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 .
Response to Arguments
Applicant’s arguments, see Applicants Remarks pages 6-12, filed 03/18/26, with respect to the rejection(s) of claim(s) 1-18, 20-22 have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of Kawaski et al US 20220309289.
Regarding claim 1, Applicant states that Johannson reference fails to teach assigning a respective persistent identifier to the monitored video object within the video footage based at least in part on commonality between video events performed by the monitored video object and motion events (Applicants Remarks pages 7-8). Examiner agrees with Applicant. Kawaski et al teaches video dataset is part of a training dataset for training a machine learning model for identifying baseball players swinging baseball bats to hit baseball pitches. The video dataset also includes labels corresponding to when the baseball player begins swinging the baseball bat and completes the swing of the baseball bat, corresponding to a label assigned to certain frames of the video dataset based on the content of the frames (paragraph 0041)
Regarding claim 16, Applicant states that Sterling does not teach the machine learning model being configured to identify video events from video footage and the computing device being configured to correlate the video events to the motion events (Applicants Remarks page 9-10). Examiner agrees with Applicant. Kawaski et al teaches video dataset is part of a training dataset for training a machine learning model for identifying baseball players swinging baseball bats to hit baseball pitches. The video dataset also includes labels corresponding to when the baseball player begins swinging the baseball bat and completes the swing of the baseball bat, corresponding to a label assigned to certain frames of the video dataset based on the content of the frames (paragraph 0041).
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1, 2, 5, 7-9, 11-16, 19 and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Johannson et al US 20190163979 in view of Kawaski et al US 20220309289 further in view of Brush et al UA 202200802636
Regarding claim 1, Johannson et al teaches a method of tracking objects within video footage (athletic performance system 100 (paragraph 0014), the method comprising:
receiving, at a computing device, video footage of a space and event data from a monitor, wherein the monitor comprises a motion sensor configured to measure movements of a monitored video object within the space, and the event data comprises motion events transmitted by the monitor (sensors within the venue 110 and the distributed ledger 120 are shown. The sensor systems can capture audience video and audio, player acceleration, athlete pace, player heartrate, coach acceleration, coach sentiment, coach heartrate, and so on as desired (paragraph 0021 and fig 3) Note: the distributed ledger 120 receives video from sensors of venue 110 which captures the movement of athletes;
Johannson et al fails to teach using a machine learning model on the computing device to identify, from the video footage, video events performed by the monitored video object
assigning a respective persistent identifier to the monitored video object within the video footage based at least in part on commonality between video events performed by the monitored video object and motion events transmitted by the monitor
Kawaski et al teaches using a machine learning model on the computing device to identify, from the video footage, video events performed by the monitored video object (training dataset data store module (“mod”) 302 receives video dataset as a component of a training dataset for training a machine learning model. video dataset is part of a training dataset for training a machine learning model for identifying baseball players swinging baseball bats to hit baseball pitches. The video dataset also includes labels corresponding to when the baseball player begins swinging the baseball bat and completes the swing of the baseball bat, corresponding to a label assigned to certain frames of the video dataset based on the content of the frames (paragraph 0041); and
(video dataset is part of a training dataset for training a machine learning model for identifying baseball players swinging baseball bats to hit baseball pitches. The video dataset also includes labels (persistent identifier) corresponding to when the baseball player begins swinging the baseball bat and completes the swing of the baseball bat, corresponding to a label assigned to certain frames of the video dataset based on the content of the frames (paragraph 0041)
Therefore, it would have been obvious to one of ordinary skill in the art to modify Johannson et al to include: assigning a respective persistent identifier to the monitored video object within the video footage based at least in part on commonality between video events performed by the monitored video object and motion events transmitted by the monitor; using a machine learning model on the computing device to identify, from the video footage, video events performed by the monitored video object.
The reason of doing so would be to accurately track an athlete’s motion and movements.
Johannson et al in view of Kawaski fails to teach assigning a respective persistent identifier to the monitored video object within the video footage based at least in part on commonality between video events performed by the monitored video object and motion events transmitted by the monitor;
Brush et al teaches assigning a respective persistent identifier to the monitored video object within the video footage based at least in part on commonality between video events performed by the monitored video object and motion events transmitted by the monitor (the AI/ML can self-correct based on inputs by the player, coach, and scout. The system may learn what a certain player looks like based on the data provided by the users, video input, meta data, and text natural language processing. The system may track that player, and auto tag actions itself based on the more data it obtains (paragraph 0087)
Therefore, it would have been obvious to one of ordinary skill in the art to modify Johannson et al in view of Kawaski to include: assigning a respective persistent identifier to the monitored video object within the video footage based at least in part on commonality between video events performed by the monitored video object and motion events transmitted by the monitor
The reason of doing so would be to accurately track an athlete’s motion and movements.
