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 Amendment
This action is in response to the amendments filed on 4/10/26 wherein the examiner acknowledges that claim 1 has been amended, additional claims 21-24 have been added and claims 15-16 have been canceled. Consequently, claims 1-14 & 21-24 are currently pending.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 22-23 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The newly added claim 22 contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Claims 22–23 are rejected under 35 U.S.C. 112(a) as failing to comply with the written description requirement. The specification does not clearly describe the limitation of claim 22 requiring “wherein the plurality of motion determinations is generated by a second machine learning model”. Furthermore, the specification also fails to provide support for the limitation of claim 23 requiring “plurality of motion determinations is generated at a monitor worn by the athlete before the plurality of motion determinations is received at the computing device”. Therefore, the disclosure does not reasonably convey to one of ordinary skill in the art that the inventor possessed the claimed invention at the time of filing.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1-14 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Burroughs et al. (US Patent Pub. 20170014684; referred to hereinafter as Burroughs).
Claim 1: Burroughs disclose a method of determining an event participated in by an athlete (0004-0005), the method comprising, receiving, at a computing device, a plurality of motion determinations generated based on motion data captured from motion data of the athlete during a monitoring window (0052), wherein the motion determinations comprise at least one of an action performed by the athlete and performance metrics of the athlete (0076-0081), classifying, by use of a machine learning model stored on the computing device and based on the motion determinations, an event based at least in part on the plurality of motion determinations, wherein the event represents a classification of the plurality of motion occur determinations (0082, 0086-0087), generating a graphical user interface visualizing the event in relation to a time-related parameter (0091-0095, performance metrics that are tracked and displayed).
Claim 2: Burroughs disclose generating, by the computing device, a timeline of events participated in by the athlete based at least in part on a set of motion determinations comprising the plurality of motion determinations by applying the machine learning model to the set of motion determinations (0090 & 0095).
Claim 3: Burroughs disclose generating the timeline of events comprises classifying individual motion determinations among the plurality of motion determinations as being generated from motion data captured during individual events among the timeline of events (0095-0180 which includes classifying motion such as jumping, running etc. associated with sports).
Claim 4: Burroughs disclose before the receiving step, training the machine learning model to identify events athletes participate in by submitting training timelines to the machine learning model, and each training timeline comprises a plurality of sample motion determinations and indications of when sample events occurred (0099, 0127-0129, provides sample motion event occurred and calibration to allow the system to ‘learn’).
Claim 5: Burroughs disclose wherein the indications of when sample events occurred include event type tags associated with sample motion determinations among the plurality of sample motion determinations (figs. 30A-C and 0130-0132).
Claim 6: Burroughs disclose generating an unfiltered timeline of events participated in by the athlete based in part on the set of motion determinations by applying the machine learning model to the set of motion determinations; and filtering the unfiltered timeline of events by changing start times of individual events within the timeline of events to comply with filtering rules (0156).
Claim 7: Burroughs disclose wherein the filtering rules comprise possible durations for events among a plurality of predetermined events (0156, provides multiple filtering processes).
Claim 8: Burroughs disclose wherein the plurality of predetermined events comprises exercise, training for a sport, and a match of the sport (0106 & 0127-0129).
Claim 9: Burroughs disclose creating a plurality of test motion determinations based on motions of a test athlete during a test window, using the machine learning model to output a test event classification of which event among the plurality of predetermined events the test athlete participated in during the test window based on the plurality of test motion determinations (0052 & 0070-0082), and correcting the test event classification based on a record of what event the test athlete participated in during the test window (0092-0096, correcting or updating event the test athlete participated).
Claim 10: Burroughs disclose determining, by the computing device, a role of the athlete in a team sport based at least in part on the plurality of motion determinations by applying the machine learning model to the plurality of motion determinations (0110).
Claims 11-12: Burroughs disclose any one or any combination of a kick, a step, dribbling a ball, and running, and furthermore any one or any combination of distance traveled, travel speed, and kick force (0082).
