DETAILED ACTION
Notice of Pre-AIA or AIA Status
In the present application, filed on or after March 16, 2013, claims 1-2, 4-10, 12-17, and 19-23 have been considered and examined under the first inventor to file provisions of the AIA .
Respond to Applicant’s Arguments/Remarks
Applicant’s arguments, see Remarks, filed 02/20/2026, with respect to the rejection(s) of claims 1-20, based solely on the limitations as amended, has been fully considered but are moot because the arguments do not apply to the new combination of references including prior art being used in the current rejection (see below for detail) under new grounds of rejection, necessitated by amendment.
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 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.
Claims 1-2, 4-7, 9-10, 12-14, 16-17, 19, and 21-23 are rejected under 35 U.S.C. 103 as being unpatentable over Tupin et al. (Tupin – WO 2015/103127 A1) in view of Madeley et al. (Madeley – WO 2021/0173571 A1) and Stivoric et al. (Stivoric – US 2017/0112391 A1).
As to claim 1, Tupin discloses a method for annotating pet health-related sensor accelerometer data, the method comprising:
receiving, by at least one processor (Tupin: FIG. 1-2 the wearable device 101 comprising the process 100), accelerometer data (Tupin: [0061]-[0066], [0094], [0112]-[0116], [0122]-[0123], [0129]-[0131], [0144], and FIG. 2: 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 accelerometer may also be used to report the possibility of a high impact event as well as corroborate and/or augment other sensor readings. In some embodiments, the accelerometer may be used to control other sensors (e.g., turn on, turn off, leave a breadcrumb, ignore a reading, etc.) from one or more sensors (Tupin: [0007], [0040], [0044]-[0046], [0077], [0080]-[0082], and FIG. 1-3 the data management system and the acceleration 210 of the wearable device) indicative of one or more movements of a pet (Tupin: [0057], [0064], [0095], [0109], [0113]-[0115], [0120]-[0122], [0134], and FIG. 1: 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 accelerometer may also be used to report the possibility of a high impact event as well as corroborate and/or augment other sensor readings. In some embodiments, the accelerometer may be used to control other sensors (e.g., turn on, turn off, leave a breadcrumb, ignore a reading, etc.). Further, the accelerometer may be used to determine which of a plurality of animals is actually wearing the wearable device 101);
converting, by the at least one processor, the accelerometer data into behavior data corresponding to one or more behaviors of the pet based on a plurality of historical relationships between historical behavior data and historical activity data (Tupin: [0040]-[0042], [0068], [0081], [0083], [0095]-[0096], [0109], [0155], FIG. 1-3 and FIG. 8-12: In any of the above embodiments, collected data may be time-stamped in order determine time-dependent inferences. That is, time stamping the various sensing activities and the ability to look backward in time allows for a root-cause analysis to determine an adverse event ( e.g. the animal was walking fine, but then played fetch and is now limping). Further, in some embodiments, time-stamping may also allow for the analysis of the rate of change which in tum can be used to predict a possible outcome ( e.g. the animal is running at an increasing rate of speed towards the outer area of the geo-zone and thus is likely to breach that zone)),
wherein the one or more behaviors of the pet comprise at least one of: a scratching behavior, a licking behavior, a walking behavior, a lying down behavior, a sleeping behavior, an eating behavior, and a drinking behavior (Tupin: [0057], [0064], [0095], [0109], [0113]-[0115], [0120]-[0122], [0134], and FIG. 1: 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 accelerometer may also be used to report the possibility of a high impact event as well as corroborate and/or augment other sensor readings. In some embodiments, the accelerometer may be used to control other sensors (e.g., turn on, turn off, leave a breadcrumb, ignore a reading, etc.). Further, the accelerometer may be used to determine which of a plurality of animals is actually wearing the wearable device 101);
receiving, by the at least one processor, contextual data (Tupin: [0040]-[0042], [0081], [0083], [0109], and FIG. 8-12: the wearable device 101 data may be combined with sensors from other sources (e.g., RSS feeds 302, owner observations 312, etc.) in performing the analysis. For example, an RSS feed 302 including the number of degree days may be compared to a number of high temperature alerts at a wearable device 101 to determine if, e.g., animal 401 is overheated or if, rather, it is just an abnormally warm month. As another example, owner's observations 312 ( e.g., observations of staggering after exertion, unusual fatigue, abnormal coughing, pale gums, etc.) may lead the DMS 301 to modify the profile or operation mode of the wearable device to employ profiles with finer granularity and sensing more often and with more sensitive thresholds for cardiopulmonary algorithms at the wearable device 101 level) associated with a pet profile of the pet from a user device (Tupin: [0043], [0079], [0081], [0093]-[0094], and [0105]: Each of these external sensors and/or mobile browser applications/installed applications may act independently, in conjunction with the wearable device 101, may be triggered by the wearable device 101, or may be triggered by the DMS on a demand, episodic, or a scheduled basis to provide additional and/or collaborative sensing information that will provide important episodic, derived, or trending information to support the animals safety, wellbeing and health),
determining, by the at least one processor, a temporal relationship between the behavior data corresponding to the one or more behaviors of the pet and the contextual data (Tupin: [0040]-[0042], [0068], [0081], [0083], [0095]-[0096], [0109], [0155], FIG. 1-3 and FIG. 8-12: In any of the above embodiments, collected data may be time-stamped in order determine time-dependent inferences. That is, time stamping the various sensing activities and the ability to look backward in time allows for a root-cause analysis to determine an adverse event ( e.g. the animal was walking fine, but then played fetch and is now limping). Further, in some embodiments, time-stamping may also allow for the analysis of the rate of change which in tum can be used to predict a possible outcome ( e.g. the animal is running at an increasing rate of speed towards the outer area of the geo-zone and thus is likely to breach that zone));
determining, by the at least one processor, a correlation between the behavior data corresponding to the one or more behaviors of the pet and the contextual data based on the temporal relationship (Tupin: [0043], [0079], [0095], [0144]-[0145], [0210], and FIG. 8-12: Because the DMS receives these divergent types of data, the DMS 301 may perform these correlations. For instance, the DMS 301 may receive high ambient temperature readings from the wearable device 101 and compare it against expected local temperatures (obtained by RSS feed 302 or Internet search 303) for the current or last identified location of the wearable device 101. If the ambient temperature is high ( for instance, over 45° C) while the predicted high temperature for the location is only 20° C), then the DMS 301 may derive that the animal is locked inside a car with its windows shut. Based on this derived event, the DMS may attempt to alert the owner as alert 314. The alert 314 may be in the form of email, SMS or other text messaging systems, social messaging systems (like Twitter and Facebook, etc.) or by calling the owner directly), and
displaying, by the at least one processor, at least one graphic depicting the correlation between the one or more behaviors of the pet and the contextual data on a user interface of the user device (Tupin: [0218]-[0226], and FIG. 25-26: The display 2601 may include one or more goals as set by the veterinarian, the owner, or the DMS 301. In this example, the goals are to walk 40 minutes per day, to keep the animal's weight below 80 pounds and to play 15 minutes. The display 2601 may further include an identification of the alert thresholds in field 2608. In this example, the alert thresholds are missing two days of a walk, a change in gait dropping 15%, and an overall drop in activity of 25%).
Tupin does not explicitly disclose wherein the contextual data includes at least one of: a medication change of the pet, a nutrition regimen change of the pet, or a life event change of the pet; wherein the correlation represents a potential cause of a pet behavior change; and
wherein the at least one graphic includes one or more annotations from the user device corresponding to the contextual data.
