DETAILED ACTION
This action is pursuant to claims filed on 11/21/2023. Claims 1-20 are pending. A first action on the merits of claims 1-20 is as follows.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Objections
Claims 6 and 16 are objected to because of the following informalities:
In claim 6, line 8, “the window” should read “a window”, as there is a lack of antecedent basis for this limitation.
In claim 16, line 6, “the window” should read “a window” , as there is a lack of antecedent basis for this limitation.
Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 4, 6, 14, and 16 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Regarding claim 4, the claim recites the limitation “the threshold” in line 8. It is unclear if this limitation is meant to refer to the threshold from claim 1, or the window threshold from claim 4. If it is referring to either of these thresholds, it needs to distinctly refer back to the particular threshold it is intending to reference. If it is referring to a different threshold, it needs to be distinguished from the threshold from claim 1 or the window threshold from claim 4. For purposes of examination, it is being interpreted as referring to the window threshold from claim 4.
Regarding claim 6, the claim recites the limitation “the plurality of windows” in line 3. There is insufficient antecedent basis for this limitation in the claim. It is unclear if this limitation is meant to read “a plurality of windows”, or if it is meant to depend on claim 4 which introduces the plurality of windows instead of claim 5. For purposes of examination, it is being interpreted as reading as “a plurality of windows”.
Regarding claim 14, the claim recites the limitation “the threshold” in line 6. It is unclear if this limitation is meant to refer to the threshold from claim 11, or the window threshold from claim 14. If it is referring to either of these thresholds, it needs to distinctly refer back to the particular threshold it is intending to reference. If it is referring to a different threshold, it needs to be distinguished from the threshold from claim 11 or the window threshold from claim 14. For purposes of examination, it is being interpreted as referring to the window threshold from claim 14.
Regarding claim 16, the claim recites the limitation “the plurality of windows” in line 2. There is insufficient antecedent basis for this limitation in the claim. It is unclear if this limitation is meant to read “a plurality of windows”, or if it is meant to depend on claim 14 which introduces the plurality of windows instead of claim 15. For purposes of examination, it is being interpreted as reading as “a plurality of windows”.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-6, 8-16, 18, and 20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. Under the two-step 101 analysis, the claims fail to satisfy the criteria for subject matter eligibility.
Regarding Step 1, claims 1-6, 8-16, 18, and 20 are all within at least one of the four statutory categories.
Claim 1 and its dependent claims disclose a method (process).
Claim 11 and its dependent claims disclose a system (machine).
Claim 18 and its dependent claims disclose a storage medium (machine).
Regarding Step 2A, Prong One, the independent claims 1, 11, and 18 recite an abstract idea. In particular the claims generally recite the following:
processing mobility data, the processing including determining one or more metrics based on the mobility data, the one or more metrics including velocity information, cadence information, acceleration information, and entropy information of the canine;
analyzing canine data corresponding to the canine to determine at least one baseline canine, wherein the at least one baseline canine is similar to the canine;
for each of the one or more metrics, comparing the one or more metrics of the canine to one or more baseline metrics of the at least one baseline canine;
determining one or more scores for each of the one or more metrics, the one or more scores based on a normal range of the one or more metrics from the one or more baseline metrics of the at least one baseline canine.
These elements recited in claims 1, 11, and 18 are drawn to abstract ideas since they involve a mental process that can be practically performed in the human mind including observation, evaluation, judgement, and opinion and using pen and paper.
Processing mobility data, the processing including determining one or more metrics based on the mobility data, the one or more metrics including velocity information, cadence information, acceleration information, and entropy information of the canine is drawn to an abstract idea since it is a mental process that can be practically performed in the human mind, or with the aid of pen and paper. A person of ordinary skill in the art could reasonably receive mobility data and determine one or more of the metrics described mentally or with the aid of pen and paper. These techniques are based on calculations, observation, and judgement, which can be performed mentally or by hand. The mathematics of determining metrics such as velocity, cadence, acceleration, or entropy are not overly complicated to perform using pen and paper given enough time, therefore these are defined as abstract ideas. There is nothing to suggest an undue level of complexity in processing mobility data, the processing including determining one or more metrics based on the mobility data, the one or more metrics including velocity information, cadence information, acceleration information, and entropy information of the canine.
Analyzing canine data corresponding to the canine to determine at least one baseline canine, wherein the at least one baseline canine is similar to the canine is drawn to an abstract idea since it is a mental process that can be practically performed in the human mind, or with the aid of pen and paper. A person of ordinary skill in the art could reasonably analyze the canine date to determine baseline data representing a baseline canine mentally or with the aid of pen and paper. These techniques are based on calculations, observation, and judgement, which can be performed mentally or by hand. The mathematics of determining baseline data is not overly complicated to perform using pen and paper given enough time, therefore these are defined as abstract ideas. There is nothing to suggest an undue level of complexity in analyzing canine data corresponding to the canine to determine at least one baseline canine, wherein the at least one baseline canine is similar to the canine.
For each of the one or more metrics, comparing the one or more metrics of the canine to one or more baseline metrics of the at least one baseline canine is drawn to an abstract idea since it is a mental process that can be practically performed in the human mind, or with the aid of pen and paper. A person of ordinary skill in the art could reasonably compare the metrics of the canine to the metrics of the baseline mentally or with the aid of pen and paper. These techniques are based on calculations, observation, and judgement, which can be performed mentally or by hand. The analysis of comparing data is not overly complicated to perform using pen and paper given enough time, therefore these are defined as abstract ideas. There is nothing to suggest an undue level of complexity in for each of the one or more metrics, comparing the one or more metrics of the canine to one or more baseline metrics of the at least one baseline canine.
