KR20230148936A - System and method for improving cognitive ability for pets - Google Patents

Abstract

A cognitive ability development system for a companion animal includes: a stimulus providing module that provides one or more visual, auditory, tactile, and olfactory stimuli to the companion animal based on a space defined by a tag in a house; a data acquisition module configured to collect learning data of the companion animal for the one or more stimuli through an electronic device; an analysis module configured to calculate a cognitive score based on the learning data; and a reward/punishment providing module that provides a reward or punishment to the companion animal based on the learning data. According to the cognitive ability development system for a companion animal, a program can be provided that allows the companion animal to develop cognitive abilities even when the owner is not present while learning about stimuli through an electronic device in the house.

Description

Cognitive ability development system and method for companion animals {SYSTEM AND METHOD FOR IMPROVING COGNITIVE ABILITY FOR PETS}

Embodiments relate to a cognitive ability development system and method for companion animals. More specifically, the embodiments relate to technology related to a cognitive ability development system for companion animals that evaluates the cognitive abilities of companion animals and provides cognitive intervention through behavior detection and digitization of companion animals.

The number of households raising pets in Korea has been continuously increasing since the Rural Development Administration's Animal Science Research Institute announced in 2008 that approximately 5 million animals are being raised in 2 million households. Accordingly, the expression 'pet' has become popular, reflecting the change in the nature of companion animals living with people and becoming family-like.

As the number of households raising companion animals continues to increase, the medical service market for companion animals is also gradually expanding. In particular, attempts to manage and treat the health of companion animals by incorporating various technologies are continuing.

Pet Tech is a compound word of pet and technology and refers to ICT for companion animals. For example, technologies such as remotely observing companion animals or providing companion animal care such as automatic feeding are being designed using cutting-edge technologies such as artificial intelligence, the Internet of Things, and robots.

Dogs, a representative companion animal, can suffer from cognitive impairment similar to Alzheimer's in humans, which is called canine dementia (Canine Cognitive Dysfunction, CCD). This is an age-related disorder in dogs that causes changes in behavior and mainly affects memory, learning and comprehension. Typically, signs of dementia are found in 50% of dogs over the age of 11, and in approximately 68% of dogs over the age of 15, it is recognized as a serious disease by pet owners.

To improve your pet's cognitive ability and prevent brain diseases such as dementia, physical training such as teaching tricks, playing games, or exercising regularly can be helpful. As technology advances, information and communication technology A cognitive ability development system incorporating .

Registered Patent Publication No. 10-2187344

According to one aspect of the present invention, the cognitive ability of the companion animal is improved by providing stimulation by reflecting the characteristics of the companion animal that are difficult to explain the rules to people and providing rewards or punishments for learning results, and the companion animal's It can perform the function of preventing brain disease, and provide a cognitive ability development system and method for companion animals and a computer program for the same that allows the owner of the companion animal to check the status of the companion animal through network communication.

A cognitive ability development system for companion animals according to an embodiment includes a stimulation providing module that provides one or more of visual, auditory, tactile, and olfactory stimulation to the companion animal based on a space defined by tags in the house; a data acquisition module configured to collect learning data of the companion animal regarding the one or more stimuli through an electronic device; an analysis module configured to calculate a recognition score based on the learning data; And it may include a reward/punishment provision module that provides a reward or punishment to the companion animal according to the learning data.

In one embodiment, the electronic device includes a camera, and the structure of the house is obtained by generating a map using the image data acquired through the camera using Simultaneous Localization and Mapping (SLAM) technology, and using the tag. The separately defined space may have updated tag information by analyzing the companion animal's behavior data collected through the camera.

In one embodiment, the reward/punishment provision module may be configured to provide the reward in a space tagged as the pet's preferred area among spaces defined by the tag when providing a reward to the companion animal.

In one embodiment, the stimulus providing module may be configured to provide one or more additional stimuli to move the position of the pet if the pet is located in an area without a camera when providing the one or more stimuli. You can.

In one embodiment, a machine configured to store learning results by machine learning using a preset training data set and determine the characteristics of cognitive ability development stimulation content to be provided to the companion animal using the cognitive score and the learning results. Additional learning modules may be included.

In one embodiment, the data acquisition module is further configured to receive the training data set, and custom stimulation content preset according to the gender, age, and past medical history information of the companion animal for the data included in the training data set. It may include a data labeling unit configured to label the training data set using and provide labeled data to the machine learning module.

A cognitive ability development method for a companion animal according to an embodiment includes the steps of the cognitive ability development system providing one or more of visual, auditory, tactile, and olfactory stimulation to the companion animal based on a space defined by tags in the house; The cognitive ability development system collecting learning data of the companion animal regarding the one or more stimuli through an electronic device; Calculating a cognitive score by the cognitive ability development system based on the learning data; And the cognitive ability development system may include providing a reward or punishment to the companion animal according to the learning data.

In one embodiment, the step of providing a reward or punishment to the companion animal includes providing a reward in a space tagged as the preferred area of the companion animal among spaces defined by the tag when providing a reward to the companion animal. May include steps.

In one embodiment, the electronic device includes a camera, and the step of providing the one or more stimuli includes adding one or more additional stimuli to move the position of the companion animal when the companion animal is located in an area without a camera. It may include providing stimulation.

A cognitive ability development method for companion animals according to an embodiment includes the steps of the cognitive ability development system generating learning results by machine learning using a preset training data set; storing the learning results by the cognitive ability development system; And it may further include the step of the cognitive ability development system determining characteristics of cognitive ability development stimulation content to be provided to the companion animal using the cognitive score and the learning result.

