or may be performed without using AI.

[0056] The collection unit can analyze the social media activities of other users and collect related information at the time of collection. For example, the collection unit analyzes the social media activities of other users and collects related information at the time of collection. Social media activities include, but are not limited to, the content of posts and the number of likes. For example, when another user posts a photo of a pet on social media, the collection unit automatically collects the pet's bark information. The collection unit can also analyze the content of other users' social media posts and collect related bark information. For example, the collection unit can collect related bark information by referring to the activities of other users' friends on social media. This allows for efficient collection of related information by analyzing other users' social media activities. Some or all of the above-described processing by the collection unit may be performed, for example, using AI or without AI.

[0057] The learning unit can optimize the learning algorithm by referring to past learning data during learning. For example, the learning unit optimizes the learning algorithm by referring to past learning data during learning. Past learning data includes, for example, learning date and time and learning content, but is not limited to these examples. The learning unit optimizes the learning algorithm based on, for example, past learning data. The learning unit can also learn specific patterns from past learning data to improve learning accuracy. For example, the learning unit analyzes past learning data and improves the learning method. This makes it possible to optimize the learning algorithm by referring to past learning data. Some or all of the above-mentioned processing in the learning unit may be performed, for example, using AI, or may be performed without using AI.

[0058] The learning unit can update the learning data by reflecting user feedback during learning. For example, the learning unit updates the learning data by reflecting user feedback during learning. Feedback includes, for example, user ratings and comments, but is not limited to these examples. The learning unit updates the learning data based on user feedback, for example. The learning unit can also learn specific patterns from user feedback and improve learning accuracy. For example, the learning unit analyzes user feedback and improves the learning method. This makes it possible to update the learning data by reflecting user feedback. Some or all of the above-mentioned processing in the learning unit may be performed, for example, using AI or without using AI.

[0059] The learning unit can weight the learning data during learning based on the time of occurrence of the cry information. For example, the learning unit weights the learning data during learning based on the time of occurrence of the cry information. The time of occurrence includes, but is not limited to, for example, the date, time, or season. For example, the learning unit learns by assigning a higher weight to recent cry information. The learning unit can also learn by assigning a higher weight to cry information generated during a specific time period. For example, the learning unit learns by assigning a higher weight to cry information generated within a period specified by the user. In this way, weighting the learning data based on the time of occurrence of the cry information enables more appropriate learning. Some or all of the above-mentioned processing in the learning unit may be performed using, for example, AI, or may be performed without using AI.

[0060] The learning unit can integrate information from different data sources during learning to expand the learning data. For example, the learning unit integrates information from different data sources during learning to expand the learning data. Data sources include, but are not limited to, audio databases and text databases. For example, the learning unit integrates and learns bird call information collected from other users. The learning unit can also integrate and learn information collected from social media. For example, information collected from public databases is integrated and learned. This makes it possible to expand the learning data by integrating information from different data sources. Some or all of the above-described processing in the learning unit may be performed using, for example, AI, or may be performed without using AI.

[0061] The providing unit can select the optimal display method by referring to the user's past operation history when providing the display. For example, the providing unit selects the optimal display method by referring to the user's past operation history when providing the display. The operation history includes, for example, operation date and time and operation content, but is not limited to such examples. For example, the providing unit suggests the optimal display method based on the user's past operation history. The providing unit can also preferentially provide a specific display method based on the user's past operation history. For example, the providing unit analyzes the user's past operation history and optimizes the display method. In this way, the optimal display method can be provided by referring to the user's past operation history. Some or all of the above-mentioned processing in the providing unit may be performed, for example, using AI or without using AI.

[0062] The providing unit can customize the display content according to the user's current task when providing the information. For example, the providing unit customizes the display content according to the user's current task when providing the information. Tasks include, but are not limited to, work content and work status, for example. For example, when the user is busy, the providing unit provides concise display content. Furthermore, when the user is relaxed, the providing unit can also provide detailed display content. For example, when the user is performing a specific task, the providing unit provides display content related to that task. In this way, by customizing the display content according to the user's current task, more appropriate information can be provided. Some or all of the above-described processing in the providing unit may be performed, for example, using AI or without using AI.

[0063] The providing unit can select the optimal display method by taking into consideration the user's device information when providing the display. For example, the providing unit selects the optimal display method by taking into consideration the user's device information when providing the display. Device information includes, for example, the device type and OS version, but is not limited to these examples. For example, if the user is using a smartphone, the providing unit provides a display method that matches the screen size. Furthermore, if the user is using a tablet, the providing unit can also provide a display method optimized for a large screen. For example, if the user is using a smartwatch, the providing unit provides a simple and highly visible display method. This makes it possible to provide the optimal display method by taking into consideration the user's device information. Some or all of the above-described processing in the providing unit may be performed, for example, using AI or without using AI.

[0064] The providing unit can make the display content multilingual according to the user's language setting when providing the information. For example, the providing unit makes the display content multilingual according to the user's language setting when providing the information. Language settings include, but are not limited to, for example, the user's language selection and the device's language setting. The providing unit automatically sets the display content based on, for example, the user's device's language setting. The providing unit can also provide a language switching function when the user uses multiple languages. For example, if the user selects a specific language, the display content is provided in that language. This makes it possible to provide more appropriate information by making the display content multilingual according to the user's language setting. Some or all of the above-described processing in the providing unit may be performed, for example, using AI or without using AI.

[0065] The providing unit can analyze the user's social media activity and provide related information at the time of providing. For example, the providing unit analyzes the user's social media activity and provides related information at the time of providing. Social media activity includes, for example, but is not limited to, the content of posts and the number of likes. For example, the providing unit provides information about places where the user has checked in on social media. The providing unit can also analyze the content of the user's social media posts and provide information about related tourist spots and stores. For example, the providing unit can provide information about related places and events by referring to the activities of the user's friends on social media. In this way, related information can be efficiently provided by analyzing the user's social media activity. Some or all of the above-described processing in the providing unit may be performed, for example, using AI or without using AI.

[0066] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0067] The pet cry translation system can further include a schedule management unit that manages the user's schedule information. The schedule management unit references the user's calendar or planner and provides that information to the analysis unit. For example, if the user is in a meeting or out, the analysis unit can provide a concise translation result of the pet cry taking that information into consideration. The schedule management unit can also reevaluate the importance of the pet cry based on the user's schedule and notify the user at an appropriate time. This allows for flexible response according to the user's schedule.

