DETAILED ACTION
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Continued Examination
2. A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 24 March 2026 [hereinafter Response] has been entered, where:
Claims 1, 6-8, 13-15, and 18- 22, have been amended.
Claims 2, 4, 9, 11, 16, and 23 have been cancelled.
Claims 1, 3, 5-8, 10, 12-15, and 17-22 are pending.
Claims 1, 3, 5-8, 10, 12-15, and 17-22 are rejected.
Claim Rejections - 35 U.S.C. § 101
3. 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.
4. Claims 1, 3, 5-8, 10, 12-15, and 17-22 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to an abstract idea without significantly more.
Claim 1 recites a method, which is a “process” and thus one of the statutory categories of patentable subject matter. (35 U.S.C. § 101).
However, under Step 2A Prong One, the claim recites the limitations of “[(c)]1 normalizing, by a pre-processing agent of the computing system, the coordinates of one or more player positions of the player tracking data,” “[(d)] initializing . . . the player tracking data to an average position of each player in the plurality of events,” “[(e)] filtering . . . the player tracking data to identify event frames corresponding to frames of tracking data in which an event occurs,” “[(f)] learning, by a prediction engine of the computing system and using the reduced set of player tracking data, an optimal formation of one or more player positions,” “[(g)] aligning . . . the optimal formation of player positions to a global template by identifying a distance between each distribution in the optimal formation and each distribution in the global template to generate a learned formation template,” “[(h)] assigning . . . a role to each player in the learned formation template,” “[(i)] clustering, by the computing system, the aligned data to identify new formations,” and “[(j)] generating a graphical representation of a structured representation of a team's formation across the subset of games for the defined context.” Each of these limitations of “[(c)] normalizing,” “[(d)] initializing,” “[(e)] filtering,” [(f)] learning,” “[(h)] assigning,” “[(i)] clustering,” and “[(j)] generating,” can practically be performed in the human mind, including, for example, observations, evaluations, judgments, and opinions, and thus recite a mental process. (MPEP § 2106.04(a)(2) subsection III).
Further, the claim recites more details or specifics of the abstract idea of “[(e)] filtering,” “[(e.1)] wherein filtering the player tracking data based on the event frames produces a reduced set of player tracking data,” and accordingly, is merely more specific to the abstract idea.
The claim also recites more details or specifics to the abstract idea of “[(h)] assigning” “[(h.1)] wherein the role assigned to each player is restricted such that only one player occupies said role within an event frame,” and accordingly, is merely more specific to the abstract idea.
Still further, the claim recites the abstract idea of “[(h)] assigning” includes the limitations of “[(h.1.1)] determining a likelihood that each player is associated with the assigned role,” “[(h.1.2)] applying a bipartite mapping between players and roles within a frame,” and “[(h.1.3)] generating . . . aligned data that is aligned with the learned formation template and comprises a per-frame ordered role assignment of players within the frame.” Each of these limitations of “[(h.1.1)] determining a likelihood,” “[(h.1.2)] applying a bipartite mapping,” and “[(h.1.3)] generating . . . aligned data,” can practically be performed in the human mind, including, for example, observations, evaluations, judgments, and opinions, and thus are a mental process. (MPEP § 2106.04(a)(2) subsection III).
Also, the limitations include more details or specifics of the abstract idea of “[(g)] aligning . . . by identifying a distance . . . . ,” and accordingly, are merely more specific to the abstract idea. The claim also recites a “prediction engine,” which requires specific mathematical calculations (probability density functions) for learning by a prediction engine and therefore encompasses the abstract idea of a mathematical concept, (MPEP § 2106.04(a)(2) sub I).
The claim also recites more details or specifics to the abstract idea of “[(f)] learning . . . an optimal formation template,” comprising “[(f.1)] initializing a K-means algorithm using the average position of each player to convergence,” “[(f.2)] initializing, via a machine learning model, one or more cluster centers of the K-means algorithm,” “[(f.3)] determining whether an eigenvalue ratio corresponding to a component or a parameter of the machine learning model is outside a threshold range of one or more acceptable values,” and “[(f.4)] upon determining that the eigenvalue ratio is outside the threshold range of one or more acceptable values, resetting the component or the parameter of the machine learning model,” and accordingly, are merely more specific to the abstract idea. Moreover, the activities of “[(f.1)] initializing a K-means algorithm, “[(f.2)] initializing . . . one or more cluster centers,” “[(f.3)] determining whether an eigenvalue ratio . . . is outside a threshold range of one or more acceptable values,” and “[(f.4)] resetting the component or the parameter of the machine learning model,” are limitations that can practically be performed in the human mind, including, for example, observations, evaluations, judgments, and opinions, and thus recite a mental process. (MPEP § 2106.04(a)(2) subsection III). Thus, claim 1 recites an abstract idea.