Determining one or more scores for each of the one or more metrics, the one or more scores based on a normal range of the one or more metrics from the one or more baseline metrics of the at least one baseline canine is drawn to an abstract idea since it is a mental process that can be practically performed in the human mind, or with the aid of pen and paper. A person of ordinary skill in the art could reasonably determine a score based on a range of the comparison of the metrics mentally or with the aid of pen and paper. These techniques are based on calculations, observation, and judgement, which can be performed mentally or by hand. The analysis of determining a score based on the range is not overly complicated to perform using pen and paper given enough time, therefore these are defined as abstract ideas. There is nothing to suggest an undue level of complexity in determining one or more scores for each of the one or more metrics, the one or more scores based on a normal range of the one or more metrics from the one or more baseline metrics of the at least one baseline canine.
Regarding Step 2A, Prong Two, claim 1, 11, and 18 do not recite additional elements that integrate the exception into a practical application. Therefore, the claims are directed to the abstract idea. The additional elements merely:
Recite the words “apply it” or an equivalent with the judicial exception, or include instructions to implement the abstract idea on a computer, or merely use the computer as a tool to perform the abstract idea (e.g., “one or more processors” in claim 1, “at least one processor” in claims 11 and 18, “at least one memory storing instructions” in claim 11, and “a non-transitory computer-readable medium storing instructions” in claim 18);
Add insignificant extra-solution activity (the pre-solution activity of: using generic data-gathering components (e.g., “a device attached to a canine” in claims 1, 11, and 18)), insignificant post-solution activity (e.g., “displaying at least one alert on one or more user interfaces of a user device, the at least one alert indicating that an average of the one or more scores is above a threshold” in claims 1, 11, and 18)).
As a whole, the additional elements merely serve to gather information to be used by the abstract idea, while generically implementing it on a computer. There is no practical application because the abstract idea is not applied, relied on, or used in a meaningful way. The processing performed remains in the abstract realm, i.e., the result is not used for a treatment. No improvement to the technology is evident. Therefore, the additional elements, alone or in combination, do not integrate the abstract idea into a practical application.
Regarding Step 2B, claims 1, 11, and 18 do not recite additional elements, alone or in combination, that are sufficient to amount to significantly more than the judicial exception (i.e., an inventive concept) for the same reasons as described above.
Claims 1, 11, and 18 do not recite additional elements that amount to significantly more than the judicial exception itself. In particular, “a device attached to a canine” does not qualify as significantly more because this limitation merely described generic data gathering steps.
The data gathering step of “a device attached to a canine” is nothing more than a generic data gathering device. Such devices are evidenced by:
US Patent Application Publication 20160170446 (Cowley) discloses conventional wearable devices that detect a user’s motion (Cowley, [0030]);
US Patent 9972187 (Srinivasan) discloses conventional devices to track a user’s motion through a device attached to the user (Srinivasan, Column 1, lines 26-30);
US Patent Application Publication 20200008714 (Hsu) discloses conventional wearable devices to monitor a user’s motion (Hsu, [0004]);
US Patent Application Publication 20220225945 (Eletr) discloses conventional wearable devices to monitor the motion of a user (Eletr, [0132]).
Further, the elements of a processor in claim 1, 11, and 18, a memory in claim 11, and a non-transitory computer-readable medium in claim 18 do not qualify as significantly more because these limitations are simply appending well-understood, routine and conventional activities previously known in the industry, specified at a high level of generality, to the judicial exception, e.g., a claim to an abstract idea requiring no more than a generic computer to perform generic computer functions that are well-understood, routine and conventional activities previously known in the industry (see Electric Power Group, 830 F.3d 1350 (Fed. Cir. 2016); Alice Corp. v. CLS Bank Int’l, 110 USPQ2d 1976 (2014)) and/or a claim to an abstract idea requiring no more than being stored on a computer readable medium which is a well-understood, routine and conventional activity previously known in the industry (see Electric Power Group, 830 F.3d 1350 (Fed. Cir. 2016); Alice Corp. v. CLS Bank Int’l, 110 USPQ2d 1976 (2014); SAP Am. v. InvestPic, 890 F.3d 1016 (Fed. Circ. 2018)).
In view of the above, the additional elements individually do not integrate the exception into a practical application and do not amount to significantly more than the above judicial exception. Looking at the limitations as an ordered combination (that is, as a whole) adds nothing that is not already present when looking at the elements individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation, i.e., the computer is simply a tool to perform the process.
