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 .
DETAILED ACTION
This action is response to the communication filed on July 2, 2025. Claims 1-20 are pending.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Regarding the claim 1, it recites, obtaining an initial set of data relating to the first action of a sporting event, the initial set of data including at least a position of a first player on a surface and a position of a target area on the surface; generating, by a prediction model, an initial prediction based on the initial set of data; generating a feature set relating to the sporting event, the feature set being derived from a position of a second player on the surface or a distance or an angle between the first player, the second player, or the target area of the surface; and modifying, by the prediction model, the initial prediction to an updated prediction using the feature set, wherein the feature set includes: a virtual line directed between the position of the first player and the position of the target area.
The claim recited the limitation of generating in both instances and modifying limitation as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind. User can mentally generate prediction the obtain data and modify the prediction by updating prediction based on feature set of data. Hence, the generating and modifying limitations are mental process.
The claim recited one additional element: obtaining an initial set of data …. The obtaining step as recited amounts to mere data gathering, which is a form of insignificant extra-solution activity, (see Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362(utilizing an intermediary computer to forward information)). Accordingly, even in combination, the additional element does not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to the abstract idea.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of obtaing step amounts to no more than mere instructions to apply the exception using a generic computer component. The courts have recognized these functions as well‐understood, routine, and conventional as they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity (see MPEP 2106.05(d) II, Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information)). Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claim is not patent eligible.
Claim 2 is dependent on claim 1 and includes all the limitations of claim 1. Therefore, claim 2 recites the same abstract idea of implementing a feature set of high-dimensional spatial data in sports predictions. The claim recites the limitations of wherein the first player and the second player are on opposing teams in the sporting event, which can be done mentally with or without the use of a physical aid (e.g., pen and paper) or with a generic computer and is not an inventive concept that meaningfully limits the abstract idea. Therefore, the limitation is a mental process.
Claim 3 is dependent on claim 1 and includes all the limitations of claim 1. Therefore, claim 3 recites the same abstract idea of implementing a feature set of high-dimensional spatial data in sports predictions. The claim recites the limitations of wherein the feature set includes a previous action type specifying a previous action performed prior to an occurrence of the first action, which can be done mentally with or without the use of a physical aid (e.g., pen and paper) or with a generic computer and is not an inventive concept that meaningfully limits the abstract idea. Therefore, the limitation is a mental process.
Claim 4 is dependent on claim 1 and includes all the limitations of claim 1. Therefore, claim 4 recites the same abstract idea of implementing a feature set of high-dimensional spatial data in sports predictions. The claim recites the limitations of wherein the initial set of data or the feature set includes historical save probabilities for the second player, which can be done mentally with or without the use of a physical aid (e.g., pen and paper) or with a generic computer and is not an inventive concept that meaningfully limits the abstract idea. Therefore, the limitation is a mental process.
Claim 5 is dependent on claim 1 and includes all the limitations of claim 1. Therefore, claim 5 recites the same abstract idea of implementing a feature set of high-dimensional spatial data in sports predictions. The claim recites the limitations of wherein the feature set further includes a second player's save probability permutation modifying a save probability for each of a set of shot locations at different angles from the virtual line between the position of the first player and the position of the target area, which can be done mentally with or without the use of a physical aid (e.g., pen and paper) or with a generic computer and is not an inventive concept that meaningfully limits the abstract idea. Therefore, the limitation is a mental process.
Claim 6 is dependent on claim 1 and includes all the limitations of claim 1. Therefore, claim 6 recites the same abstract idea of implementing a feature set of high-dimensional spatial data in sports predictions. The claim recites the limitations of wherein the feature set includes a presence of any additional player within a proximity of the first player, second player, or target area, which can be done mentally with or without the use of a physical aid (e.g., pen and paper) or with a generic computer and is not an inventive concept that meaningfully limits the abstract idea. Therefore, the limitation is a mental process.
Claim 7 is dependent on claim 1 and includes all the limitations of claim 1. Therefore, claim 7 recites the same abstract idea of implementing a feature set of high-dimensional spatial data in sports predictions. The claim recites the limitations of wherein the prediction model comprises a generative adversarial network (GAN) model, and wherein the GAN model includes at least one of a set of monotonic constraints or a weight for each of the monotonic constraints, which can be done mentally with or without the use of a physical aid (e.g., pen and paper) or with a generic computer and is not an inventive concept that meaningfully limits the abstract idea. Therefore, the limitation is a mental process.
As to claims 8-20, they have similar limitations as of claims 1-7 above. Hence, they are rejected under the same rational as of claims 1-7 above.
