System

Abstract

An object of the system according to the embodiment is to accurately understand the feeling and words of the cat and convey them to the owner.SOLUTION: A system according to an embodiment includes an information collection unit, a decoder, an estimation unit, and a display unit. The information collection unit collects information on the cat's voice, movement, smell, and body temperature. The decoder decodes the information collected by the information collector. The estimation unit estimates feelings and words of the cat based on the information verbalized by the verbalization unit. The display part displays the information estimated by the estimation part on the smartphone of the owner.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] Conventional technology has had the problem of making it difficult to accurately understand a cat's feelings and words.

[0005] The system according to the embodiment aims to accurately understand a cat's feelings and words and convey them to its owner. [Means for solving the problem]

[0006] The system according to the embodiment includes an information collection unit, a verbalization unit, an estimation unit, and a display unit. The information collection unit collects information on the cat's voice, movements, smell, and body temperature. The verbalization unit verbalizes the information collected by the information collection unit. The estimation unit estimates the cat's feelings and words based on the information verbalized by the verbalization unit. The display unit displays the information estimated by the estimation unit on the owner's smartphone. [Effects of the Invention]

[0007] The system according to the embodiment can accurately understand the feelings and words of cats and convey them to their owners. [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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[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 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[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 system according to an embodiment of the present invention uses a collar-type IoT device to collect information such as a cat's voice, movements, smell, and body temperature, and verbalizes the collected information. This system uses a generative AI to learn the verbalized information, inferring the cat's feelings and words and displaying them on the owner's smartphone. This allows the system to help owners better understand their cat's feelings.

[0029] The system according to the embodiment includes an information collection unit, a verbalization unit, an estimation unit, and a display unit. The information collection unit collects information on the cat's voice, movements, smell, and body temperature. For example, when a cat meows, the information collection unit uses a sensor to detect the sound and record it as data. The information collection unit also measures the cat's speed using an acceleration sensor and GPS, determining that 0 km / h indicates sitting, 3 km / h indicates walking slowly, 5 km / h indicates walking, and 8 km / h indicates running. The information collection unit also measures the smell using an odor sensor and classifies the smell as 0 = no odor, 1 = slight odor, 2 = odor, and 3 = very odor. Regarding body temperature, the temperature sensor determines that 37 degrees Celsius is low, 38 degrees Celsius is normal, 39 degrees Celsius is a slight fever, and 40 degrees Celsius is a high fever. The verbalization unit verbalizes the information collected by the information collection unit. For example, the verbalization unit verbalizes "Good morning, in a good mood" based on the information on the voice "meow," the movement of "0 km / h (sitting)," the smell of "0 (odorless)," and the body temperature of "38 degrees (normal temperature)." The inference unit infers the cat's feelings and words based on the information verbalized by the verbalization unit. For example, the inference unit infers "The litter box stinks! Clean it up!" based on the information on the voice "meow," the movement of "3 km / h (walking slowly)," the smell of "2 (smelly)," and the body temperature of "38 degrees (normal temperature)." The display unit displays the information inferred by the inference unit on the owner's smartphone. For example, the display unit displays "Good morning, in a good mood" or "The litter box stinks! Clean it up!" on the owner's smartphone. In this way, the system according to the embodiment can make it easier for owners to understand their cat's feelings.

[0030] The information collection unit can analyze the frequency spectrum of a cat's voice and estimate its emotions based on specific frequency patterns. The information collection unit, for example, collects cat voices and analyzes their frequency spectra. For example, when a cat meows, the frequency components of the voice are analyzed in detail to extract specific frequency patterns. This provides basic data for estimating the cat's emotions. This allows for more accurate estimation of the cat's emotions.

[0031] In addition to collecting data on the cat's movements, the information collection unit can also analyze its posture using image recognition technology to collect more detailed behavioral patterns. For example, the information collection unit is equipped with a camera in a collar-type device and analyzes the cat's posture using image recognition technology. For example, it can detect the cat's posture, such as standing, lying down, or sitting, in real time and record the behavioral patterns in detail. This allows for more detailed collection of the cat's behavioral patterns.