In one embodiment, generating a learning result by machine learning includes: receiving the training data set by the cognitive ability development system; And the cognitive ability development system may further include labeling the training data set using customized stimulus content preset according to the gender, age, and past medical history information of the companion animal for the data included in the training data set. You can.

The computer program according to one embodiment is combined with hardware to execute the cognitive ability development method for companion animals according to the above-described embodiments, and may be stored in a computer-readable recording medium.

According to the cognitive ability development system and method for companion animals according to one aspect of the present invention, it is possible to provide a system that can evaluate and improve (cognitive intervention) the cognitive abilities of companion animals.

In addition, the cognitive ability development system and method for companion animals according to one aspect of the present invention trains the brain's multi-sensory information cognitive ability for sensory stimulation such as sight, hearing, smell, and touch according to the movement of the companion animal and changes the brain's cognitive abilities. It has the advantage of measuring brain cognitive ability.

In addition, the cognitive ability development system and method for companion animals according to one aspect of the present invention can be implemented as a home-based program that performs behavior management of companion animals through a network, and can provide stimulation at home even in the absence of the companion animal owner. Configured to perform training, So? can provide convenience by allowing you to check your pet's condition through the application.

Furthermore, using the cognitive ability development system and method for companion animals according to one aspect of the present invention has the advantage of strengthening continuous management by continuously providing and evaluating a cognitive improvement environment for companion animals in their home living space.

1 is a diagram illustrating an example of an operating environment of a system according to an embodiment.
Figure 2 is a block diagram for explaining the internal configuration of a computing device according to an embodiment.
Figure 3 is a schematic block diagram of a cognitive ability development system for companion animals according to an embodiment.
Figure 4 is a flowchart showing each step of a cognitive ability development method for companion animals according to an embodiment.
Figure 5 is an exemplary diagram of an apartment floor plan, which is an area where companion animals are active, used in a cognitive ability development system for companion animals according to an embodiment.
Figure 6 is an exemplary diagram illustrating the concept of learning of a companion animal through a cognitive ability development system for companion animals according to an embodiment.

In describing the embodiments of the present specification, if it is determined that a detailed description of a known configuration or function may obscure the gist of the embodiments of the present specification, the detailed description thereof will be omitted. In addition, in the drawings, parts that are not related to the description of the embodiments of the present specification are omitted, and similar parts are given similar reference numerals.

In the embodiments of the present specification, when a component is said to be “connected,” “coupled,” or “connected” to another component, this refers not only to a direct connection relationship, but also to an indirect relationship where another component exists in between. Connection relationships may also be included. In addition, when a component is said to "include" or "have" another component, this does not mean excluding the other component, but may further include another component, unless specifically stated to the contrary. .

In the embodiments of this specification, terms such as first, second, etc. are used only for the purpose of distinguishing one component from other components, and do not limit the order or importance between components unless specifically mentioned. No. Therefore, within the scope of the embodiments of the present specification, the first component in an embodiment may be referred to as a second component in another embodiment, and similarly, the second component in the embodiment may be referred to as the first component in another embodiment. It may also be called.

In the embodiments of the present specification, distinct components are intended to clearly explain each feature, and do not necessarily mean that the components are separated. That is, a plurality of components may be integrated to form one hardware or software unit, or one component may be distributed to form a plurality of hardware or software units. Therefore, even if not specifically mentioned, such integrated or distributed embodiments are also included in the scope of the embodiments of the present specification.

In this specification, a network may be a concept that includes both wired and wireless networks. At this time, the network may refer to a communication network in which data exchange between devices, systems, and devices can be performed, and is not limited to a specific network.

Embodiments described herein may have aspects that are entirely hardware, partly hardware and partly software, or entirely software. In this specification, “unit,” “device,” or “system” refers to computer-related entities such as hardware, a combination of hardware and software, or software. For example, in this specification, a part, module, device, or system refers to a running process, processor, object, executable, thread of execution, program, and/or computer. It may be a (computer), but is not limited thereto. For example, both an application running on a computer and the computer may correspond to a part, module, device, or system in the present specification.

Additionally, in this specification, the device may be a mobile device such as a smartphone, tablet PC, wearable device, and HMD (Head Mounted Display), as well as a fixed device such as a PC or a home appliance with a display function. Additionally, as an example, the device may be a cluster within a vehicle or an Internet of Things (IoT) device. That is, in this specification, a device may refer to devices capable of operating an application, and is not limited to a specific type. In the following, for convenience of explanation, the device on which the application runs is referred to as a device.

In this specification, the network communication method is not limited, and connections between each component may not be connected through the same network method. The network may include not only a communication method utilizing a communication network (for example, a mobile communication network, wired Internet, wireless Internet, broadcasting network, satellite network, etc.), but also short-range wireless communication between devices. For example, a network can include objects and any communication method through which objects can be networked, and is not limited to wired communication, wireless communication, 3G, 4G, 5G, or other methods. For example, wired and/or networks include Local Area Network (LAN), Metropolitan Area Network (MAN), Global System for Mobile Network (GSM), Enhanced Data GSM Environment (EDGE), High Speed Downlink Packet Access (HSDPA), W-CDMA (Wideband Code Division Multiple Access), CDMA (Code Division Multiple Access), TDMA (Time Division Multiple Access), Bluetooth, Zigbee, Wi-Fi, VoIP (Voice over Internet Protocol), LTE Advanced, IEEE802.16m, WirelessMAN-Advanced, HSPA+, 3GPP Long Term Evolution (LTE), Mobile WiMAX (IEEE 802.16e), UMB (formerly EV-DO Rev. C), Flash-OFDM, iBurst and It may refer to a communication network using one or more communication methods selected from the group consisting of MBWA (IEEE 802.20) systems, HIPERMAN, Beam-Division Multiple Access (BDMA), Wi-MAX (World Interoperability for Microwave Access), and ultrasonic communication. However, it is not limited to this.