[0068] The pet bark translation system can further include a behavioral pattern learning unit that learns the user's past behavioral patterns. The behavioral pattern learning unit collects the user's past behavioral data and provides it to the analysis unit. For example, if the user often plays with their pet during a specific time period, the analysis unit can take that information into consideration when providing translation results for the pet's barks. The behavioral pattern learning unit can also reevaluate the importance of the pet's barks based on the user's behavioral patterns and provide notifications at appropriate times. This allows for flexible responses according to the user's behavioral patterns.

[0069] The pet cry translation system can further include a location information providing unit that provides translation results of pet cries taking into account the user's geographical location information. The location information providing unit acquires the user's current location in real time and provides the data to the analysis unit. For example, if the user is at home, it can provide detailed translation results, and if the user is out, it can provide concise translation results. The location information providing unit can also reevaluate the importance of the pet cry based on the user's geographical location information and notify the user at an appropriate time. This allows for flexible response according to the user's location information.

[0070] The pet cry translation system can further include a social media analysis unit that analyzes the user's social media activity and provides related information. The social media analysis unit analyzes the content of the user's social media posts and the number of likes, and provides the data to the analysis unit. For example, if a user posts a photo of their pet, the situation can be automatically registered and reflected in the translation results. The social media analysis unit can also reevaluate the importance of the pet cry based on the user's social media activity and notify the user at an appropriate time. This allows for flexible responses to the user's social media activity.

[0071] The pet cry translation system can further include a feedback reflection unit that reflects the user's past feedback to customize the translation results. The feedback reflection unit collects the user's past ratings and comments and provides that data to the analysis unit. For example, if a user gives a high rating to a particular translation result, that pattern can be used preferentially. The feedback reflection unit can also reevaluate the importance of the pet cry based on the user's past feedback and notify the user at an appropriate time. This allows for flexible responses to user feedback.

[0072] The pet cry translation system can further include an expertise customization unit that customizes the translation results according to the user's level of expertise. The expertise customization unit evaluates the user's level of expertise and provides the data to the analysis unit. For example, if the user is a beginner, it can provide a concise and easy-to-understand translation result, and if the user is an expert, it can provide a detailed and specialized translation result. The expertise customization unit can also reevaluate the importance of the pet cry based on the user's level of expertise and notify the user at an appropriate time. This allows for flexible response according to the user's level of expertise.

[0073] The pet voice translation system can further include a device information providing unit that provides translation results taking into account the user's device information. The device information providing unit acquires the user's device type and OS version and provides that data to the providing unit. For example, if the user is using a smartphone, it can provide a display method that matches the screen size, and if the user is using a tablet, it can provide a display method optimized for a large screen. The device information providing unit can also reevaluate the importance of the pet voice based on the user's device information and notify the user at an appropriate time. This enables flexible response based on the user's device information.

[0074] The processing flow of the first embodiment will be briefly explained below.

[0075] Step 1: In the registration section, the user registers the pet's cries and situations. For example, the user records the cries of the pet when it begs for food or when it begs to play, and registers each situation. This information is input into the generation AI. Step 2: The analysis unit uses the generation AI to analyze the information registered by the registration unit. The generation AI learns the bark information and situation information registered by the user and identifies the pattern of the pet's bark. For example, if a specific bark means the pet is asking for food, it learns that bark pattern. Step 3: The translation unit uses the generation AI to translate the pet's cry based on the information analyzed by the analysis unit. The generation AI translates the pet's cry based on the learned cry patterns. For example, if a user records their pet's cry and inputs it into the generation AI, the generation AI analyzes the cry and infers what the pet is saying.

[0076] (Example 2) A pet voice translation system according to an embodiment of the present invention is a system in which a user registers a pet's bark and a situation, and a generation AI analyzes the information to translate the pet's bark. In the pet voice translation system, a user registers a pet's bark and a situation, and a generation AI analyzes the information to translate the pet's bark. For example, a user records a pet's bark and registers the situation in which the bark occurred. The generation AI then learns the bark information and situation information registered by the user. The generation AI analyzes the bark information and situation information collected from multiple users and identifies the pet's bark pattern. The generation AI translates the pet's bark based on the learned bark pattern. For example, when a user records a pet's bark and inputs the bark into the generation AI, the generation AI analyzes the bark and infers what the pet is saying. This allows the pet cry translation system to make it easier for users to understand their pet's cry, improving communication with their pet. This allows the pet cry translation system to make it easier for users to understand their pet's cry, improving communication with their pet. For example, by accurately understanding what your pet wants, you can respond appropriately. Furthermore, by utilizing the cry information registered by other users, more accurate translations are possible.

[0077] A pet bark translation system according to an embodiment includes a registration unit, an analysis unit, and a translation unit. The registration unit allows a user to register a pet's bark and a situation. For example, the user may record the pet's bark when begging for food or when begging to play, and register each situation. This information is input to the generation AI. The analysis unit uses the generation AI to analyze the information registered by the registration unit. The generation AI learns the bark information and situation information registered by the user and identifies the pet's bark pattern. For example, if a specific bark means a begging for food, the generation AI learns that bark pattern. The translation unit uses the generation AI to translate the pet's bark based on the information analyzed by the analysis unit. The generation AI translates the pet's bark based on the learned bark pattern. For example, if a user records a pet's cry and inputs the cry into the generation AI, the generation AI will analyze the cry and infer what the pet is saying. This makes it easier for users to understand their pet's cry, improving communication with their pet.

[0078] The pet bark translation system includes a collection unit that collects bark information registered by other users. The collection unit collects the bark information registered by other users. The bark information includes, but is not limited to, audio data and text data, for example. The collection unit, for example, automatically collects the bark information registered by other users. The collection unit can also manually collect bark information provided by other users. For example, the collection unit collects audio data uploaded by other users and stores it in a database. By collecting the bark information of other users, the accuracy of the translation is improved. Some or all of the above-described processing in the collection unit may be performed, for example, using AI, or may be performed without using AI.

[0079] The pet bark translation system includes a learning unit that learns the collected information. The learning unit learns the collected information using a generation AI. The generation AI learns the collected data using technologies such as machine learning and deep learning. For example, the learning unit analyzes the collected bark information and situation information to identify the pet bark pattern. The learning unit can also update the generation AI model based on the collected data. For example, the learning unit retrains the generation AI model using newly collected data to improve translation accuracy. In this way, the accuracy of the system is improved by learning the collected information. Some or all of the above-described processing in the learning unit may be performed using AI, for example, or without AI.