Regarding the dependent claims, claims 2-6 and 8-10 depend on claim 1, claims 12-16 depend on claim 11, and claim 20 depends on claim 18. The dependent claims merely further define the abstract idea or are additional data output that is well-understood, routine, and previously known in the industry. For example, the following are dependent claims reciting abstract ideas and can be performed in the human mind:
(Claim 2): “wherein the at least one alert includes positive reinforcement” further describes insignificant post-solution activity;
(Claim 3): “analyzing, by the one or more processors, the mobility data to determine at least one portion of the mobility data that does not include walking data; and removing, by the one or more processors, the at least one portion from the mobility data” is drawn to an abstract idea since it is a mental process that can practically be performed in the human mind. One of ordinary skill in the art could reasonably analyze the data to determine walking data and exclude the walking data from analysis mentally or with the aid of pen and paper. These techniques are based in calculations, observations, and judgement, which can be performed mentally or by hand given enough time, therefore it is drawn to an abstract idea;
(Claim 4): “segmenting, by the one or more processors, the mobility data into a plurality of windows based on at least one time interval; analyzing, by the one or more processors, each of the plurality of windows to determine whether a threshold number of the plurality of windows is below a window threshold; and removing, by the one or more processors, each of the plurality of windows that falls below the threshold” is drawn to an abstract idea since it is a mental process that can practically be performed in the human mind. One of ordinary skill in the art could reasonably segment the data into windows and remove the windows that are below a threshold mentally or with the aid of pen and paper. These techniques are based in calculations, evaluations, and judgement, which can be performed mentally or by hand given enough time, therefore it is drawn to an abstract idea;
(Claim 5): “wherein the normal range includes an upper mobility bound and a lower mobility bound” further defines the abstract idea as it further defines the range used to perform the analysis mentally or with the aid of pen and paper;
(Claim 6): “determining, by the one or more processors, a highest oscillation frequency of the plurality of windows; analyzing, by the one or more processors, the highest oscillation frequency to determine whether the highest oscillation frequency falls outside of an oscillation range; and in response to determining that the highest oscillation frequency does fall outside of the oscillation range, removing, by the one or more processors, the window of the plurality of windows that corresponds to the highest oscillation frequency from the mobility data” is drawn to an abstract idea since it is a mental process that can practically be performed in the human mind. One of ordinary skill in the art could reasonably analyze the plurality of windows and removing the window that corresponds to the highest oscillation frequency mentally or with the aid of pen and paper. These techniques are based in calculations, evaluations, and judgement, which can be performed mentally or by hand given enough time, therefore it is drawn to an abstract idea;
(Claim 8): “receiving, by the one or more processors, canine veterinary data associated with the canine from one or more external systems; receiving, by the one or more processors, baseline canine veterinary data associated with the at least one baseline canine from one or more data stores; analyzing, by the one or more processors, the canine veterinary data, the one or more scores, the baseline canine veterinary data, and the one or more baseline metrics; based on the analyzing, determining, by the one or more processors, that the canine has a mobility issue; and displaying, by the one or more processors, at least one mobility alert indicating the mobility issue on the one or more user interfaces of the user device” is insignificant pre-solution activity and insignificant post-solution activity. Receiving the data is insignificant pre-solution activity, analyzing the data is drawn to the abstract idea as it can be performed mentally or with the aid of pen and paper, and displaying the alert is insignificant post-solution activity;
(Claim 9): “the canine veterinary data including canine medication data including at least one medication dosage amount, at least one medication description, at least one medication administrator, or at least one medication administration timestamp” is insignificant pre-solution activity;
(Claim 10): “the canine data including age data of the canine, breed data of the canine, weight data of the canine, one or more risk factors of the canine, or medical history of the canine” is insignificant pre-solution activity;
(Claim 12): “wherein the at least one alert includes positive reinforcement” further describes insignificant post-solution activity;
(Claim 13): “analyzing the mobility data to determine at least one portion of the mobility data that does not include walking data; and removing the at least one portion from the mobility data” is drawn to an abstract idea since it is a mental process that can practically be performed in the human mind. One of ordinary skill in the art could reasonably analyze the data to determine walking data and exclude the walking data from analysis mentally or with the aid of pen and paper. These techniques are based in calculations, observations, and judgement, which can be performed mentally or by hand given enough time, therefore it is drawn to an abstract idea;
(Claim 14): “segmenting the mobility data into a plurality of windows based on at least one time interval; analyzing each of the plurality of windows to determine whether a threshold number of the plurality of windows is below a window threshold; and removing each of the plurality of windows that falls below the threshold” is drawn to an abstract idea since it is a mental process that can practically be performed in the human mind. One of ordinary skill in the art could reasonably segment the data into windows and remove the windows that are below a threshold mentally or with the aid of pen and paper. These techniques are based in calculations, evaluations, and judgement, which can be performed mentally or by hand given enough time, therefore it is drawn to an abstract idea;
(Claim 15): “wherein the normal range includes an upper mobility bound and a lower mobility bound” further defines the abstract idea as it further defines the range used to perform the analysis mentally or with the aid of pen and paper;
(Claim 16): “determining a highest oscillation frequency of the plurality of windows; analyzing the highest oscillation frequency to determine whether the highest oscillation frequency falls outside of an oscillation range; and in response to determining that the highest oscillation frequency does fall outside of the oscillation range, removing the window of the plurality of windows that corresponds to the highest oscillation frequency from the mobility data” is drawn to an abstract idea since it is a mental process that can practically be performed in the human mind. One of ordinary skill in the art could reasonably analyze the plurality of windows and removing the window that corresponds to the highest oscillation frequency mentally or with the aid of pen and paper. These techniques are based in calculations, evaluations, and judgement, which can be performed mentally or by hand given enough time, therefore it is drawn to an abstract idea;
(Claim 20): “the canine data including age data of the canine, breed data of the canine, weight data of the canine, one or more risk factors of the canine, or medical history of the canine” is insignificant pre-solution activity.
The dependent claims do not recite significantly more than the abstract ideas. Therefore, claims 1-6, 8-16, 18, and 20 are rejected as being directed to non-statutory subject matter.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1, 4-5, 7-11, 14-15, and 17-20 are rejected under 35 U.S.C. 103 as being unpatentable over Couse (US 20150182322) in further view of Cordonnier (US 11443838).
Regarding independent claim 1, Couse teaches a computer-implemented method for canine mobility detection (Abstract: “A system and method for monitoring the health of an animal using multiple sensors is described”), the method comprising:
processing, by one or more processors ([0046]: “wearable device 101 includes a processor 100 (or multiple processors as known in the art)”), mobility data captured by a device attached to a canine ([0063]: “Wearable device 101 may further accelerometer providing the acceleration signal 210. The accelerometer may be used to report levels of specific activities of an animal. For example, readings from the accelerometer may be interpreted as the animal being currently engaged in walking, running, sleeping, drinking, barking, scratching, shaking, etc.”), the processing including determining one or more metrics based on the mobility data, the one or more metrics including velocity information, cadence information, acceleration information, and entropy information of the canine ([0132]: “the accelerometer (n3) changes (as being controlled by processor 100) from being in an interrupt mode (e.g., looking for episodic events) to a real-time monitoring of motion activities. This real-time monitoring may be compared to a profile to determine if the animal's gait has changed dramatically as determined in step 1120. At step 1117, the GPS sensor (n4) is instructed (i.e., controlled by processor 100) to determine location, speed, and/or direction of the animal 401”; [0063]: “readings from the accelerometer may be interpreted as the animal being currently engaged in walking, running, sleeping, drinking, barking, scratching, shaking, etc.”; [0063]: “Wearable device 101 may further accelerometer providing the acceleration signal 210”);
analyzing, by the one or more processors, canine data corresponding to the canine to determine at least one baseline canine, wherein the at least one baseline canine is similar to the canine ([0120]: “a base line measurement of animal 401 may be determined and then compared to subsequent data collection to determine, e.g., one or more of the inferences discussed herein. In some embodiments, data received from two or more sensors may be used to determine, e.g., that it is an appropriate time to collect this baseline data”);
for each of the one or more metrics, comparing, by the one or more processors, the one or more metrics of the canine to one or more baseline metrics of the at least one baseline canine ([0120]: “a base line measurement of animal 401 may be determined and then compared to subsequent data collection to determine, e.g., one or more of the inferences discussed herein”).