Components described in various embodiments do not necessarily mean essential components, and some may be optional components. Accordingly, embodiments consisting of a subset of the elements described in the embodiments are also included in the scope of the embodiments of the present specification. In addition, embodiments that include other components in addition to the components described in the various embodiments are also included in the scope of the embodiments of the present specification.

Hereinafter, embodiments of the present specification will be examined in detail with reference to the drawings.

1 is a diagram illustrating an example of the operating environment of a system according to an embodiment of the present specification. Referring to FIG. 1, a user device 110 and one or more servers 120, 130, and 140 are connected through a network 1. Figure 1 is an example for explaining the invention, and the number of user devices or servers is not limited as in Figure 1.

The user device 110 may be a fixed terminal implemented as a computer system or a mobile terminal. The user device 110 includes, for example, a smart phone, a mobile phone, a navigation device, a computer, a laptop, a digital broadcasting terminal, a Personal Digital Assistant (PDA), a Portable Multimedia Player (PMP), a tablet PC, or a game console. consoles, wearable devices, IoT (internet of things) devices, VR (virtual reality) devices, AR (augmented reality) devices, etc. For example, in embodiments, user device 110 may be one of a variety of physical computer systems capable of communicating with other servers 120 - 140 over network 1 using substantially wireless or wired communication methods. It can mean.

Each server may be implemented as a computer device or a plurality of computer devices that communicate with the user device 110 and the network 1 to provide commands, codes, files, content, services, etc. For example, the server may be a system that provides each service to the user device 110 connected through the network 1. As a more specific example, the server may provide a service (for example, provision of information, etc.) targeted by the application to the user device 110 through an application as a computer program installed and running on the user device 110. As another example, the server may distribute files for installing and running the above-described application to the user device 110, receive user input information, and provide a corresponding service. For example, the current state of the pet can be transmitted through the user device 110, or the pet's performance achievements in response to stimuli such as cognitive scores and scorecards can be displayed on the user device 110, including a graph. The user device 110 may have an application required for this installed and run.

As an example, each user device below may operate based on any one of the user devices in FIG. 1. As an example, a user device may communicate and exchange information with another user device or server (or system) through a network. Additionally, as an example, the user device described below may be a medium on which a game provided based on software or an application is recorded. That is, each user device may be a device on which software, applications, or programs for the games described below are installed, and communicates with other user devices or servers through a network based on the installed information, through which software or applications are installed. It may be a device that provides services to users based on an application. In other words, each of the services provided to the user may operate based on software or applications installed on the user device, but for convenience of explanation, the description below is based on the operation of the user device.

Figure 2 is a block diagram for explaining the internal configuration of the computing device 200 in one embodiment of the present specification. This computing device 200 can be applied to one or more user devices 110-1, 110-2 or servers 120-140 described above with reference to FIG. 1, and each device and server may add some components or By being configured by excluding them, they may have the same or similar internal configuration.

Referring to FIG. 2 , the computing device 200 may include a memory 210, a processor 220, a communication module 230, and a transceiver 240. The memory 210 is a non-transitory computer-readable recording medium, and is a non-perishable large capacity device such as random access memory (RAM), read only memory (ROM), disk drive, solid state drive (SSD), flash memory, etc. It may include a permanent mass storage device. Here, non-perishable mass storage devices such as ROM, SSD, flash memory, disk drive, etc. may be included in the above-mentioned device or server as a separate persistent storage device that is distinct from the memory 210. In addition, the memory 210 stores an operating system and at least one program code (for example, code for a browser installed and running on the user device 110, etc., or an application installed on the user device 110 to provide a specific service, etc.). It can be. These software components may be loaded from a computer-readable recording medium separate from the memory 210. Such separate computer-readable recording media may include computer-readable recording media such as floppy drives, disks, tapes, DVD/CD-ROM drives, and memory cards.

In another embodiment, software components may be loaded into the memory 210 through the communication module 230 rather than a computer-readable recording medium. For example, at least one program is a computer program (for example, installed by files provided through the network 1) by developers or a file distribution system (for example, the server described above) that distributes the installation file of the application. It may be loaded into the memory 210 based on the above-described application).

The processor 220 may be configured to process instructions of a computer program by performing basic arithmetic, logic, and input/output operations. Commands may be provided to the processor 220 by the memory 210 or the communication module 230. For example, processor 220 may be configured to execute received instructions according to program code stored in a recording device such as memory 210.

The communication module 230 may provide a function for the user device 110 and the servers 120 - 140 to communicate with each other through the network 1, and the device 110 and/or the servers 120 - 140, respectively. This can provide functions for communicating with other electronic devices.

The transceiver unit 240 may be a means for interfacing with an external input/output device (not shown). For example, external input devices may include devices such as a keyboard, mouse, microphone, and camera, and external output devices may include devices such as a display, speaker, and haptic feedback device.

As another example, the transceiver 240 may be a means for interfacing with a device that integrates input and output functions, such as a touch screen.