[0032] The information collection unit can use the emotion estimation function to collect emotions estimated from the cat's voice and movements in real time and reflect them in the data. The information collection unit, for example, uses the emotion estimation function to build a system that collects emotions estimated from the cat's voice and movements in real time. For example, when a cat meows, the characteristics of the voice are analyzed, and the emotion of joy is estimated and reflected in the data. In this way, the cat's emotions can be collected in real time and reflected in the data.

[0033] The information collection unit is equipped with a camera in the collar-type device, and collects video from the cat's perspective, which can be used as background information for the cat's behavior. The information collection unit is, for example, equipped with a camera in the collar-type device, and collects video from the cat's perspective. For example, it records video from the cat's perspective while it is walking, and analyzes background information for the cat's behavior based on that video. In this way, video from the cat's perspective can be collected and used as background information for the cat's behavior.

[0034] The information collection unit develops a general-purpose IoT device that can be applied to other pets, and can collect behavioral data of different animals. For example, the information collection unit develops a collar-type IoT device as a general-purpose device that can be applied to other pets. For example, it collects behavioral data of dogs and birds and analyzes the behavioral patterns of each animal. This makes it possible to collect behavioral data of different animals.

[0035] The information collection unit can use the emotion estimation function to collect environmental factors when a cat behaves in a specific way and use the collected information as background information for the behavior. For example, the information collection unit uses the emotion estimation function to build a system that collects environmental factors when a cat behaves in a specific way. For example, the temperature and humidity when a cat meows can be recorded and analyzed as background information for the behavior. This allows background information for the cat's behavior to be collected and analyzed.

[0036] The verbalization unit uses data on cat voices and movements to allow the generation AI to learn the cat's personality and character, enabling more personalized verbalization. For example, the verbalization unit collects data on cat voices and movements, and builds a system in which the generation AI learns the cat's personality and character. For example, it analyzes the frequency with which a cat meows and its movement patterns, and generates verbalization that reflects the cat's personality and character. This makes it possible to generate verbalization that reflects the cat's personality and character.

[0037] When verbalizing, the verbalization unit can refer to the cat's past behavioral history and generate appropriate words according to the context. The verbalization unit, for example, builds a system that refers to the cat's past behavioral history and generates appropriate words according to the context. For example, the verbalization unit analyzes the cat's past behavioral history when it meows and generates appropriate words. This makes it possible to generate words according to the context by referring to the cat's past behavioral history.

[0038] The verbalization unit uses the emotion estimation function to perform verbalization based on the cat's emotion, and can reflect the intensity and type of emotion. The verbalization unit, for example, uses the emotion estimation function to build a system that performs verbalization based on the cat's emotion. For example, the emotion when a cat meows "meows" is estimated, and words that reflect the intensity and type of that emotion are generated. This makes it possible to perform verbalization that reflects the intensity and type of the cat's emotion.

[0039] The language generation unit can make the language-generated information multilingual, making it possible to accommodate owners who speak different languages. The language generation unit, for example, builds a system that makes the language-generated information multilingual. For example, it translates words generated based on data on cat voices and movements into multiple languages, such as English, French, and Chinese. This makes it possible to accommodate owners who speak different languages.

[0040] The verbalization unit can output the verbalized information by voice using voice synthesis technology, allowing the owner to understand the cat's feelings through the voice. The verbalization unit, for example, builds a system that outputs the verbalized information by voice using voice synthesis technology. For example, it can output the words that a cat says when it meows, allowing the owner to understand the cat's feelings through the voice. This allows the owner to understand the cat's feelings through the voice.

[0041] The verbalization unit uses the emotion estimation function to perform storytelling based on the cat's emotions, and can convey the cat's daily events to the owner in a narrative format. The verbalization unit, for example, uses the emotion estimation function to build a system that performs storytelling based on the cat's emotions. For example, it estimates the emotion a cat feels when it meows, and tells the cat's daily events in a narrative format based on that emotion. This makes it possible to convey the cat's daily events to the owner in a narrative format.