Couse discloses comparing the one or more metrics to the at least one baseline canine, however Couse is silent on what value is determined to represent the comparison.
Cordonnier discloses systems and methods for managing healthcare data. Specifically, Cordonnier teaches the step of determining, by the one or more processors, one or more scores for each of the one or more metrics (Column 15, lines 29-32: “The parameter(s) can be used to calculate a similarity score for each reference patient. The similarity score can represent a statistical correlation between the patient data set 108 and the reference patient data set”). Couse and Cordonnier are analogous arts as they are both related to systems used to monitor physiological parameters of a user.
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to include the score from Cordonnier into the method from Couse as Couse is silent on the value used to represent the comparison, and Cordonnier provides a suitable value in an analogous device.
The Couse/Cordonnier combination teaches the one or more scores based on a normal range of the one or more metrics from the one or more baseline metrics of the at least one baseline canine (Cordonnier, Column 15, lines 29-32: “The parameter(s) can be used to calculate a similarity score for each reference patient. The similarity score can represent a statistical correlation between the patient data set 108 and the reference patient data set”; Couse, [0050]: “processor 100 only stores indications that a sensor has provided a reading outside of a normal range. The normal range may be set by the current profile and/or operating mode and may include one or more thresholds for each sensor signal”); and
displaying, by the one or more processors, at least one alert on one or more user interfaces of a user device, the at least one alert indicating that an average of the one or more scores is above a threshold (Couse, [0103]: “the received sensor data is compared to a threshold value. At step 803, the relationship of the compared data to the threshold value may be such that nothing of interest is happening. In such a situation, the data may be ignored as indicated by step 809, and the method will return step 801 to receive additional data. However, if the compared data exceeds the threshold, this occurrence is written to storage in step 805. Optionally or in addition to step 805, an alert may be provided to a pet owner or sent to the DMS as shown in step 807. The alert may be local (e.g., an audible alarm on the wearable device 101) and/or may be remote (e.g., on a pet owner's personal mobile device, within a veterinary dashboard, etc.)”).
Regarding claim 4, the Couse/Cordonnier combination teaches the computer-implemented method of claim 1, the analyzing including: segmenting, by the one or more processors, the mobility data into a plurality of windows based on at least one time interval (Couse, [0093]: “the accelerometer {x,y,z} g values may be averaged over a fixed window (for instance, a one second window)”; [0203]: “FIG. 23 shows data dump points 2305, 2306, and 2307 after which insignificant signal readings are dumped from the memory of processor 100 and/or storage 105. Interestingly, the data dump points 2305, 2306, and 2307 do not have to be at the same time window from the present. Rather each may have its own separate window length during which signal levels are maintained”; Fig. 23 shows a plurality of time windows analyzed.); analyzing, by the one or more processors, each of the plurality of windows to determine whether a threshold number of the plurality of windows is below a window threshold; and removing, by the one or more processors, each of the plurality of windows that falls below the threshold (Couse, [0202]: “an individual signal value different from a maximum value above a threshold having been reached during a time interval is less relevant than the signal having reached the threshold during the time window. Stated differently, once it has been determined that a light signal is above the light threshold {Threshold(light)} for sensor reading 2310, other readings between levels 2312 and 2313 are not considered for this threshold analysis. Similarly, variants between sound level 2316 and 2317 are less relevant than the sound level 2314 having passed the sound threshold level {Threshold(sound)} as the sound threshold has already been met.”).
Regarding claim 5, the Couse/Cordonnier combination teaches the computer-implemented method of claim 1, wherein the normal range includes an upper mobility bound and a lower mobility bound (Couse, [0189]: “FIG. 16G describes a seventh profile, Profile 6, which relates to an enhanced monitoring profile set by the veterinarian in which some sensors are operated continuously as opposed to their standard intermittent usage. The profile type identified in cell 1603G and its title identified in cell 1604G. Here, the range between the low threshold 1605A and the high threshold 1606A is set relatively [n]arrow, the frequency of operation of each sensor depends on its importance”. Fig. 16G shows a low threshold and a high threshold for the accelerometer data.).
Regarding claim 7, the Couse/Cordonnier combination teaches the computer-implemented method of claim 1, wherein the device is attached to a collar of the canine (Couse, [0038]: “the device may be a collar, harness, or other device placed on an animal by a human (e.g., a pet's owner)”).
