Description:FORM 2
THE PATENTS ACT, 1970
(39 of 1970)
&
THE PATENTS RULES, 2003
COMPLETE SPECIFICATION
[See section 10 and rule 13]
1. TITLE OF THE DISCLOSURE: DEVICE AND METHOD FOR DETECTING POTENTIAL HEALTH RISKS IN PETS

2. APPLICANT:
(a) Name – BVUR INNOVATIONS (INDIA) PRIVATE LIMITED
(b) Nationality – an Indian company
(c) Address - S Office No 503, 504, 5th Floor, Majestique Cityview, Shankar Sheth Road, Seven Loves Chowk, Gultekdi, Pune, Maharashtra, INDIA, 411037

THE FOLLOWING SPECIFICATION PARTICULARLY DESCRIBES THE DISCLOSURE AND THE MANNER IN WHICH IT IS TO BE PERFORMED.

FIELD
[0001] The present disclosure generally relates to a field of the pet management. In particular, the present disclosure relates to device and method for detecting and mitigate potential health risks in pets.
BACKGROUND
[0002] Pets often exhibit subtle signs of health issues that can be challenging for owners to detect, analyse, and mitigate health risks, without professional assistance. Traditional methods of monitoring pet health typically rely on periodic veterinary check-ups, which may not capture real-time changes or early indicators of potential problems. Moreover, lack of continuous monitoring tools makes it difficult to track various physiological and behavioural parameters that could signal the onset of health issues, such as musculoskeletal, neurological, respiratory, and cardiovascular issues. Consequently, pet owners are often left unaware of their pets' health status until the issues become severe, thereby leading to delayed interventions and increased risks to well-being of the pet.
SUMMARY
[0003] The present invention as claimed in the claims of the present specification is a pet interface device. The device comprises a processor, a connectivity module, and a memory including stored data, tables, and executable program instructions. The device further comprises two or more sensors, wherein each sensor senses a physiological parameter corresponding to a pet. The device includes a pet health monitoring module configured to monitor gait patterns, sudden activities, respiratory activities, and vital signs of the pet based on two or more physiological parameters received from two or more corresponding sensors. The pet health monitoring module is further configured to analyse data corresponding to the two or more physiological parameters to detect and predict potential health issues, including musculoskeletal, neurological, respiratory, or cardiovascular conditions.
[0004] In one implementation, the two or more sensors includes a body temperature sensor, a heart rate sensor, an accelerometer, a Global Positioning System (GPS) module, a gyroscope, a barometer, a pedometer, an SPO2 sensor, and a camera.
[0005] In one implementation, the pet health monitoring module comprises a gait analysis module configured to analyse gait patterns using data from the accelerometer and the gyroscope to detect limping or irregular movements indicating musculoskeletal problems.
[0006] In one implementation, the pet health monitoring module comprises a movement monitoring module configured to monitor movement patterns and detect changes in travel distances of the pet, wherein the changes are indicative of anxiety, disorientation, or other health issues.
[0007] In one implementation, the pet health monitoring module comprises a behaviour tracking module using the camera to monitor eating and drinking patterns, and interactions with the environment and other animals, to detect signs of stress, aggression, or health problems.
[0008] In one implementation, the pet health monitoring module is an Artificial Intelligence and Machine Learning (AI/ML) module configured to detect anomalies in the data, wherein the anomalies are indicative of potential health issues, thereby providing alerts for early intervention, and perform predictive analytics by analysing historical data to predict potential health issues before the potential health issues become severe.
[0009] In one implementation, the pet interface device further comprises a customizable health plan module allowing pet owners to personalize monitoring parameters based on the pet's age, breed, and known health conditions, providing tailored health recommendations and interventions.
[0010] The present invention as claimed in the claims of the present specification also includes a method for detecting, analysing, and mitigating health issues of a pet. The method comprises obtaining from each of two or more sensors data related to a physiological parameter corresponding to a pet, including gait patterns, sudden activities, respiratory activities, and vital signs of the pet by a pet health monitoring module; monitoring and analysing the gait patterns, the sudden activities, the respiratory activities, and the vital signs of the pet by the pet health monitoring module; and implementing machine learning algorithms to recognize patterns based on the data related to the two or more physiological parameters for detecting, analysing, predicting, and mitigating potential health issues of the pet, including musculoskeletal, neurological, respiratory, and cardiovascular conditions.
[0011] In one implementation, the two or more sensors includes a body temperature sensor, a heart rate sensor, an accelerometer, a Global Positioning System (GPS) module, a gyroscope, a barometer, a pedometer, an SPO2 sensor, and a camera.