[0080] The pet bark translation system includes a providing unit that provides the translation result to the user. The providing unit provides the translation result to the user. The providing unit provides the translation result by, for example, a method such as text display or audio output. For example, the providing unit displays the translation result produced by the generation AI as text. The providing unit can also output the translation result as audio. For example, the providing unit outputs the translation result as audio using speech synthesis technology. In this way, providing the translation result to the user makes it easier for the user to understand the pet's intentions. Some or all of the above-described processing in the providing unit may be performed, for example, using AI, or may be performed without using AI.

[0081] The analysis unit can extract features of the audio waveform. The analysis unit, for example, extracts features of the audio waveform. Features of the audio waveform include, but are not limited to, frequency components and amplitude. The analysis unit, for example, analyzes the frequency components of the audio waveform to extract features. The analysis unit can also analyze the amplitude of the audio waveform to extract features. For example, the analysis unit analyzes the peak values ​​of the audio waveform to extract features. By extracting features of the audio waveform in this way, the accuracy of the analysis is improved. Some or all of the above-described processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI.

[0082] The learning unit can learn the collected data. The learning unit, for example, learns the collected data. The collected data includes, for example, voice data and text data, but is not limited to these examples. The learning unit, for example, analyzes and learns the collected voice data. The learning unit can also analyze and learn the collected text data. For example, the learning unit learns by combining the collected voice data and text data. In this way, the accuracy of the system is improved by learning the collected data. Some or all of the above-mentioned processing in the learning unit may be performed, for example, using AI, or may be performed without using AI.

[0083] The registration unit can estimate the user's emotions and adjust the timing of registering the cries and situations based on the estimated user emotions. The registration unit, for example, estimates the user's emotions and adjusts the timing of registering the cries and situations based on the estimated user emotions. Examples of user emotions include, but are not limited to, stress and relaxation. For example, when the user is stressed, the registration unit provides a simple interface and minimizes the registration procedure. Furthermore, when the user is relaxed, the registration unit can provide detailed registration options and suggest a customizable registration method. For example, when the user is in a hurry, voice input can be prioritized to quickly register the cries and situations. This allows more appropriate information to be registered by adjusting the registration timing according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the registration unit may be performed using AI, for example, or may be performed without using AI.

[0084] The registration unit can analyze the user's past registration history and select the optimal registration method. The registration unit, for example, analyzes the user's past registration history and selects the optimal registration method. The past registration history includes, for example, date and time, content, frequency, etc., but is not limited to these examples. The registration unit, for example, automatically displays situations that the user has frequently registered in the past as candidates. The registration unit can also preferentially suggest registration methods (voice, text, etc.) that the user has used in the past. For example, the registration unit predicts and suggests situations to be used in a specific time period based on the user's past registration history. In this way, by analyzing the past registration history, the optimal registration method can be provided to the user. Some or all of the above-mentioned processing in the registration unit may be performed, for example, using AI or without using AI.

[0085] The registration unit may filter the pet sounds and situations based on the user's current living situation and areas of interest when registering the pet sounds and situations. For example, the registration unit may filter the pet sounds and situations based on the user's current living situation and areas of interest when registering the pet sounds and situations. Examples of living situations include, but are not limited to, a home environment and a work situation. Examples of areas of interest include, but are not limited to, hobbies and topics of interest. For example, if the user is busy, the registration unit may provide a simple registration procedure and omit the need to enter detailed information. Furthermore, if the user is interested in the health of their pet, the registration unit may prioritize displaying health-related situations. For example, if the user is traveling, the registration unit may prioritize displaying travel-related situations. By filtering based on the user's living situation and areas of interest, more relevant information can be registered. Some or all of the above-described processing in the registration unit may be performed using, for example, AI, or may be performed without using AI.

[0086] The registration unit can select the optimal registration means depending on the user's input method when registering the bird cry and the situation. For example, the registration unit selects the optimal registration means depending on the user's input method (voice, text, image, etc.) when registering the bird cry and the situation. Input methods include, but are not limited to, voice input, text input, image input, etc. For example, when a user registers a bird cry using voice, the registration unit automatically converts the voice into text using voice recognition technology. Furthermore, when a user registers a situation using text, the registration unit can provide an input completion function to enable quick input. For example, when a user registers a situation using an image, the registration unit automatically identifies the situation using image recognition technology. This selects the optimal registration means depending on the user's input method, thereby streamlining the registration process. Some or all of the above-described processing in the registration unit may be performed using, for example, AI or without AI.

[0087] The registration unit can estimate the user's emotions and determine the priority of the cries and situations to be registered based on the estimated user emotions. The registration unit, for example, estimates the user's emotions and determines the priority of the cries and situations to be registered based on the estimated user emotions. Examples of user emotions include, but are not limited to, stress and relaxation. For example, if the user is feeling stressed, the registration unit can prioritize registering important situations. Furthermore, if the user is relaxed, the registration unit can prioritize registering detailed situations. For example, if the user is in a hurry, the registration unit can prioritize registering simple situations. This allows important information to be prioritized by determining the priority according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the registration unit may be performed using, for example, AI, or without AI.

[0088] The registration unit can prioritize registering highly relevant information in consideration of the user's geographical location information when registering a cry and a situation. For example, the registration unit prioritizes registering highly relevant information in consideration of the user's geographical location information when registering a cry and a situation. Geographical location information includes, but is not limited to, GPS data and address information. For example, when the user is at home, the registration unit prioritizes registering home-related situations. Furthermore, when the user is at a park, the registration unit can also prioritize registering park-related situations. For example, when the user is at a veterinary clinic, the registration unit prioritizes registering health-related situations. In this way, highly relevant information can be prioritized by considering the user's geographical location information. Some or all of the above-described processing in the registration unit may be performed, for example, using AI or without using AI.

[0089] The registration unit can analyze the user's social media activity and register related information when registering the pet's cry and situation. For example, the registration unit analyzes the user's social media activity and registers related information when registering the pet's cry and situation. Social media activity includes, but is not limited to, the content of posts and the number of likes. For example, when a user posts a photo of their pet on social media, the registration unit automatically registers the situation. The registration unit can also analyze the content of the user's social media posts and suggest related situations. For example, the registration unit can suggest related situations based on the activities of the user's friends on social media. This allows related information to be efficiently registered by analyzing the user's social media activity. Some or all of the above-mentioned processing in the registration unit may be performed using, for example, AI, or may be performed without using AI.