Claim Rejections - 35 USC § 102
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 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, 3, 7-8, 10, 14-15, 17 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Schnurr et al. (Pub. No. : US 20170061314 A1)
As to claim 1 Schnurr teaches a method for generating a probability for a first action of a sporting event by implementing a feature set, the method comprising:
obtaining an initial set of data relating to the first action of a sporting event, the initial set of data including at least a position of a first player on a surface and a position of a target area on the surface (paragraphs [0081], [0043], [0086]: receiving, during a live-action sporting event, live-action sporting event data from a live-action sporting event reporting service, wherein the data includes which team poses the ball, positions of players on the field, position of the ball on the field, etc.);
generating, by a prediction model, an initial prediction based on the initial set of data (paragraph [0083]: the method 600 may include providing the live-action sporting event data to a machine-learning prediction machine to output a prediction);
generating a feature set relating to the sporting event, the feature set being derived from a position of a second player on the surface or a distance or an angle between the first player, the second player, or the target area of the surface (paragraphs [0033], [0042], [0043], [0086]: Data collected from these information trackers may be subjected to one or more processing/analysis steps in order to generate a variety of useful information pertaining to the real-world sporting event, the data including positions relative to each other, to any game balls or other important objects, positions of players on the field, position of the ball on the field, etc. wherein the prediction trainer computing system 208 may be configured to parse the game data to identify particular characteristics (i.e. feature set) that may be used to generate different prediction models); and
modifying, by the prediction model, the initial prediction to an updated prediction using the feature set, wherein the feature set includes: a virtual line directed between the position of the first player and the position of the target area (paragraphs [0054], [0076], [0043], [0086]: prediction trainer computing system 208 may update the prediction models 210 based the game data. In other words, the prediction models 210 may be dynamically updated to account for information produced by a football game that is currently taking place, wherein the data including positions relative to each other, to any game balls or other important objects, positions of players on the field, position of the ball on the field, etc. wherein the prediction trainer computing system 208 may be configured to parse the game data to identify particular characteristics (i.e. feature set) that may be used to generate different prediction models).
As to claim 3 Schnurr teaches wherein the feature set includes a previous action type specifying a previous action performed prior to an occurrence of the first action (paragraphs [0041]-[0043]: Prediction trainer computing system 208 may be configured to parse the game data to identify particular characteristics that may be used to generate different prediction models, wherein the data includes historical data).
As to claim 7 Schnurr teaches wherein the prediction model comprises a generative adversarial network (GAN) model, and wherein the GAN model includes at least one of a set of monotonic constraints or a weight for each of the monotonic constraints (paragraphs [0046], [0083]: the method 600 may include providing the live-action sporting event data to a machine-learning prediction machine to output a prediction for an upcoming play of the live-action sporting event, wherein the machine learning algorithms including, but not limited to, learning trees (e.g., Boosted, Bagging, Random forest), support vector machines, neural networks, and other suitable machine-learning algorithms).
As to claims 8, 10, 14-15, 17, they have similar limitations as of claims 1, 3, and 7 above. Hence, they are rejected under the same rational as of claims 1, 3 and 7 above.
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.
Claims 2, 4-6, 9, 11-13, 16, 18-19 are rejected under 35 U.S.C. 103 as being unpatentable over Schnurr et al. (Pub. No. : US 20170061314 A1) in the view of Marty et al. (Patent No. : US 10956723 B2)
As to claim 2 all of the limitations of claim 1 have been addressed above. Schnurr does not explicitly disclose but Marty teaches wherein the first player and the second player are on opposing teams in the sporting event (Column 44 lines 30-33: the measurement logic 1597 can provide the defender's defensive characteristics relative to individual offensive players (e.g., a specific person dribbling the ball)). It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to modify Schnurr by adding above limitation as taught by Marty to improve their skill level so that they can become more competitive in sporting events (Marty, column 1 lines 21-22).
As to claim 4 Schnurr together with Marty teaches a method according to claim 1. Marty teaches wherein the initial set of data or the feature set includes historical save probabilities for the second player (Column 24 lines 36-67).
As to claim 5 Schnurr together with Marty teaches a method according to claim 1. Marty teaches wherein the feature set further includes a second player's save probability permutation modifying a save probability for each of a set of shot locations at different angles from the virtual line between the position of the first player and the position of the target area (Column 3 lines 39-58).
As to claim 6 Schnurr together with Marty teaches a method according to claim 1. Marty teaches wherein the feature set includes a presence of any additional player within a proximity of the first player, second player, or target area (Column 30 lines 32-64).
As to claims 9, 11-13, 16, 18-19, they have similar limitations as of claims 2, 4-6 above. Hence, they are rejected under the same rational as of claims 2, 4-6 above.
Examiner's Note: Examiner has cited particular columns and line numbers or paragraphs in the references as applied to the claims above for the convenience of the applicant. Although the specified citations are representative of the teachings of the art and are applied to the specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested from the applicant in preparing responses, to fully consider the references in its entirety as potentially teaching of all or part of the claimed invention, as well as the context.
Conclusion
The prior art made of record, listed on form PTO-892, and not relied upon, if any, is considered pertinent to applicant's disclosure.
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/MD I UDDIN/Primary Examiner, Art Unit 2169
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