Systems, devices, and methods for determining a change in a surface temperature of a pet

Abstract

Systems, methods, and devices for determining a change in a surface temperature of a pet are provided. Surface temperature data of the pet generated by a sensor device may be received. Surface temperature data of the pet may be compared with a baseline surface temperature of the pet. The change in the surface temperature of the pet may be determined, based on comparing the surface temperature of the pet with the baseline surface temperature of the pet. A notification including information identifying the change in the surface temperature of the pet may be generated. The notification may be caused to be displayed.

Description

SYSTEMS, DEVICES, AND METHODS FOR DETERMINING A CHANGE IN A SURFACE TEMPERATURE OF A PETCROSS-REFERENCE TO RELATED APPLICATION(S)

[0001] This patent application claims the benefit of priority to U.S. Provisional Application No. 63 / 656,329, filed on June 5, 2024, the entirety of which is incorporated herein by reference.TECHNICAL FIELD

[0002] The present disclosure relates, generally, to systems, devices, and methods for determining a change in a surface temperature of a pet.BACKGROUND

[0003] A change in a temperature of a pet can be an important indicator of the pet’s health. For example, a change in a temperature of the pet can be indicative of the pet having an underlying need for medical attention, the pet being located in a stressful environment, the pet needing a dietary adjustment, or the like. Accordingly, a pet owner should routinely monitor the pet’s temperature. To do so, a pet owner can use a physical device such as a thermometer to measure the pet’s internal temperature. However, this technique may be invasive and stressful for the pet. Also, this technique might be inconvenient, which might result in the temperature of the pet being measured infrequently. As such, there is a need for a technique for quickly, practically, easily, frequently, less-intrusively, safely, comfortably, and accurately monitoring a pet’s temperature and determining a change in a pet’s temperature.SUMMARY

[0004] According to an aspect, a method for determining a change in a surface temperature of a pet may include receiving surface temperature data of the pet generated by a sensor device; comparing the surface temperature data of the pet with a baseline surface temperature of the pet; determining the change in the surface temperature of the pet, based on comparing the surface temperature of the pet with the baseline surface temperature of the pet; generating a notification including information identifying the change in the surface temperature of the pet; and causing the notification to be displayed.

[0005] According to an aspect, a device for determining a change in a surface temperature of a pet may include a memory configured to store instructions; and one or more processors configured to execute the instructions to perform operationscomprising: receiving surface temperature data of the pet generated by a sensor device; comparing the surface temperature data of the pet with a baseline surface temperature of the pet; determining the change in the surface temperature of the pet, based on comparing the surface temperature of the pet with the baseline surface temperature of the pet; generating a notification including information identifying the change in the surface temperature of the pet; and causing the notification to be displayed.

[0006] According to an aspect, a non-transitory computer-readable medium may be configured to store instructions that, when executed by one or more processors of a device for determining a change in a surface temperature of a pet, cause the one or more processors to perform operations comprising: receiving surface temperature data of the pet generated by a sensor device; comparing the surface temperature data of the pet with a baseline surface temperature of the pet; determining the change in the surface temperature of the pet, based on comparing the surface temperature of the pet with the baseline surface temperature of the pet; generating a notification including information identifying the change in the surface temperature of the pet; and causing the notification to be displayed.

[0007] It may be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the invention, as claimed.BRIEF DESCRIPTION OF THE DRAWINGS

[0008] FIG. 1 is a diagram of an example system for determining a change in a surface temperature of a pet.

[0009] FIG. 2 is a diagram of a device that may correspond to one or more devices of FIG. 1.

[0010] FIG. 3 is a diagram of a sensor device for generating surface temperature data.

[0011] FIGS. 4A and 4B are diagrams of a sensor device for generating surface temperature data.

[0012] FIG. 5 is a flowchart of an example process for determining a change in a surface temperature of a pet.

[0013] FIG. 6 is a diagram of an example user interface for displaying a notification including information identifying the change in the surface temperature of the pet.

[0014] FIG. 7 is a diagram of an example process for training an artificial intelligence (Al) model for generating a baseline surface temperature of the pet.DETAILED DESCRIPTION

[0015] FIG. 1 is a diagram of an example system 100 for determining a change in a surface temperature of a pet. As shown in FIG. 1 , the system 100 may include a sensor device 110, a user device 120, a server 130, an artificial intelligence (Al) model 140, and a network 150.