Claim 13: Burroughs disclose event among a plurality of predetermined events the athlete participated in during the monitoring window is further based on video footage of the athlete during the monitoring window (0057).
Claim 14: Burroughs disclose creating tagged footage by tagging training video footage of athletes participating in events among the plurality of predetermined events with start times of the events among the plurality of predetermined events (0100-0101) and training the machine learning model on the tagged footage to recognize participation in the events among the plurality of predetermined events (0102-0106).
Claim Rejections - 35 USC § 103
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 21-22 are rejected under 35 U.S.C. 103 as being unpatentable over Burroughs as applied to claims above, and further in view of Pilon et al. (US Patent Pub. 20240367004; referred to hereinafter as Pilon).
Claim 21: Burroughs disclose a method of determining an event participated in by an athlete (0004-0005), the method comprising, receiving, at a computing device, a plurality of motion determinations generated based on motion data captured from motion data of the athlete during a monitoring window (0052), wherein the motion determinations comprise at least one of an action performed by the athlete and performance metrics of the athlete (0076-0081), classifying, by use of a machine learning model stored on the computing device. Burroughs, however does not explicitly disclose the MLM comprising a neural network. In an analogous art, Pilon teach a physical analysis system which utilizes AI for a target individual. The AI is configured to execute a training improvement method by receiving a feed of a defined environment and physical feedback from sensors (0020-0022). Pilon further teaches the machine learning model includes neural network (0022). It would have been obvious for one with ordinary skill in the art, at the time of applicant’s invention to modify the system disclosed by Burroughs to include a neural network, as taught by Pilon to provide training improvements (0013).
Claim 22: The combination of Burroughs and Pilon teach the machine learning model is a first machine learning model, and wherein the plurality of motion determinations is generated by a second machine learning model (0023-0026 Pilon).
Claim 23: The combination of Burroughs and Pilon teach the plurality of motion determinations is generated at a monitor worn by the athlete before the plurality of motion determinations is received at the computing device, and wherein the monitor comprises a sensor configured to capture the motion data (0025-0026 & 0029-0031 Pilon).
Claim 24: The combination of Burroughs and Pilon teach the classifying the event is further based on sensor data received at the computing device from an object monitor, and wherein the object monitor is integrated into a sports object comprising a ball, a sport stick, a glove, a bicycle, an oar, a ski, a skateboard, or a surfboard (0025 Pilon).
Response to Arguments
Applicant's arguments filed 4/16/2026 have been fully considered but they are not persuasive.
In response to applicant's argument that the references fail to show certain features of the invention, it is noted that the features upon which applicant relies (i.e., “machine learning model”) are not recited in the rejected claims. Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993).
Regarding the applicant’s argument that Burroughs fails to disclose "machine learning model" because the reference does not explicitly use that term and because said term machine learning model is alleged as to having a specific meaning. The examiner respectfully disagrees.
The anticipation inquiry is based on what the reference discloses explicitly and/or implicitly, not whether it uses the identical terminology as the claims. A prior art reference need not explicitly recite the words "machine learning model" if it discloses all the required features of the claimed subject matter; see In re Gleave, 560 F.3d 1331, 1334 (Fed. Cir. 2009). Burroughs discloses receiving motion data, generating motion determinations, and classifying an event based on those determinations using a trained classification process (see the rejection above).
Applicant also asserts that the term "machine learning model" excludes all computer-based classification processes. However, applicant has not identified any explicit definition or disclaimer in the specification that limits the claimed machine learning model to a particular machine learning architecture, training algorithm, or implementation. In the absence of such a limiting definition, the claim term is afforded its ordinary and customary meaning as understood by one of ordinary skill in the art. Under that meaning, cited reference Burroughs falls within the scope of the claimed machine learning model.
Accordingly, the Examiner maintains that Burroughs discloses the claimed limitations, and the rejection is maintained.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SUNIT PANDYA whose telephone number is (571)272-2823. The examiner can normally be reached M-F 9:30-6:30PM.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Peter Vasat can be reached at 571-270-7625. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/SUNIT PANDYA/Primary Examiner, Art Unit 3715
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