[0042] The prediction unit allows the generation AI to refer to the owner's past evaluation data to improve the accuracy of predictions when predicting the cat's feelings and words. For example, the prediction unit builds a system in which the generation AI refers to the owner's past evaluation data to improve the accuracy of predictions of the cat's feelings and words. For example, based on data in which the owner responds to the sound "meow" with "good morning," the prediction accuracy is improved from the next time onwards. This makes it possible to improve the accuracy of predictions by referring to the owner's past evaluation data.

[0043] The inference unit allows the generation AI to suggest specific actions to the owner based on the inferred feelings and words. For example, the inference unit will build a system in which the generation AI suggests specific actions to the owner based on the inferred feelings and words of a cat. For example, when a cat meows, it infers that this means "good morning" and suggests an action such as "feed the cat" to the owner. This allows the owner to be advised of specific actions, improving communication with their cat.

[0044] The estimation unit uses the emotion estimation function to collect the owner's emotional responses in real time when estimating the cat's feelings and words, thereby improving the accuracy of the estimation. For example, the estimation unit uses the emotion estimation function to build a system that collects the owner's emotional responses in real time when estimating the cat's feelings and words. For example, if the owner shows an emotion of joy in response to the sound of a "meow," the accuracy of the estimation is improved based on that data. This allows the owner's emotional responses to be collected in real time, improving the accuracy of the estimation.

[0045] The estimation unit can link with smart home devices and notify the owner of the inferred feelings and words by voice. For example, the estimation unit can link with smart home devices and build a system that notifies the owner of the inferred feelings and words of a cat by voice. For example, when a cat meows "meow," it can infer that the meaning is "good morning" and notify the owner by voice via a smart speaker. This allows the system to link with smart home devices and notify the owner by voice.

[0046] The prediction unit can share the predicted feelings and words on social media or a dedicated app, allowing for information exchange with other pet owners. The prediction unit can, for example, build a system that shares the predicted feelings and words of a cat on social media or a dedicated app. For example, when a cat meows "meow," it can predict that the message is "good morning," share that information on social media, and exchange information with other pet owners. This allows for information exchange with other pet owners on social media or a dedicated app.

[0047] The estimation unit uses the emotion estimation function to collect the owner's emotional reactions to the inferred feelings and words, and the generation AI can learn from these reactions and reflect them in future estimations. For example, the estimation unit uses the emotion estimation function to build a system that collects the owner's emotional reactions to the inferred feelings and words of a cat. For example, if the owner expresses joy in response to the sound of a "meow," the generation AI can learn from this data and reflect it in future estimations. This makes it possible to collect the owner's emotional reactions and reflect them in future estimations.

[0048] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0049] The information collection unit can add a function to measure the cat's weight. For example, a weight sensor could be installed in a collar-type device to measure the cat's weight when it sits in a specific location. This would allow the cat's weight changes to be monitored and used to help manage its health. The weight data could also be used to provide advice on adjusting the amount of food and exercise the cat receives.

[0050] The information collection unit can be equipped with a function to measure the cat's heart rate. For example, a collar-type device could be equipped with a heart rate sensor to measure the cat's heart rate in real time. This would allow the cat's stress level and health to be monitored. Furthermore, the heart rate data could be used to estimate the cat's state of relaxation or excitement and notify the owner.

[0051] The information collection unit can be equipped with a function to monitor the cat's food and water intake. For example, a collar-type device could be equipped with a food sensor and a water sensor to collect data on the cat's food and water intake. This allows the cat's food and water intake to be monitored and used for health management. It can also notify the owner if an abnormal intake is detected.

[0052] The information collection unit can be equipped with a function to monitor a cat's sleep patterns. For example, a sleep sensor could be installed in the collar-type device to measure the amount of sleep and quality of the cat's sleep. This would allow monitoring of the cat's sleep patterns and help with health management. It would also be possible to estimate the cat's stress level and health condition based on the sleep data and notify the owner.

[0053] The information collection unit can collect environmental sounds around the cat and analyze factors that influence the cat's behavior. For example, a microphone can be installed in the collar-type device to collect environmental sounds around the cat. This allows the cat's reaction to specific sounds to be analyzed and notified to the owner. The environmental sound data can also be used to estimate the cat's stress level and relaxation state.

[0054] The processing flow of the first embodiment will be briefly explained below.