[0016] The sensor device 110 may be configured to generate surface temperature data of the pet that is indicative of a surface temperature of the pet. For example, the sensor device 110 may be a smart pet collar, a smart pet feeder, or the like. In some implementations, the sensor device 110 may be a smart pet collar, and sense the surface temperature of the pet’s neck. Alternatively, the sensor device 110 may be affixed to another component provided at a different location with respect to the pet, and may sense the surface temperature of a different anatomical location of the pet.

[0017] The user device 120 may be configured to display a notification including information identifying the change in the surface temperature of the pet. For example, the user device 120 may be a smartphone, a desktop computer, a tablet computer, a laptop computer, a smart speaker, a wearable device, or the like.

[0018] The server 130 may be configured to receive surface temperature data of the pet generated by the sensor device 110, compare the surface temperature data of the pet with a baseline surface temperature of the pet, determine the change in the surface temperature of the pet, based on comparing the surface temperature of the pet with the baseline surface temperature of the pet, generate a notification including information identifying the change in the surface temperature of the pet, and cause the notification to be displayed via the user device 120. For example, the server 130 may be a cloud-server, a virtual server, or the like.

[0019] The Al model 140 may be configured to receive surface temperature sensor data of the pet, and generate a baseline surface temperature of the pet. For example, the Al model 140 may be a neural network, a support vector machine, a Bayesian network, or the like. In some implementations, and as described in more detail in FIG. 7, the Al model 140 may be trained using a training technique and training data.

[0020] The network 150 may be configure to permit communication between the devices of FIG. 1. For example, the network 150 may be a cellular network (e.g., a fifth generation (5G) network, a long-term evolution (LTE) network, a third generation (3G) network, a code division multiple access (CDMA) network, etc.), a public land mobile network (PLMN), a local area network (l_AN), a wide area network (WAN), a metropolitan area network (MAN), a telephone network (e.g., the Public Switched Telephone Network (PSTN)), a private network, an ad hoc network, an intranet, the Internet, a fiber optic-based network, or the like, and / or a combination of these or other types of networks.

[0021] The number and arrangement of devices shown in FIG. 1 are provided as an example. In practice, there may be additional devices, fewer devices, different devices, or differently arranged devices than those shown in FIG. 1 . Furthermore, two or more devices shown in FIG. 1 may be implemented within a single device, or a single device shown in FIG. 1 may be implemented as multiple, distributed devices. Additionally, or alternatively, a set of devices (e.g., one or more devices) of the system 100 may perform one or more functions described as being performed by another set of devices of the system 100.

[0022] FIG. 2 is a diagram of a device 200 that may correspond to one or more devices of FIG. 1 . For example, the device 200 may be the sensor device 110, the user device 120, and / or the server 130.

[0023] As shown in FIG. 2, the device 200 may include a bus 210, a processor 220, a memory 230, a storage component 240, an input component 250, an output component 260, and a communication interface 270.

[0024] The bus 210 includes a component that permits communication among the components of the device 200. The processor 220 may be implemented in hardware, firmware, or a combination of hardware and software. The processor 220 may be a central processing unit (CPU), a graphics processing unit (GPU), an accelerated processing unit (APU), a microprocessor, a microcontroller (MCU), a digital signal processor (DSP), a field-programmable gate array (FPGA), an application-specific integrated circuit (ASIC), or another type of processing component.

[0025] The processor 220 may include one or more processors capable of being programmed to perform a function. The memory 230 may include a random access memory (RAM), a read only memory (ROM), and / or another type of dynamicor static storage device (e.g., a flash memory, a magnetic memory, and / or an optical memory) that stores information and / or instructions for use by the processor 220.

[0026] The storage component 240 may store information and / or software related to the operation and use of the device 200. For example, the storage component 240 may include a hard disk (e.g., a magnetic disk, an optical disk, a magneto-optic disk, and / or a solid state disk), a compact disc (CD), a digital versatile disc (DVD), a floppy disk, a cartridge, a magnetic tape, and / or another type of non- transitory computer-readable medium, along with a corresponding drive.

[0027] The input component 250 may include a component that permits the device 200 to receive information, such as via user input (e.g., a touch screen display, a keyboard, a keypad, a mouse, a button, a switch, and / or a microphone for receiving the reference sound input). Additionally, or alternatively, the input component 250 may include a sensor for sensing information (e.g., a temperature sensor, a pressure sensor, a global positioning system (GPS) component, an accelerometer, a gyroscope, and / or an actuator). The output component 260 may include a component that provides output information from the device 200 (e.g., a display, a speaker for outputting sound at the output sound level, and / or one or more light-emitting diodes (LEDs)).