Additionally, in other embodiments, computing device 200 may include more components than those of FIG. 2 depending on the nature of the device to which it is applied. For example, when the computing device 200 is applied to the user device 110, it is implemented to include at least some of the above-described input/output devices, or a transceiver, a Global Positioning System (GPS) module, a camera, various sensors, It may further include other components such as a database, etc. As a more specific example, if the user device is a smartphone, the smartphone generally includes various devices such as acceleration sensors, gyro sensors, camera modules, various physical buttons, buttons using a touch panel, input/output ports, and vibrators for vibration. It can be implemented to include more components.

As an example, the user device and server (or system) below may be devices that operate based on FIG. 2. That is, in the following, the user device and the server may be entities that are connected to each other through a network and perform communication.

Here, each subject is equipped with a memory, processor, communication module, transceiver and other components based on FIG. 2, and based on this, at least one of software, applications and programs for providing services according to the game is recorded. It may be a computing device that operates as a medium, and may not be limited to a specific computing device.

Figure 3 is a schematic block diagram of a cognitive ability development system 2 for companion animals according to an embodiment.

Referring to FIG. 3, the cognitive ability development system 2 according to this embodiment includes a stimulus provision module 21, a data acquisition module 22, an analysis module 23, and a machine learning module 24. Includes. Additionally, each module 21-24 may include one or more functional units.

The cognitive ability development system 2 according to embodiments and each module 21 to 24 or units included therein may be entirely hardware, or may have aspects that are partly hardware and partly software. For example, each module 21-24 or unit of the cognitive ability development system 2 may collectively refer to hardware and software related thereto for processing data of a specific format and content and/or exchanging data through electronic communication. In this specification, terms such as “unit,” “module,” “device,” “terminal,” “server,” or “system” are intended to refer to a combination of hardware and software driven by the hardware. For example, hardware may be a data processing device that includes a CPU or other processor. Additionally, software driven by hardware may refer to a running process, object, executable, thread of execution, program, etc.

Additionally, each element constituting the cognitive ability development system 2 is not necessarily intended to refer to a separate device that is physically distinct from each other. That is, the stimulation providing module 21, data acquisition module 22, analysis module 23, and machine learning module 24 in FIG. 3 are the hardware that constitutes the cognitive ability development system (2) and are performed by the hardware. It is only divided functionally according to operation, and each part does not necessarily have to be provided independently from each other. Of course, depending on the embodiment, one or more of the stimulus providing module 21, data acquisition module 22, analysis module 23, and machine learning module 24 may be implemented as separate devices that are physically separated from each other. do.

The stimulation provision module 21 is configured to provide stimulation for cognitive ability development to the companion animal. For example, the stimulus providing module 21 is an electronic device ( 300 ) that can present one or more stimuli selected from sight, hearing, smell, and touch using display means and sound output means provided in the electronic device 300. This may be a part for transmitting a control command to 300). For the above operations, the cognitive ability development system 2 may communicate with the electronic device 300 through a wired and/or wireless network. In this specification, communication methods through wired and/or wireless networks may include objects and all communication methods through which objects can be networked, and are not limited to wired communication, wireless communication, 3G, 4G, or other methods. .

In FIG. 3, the electronic device 300 is shown in the form of a tablet computer, but this is an example. The electronic device 300 is a user device 110 configured as shown in FIG. 2 and is a mobile computing device such as a smartphone. It may consist of any computing device, such as a device, a laptop computer, or a personal computer (PC). In addition, the electronic device 300 may be an independently installed device in the home for cognitive ability development, and may be implemented as various electronic devices such as a TV, refrigerator, robot vacuum cleaner, set-top box, AI speaker, camera, etc. no limits.

In one embodiment, the cognitive ability development system 2 performs, at least in part, the function of an application server that enables the performance of the application's functions by communicating with an application (or app) running on the electronic device 300. can do. Or/Also, the cognitive ability development system 2 is, at least in part, a web server that provides a predetermined web page accessible through a web browser running on the electronic device 300. ) can also perform the function. Cognitive development system 2 may be implemented on one or more computing devices 200.

However, this is an example, and in other embodiments, the cognitive ability development system 2 itself may be configured in the form of a device 110 used by the user, and in this case, the user directly submits the feedback through the cognitive ability development system 2. Since it is possible to design and use a training program by selecting and controlling the stimulation provided to the animal and checking the cognitive ability evaluation score, etc., the electronic device 300 shown in FIG. 3 can be used only as a stimulation output device that provides stimulation to the companion animal. It may be possible.

In embodiments of the present invention, cognitive ability development stimulation content presented through the stimulation providing module 21 may be related to a plurality of cognitive areas. In this specification, a plurality of cognitive domains refers to a multifaceted domain for inducing activities to improve cognitive ability in companion animals from different perspectives in order to improve cognitive ability, such as preventing or slowing down the decline in cognitive ability due to dementia, etc. It means fields.

For example, in one embodiment, the cognitive ability development system 2 may divide content that needs to be managed to improve cognitive ability into five cognitive areas: vascular disease management, cognitive training, exercise, nutritional management, and motivation enhancement. . At this time, the cognitive ability development stimulation content provided by the cognitive ability development system (2) aims to improve the ability of one or multiple cognitive domains among the five cognitive domains described above, and is developed with the goal of strengthening a specific cognitive domain. This may be the case, and in this specification, this is referred to as the characteristics of cognitive ability development stimulating content. For example, content that stimulates cognitive development may take the form of training or games that perform actions according to the owner's voice commands for the purpose of strengthening abilities in a specific field.

The data acquisition module 22 is configured to receive the companion animal's learning data about the stimulus content provided to the companion animal through the stimulus provision module 21 from the electronic device 300 . The electronic device 300 may include, for example, a refrigerator, TV, robot vacuum cleaner, set-top box, AI speaker, or device equipped with a camera such as CCTV, and the data acquisition module 22 is the electronic device 300. Feedback learning data from your pet about stimulation can be received through input means (not shown) such as a microphone, camera, or motion detection sensor.