[0055] Step 1: The information gathering unit collects information on the cat's voice, movements, smell, and body temperature. For example, if a cat meows, the sensor detects the sound and records it as data. It also uses an acceleration sensor and GPS to measure the cat's movements and determines whether it is sitting, walking, running, etc. based on its speed. It uses an odor sensor to measure the smell and classify it from odorless to very odorous. It uses a body temperature sensor to measure body temperature and classify it from low to high fever. Step 2: The verbalization unit verbalizes the information collected by the information collection unit. For example, based on the information about the sound "meow," the movement "0km / h (sitting)," the smell "0 (odorless)," and the body temperature "38 degrees (normal temperature)," it verbalizes "I'm in a good mood, good morning." Step 3: The inference unit infers the cat's feelings and words based on the information verbalized by the verbalization unit. For example, based on the sound of "meow," the movement of "3km / h (walking slowly)," the smell of "2 (smelly)," and the body temperature of "38 degrees (normal temperature)," the inference unit infers that "The litter box stinks! It's angry and wants you to clean it!" Step 4: The display unit displays the information inferred by the inference unit on the owner's smartphone. For example, it displays "Good morning, in a good mood" or "The litter box stinks! Clean it!"

[0056] (Example 2) A system according to an embodiment of the present invention uses a collar-type IoT device to collect information such as a cat's voice, movements, smell, and body temperature, and verbalizes the collected information. This system uses a generative AI to learn the verbalized information, inferring the cat's feelings and words and displaying them on the owner's smartphone. This allows the system to help owners better understand their cat's feelings.

[0057] The system according to the embodiment includes an information collection unit, a verbalization unit, an estimation unit, and a display unit. The information collection unit collects information on the cat's voice, movements, smell, and body temperature. For example, when a cat meows, the information collection unit uses a sensor to detect the sound and record it as data. The information collection unit also measures the cat's speed using an acceleration sensor and GPS, determining that 0 km / h indicates sitting, 3 km / h indicates walking slowly, 5 km / h indicates walking, and 8 km / h indicates running. The information collection unit also measures the smell using an odor sensor and classifies the smell as 0 = no odor, 1 = slight odor, 2 = odor, and 3 = very odor. Regarding body temperature, the temperature sensor determines that 37 degrees Celsius is low, 38 degrees Celsius is normal, 39 degrees Celsius is a slight fever, and 40 degrees Celsius is a high fever. The verbalization unit verbalizes the information collected by the information collection unit. For example, the verbalization unit verbalizes "Good morning, in a good mood" based on the information on the voice "meow," the movement of "0 km / h (sitting)," the smell of "0 (odorless)," and the body temperature of "38 degrees (normal temperature)." The inference unit infers the cat's feelings and words based on the information verbalized by the verbalization unit. For example, the inference unit infers "The litter box stinks! Clean it up!" based on the information on the voice "meow," the movement of "3 km / h (walking slowly)," the smell of "2 (smelly)," and the body temperature of "38 degrees (normal temperature)." The display unit displays the information inferred by the inference unit on the owner's smartphone. For example, the display unit displays "Good morning, in a good mood" or "The litter box stinks! Clean it up!" on the owner's smartphone. In this way, the system according to the embodiment can make it easier for owners to understand their cat's feelings.

[0058] The information collection unit can analyze the frequency spectrum of a cat's voice and estimate its emotions based on specific frequency patterns. The information collection unit, for example, collects cat voices and analyzes their frequency spectra. For example, when a cat meows, the frequency components of the voice are analyzed in detail to extract specific frequency patterns. This provides basic data for estimating the cat's emotions. This allows for more accurate estimation of the cat's emotions.

[0059] In addition to collecting data on the cat's movements, the information collection unit can also analyze its posture using image recognition technology to collect more detailed behavioral patterns. For example, the information collection unit is equipped with a camera in a collar-type device and analyzes the cat's posture using image recognition technology. For example, it can detect the cat's posture, such as standing, lying down, or sitting, in real time and record the behavioral patterns in detail. This allows for more detailed collection of the cat's behavioral patterns.

