DETAILED ACTION
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 .
Status of Claims
Claims 1-20 are pending.
This communication is in response to the communication filed January 2, 2024.
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
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 set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied 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 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Tran et al. US20210233656 in view of Shmigelsky et al. US20230337636.
As per claim 1, Tran teaches
a computer-implemented method comprising: receiving, at a wireless data relay device, from a sensor device, (Tran par. 23 teaches sensor devices connected via wireless communication interface)
first data representative of measurements associated with gait of an animal; (Tran par. 217, 229 teaches Temporal characteristics, Electromyographic signals, Kinematics of limb segments, and Kinetics of gait and limb movements for asymmetric gaits in a gallop of a horse, where kinematic modeling may )
wherein the sensor device is attached to the animal and the sensor device comprises (Tran par. 25, 37, 45, 220-221 teaches data from pressure sensor, force sensor, audio component of actuator, accelerometer for kinematic data, GPS. Signals may be captured from a wearable device with sensors, for example sensors attached to the left elbow (LE), right elbow (RE), left knee (LK), and right knee (RK) connect the upper and the lower extremities, among others)
a first wireless transceiver communicatively coupled to one or more first sensors; (Tran par. 22, 37, 73 teaches sensor data can be transmitted to a disease management server in a data center by a long-range communication network such as a 5G transceiver)
determining start times and stop times of movement events associated with the first data; (Tran par. 185, 217 teaches temporal analysis of gait provides some norms for the average velocity of walking as well as time durations for the two phases of gait: the stance phase and the swing phase, interpreted as start and stop times for movement events)
storing movement event data, wherein the movement event data includes the first data; (Tran par. 35, 185 teaches system receives and stores a set of body metric measurement data over the network from the user and a portion of the participants of the matched group functions to gather data from which to generate feedback in support of the health regimen. This step is preferably repeated over time such that a time series of body metric measurement data may be received in regular intervals (e.g., hourly, daily, weekly, biweekly) or irregular intervals from the user and at least one other user of the matched group)
generating one or more treatment recommendations based on the…gait asymmetry for the animal; (Tran par. 229 teaches same architecture can also recommend treatment based on sensor data captured over time and based on treatment data for a population of users. For example, during examination, a doctor uses a smartphone to review sensor data from a biologic such as a human or an animal. Par. 160 teaches recommendation for levels of a biometrics.)
displaying, on a client computing device, the one or more treatment recommendations and the…gait asymmetry for the animal (Tran par. 211 teaches In one embodiment, a visual display can be used for displaying suggestions. For example, the suggestions displayed include (a) a suggestion regarding recovery and sprint, and (b) a suggestion regarding increase and reduction of velocity. The suggestion (a) is provided for determining whether to take dynamic recovery (reduce exercise strength) or sprint (increase exercise strength or speeding). The suggestion (b) is provided for the determining whether to increase or decrease velocity so that the user can best approach the predetermined target but within the user's physiological tolerance based on a predetermined target time and physiological situation.).
Tran teaches levels of biometric measurements from sensors, but does not specifically teach the following limitations met by Shmigelsky levels of gait asymmetry; (Shmigelsky par. 61, 174, 277 teaches levels of gait asymmetry)
using a first machine learning model to generate levels of gait asymmetry for the animal, (Shmigelsky par. 61 teaches the Al pipeline comprises a classification layer having a lameness Al model that is trained to determine a level of lameness that the detected animal is incurring, wherein the lameness Al model is adapted to receive at least one leg section and key points thereof and generates an ordinal output result for the level of lameness being incurred by the detected animal)
wherein the movement event data is provided as input for the first machine learning model, and wherein the first machine learning model is trained to predict levels of gait asymmetry using the movement event data; (Shmigelsky par. 174, 277 teaches the system may be programmed to track the animal’s strides over time to make a prediction for its gait. An example of this is described with reference to FIG. 20 which only shows one way to use certain angles and distances as input to a gait AI model that can be used to determine a characteristic of the animal’s gait.)