However, it has been known in the art of monitoring conditions of animals to implement wherein the contextual data includes at least one of: a medication change of the pet, a nutrition regimen change of the pet, or a life event change of the pet; wherein the correlation represents a potential cause of a pet behavior change, as suggested by Madeley, which discloses
receiving, by at least one processor (FIG. 1 the embedded controller 206) , accelerometer data (Madeley: [0036]-[0041], [0043]-[0050], [0056], [0065]-[0066], and FIG. 5-8: the sensor data is communicated to the processing unit 106. If the sensors 202 included a tri-axis accelerometer 220 and tri-axis gyroscope 220, the sensor data can include 9 sensor values per instance per sensing unit 104 including three values related to acceleration in the 3 axes, three values related to angular velocity in the 3 axes, and three values related to orientation relative to magnetic north and gravity) from one or more sensors (Madeley: FIG. 2 the sensor 202 comprising the accelerometer 220 and the gyroscope 222) indicative of one or more movements of a pet (Madeley: [0036]-[0041], [0043]-[0050], [0056], and FIG. 2-3: One such module is an activity detection module 316 that is configured to process data received from the sensing unit 104 and identify a particular activity the corresponding animal may be engaged in based on the data in certain embodiments. For example, the activity detection module 316 can access sensor data and/or location data to determine whether the animal is walking, trotting, climbing up or down stairs, standing or sitting.);
converting, by the at least one processor, the accelerometer data into behavior data corresponding to one or more behaviors of the pet (Madeley: [0029], [0056], [0065], [0072], [0087], [0100]-[0101], and FIG. 5-8: One such module is an activity detection module 316 that is configured to process data received from the sensing unit 104 and identify a particular activity the corresponding animal may be engaged in based on the data in certain embodiments. For example, the activity detection module 316 can access sensor data and/or location data to determine whether the animal is walking, trotting, climbing up or down stairs, standing or sitting) based on a plurality of historical relationships between historical behavior data and historical activity data (Madeley: [0008], [0053], [0060], [0065], [0070], [0092], [0100], [0118], and FIG. 5-6: During the training process, sensor values may be fed to the processing unit I 06 and based on the weights of the neural networks, an activity type is predicted. If the output is incorrect, the CNN changes its weights to be more likely to produce the correct output. This process is repeated numerous times with multiple sensor values, until the CNN can correctly determine the output most of the times),
wherein the one or more behaviors of the pet comprise at least one of: a scratching behavior, a licking behavior, a walking behavior, a lying down behavior, a sleeping behavior, an eating behavior, and a drinking behavior (Madeley: [0029], [0056], [0065], [0072], [0087], [0100]-[0101], and FIG. 5-8: One such module is an activity detection module 316 that is configured to process data received from the sensing unit 104 and identify a particular activity the corresponding animal may be engaged in based on the data in certain embodiments. For example, the activity detection module 316 can access sensor data and/or location data to determine whether the animal is walking, trotting, climbing up or down stairs, standing or sitting);
receiving, by the at least one processor, contextual data associated with a pet profile of the pet from a user device (Madeley: [0039]-[0040], [0050], [0054], [0058]-[0059], [0061]-[0063], [0112], [0114]-[0115], and FIG. 4), wherein the contextual data includes at least one of:
a medication change of the pet (Madeley: [0032], [0113], and FIG. 1: if the movement score has improved from the previously generated score and the user of the application has input new medication details in the diagnostic application 410, the diagnostic application 410 may display the movement score and a message stating, e.g., “[the subject] appears to be doing much better - looks like the new medication is taking affect.”. Similarly, if the subject's movement score has declined since the last score was calculated, the diagnostic application 410 may display the movement score and a message stating, e.g., “[the subject's] condition appears to be deteriorating ),
a nutrition regimen change of the pet, or
a life event change of the pet;
determining, by the at least one processor, a temporal relationship between the behavior data corresponding to the one or more behaviors of the pet and the contextual data (Madeley: [0032], [0113], and FIG. 1);
determining, by the at least one processor, a correlation between the behavior data corresponding to the one or more behaviors of the pet and the contextual data based on the temporal relationship, wherein the correlation represents a potential cause of a pet behavior change (Madeley: [0032], [0113], and FIG. 1); and
displaying, by the at least one processor, at least one graphic depicting the correlation between the one or more behaviors of the pet and the contextual data on a user interface of the user device (Madeley: [0032], [0113], and FIG. 1: In some examples, the diagnostic application 410 maintains a database of movement scores received for the subject over time. It may compare the received movement score with one or more immediately preceding scores and generate one or more insights based on the movement score. For example, if the movement score has improved from the previously generated score and the user of the application has input new medication details in the diagnostic application 410, the diagnostic application 410 may display the movement score and a message stating, e.g., "[the subject] appears to be doing much better- looks like the new medication is taking affect.". Similarly, if the subject's movement score has declined since the last score was calculated, the diagnostic application 410 may display the movement score and a message stating, e.g., "[the subject's] condition appears to be deteriorating.).
Therefore, in view of teachings by Tupin and Madeley, it would have been obvious to one of the ordinary skills in the art before the effective filing date of the claimed invention to implement in the animal monitoring system of Tupin to include wherein the contextual data includes at least one of: a medication change of the pet, a nutrition regimen change of the pet, or a life event change of the pet; wherein the correlation represents a potential cause of a pet behavior change, as suggested by Madeley. The motivation for this is to provide conditions of an animal to an animal owner.
The combination of Tupin and Madeley does not explicitly disclose wherein the at least one graphic includes one or more annotations from the user device corresponding to the contextual data.
However it has been known in the art of providing information to users to implement wherein the at least one graphic includes one or more annotations from the user device corresponding to the contextual data, as suggested by Stivoric, which discloses wherein the at least one graphic includes one or more annotations from the user device corresponding to the contextual data (Stivoric: Abstract, [0082]-[0085], [0088], [0091], [0094]-[0097], [0129]-[0130], [0137], and FIG. 5-11: Analytical status data is characterized by the application of certain utilities or algorithms to convert one or more of the data indicative of various physiological parameters generated by sensor device 10, the data derived from the data indicative of various physiological parameters, the data indicative of various contextual parameters generated by sensor device 10, and the data input by the user into calculated health, wellness and lifestyle indicators. For example, based on data input by the user relating to the foods he or she has eaten, things such as calories and amounts of proteins, fats, carbohydrates, and certain vitamins can be calculated. As another example, skin temperature, heart rate, respiration rate, heat flow and/or GSR can be used to provide an indicator to the user of his or her stress level over a desired time period).
Therefore, in view of teachings by Tupin, Madeley, and Stivoric it would have been obvious to one of the ordinary skills in the art before the effective filing date of the claimed invention to implement in the animal monitoring system of Tupin and Madeley to include wherein the at least one graphic includes one or more annotations from the user device corresponding to the contextual data, as suggested by Stivoric. The motivation for this is to provide conditions of a monitored subject based on various information resources.
As to claim 2, Tupin, Madeley, and Stivoric discloses the limitations of claim 1 further comprising the method of claim 1, wherein the contextual data comprises at least one of:
a mobility state of the pet (Tupin: [0057], [0064], [0095], [0109], [0113]-[0115], [0120]-[0122], [0134], and FIG. 1: 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 accelerometer may also be used to report the possibility of a high impact event as well as corroborate and/or augment other sensor readings. In some embodiments, the accelerometer may be used to control other sensors (e.g., turn on, turn off, leave a breadcrumb, ignore a reading, etc.). Further, the accelerometer may be used to determine which of a plurality of animals is actually wearing the wearable device 101 and Madeley: [0006], [0030]-[0032], [0085]-[0087], [0089]-[0092], and FIG. 5-8: the disclosed diagnostic systems and methods calculate an overall movement score for an animal. As used in this disclosure, the overall movement score indicates the quality of movement of an animal and includes factors such as the ease and flow of movement and the power and efficiency of the movement. It was observed by the inventors that animals that suffer from pain or any other related conditions typically make stiff, jerky movements and/or moved in a rather inefficient manner or with reduced power. Accordingly, a calculated overall movement score can indicate the amount of pain an animal may be suffering as a result of OA or other conditions that affect movement); and
an emotional state of the pet (Tupin: [0003], [0090], [0094], [0108], [0190], [0220]-[0021], and FIG. 25: the wearable device 101 may monitor the animal's barking over time to ensure the animal 401 is complying with local by-laws or to interpret continued barking as a potential stress indicator).