[0060] The information collection unit can use the emotion estimation function to collect emotions estimated from the cat's voice and movements in real time and reflect them in the data. The information collection unit, for example, uses the emotion estimation function to build a system that collects emotions estimated from the cat's voice and movements in real time. For example, when a cat meows, the characteristics of the voice are analyzed, and the emotion of joy is estimated and reflected in the data. In this way, the cat's emotions can be collected in real time and reflected in the data.

[0061] The information collection unit is equipped with a camera in the collar-type device, and collects video from the cat's perspective, which can be used as background information for the cat's behavior. The information collection unit is, for example, equipped with a camera in the collar-type device, and collects video from the cat's perspective. For example, it records video from the cat's perspective while it is walking, and analyzes background information for the cat's behavior based on that video. In this way, video from the cat's perspective can be collected and used as background information for the cat's behavior.

[0062] The information collection unit develops a general-purpose IoT device that can be applied to other pets, and can collect behavioral data of different animals. For example, the information collection unit develops a collar-type IoT device as a general-purpose device that can be applied to other pets. For example, it collects behavioral data of dogs and birds and analyzes the behavioral patterns of each animal. This makes it possible to collect behavioral data of different animals.

[0063] The information collection unit can use the emotion estimation function to collect environmental factors when a cat behaves in a specific way and use the collected information as background information for the behavior. For example, the information collection unit uses the emotion estimation function to build a system that collects environmental factors when a cat behaves in a specific way. For example, the temperature and humidity when a cat meows can be recorded and analyzed as background information for the behavior. This allows background information for the cat's behavior to be collected and analyzed.

[0064] The verbalization unit uses data on cat voices and movements to allow the generation AI to learn the cat's personality and character, enabling more personalized verbalization. For example, the verbalization unit collects data on cat voices and movements, and builds a system in which the generation AI learns the cat's personality and character. For example, it analyzes the frequency with which a cat meows and its movement patterns, and generates verbalization that reflects the cat's personality and character. This makes it possible to generate verbalization that reflects the cat's personality and character.

[0065] When verbalizing, the verbalization unit can refer to the cat's past behavioral history and generate appropriate words according to the context. The verbalization unit, for example, builds a system that refers to the cat's past behavioral history and generates appropriate words according to the context. For example, the verbalization unit analyzes the cat's past behavioral history when it meows and generates appropriate words. This makes it possible to generate words according to the context by referring to the cat's past behavioral history.

[0066] The verbalization unit uses the emotion estimation function to perform verbalization based on the cat's emotion, and can reflect the intensity and type of emotion. The verbalization unit, for example, uses the emotion estimation function to build a system that performs verbalization based on the cat's emotion. For example, the emotion when a cat meows "meows" is estimated, and words that reflect the intensity and type of that emotion are generated. This makes it possible to perform verbalization that reflects the intensity and type of the cat's emotion.

[0067] The language generation unit can make the language-generated information multilingual, making it possible to accommodate owners who speak different languages. The language generation unit, for example, builds a system that makes the language-generated information multilingual. For example, it translates words generated based on data on cat voices and movements into multiple languages, such as English, French, and Chinese. This makes it possible to accommodate owners who speak different languages.

[0068] The verbalization unit can output the verbalized information by voice using voice synthesis technology, allowing the owner to understand the cat's feelings through the voice. The verbalization unit, for example, builds a system that outputs the verbalized information by voice using voice synthesis technology. For example, it can output the words that a cat says when it meows, allowing the owner to understand the cat's feelings through the voice. This allows the owner to understand the cat's feelings through the voice.

[0069] The verbalization unit uses the emotion estimation function to perform storytelling based on the cat's emotions, and can convey the cat's daily events to the owner in a narrative format. The verbalization unit, for example, uses the emotion estimation function to build a system that performs storytelling based on the cat's emotions. For example, it estimates the emotion a cat feels when it meows, and tells the cat's daily events in a narrative format based on that emotion. This makes it possible to convey the cat's daily events to the owner in a narrative format.