The analysis module 23 is configured to analyze the feedback learning data input to the data acquisition module 22 and calculate performance information for a plurality of cognitive areas. For example, the analysis module 23 determines performance based on cognitive scores for stimulation content (e.g., training, games) in each cognitive domain, time spent on correct stimulus feedback in each domain, progress rate and/or participation rate for each domain, etc. information can be determined. Stimulation for each area includes visual, auditory, tactile, and olfactory senses.

To this end, in one embodiment, the analysis module 23 includes a result analysis unit 231 that analyzes the cognitive score, which is the result of the pet's learning feedback for each area, and/or determines the amount required to achieve the goal of the stimulation content in each area. It may include a process analysis unit 232 that analyzes information on time consumption, such as time and the pet's response time to stimulus content.

Also, in one embodiment, the analysis module 23 further includes a profiling unit 233. In this embodiment, information such as the gender, age, and past medical history of the companion animal can be input from the user through the data acquisition module 22. In addition, through the data acquisition module 22, the companion animal's movement path within the house can be collected using camera shooting information, motion sensor information, etc. The profiling unit 233 is configured to determine a reference value for deriving performance information from feedback learning data, based on this information about the companion animal. At this time, the standard value is information about the section for converting the cognitive score or consumption time into a performance value, weight information to be applied to the cognitive score or consumption time, or the gender/age/medical history of the companion animal, etc. that affect the performance value. It may refer to an adjustment value in any other arbitrary way.

The machine learning module 24 stores the results of learning by machine learning using a pre-entered training data set, and performs machine learning on the performance information obtained by the analysis module 23. It functions to determine the characteristics of personalized stimulation content for companion animals by applying the learning results.

For the above operation, the machine learning module 24 includes a machine learning model learning unit 241 that performs machine learning using a training data set, and a machine learning model that determines customized stimulation content for the performance information of the companion animal based on the learning results. It may include a machine learning model determination unit 242.

In one embodiment, the data acquisition module 22 may further perform the function of receiving a training data set and inputting it to the machine learning model learning unit 241 for machine learning. At this time, the data acquisition module 22 may include a data storage unit 221 and a data labeling unit 222. The data storage unit 221 can classify and define spaces using a space map or house floor plan DB automatically generated using SLAM technology, and provide stimulation according to analysis of the companion animal's behavior patterns in each space. . The data storage unit 221 can detect and digitize the companion animal's behavior from collected video data, etc., and extract tag information for each area where the companion animal is active from this behavioral data and create a database. DB-based tag information can be updated manually or automatically by user input information or additionally collected companion animal behavior data. The data labeling unit 222 uses customized stimulus content information (i.e., correct answer) determined by experts or clinical results as suitable for improving the cognitive ability of the companion animal that provided the data for the data included in the training data set. Thus, the data in the training data set can be labeled, and the labeled data can be provided to the machine learning model learning unit 241.

Additionally, the data acquisition module 22 may further include a behavior detection unit 223. The behavior detection unit 223 receives sensor data according to the behavior of the companion animal from various sensors installed in the house linked to the cognitive ability development system 2, and can determine the behavior and movement of the companion animal from these data. . Learning results can be analyzed according to the results of analyzing the pet's behavior, and the pet's achievement of the stimulation task can be evaluated to determine whether to provide rewards or punishments. In addition, for example, after identifying the pet's movement path with a camera, the area where the pet frequently stays, preferred area, and resting area in the house structure is updated with tag information, and stimulation is centered on the pet's movement path. Cognitive development content can be determined to provide .

The machine learning model determination unit 242 of the machine learning module 24 determines the type of companion animal corresponding to the performance information of the companion animal based on data previously accumulated by the machine learning model learning unit 241, You can determine the characteristics of customized stimulation content suitable for the pet. For example, determining the type of companion animal may mean classifying or clustering companion animals based on patterns of performance shown by the companion animal in multiple cognitive domains, and machine learning model version. The government 242 can use these types to determine customized stimulation content suitable for improving the cognitive ability of the corresponding companion animal classification or group.

The reward/punishment provision module 25 analyzes the cognitive score, which is the result of the pet's learning feedback for each area in the analysis module 23, and provides a reward when it is determined that the pet has given appropriate feedback to the stimulus. Punishment can be provided if it is determined that the pet did not provide appropriate feedback to the stimulus. For example, the reward/punishment provision module 25 is linked to a speaker, a feeding device, etc. to provide rewards such as the owner's praising voice, provision of a favorite snack, etc., or the owner's scolding voice, preventing entry to a favorite place, etc. punishment can be provided.

Figure 4 is a flowchart showing each step of a cognitive ability development method for companion animals according to an embodiment. For convenience of explanation, the cognitive ability development method for companion animals according to this embodiment will be described with reference to FIGS. 3 and 4.

Referring to FIG. 4, first, as a machine learning-based learning process, the data acquisition module 22 of the cognitive ability development system 2 can receive a training data set (S11). The training data set may be input or transmitted to the data acquisition module 22 from the manager's electronic device, or may be received from an external server (not shown).

As a preprocessing process for performing machine learning using a training data set, the data labeling unit 222 of the data acquisition module 22 provides customized stimulus content information determined based on expert or clinical information based on each data in the training data set. You can label the data using (S12).

