Prosecution Insights
Last updated: July 17, 2026
Application No. 19/172,538

SYSTEMS AND METHODS FOR SPORTS TRACKING DATA COLLECTION, PROCESSING, AND CORRECTION

Non-Final OA §102§103
Filed
Apr 07, 2025
Priority
Apr 09, 2024 — provisional 63/631,688
Examiner
ZHAO, DAQUAN
Art Unit
2484
Tech Center
2400 — Computer Networks
Assignee
Stats LLC
OA Round
1 (Non-Final)
77%
Grant Probability
Favorable
1-2
OA Rounds
1y 6m
Est. Remaining
92%
With Interview

Examiner Intelligence

Grants 77% — above average
77%
Career Allowance Rate
802 granted / 1040 resolved
+19.1% vs TC avg
Moderate +14% lift
Without
With
+14.5%
Interview Lift
resolved cases with interview
Typical timeline
2y 9m
Avg Prosecution
18 currently pending
Career history
1059
Total Applications
across all art units

Statute-Specific Performance

§101
4.2%
-35.8% vs TC avg
§103
72.3%
+32.3% vs TC avg
§102
8.4%
-31.6% vs TC avg
§112
5.8%
-34.2% vs TC avg
Black line = Tech Center average estimate • Based on career data from 1040 resolved cases

Office Action

§102 §103
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 . 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 1-5, 7-12, and 14-20 are rejected under 35 U.S.C. 102(a)(1) as being described by Chang et al (US 2017/0238055). For claim 1, Chang et al teach a method for processing and correcting data for a sports event, the method comprising: receiving, by a computing system, one or more data feeds for a sports event, wherein the one or more data feeds includes at least one data entry (paragraph 23: “ …machine learning to develop an understanding of a plurality of events and to determine at least one event type for each of the plurality of events within the at least one video feed. The at least one event type includes an entry in a relationship library at least detailing a relationship between two visible features of the at least one video feed.” Figure 10 shows the “understanding” of a plurality of events. For example, “Field Goal Attempts”, “handoffs”, “isolations”…etc. Figure 14 also shows the understanding of events in the “Description”, also Figure 15 shows the marking of events ); receiving, by the computing system, one or more video feeds for the sports event, wherein the one or more video feeds includes event data (e.g. paragraph 13: “taking a video feed of an event; using machine learning to develop an understanding of the event; automatically, under computer control, aligning the video feed with the understanding; and producing a transformed video feed that includes at least one highlight that may be extracted from the machine learning of the event. In embodiments, the event may be a sporting event.” ); identifying, by the computing system, a data feed error, wherein the data feed error is a difference between the at least one data entry and the event data in the one or more video feeds (e.g. paragraph 279: “For example, if two players' XY positions are switched, then “over” vs “under” defense would be incorrectly characterized, since the players' relative positioning is used as a critical feature for the classification. Even player-by-player data sources are occasionally incorrect, such as associating identified events with the wrong player.”) and correcting, by the computing system, the data feed error, wherein correction of the data feed error includes altering the at least one data entry to be consistent with the event data (e.g. paragraph 280: “validation algorithms are used to detect all events, including the basic events such as possession, pass, dribble, shot, and rebound that are provided with the XYZ data.”). For claim 8, Chang et al teach a system for processing and correcting data for a sports event, the system comprising: a non-transitory computer readable medium configured to store processor-readable instructions; and a processor operatively connected to the non-transitory computer readable medium (e.g. paragraph 539: “The methods and systems described herein may be deployed in part or in whole through a machine that executes computer software, program codes, and/or instructions on a processor. ”), and configured to execute the instructions to perform operations comprising: receiving one or more data feeds for a sports event, wherein the one or more data feeds includes at least one data entry (e.g. see discussion of claim 1 above); receiving one or more video feeds for the sports event, wherein the one or more video feeds includes event data (e.g. see discussion of claim 1 above); identifying a data feed error, wherein the data feed error is a difference between the at least one data entry and the event data in the one or more video feeds (e.g. see discussion of claim 1 above); and correcting the data feed error, wherein correction of the data feed error includes altering the at least one data entry to be consistent with the event data (e.g. see discussion of claim 1 above). Claim 15 is rejected for the same reasons as discussed in claim 8 above. For claims 2, 9 and 16, Chang et al teach the at least one data entry includes player identification data (e.g. figure 4: “PLAYER4 STEALS THE BALL FROM PLAYER3”). For claims 3, 10 and 17, Chang et al teach the at least one data entry includes player tracking data (e.g. paragraph 148: chip base player tracking system. Or figure 20, paragraph 195: player detection). For claims 4,11 and 18, Chang et al teach the at least one data entry includes jersey identification data (e.g. figure 11: Player 4: #35). For claims 5, 12 and 19, Chang et al teach the at least one data entry includes team association data (e.g. figure 19: “Team 1”, “Team 2” or figure 5A “TEAM”). For claims 7, 14 and 20, Chang et al teach the data in the corrected data feed error is reincorporated into the one or more data feeds in real time (e.g. paragraph 485: displaying machine extracted, real time, contextualized content based on machine identification of a type of event occurring in a live video stream. e.g. paragraph 280: “validation algorithms are used to detect all events, including the basic events such as possession, pass, dribble, shot, and rebound that are provided with the XYZ data.”). 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 6 and 13 are rejected under 35 U.S.C. 103 as being unpatentable over Chang et al, as applied to claims 1-5, 7-12 and 14-20 above, and further in view of Lavin (US 2017/0333778). For claims 6 and 13, Chang et al do not further disclose the data feed error includes duplicate player identification data. Lavin teaches the data feed error includes duplicate player identification data (e.g. paragraph 35: errors in the line-up, such as duplicate players at a single position). It would have been obvious to one ordinary skill in the art before the effective filing date of the claim invention to incorporate the teaching of Lavin into the teaching of Chang et al to detect and correct error to improve the quality of the video. Conclusion The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Gershey et al (WO 2022/119605, see foreign reference filed on 7/7/2025, see paragraph 6: receiving live data feed and detect anomaly in the data feed and resolving the anomaly of the multimedia content). Any inquiry concerning this communication or earlier communications from the examiner should be directed to DAQUAN ZHAO whose telephone number is (571)270-1119. The examiner can normally be reached M-Thur: 7:00 am-5:00 pm. Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Thai Tran can be reached on 571-272-7382. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. Email: daquan.zhao1@uspto.gov. Phone: (571)270-1119 /DAQUAN ZHAO/Primary Examiner, Art Unit 2484
Read full office action

Prosecution Timeline

Apr 07, 2025
Application Filed
Jul 09, 2026
Non-Final Rejection mailed — §102, §103 (current)

Precedent Cases

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Study what changed to get past this examiner. Based on 5 most recent grants.

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Prosecution Projections

1-2
Expected OA Rounds
77%
Grant Probability
92%
With Interview (+14.5%)
2y 9m (~1y 6m remaining)
Median Time to Grant
Low
PTA Risk
Based on 1040 resolved cases by this examiner. Grant probability derived from career allowance rate.

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