[0090] The registration unit can customize the registration method by reflecting the user's past feedback when registering the bird's cry and the situation. For example, the registration unit customizes the registration method by reflecting the user's past feedback when registering the bird's cry and the situation. Past feedback includes, but is not limited to, user ratings and comments. For example, the registration unit suggests an optimal registration procedure based on feedback provided by the user in the past. The registration unit can also preferentially register specific situations based on the user's past feedback. For example, the registration unit analyzes the user's past feedback and customizes the registration interface. This allows the registration method to be optimized by reflecting the user's past feedback. Some or all of the above-described processing in the registration unit may be performed using, for example, AI, or may be performed without using AI.

[0091] The analysis unit can estimate the user's emotion and adjust the presentation method of the analysis based on the estimated user's emotion. The analysis unit, for example, estimates the user's emotion and adjusts the presentation method of the analysis based on the estimated user's emotion. User emotions include, but are not limited to, relaxed and rushed. For example, the analysis unit provides detailed analysis results when the user is relaxed. Furthermore, the analysis unit can provide concise analysis results when the user is rushed. For example, the analysis unit can provide visually stimulating analysis results when the user is excited. This allows for adjusting the presentation method of the analysis according to the user's emotion, thereby providing more appropriate analysis results. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or without AI.

[0092] The analysis unit can adjust the level of detail of the analysis based on the importance of the call during analysis. For example, the analysis unit adjusts the level of detail of the analysis based on the importance of the call during analysis. The importance of the call includes, for example, frequency and volume, but is not limited to these examples. For example, the analysis unit performs a detailed analysis in the case of an important call. Furthermore, the analysis unit can also perform a concise analysis in the case of a general call. For example, in the case of a call related to a specific situation, an analysis specialized for that situation is performed. In this way, adjusting the level of detail of the analysis based on the importance of the call enables efficient analysis. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI.

[0093] The analysis unit can apply different analysis algorithms depending on the category of the cry during analysis. For example, the analysis unit applies different analysis algorithms depending on the category of the cry during analysis. Categories of cries include, for example, warning sounds and demanding sounds, but are not limited to these examples. For example, the analysis unit applies a specific algorithm to cries requesting food. The analysis unit can also apply a different algorithm to cries requesting play. For example, the analysis unit applies yet another algorithm to cries indicating a health condition. In this way, by applying different analysis algorithms depending on the category of the cry, the accuracy of the analysis is improved. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI.

[0094] The analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results during analysis. For example, the analysis unit improves the accuracy of the analysis by referring to the user's past analysis results during analysis. Past analysis results include, for example, analysis date and time and analysis content, but are not limited to these examples. For example, the analysis unit optimizes the analysis algorithm based on the user's past analysis results. The analysis unit can also learn specific patterns from the user's past analysis results and improve the analysis accuracy. For example, the analysis unit analyzes the user's past analysis results and improves the analysis method. By referring to the user's past analysis results, the accuracy of the analysis is improved. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI or without using AI.

[0095] The analysis unit can estimate the user's emotion and adjust the length of the analysis based on the estimated user's emotion. The analysis unit, for example, estimates the user's emotion and adjusts the length of the analysis based on the estimated user's emotion. User emotions include, but are not limited to, being in a hurry or being relaxed. For example, if the user is in a hurry, the analysis unit can provide a short and concise analysis result. Furthermore, if the user is relaxed, the analysis unit can provide a detailed analysis result. For example, if the user is excited, the analysis unit can provide a visually stimulating analysis result. By adjusting the length of the analysis according to the user's emotion, more appropriate analysis results can be provided. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the analysis unit may be performed using, for example, an AI, or without an AI.

[0096] The analysis unit can determine the priority of analysis based on the time of occurrence of the cries during analysis. For example, the analysis unit determines the priority of analysis based on the time of occurrence of the cries during analysis. The time of occurrence of the cries includes, but is not limited to, for example, the date, time, season, etc. The analysis unit, for example, prioritizes analysis of recent cries. The analysis unit can also prioritize analysis of cries occurring during a specific time period. For example, the analysis unit prioritizes analysis of cries occurring within a period specified by the user. This enables efficient analysis by determining the priority of analysis based on the time of occurrence of the cries. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI.

[0097] The analysis unit can adjust the order of analysis based on the relevance of the cries during analysis. For example, the analysis unit adjusts the order of analysis based on the relevance of the cries during analysis. The relevance of the cries includes, but is not limited to, for example, co-occurrence frequency and semantic relevance. For example, the analysis unit prioritizes analysis of highly relevant cries. The analysis unit can also prioritize analysis of cries related to a specific situation. For example, the analysis unit prioritizes analysis of highly relevant cries specified by the user. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the cries. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI.

[0098] The analysis unit can adjust the use of technical terminology during analysis according to the user's level of expertise. For example, the analysis unit adjusts the use of technical terminology during analysis according to the user's level of expertise. Expertise levels include, but are not limited to, beginner, intermediate, and advanced levels. For example, the analysis unit uses detailed technical terminology when the user has specialized knowledge. Furthermore, the analysis unit can also use concise terminology when the user has general knowledge. For example, if the user is a beginner, it uses easy-to-understand terminology. By adjusting the technical terminology during analysis according to the user's level of expertise, it is possible to provide analysis results that are easier to understand. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI.

[0099] The translation unit can estimate the user's emotion and adjust the translation expression based on the estimated user's emotion. The translation unit, for example, estimates the user's emotion and adjusts the translation expression based on the estimated user's emotion. User emotions include, but are not limited to, relaxed and rushed. For example, the translation unit provides a detailed translation result when the user is relaxed. The translation unit can also provide a concise translation result when the user is rushed. For example, the translation unit can provide a visually stimulating translation result when the user is excited. This allows the translation expression to be adjusted according to the user's emotion, thereby providing a more appropriate translation result. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the translation unit may be performed using, for example, AI, or without AI.