The machine learning module 24 of the cognitive ability development system 2 can receive data for which labeling has been completed and use it to generate learning results for determining customized stimulation content from the learning data (S13). For example, the learning result refers to determining the type of companion animal based on the pattern of the learning data and the characteristics of the stimulus content suitable for application to that type of companion animal, such as the type and number of cognitive areas corresponding to the stimulus content. It may be to decide, etc.

The machine learning learning process (S11-S13) described above may be performed by the machine learning model learning unit 241 of the machine learning module 24. However, in another embodiment, the machine learning module 24 may receive algorithms and parameters corresponding to machine learning results from an external device or server and operate based on them. In this case, the above-described learning process (S11- S13) may be omitted.

In order to provide customized stimulation content to the target companion animal based on the learning results by machine learning, first, the stimulation provision module 21 of the cognitive ability development system 2 provides cognitive ability development stimulus content with characteristics of a plurality of cognitive domains. can be provided to companion animals (S21).

Next, the data acquisition module 22 of the cognitive ability development system 2 may receive the companion animal's learning data about the provided stimulus content (S22). For example, learning data refers to the cognitive score achieved by a pet by solving a task provided through stimulus content in a game-like format, the time the pet spends on completing the task, or a series of stimulus content. It may include the pet's progress or participation rate in tasks.

In one embodiment, the data acquisition module 22 may label information such as gender/age/medical history of the companion animal that receives the stimulus content simultaneously with or before or after receiving the learning data (S23). .

The data acquisition module 22 of the cognitive ability development system 2 can acquire the structure of the house, provide stimulation to the companion animal at an appropriate location, and receive behavioral data of the companion animal in response to the stimulation (S24). The cognitive ability development system 2 acquires a map of the pet's activity area, for example, an apartment floor plan as shown in Figure 3, and collects the pet's behavior data through various sensors installed inside the house, such as cameras and motion sensors. By collecting this, you can define the path your pet moves and the area it stays in, and based on these, you can configure areas that will provide appropriate stimulation for each cognitive area of your pet. Pet behavioral data in response to stimuli provided in each area can be collected through various sensors installed inside the house, such as cameras, motion sensors, and microphones.

The analysis module 23 of the cognitive ability development system 2 can derive performance information suitable for determining the type of pet indicated by the behavior data from the behavior data of the companion animal and calculate a cognitive score (S25). . For example, performance information refers to the recognition score, time spent, progress rate, participation rate, etc. achieved by the pet in response to the stimulus, or this information is used to measure performance based on predetermined criteria (grade, threshold, weight, etc.) It may have been converted to a value.

The reward/punishment provision module 25 of the cognitive ability development system 2 may determine what reward or punishment to provide to the companion animal based on performance information such as the cognitive score derived by the analysis module 23 (S26 ). In this regard, based on the type of companion animal determined through the machine learning model, the characteristics (type, difficulty, time limit, etc.) of cognitive ability development stimulation content suitable to be presented to the companion animal next can be determined. For example, if a pet is not allowed to climb on a red cushion and is taught to only climb on a blue cushion, the pet can be taught to do so by scolding the pet with the owner's voice when it climbs on the red cushion and giving a treat reward when it climbs on the green cushion. Through such training, if the cognitive score (the pet's achievement level) falls below a certain level, an electronic device (for example, a robot vacuum cleaner) equipped with a means of transportation moves to the relevant location and ignites so that the scolding voice can be heard in the owner's room. Alternatively, punishment can be provided in a way that prevents the pet from moving to its preferred location. Even when providing a reward, if the recognition score is above a certain point, the electronic device checks the tag information from the DB, moves to the pet's preferred area, and provides a snack or water reward to further maximize the effectiveness of the training.

In the cognitive ability development method according to embodiments of the present invention, information such as movement line, progress rate, and/or cognitive score is derived from the companion animal's behavior data and learning data, and this is used as performance information to be provided to the companion animal. Customized stimulation content can be determined.

Figure 5 is an exemplary diagram of an apartment floor plan, which is an area where companion animals are active, used in a cognitive ability development system for companion animals according to an embodiment.

The floor plan may be information input by the user through the data acquisition module 22, or the map may be automatically generated. Specifically, SLAM (Simultaneous Localization and Mapping) technology can be used to extract key frames, landmarks, etc. from images obtained from a camera to create a SLAM map.

According to an embodiment of the present invention, each area where a companion animal is active may have various tag information automatically entered through area recognition or entered through user input, and may be updated automatically or manually by receiving user input. there is. For example, the master bedroom is tagged as #male owner #sleeping place #pet activity restricted area, the bed in the master bedroom is a #pet-free zone, the bathroom and in front of the bathroom are #a place where pets go when they are scolded, and the entrance. The door can be entered with tag information such as a place where the pet wants to go out.

A pet's behavior can be detected through various sensors installed in the house, such as cameras. If the companion animal is in a location that cannot be detected by cameras, etc., the cognitive ability development system 2 can provide instructions through voice, light, food, etc. to help the companion animal move to an area that can be detected.

The data acquisition module 22 of the cognitive ability development system 2 can create a database of the companion animal's movement path by collecting behavioral data while the owner is in the house and when the owner is not. At this time, the space where the pet is located when the owner is not present can automatically update the tag information to the #pet preference area. When a companion animal performs a stimulation task appropriately and provides a reward, it can be used to increase reward efficiency by providing a reward such as the owner's voice or a snack using the pet's preferred area.

Figure 6 is an exemplary diagram illustrating the concept of learning of a companion animal through a cognitive ability development system for companion animals according to an embodiment.

