DETAILED ACTION
Applicant’s arguments, filed on 07/09/2026, have been fully considered. The following rejections and/or objections are either reiterated or newly applied. They constitute the complete set presently being applied to the instant application.
Applicants have amended their claims, filed on 07/06/2026, and therefore rejections newly made in the instant office action have been necessitated by amendment.
Claims 1-6, 8-16, 18, and 21-23 are the current claims hereby under examination.
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 9, “the window” should read “a window”, as there is a lack of antecedent basis for this limitation.
In claim 16, line 7, “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 § 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, 8-11, 14-15, 18, and 20-21 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 collar of 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.”; [0038]: “the device may be a collar, harness, or other device placed on an animal by a human (e.g., a pet's owner)”), the processing including:
identifying one or more time periods corresponding to a predicted mobility event of the canine ([0129]: “The previous readings from the slave sensors are reviewed to look for episodic threshold events to create a more accurate picture as to what has transpired over the previous time interval and possibly confirm a possible high impact event from accelerometer n3. Thus, at step 1105 processor 100 retrieves stored data from the microphone/peak sound sensor (n2) for a time period immediately preceding and overlapping with the high acceleration reading, and at step 1107 processor 100 retrieves stored data from the light meter n1 for a time period immediately preceding and overlapping with the high acceleration reading”. The high impact event is the predicted mobility event.),
filtering the mobility data based on the identified one or more time periods corresponding to the predicted mobility event ([0130]: “At steps 1104-1106, the data received from each sensor may be weighted and combined into a single result to determine in step 1107 if the constructed profile meets a high degree of probability that an event of interest (e.g., impact) has occurred. For example, if the light meter (n1) sensed a high incidence of light (potentially indicative of headlights), and/or if the microphone/peak sound sensor (n2) sensed a loud noise (potentially indicative of a being impacted by a vehicle), then the method may determine at step 1107 that an impact has in fact occurred”; [0131]: “If the combined and corroborated data meets certain conditions (e.g., each is indicative of an impact event) in step 1107, the master sensor (in the depicted embodiment, accelerometer n3) may trigger and/or change states other sensors (including itself) in order to, e.g., take individual spot readings, schedule-based readings, or change each sensor's sensing configurations”; [0132]: “at step 1109, 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”), and
determining one or more metrics based on the filtered 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”).
However, Couse does not teach inputting the canine data into a trained machine-learning model.
Cordonnier discloses systems and methods for managing healthcare data. Specifically, Cordonnier teaches the step of inputting the canine data into a trained machine-learning model (Column 3, lines 58-64: “The data management system can select a model (e.g., machine learning model) selected based on the available data. If new data comes available, the data management system can identify one or more models suitable for analyzing the newly available data. This allows the data management system to adaptively select machine learning models to enhance analytics”; Column 17, lines 57-59: “the machine learning model (e.g., a neural network or a naïve Bayes classifier) may be trained on the training data set using a supervised learning method”). 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 use the machine learning model from Cordonnier into the method from Couse as it allows Couse to process the data quickly and efficiently, which can produce faster, more dynamic analysis.
The Couse/Cordonnier combination teaches 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 the Couse/Cordonnier combination is silent on what value is determined to represent the comparison.
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”).
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 window 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 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 collar of 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.”; [0038]: “the device may be a collar, harness, or other device placed on an animal by a human (e.g., a pet's owner)”), the processing including:
identifying one or more time periods corresponding to a predicted mobility event of the canine ([0129]: “The previous readings from the slave sensors are reviewed to look for episodic threshold events to create a more accurate picture as to what has transpired over the previous time interval and possibly confirm a possible high impact event from accelerometer n3. Thus, at step 1105 processor 100 retrieves stored data from the microphone/peak sound sensor (n2) for a time period immediately preceding and overlapping with the high acceleration reading, and at step 1107 processor 100 retrieves stored data from the light meter n1 for a time period immediately preceding and overlapping with the high acceleration reading”. The high impact event is the predicted mobility event.),
filtering the mobility data based on the identified one or more time periods corresponding to the predicted mobility event ([0130]: “At steps 1104-1106, the data received from each sensor may be weighted and combined into a single result to determine in step 1107 if the constructed profile meets a high degree of probability that an event of interest (e.g., impact) has occurred. For example, if the light meter (n1) sensed a high incidence of light (potentially indicative of headlights), and/or if the microphone/peak sound sensor (n2) sensed a loud noise (potentially indicative of a being impacted by a vehicle), then the method may determine at step 1107 that an impact has in fact occurred”; [0131]: “If the combined and corroborated data meets certain conditions (e.g., each is indicative of an impact event) in step 1107, the master sensor (in the depicted embodiment, accelerometer n3) may trigger and/or change states other sensors (including itself) in order to, e.g., take individual spot readings, schedule-based readings, or change each sensor's sensing configurations”; [0132]: “at step 1109, 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”), and
determining one or more metrics based on the filtered 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”).