[0070] The prediction unit allows the generation AI to refer to the owner's past evaluation data to improve the accuracy of predictions when predicting the cat's feelings and words. For example, the prediction unit builds a system in which the generation AI refers to the owner's past evaluation data to improve the accuracy of predictions of the cat's feelings and words. For example, based on data in which the owner responds to the sound "meow" with "good morning," the prediction accuracy is improved from the next time onwards. This makes it possible to improve the accuracy of predictions by referring to the owner's past evaluation data.

[0071] The inference unit allows the generation AI to suggest specific actions to the owner based on the inferred feelings and words. For example, the inference unit will build a system in which the generation AI suggests specific actions to the owner based on the inferred feelings and words of a cat. For example, when a cat meows, it infers that this means "good morning" and suggests an action such as "feed the cat" to the owner. This allows the owner to be advised of specific actions, improving communication with their cat.

[0072] The estimation unit uses the emotion estimation function to collect the owner's emotional responses in real time when estimating the cat's feelings and words, thereby improving the accuracy of the estimation. For example, the estimation unit uses the emotion estimation function to build a system that collects the owner's emotional responses in real time when estimating the cat's feelings and words. For example, if the owner shows an emotion of joy in response to the sound of a "meow," the accuracy of the estimation is improved based on that data. This allows the owner's emotional responses to be collected in real time, improving the accuracy of the estimation.

[0073] The estimation unit can link with smart home devices and notify the owner of the inferred feelings and words by voice. For example, the estimation unit can link with smart home devices and build a system that notifies the owner of the inferred feelings and words of a cat by voice. For example, when a cat meows "meow," it can infer that the meaning is "good morning" and notify the owner by voice via a smart speaker. This allows the system to link with smart home devices and notify the owner by voice.

[0074] The prediction unit can share the predicted feelings and words on social media or a dedicated app, allowing for information exchange with other pet owners. The prediction unit can, for example, build a system that shares the predicted feelings and words of a cat on social media or a dedicated app. For example, when a cat meows "meow," it can predict that the message is "good morning," share that information on social media, and exchange information with other pet owners. This allows for information exchange with other pet owners on social media or a dedicated app.

[0075] The estimation unit uses the emotion estimation function to collect the owner's emotional reactions to the inferred feelings and words, and the generation AI can learn from these reactions and reflect them in future estimations. For example, the estimation unit uses the emotion estimation function to build a system that collects the owner's emotional reactions to the inferred feelings and words of a cat. For example, if the owner expresses joy in response to the sound of a "meow," the generation AI can learn from this data and reflect it in future estimations. This makes it possible to collect the owner's emotional reactions and reflect them in future estimations.

[0076] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0077] The information collection unit can add a function to measure the cat's weight. For example, a weight sensor could be installed in a collar-type device to measure the cat's weight when it sits in a specific location. This would allow the cat's weight changes to be monitored and used to help manage its health. The weight data could also be used to provide advice on adjusting the amount of food and exercise the cat receives.

[0078] The information collection unit can be equipped with a function to measure the cat's heart rate. For example, a collar-type device could be equipped with a heart rate sensor to measure the cat's heart rate in real time. This would allow the cat's stress level and health to be monitored. Furthermore, the heart rate data could be used to estimate the cat's state of relaxation or excitement and notify the owner.

[0079] The information collection unit can be equipped with a function to monitor the cat's food and water intake. For example, a collar-type device could be equipped with a food sensor and a water sensor to collect data on the cat's food and water intake. This allows the cat's food and water intake to be monitored and used for health management. It can also notify the owner if an abnormal intake is detected.

[0080] The information collection unit can be equipped with a function to monitor a cat's sleep patterns. For example, a sleep sensor could be installed in the collar-type device to measure the amount of sleep and quality of the cat's sleep. This would allow monitoring of the cat's sleep patterns and help with health management. It would also be possible to estimate the cat's stress level and health condition based on the sleep data and notify the owner.

[0081] The information collection unit can collect environmental sounds around the cat and analyze factors that influence the cat's behavior. For example, a microphone can be installed in the collar-type device to collect environmental sounds around the cat. This allows the cat's reaction to specific sounds to be analyzed and notified to the owner. The environmental sound data can also be used to estimate the cat's stress level and relaxation state.