[0100] The translation unit can adjust the level of detail of the translation based on the importance of the cry during translation. For example, the translation unit adjusts the level of detail of the translation based on the importance of the cry during translation. The importance of the cry includes, but is not limited to, for example, frequency and volume. For example, the translation unit provides a detailed translation for an important cry. The translation unit can also provide a concise translation for a general cry. For example, for a cry related to a specific situation, the translation unit provides a translation specialized for that situation. This enables efficient translation by adjusting the level of detail of the translation based on the importance of the cry. Some or all of the above-mentioned processing in the translation unit may be performed using, for example, AI, or may be performed without using AI.

[0101] The translation unit can apply different translation algorithms depending on the category of the cry during translation. For example, the translation unit applies different translation algorithms depending on the category of the cry during translation. Categories of cries include, but are not limited to, warning sounds and request sounds. For example, the translation unit applies a specific algorithm to cries that are asking for food. The translation unit can also apply a different algorithm to cries that are asking for play. For example, the translation unit applies yet another algorithm to cries that indicate a health condition. In this way, by applying different translation algorithms depending on the category of the cry, the accuracy of the translation is improved. Some or all of the above-mentioned processing in the translation unit may be performed using, for example, AI, or may be performed without using AI.

[0102] The translation unit can improve the accuracy of translation by referring to the user's past translation results during translation. For example, the translation unit improves the accuracy of translation by referring to the user's past translation results during translation. Past translation results include, but are not limited to, for example, the translation date and time and the translation content. For example, the translation unit optimizes the translation algorithm based on the user's past translation results. The translation unit can also learn specific patterns from the user's past translation results to improve the translation accuracy. For example, the translation unit analyzes the user's past translation results and improves the translation method. In this way, the accuracy of translation is improved by referring to the user's past translation results. Some or all of the above-mentioned processing in the translation unit may be performed, for example, using AI, or may be performed without using AI.

[0103] The translation unit can estimate the user's emotion and adjust the length of the translation based on the estimated user's emotion. The translation unit, for example, estimates the user's emotion and adjusts the length of the translation based on the estimated user's emotion. Examples of user emotions include, but are not limited to, being in a hurry or being relaxed. For example, if the user is in a hurry, the translation unit can provide a short and to-the-point translation result. Furthermore, if the user is relaxed, the translation unit can provide a detailed translation result. For example, if the user is excited, the translation unit can provide a visually stimulating translation result. This allows for adjusting the length of the translation according to the user's emotion, thereby providing a more appropriate translation result. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the translation unit may be performed using, for example, AI, or without AI.

[0104] The translation unit can determine the priority of translation based on the time when the bird cries occurred during translation. For example, the translation unit determines the priority of translation based on the time when the bird cries occurred during translation. The time when the bird cries occurred includes, but is not limited to, for example, the date, time, or season. For example, the translation unit prioritizes translating recent bird cries. The translation unit can also prioritize translating bird cries that occurred during a specific time period. For example, the translation unit prioritizes translating bird cries that occurred within a period specified by the user. This enables efficient translation by determining the priority of translation based on the time when the bird cries occurred. Some or all of the above-described processing in the translation unit may be performed using AI, for example, or without using AI.

[0105] The translation unit can adjust the order of translation based on the relevance of the cries during translation. For example, the translation unit adjusts the order of translation based on the relevance of the cries during translation. The relevance of the cries includes, but is not limited to, co-occurrence frequency and semantic relevance. For example, the translation unit prioritizes translating highly relevant cries. The translation unit can also prioritize translating cries related to a specific situation. For example, the translation unit prioritizes translating highly relevant cries specified by the user. This enables efficient translation by adjusting the order of translation based on the relevance of the cries. Some or all of the above-described processing in the translation unit may be performed using AI, for example, or may be performed without using AI.

[0106] The translation unit can adjust the use of technical terminology in the translation according to the user's level of expertise during translation. For example, the translation unit adjusts the use of technical terminology in the translation according to the user's level of expertise during translation. Expertise levels include, but are not limited to, beginner, intermediate, and advanced. For example, the translation unit uses detailed technical terminology when the user has specialized knowledge. The translation unit can also use concise terminology when the user has general knowledge. For example, if the user is a beginner, it uses easy-to-understand terminology. In this way, by adjusting the technical terminology in the translation according to the user's level of expertise, it is possible to provide a translation result that is easier to understand. Some or all of the above-mentioned processing in the translation unit may be performed using, for example, AI, or may be performed without using AI.

[0107] The collection unit can estimate the user's emotions and determine the priority of the cries information to be collected based on the estimated user emotions. The collection unit, for example, estimates the user's emotions and determines the priority of the cries information to be collected based on the estimated user emotions. User emotions include, but are not limited to, stress and relaxation. For example, when the user is stressed, the collection unit prioritizes collecting important cries information. Furthermore, when the user is relaxed, the collection unit can prioritize collecting detailed cries information. For example, when the user is in a hurry, the collection unit prioritizes collecting simple cries information. In this way, by determining the priority of the cries information to be collected according to the user's emotions, important information can be collected preferentially. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI.

[0108] The collection unit can analyze other users' past collection histories at the time of collection and select the optimal collection method. For example, the collection unit analyzes other users' past collection histories at the time of collection and selects the optimal collection method. Past collection history includes, for example, collection date and time and collection content, but is not limited to such examples. The collection unit proposes the optimal collection method, for example, based on bird call information frequently collected by other users. The collection unit can also predict and propose bird call information to be collected during a specific time period from other users' past collection histories. For example, the collection unit analyzes other users' past collection histories and optimizes the collection method. In this way, the optimal collection method can be provided by analyzing other users' past collection histories. Some or all of the above-described processing in the collection unit may be performed, for example, using AI or without using AI.

[0109] The collection unit may perform filtering based on the other user's current living situation and areas of interest at the time of collection. For example, the collection unit may perform filtering based on the other user's current living situation and areas of interest at the time of collection. Examples of living situations include, but are not limited to, home environment and work situation. Examples of areas of interest include, but are not limited to, hobbies and topics of interest. For example, if the other user is busy, the collection unit may provide a simple collection procedure and omit detailed information input. Furthermore, if the other user is interested in the health of their pet, the collection unit may prioritize collecting health-related bark information. For example, if the other user is traveling, the collection unit may prioritize collecting travel-related bark information. By performing filtering based on the other user's living situation and areas of interest, more relevant information can be collected. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI.