However, Couse does not teach inputting the canine data into a trained machine-learning model.
Cordonnier discloses systems and methods for managing healthcare data. Specifically, Cordonnier teaches the step of inputting the canine data into a trained machine-learning model (Column 3, lines 58-64: “The data management system can select a model (e.g., machine learning model) selected based on the available data. If new data comes available, the data management system can identify one or more models suitable for analyzing the newly available data. This allows the data management system to adaptively select machine learning models to enhance analytics”; Column 17, lines 57-59: “the machine learning model (e.g., a neural network or a naïve Bayes classifier) may be trained on the training data set using a supervised learning method”). 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 use the machine learning model from Cordonnier into the method from Couse as it allows Couse to process the data quickly and efficiently, which can produce faster, more dynamic analysis.
The Couse/Cordonnier combination teaches 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 the Couse/Cordonnier combination is silent on what value is determined to represent the comparison.
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”).
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 window 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 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 collar of 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.”; [0038]: “the device may be a collar, harness, or other device placed on an animal by a human (e.g., a pet's owner)”), the processing including:
identifying one or more time periods corresponding to a predicted mobility event of the canine ([0129]: “The previous readings from the slave sensors are reviewed to look for episodic threshold events to create a more accurate picture as to what has transpired over the previous time interval and possibly confirm a possible high impact event from accelerometer n3. Thus, at step 1105 processor 100 retrieves stored data from the microphone/peak sound sensor (n2) for a time period immediately preceding and overlapping with the high acceleration reading, and at step 1107 processor 100 retrieves stored data from the light meter n1 for a time period immediately preceding and overlapping with the high acceleration reading”. The high impact event is the predicted mobility event.),
filtering the mobility data based on the identified one or more time periods corresponding to the predicted mobility event ([0130]: “At steps 1104-1106, the data received from each sensor may be weighted and combined into a single result to determine in step 1107 if the constructed profile meets a high degree of probability that an event of interest (e.g., impact) has occurred. For example, if the light meter (n1) sensed a high incidence of light (potentially indicative of headlights), and/or if the microphone/peak sound sensor (n2) sensed a loud noise (potentially indicative of a being impacted by a vehicle), then the method may determine at step 1107 that an impact has in fact occurred”; [0131]: “If the combined and corroborated data meets certain conditions (e.g., each is indicative of an impact event) in step 1107, the master sensor (in the depicted embodiment, accelerometer n3) may trigger and/or change states other sensors (including itself) in order to, e.g., take individual spot readings, schedule-based readings, or change each sensor's sensing configurations”; [0132]: “at step 1109, 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”), and
determining one or more metrics based on the filtered 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”).
However, Couse does not teach inputting the canine data into a trained machine-learning model.
Cordonnier discloses systems and methods for managing healthcare data. Specifically, Cordonnier teaches the step of inputting the canine data into a trained machine-learning model (Column 3, lines 58-64: “The data management system can select a model (e.g., machine learning model) selected based on the available data. If new data comes available, the data management system can identify one or more models suitable for analyzing the newly available data. This allows the data management system to adaptively select machine learning models to enhance analytics”; Column 17, lines 57-59: “the machine learning model (e.g., a neural network or a naïve Bayes classifier) may be trained on the training data set using a supervised learning method”). 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 use the machine learning model from Cordonnier into the method from Couse as it allows Couse to process the data quickly and efficiently, which can produce faster, more dynamic analysis.