[0012] In one implementation, the method further comprises a step of tracking gait patterns of the pet, by a gait analysis module, using data from the accelerometer and the gyroscope to detect limping or irregular movements indicating musculoskeletal problems.
[0013] In one implementation, the method further comprises step of monitoring movement patterns of the pet by a movement monitoring module. The method further includes detecting changes in travel distances of the pet by the movement monitoring module, wherein the changes are indicative of anxiety, disorientation, or other health issues.
[0014] In one implementation, the method further comprises step of tracking eating and drinking patterns of the pet and interactions of the pet with the environment and other animals by a behaviour tracking module. The method further includes detecting signs of stress, aggression, or other health issues by the behaviour tracking module based on the tracking.
[0015] In one implementation, the method further comprises steps of facilitating personalization of monitoring parameters of the pet by a customizable health plan module, wherein the personalization is based on the pet's age, breed, and known health conditions. The method further includes providing tailored health recommendations based on the personalization.
BRIEF DESCRIPTION OF FIGURES
[0016] The aspects and other features of the subject matter will be better understood with regard to the following description, appended claims, and accompanying figures. The use of the same reference number in different figures indicates similar or identical items.
[0017] FIG. 1A illustrates a block diagram of a pet interface device 104A, in accordance with an embodiment of the present disclosure.
[0018] FIG. 1B illustrates a block diagram of a pet interface device as a part of a pet management system, in accordance with another embodiment of the present disclosure.
[0019] FIG. 2 illustrates a block diagram of a pet interface device as a part of a pet management system, in accordance with another embodiment of the present disclosure.
[0020] FIG. 3 illustrates a block diagram for a method for pet health management, in accordance with another embodiment of the present disclosure.
DETAILED DESCRIPTION
[0021] In the present disclosure, a reference to a term “module” is understood as a set of instructions stored in the memory of the device to achieve a specific purpose.
[0022] In the present disclosure, a reference to a term “connectivity module” is understood as the hardware component of the device responsible for enabling communication between the device and other devices or systems.
[0023] In the present disclosure, a reference to a term “pet health monitoring module” is understood as a module configured for monitoring gait patterns, sudden activities, respiratory activities, and vital signs of the pet based on inputs from the two or more sensors.
[0024] In the present disclosure, a reference to a term “server” is understood as a remote hardware component in the pet management system that processes and stores data associated with the physiological parameters of the pet captured by the two or more sensors.
[0025] In the present disclosure, a reference to a term “user interface device” is understood as a smart device used by the pet owner to interact with the server, which may include a smartphone, tablet, laptop, or any other smart device.
[0026] In the present disclosure, a reference to a term “accelerometer” is understood as a sensor within the two or more sensors used to measure the pet's acceleration and movement patterns.
[0027] In the present disclosure, a reference to a term “gyroscope” is understood as a sensor within the two or more sensors used to measure the pet's orientation and rotational movements.
[0028] In the present disclosure, a reference to a term “GPS module” is understood as a sensor within the two or more sensors used to track the geographical location and travel distances of the pet.
[0029] In the present disclosure, a reference to a term “camera” is understood as a sensor within the two or more sensors used to capture visual data of the pet’s activities and interactions.
[0030] In the present disclosure, a reference to a term “AI/ML algorithms” is understood as artificial intelligence and machine learning algorithms used within the pet health monitoring module to analyse data, detect anomalies, and perform predictive analytics.
[0031] FIG. 1A illustrates a block diagram of a pet interface device 104A, in accordance with an embodiment of the present disclosure. The device 104A includes a processor 106 and a connectivity module 106A. The device 104A further includes a memory 108 for storing data, tables, and executable program instructions. The device 104A further includes a two or more sensors 110, where each sensor is provided to sense a physiological parameter corresponding to a pet. The device 104A further includes a pet health monitoring module 112 configured for monitoring gait patterns, sudden activities, respiratory activities, and vital signs of the pet based on two or more physiological parameters received from the two or more sensors 110. The pet health monitoring module 112 is further configured to analyse data corresponding to the two or more physiological parameters to detect and predict potential health issues, including musculoskeletal, neurological, respiratory, and cardiovascular conditions.
[0032] FIG. 1B illustrates a block diagram of a pet interface device 104A as a part of a pet management system 100, in accordance with another embodiment of the present disclosure. As shown in FIG. 1B, the pet interface device 104A is part of a pet management system 100 that includes a server 102. In the system 100, the pet interface device 104A, the server 102, and a user interface device 104B may be communicatively coupled by the connectivity module 106A to facilitate data transmission among the pet interface device 104A, the server 102, and the user interface device 104B. In one implementation, the connectivity module 106A facilitates the communication through any communication system, including but not limited to, technologies such as Internet, GPS, GSM, or LTE. A user may interact with the server 102 using the user interface device 104B, where such user interface device 104B may be a smart device of a pet owner of the pet. The user interface device may be a smartphone, a tablet, a laptop, or any other smart device.