Regarding claim 8, the Couse/Cordonnier combination teaches the computer-implemented method of claim 1, the method further comprising: receiving, by the one or more processors, canine veterinary data associated with the canine from one or more external systems (Couse, [0040]: “the wearable device would receive data from its own sensors as well as information from either sensors not located on the wearable device and/or additional content provided by the owner, veterinarian, or third party.”); receiving, by the one or more processors, baseline canine veterinary data associated with the at least one baseline canine from one or more data stores (Couse, [0046]: “The wearable device 101 may also include a storage 105”; [0120]: “a base line measurement of animal 401 may be determined and then compared to subsequent data collection to determine, e.g., one or more of the inferences discussed herein. In some embodiments, data received from two or more sensors may be used to determine, e.g., that it is an appropriate time to collect this baseline data”); analyzing, by the one or more processors, the canine veterinary data, the one or more scores, the baseline canine veterinary data, and the one or more baseline metrics (Couse, [0082]: “DMS 301 is a data receiving and processing system that receives data and/or wearable device-derived events from the wearable device 101 and analyzes that content directly, or in conjunction with older data or past analyses of older data from the wearable device, or in conjunction with data from other sources, or any combination thereof”); based on the analyzing, determining, by the one or more processors, that the canine has a mobility issue (Couse, [0088]: “the following lists typical inferences that may be reported to owners: the animal is outside of designated safe zones; there is a potential situation where the animal may be overheating or freezing; the animal may have been in an accident (high impact event of various levels of severity); the animal's activity level has been decreasing even after applied filters for owner and pet lifestyle profiles; the animal is limping (based on a change in gait); the animal appears to be in potentially dangerous environment based on extreme noise and light indicators; the animal is very listless during sleep (as an indication of pain, digestive issues, respiration issues, or past physiological trauma); the animal's heart rate variability is abnormal; the animal's respiration rate and quality is abnormal; the animal appears to be in distress/pain (yelps when there is large gross movement); and the wearable device is not on the animal that it was initially assigned to by means of examining its gate profile versus the one on file or other vital sign indicators that are part of their electronic profile”); and displaying, by the one or more processors, at least one mobility alert indicating the mobility issue on the one or more user interfaces of the user device (Couse, [0103]: “the received sensor data is compared to a threshold value. At step 803, the relationship of the compared data to the threshold value may be such that nothing of interest is happening. In such a situation, the data may be ignored as indicated by step 809, and the method will return step 801 to receive additional data. However, if the compared data exceeds the threshold, this occurrence is written to storage in step 805. Optionally or in addition to step 805, an alert may be provided to a pet owner or sent to the DMS as shown in step 807. The alert may be local (e.g., an audible alarm on the wearable device 101) and/or may be remote (e.g., on a pet owner's personal mobile device, within a veterinary dashboard, etc.)”).
Regarding claim 9, the Couse/Cordonnier combination teaches the computer-implemented method of claim 8, the canine veterinary data including canine medication data including at least one medication dosage amount, at least one medication description, at least one medication administrator, or at least one medication administration timestamp (Couse, [0041]: “the veterinarian may provide information to the DMS 301 including breed, age, weight, existing medical conditions, suspected medical conditions, appointment compliance and/or scheduling, current and past medications, and the like”).
Regarding claim 10, the Couse/Cordonnier combination teaches the computer-implemented method of claim 1, the canine data including age data of the canine, breed data of the canine, weight data of the canine, one or more risk factors of the canine, or medical history of the canine (Couse, [0041]: “the veterinarian may provide information to the DMS 301 including breed, age, weight, existing medical conditions, suspected medical conditions, appointment compliance and/or scheduling, current and past medications, and the like”; Claim 4: “the fine adjustment is an offset based on one or more conditions of the animal including at least one of: age, breed, hair length, sex, altered status, menstruation, gestation, lactation, and sickness or illness.”).
Regarding independent claim 11, Couse teaches a computer system for canine mobility detection (Abstract: “A system and method for monitoring the health of an animal using multiple sensors is described”), the computer system comprising:
at least one memory storing instructions ([0046]: “The wearable device 101 may also include a storage 105”); and
at least one processor configured to execute the instructions to perform operations comprising ([0046]: “wearable device 101 includes a processor 100 (or multiple processors as known in the art)”):
processing mobility data captured by a device attached to a canine ([0063]: “Wearable device 101 may further accelerometer providing the acceleration signal 210. The accelerometer may be used to report levels of specific activities of an animal. For example, readings from the accelerometer may be interpreted as the animal being currently engaged in walking, running, sleeping, drinking, barking, scratching, shaking, etc.”), the processing including determining one or more metrics based on the mobility data, the one or more metrics including velocity information, cadence information, acceleration information, and entropy information of the canine ([0132]: “the accelerometer (n3) changes (as being controlled by processor 100) from being in an interrupt mode (e.g., looking for episodic events) to a real-time monitoring of motion activities. This real-time monitoring may be compared to a profile to determine if the animal's gait has changed dramatically as determined in step 1120. At step 1117, the GPS sensor (n4) is instructed (i.e., controlled by processor 100) to determine location, speed, and/or direction of the animal 401”; [0063]: “readings from the accelerometer may be interpreted as the animal being currently engaged in walking, running, sleeping, drinking, barking, scratching, shaking, etc.”; [0063]: “Wearable device 101 may further accelerometer providing the acceleration signal 210”);
analyzing canine data corresponding to the canine to determine at least one baseline canine, wherein the at least one baseline canine is similar to the canine ([0120]: “a base line measurement of animal 401 may be determined and then compared to subsequent data collection to determine, e.g., one or more of the inferences discussed herein. In some embodiments, data received from two or more sensors may be used to determine, e.g., that it is an appropriate time to collect this baseline data”);
for each of the one or more metrics, comparing the one or more metrics of the canine to one or more baseline metrics of the at least one baseline canine ([0120]: “a base line measurement of animal 401 may be determined and then compared to subsequent data collection to determine, e.g., one or more of the inferences discussed herein”).
Couse discloses comparing the one or more metrics to the at least one baseline canine, however Couse is silent on what value is determined to represent the comparison.