[0028] The communication interface 270 may include a transceiver-like component (e.g., a transceiver and / or a separate receiver and transmitter) that enables the device 200 to communicate with other devices, such as via a wired connection, a wireless connection, or a combination of wired and wireless connections. The communication interface 270 may permit the device 200 to receive information from another device and / or provide information to another device. For example, the communication interface 270 may include an Ethernet interface, an optical interface, a coaxial interface, an infrared interface, a radio frequency (RF) interface, a universal serial bus (USB) interface, a Wi-Fi interface, a cellular network interface, or the like.

[0029] The device 200 may perform one or more processes described herein. The device 200 may perform these processes based on the processor 220 executing software instructions stored by a non-transitory computer-readable medium, such as the memory 230 and / or the storage component 240. A computer-readable medium may be defined herein as a non-transitory memory device. A memory device mayinclude memory space within a single physical storage device or memory space spread across multiple physical storage devices.

[0030] The software instructions may be read into the memory 230 and / or the storage component 240 from another computer-readable medium or from another device via the communication interface 270. When executed, the software instructions stored in the memory 230 and / or the storage component 240 may cause the processor 220 to perform one or more processes described herein. Additionally, or alternatively, hardwired circuitry may be used in place of or in combination with software instructions to perform one or more processes described herein. Thus, implementations described herein are not limited to any specific combination of hardware circuitry and software.

[0031] The number and arrangement of the components shown in FIG. 2 are provided as an example. In practice, the device 200 may include additional components, fewer components, different components, or differently arranged components than those shown in FIG. 2. Additionally, or alternatively, a set of components (e.g., one or more components) of the device 200 may perform one or more functions described as being performed by another set of components of the device 200.

[0032] FIG. 3 is a diagram 300 of a sensor device 110 for generating surface temperature data. As shown in FIG. 3, the sensor device 110 may include a core device 112 and a temperature sensor 114. The core device 112 may be configured to generate the surface temperature data of the pet that is indicative of a surface temperature of the pet. The temperature sensor 114 may be configured to sense the surface temperature of the pet. Further, the core device 112 may be configured to transmit the surface temperature data to the user device 120 and / or the server 130.

[0033] FIGS. 4A and 4B are diagrams 400 of a sensor device 110 for generating surface temperature data. As shown in FIG. 4A, the core device 112 may be configured to connect to the temperature sensor 114. In this way, the core device 112 is modular in that different types of sensors can be connected to the core device 112 to permit different functionality of the core device depending on which type of sensor, or sensors, is or are connected to the core device 112. As shown in FIG. 4B, the temperature sensor 114 may be connectable to a strap attached to the pet.

[0034] FIG. 5 is a flowchart of an example process 500 for determining a change in a surface temperature of a pet. According to an embodiment, the server130 may be configured to perform one or more operations of the process 500. Additionally, or alternatively, one or more other devices of the system 100 may be configured to perform one or more operations of the process 500.

[0035] As shown in FIG. 5, the process 500 may include receiving surface temperature data of the pet generated by a sensor device (operation 510). The sensor device 110 may sense the surface temperature of the pet, generate surface temperature data, and transmit the surface temperature data to the user device 120. According to an embodiment, the sensor device 110 may sense the surface temperature of the pet based on detecting motion of the pet. For example, the sensor device 110 may detect that the pet is moving, or has moved, and sense the surface temperature of the pet based on detecting that the pet is moving or has moved. According to another embodiment, the sensor device 110 may sense the surface temperature of the pet based on a predetermined time frame (e.g., every minute, every five minutes, every hour, or the like).

[0036] The user device 120 may receive the surface temperature data from the sensor device 110, and transmit the surface temperature data to the server 130. The server 130 may receive the surface temperature data from the user device 120.

[0037] The server 130 may receive the surface temperature data based on a request, a predetermined timeframe, an application of the user device 120 being executed, a user input being received via the user device 120, motion data of the pet, location data of the pet, feeding data of the pet, vital data of the pet, blood data of the pet, stool data of the pet, weather data, sound data of the pet, sleeping data of the pet, or the like.

[0038] The surface temperature data may include a surface temperature value of the pet, a temperature value of the sensor device 110, an ambient temperature value, a timestamp, a pet identifier of the pet, a device identifier of the sensor device 110, a device identifier of the user device 120, an account identifier of the user device 120, or the like.