[0033] In one implementation, the data associated with the physiological parameters of the pet captured by the two or more sensors 110 may be remotely processed and stored on the server 102. In another implementation, the data may be processed on-board the pet interface device 104A and can be transferred to the server 102 for remote storage. Any data that is stored at the server 102 may be accessible to the user by the user interface device 104B, where the user may use an application or a website to interface with said data.
[0034] FIG. 2 illustrates a block diagram of a pet interface device as a part of a pet management system, in accordance with another embodiment of the present disclosure. As shown in FIG. 2, the pet interface device 204A, in accordance with one implementation, includes a processor 206, a connectivity module 206A, and a memory 208. In one implementation, the pet interface device 204A is a part of a system 200 that includes a server 202 and a user interface device 204B. The pet interface device 204A further includes two or more sensors 210 and a pet health monitoring module 212. The present embodiment is similar to the pet interface device 104A of system 100. To avoid repetition, the description of similar elements is omitted here for brevity.
[0035] The device 204A further includes the pet health monitoring module 212 configured for monitoring gait patterns, sudden activities, respiratory activities, and vital signs of the pet based on inputs from the two or more sensors 210 related to two or more physiological parameters. In one implementation, the two or more sensors includes a body temperature sensor, a heart rate sensor, an accelerometer, a Global Positioning System (GPS) module, a gyroscope, a barometer, a pedometer, an SPO2 sensor, and a camera.
[0036] The pet health monitoring module 212 is further configured to analyse data from the two or more sensors 210 corresponding to the two or more physiological parameters to detect and predict potential health issues, including musculoskeletal, neurological, respiratory, and cardiovascular conditions. In one implementation, the pet health monitoring module 212 is an Artificial Intelligence and Machine Learning (AI/ML) module configured to detect anomalies in the data, wherein the anomalies are indicative of potential health issues, thereby providing alerts for early intervention. The pet health monitoring module 212 may be further configured for performing predictive analytics by analysing historical data to predict potential health issues before the potential health issues become severe. The term historical data may refer to the data captured by the two or more sensors corresponding to the two or more physiological parameters over a pre-defined period of time. In one implementation, the pre-defined period of time may be a period ranging from 3 months to 2 years.
[0037] In one implementation, the pet health monitoring module 212 comprises a gait analysis module 220 configured to analyse gait patterns of the pet using data from the accelerometer and the gyroscope to detect limping or irregular movements indicating musculoskeletal problems. In one implementation, the gait analysis module 220 is configured to use data from the accelerometer and the gyroscope, which are included in the two or more sensors 210. .
[0038] In another implementation, the gait analysis module 220 may further utilize data from additional sensors, apart from the accelerometer and the gyroscope, such as a pedometer or a camera to enhance the accuracy of the gait analysis. The pedometer may provide data on the number of steps taken by the pet, while the camera may capture visual data of the pet's movements, which may be used for more detailed analysis of gait patterns.
[0039] Further, the gait analysis module 220 may be configured to employ machine learning algorithms to improve the detection and prediction of musculoskeletal problems over time. By analysing historical gait data, the gait analysis module 220 may be configured to recognize patterns and anomalies that may indicate developing health issues. The gait analysis module 220 may also generate alerts for early intervention when irregular gait patterns are detected.
[0040] In one implementation, the pet health monitoring module 212 comprises a movement monitoring module 222 configured to monitor movement patterns and detect changes in travel distances of the pet, wherein the changes are indicative of anxiety, disorientation, or other health issues. The movement monitoring module 222 utilizes data from the two or more sensors 210, including but not limited to the accelerometer, GPS module, and gyroscope, to track and analyse the pet's movement patterns.
[0041] In another implementation, the movement monitoring module 222 may be configured to detect sudden movements during both sleep and awake states of the pet to indicate disorders or diseases based on inputs from different sensors. The movement monitoring module 222 may utilize data from the accelerometer and gyroscope from the two or more sensors 210 to capture and analyse the pet's movement patterns. For example, the movement monitoring module 222 may detect sudden movements that could indicate tick infestation, where the pet frequently scratches or shakes its body, or seizures, characterized by abrupt, uncontrolled movements.