Regarding claim 2, Johanson et al in view of Kawaski et al teaches wherein the monitored video object is one among multiple video objects, the monitor is one among multiple monitors that each comprise a motion sensor configured to measure movements of a respective monitored video object, the method comprises assigning a respective persistent identifier to each of the multiple monitored video objects (Johannson et al: Once the image is segmented, the image forms that represent different body parts are identified. This process includes supervised machine learning models that distinguish arms, legs, torso, head, facial features, and so on. The relationships between these identified elements are determined, so that the location of joints between body parts are returned. In some embodiments, the locations are determined within a reference frame internal to the video frame. Therefore, the relative location of different body parts to a specific frame reference can be used across video frames (paragraph 0036) Note: the body parts are read as identifiers, and assigning the respective persistent identifiers comprises:
assigning respective preliminary identifiers to the monitored video objects within the video footage at multiple different times within the video footage (Johannson et al: This process includes supervised machine learning models that distinguish arms, legs, torso, head, facial features, and so on. location of different body parts to a specific frame reference can be used across video frames. (paragraph 0036);
finding preliminary identifiers assigned to a same monitored video object within different portions of the video footage based at least in part on commonality between video events performed by the same monitored video object and motion events transmitted by one of the monitors (Johannson et al: the locations of the body joints (preliminary identifiers) of the reference athlete (video object) in one frame can be compared to the locations of body parts of the user in the associated frame (paragraph 0039);
consolidating preliminary identifiers found to be assigned to the same monitored video object into a single preliminary identifier (Johannson et al: After this association is known, the locations of the body joints of the reference athlete in one frame can be compared to the locations of body parts of the user in the associated frame (paragraph 0039). Accumulating metrics from one frame to the next yields a cumulative measure of the similarity between the reference video of the athlete and the video submitted by the user (paragraph 0040) ; and
after the consolidating step, converting the preliminary identifiers to the persistent identifiers (Johannson et al: measure of similarity is then reported to the user as a similarity score. In some embodiments, the cumulative similarity score can be obtained using a summation of all individual similarity scores or a product of the individual scores (paragraph 0040).
Regarding claim 5, Johannson et al in view of Kawaski et al teaches processing motion data captured by the motion sensor of the wearable monitor to identify the motion events before transmitting the event data to the computing device (Johannson et al: the reference athlete can wear a motion sensor that measures acceleration and rotation (paragraph 0042).
Regarding claim 7, Johannson et al in view of Kawaski et al teaches identifying, for each persistent identifier, a corresponding one of the monitors that comprises the motion sensor configured to measure the movements of the monitored video object to which the persistent identifier is assigned (Johannson et al: In the basketball example, the sensor on the basketball player communicates its data to a nearby access point (paragraph 0026). platform 130 can look for various triggers to determine whether a given move, drill, or routine should be selected for presentation (paragraph 0027 and table paragraph 0028)
Regarding claim 8, Johannson et al in view of Kawaski et al teaches applying a timestamp to one of the motion events transmitted by the corresponding one of the monitors matching a time when a corresponding video event is performed by the monitored video object to which the persistent identifier is assigned (Johannson et al: A time series of joint positions describes the movements of the athlete. This sequence of joint positions is used to compare to the video submitted by the user. For instance, the joint positions extracted from the video submitted by the user can be compared to those of the reference athlete to determine a similarity between the respective time-series of joint positions (paragraph 0037).
Regarding claim 9, Johannson et al in view of Kawaski et al teaches applying a timestamp to at least one of the motion events to match a time at which one of the video events occurred (Johannson et al: A time series of joint positions describes the movements of the athlete. This sequence of joint positions is used to compare to the video submitted by the user. For instance, the joint positions extracted from the video submitted by the user can be compared to those of the reference athlete to determine a similarity between the respective time-series of joint positions. initial similarity measure describes the comparison of each frame in one video to a frame in a second video. This set of initial similarity measures can be aggregated across all frames in the video to produce a single comprehensive similarity score (paragraph 0037).
Regarding claim 11, Johannson et al in view of Kawaski et al teaches wherein the video objects are athletes engaged in a sporting event (Johannson et al: the reference athlete can wear a motion sensor that measures acceleration and rotation (paragraph 0042).
Regarding claim 12, Johannson et al in view of Kawaski et al teaches wherein each athlete wears one of the monitors (Johannson et al: the reference athlete can wear a motion sensor that measures acceleration and rotation (paragraph 0042).
