PROCESSOR-BASED METHODS, SYSTEMS AND PROGRAMS FOR REMOTE ANIMAL HEALTH MONITORING, ASSESSMENT, DIAGNOSIS, AND MANAGEMENT

• US 20150039239A1
• Filed: 07/30/2014
• Published: 02/05/2015
• Est. Priority Date: 07/31/2013
• Status: Abandoned Application

• Current Assignee: AgLogica Holdings, LLC
• Original Assignee: AgLogica Holdings, LLC
• Inventors: Shuler, Richard, Sarzen, Marcel, Hu, Yongguo
• Application Number:
  US14/446,908
• Publication Number:
  US 20150039239A1
• Time in Patent Office: Days
• US Class Current: 702/19
• CPC Class Codes: A61B 2503/40 Animals
• CPC Class Codes: A61B 5/0022 Monitoring a patient using a global network, e.g. telephone networks, internet
• CPC Class Codes: A61B 5/0024 for multiple sensor units attached to the patient, e.g. using a body or personal area network
• CPC Class Codes: A61B 5/7275 Determining trends in physiological measurement data; Predicting development of a medical condition based on physiological measurements, e.g. determining a risk factor
• CPC Class Codes: G16H 40/67 for remote operation
• CPC Class Codes: G16H 50/20 for computer-aided diagnosis, e.g. based on medical expert systems

Abstract

Processor-based methods and systems and computer programs for remote animal health monitoring receive and process data relating to animal health parameters obtained from a plurality of different types of sensors. Baseline data signatures are determined from the data obtained for individual animals, and as data is subsequently collected is compared to the current data signature to assess animal health. Deviations from the signatures may serve to predictively diagnose certain conditions and facilitate medical intervention before adverse physical symptoms are manifested. Informational data and analytics are made available to animal owners, health care providers, and other interested persons.

Claims

1. A method for remotely assessing a health state of at least one non-human animal, said method being implemented with at least one computing device including at least one processor in communication with at least one memory, the method comprising:

generating a baseline data signature of at least one non-human animal using the at least one processor based on a first set of collected data;

generating, by the at least one processor, a first current data signature of the at least one non-human animal based on a second set of collected data and at least one reference health state profile;

multiplying the baseline data signature by a threshold factor to generate a baseline threshold data signature;

comparing by the at least one processor, the baseline threshold data signature to the first current data signature; and

generating a health assessment of the at least one non-human animal.

2. The method of claim 1, wherein generating the first current data signature further comprises:

collecting the first set of collected data from a plurality of sensors of different types at a plurality of predetermined time intervals over a first predetermined time period, wherein each one of the different types of sensors in the plurality of sensors of different types collects data related to a particular health parameter of the at least one non-human animal;

collecting the second set of collected data from the plurality of sensors of different types at the plurality of predetermined time intervals over a second pre-determined time period;

determining a plurality of behavioral event scores using the at least one processor based on the first and second collected data sets, wherein each of the plurality of behavioral event scores represents a sum of variances of a particular health parameter measured by a first type of sensor of the plurality of sensors of different types in the first and second collected data sets over corresponding predetermined time intervals; and

applying a weighing factor to each of the plurality of behavioral event scores, wherein the weighing factor is based on the at least one reference health state profile, and wherein each behavioral event score is weighed based on the significance of the particular health parameter of the behavioral health score to the at least one reference health state profile.

3. The method of claim 2, wherein at least one reference health state profile includes a predetermined health, behavior, or physiological state profile.

4. The method of claim 1 further comprising generating a second current data signature using the at least one processor based on the second set of collected data and at least another reference health state profile that is different from the at least one health state profile.

5. The method of claim 1, wherein multiplying the baseline data signature by the threshold factor further comprises multiplying a multiple of a standard deviation of the first data set to the baseline data signature.

6. The method of claim 1, wherein generating the health assessment of the animal includes a predictive diagnosis of a health condition

7. The method of claim 6, further comprising generating at least one alert relating to the predictive diagnosis so that medical intervention may occur before adverse physical symptoms are manifested in the non-human animal.

8. The method of claim 1, wherein generating a baseline data signature of at least one non-human animal using the at least one processor based on a first set of collected data comprising dynamically generating a revised baseline data signature based upon at least one collected set of data subsequent to the first set of collected data.

9. A system for remotely monitoring the health state of at least one non-human animal, said system including at least one computing device including at least one processor in communication with at least one memory, said at least one processor programmed to:

generate a baseline data signature of the at least one non-human animal based on a first set of collected data;

generate a first current data signature of the at least one non-human animal based on a second set of collected data;

multiply the baseline data signature by a threshold factor to generate a baseline threshold data signature for the at least one non-human animal;

compare the baseline threshold data signature to the first current data signature; and

generate a health assessment of the at least one non-human animal.

10. The system of claim 9, wherein the at least one processor is further programmed to receive observed data related to the health state of the at least one non-human animal, wherein the observed data is input by a human user.

11. The system of claim 9, wherein the baseline and current data signatures are based on data collected from a plurality of sensors of different types.

12. The system of claim 9, wherein the at least one non-human animal comprises a plurality of non-human animals, and wherein the at least one processor is further programmed to generate a health state assessment of each of the plurality of non-human animals.

13. The system of claim 9, wherein the at least one processor is programmed to predictively diagnosis a health condition of the at least one non-human animal.

14. The system of claim 9, wherein the at least one processor is programmed to dynamically generate a revised baseline data signature based upon at least one collected set of data subsequent to the first set of collected data.

15. The system of claim 9, wherein the at least one processor is programmed to:

when a variation between the baseline threshold data signature and the first current data signature exceeds a predetermined amount, indicate a significant change in health of the at least one non-human animal in at least one aspect; and

when a variation between the baseline threshold data signature and the first current data signature exceeds is less than a predetermined amount, indicate the at least one non-human animal to be in good health.

16. A computer program embodied on a non-transitional computer readable medium for evaluating and assessing a health state of at least one non-human animal, the program comprising at least one code segment for instructing at least one computing device including at least one memory and at least one processor in communication with the memory to:

generate a baseline data signature of the at least one non-human animal based on a first set of collected data;

generate a first current data signature of the at least one non-human animal based on a second set of collected data;

multiply the baseline data signature by a threshold factor to generate a baseline threshold data signature for the at least one non-human animal;

compare the baseline threshold data signature to the first current data signature; and

generate a health assessment of the at least one non-human animal.

17. The computer program of claim 16, wherein the baseline and current data signatures are based on data collected from a plurality of sensors of different types.

18. The computer program of claim 16, wherein the at least one non-human animal comprises a plurality of non-human animals of different types, and the computer program comprises at least one code segment for instructing the at least one processor to generate a health state assessment of each different type of animal in the plurality of animals.

19. The computer program of claim 16, wherein the computer program comprises at least one code segment for instructing at least one processor to predictively diagnosis a health condition of the at least one non-human animal.

20. The computer program of claim 16, wherein the computer program comprises at least one code segment for instructing the at least one processor to dynamically generate a revised baseline data signature based upon at least one collected set of data subsequent to the first set of collected data.