Referring to FIG. 6, a task through voice stimulation is given to the companion animal through the electronic device 300, and a reward is given when the companion animal completes appropriate performance, and punishment is provided when the companion animal does not perform appropriately. For example, if a pet is not allowed to climb on a red cushion and is taught to only climb on a blue cushion, the pet can be taught to do so by scolding the pet with the owner's voice when it climbs on the red cushion and giving a treat reward when it climbs on the green cushion. At this time, the electronic device 300 may move to a relevant location and ignite so that the scolding voice can be heard in the owner's room, or may provide punishment by preventing the pet from moving to its preferred location. Here, the preferred place may mean a place where the pet is mainly located, a place where the pet sleeps, or a place where the pet's biological signals are expressed stably. To check the above biosignals, a wearable device or a vision recognition device can be used to sense pulse, body temperature, electrocardiogram, and behavior.

Even when providing a reward, the electronic device 300 can check the tag information from the DB, move to the pet's preferred area, and provide a snack or water reward, thereby further maximizing the effect of the training. To this end, the electronic device 300 itself may be equipped with a means of movement as shown in FIG. 6, and can provide the desired reward or punishment to the pet by controlling it in conjunction with available electronic devices such as a robot vacuum cleaner. You can.

The cognitive ability development system according to one embodiment may determine the cognitive score of the cognitive training area based on the level of difficulty (i.e., level) achieved by the companion animal for the cognitive training content or the time the companion animal spent on completing the task. .

As shown in Figure 6, the pet owner can check the results of the pet's performance information, cognitive score information, degree of cognitive coordination in each area, etc. through the application installed on the user device, and can remotely feed the pet. , you can control the provision of snacks and water, and you can also select various pet information, customized stimulation content provided to the pet, and training programs through an application installed on the user device.

The cognitive ability development system according to embodiments of the present invention uses the training progress rate in addition to the cognitive score for each cognitive area calculated as described above to calculate the pet's performance information for the content and provide it to the user device. You can.

In this specification, the cognitive score may refer to the degree of achievement in which a companion animal appropriately performs tasks for all training tasks provided in the entire stimulus content. For example, if a training task is given to a companion animal 5 times as content stimulating cognitive development, and the companion animal performs the task appropriately 3 times, the cognitive score can be defined as 60 points. In addition, for tasks provided through stimulation content based on multi-domain mediation with multiple cognitive domains, it may refer to scoring the degree of achievement in each domain achieved by the companion animal. For example, if the content that stimulates cognitive development is content that has four cognitive domains: vascular disease management, cognitive training, exercise, and nutritional management, the cognitive score is the score for each cognitive domain, such as {30, 40, 10, 5}. It may be a data set containing data records. However, this is an example, and in other embodiments, the progress rate may be defined for each cognitive domain.

In this specification, the progress rate refers to the companion animal's achievement rate for the training tasks provided throughout the stimulus content. For example, if content that stimulates cognitive ability development for a companion animal includes five training tasks, and the companion animal achieves one of these tasks, the progress rate can be defined as 20%. However, this is an example, and in other embodiments, the progress rate may be defined for each cognitive domain.

The cognitive ability development system according to embodiments can derive cognitive scores and progress rates derived from learning data through the above process as performance information of the companion animal. In addition, the cognitive ability development system determines the type of pet by analyzing the pet's performance information based on the learning results of machine learning, and determines customized stimulation content suitable for improving the pet's cognitive ability to provide to the pet. can be presented.

For example, learning results using a training data set may mean clustering companion animals into multiple groups according to performance information and matching the characteristics of content suitable for improving the cognitive abilities of companion animals in each group for each group. You can. For example, based on the results of a comprehensive analysis of users' cognitive scores and progress rates in the training data set, the users' performance information is 4, which represents the performance of each cognitive area of vascular disease management, cognitive training, exercise, and nutrition management. It can be displayed in the form of dimensional data records. Information that requires user input, such as vascular disease management and nutritional management, can be managed comprehensively if input is input from the pet owner, and if there is no input, it can be expressed as lower-dimensional data.

Each learning data included in the training data set is expressed in the form of a 4-dimensional data record, and through clustering, the data record corresponding to the center point of each group (i.e., cluster) can be determined. At this time, the cognitive ability development content may have characteristic information that defines areas that can be improved through the content among the four cognitive areas of vascular disease management, cognitive training, exercise, and nutrition management. Characteristic information can be set in advance by an expert, and in some cases, multiple areas are strengthened through one content, so cognitive ability development contents are also four-dimensional data with characteristic values for each of the four areas. It can be expressed in record form.

That is, in this embodiment, the learning result by machine learning is four-dimensional data corresponding to the central data record of each data group generated by clustering the learning data of the companion animal in the training data set, and the cognitive ability of each data group. It may include four-dimensional characteristic values of cognitive ability development content set as suitable for development.

At this time, when the learning data of the target companion animal is received, the cognitive ability development system can determine which data group the performance information of the target companion animal corresponds to based on the learning results. In other words, this means determining the user's type based on the pet's performance information. To determine the type of companion animal, calculate the distance between the data records corresponding to the center point of each data group and the user's performance information within a four-dimensional data space with four coordinate axes corresponding to each cognitive area, and this This can be done by determining a data group with the closest data record as the center point. Next, the cognitive ability development content determined for the corresponding data group can be determined as customized stimulation content for the companion animal.

In another embodiment, determining the type of companion animal does not necessarily mean determining that the companion animal's performance information belongs to any one data group, and calculates the similarity of the companion animal's performance information for each of a plurality of data groups. And based on this, the customized stimulation content for the pet can be determined by merging the customized content corresponding to each data group.