[0110] The collection unit can estimate the user's emotions and adjust the display method of the collected bird cry information based on the estimated user emotions. The collection unit, for example, estimates the user's emotions and adjusts the display method of the collected bird cry information based on the estimated user emotions. Examples of user emotions include, but are not limited to, stress and relaxation. For example, when the user is stressed, the collection unit provides a simple display method to reduce visual stress. Furthermore, when the user is relaxed, the collection unit can provide a detailed display method to make the collection process more enjoyable. For example, when the user is in a hurry, the collection unit provides a concise display method to enable quick collection. This allows for more appropriate display by adjusting the display method of the collected bird cry information according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or without AI.

[0111] The collection unit can prioritize collecting highly relevant information by taking into account the geographical location information of other users when collecting information. For example, the collection unit prioritizes collecting highly relevant information by taking into account the geographical location information of other users when collecting information. Geographical location information includes, but is not limited to, GPS data and address information. For example, when another user is at home, the collection unit prioritizes collecting home-related cry information. Furthermore, when another user is at a park, the collection unit can also prioritize collecting park-related cry information. For example, when another user is at a veterinary clinic, the collection unit prioritizes collecting health-related cry information. In this way, highly relevant information can be prioritized by taking into account the geographical location information of other users. Some or all of the above-described processing by the collection unit may be performed using AI, for example, or may be performed without using AI.

[0112] The collection unit can analyze the social media activities of other users and collect related information at the time of collection. For example, the collection unit analyzes the social media activities of other users and collects related information at the time of collection. Social media activities include, but are not limited to, the content of posts and the number of likes. For example, when another user posts a photo of a pet on social media, the collection unit automatically collects the pet's bark information. The collection unit can also analyze the content of other users' social media posts and collect related bark information. For example, the collection unit can collect related bark information by referring to the activities of other users' friends on social media. This allows for efficient collection of related information by analyzing other users' social media activities. Some or all of the above-described processing by the collection unit may be performed, for example, using AI or without AI.

[0113] The learning unit can estimate the user's emotions and select training data based on the estimated user emotions. The learning unit, for example, estimates the user's emotions and selects training data based on the estimated user emotions. User emotions include, but are not limited to, being relaxed or in a hurry. For example, if the user is relaxed, the learning unit selects detailed training data. Furthermore, if the user is in a hurry, the learning unit can select concise training data. For example, if the user is excited, the learning unit selects visually stimulating training data. This selection of training data according to the user's emotions enables more appropriate learning. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the learning unit may be performed using, for example, an AI, or without an AI.

[0114] The learning unit can optimize the learning algorithm by referring to past learning data during learning. For example, the learning unit optimizes the learning algorithm by referring to past learning data during learning. Past learning data includes, for example, learning date and time and learning content, but is not limited to these examples. The learning unit optimizes the learning algorithm based on, for example, past learning data. The learning unit can also learn specific patterns from past learning data to improve learning accuracy. For example, the learning unit analyzes past learning data and improves the learning method. This makes it possible to optimize the learning algorithm by referring to past learning data. Some or all of the above-mentioned processing in the learning unit may be performed, for example, using AI, or may be performed without using AI.

[0115] The learning unit can update the learning data by reflecting user feedback during learning. For example, the learning unit updates the learning data by reflecting user feedback during learning. Feedback includes, for example, user ratings and comments, but is not limited to these examples. The learning unit updates the learning data based on user feedback, for example. The learning unit can also learn specific patterns from user feedback and improve learning accuracy. For example, the learning unit analyzes user feedback and improves the learning method. This makes it possible to update the learning data by reflecting user feedback. Some or all of the above-mentioned processing in the learning unit may be performed, for example, using AI or without using AI.

[0116] The learning unit can estimate the user's emotions and adjust the frequency of learning based on the estimated user emotions. For example, the learning unit estimates the user's emotions and adjusts the frequency of learning based on the estimated user emotions. User emotions include, but are not limited to, relaxed and in a hurry. For example, the learning unit performs learning frequently when the user is relaxed. The learning unit can also reduce the learning frequency when the user is in a hurry. For example, the learning unit adjusts the learning frequency when the user is excited. This enables more appropriate learning by adjusting the learning frequency according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the learning unit may be performed using, for example, an AI, or may be performed without using an AI.

[0117] The learning unit can weight the learning data during learning based on the time of occurrence of the cry information. For example, the learning unit weights the learning data during learning based on the time of occurrence of the cry information. The time of occurrence includes, but is not limited to, for example, the date, time, or season. For example, the learning unit learns by assigning a higher weight to recent cry information. The learning unit can also learn by assigning a higher weight to cry information generated during a specific time period. For example, the learning unit learns by assigning a higher weight to cry information generated within a period specified by the user. In this way, weighting the learning data based on the time of occurrence of the cry information enables more appropriate learning. Some or all of the above-mentioned processing in the learning unit may be performed using, for example, AI, or may be performed without using AI.

[0118] The learning unit can integrate information from different data sources during learning to expand the learning data. For example, the learning unit integrates information from different data sources during learning to expand the learning data. Data sources include, but are not limited to, audio databases and text databases. For example, the learning unit integrates and learns bird call information collected from other users. The learning unit can also integrate and learn information collected from social media. For example, information collected from public databases is integrated and learned. This makes it possible to expand the learning data by integrating information from different data sources. Some or all of the above-described processing in the learning unit may be performed using, for example, AI, or may be performed without using AI.

[0119] The providing unit can estimate the user's emotion and adjust the display method of the translation result based on the estimated user's emotion. For example, the providing unit can estimate the user's emotion and adjust the display method of the translation result based on the estimated user's emotion. User emotions include, but are not limited to, tension and relaxation. For example, if the user is tension, the providing unit can provide a simple, highly visible display method. Furthermore, if the user is relaxed, the providing unit can also provide a display method including detailed information. For example, if the user is in a hurry, the providing unit can provide a display method that focuses on the main points. This allows for a more appropriate display by adjusting the display method of the translation result according to the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or without AI.

[0120] The providing unit can select the optimal display method by referring to the user's past operation history when providing the display. For example, the providing unit selects the optimal display method by referring to the user's past operation history when providing the display. The operation history includes, for example, operation date and time and operation content, but is not limited to such examples. For example, the providing unit suggests the optimal display method based on the user's past operation history. The providing unit can also preferentially provide a specific display method based on the user's past operation history. For example, the providing unit analyzes the user's past operation history and optimizes the display method. In this way, the optimal display method can be provided by referring to the user's past operation history. Some or all of the above-mentioned processing in the providing unit may be performed, for example, using AI or without using AI.