It would have been obvious to one of ordinary skill in the art at the time the invention was filed to modify the systems and methods as taught by Tran to use machine learning to generate levels of gait asymmetry and using movement event data as input for the machine learning model to predict levels of gait asymmetry as taught by Shmigelsky with the motivation to of solving a need for efficient systems and methods for tracking and managing livestock (par. 3-17).
As per claim 2, Tran and Shmigelsky teach all the limitations of claim 1 and further teach wherein the first data includes at least one of: kinematic data of the animal, force and pressure data of paws of the animal, GPS data indicating movement of the animal, video data capturing movement of the animal and movement of limbs of the animal, and audio data associated with movement of the animal (Tran par. 25, 37, 45 teaches data from pressure sensor, force sensor, audio component of actuator, accelerometer for kinematic data, GPS).
As per claim 3, Tran and Shmigelsky teach all the limitations of claim 1 and further teach wherein the sensor device is attached to the animal using one or more straps to attach the sensor device to a limb of the animal (Tran par. 37, 220-221 teaches an HMM is used to track patient motor skills or patient movement patterns. The muscular groups attached at various locations along the skeletal structure often have multiple functions. The majority of energy expended during walking is for vertical motion of the body. Wireless sensors with tri-axial accelerometers are mounted to the patient on different body locations for recording, for example the tree structure as shown in FIG. 16D; similar comparisons can be made for sensors attached to the left elbow (LE), right elbow (RE), left knee (LK), and right knee (RK) connect the upper and the lower extremities, among others).
As per claim 4, Tran and Shmigelsky teach all the limitations of claim 1 and further teach wherein the sensor device is affixed to a wearable device worn by the animal (Tran par. 37, 220-221 teaches a wearable device with sensors).
As per claim 5, Tran and Shmigelsky teach all the limitations of claim 1 and further teach wherein the sensor device is part of a footwear element worn on a paw of the animal (Tran par. 21 teaches wearing sensor devices on various parts of the body including legs and ankles).
As per claim 6, Tran and Shmigelsky teach all the limitations of claim 1 and further teach
receiving, at the wireless data relay device, from a second sensor device, second data representative of measurements associated with gait of the animal, (Tran par. 185, 217 teaches temporal analysis of gait provides some norms for the average velocity of walking as well as time durations for the two phases of gait: the stance phase and the swing phase, where the swing phase may be interpreted as second data representative of measurements associated with gait. Moreover, any one of Temporal characteristics, Electromyographic signals, Kinematics of limb segments, and Kinetics of the foot-floor and joint resultants may be first or second data.)
wherein the second sensor device is attached to the animal and the second sensor device comprises a second wireless transceiver communicatively coupled to one or more second sensors; (Tran par. 25, 37, 45, 220-221 teaches data from pressure sensor, force sensor, audio component of actuator, accelerometer for kinematic data, GPS. Signals may be captured from a wearable device with sensors, for example sensors attached to the left elbow (LE), right elbow (RE), left knee (LK), and right knee (RK) connect the upper and the lower extremities, among others. Here a first sensor may be a left elbow sensor and a second sensor may be any other sensor.)
determining second start times and second stop times of second movement events associated with the second data; (Shmigelsky par. 282 teaches The time stamp for an image when the given animal begins performing the activity can be subtracted from the time stamp of the image frame in which the given animal stops performing the activity to determine the time interval for performing the activity. Images in which the animal is performing a certain activity can be determined by a corresponding AI model as was described previously for determining when an animal is eating when an image of the animal is acquired. This can be repeated to determine all of the time intervals where the given animal is performing the activity during the desired time period. The time intervals can then be added to determine the overall time duration that the given animal spent engaging in the activity in the desired time period. Here, second data may be any data that is not first data and a second start and stop time may be any labeled for a second activity including the swing phase.)
wherein storing movement event data comprises storing movement event data that includes the first data and the second data (Tran par. 35, 185 teaches system receives and stores a set of body metric measurement data over the network from the user and a portion of the participants of the matched group functions to gather data from which to generate feedback in support of the health regimen. This step is preferably repeated over time such that a time series of body metric measurement data may be received in regular intervals (e.g., hourly, daily, weekly, biweekly) or irregular intervals from the user and at least one other user of the matched group).