As to claim 4, Tupin, Madeley, and Stivoric discloses the limitations of claim 1 further comprising the method of claim 1, further comprising:
transmitting, by the at least one processor to the user device (Tupin: [0079], [0081], [0093]-[0094], and [0105]: Each of these external sensors and/or mobile browser applications/installed applications may act independently, in conjunction with the wearable device 101, may be triggered by the wearable device 101, or may be triggered by the DMS on a demand, episodic, or a scheduled basis to provide additional and/or collaborative sensing information that will provide important episodic, derived, or trending information to support the animals safety, wellbeing and health), a prompt requesting input of the contextual data in response to receiving the accelerometer data (Tupin: [0040], [0081], [0093-[0096], [0109], [0151], and FIG. 3).
As to claim 5, Tupin, Madeley, and Stivoric disclose the limitations of claim 4 further comprising the method of claim 4, wherein the prompt comprises a plurality of selectable options (Tupin: [0079], [0081], [0093]-[0096], and [0105]: Each of these external sensors and/or mobile browser applications/installed applications may act independently, in conjunction with the wearable device 101, may be triggered by the wearable device 101, or may be triggered by the DMS on a demand, episodic, or a scheduled basis to provide additional and/or collaborative sensing information that will provide important episodic, derived, or trending information to support the animals safety, wellbeing and health), wherein each of the plurality of selectable options corresponds to a life event of the pet (Tupin: [0004], [0090], [0094], [0117], [0134], [0152], [0220], and FIG. 12-13: 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 ).
As to claim 6, Tupin, Madeley, and Stivoric disclose the limitations of claim 1 further comprising the method of claim 1, wherein the contextual data includes mood data, the mood data including an emotional state of the pet (Tupin: [0003], [0090], [0094], [0108], [0190], [0220]-[0021], and FIG. 25: the wearable device 101 may monitor the animal's barking over time to ensure the animal 401 is complying with local by-laws or to interpret continued barking as a potential stress indicator ) and a tag indicating a justification for the emotional state (Tupin: [0090]-[0091], [0108], [0190], [0218]-[0226], and FIG. 25-26: 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).
As to claim 7, Tupin, Madeley, and Stivoric disclose the limitations of claim 1 further comprising the method of claim 1, wherein the at least one graphic comprises a graph or plot of the one or more behaviors of the pet over a plurality of data points overlaid with at least one annotation of a life event of the pet corresponding to the contextual data determined to be correlated with the one or more behaviors (Tupin: [0218]-[0226], and FIG. 25-26: The display 2601 may include one or more goals as set by the veterinarian, the owner, or the DMS 301. In this example, the goals are to walk 40 minutes per day, to keep the animal's weight below 80 pounds and to play 15 minutes. The display 2601 may further include an identification of the alert thresholds in field 2608. In this example, the alert thresholds are missing two days of a walk, a change in gait dropping 15%, and an overall drop in activity of 25%).
As to claim 9, Tupin discloses a system for annotating pet health-related accelerometer sensor data, the system comprising:
at least one memory having processor-readable instructions stored therein; and at least one processor (Tupin: FIG. 1-2 the wearable device 101 comprising the process 100) configured to access the at least one memory and execute the processor-readable instructions, which when executed by the at least one processor cause the at least one processor to perform a plurality of functions (Tupin: [0047], [0053], [0084], and FIG. 1-3), including functions for:
receiving, from one or more sensors, accelerometer data (Tupin: [0061]-[0066], [0094], [0112]-[0116], [0122]-[0123], [0129]-[0131], [0144], and FIG. 2: 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 accelerometer may also be used to report the possibility of a high impact event as well as corroborate and/or augment other sensor readings. In some embodiments, the accelerometer may be used to control other sensors (e.g., turn on, turn off, leave a breadcrumb, ignore a reading, etc.) indicative of one or more movements of a pet (Tupin: [0057], [0064], [0095], [0109], [0113]-[0115], [0120]-[0122], [0134], and FIG. 1: 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 accelerometer may also be used to report the possibility of a high impact event as well as corroborate and/or augment other sensor readings. In some embodiments, the accelerometer may be used to control other sensors (e.g., turn on, turn off, leave a breadcrumb, ignore a reading, etc.). Further, the accelerometer may be used to determine which of a plurality of animals is actually wearing the wearable device 101);
converting the accelerometer data into behavior data corresponding to one or more behaviors of the pet based on a plurality of historical relationships between historical behavior data and historical activity data (Tupin: [0040]-[0042], [0068], [0081], [0083], [0095]-[0096], [0109], [0155], FIG. 1-3 and FIG. 8-12: In any of the above embodiments, collected data may be time-stamped in order determine time-dependent inferences. That is, time stamping the various sensing activities and the ability to look backward in time allows for a root-cause analysis to determine an adverse event ( e.g. the animal was walking fine, but then played fetch and is now limping). Further, in some embodiments, time-stamping may also allow for the analysis of the rate of change which in tum can be used to predict a possible outcome ( e.g. the animal is running at an increasing rate of speed towards the outer area of the geo-zone and thus is likely to breach that zone)),
wherein the one or more behaviors of the pet comprise at least one of: a scratching behavior, a licking behavior, a walking behavior, a lying down behavior, a sleeping behavior, an eating behavior, and a drinking behavior (Tupin: [0057], [0064], [0095], [0109], [0113]-[0115], [0120]-[0122], [0134], and FIG. 1: 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 accelerometer may also be used to report the possibility of a high impact event as well as corroborate and/or augment other sensor readings. In some embodiments, the accelerometer may be used to control other sensors (e.g., turn on, turn off, leave a breadcrumb, ignore a reading, etc.). Further, the accelerometer may be used to determine which of a plurality of animals is actually wearing the wearable device 101);
receiving contextual data (Tupin: [0040]-[0042], [0081], [0083], [0109], and FIG. 8-12: the wearable device 101 data may be combined with sensors from other sources (e.g., RSS feeds 302, owner observations 312, etc.) in performing the analysis. For example, an RSS feed 302 including the number of degree days may be compared to a number of high temperature alerts at a wearable device 101 to determine if, e.g., animal 401 is overheated or if, rather, it is just an abnormally warm month. As another example, owner's observations 312 ( e.g., observations of staggering after exertion, unusual fatigue, abnormal coughing, pale gums, etc.) may lead the DMS 301 to modify the profile or operation mode of the wearable device to employ profiles with finer granularity and sensing more often and with more sensitive thresholds for cardiopulmonary algorithms at the wearable device 101 level) associated with a pet profile of the pet from a user device (Tupin: [0043], [0079], [0081], [0093]-[0094], and [0105]: Each of these external sensors and/or mobile browser applications/installed applications may act independently, in conjunction with the wearable device 101, may be triggered by the wearable device 101, or may be triggered by the DMS on a demand, episodic, or a scheduled basis to provide additional and/or collaborative sensing information that will provide important episodic, derived, or trending information to support the animals safety, wellbeing and health),
determining a temporal relationship between the behavior data corresponding to the one or more behaviors of the pet and the contextual data (Tupin: [0040]-[0042], [0068], [0081], [0083], [0095]-[0096], [0109], [0155], FIG. 1-3 and FIG. 8-12: In any of the above embodiments, collected data may be time-stamped in order determine time-dependent inferences. That is, time stamping the various sensing activities and the ability to look backward in time allows for a root-cause analysis to determine an adverse event ( e.g. the animal was walking fine, but then played fetch and is now limping). Further, in some embodiments, time-stamping may also allow for the analysis of the rate of change which in tum can be used to predict a possible outcome ( e.g. the animal is running at an increasing rate of speed towards the outer area of the geo-zone and thus is likely to breach that zone));
determining a correlation between the behavior data corresponding to the one or more behaviors of the pet and the contextual data based on the temporal relationship (Tupin: [0043], [0079], [0095], [0144]-[0145], [0210], and FIG. 8-12: Because the DMS receives these divergent types of data, the DMS 301 may perform these correlations. For instance, the DMS 301 may receive high ambient temperature readings from the wearable device 101 and compare it against expected local temperatures (obtained by RSS feed 302 or Internet search 303) for the current or last identified location of the wearable device 101. If the ambient temperature is high ( for instance, over 45° C) while the predicted high temperature for the location is only 20° C), then the DMS 301 may derive that the animal is locked inside a car with its windows shut. Based on this derived event, the DMS may attempt to alert the owner as alert 314. The alert 314 may be in the form of email, SMS or other text messaging systems, social messaging systems (like Twitter and Facebook, etc.) or by calling the owner directly), and
displaying at least one graphic depicting the correlation between the one or more behaviors of the pet and the contextual data on a user interface of the user device (Tupin: [0218]-[0226], and FIG. 25-26: The display 2601 may include one or more goals as set by the veterinarian, the owner, or the DMS 301. In this example, the goals are to walk 40 minutes per day, to keep the animal's weight below 80 pounds and to play 15 minutes. The display 2601 may further include an identification of the alert thresholds in field 2608. In this example, the alert thresholds are missing two days of a walk, a change in gait dropping 15%, and an overall drop in activity of 25%).