[0121] The providing unit can customize the display content according to the user's current task when providing the information. For example, the providing unit customizes the display content according to the user's current task when providing the information. Tasks include, but are not limited to, work content and work status, for example. For example, when the user is busy, the providing unit provides concise display content. Furthermore, when the user is relaxed, the providing unit can also provide detailed display content. For example, when the user is performing a specific task, the providing unit provides display content related to that task. In this way, by customizing the display content according to the user's current task, more appropriate information can be provided. Some or all of the above-described processing in the providing unit may be performed, for example, using AI or without using AI.

[0122] The providing unit can estimate the user's emotions and adjust the operation procedures of the translation result based on the estimated user's emotions. The providing unit, for example, estimates the user's emotions and adjusts the operation procedures of the translation result based on the estimated user's emotions. User emotions include, but are not limited to, tension and relaxation. For example, if the user is tension, the providing unit provides simple and intuitive operation procedures. Furthermore, if the user is relaxed, the providing unit can also provide detailed operation procedures. For example, if the user is in a hurry, the providing unit provides procedures that allow for quick operation. This allows for more appropriate operation by adjusting the operation procedures of the translation result according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or without AI.

[0123] The providing unit can select the optimal display method by taking into consideration the user's device information when providing the display. For example, the providing unit selects the optimal display method by taking into consideration the user's device information when providing the display. Device information includes, for example, the device type and OS version, but is not limited to these examples. For example, if the user is using a smartphone, the providing unit provides a display method that matches the screen size. Furthermore, if the user is using a tablet, the providing unit can also provide a display method optimized for a large screen. For example, if the user is using a smartwatch, the providing unit provides a simple and highly visible display method. This makes it possible to provide the optimal display method by taking into consideration the user's device information. Some or all of the above-described processing in the providing unit may be performed, for example, using AI or without using AI.

[0124] The providing unit can make the display content multilingual according to the user's language setting when providing the information. For example, the providing unit makes the display content multilingual according to the user's language setting when providing the information. Language settings include, but are not limited to, for example, the user's language selection and the device's language setting. The providing unit automatically sets the display content based on, for example, the user's device's language setting. The providing unit can also provide a language switching function when the user uses multiple languages. For example, if the user selects a specific language, the display content is provided in that language. This makes it possible to provide more appropriate information by making the display content multilingual according to the user's language setting. Some or all of the above-described processing in the providing unit may be performed, for example, using AI or without using AI.

[0125] The providing unit can analyze the user's social media activity and provide related information at the time of providing. For example, the providing unit analyzes the user's social media activity and provides related information at the time of providing. Social media activity includes, for example, but is not limited to, the content of posts and the number of likes. For example, the providing unit provides information about places where the user has checked in on social media. The providing unit can also analyze the content of the user's social media posts and provide information about related tourist spots and stores. For example, the providing unit can provide information about related places and events by referring to the activities of the user's friends on social media. In this way, related information can be efficiently provided by analyzing the user's social media activity. Some or all of the above-described processing in the providing unit may be performed, for example, using AI or without using AI. === Hard Collateral 1-1 === Each of the multiple elements, including the registration unit, analysis unit, translation unit, collection unit, learning unit, and provision unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the registration unit allows a user to register a pet's cry and a situation using the reception device 38 of the smart device 14. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the registered information using a generation AI. The translation unit is realized by the specific processing unit 290 of the data processing device 12 and translates the pet's cry based on the analyzed information. The collection unit collects cry information registered by other users using the communication I / F 44 of the smart device 14. The learning unit is realized by the specific processing unit 290 of the data processing device 12 and learns the collected information. The provision unit provides the translation result to the user using the output device 40 of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements, including the above-mentioned registration unit, analysis unit, translation unit, collection unit, learning unit, and provision unit, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the registration unit allows a user to register a pet's cry and a situation using the microphone 238 of the smart glasses 214. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the registered information using a generation AI. The translation unit is realized by the specific processing unit 290 of the data processing device 12 and translates the pet's cry based on the analyzed information. The collection unit collects cry information registered by other users using the communication I / F 44 of the smart glasses 214. The learning unit is realized by the specific processing unit 290 of the data processing device 12 and learns the collected information. The provision unit provides the translation result to the user using the speaker 240 of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned registration unit, analysis unit, translation unit, collection unit, learning unit, and provision unit is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the registration unit allows a user to register a pet's cry and a situation using the microphone 238 of the headset-type terminal 314. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the registered information using a generation AI. The translation unit is realized by the specific processing unit 290 of the data processing device 12 and translates the pet's cry based on the analyzed information. The collection unit collects cry information registered by other users using the communication I / F 44 of the headset-type terminal 314. The learning unit is realized by the specific processing unit 290 of the data processing device 12 and learns the collected information. The provision unit provides the translation result to the user using the speaker 240 of the headset-type terminal 314. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned registration unit, analysis unit, translation unit, collection unit, learning unit, and provision unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the registration unit allows a user to register a pet's cry and a situation using the microphone 238 of the robot 414. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the registered information using a generation AI. The translation unit is realized by the specific processing unit 290 of the data processing device 12 and translates the pet's cry based on the analyzed information. The collection unit collects cry information registered by other users using the communication I / F 44 of the robot 414. The learning unit is realized by the specific processing unit 290 of the data processing device 12 and learns the collected information. The provision unit provides the translation result to the user using the speaker 240 of the robot 414.

[0126] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0127] The pet voice translation system can further include a health monitoring unit that monitors the user's health condition. The health monitoring unit measures the user's heart rate and stress level and provides the data to the analysis unit. For example, if the user indicates a high stress level, the analysis unit can adjust the translation results of the pet voice taking that information into account. The health monitoring unit can also reevaluate the importance of the pet voice based on the user's health condition and prioritize translation of more urgent voices. This allows for appropriate responses according to the user's health condition.

[0128] The pet cry translation system can further include a schedule management unit that manages the user's schedule information. The schedule management unit references the user's calendar or planner and provides that information to the analysis unit. For example, if the user is in a meeting or out, the analysis unit can provide a concise translation result of the pet cry taking that information into consideration. The schedule management unit can also reevaluate the importance of the pet cry based on the user's schedule and notify the user at an appropriate time. This allows for flexible response according to the user's schedule.