It would have been obvious to one of ordinary skill in the art at the time the invention was filed to modify the systems and methods as taught by Tran to determine second start and stop times of second movement events as taught by Shmigelsky with the motivation to of solving a need for efficient systems and methods for tracking and managing livestock (par. 3-17)
As per claim 7, Tran and Shmigelsky teach all the limitations of claim 1 and further teach
training the first machine learning model using a training corpus with data representing a plurality of sensor data representing force and pressure data, kinematic data, GPS data, video data, and audio data, (Tran par. 25, 37, 45 teaches data from pressure sensor, force sensor, audio component of actuator, accelerometer for kinematic data, GPS)(Shmigelsky par. 43-44, 180, 196, 316 teaches video camera data, audio data, and lidar sensor data used to train AI models)
for a plurality of animals representing different species of animals, different breeds of each of the species of animals, and different conformations of each of the species and breeds of animals (Shmigelsky par. 7 teaches tracking and managing livestock and wildlife including different species, breeds, and heard affiliation).
It would have been obvious to one of ordinary skill in the art at the time the invention was filed to modify the systems and methods as taught by Tran to use video data, and animals representing different species, breeds, and conformations as taught by Shmigelsky with the motivation to of solving a need for efficient systems and methods for tracking and managing livestock (par. 3-17).
As per claim 8, Tran and Shmigelsky teach all the limitations of claim 1 and further teach wherein the first machine learning model uses characteristics of the animal to generate the levels of gait asymmetry for the animal, wherein the characteristics of the animal include at least one of: age of the animal, gender of the animal, neutering status of the animal, breed of the animal, weight of the animal, medical history of the animal, observed physical tendencies of the animal, and historical gait observations of the animal (Shmigelsky par. 343 teaches data may be provided for displaying data about the age, species, health, cumulative weight information, respectively, for a herd of animals at the site.).
It would have been obvious to one of ordinary skill in the art at the time the invention was filed to modify the systems and methods as taught by Tran to use age, breed, and weight data for animals as taught by Shmigelsky with the motivation to of solving a need for efficient systems and methods for tracking and managing livestock (par. 3-17).
As per claim 9, Tran and Shmigelsky teach all the limitations of claim 1 and further teach wherein generating the one or more treatment recommendations comprises, using a second machine learning model to generate the one or more treatment recommendations for the animal, wherein the second machine learning model is trained to predict the one or more treatment recommendations based on the levels of gait asymmetry for the animal and characteristics of the animal (Tran par. 160, 229 teaches treatment recommendations based on sensor data captured over time. Sensor data may output levels of lameness, which is gait asymmetry.).
As per claim 10, Tran and Shmigelsky teach all the limitations of claim 1 and further teach displaying, on a graphical user interface on the client computing device, the movement event data including an indication as to whether a limb of the animal is affected by the gait asymmetry (Shmigelsky par. 342 teaches animal management and monitoring tools, where the menu includes a Dashboard option, an AI View option, an Animals option, a Herds option, an Activity option, a Nutrition option, a Health option, a Weight option, a Temperature option, a Respiration option, a Stress option and a Lameness option.).
It would have been obvious to one of ordinary skill in the art at the time the invention was filed to modify the systems and methods as taught by Tran to use display indication of gait asymmetry affecting a limb of an animal as taught by Shmigelsky with the motivation to of solving a need for efficient systems and methods for tracking and managing livestock (par. 3-17).
As per claims 11-20, Tran and Shmigelsky teaches (see claims 1-10 rejections)
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JAY M. PATEL whose telephone number is (571)272-6793 and email is jay.patel2@uspto.gov. The examiner can normally be reached on Monday-Friday 8AM-4:30PM.
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/JAY M. PATEL/Primary Examiner, Art Unit 3686
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