Tupin does not explicitly disclose wherein the contextual data includes at least one of: a medication change of the pet, a nutrition regimen change of the pet, or a life event change of the pet; wherein the correlation represents a potential cause of a pet behavior change; and
wherein the at least one graphic includes one or more annotations from the user device corresponding to the contextual data.
However, it has been known in the art of monitoring conditions of animals to implement wherein the contextual data includes at least one of: a medication change of the pet, a nutrition regimen change of the pet, or a life event change of the pet; wherein the correlation represents a potential cause of a pet behavior change, as suggested by Madeley, which discloses
receiving, by at least one processor (FIG. 1 the embedded controller 206) , accelerometer data (Madeley: [0036]-[0041], [0043]-[0050], [0056], [0065]-[0066], and FIG. 5-8: the sensor data is communicated to the processing unit 106. If the sensors 202 included a tri-axis accelerometer 220 and tri-axis gyroscope 220, the sensor data can include 9 sensor values per instance per sensing unit 104 including three values related to acceleration in the 3 axes, three values related to angular velocity in the 3 axes, and three values related to orientation relative to magnetic north and gravity) from one or more sensors (Madeley: FIG. 2 the sensor 202 comprising the accelerometer 220 and the gyroscope 222) indicative of one or more movements of a pet (Madeley: [0036]-[0041], [0043]-[0050], [0056], and FIG. 2-3: One such module is an activity detection module 316 that is configured to process data received from the sensing unit 104 and identify a particular activity the corresponding animal may be engaged in based on the data in certain embodiments. For example, the activity detection module 316 can access sensor data and/or location data to determine whether the animal is walking, trotting, climbing up or down stairs, standing or sitting.);
converting, by the at least one processor, the accelerometer data into behavior data corresponding to one or more behaviors of the pet (Madeley: [0029], [0056], [0065], [0072], [0087], [0100]-[0101], and FIG. 5-8: One such module is an activity detection module 316 that is configured to process data received from the sensing unit 104 and identify a particular activity the corresponding animal may be engaged in based on the data in certain embodiments. For example, the activity detection module 316 can access sensor data and/or location data to determine whether the animal is walking, trotting, climbing up or down stairs, standing or sitting) based on a plurality of historical relationships between historical behavior data and historical activity data (Madeley: [0008], [0053], [0060], [0065], [0070], [0092], [0100], [0118], and FIG. 5-6: During the training process, sensor values may be fed to the processing unit I 06 and based on the weights of the neural networks, an activity type is predicted. If the output is incorrect, the CNN changes its weights to be more likely to produce the correct output. This process is repeated numerous times with multiple sensor values, until the CNN can correctly determine the output most of the times),
wherein the one or more behaviors of the pet comprise at least one of: a scratching behavior, a licking behavior, a walking behavior, a lying down behavior, a sleeping behavior, an eating behavior, and a drinking behavior (Madeley: [0029], [0056], [0065], [0072], [0087], [0100]-[0101], and FIG. 5-8: One such module is an activity detection module 316 that is configured to process data received from the sensing unit 104 and identify a particular activity the corresponding animal may be engaged in based on the data in certain embodiments. For example, the activity detection module 316 can access sensor data and/or location data to determine whether the animal is walking, trotting, climbing up or down stairs, standing or sitting);
receiving, by the at least one processor, contextual data associated with a pet profile of the pet from a user device (Madeley: [0039]-[0040], [0050], [0054], [0058]-[0059], [0061]-[0063], [0112], [0114]-[0115], and FIG. 4), wherein the contextual data includes at least one of:
a medication change of the pet (Madeley: [0032], [0113], and FIG. 1: if the movement score has improved from the previously generated score and the user of the application has input new medication details in the diagnostic application 410, the diagnostic application 410 may display the movement score and a message stating, e.g., “[the subject] appears to be doing much better - looks like the new medication is taking affect.”. Similarly, if the subject's movement score has declined since the last score was calculated, the diagnostic application 410 may display the movement score and a message stating, e.g., “[the subject's] condition appears to be deteriorating ),
a nutrition regimen change of the pet, or
a life event change of the pet;
determining, by the at least one processor, a temporal relationship between the behavior data corresponding to the one or more behaviors of the pet and the contextual data (Madeley: [0032], [0113], and FIG. 1);
determining, by the at least one processor, a correlation between the behavior data corresponding to the one or more behaviors of the pet and the contextual data based on the temporal relationship, wherein the correlation represents a potential cause of a pet behavior change (Madeley: [0032], [0113], and FIG. 1); and
displaying, by the at least one processor, at least one graphic depicting the correlation between the one or more behaviors of the pet and the contextual data on a user interface of the user device (Madeley: [0032], [0113], and FIG. 1: In some examples, the diagnostic application 410 maintains a database of movement scores received for the subject over time. It may compare the received movement score with one or more immediately preceding scores and generate one or more insights based on the movement score. For example, if the movement score has improved from the previously generated score and the user of the application has input new medication details in the diagnostic application 410, the diagnostic application 410 may display the movement score and a message stating, e.g., "[the subject] appears to be doing much better- looks like the new medication is taking affect.". Similarly, if the subject's movement score has declined since the last score was calculated, the diagnostic application 410 may display the movement score and a message stating, e.g., "[the subject's] condition appears to be deteriorating.).
Therefore, in view of teachings by Tupin and Madeley, it would have been obvious to one of the ordinary skills in the art before the effective filing date of the claimed invention to implement in the animal monitoring system of Tupin to include wherein the contextual data includes at least one of: a medication change of the pet, a nutrition regimen change of the pet, or a life event change of the pet; wherein the correlation represents a potential cause of a pet behavior change, as suggested by Madeley. The motivation for this is to provide conditions of an animal to an animal owner.
The combination of Tupin and Madeley does not explicitly disclose wherein the at least one graphic includes one or more annotations from the user device corresponding to the contextual data.
However it has been known in the art of providing information to users to implement wherein the at least one graphic includes one or more annotations from the user device corresponding to the contextual data, as suggested by Stivoric, which discloses wherein the at least one graphic includes one or more annotations from the user device corresponding to the contextual data (Stivoric: Abstract, [0082]-[0085], [0088], [0091], [0094]-[0097], [0129]-[0130], [0137], and FIG. 5-11: Analytical status data is characterized by the application of certain utilities or algorithms to convert one or more of the data indicative of various physiological parameters generated by sensor device 10, the data derived from the data indicative of various physiological parameters, the data indicative of various contextual parameters generated by sensor device 10, and the data input by the user into calculated health, wellness and lifestyle indicators. For example, based on data input by the user relating to the foods he or she has eaten, things such as calories and amounts of proteins, fats, carbohydrates, and certain vitamins can be calculated. As another example, skin temperature, heart rate, respiration rate, heat flow and/or GSR can be used to provide an indicator to the user of his or her stress level over a desired time period).