The Couse/Cordonnier combination teaches 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 the Couse/Cordonnier combination is silent on what value is determined to represent the comparison.
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”).
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 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.”).
Regarding claim 21, the Couse/Cordonnier combination teaches the non-transitory computer-readable medium of claim 18, 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.).
Claims 2-3, 12-13, and 22-23 are rejected under 35 U.S.C. 103 as being unpatentable over the Couse/Cordonnier combination as applied to claims 1, 11, and 18 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.
Regarding claim 22, the Couse/Cordonnier combination teaches the non-transitory computer-readable of claim 18, 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.
Regarding claim 23, the Couse/Cordonnier combination teaches the non-transitory computer-readable medium of claim 18.
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.
Claims 6 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over the Couse/Cordonnier combination as applied to claims 4 and 14 above, and further in view of Yuan (US 20200113442).
Regarding claim 6, the Couse/Cordonnier combination teaches the computer-implemented method of claim 4.
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 14.
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.
Response to Arguments
All of applicant’s argument regarding the rejections and objections previously set forth have been fully considered and are persuasive unless directly addressed subsequently.
Applicant has amended claims 6 and 16 in an attempt to overcome the claim objections, however the change in dependency has not introduced proper antecedent basis for the limitation “the window”, therefore the claim objections are maintained.
Applicant's arguments regarding the 103 rejections have been fully considered but they are not persuasive. Applicant argues that Couse does not teach the new limitations of “identifying one or more time periods corresponding to a predicted mobility event of the canine, filtering the mobility data based on the identified one or more time periods corresponding to the predicted mobility event”, however as stated in the 103 rejection above, Couse does teach this limitations ([0129]: “The previous readings from the slave sensors are reviewed to look for episodic threshold events to create a more accurate picture as to what has transpired over the previous time interval and possibly confirm a possible high impact event from accelerometer n3. Thus, at step 1105 processor 100 retrieves stored data from the microphone/peak sound sensor (n2) for a time period immediately preceding and overlapping with the high acceleration reading, and at step 1107 processor 100 retrieves stored data from the light meter n1 for a time period immediately preceding and overlapping with the high acceleration reading”. The high impact event is the predicted mobility event; ([0130]: “At steps 1104-1106, the data received from each sensor may be weighted and combined into a single result to determine in step 1107 if the constructed profile meets a high degree of probability that an event of interest (e.g., impact) has occurred. For example, if the light meter (n1) sensed a high incidence of light (potentially indicative of headlights), and/or if the microphone/peak sound sensor (n2) sensed a loud noise (potentially indicative of a being impacted by a vehicle), then the method may determine at step 1107 that an impact has in fact occurred”; [0131]: “If the combined and corroborated data meets certain conditions (e.g., each is indicative of an impact event) in step 1107, the master sensor (in the depicted embodiment, accelerometer n3) may trigger and/or change states other sensors (including itself) in order to, e.g., take individual spot readings, schedule-based readings, or change each sensor's sensing configurations”; [0132]: “at step 1109, 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”), therefore this argument is not persuasive.
Additionally, Applicant argues that Cordonnier does not teach the limitation of “inputting the canine data into a trained machine-learning model”, however as stated in the 103 rejection above, Cordonnier does teach this limitation (Column 3, lines 58-64: “The data management system can select a model (e.g., machine learning model) selected based on the available data. If new data comes available, the data management system can identify one or more models suitable for analyzing the newly available data. This allows the data management system to adaptively select machine learning models to enhance analytics”; Column 17, lines 57-59: “the machine learning model (e.g., a neural network or a naïve Bayes classifier) may be trained on the training data set using a supervised learning method”), therefore this argument is not persuasive.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ERIN K MCCORMACK whose telephone number is (703)756-1886. The examiner can normally be reached Mon-Fri 7:30-5.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Jason Sims can be reached at 5712727540. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/E.K.M./Examiner, Art Unit 3791
/MATTHEW KREMER/Primary Examiner, Art Unit 3791
Prosecution Insights