[0042] In one implementation, the movement monitoring module 222 can process the data from two or more sensors 210 to analyse the nature of the movements by distinguishing between normal activities and potential signs of health issues. The movement monitoring module 222 may be configured to identify patterns in the data, such as repetitive or unusual movements during sleep, which may suggest underlying disorders such as sleep apnea or restless leg syndrome.
[0043] Additionally, the movement monitoring module 222 may be configured to differentiate between various types of sudden movements. For example, movements caused by external factors, such as environmental noises or interactions with other animals, may be filtered out to focus on movements that are more indicative of health issues. The movement monitoring module 222 can achieve this by integrating data from additional sensors, such as a microphone or a camera, to provide context to the detected movements.
[0044] In one implementation, the pet health monitoring module 212 comprises a behaviour tracking module 224 that uses the camera to monitor eating and drinking patterns of the pet, and interactions of the pet with the environment and other animals. Based on the aforementioned inputs, the behaviour tracking module 224 is configured to detect signs of stress, aggression, or health problems. The behaviour tracking module 224 captures and analyses visual data through the camera, which is part of the two or more sensors 210, to observe the daily activities and interactions of the pet.
[0045] In another implementation, the behaviour tracking module 224 may utilize additional sensors, such as a microphone to capture audio cues, and an accelerometer to detect physical activities, thereby providing a comprehensive analysis of the behaviour of the pet. The behaviour tracking module 224 can process the combined data to identify changes in the pet's routine or behaviour, which may indicate potential health issues or environmental stressors.
[0046] The behaviour tracking module 224 may be configured to use machine learning algorithms to analyse patterns in the collected data, enhancing its ability to detect subtle changes in behaviour over time. By recognizing deviations from normal behaviour, the behaviour tracking module 224 may alert the user to potential issues such as decreased appetite, excessive aggression, or signs of stress.
[0047] In one implementation, the pet interface device 204A further comprises a customizable health plan module 226 allowing pet owners to personalize monitoring parameters based on age, breed, and known health conditions of the pet, thereby providing tailored health recommendations and interventions. The customizable health plan module 226 enables users to input specific details about the pet, ensuring that the monitoring and analysis are reasonably accurately aligned with the unique characteristics of the pet.
[0048] The customizable health plan module 226 can be used to specifically address issues when the pet has prior health conditions. For example, if the pet has only three limbs instead of the regular four, the module 226 allows the user to manually input such parameters into the pet health monitoring module 212. Customization helps to avoid false positives in the detection of potential health issues, as the system will be aware of and account for the pet's specific condition.
[0049] FIG. 3 illustrates a block diagram for a method for pet health management 300 (hereinafter referred to as method 300), in accordance with another embodiment of the present disclosure.
[0050] At block 302, the method 300 includes a step of obtaining from each of two or more sensors data related to a physiological parameter corresponding to a pet, including gait patterns, sudden activities, respiratory activities, and vital signs of the pet by a pet health monitoring module. .
[0051] At block 304, the method 300 further includes a step of monitoring and analysing the gait patterns, the sudden activities, the respiratory activities, and the vital signs of the pet by the pet health monitoring module.
[0052] At block 306, the method 300 further includes a step of implementing machine learning algorithms to recognize patterns based on the data related to the two or more physiological parameters for detecting, analysing, predicting, and mitigating potential health issues of the pet, including musculoskeletal, neurological, respiratory, and cardiovascular conditions.
[0053] In one implementation, the method 300 further includes a step of tracking, by a gait analysis module 220, gait patterns using data from the accelerometer and the gyroscope within the two or more sensors 210 to detect limping or irregular movements indicating musculoskeletal problems. The data captured by the accelerometer and gyroscope is processed by the processor 206, which is in communication with the gait analysis module 220, allowing for reasonably accurate detection and analysis of gait patterns.
[0054] In one implementation, the method 300 further includes a step of monitoring movement patterns by a movement monitoring module 222. The method further includes detecting changes in travel distances of the pet by the movement monitoring module 222, wherein the changes are indicative of anxiety, disorientation, or other health issues. The movement monitoring module 222 utilizes data from the GPS module, accelerometer, and gyroscope from within the two or more sensors 210. The processor 206 processes the data to provide real-time monitoring and analysis of the pet's movements.
[0055] In one implementation, the method 300 further includes a step of tracking of eating and drinking patterns of the pet and interactions of the pet with environment and other animals by a behaviour tracking module. The method further includes the step of detecting signs of stress, aggression, or other health issues by the behaviour tracking module based on the tracking. . In one implementation, the camera within the two or more sensors 210 captures visual data, which is processed by the processor 206 and analysed by the behaviour tracking module 224 to monitor the pet's behaviour and interactions.