Therefore, in view of teachings by Tupin, Madeley, and Stivoric it would have been obvious to one of the ordinary skills in the art before the effective filing date of the claimed invention to implement in the animal monitoring system of Tupin and Madeley to include wherein the at least one graphic includes one or more annotations from the user device corresponding to the contextual data, as suggested by Stivoric. The motivation for this is to provide conditions of a monitored subject based on various information resources.
As to claim 10, Tupin, Madeley, and Stivoric discloses the limitations of claim 9 further comprising the system of claim 9, wherein the contextual data comprises at least one of:
a mobility state of the pet (Tupin: [0057], [0064], [0095], [0109], [0113]-[0115], [0120]-[0122], [0134], and FIG. 1: 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 accelerometer may also be used to report the possibility of a high impact event as well as corroborate and/or augment other sensor readings. In some embodiments, the accelerometer may be used to control other sensors (e.g., turn on, turn off, leave a breadcrumb, ignore a reading, etc.). Further, the accelerometer may be used to determine which of a plurality of animals is actually wearing the wearable device 101 and Madeley: [0006], [0030]-[0032], [0085]-[0087], [0089]-[0092], and FIG. 5-8: the disclosed diagnostic systems and methods calculate an overall movement score for an animal. As used in this disclosure, the overall movement score indicates the quality of movement of an animal and includes factors such as the ease and flow of movement and the power and efficiency of the movement. It was observed by the inventors that animals that suffer from pain or any other related conditions typically make stiff, jerky movements and/or moved in a rather inefficient manner or with reduced power. Accordingly, a calculated overall movement score can indicate the amount of pain an animal may be suffering as a result of OA or other conditions that affect movement); and
an emotional state of the pet (Tupin: [0003], [0090], [0094], [0108], [0190], [0220]-[0021], and FIG. 25: the wearable device 101 may monitor the animal's barking over time to ensure the animal 401 is complying with local by-laws or to interpret continued barking as a potential stress indicator).
As to claim 12, Tupin, Madeley, and Stivoric discloses the limitations of claim 9 further comprising the system of claim 9, wherein the plurality of functions further includes a function for:
transmitting (Tupin: [0079], [0081], [0093]-[0094], and [0105]: Each of these external sensors and/or mobile browser applications/installed applications may act independently, in conjunction with the wearable device 101, may be triggered by the wearable device 101, or may be triggered by the DMS on a demand, episodic, or a scheduled basis to provide additional and/or collaborative sensing information that will provide important episodic, derived, or trending information to support the animals safety, wellbeing and health) a prompt requesting input of the contextual data in response to receiving the accelerometer data (Tupin: [0040], [0081], [0093-[0096], [0109], [0151], and FIG. 3).
As to claim 13, Tupin, Madeley, and Stivoric disclose the limitations of claim 9 further comprising the system of claim 9, wherein the contextual data includes mood data, the mood data including an emotional state of the pet (Tupin: [0003], [0090], [0094], [0108], [0190], [0220]-[0021], and FIG. 25: the wearable device 101 may monitor the animal's barking over time to ensure the animal 401 is complying with local by-laws or to interpret continued barking as a potential stress indicator ) and a tag indicating a justification for the emotional state (Tupin: [0090]-[0091], [0108], [0190], [0218]-[0226], and FIG. 25-26: 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).
As to claim 14, Tupin, Madeley, and Stivoric disclose the limitations of claim 9 further comprising the system of claim 9, wherein the at least one graphic comprises a graph or plot of the one or more behaviors of the pet over a plurality of data points overlaid with at least one annotation of a life event of the pet corresponding to the contextual data determined to be correlated with the one or more behaviors (Tupin: [0218]-[0226], and FIG. 25-26: The display 2601 may include one or more goals as set by the veterinarian, the owner, or the DMS 301. In this example, the goals are to walk 40 minutes per day, to keep the animal's weight below 80 pounds and to play 15 minutes. The display 2601 may further include an identification of the alert thresholds in field 2608. In this example, the alert thresholds are missing two days of a walk, a change in gait dropping 15%, and an overall drop in activity of 25%).
As to claim 16, Tupin discloses a non-transitory computer-readable medium configured to store instructions that, when executed by at least one processor of a device for annotating pet health-related accelerometer data, cause the at least one processor to perform operations comprising:
receiving, from one or more sensors, accelerometer data ([0061]-[0066], [0094], [0112]-[0116], [0122]-[0123], [0129]-[0131], [0144], and FIG. 2: 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 accelerometer may also be used to report the possibility of a high impact event as well as corroborate and/or augment other sensor readings. In some embodiments, the accelerometer may be used to control other sensors (e.g., turn on, turn off, leave a breadcrumb, ignore a reading, etc.) indicative of one or more movements of a pet (Tupin: [0057], [0064], [0095], [0109], [0113]-[0115], [0120]-[0122], [0134], and FIG. 1: 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 accelerometer may also be used to report the possibility of a high impact event as well as corroborate and/or augment other sensor readings. In some embodiments, the accelerometer may be used to control other sensors (e.g., turn on, turn off, leave a breadcrumb, ignore a reading, etc.). Further, the accelerometer may be used to determine which of a plurality of animals is actually wearing the wearable device 101);
converting the accelerometer data into behavior data corresponding to one or more behaviors of the pet based on a plurality of historical relationships between historical behavior data and historical activity data (Tupin: [0040]-[0042], [0068], [0081], [0083], [0095]-[0096], [0109], [0155], FIG. 1-3 and FIG. 8-12: In any of the above embodiments, collected data may be time-stamped in order determine time-dependent inferences. That is, time stamping the various sensing activities and the ability to look backward in time allows for a root-cause analysis to determine an adverse event ( e.g. the animal was walking fine, but then played fetch and is now limping). Further, in some embodiments, time-stamping may also allow for the analysis of the rate of change which in tum can be used to predict a possible outcome ( e.g. the animal is running at an increasing rate of speed towards the outer area of the geo-zone and thus is likely to breach that zone)),
wherein the one or more behaviors of the pet comprise at least one of: a scratching behavior, a licking behavior, a walking behavior, a lying down behavior, a sleeping behavior, an eating behavior, and a drinking behavior (Tupin: [0057], [0064], [0095], [0109], [0113]-[0115], [0120]-[0122], [0134], and FIG. 1: 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 accelerometer may also be used to report the possibility of a high impact event as well as corroborate and/or augment other sensor readings. In some embodiments, the accelerometer may be used to control other sensors (e.g., turn on, turn off, leave a breadcrumb, ignore a reading, etc.). Further, the accelerometer may be used to determine which of a plurality of animals is actually wearing the wearable device 101);
receiving contextual data (Tupin: [0040]-[0042], [0081], [0083], [0109], and FIG. 8-12: the wearable device 101 data may be combined with sensors from other sources (e.g., RSS feeds 302, owner observations 312, etc.) in performing the analysis. For example, an RSS feed 302 including the number of degree days may be compared to a number of high temperature alerts at a wearable device 101 to determine if, e.g., animal 401 is overheated or if, rather, it is just an abnormally warm month. As another example, owner's observations 312 ( e.g., observations of staggering after exertion, unusual fatigue, abnormal coughing, pale gums, etc.) may lead the DMS 301 to modify the profile or operation mode of the wearable device to employ profiles with finer granularity and sensing more often and with more sensitive thresholds for cardiopulmonary algorithms at the wearable device 101 level) associated with a pet profile of the pet from a user device (Tupin: [0043], [0079], [0081], [0093]-[0094], and [0105]: Each of these external sensors and/or mobile browser applications/installed applications may act independently, in conjunction with the wearable device 101, may be triggered by the wearable device 101, or may be triggered by the DMS on a demand, episodic, or a scheduled basis to provide additional and/or collaborative sensing information that will provide important episodic, derived, or trending information to support the animals safety, wellbeing and health),
determining a temporal relationship between the behavior data corresponding to the one or more behaviors of the pet and the contextual data (Tupin: [0040]-[0042], [0068], [0081], [0083], [0095]-[0096], [0109], [0155], FIG. 1-3 and FIG. 8-12: In any of the above embodiments, collected data may be time-stamped in order determine time-dependent inferences. That is, time stamping the various sensing activities and the ability to look backward in time allows for a root-cause analysis to determine an adverse event ( e.g. the animal was walking fine, but then played fetch and is now limping). Further, in some embodiments, time-stamping may also allow for the analysis of the rate of change which in tum can be used to predict a possible outcome ( e.g. the animal is running at an increasing rate of speed towards the outer area of the geo-zone and thus is likely to breach that zone));
determining a correlation between the behavior data corresponding to the one or more behaviors of the pet and the contextual data based on the temporal relationship (Tupin: [0043], [0079], [0095], [0144]-[0145], [0210], and FIG. 8-12: Because the DMS receives these divergent types of data, the DMS 301 may perform these correlations. For instance, the DMS 301 may receive high ambient temperature readings from the wearable device 101 and compare it against expected local temperatures (obtained by RSS feed 302 or Internet search 303) for the current or last identified location of the wearable device 101. If the ambient temperature is high ( for instance, over 45° C) while the predicted high temperature for the location is only 20° C), then the DMS 301 may derive that the animal is locked inside a car with its windows shut. Based on this derived event, the DMS may attempt to alert the owner as alert 314. The alert 314 may be in the form of email, SMS or other text messaging systems, social messaging systems (like Twitter and Facebook, etc.) or by calling the owner directly), and
displaying at least one graphic depicting the correlation between the one or more behaviors of the pet and the contextual data on a user interface of the user device (Tupin: [0218]-[0226], and FIG. 25-26: The display 2601 may include one or more goals as set by the veterinarian, the owner, or the DMS 301. In this example, the goals are to walk 40 minutes per day, to keep the animal's weight below 80 pounds and to play 15 minutes. The display 2601 may further include an identification of the alert thresholds in field 2608. In this example, the alert thresholds are missing two days of a walk, a change in gait dropping 15%, and an overall drop in activity of 25%).