[0082] The information collection unit uses the emotion estimation function to collect emotions inferred from the cat's voice and movements in real time and reflect them in the data. For example, if a cat meows, the characteristics of its voice are analyzed, and the emotion of joy is estimated and reflected in the data. This allows the cat's emotions to be collected in real time and reflected in the data.

[0083] The information collection unit uses the emotion estimation function to collect environmental factors that cause a cat to behave in a specific way, and can use this information as background information for that behavior. For example, the temperature and humidity when a cat meows can be recorded and analyzed as background information for the behavior. This allows background information on a cat's behavior to be collected and analyzed.

[0084] The verbalization unit uses the emotion estimation function to generate verbalization based on the cat's emotions, reflecting the intensity and type of emotion. For example, it estimates the emotion a cat feels when it meows and generates words that reflect the intensity and type of emotion. This makes it possible to generate verbalization that reflects the intensity and type of the cat's emotions.

[0085] The language generation unit uses the emotion estimation function to perform storytelling based on the cat's emotions, and can convey the cat's daily events to the owner in a narrative format. For example, it can estimate the emotion a cat feels when it meows, and convey the cat's daily events in a narrative format based on that emotion. This allows the cat's daily events to be conveyed to the owner in a narrative format.

[0086] The estimation unit uses the emotion estimation function to collect the owner's emotional response in real time when estimating the cat's feelings and words, thereby improving the accuracy of the estimation. For example, if the owner shows joy in response to the sound of a "meow," the accuracy of the estimation is improved based on that data. This allows the owner's emotional response to be collected in real time, improving the accuracy of the estimation.

[0087] The processing flow of the second embodiment will be briefly explained below.

[0088] Step 1: The information gathering unit collects information on the cat's voice, movements, smell, and body temperature. For example, if a cat meows, the sensor detects the sound and records it as data. It also uses an acceleration sensor and GPS to measure the cat's movements and determines whether it is sitting, walking, running, etc. based on its speed. It uses an odor sensor to measure the smell and classify it from odorless to very odorous. It uses a body temperature sensor to measure body temperature and classify it from low to high fever. Step 2: The verbalization unit verbalizes the information collected by the information collection unit. For example, based on the information about the sound "meow," the movement "0km / h (sitting)," the smell "0 (odorless)," and the body temperature "38 degrees (normal temperature)," it verbalizes "I'm in a good mood, good morning." Step 3: The inference unit infers the cat's feelings and words based on the information verbalized by the verbalization unit. For example, based on the sound of "meow," the movement of "3km / h (walking slowly)," the smell of "2 (smelly)," and the body temperature of "38 degrees (normal temperature)," the inference unit infers that "The litter box stinks! It's angry and wants you to clean it!" Step 4: The display unit displays the information inferred by the inference unit on the owner's smartphone. For example, it displays "Good morning, in a good mood" or "The litter box stinks! Clean it!"

[0089] 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.

[0090] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> 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.

[0091] 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.

[0092] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0093] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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).

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0102] 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. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0108] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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).

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0117] 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 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0123] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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).

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0133] In the robot 414, the processor 46 performs the identification process. 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. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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).

[0142] 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.

[0143] 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."

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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. [Explanation of symbols]

[0156] 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. The information gathering section gathers information on cats' voices, movements, smells, and body temperatures. a verbalization unit that verbalizes the information collected by the information collection unit; an inference unit that infers the cat's feelings and words based on the information verbalized by the verbalization unit; a display unit that displays the information estimated by the estimation unit on the owner's smartphone. A system characterized by:

2. The information collecting unit A camera is attached to the collar-type device to collect images from the cat's point of view and use them as background information on its behavior.

2. The system of claim 1.

3. The verbalization unit Using data on the cat's voice and movements, the generation AI learns the cat's personality and character and produces more personalized verbalization.

2. The system of claim 1.

4. The estimation unit In the estimation of the feelings and words of the cat, the generation AI refers to the owner's past evaluation data and improves the accuracy of the estimation.

2. The system of claim 1.

5. The verbalization unit The cat's verbalization is based on its emotions and reflects the intensity and type of the emotions.

2. The system of claim 1.

Images