In one embodiment, the learning results using the training data set cluster companion animals into a plurality of groups according to age, gender, and past medical history information, and provide stimulation content suitable for improving the cognitive abilities of companion animals in each group for each group. This may mean matching characteristics. For the data included in the training data set, the training data set can be labeled using customized stimulation content to determine which training program is appropriate for improving cognitive abilities of companion animals based on age, gender, and past medical history information. Continuously collects the pet's learning results for the training data set and considers the cognitive score along with the relevant age, gender, and past medical history information to provide appropriate training for each cognitive score based on the type of pet and the specific age, gender, and past medical history information. What kind of stimulus content could be can be derived through machine learning.

However, the method of determining customized content for companion animals described above is an example, and the method in which the cognitive ability development system for companion animals according to embodiments determines customized stimulus content from the learning data of companion animals based on machine learning is It is not limited to the examples described above.

Meanwhile, operations by the cognitive ability development method according to the embodiments described above may be at least partially implemented as a computer program and recorded on a computer-readable recording medium. Computer-readable recording media on which programs for implementing operations according to cognitive ability development methods according to embodiments are recorded include all types of recording devices that store data that can be read by a computer. Examples of computer-readable recording media include ROM, RAM, CD-ROM, magnetic tape, floppy disk, and optical data storage devices. Additionally, computer-readable recording media may be distributed across computer systems connected to a network, and computer-readable codes may be stored and executed in a distributed manner. Additionally, functional programs, codes, and code segments for implementing this embodiment can be easily understood by those skilled in the art to which this embodiment belongs.

The present invention discussed above has been described with reference to the embodiments shown in the drawings, but these are merely illustrative examples, and those skilled in the art will understand that various modifications and modifications of the embodiments are possible therefrom. However, such modifications should be considered within the technical protection scope of the present invention. Therefore, the true technical protection scope of the present invention should be determined by the technical spirit of the attached claims.

Claims (12)

A stimulation providing module that provides one or more of visual, auditory, tactile and olfactory stimulation to a companion animal based on a space defined by a tag in the house;
a data acquisition module configured to collect learning data of the companion animal regarding the one or more stimuli through an electronic device;
an analysis module configured to calculate a recognition score based on the learning data; and
A cognitive ability development system for companion animals, including a reward/punishment provision module that provides rewards or punishments to the companion animal according to the learning data.
According to paragraph 1,
The electronic device includes a camera,
The structure of the house is obtained by creating a map using the image data acquired through the camera using SLAM (Simultaneous Localization and Mapping) technology,
A cognitive ability development system for companion animals, wherein the space defined by the tag has updated tag information by analyzing the companion animal's behavior data collected through the camera.
According to clause 1,
The reward/punishment provision module is configured to provide a reward in a space tagged as a preferred area of the companion animal among spaces defined by the tag when providing a reward to the companion animal. A cognitive ability development system for a companion animal.
According to clause 2,
The stimulus providing module is configured to provide one or more additional stimuli to move the position of the pet when the pet is located in an area without a camera when providing the one or more stimuli. Competency development system.
According to paragraph 1,
It stores learning results by machine learning using a preset training data set, and further includes a machine learning module configured to determine the characteristics of cognitive ability development stimulation content to be provided to the companion animal using the cognitive score and the learning results. A cognitive ability development system for companion animals.
According to clause 5,
The data acquisition module is,
further configured to receive the training data set,
Label the training data set using customized stimulus content preset according to the gender, age, and past medical history information of the companion animal for the data included in the training data set, and provide the labeled data to the machine learning module. A cognitive ability development system for companion animals, including a configured data labeling unit.
A cognitive ability development system providing stimulation of one or more of visual, auditory, tactile and olfactory senses to the companion animal based on a space defined by tags in the house;
The cognitive ability development system collecting learning data of the companion animal regarding the one or more stimuli through an electronic device;
Calculating a cognitive score by the cognitive ability development system based on the learning data; and
A cognitive ability development method for a companion animal, comprising the step of the cognitive ability development system providing a reward or punishment to the companion animal according to the learning data.
In clause 7,
The step of providing a reward or punishment to the companion animal is,
When providing a reward to the companion animal, providing the reward in a space tagged as the preferred area of the companion animal among spaces defined by the tags. A cognitive ability development method for a companion animal.
In clause 7,
The electronic device includes a camera,
The step of providing the one or more stimuli includes:
A cognitive ability development method for a companion animal, comprising providing one or more additional stimuli to move the location of the companion animal when the companion animal is located in an area without a camera.
In clause 7,
Generating, by the cognitive ability development system, a learning result by machine learning using a preset training data set;
storing the learning results by the cognitive ability development system; and
A cognitive ability development method for a companion animal, further comprising the step of the cognitive ability development system determining characteristics of cognitive ability development stimulation content to be provided to the companion animal using the cognitive score and the learning result.
According to clause 10,
The step of generating learning results by machine learning is,
The cognitive ability development system receiving the training data set; and
The cognitive ability development system further includes the step of labeling the training data set using customized stimulus content preset according to the gender, age, and past medical history information of the companion animal for the data included in the training data set, How to develop cognitive abilities for pets.
Combined with hardware,
Providing one or more of visual, auditory, tactile, and olfactory stimulation to a companion animal based on a space defined by a tag in the house;
collecting learning data of the companion animal regarding the one or more stimuli through an electronic device;
calculating a recognition score based on the learning data; and
A computer program stored in a computer-readable recording medium to execute the step of providing a reward or punishment to the companion animal according to the learning data.

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