Tupin does not explicitly disclose wherein the contextual data includes at least one of: a medication change of the pet, a nutrition regimen change of the pet, or a life event change of the pet; wherein the correlation represents a potential cause of a pet behavior change; and
wherein the at least one graphic includes one or more annotations from the user device corresponding to the contextual data.
However, it has been known in the art of monitoring conditions of animals to implement wherein the contextual data includes at least one of: a medication change of the pet, a nutrition regimen change of the pet, or a life event change of the pet; wherein the correlation represents a potential cause of a pet behavior change, as suggested by Madeley, which discloses
receiving, by at least one processor (FIG. 1 the embedded controller 206) , accelerometer data (Madeley: [0036]-[0041], [0043]-[0050], [0056], [0065]-[0066], and FIG. 5-8: the sensor data is communicated to the processing unit 106. If the sensors 202 included a tri-axis accelerometer 220 and tri-axis gyroscope 220, the sensor data can include 9 sensor values per instance per sensing unit 104 including three values related to acceleration in the 3 axes, three values related to angular velocity in the 3 axes, and three values related to orientation relative to magnetic north and gravity) from one or more sensors (Madeley: FIG. 2 the sensor 202 comprising the accelerometer 220 and the gyroscope 222) indicative of one or more movements of a pet (Madeley: [0036]-[0041], [0043]-[0050], [0056], and FIG. 2-3: One such module is an activity detection module 316 that is configured to process data received from the sensing unit 104 and identify a particular activity the corresponding animal may be engaged in based on the data in certain embodiments. For example, the activity detection module 316 can access sensor data and/or location data to determine whether the animal is walking, trotting, climbing up or down stairs, standing or sitting.);
converting, by the at least one processor, the accelerometer data into behavior data corresponding to one or more behaviors of the pet (Madeley: [0029], [0056], [0065], [0072], [0087], [0100]-[0101], and FIG. 5-8: One such module is an activity detection module 316 that is configured to process data received from the sensing unit 104 and identify a particular activity the corresponding animal may be engaged in based on the data in certain embodiments. For example, the activity detection module 316 can access sensor data and/or location data to determine whether the animal is walking, trotting, climbing up or down stairs, standing or sitting) based on a plurality of historical relationships between historical behavior data and historical activity data (Madeley: [0008], [0053], [0060], [0065], [0070], [0092], [0100], [0118], and FIG. 5-6: During the training process, sensor values may be fed to the processing unit I 06 and based on the weights of the neural networks, an activity type is predicted. If the output is incorrect, the CNN changes its weights to be more likely to produce the correct output. This process is repeated numerous times with multiple sensor values, until the CNN can correctly determine the output most of the times),
wherein the one or more behaviors of the pet comprise at least one of: a scratching behavior, a licking behavior, a walking behavior, a lying down behavior, a sleeping behavior, an eating behavior, and a drinking behavior (Madeley: [0029], [0056], [0065], [0072], [0087], [0100]-[0101], and FIG. 5-8: One such module is an activity detection module 316 that is configured to process data received from the sensing unit 104 and identify a particular activity the corresponding animal may be engaged in based on the data in certain embodiments. For example, the activity detection module 316 can access sensor data and/or location data to determine whether the animal is walking, trotting, climbing up or down stairs, standing or sitting);
receiving, by the at least one processor, contextual data associated with a pet profile of the pet from a user device (Madeley: [0039]-[0040], [0050], [0054], [0058]-[0059], [0061]-[0063], [0112], [0114]-[0115], and FIG. 4), wherein the contextual data includes at least one of:
a medication change of the pet (Madeley: [0032], [0113], and FIG. 1: if the movement score has improved from the previously generated score and the user of the application has input new medication details in the diagnostic application 410, the diagnostic application 410 may display the movement score and a message stating, e.g., “[the subject] appears to be doing much better - looks like the new medication is taking affect.”. Similarly, if the subject's movement score has declined since the last score was calculated, the diagnostic application 410 may display the movement score and a message stating, e.g., “[the subject's] condition appears to be deteriorating ),
a nutrition regimen change of the pet, or
a life event change of the pet;
determining, by the at least one processor, a temporal relationship between the behavior data corresponding to the one or more behaviors of the pet and the contextual data (Madeley: [0032], [0113], and FIG. 1);
determining, by the at least one processor, a correlation between the behavior data corresponding to the one or more behaviors of the pet and the contextual data based on the temporal relationship, wherein the correlation represents a potential cause of a pet behavior change (Madeley: [0032], [0113], and FIG. 1); and
displaying, by the at least one processor, at least one graphic depicting the correlation between the one or more behaviors of the pet and the contextual data on a user interface of the user device (Madeley: [0032], [0113], and FIG. 1: In some examples, the diagnostic application 410 maintains a database of movement scores received for the subject over time. It may compare the received movement score with one or more immediately preceding scores and generate one or more insights based on the movement score. For example, if the movement score has improved from the previously generated score and the user of the application has input new medication details in the diagnostic application 410, the diagnostic application 410 may display the movement score and a message stating, e.g., "[the subject] appears to be doing much better- looks like the new medication is taking affect.". Similarly, if the subject's movement score has declined since the last score was calculated, the diagnostic application 410 may display the movement score and a message stating, e.g., "[the subject's] condition appears to be deteriorating.).
Therefore, in view of teachings by Tupin and Madeley, it would have been obvious to one of the ordinary skills in the art before the effective filing date of the claimed invention to implement in the animal monitoring system of Tupin to include wherein the contextual data includes at least one of: a medication change of the pet, a nutrition regimen change of the pet, or a life event change of the pet; wherein the correlation represents a potential cause of a pet behavior change, as suggested by Madeley. The motivation for this is to provide conditions of an animal to an animal owner.
The combination of Tupin and Madeley does not explicitly disclose wherein the at least one graphic includes one or more annotations from the user device corresponding to the contextual data.