Cordonnier discloses systems and methods for managing healthcare data. Specifically, Cordonnier teaches the step of determining one or more scores for each of the one or more metrics (Column 15, lines 29-32: “The parameter(s) can be used to calculate a similarity score for each reference patient. The similarity score can represent a statistical correlation between the patient data set 108 and the reference patient data set”). Couse and Cordonnier are analogous arts as they are both related to systems used to monitor physiological parameters of a user.
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to include the score from Cordonnier into the method from Couse as Couse is silent on the value used to represent the comparison, and Cordonnier provides a suitable value in an analogous device.
The Couse/Cordonnier combination teaches the one or more scores based on a normal range of the one or more metrics from the one or more baseline metrics of the at least one baseline canine (Cordonnier, Column 15, lines 29-32: “The parameter(s) can be used to calculate a similarity score for each reference patient. The similarity score can represent a statistical correlation between the patient data set 108 and the reference patient data set”; Couse, [0050]: “processor 100 only stores indications that a sensor has provided a reading outside of a normal range. The normal range may be set by the current profile and/or operating mode and may include one or more thresholds for each sensor signal”); and
displaying at least one alert on one or more user interfaces of a user device, the at least one alert indicating that an average of the one or more scores is above a threshold (Couse, [0103]: “the received sensor data is compared to a threshold value. At step 803, the relationship of the compared data to the threshold value may be such that nothing of interest is happening. In such a situation, the data may be ignored as indicated by step 809, and the method will return step 801 to receive additional data. However, if the compared data exceeds the threshold, this occurrence is written to storage in step 805. Optionally or in addition to step 805, an alert may be provided to a pet owner or sent to the DMS as shown in step 807. The alert may be local (e.g., an audible alarm on the wearable device 101) and/or may be remote (e.g., on a pet owner's personal mobile device, within a veterinary dashboard, etc.)”).
Regarding claim 14, the Couse/Cordonnier combination teaches the computer system of claim 11, the analyzing including: segmenting the mobility data into a plurality of windows based on at least one time interval (Couse, [0093]: “the accelerometer {x,y,z} g values may be averaged over a fixed window (for instance, a one second window)”; [0203]: “FIG. 23 shows data dump points 2305, 2306, and 2307 after which insignificant signal readings are dumped from the memory of processor 100 and/or storage 105. Interestingly, the data dump points 2305, 2306, and 2307 do not have to be at the same time window from the present. Rather each may have its own separate window length during which signal levels are maintained”; Fig. 23 shows a plurality of time windows analyzed.); analyzing each of the plurality of windows to determine whether a threshold number of the plurality of windows is below a window threshold; and removing each of the plurality of windows that falls below the threshold (Couse, [0202]: “an individual signal value different from a maximum value above a threshold having been reached during a time interval is less relevant than the signal having reached the threshold during the time window. Stated differently, once it has been determined that a light signal is above the light threshold {Threshold(light)} for sensor reading 2310, other readings between levels 2312 and 2313 are not considered for this threshold analysis. Similarly, variants between sound level 2316 and 2317 are less relevant than the sound level 2314 having passed the sound threshold level {Threshold(sound)} as the sound threshold has already been met.”).
Regarding claim 15, the Couse/Cordonnier combination teaches the computer system of claim 11, wherein the normal range includes an upper mobility bound and a lower mobility bound (Couse, [0189]: “FIG. 16G describes a seventh profile, Profile 6, which relates to an enhanced monitoring profile set by the veterinarian in which some sensors are operated continuously as opposed to their standard intermittent usage. The profile type identified in cell 1603G and its title identified in cell 1604G. Here, the range between the low threshold 1605A and the high threshold 1606A is set relatively [n]arrow, the frequency of operation of each sensor depends on its importance”. Fig. 16G shows a low threshold and a high threshold for the accelerometer data. ).
Regarding claim 17, the Couse/Cordonnier combination teaches the computer system of claim 11, wherein the device is attached to a collar of the canine (Couse, [0038]: “the device may be a collar, harness, or other device placed on an animal by a human (e.g., a pet's owner)”).
Regarding independent claim 18, Couse teaches a non-transitory computer-readable medium storing instructions ([0046]: “The wearable device 101 may also include a storage 105”) that, when executed by at least one processor, cause the at least one processor to perform operations for canine mobility detection ([0046]: “wearable device 101 includes a processor 100 (or multiple processors as known in the art)”), the operations comprising:
processing mobility data captured by a device attached to a canine ([0063]: “Wearable device 101 may further accelerometer providing the acceleration signal 210. The accelerometer may be used to report levels of specific activities of an animal. For example, readings from the accelerometer may be interpreted as the animal being currently engaged in walking, running, sleeping, drinking, barking, scratching, shaking, etc.”), the processing including determining one or more metrics based on the mobility data, the one or more metrics including velocity information, cadence information, acceleration information, and entropy information of the canine ([0132]: “the accelerometer (n3) changes (as being controlled by processor 100) from being in an interrupt mode (e.g., looking for episodic events) to a real-time monitoring of motion activities. This real-time monitoring may be compared to a profile to determine if the animal's gait has changed dramatically as determined in step 1120. At step 1117, the GPS sensor (n4) is instructed (i.e., controlled by processor 100) to determine location, speed, and/or direction of the animal 401”; [0063]: “readings from the accelerometer may be interpreted as the animal being currently engaged in walking, running, sleeping, drinking, barking, scratching, shaking, etc.”; [0063]: “Wearable device 101 may further accelerometer providing the acceleration signal 210”);
analyzing canine data corresponding to the canine to determine at least one baseline canine, wherein the at least one baseline canine is similar to the canine ([0120]: “a base line measurement of animal 401 may be determined and then compared to subsequent data collection to determine, e.g., one or more of the inferences discussed herein. In some embodiments, data received from two or more sensors may be used to determine, e.g., that it is an appropriate time to collect this baseline data”);
for each of the one or more metrics, comparing the one or more metrics of the canine to one or more baseline metrics of the at least one baseline canine ([0120]: “a base line measurement of animal 401 may be determined and then compared to subsequent data collection to determine, e.g., one or more of the inferences discussed herein”).