[0056] In one implementation, the method 300 further includes a step of facilitating personalization of monitoring parameters of the pet, by a customizable health plan module, wherein the personalization is based on pet's age, breed, and known health conditions. The method further includes the step of providing tailored health recommendations based on the personalization. . The customizable health plan module 226 allows users to input specific parameters into the pet health monitoring module 212. The processor 206 processes these inputs to adjust the monitoring parameters, ensuring that the system provides reasonably accurate and relevant health recommendations based on the pet's unique characteristics.
, Claims:We claim:
1. A pet interface device comprising:
a processor;
a connectivity module;
a memory including stored data, tables, and executable program instructions;
a two or more sensors, wherein each sensor sensing a physiological parameter corresponding to a pet;
a pet health monitoring module configured to monitor gait patterns, sudden activities, respiratory activities, and vital signs of the pet, based on two or more physiological parameters received from two or more corresponding sensors, and to analyse data corresponding to the two or more physiological parameters to detect and predict potential health issues, including musculoskeletal, neurological, respiratory, or cardiovascular conditions.
2. The pet interface device as claimed in claim 1, wherein the plurality of sensors includes a body temperature sensor, a heart rate sensor, an accelerometer, a Global Positioning System (GPS) module, a gyroscope, a barometer, a pedometer, an SPO2 sensor, and a camera.
3. The pet interface device as claimed in claim 2, wherein the pet health monitoring module comprises a gait analysis module configured to analyse gait patterns using data from the accelerometer and the gyroscope to detect limping or irregular movements indicating musculoskeletal problems.
4. The pet interface device as claimed in claim 1, wherein the pet health monitoring module comprises a movement monitoring module configured to monitor movement patterns and detect changes in travel distances of the pet, wherein the changes are indicative of anxiety, disorientation, or other health issues.
5. The pet interface device as claimed in claim 2, wherein the pet health monitoring module comprises a behaviour tracking module using the camera to monitor eating and drinking patterns, and interactions with environment and other animals, to detect signs of stress, aggression, or health problems.
6. The pet interface device as claimed in claim 1, wherein the pet health monitoring module is an Artificial Intelligence and Machine Learning (AI/ML) module configured to:
detect anomalies in the data, wherein the anomalies are indicative of potential health issues, thereby providing alerts for early intervention; and
perform predictive analytics by analysing historical data to predict potential health issues before the potential health issues become severe.
7. The pet interface device as claimed in claim 1, further comprising a customizable health plan module allowing pet owners to personalize monitoring parameters based on the pet's age, breed, and known health conditions, providing tailored health recommendations and interventions.
8. A method for detecting, analysing, and mitigating health issues of a pet, the method comprising:
obtaining from each of two or more sensors data related to a physiological parameter corresponding to a pet, including gait patterns, sudden activities, respiratory activities, and vital signs of the pet by a pet health monitoring module;
monitoring and analysing the gait patterns, the sudden activities, the respiratory activities, and the vital signs of the pet by the pet health monitoring module; and
implementing machine learning algorithms to recognize patterns based on the data related to the two or more physiological parameters for detecting, analysing, predicting, and mitigating potential health issues of the pet, including musculoskeletal, neurological, respiratory, and cardiovascular conditions.
9. The method for pet health management as claimed in claim 8, wherein the plurality of sensors includes a body temperature sensor, a heart rate sensor, an accelerometer, a Global Positioning System (GPS) module, a gyroscope, a barometer, a pedometer, an SPO2 sensor, and a camera.
10. The method for pet health management as claimed in claim 9, further comprising a step of tracking gait patters of the pet, by a gait analysis module, using data from the accelerometer and the gyroscope to detect limping or irregular movements indicating musculoskeletal problems.

11. The method for pet health management as claimed in claim 8, further comprising steps of:
monitoring movement patterns of the pet by a movement monitoring module; and
detecting changes in travel distances of the pet by the movement monitoring module, wherein the changes are indicative of anxiety, disorientation, or other health issues.
12. The method for pet health management as claimed in claim 8, further comprising steps of:
tracking of eating and drinking patterns of the pet and interactions of the pet with environment and other animals by a behaviour tracking module; and
detecting signs of stress, aggression, or other health issues by the behaviour tracking module based on the tracking.

13. The method for pet health management as claimed in claim 8, further comprising steps of:
facilitating personalization of monitoring parameters of the pet by a customizable health plan module, wherein the personalization is based on pet's age, breed, and known health conditions; and
providing tailored health recommendations based on the personalization.
Dated this

Saad Jawed Shaikh (IN/PA 3775)
Agent for the Applicant
To,
The Controller of Patents,
The Patent Office, Mumbai