Regarding claim 13, Johannson et al in view of Kawaski et al teaches wherein the motion events comprise any one or any combination of kicking, running, walking, and standing (Johannson et al: the motion data that represents the spinning dribble is directly compared between the basketball player and fan, to determine the degree of similarity between the player and fan. The motion data representing the dunk is also compared, to determine how high the fan jumped, relative to the player (paragraph 0047).
Regarding claim 14, Johannson et al in view Kawaski et al teaches wherein the motion events comprise magnitude information in the form of any one or any combination of kick force, distance traveled, and speed (Johannson et al: the motion data that represents the spinning dribble is directly compared between the basketball player and fan, to determine the degree of similarity between the player and fan. The motion data representing the dunk is also compared, to determine how high the fan jumped, relative to the player (paragraph 0047)
Regarding claim 15, Johannson et al in view of Kawaski et al teaches wherein the monitor comprises a controller configured to identify the motion events from the motion sensor of the monitor (Johannson et al: performance measurement system 100 also maintains a list of continuous indices. For example, facial expressions, team energy, fan excitement, pressure, coach tension can be maintained. These continuous indices can be used for identifying exciting moments in a sporting event, and for annotating content to report the characteristics of actions and/or behaviors (paragraph 0046)
Regarding claim 16, Johannson et al teaches a system comprising:
a wearable monitor comprising a motion sensor and a controller configured to identify motion events from measurements acquired by the motion sensor (a professional basketball player can wear a motion sensor fitted with an ultra-wideband (UWB) wireless interface communicating with the analytic processor over a wireless data network. The UWB interface can communicate data recorded of the professional basketball player while performing a spin move while dribbling and dunking the basketball (paragraph 0021). the reference athlete can wear a motion sensor that measures acceleration and rotation (paragraph 0042); and
a computing device configured to receive event data transmitted by the wearable monitor, the event data comprising motion events (sensors within the venue 110 and the distributed ledger 120 are shown. The sensor systems can capture audience video and audio, player acceleration, athlete pace, player heartrate, coach acceleration, coach sentiment, coach heartrate, and so on as desired (paragraph 0021 and fig 3) Note: the distributed ledger 120 receives video from sensors of venue 110 which captures the movement of athletes,
Johannson et al fails to teach wherein the computing device comprises a non-transitory computer readable medium on which a machine learning model is stored,
the machine learning model being configured to identify video events from video footage, and the computing device being configured to correlate the video events to the motion events,
Kawaski et al teaches wherein the computing device comprises a non-transitory computer readable medium on which a machine learning model is stored (Memory 208 and persistent storage 210 are computer-readable storage media. In general, memory 208 can include any suitable volatile or non-volatile computer-readable storage media (paragraph 0034),
the machine learning model being configured to identify video events from video footage, and the computing device being configured to correlate the video events to the motion events (training dataset data store module (“mod”) 302 receives video dataset as a component of a training dataset for training a machine learning model. video dataset is part of a training dataset for training a machine learning model for identifying baseball players swinging baseball bats to hit baseball pitches. The video dataset also includes labels corresponding to when the baseball player begins swinging the baseball bat and completes the swing of the baseball bat, corresponding to a label assigned to certain frames of the video dataset based on the content of the frames (paragraph 0041).
Therefore, it would have been obvious to one of ordinary skill in the art to modify Johannson et al to include: wherein the computing device comprises a non-transitory computer readable medium on which a machine learning model is stored, the machine learning model being configured to identify video events from video footage, and the computing device being configured to correlate the video events to the motion events.
The reason of doing so would be to accurately track an athlete’s motion and movements.
Johannson et al in view of Kawaski fails to teach assigning a respective persistent identifier to the monitored video object within the video footage based at least in part on commonality between video events performed by the monitored video object and motion events transmitted by the monitor;
Brush et al teaches assigning a respective persistent identifier to the monitored video object within the video footage based at least in part on commonality between video events performed by the monitored video object and motion events transmitted by the monitor (the AI/ML can self-correct based on inputs by the player, coach, and scout. The system may learn what a certain player looks like based on the data provided by the users, video input, meta data, and text natural language processing. The system may track that player, and auto tag actions itself based on the more data it obtains (paragraph 0087)
Therefore, it would have been obvious to one of ordinary skill in the art to modify Johannson et al in view of Kawaski to include: assigning a respective persistent identifier to the monitored video object within the video footage based at least in part on commonality between video events performed by the monitored video object and motion events transmitted by the monitor
The reason of doing so would be to accurately track an athlete’s motion and movements.