However it has been known in the art of providing information to users to implement wherein the at least one graphic includes one or more annotations from the user device corresponding to the contextual data, as suggested by Stivoric, which discloses wherein the at least one graphic includes one or more annotations from the user device corresponding to the contextual data (Stivoric: Abstract, [0082]-[0085], [0088], [0091], [0094]-[0097], [0129]-[0130], [0137], and FIG. 5-11: Analytical status data is characterized by the application of certain utilities or algorithms to convert one or more of the data indicative of various physiological parameters generated by sensor device 10, the data derived from the data indicative of various physiological parameters, the data indicative of various contextual parameters generated by sensor device 10, and the data input by the user into calculated health, wellness and lifestyle indicators. For example, based on data input by the user relating to the foods he or she has eaten, things such as calories and amounts of proteins, fats, carbohydrates, and certain vitamins can be calculated. As another example, skin temperature, heart rate, respiration rate, heat flow and/or GSR can be used to provide an indicator to the user of his or her stress level over a desired time period).
Therefore, in view of teachings by Tupin, Madeley, and Stivoric it would have been obvious to one of the ordinary skills in the art before the effective filing date of the claimed invention to implement in the animal monitoring system of Tupin and Madeley to include wherein the at least one graphic includes one or more annotations from the user device corresponding to the contextual data, as suggested by Stivoric. The motivation for this is to provide conditions of a monitored subject based on various information resources.
As to claim 17, Tupin, Madeley, and Stivoric discloses the limitations of claim 16 further comprising the non-transitory computer-readable medium of claim 16, wherein the contextual data comprises at least one of:
a mobility state of the pet (Tupin: [0057], [0064], [0095], [0109], [0113]-[0115], [0120]-[0122], [0134], and FIG. 1: 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 accelerometer may also be used to report the possibility of a high impact event as well as corroborate and/or augment other sensor readings. In some embodiments, the accelerometer may be used to control other sensors (e.g., turn on, turn off, leave a breadcrumb, ignore a reading, etc.). Further, the accelerometer may be used to determine which of a plurality of animals is actually wearing the wearable device 101 and Madeley: [0006], [0030]-[0032], [0085]-[0087], [0089]-[0092], and FIG. 5-8: the disclosed diagnostic systems and methods calculate an overall movement score for an animal. As used in this disclosure, the overall movement score indicates the quality of movement of an animal and includes factors such as the ease and flow of movement and the power and efficiency of the movement. It was observed by the inventors that animals that suffer from pain or any other related conditions typically make stiff, jerky movements and/or moved in a rather inefficient manner or with reduced power. Accordingly, a calculated overall movement score can indicate the amount of pain an animal may be suffering as a result of OA or other conditions that affect movement); and
an emotional state of the pet (Tupin: [0003], [0090], [0094], [0108], [0190], [0220]-[0021], and FIG. 25: the wearable device 101 may monitor the animal's barking over time to ensure the animal 401 is complying with local by-laws or to interpret continued barking as a potential stress indicator ).
As to claim 19, Tupin, Madeley, and Stivoric disclose the limitations of claim 17 further comprising the non-transitory computer-readable medium of claim 17, wherein the operations further comprise:
transmitting, by the at least one processor to the user device (Tupin: [0079], [0081], [0093]-[0094], and [0105]: Each of these external sensors and/or mobile browser applications/installed applications may act independently, in conjunction with the wearable device 101, may be triggered by the wearable device 101, or may be triggered by the DMS on a demand, episodic, or a scheduled basis to provide additional and/or collaborative sensing information that will provide important episodic, derived, or trending information to support the animals safety, wellbeing and health), a prompt requesting input of the contextual data in response to receiving the accelerometer data (Tupin: [0040], [0081], [0093-[0096], [0109], [0151], and FIG. 3).
As to claim 21, Tupin, Madeley, and Stivoric discloses the limitations of claim 1 further comprising the method of claim 1, further comprising:
generating, by the at least one processor, a personalized wellness plan for the pet based on the potential cause of the pet behavior change (Tupin: Abstract, [0091], [0223]-[0225], and FIG. 25-26); and
displaying, by the at least one processor, the personalized wellness plan on the user interface of the user device (Tupin: Abstract, [0091], [0223]-[0225], and FIG. 25-26: Figure 26 shows activity level for that particular animal in accordance with aspects of the disclosure. The Owner Level Detail screen allows the owner to drill down on a specific item from the dashboard and review goals, alerts, recommendations, and more detailed, long term analyses information. For instance, the display 2601 of Figure 26 includes an identification of the animal 2602, a current indicator 2603 for the detail screen (in this example, the activity of the animal), and an alert message box 2604 identifying an alert determined by the wearable device 101 and or the DMS 301 (in this example that the animal missed two consecutive days of walks with an identification of the date and time of when the walks were missed). Next, the display 2601 may further include recommendations in field 2605 to improve the health of the animal (for instance, to resume daily walks). The display 2601 may include one or more goals as set by the veterinarian, the owner, or the DMS 301. In this example, the goals are to walk 40 minutes per day, to keep the animal's weight below 80 pounds and to play 15 minutes. The display 2601 may further include an identification of the alert thresholds in field 2608. In this example, the alert thresholds are missing two days of a walk, a change in gait dropping 15%, and an overall drop in activity of 25%).
As to claim 22, Tupin, Madeley, and Stivoric discloses the limitations of claim 9 further comprising the system of claim 9, further comprising: generating a personalized wellness plan for the pet based on the potential cause of the pet behavior change (Tupin: Abstract, [0091], [0223]-[0225], and FIG. 25-26); and
displaying the personalized wellness plan on the user interface of the user device (Tupin: Abstract, [0091], [0223]-[0225], and FIG. 25-26: Figure 26 shows activity level for that particular animal in accordance with aspects of the disclosure. The Owner Level Detail screen allows the owner to drill down on a specific item from the dashboard and review goals, alerts, recommendations, and more detailed, long term analyses information. For instance, the display 2601 of Figure 26 includes an identification of the animal 2602, a current indicator 2603 for the detail screen (in this example, the activity of the animal), and an alert message box 2604 identifying an alert determined by the wearable device 101 and or the DMS 301 (in this example that the animal missed two consecutive days of walks with an identification of the date and time of when the walks were missed). Next, the display 2601 may further include recommendations in field 2605 to improve the health of the animal (for instance, to resume daily walks). The display 2601 may include one or more goals as set by the veterinarian, the owner, or the DMS 301. In this example, the goals are to walk 40 minutes per day, to keep the animal's weight below 80 pounds and to play 15 minutes. The display 2601 may further include an identification of the alert thresholds in field 2608. In this example, the alert thresholds are missing two days of a walk, a change in gait dropping 15%, and an overall drop in activity of 25%).
As to claim 23, Tupin, Madeley, and Stivoric discloses the limitations of claim 16 further comprising the non-transitory computer-readable medium of claim 16, wherein the operations further comprise:
generating a personalized wellness plan for the pet based on the potential cause of the pet behavior change (Tupin: Abstract, [0091], [0223]-[0225], and FIG. 25-26); and
displaying the personalized wellness plan on the user interface of the user device (Tupin: Abstract, [0091], [0223]-[0225], and FIG. 25-26: Figure 26 shows activity level for that particular animal in accordance with aspects of the disclosure. The Owner Level Detail screen allows the owner to drill down on a specific item from the dashboard and review goals, alerts, recommendations, and more detailed, long term analyses information. For instance, the display 2601 of Figure 26 includes an identification of the animal 2602, a current indicator 2603 for the detail screen (in this example, the activity of the animal), and an alert message box 2604 identifying an alert determined by the wearable device 101 and or the DMS 301 (in this example that the animal missed two consecutive days of walks with an identification of the date and time of when the walks were missed). Next, the display 2601 may further include recommendations in field 2605 to improve the health of the animal (for instance, to resume daily walks). The display 2601 may include one or more goals as set by the veterinarian, the owner, or the DMS 301. In this example, the goals are to walk 40 minutes per day, to keep the animal's weight below 80 pounds and to play 15 minutes. The display 2601 may further include an identification of the alert thresholds in field 2608. In this example, the alert thresholds are missing two days of a walk, a change in gait dropping 15%, and an overall drop in activity of 25%).