[0039] As further shown in FIG. 5, the process 500 may include comparing the surface temperature data of the pet with a baseline surface temperature of the pet (operation 520), and determining the change in the surface temperature of the pet, based on comparing the surface temperature of the pet with the baseline surface temperature of the pet (operation 530). The baseline surface temperature of the pet may be a baseline from which a change in a surface temperature of the pet can bedetermined. For example, the baseline representation of the surface temperature of the pet may be the immediately preceding determined surface temperature of the pet, may be an average of previously determined surface temperatures of the pet, may be the first previously determined surface temperature of the pet, an average surface temperature of a type of the pet, a user-reported surface temperature of the pet, or the like.

[0040] The server 130 may determine the baseline surface temperature of the pet using the Al model 140. For example, the server 130 may input surface temperature data of the pet, sensor data, and / or external data into the Al model 140, and determine the baseline surface temperature of the pet based on an output of the model 140.

[0041] The server 130 may determine a particular baseline surface temperature of the pet to which to compare the surface temperature of the pet. For example, the server 130 may store a set of baseline representations of the surface temperature of the pet, and determine a particular baseline surface temperature of the pet from the set of stored baseline surface temperatures of the pet.

[0042] The server 130 may determine a timeframe (e.g., time of day, day of the week, etc.) associated with the surface temperature data generated by the sensor device 110, and determine a baseline surface temperature of the pet that corresponds to the timeframe. For example, the server 130 may determine a timeframe that is associated with the surface temperature data, and determine a baseline surface temperature that is associated with the timeframe. In this way, the server 130 may more accurately determine a change in a characteristic of the pet by comparing the surface temperature of the pet with a baseline the surface temperature of the pet that more closely corresponds to the timeframe at which the surface temperature data was generated.

[0043] The server 130 may compare the surface temperature data of the pet that was generated by the sensor device 110 with the baseline surface temperature of the pet, and determine the change in the surface temperature of the pet, based on comparing the surface temperature of the pet with the baseline surface temperature of the pet. For example, if the surface temperature of the pet is 39°C and the baseline surface temperature is 38.8°C, then the change in the surface temperature of the pet may be is 0.2°C.

[0044] As further shown in FIG. 5, the process 500 may include generating a notification including information identifying the change in the surface temperature of the pet (operation 540), and causing the notification to be displayed (operation 550).

[0045] The server 130 may generate a notification including information identifying the change in the surface temperature of the pet, and transmit the notification to the user device 120 to cause the user device 120 to display the information identifying the change in the surface temperature of the pet.

[0046] The server 130 may generate the notification including information identifying the change in the surface temperature of the pet, and cause the notification including information identifying the change in the surface temperature of the pet to be displayed, based on the change in the surface temperature of the pet satisfying a threshold. For example, the server 130 may compare the surface temperature of the pet with the baseline surface temperature of the pet, and determine that a difference between the surface temperature of the pet and the baseline surface temperature of the pet is greater a threshold.

[0047] The information identifying the change in the surface temperature of the pet may include text or audio information that identifies the change in the surface temperature of the pet. For example, the information identifying the change in the surface temperature of the pet may be text that indicates that “your pet has an increased temperature!,” “your pet has a decreased temperature!,” or the like.

[0048] The information identifying the change in the surface temperature of the pet may include a value of the surface temperature of the pet. For example, the information identifying the change in the surface temperature of the pet may include a particular value such as “39°C,” or the like.

[0049] The information identifying the change in the surface temperature of the pet may include a value of a difference between the surface temperature of the pet and the baseline surface temperature of the pet. For example, the information identifying the change in the surface temperature of the pet may include a particular value such as “+2°C,” “-2°C,” or the like.

[0050] The information identifying the change in the surface temperature of the pet may include a value of an internal body temperature of the pet. The server 130 may store mapping information that maps a surface temperature of the pet with an internal body temperature of the pet, and determine the internal body temperature of the pet. In other words, the surface temperature of the pet may be different thanan internal body temperature of the pet, and a change in a surface temperature of the pet may be a proxy for a change in the internal body temperature of the pet. It should be understood that the embodiments herein that sense the surface temperature of the pet are less-invasive than techniques that measure the internal body temperature of the pet.

[0051] The information identifying the change in the surface temperature of the pet may include information that provides a recommendation or suggestion to the pet owner. For example, the information may include an instruction for the pet owner to seek medical attention, to schedule an appointment, to adjust behavior of the pet, to adjust care of the pet, or the like.