Couse discloses comparing the one or more metrics to the at least one baseline canine, however Couse is silent on what value is determined to represent the comparison.
Cordonnier discloses systems and methods for managing healthcare data. Specifically, Cordonnier teaches the step of determining one or more scores for each of the one or more metrics (Column 15, lines 29-32: “The parameter(s) can be used to calculate a similarity score for each reference patient. The similarity score can represent a statistical correlation between the patient data set 108 and the reference patient data set”). Couse and Cordonnier are analogous arts as they are both related to systems used to monitor physiological parameters of a user.
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to include the score from Cordonnier into the method from Couse as Couse is silent on the value used to represent the comparison, and Cordonnier provides a suitable value in an analogous device.
The Couse/Cordonnier combination teaches the one or more scores based on a normal range of the one or more metrics from the one or more baseline metrics of the at least one baseline canine (Cordonnier, Column 15, lines 29-32: “The parameter(s) can be used to calculate a similarity score for each reference patient. The similarity score can represent a statistical correlation between the patient data set 108 and the reference patient data set”; Couse, [0050]: “processor 100 only stores indications that a sensor has provided a reading outside of a normal range. The normal range may be set by the current profile and/or operating mode and may include one or more thresholds for each sensor signal”); and
displaying at least one alert on one or more user interfaces of a user device, the at least one alert indicating that an average of the one or more scores is above a threshold (Couse, [0103]: “the received sensor data is compared to a threshold value. At step 803, the relationship of the compared data to the threshold value may be such that nothing of interest is happening. In such a situation, the data may be ignored as indicated by step 809, and the method will return step 801 to receive additional data. However, if the compared data exceeds the threshold, this occurrence is written to storage in step 805. Optionally or in addition to step 805, an alert may be provided to a pet owner or sent to the DMS as shown in step 807. The alert may be local (e.g., an audible alarm on the wearable device 101) and/or may be remote (e.g., on a pet owner's personal mobile device, within a veterinary dashboard, etc.)”).
Regarding claim 19, the Couse/Cordonnier combination teaches the non-transitory computer-readable medium of claim 18, wherein the device is attached to a collar of the canine (Couse, [0038]: “the device may be a collar, harness, or other device placed on an animal by a human (e.g., a pet's owner)”).
Regarding claim 20, the Couse/Cordonnier combination teaches the non-transitory computer-readable medium of claim 18, the canine data including age data of the canine, breed data of the canine, weight data of the canine, one or more risk factors of the canine, or medical history of the canine (Couse, [0041]: “the veterinarian may provide information to the DMS 301 including breed, age, weight, existing medical conditions, suspected medical conditions, appointment compliance and/or scheduling, current and past medications, and the like”; Claim 4: “the fine adjustment is an offset based on one or more conditions of the animal including at least one of: age, breed, hair length, sex, altered status, menstruation, gestation, lactation, and sickness or illness.”).
Claims 2-3 and 12-13 are rejected under 35 U.S.C. 103 as being unpatentable over the Couse/Cordonnier combination as applied to claims 1 and 11 above, and further in view of Winterbach (US 20240212866).
Regarding claim 2, the Couse/Cordonnier combination teaches the computer-implemented method of claim 1.
However, the Couse/Cordonnier combination does not teach wherein the at least one alert includes positive reinforcement.
Winterbach discloses systems and methods for presenting motion feedback for a patient. Specifically, Winterbach teaches wherein the at least one alert includes positive reinforcement ([0045]: “The device 302 may reduce surgeon communication burden, such as by providing proactive positive reinforcement that rehab is going well if that is what the data indicates”; [0115]: “The information indicative of the comparison may include quantitative information or qualitative information. The quantitative information may include a score (e.g., a range of motion score or a pain score). The qualitative information may include feedback, such as positive reinforcement (e.g., ‘good job’), instructions (e.g., ‘try walking for 5 minutes each hour’), or adherence information related to the task, for example based on a milestone (e.g., completing a specified range of motion without pain)”). Couse, Cordonnier, and Winterbach are analogous arts as they are all related to systems that monitor the physiological parameters of a user and output the data.
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to include the positive reinforcement from Winterbach into the Couse/Cordonnier combination as it allows the device to deliver positive reinforcement when necessary, which can further influence the effectiveness of the alerts presented to the user.
Regarding claim 3, the Couse/Cordonnier combination teaches the computer-implemented method of claim 1, the method further comprising: analyzing, by the one or more processors, the mobility data to determine at least one portion of the mobility data that does not include walking data (Couse, [0063]: “readings from the accelerometer may be interpreted as the animal being currently engaged in walking, running, sleeping, drinking, barking, scratching, shaking, etc.”).
However, the Couse/Cordonnier combination does not teach removing, by the one or more processors, the at least one portion from the mobility data.
Winterbach teaches removing, by the one or more processors, the at least one portion from the mobility data (Claim 1: “identifying a pre-operative gait of the patient based on walking movement performed by the patient in the pre-operative video; determining a gait type by comparing the pre-operative gait to a plurality of stored gaits; generating an orthopedic intervention plan for the patient based on the gait type; and outputting information indicative of the orthopedic intervention plan for display”. It would be obvious to only utilize the walking portions of the mobility data, as it allows the method to compare the different measurements more accurately and analyze only the walking parameters.).
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to include the step of removing the non-walking data from Winterbach into the Couse/Cordonnier combination as it would allow the method to analyze the walking patterns of the user, which can allow for easier comparison and more accurate analysis.
Regarding claim 12, the Couse/Cordonnier combination teaches the computer system of claim 11.
However, the Couse/Cordonnier combination does not teach wherein the at least one alert includes positive reinforcement.