[0129] The pet bark translation system can further include a behavioral pattern learning unit that learns the user's past behavioral patterns. The behavioral pattern learning unit collects the user's past behavioral data and provides it to the analysis unit. For example, if the user often plays with their pet during a specific time period, the analysis unit can take that information into consideration when providing translation results for the pet's barks. The behavioral pattern learning unit can also reevaluate the importance of the pet's barks based on the user's behavioral patterns and provide notifications at appropriate times. This allows for flexible responses according to the user's behavioral patterns.

[0130] The pet cry translation system may further include an emotion customization unit that estimates the user's emotion and customizes the translation results of the pet cry based on the estimated user emotion. The emotion customization unit monitors the user's emotion in real time and provides the data to the analysis unit. For example, if the user is relaxed, it can provide a detailed translation result, and if the user is in a hurry, it can provide a concise translation result. The emotion customization unit can also reevaluate the importance of the pet cry based on the user's emotion and notify the user at an appropriate time. This allows for flexible response according to the user's emotion.

[0131] The pet cry translation system can further include a location information providing unit that provides translation results of pet cries taking into account the user's geographical location information. The location information providing unit acquires the user's current location in real time and provides the data to the analysis unit. For example, if the user is at home, it can provide detailed translation results, and if the user is out, it can provide concise translation results. The location information providing unit can also reevaluate the importance of the pet cry based on the user's geographical location information and notify the user at an appropriate time. This allows for flexible response according to the user's location information.

[0132] The pet cry translation system can further include a social media analysis unit that analyzes the user's social media activity and provides related information. The social media analysis unit analyzes the content of the user's social media posts and the number of likes, and provides the data to the analysis unit. For example, if a user posts a photo of their pet, the situation can be automatically registered and reflected in the translation results. The social media analysis unit can also reevaluate the importance of the pet cry based on the user's social media activity and notify the user at an appropriate time. This allows for flexible responses to the user's social media activity.

[0133] The pet cry translation system can further include a feedback reflection unit that reflects the user's past feedback to customize the translation results. The feedback reflection unit collects the user's past ratings and comments and provides that data to the analysis unit. For example, if a user gives a high rating to a particular translation result, that pattern can be used preferentially. The feedback reflection unit can also reevaluate the importance of the pet cry based on the user's past feedback and notify the user at an appropriate time. This allows for flexible responses to user feedback.

[0134] The pet cry translation system can further include an expertise customization unit that customizes the translation results according to the user's level of expertise. The expertise customization unit evaluates the user's level of expertise and provides the data to the analysis unit. For example, if the user is a beginner, it can provide a concise and easy-to-understand translation result, and if the user is an expert, it can provide a detailed and specialized translation result. The expertise customization unit can also reevaluate the importance of the pet cry based on the user's level of expertise and notify the user at an appropriate time. This allows for flexible response according to the user's level of expertise.

[0135] The pet cry translation system may further include an emotion display adjustment unit that estimates the user's emotion and adjusts the display method of the translation result based on the estimated user emotion. The emotion display adjustment unit monitors the user's emotion in real time and provides the data to the providing unit. For example, if the user is relaxed, a detailed display method can be provided, and if the user is in a hurry, a concise display method can be provided. The emotion display adjustment unit can also reevaluate the importance of the pet cry based on the user's emotion and notify at an appropriate time. This enables flexible response according to the user's emotion.

[0136] The pet voice translation system can further include a device information providing unit that provides translation results taking into account the user's device information. The device information providing unit acquires the user's device type and OS version and provides that data to the providing unit. For example, if the user is using a smartphone, it can provide a display method that matches the screen size, and if the user is using a tablet, it can provide a display method optimized for a large screen. The device information providing unit can also reevaluate the importance of the pet voice based on the user's device information and notify the user at an appropriate time. This enables flexible response based on the user's device information.

[0137] The processing flow of the second embodiment will be briefly explained below.

[0138] Step 1: In the registration section, the user registers the pet's cries and situations. For example, the user records the cries of the pet when it begs for food or when it begs to play, and registers each situation. This information is input into the generation AI. Step 2: The analysis unit uses the generation AI to analyze the information registered by the registration unit. The generation AI learns the bark information and situation information registered by the user and identifies the pattern of the pet's bark. For example, if a specific bark means the pet is asking for food, it learns that bark pattern. Step 3: The translation unit uses the generation AI to translate the pet's cry based on the information analyzed by the analysis unit. The generation AI translates the pet's cry based on the learned cry patterns. For example, if a user records their pet's cry and inputs it into the generation AI, the generation AI analyzes the cry and infers what the pet is saying.

[0139] 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.

[0140] 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 generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. 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 can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0141] 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.

[0142] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0143] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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).

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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 containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. 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 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 can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0157] 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.

[0158] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0159] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0160] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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).

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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 containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. 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 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 can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0173] 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.

[0174] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0175] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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).

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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 containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. 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 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 can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0190] 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.

[0191] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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).

[0196] 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.

[0197] 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."

[0198] 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.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] 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.

[0203] 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.

[0204] The hardware resource that executes the specific process 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 process may be a single processor.

[0205] 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.

[0206] 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.

[0207] 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.

[0208] 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.

[0209] 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.

[0210] [Explanation of symbols]

[0211] 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 registration unit for allowing a user to register the sounds and situations of their pets; an analysis unit that analyzes the information registered by the registration unit; a translation unit that translates the pet's cry based on the information analyzed by the analysis unit; Equipped with A system characterized by:

2. A collection unit is provided to collect bird call information registered by other users.

2. The system of claim 1.

3. Equipped with a learning unit that learns from collected information 3. The system of claim 2.

4. A providing unit is provided to provide the translation results to the user.

2. The system of claim 1.

5. The analysis unit Extracting features from speech waveforms 2. The system of claim 1.

6. The learning unit Learning from collected data 4. The system of claim 3.

7. The registration unit Estimate the user's emotions and adjust the timing of recording sounds and situations based on the estimated user emotions.

2. The system of claim 1.

8. The registration unit Analyze users' past registration history and select the optimal registration method 2. The system of claim 1.

9. The registration unit When registering sounds and situations, filtering is performed based on the user's current living situation and areas of interest.

2. The system of claim 1.

Images