[0052] FIG. 6 is a diagram of an example user interface 600 for displaying a notification including information identifying the change in the surface temperature of the pet. As shown in FIG. 6, the user device 120 may display information identifying the change in the surface temperature of the pet. The user device 120 may also display an icon (“view suggestions”) that permits the owner to obtain additional information, such as suggestions or recommendations, regarding the change in the surface temperature of the pet.

[0053] FIG. 7 is a diagram of an example process for training an artificial intelligence (Al) model for generating a baseline surface temperature of the pet. The server 130 may generate, store, train, and / or use the Al model 140. According to an embodiment, the server 130 may include the Al model 140 and / or instructions associated with the Al model 140. For example, the server 130 may include instructions for generating the Al model 140, training the Al model 140, using the Al model 140, etc. According to another embodiment, a system or device other than the server 130 may be used to generate and / or train the Al model 140. For example, a system or device may include instructions for generating the Al model 140, and / or instructions for training the Al model 140. The system or device may provide a resulting trained Al model 140 to the server 130 for use.

[0054] As shown in FIG. 7, according to an embodiment, the process 700 may include a training phase 702, a deployment phase 708, and a monitoring phase 714. In the training phase 702, at operation 706, the process 700 may include receiving and processing training data 704 to generate a trained Al model 140 for performing one or more operations of any one of the process 500. The training data 704 may include information related to a surface temperature of the pet. For example, thetraining data 704 may include an internal temperature of the pet, a surface temperature value of the pet, a temperature value of the sensor device 110, an ambient temperature value, or the like.

[0055] Generally, the Al model 140 may include a set of variables (e.g., nodes, neurons, filters, or the like) that are tuned (e.g., weighted, biased, or the like) to different values via the application of the training data 704. According to an embodiment, the training process at operation 706 may employ supervised, unsupervised, semi-supervised, and / or reinforcement learning processes to train the Al model 140. According to an embodiment, a portion of the training data 704 may be withheld during training and / or used to validate the trained Al model 140.

[0056] For supervised learning processes, the training data 704 may include labels or scores that may facilitate the training process by providing a ground truth. The Al model 140 may have variables set at initialized values (e.g., at random, based on Gaussian noise, based on pre-trained values, or the like). The Al model 140 may provide an output, and the output may be compared with the corresponding label or score (e.g., the ground truth), which may then be back-propagated through the Al model 140 to adjust the values of the variables. This process may be repeated for a plurality of samples at least until a determined loss or error is below a predefined threshold. According to an embodiment, some of the training data 704 may be withheld and used to further validate or test the trained Al model 140.

[0057] For unsupervised learning processes, the training data 704 may not include pre-assigned labels or scores to aid the learning process. Instead, unsupervised learning processes may include clustering, classification, or the like, to identify naturally occurring patterns in the training data 704. As an example, the training data 704 may be clustered into groups based on identified similarities and / or patterns. K-means clustering or K-Nearest Neighbors may also be used, which may be supervised or unsupervised. Combinations of K-Nearest Neighbors and an unsupervised cluster technique may also be used. For semi-supervised learning, a combination of training data 704 with pre-assigned labels or scores and training data 704 without pre-assigned labels or scores may be used to train the Al model 140.

[0058] When reinforcement learning is employed, an agent (e.g., an algorithm) may be trained to make a decision regarding the data quality from the training data 704 through trial and error. For example, based on making a decision, the agent may then receive feedback (e.g., a positive reward if the prediction was above apredetermined threshold), adjust its next decision to maximize the reward, and repeat until a loss function is optimized.

[0059] After being trained, the trained Al model 140 may be stored and subsequently applied by the server 130 during the deployment phase 708. For example, during the deployment phase 708, the trained Al model 140 executed by the server 130 may receive input data 710 for performing one or more operations of any one of the process 500.

[0060] After being applied by the server 130 during the deployment phase 708, the trained Al model 140 may be monitored during the monitoring phase 714. The monitoring data 716 may include data that is output by the Al model 140. During the monitoring process 718, the monitoring data 716 may be analyzed along with the predicted output data 712 and input data 710 to determine an accuracy of the trained Al model 140. According to an embodiment, based on the analysis, the process 700 may return to the training phase 702, where at operation 706 values of one or more variables of the model may be adjusted to improve the accuracy of the Al model 140.

[0061] The example process 700 described above is provided merely as an example, and may include additional, fewer, different, or differently arranged aspects than depicted in FIG. 7.