Winterbach discloses systems and methods for presenting motion feedback for a patient. Specifically, Winterbach teaches wherein the at least one alert includes positive reinforcement ([0045]: “The device 302 may reduce surgeon communication burden, such as by providing proactive positive reinforcement that rehab is going well if that is what the data indicates”; [0115]: “The information indicative of the comparison may include quantitative information or qualitative information. The quantitative information may include a score (e.g., a range of motion score or a pain score). The qualitative information may include feedback, such as positive reinforcement (e.g., ‘good job’), instructions (e.g., ‘try walking for 5 minutes each hour’), or adherence information related to the task, for example based on a milestone (e.g., completing a specified range of motion without pain)”). Couse, Cordonnier, and Winterbach are analogous arts as they are all related to systems that monitor the physiological parameters of a user and output the data.
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to include the positive reinforcement from Winterbach into the Couse/Cordonnier combination as it allows the device to deliver positive reinforcement when necessary, which can further influence the effectiveness of the alerts presented to the user.
Regarding claim 13, the Couse/Cordonnier combination teaches the computer system of claim 11, the operations further comprising: analyzing the mobility data to determine at least one portion of the mobility data that does not include walking data (Couse, [0063]: “readings from the accelerometer may be interpreted as the animal being currently engaged in walking, running, sleeping, drinking, barking, scratching, shaking, etc.”).
However, the Couse/Cordonnier combination does not teach removing the at least one portion from the mobility data.
Winterbach teaches removing the at least one portion from the mobility data (Claim 1: “identifying a pre-operative gait of the patient based on walking movement performed by the patient in the pre-operative video; determining a gait type by comparing the pre-operative gait to a plurality of stored gaits; generating an orthopedic intervention plan for the patient based on the gait type; and outputting information indicative of the orthopedic intervention plan for display”. It would be obvious to only utilize the walking portions of the mobility data, as it allows the method to compare the different measurements more accurately and analyze only the walking parameters.).
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to include the step of removing the non-walking data from Winterbach into the Couse/Cordonnier combination as it would allow the method to analyze the walking patterns of the user, which can allow for easier comparison and more accurate analysis. \
Claims 6 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over the Couse/Cordonnier combination as applied to claims 5 and 15 above, and further in view of Yuan (US 20200113442).
Regarding claim 6, the Couse/Cordonnier combination teaches the computer-implemented method of claim 5.
However, the Couse/Cordonnier combination does not teach the analyzing further including: determining, by the one or more processors, a highest oscillation frequency of the plurality of windows; analyzing, by the one or more processors, the highest oscillation frequency to determine whether the highest oscillation frequency falls outside of an oscillation range; and in response to determining that the highest oscillation frequency does fall outside of the oscillation range, removing, by the one or more processors, the window of the plurality of windows that corresponds to the highest oscillation frequency from the mobility data.
Yuan discloses apparatuses and methods for determining blood pressure of a user. Specifically, Yuan teaches the analyzing further including: determining, by the one or more processors, a highest oscillation frequency of the plurality of windows; analyzing, by the one or more processors, the highest oscillation frequency to determine whether the highest oscillation frequency falls outside of an oscillation range; and in response to determining that the highest oscillation frequency does fall outside of the oscillation range, removing, by the one or more processors, the window of the plurality of windows that corresponds to the highest oscillation frequency from the mobility data ([0096]: “The process 918 begins at block 920 where measured pressure data from the force sensor 104 is filtered through a bandpass filter to provide AC pressure oscillations at frequencies near the expected user heartrate. For example, the bandpass filter may allow pressure oscillation data in the frequency range of 0.90 to 1.05 Hz. Other bandpass frequency ranges and types of filters may be used if desired, provided it provides the function and performance described herein”). Couse, Cordonnier, and Yuan are analogous arts as they are all related to monitoring physiological parameters of a user.
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to include the oscillation frequency analysis from Yuan into the Couse/Cordonnier combination as it allows the combination to filter out the windows in unwanted oscillation frequency range, which can ensure only the specific data used in the analysis is processed, which leads to a more accurate analysis.
Regarding claim 16, the Couse/Cordonnier combination teaches the computer system of claim 15.
However, the Couse/Cordonnier combination does not teach the analyzing further including: determining a highest oscillation frequency of the plurality of windows; analyzing the highest oscillation frequency to determine whether the highest oscillation frequency falls outside of an oscillation range; and in response to determining that the highest oscillation frequency does fall outside of the oscillation range, removing the window of the plurality of windows that corresponds to the highest oscillation frequency from the mobility data.
Yuan discloses apparatuses and methods for determining blood pressure of a user. Specifically, Yuan teaches the analyzing further including: determining a highest oscillation frequency of the plurality of windows; analyzing the highest oscillation frequency to determine whether the highest oscillation frequency falls outside of an oscillation range; and in response to determining that the highest oscillation frequency does fall outside of the oscillation range, removing the window of the plurality of windows that corresponds to the highest oscillation frequency from the mobility data ([0096]: “The process 918 begins at block 920 where measured pressure data from the force sensor 104 is filtered through a bandpass filter to provide AC pressure oscillations at frequencies near the expected user heartrate. For example, the bandpass filter may allow pressure oscillation data in the frequency range of 0.90 to 1.05 Hz. Other bandpass frequency ranges and types of filters may be used if desired, provided it provides the function and performance described herein”). Couse, Cordonnier, and Yuan are analogous arts as they are all related to monitoring physiological parameters of a user.
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to include the oscillation frequency analysis from Yuan into the Couse/Cordonnier combination as it allows the combination to filter out the windows in unwanted oscillation frequency range, which can ensure only the specific data used in the analysis is processed, which leads to a more accurate analysis.
Conclusion
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/E.K.M./Examiner, Art Unit 3791
/MATTHEW KREMER/Primary Examiner, Art Unit 3791