Claims 8, 15, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Tupin et al. (Tupin – WO 2015/103127 A1) in view of Madeley et al. (Madeley – WO 2021/0173571 A1) and Stivoric et al. (Stivoric – US 2017/0112391 A1) and further in view of Jang (Jang – US 2022/0165294 A1) .
As to claim 8, Tupin, Madeley, and Stivoric discloses the limitations of claim 1 except for the claimed limitations of the method of claim 1, further comprising:
providing, by the at least one processor, a second user interface for display on the user device, wherein the second user interface includes at least one of:
a digital journal including mood data received from the user device;
a calendar indicating one or more days on which mood data was received from the user device; and
a chart indicating a frequency at which mood data was received from the user device.
However, it has been known in the art of monitoring conditions of animals to implement providing, by the at least one processor, a second user interface for display on the user device, wherein the second user interface includes at least one of: a digital journal including mood data received from the user device; a calendar indicating one or more days on which mood data was received from the user device; and a chart indicating a frequency at which mood data was received from the user device, as suggested by Jang, which discloses providing, by the at least one processor, a second user interface for display on the user device (Jang: Abstract, [0047]-[0051], and FIG. 1-2 the user terminal device 100), wherein the second user interface includes at least one of: a digital journal including mood data received from the user device; a calendar indicating one or more days on which mood data was received from the user device; and a chart indicating a frequency at which mood data was received from the user device (Jang: Abstract, [0044]-[0051], [0113]-[0123], FIG. 1-2 and FIG. 6: a chatbot according to the present disclosure provides an answer to the user's question and provides an answer containing the companion animal's emotions at the time. For example, when a chatting value input by the user asks “how is the weather today?”, and if the companion animal is in a good mood, an answer value is displayed as “today's weather is sunny and good for going out”, and in contrast to this, if the companion animal is in a bad mood, the answer value is displayed as “it is sunny today, but I don't want to go out”. That is, it is characterized in that a context or a tone is slightly different depending on the companion animal's condition even when the answer value has the same topic).
Therefore, in view of teachings by Tupin, Madeley, Stivoric, and Jang, it would have been obvious to one of the ordinary skills in the art before the effective filing date of the claimed invention to implement in the animal monitoring system of Tupin, Madeley, and Stivoric to include providing, by the at least one processor, a second user interface for display on the user device, wherein the second user interface includes at least one of: a digital journal including mood data received from the user device; a calendar indicating one or more days on which mood data was received from the user device; and a chart indicating a frequency at which mood data was received from the user device, as suggested by Jang. The motivation for this is to provide mood conditions of an animal to an animal owner.
As to claim 15, Tupin, Madeley, and Stivoric discloses the limitations of claim 9 except for the claimed limitations of the system of claim 9, wherein the plurality of functions further includes a function for:
providing a second user interface for display on the user device, wherein the second user interface includes at least one of:
a digital journal including mood data received from the user device;
a calendar indicating one or more days on which mood data was received from the user device; and
a chart indicating a frequency at which mood data was received from the user device.
However, it has been known in the art of monitoring conditions of animals to implement wherein the plurality of functions further includes a function for:
providing a second user interface for display on the user device, wherein the second user interface includes at least one of:
a digital journal including mood data received from the user device;
a calendar indicating one or more days on which mood data was received from the user device; and a chart indicating a frequency at which mood data was received from the user device, as suggested by Jang, which discloses wherein the plurality of functions further includes a function for:
providing a second user interface for display on the user device (Jang: Abstract, [0047]-[0051], and FIG. 1-2 the user terminal device 100), wherein the second user interface includes at least one of:
a digital journal including mood data received from the user device;
a calendar indicating one or more days on which mood data was received from the user device; and a chart indicating a frequency at which mood data was received from the user device (Jang: Abstract, [0044]-[0051], [0113]-[0123], FIG. 1-2 and FIG. 6: a chatbot according to the present disclosure provides an answer to the user's question and provides an answer containing the companion animal's emotions at the time. For example, when a chatting value input by the user asks “how is the weather today?”, and if the companion animal is in a good mood, an answer value is displayed as “today's weather is sunny and good for going out”, and in contrast to this, if the companion animal is in a bad mood, the answer value is displayed as “it is sunny today, but I don't want to go out”. That is, it is characterized in that a context or a tone is slightly different depending on the companion animal's condition even when the answer value has the same topic).
Therefore, in view of teachings by Tupin, Madeley, Stivoric, and Jang, it would have been obvious to one of the ordinary skills in the art before the effective filing date of the claimed invention to implement in the animal monitoring system of Tupin, Madeley, and Stivoric to include wherein the plurality of functions further includes a function for:
providing a second user interface for display on the user device, wherein the second user interface includes at least one of:
a digital journal including mood data received from the user device;
a calendar indicating one or more days on which mood data was received from the user device; and a chart indicating a frequency at which mood data was received from the user device, as suggested by Jang. The motivation for this is to provide mood conditions of an animal to an animal owner.
As to claim 20, Tupin, Madeley, and Stivoric discloses the limitations of claim 16 except for the claimed limitations of the non-transitory computer-readable medium of claim 16, wherein the operations further comprise:
providing a second user interface for display on the user device, wherein the second user interface includes at least one of:
a digital journal including mood data received from the user device;
a calendar indicating one or more days on which mood data was received from the user device; and
a chart indicating a frequency at which mood data was received from the user device.
However, it has been known in the art of monitoring conditions of animals to implement wherein the operations further comprise:
providing a second user interface for display on the user device, wherein the second user interface includes at least one of:
a digital journal including mood data received from the user device; a calendar indicating one or more days on which mood data was received from the user device; and a chart indicating a frequency at which mood data was received from the user device, as suggested by Jang, which discloses wherein the operations further comprise:
providing a second user interface for display on the user device (Jang: Abstract, [0047]-[0051], and FIG. 1-2 the user terminal device 100), wherein the second user interface includes at least one of:
a digital journal including mood data received from the user device; a calendar indicating one or more days on which mood data was received from the user device; and a chart indicating a frequency at which mood data was received from the user device (Jang: Abstract, [0044]-[0051], [0113]-[0123], FIG. 1-2 and FIG. 6: a chatbot according to the present disclosure provides an answer to the user's question and provides an answer containing the companion animal's emotions at the time. For example, when a chatting value input by the user asks “how is the weather today?”, and if the companion animal is in a good mood, an answer value is displayed as “today's weather is sunny and good for going out”, and in contrast to this, if the companion animal is in a bad mood, the answer value is displayed as “it is sunny today, but I don't want to go out”. That is, it is characterized in that a context or a tone is slightly different depending on the companion animal's condition even when the answer value has the same topic).
Therefore, in view of teachings by Tupin, Madeley, Stivoric, and Jang, it would have been obvious to one of the ordinary skills in the art before the effective filing date of the claimed invention to implement in the animal monitoring system of Tupin, Madeley, and Stivoric to include wherein the operations further comprise:
providing a second user interface for display on the user device, wherein the second user interface includes at least one of:
a digital journal including mood data received from the user device; a calendar indicating one or more days on which mood data was received from the user device; and a chart indicating a frequency at which mood data was received from the user device, as suggested by Jang. The motivation for this is to provide mood conditions of an animal to an animal owner.
Citation of Pertinent Art
The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure:
Fukada et al., US 2025/0331503 A1, discloses animal monitoring system, animal monitoring server, animal monitoring method, and animal monitoring programs.
Nakai et al., US 2025/0212842 A1, discloses animal behavior recording device, animal behavior recording method, and program.
Rahat et al., US 2024/0005616 A1, dsiclsoes wearable device and controlling method thereof.
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 extension fee 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 QUANG PHAM whose telephone number is (571)-270-3668. The examiner can normally be reached 09:00 AM - 05:00 PM.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, QUAN-ZHEN WANG can be reached at (571)-272-3114. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/QUANG PHAM/Primary Examiner, Art Unit 2685
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