[0062] Although the implementations herein are described in connection with a pet, it should be understood that the implementations are applicable to any other type of mobile object, such as a human, an animal, etc. Moreover, although implementations herein are described in connection with a pet, it should be understood that the implementations herein are applicable to animals that are not pets, such as livestock, zoo animals, undomesticated animals, or the like.

[0063] While principles of the present disclosure are described herein with reference to illustrative embodiments for particular applications, it should be understood that the disclosure is not limited thereto. Those having ordinary skill in the art and access to the teachings provided herein will recognize additional modifications, applications, embodiments, and substitution of equivalents all fall within the scope of the embodiments described herein. Accordingly, the invention is not to be considered as limited by the foregoing description.

Claims

CLAIMSWhat is claimed is:1 . A method for determining a change in a surface temperature of a pet, the method comprising: receiving surface temperature data of the pet generated by a sensor device; comparing the surface temperature data of the pet with a baseline surface temperature of the pet; determining the change in the surface temperature of the pet, based on comparing the surface temperature of the pet with the baseline surface temperature of the pet; generating a notification including information identifying the change in the surface temperature of the pet; and causing the notification to be displayed.

2. The method of claim 1 , wherein the sensor device is configured to generate the surface temperature data of the pet based on detecting motion of the pet.

3. The method of claim 1 , wherein the surface temperature data includes one or more of a surface temperature value of the pet, or a temperature value of the sensor device, an ambient temperature value.

4. The method of claim 1 , wherein the baseline surface temperature includes an average of previously determined surface temperatures of the pet.

5. The method of claim 1 , wherein the baseline surface temperature includes a user-reported surface temperature of the pet.

6. The method of claim 1 , further comprising: determining a timeframe; and determining the baseline surface temperature based on the timeframe.

7. The method of claim 1 , further comprising:determining that the change in the surface temperature of the pet satisfies a threshold, wherein the generating the notification comprises generating the notification based on determining that the change in the surface temperature of the pet satisfies the threshold.

8. A device for determining a change in a surface temperature of a pet, the device comprising: a memory configured to store instructions; and one or more processors configured to execute the instructions to perform operations comprising: receiving surface temperature data of the pet generated by a sensor device; comparing the surface temperature data of the pet with a baseline surface temperature of the pet; determining the change in the surface temperature of the pet, based on comparing the surface temperature of the pet with the baseline surface temperature of the pet; generating a notification including information identifying the change in the surface temperature of the pet; and causing the notification to be displayed.

9. The device of claim 8, wherein the sensor device is configured to generate the surface temperature data of the pet based on detecting motion of the pet.

10. The device of claim 8, wherein the surface temperature data includes one or more of a surface temperature value of the pet, or a temperature value of the sensor device, an ambient temperature value.11 . The device of claim 8, wherein the baseline surface temperature includes an average of previously determined surface temperatures of the pet.

12. The device of claim 8, wherein the baseline surface temperature includes a user-reported surface temperature of the pet.

13. The device of claim 8, wherein the operations further comprise: determining a timeframe; and determining the baseline surface temperature based on the timeframe.

14. The device of claim 8, wherein the operations further comprise: determining that the change in the surface temperature of the pet satisfies a threshold, wherein the generating the notification comprises generating the notification based on determining that the change in the surface temperature of the pet satisfies the threshold.

15. A non-transitory computer-readable medium configured to store instructions that, when executed by one or more processors of a device for determining a change in a surface temperature of a pet, cause the one or more processors to perform operations comprising: receiving surface temperature data of the pet generated by a sensor device; comparing the surface temperature data of the pet with a baseline surface temperature of the pet; determining the change in the surface temperature of the pet, based on comparing the surface temperature of the pet with the baseline surface temperature of the pet; generating a notification including information identifying the change in the surface temperature of the pet; and causing the notification to be displayed.

16. The non-transitory computer-readable medium of claim 15, wherein the sensor device is configured to generate the surface temperature data of the pet based on detecting motion of the pet.

17. The non-transitory computer-readable medium of claim 15, wherein the surface temperature data includes one or more of a surface temperature value of the pet, or a temperature value of the sensor device, an ambient temperature value.

18. The non-transitory computer-readable medium of claim 15, wherein the baseline surface temperature includes an average of previously determined surface temperatures of the pet.

19. The non-transitory computer-readable medium of claim 15, wherein the baseline surface temperature includes a user-reported surface temperature of the pet.

20. The non-transitory computer-readable medium of claim 15, wherein the operations further comprise: determining a timeframe; and determining the baseline surface temperature based on the timeframe.

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