Regarding claim 20, Johannson et al in view of Kawaski et al teaches wherein the machine learning model is configured to track video objects within video footage based on the event data (Kawaski et al: the video dataset is a video of a baseball player swinging a baseball bat as if to strike a baseball thrown during a baseball pitch. This video dataset is part of a training dataset for training a machine learning model for identifying baseball players swinging baseball bats to hit baseball pitches. (paragraph 0041)
Regarding claim 21, Johannson et al in view of Kawaski et al teaches wherein the respective persistent identifier remains associated with the object throughout the video footage (Kawaski et al: the video dataset is a video of a baseball player swinging a baseball bat as if to strike a baseball thrown during a baseball pitch. This video dataset is part of a training dataset for training a machine learning model for identifying baseball players swinging baseball bats to hit baseball pitches. (paragraph 0041)
Regarding claim 22, Johannson et al in view of Kawaski et al teaches wherein the computing device is configured to use correlations between the video events and motion events to track movement of a video object in the video footage (Kawaski et al: the video dataset is a video of a baseball player swinging a baseball bat as if to strike a baseball thrown during a baseball pitch. This video dataset is part of a training dataset for training a machine learning model for identifying baseball players swinging baseball bats to hit baseball pitches. (paragraph 0041)
Claim(s) 6 is/are rejected under 35 U.S.C. 103 as being unpatentable over Johannson et al US 20190163979 in view of Kawaski et al US 20220309289 further in view of Power et al US
Regarding claim 6, Johannson et al in view of Kawaski et al teaches all of the limitations of claim 1
Johannson et al in view of Kawaski et al fails to teach wherein the machine learning model is a first machine learning model, and the processing of the motion data comprises applying a second machine learning model hosted by the wearable monitor to the motion data.
Power et al teaches wherein the machine learning model is a first machine learning model, and the processing of the motion data comprises applying a second machine learning model hosted by the wearable monitor to the motion data (Motion sensor device 104 (wearable monitor) can include device circuit 302. Device circuit 302 can include one or more of the components of device circuit 202 with the addition of microprocessor 304, memory 306 (paragraph 0054), memory 306 can store computer executable components, including analysis component 308 (paragraph 0055) and analysis component 308 can employ machine learning to techniques (paragraph 0064) Note: the motion sensor device 104 comprises analysis component 308 that employs machine learning techniques. inference component 506 can also employ machine learning techniques (first machine learning model) (paragraph 0078)
Therefore, it would have been obvious to one of ordinary skill in the art to modify Johannson et al in view of Kawaski et al to include: wherein the machine learning model is a first machine learning model, and the processing of the motion data comprises applying a second machine learning model hosted by the wearable monitor to the motion data.
The reason of doing so would be to accurately track an athlete’s motion and movements.
Claim(s) 17 and 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Johannson et al US 20190163979 in view of Kawaski et al US 20220309289 in view McDaid US 2022/0409098.
Regarding claim 17, Johannson et al in view of Kawaski et al teaches all of the limitations of claim 16
Johannson et al in view of Kawaski et al fails to teach wherein the wearable monitor is configured to be integrated into an article of wear
McDaid teaches wherein the wearable monitor is configured to be integrated into an article of wear (A wearable device may comprise any device which is worn, held, carried by, attached to or mounted on a human or animal in any suitable manner or form, including wearable activity trackers, smart watches, smart phones, smart shoes and other devices able to be carried (paragraph 0015)
Therefore, it would have been obvious to one of ordinary skill in the art to modify Johannson et al in view of Kawaski et al to include: wherein the wearable monitor is configured to be integrated into an article of wear.
The reason of doing so would be to accurately track an athlete’s motion and movements.
Regarding claim 18, Johannson et al in view of Kawaski et al further in view of McDaid teaches wherein the article of wear is a shoe insole (McDaid: A wearable device may comprise any device which is worn, held, carried by, attached to or mounted on a human or animal in any suitable manner or form, including wearable activity trackers, smart watches, smart phones, smart shoes and other devices able to be carried (paragraph 0015)
Therefore, it would have been obvious to one of ordinary skill in the art to modify Johannson et al in view of Kawaski et al to include: wherein the article of wear is a shoe insole.
The reason of doing so would be to accurately track an athlete’s motion and movements.
Allowable Subject Matter
Claims 3, 4, 10 objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Conclusion
Any inquiry concerning this communication should be directed to Michael Burleson whose telephone number is (571) 272-7460 and fax number is (571) 273-7460. The examiner can normally be reached Monday thru Friday from 8:00 a.m. – 4:30p.m. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Akwasi Sarpong can be reached at (571) 270- 3438.
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Michael Burleson
Patent Examiner
Art Unit 2683
Michael Burleson
June 3, 2026
/MICHAEL BURLESON/
/AKWASI M SARPONG/SPE, Art Unit 2681 6/8/2026
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