Under Step 2A Prong Two, the abstract idea of claim 1 is not integrated into a practical application, because additional elements beyond the identified judicial exception recited in the claim include a computing system, an application server of the computer system, and a client device, which are generic computer components used to implement the abstract idea, (MPEP § 2106.05(f)), and cannot integrate the abstract idea into a practical application. Also, the claim recites a “prediction engine of the computing system,” and “a machine learning model,” which are recited at a high-level of generality, and accordingly, are generic computer components used to implement the abstract idea, (MPEP § 2106.05(f)), that does not serve to integrate the abstract idea into a practical application.
Also, the limitations of “[(a)] receiving, via a client device, a request to identify a team’s formation and role assignment,” and “[(b)] retrieving . . . player tracking data for a plurality of players across a plurality of events, the player tracking data comprising coordinates of player positions during each event,” is an insignificant extra-solution activity of storing and retrieving information in memory. (MPEP § 2106.05(d) subsection II.iv), that does not integrate the abstract idea into a practical application.
The limitation of “[(k)] transmitting the graphical representation for display on the client device” is a post-processing, insignificant extra-solution activity of a result output, (MPEP § 2106.05(g)), that does not amount to significantly more than the abstract idea. Therefore, claim 1 is directed to the abstract idea.
Finally, under Step 2B, the additional limitations, taken alone or in combination, do not represent significantly more than the abstract idea itself. The additional limitations include a computing system, an application server of the computer system, and a client device, which is are generic computer components used to implement the abstract idea, merely using a computer as a tool to perform an abstract idea, (MPEP § 2106.05(f)), and do not amount to significantly more than the abstract idea. Also, the claim recites a “prediction engine of the computing system,” and “a machine learning model,” which are recited at a high-level of generality, and accordingly, are generic computer components used to implement the abstract idea, (MPEP § 2106.05(f)), that does not amount to significantly more than the abstract idea.
Also, the limitations of “[(a)] receiving, via a client device, a request to identify a team’s formation and role assignment,” and “[(b)] retrieving . . . player tracking data for a plurality of players across a plurality of events, the player tracking data comprising coordinates of player positions during each event,” are well-understood, routine, and conventional activity of storing and retrieving information in memory, (MPEP § 2106.05(d) subsection II.iv), which is not significantly more than the abstract idea itself.
The limitation of “[(k)] transmitting the graphical representation for display on the client device” is a well-understood, routine, and conventional activity of transmitting or receiving data over a network, (MPEP § 2106.05(d) sub II.i), that does not amount to significantly more than the abstract idea. Therefore, claim 1 is subject-matter ineligible.
Claim 8 recites a non-transitory computer readable medium, which is a “product” and thus one of the statutory categories of patentable subject matter. (35 U.S.C. § 101).
However, under Step 2A Prong One, the claim recites the limitations of “[(c)] normalizing, by a pre-processing agent of the computing system, the coordinates of one or more player positions of the player tracking data,” “[(d)] initializing . . . the player tracking data to an average position of each player in the plurality of events,” “[(e)] filtering . . . the player tracking data to identify event frames corresponding to frames of tracking data in which an event occurs,” “[(f)] learning, by a prediction engine of the computing system and using the reduced set of player tracking data, an optimal formation of one or more player positions,” “[(g)] aligning . . . the optimal formation of player positions to a global template by identifying a distance between each distribution in the optimal formation and each distribution in the global template to generate a learned formation template,” “[(h)] assigning . . . a role to each player in the learned formation template,” “[(i)] clustering, by the computing system, the aligned data to identify new formations,” and “[(j)] generating a graphical representation of a structured representation of a team's formation across the subset of games for the defined context.” Each of these limitations of “[(c)] normalizing,” “[(d)] initializing,” “[(e)] filtering,” [(f)] learning,” “[(h)] assigning,” “[(i)] clustering,” and “[(j)] generating,” can practically be performed in the human mind, including, for example, observations, evaluations, judgments, and opinions, and thus recite a mental process. (MPEP § 2106.04(a)(2) subsection III).
Further, the claim recites more details or specifics of the abstract idea of “[(e)] filtering,” “[(e.1)] wherein filtering the player tracking data based on the event frames produces a reduced set of player tracking data,” and accordingly, is merely more specific to the abstract idea.
The claim also recites more details or specifics to the abstract idea of “[(h)] assigning” “[(h.1)] wherein the role assigned to each player is restricted such that only one player occupies said role within an event frame,” and accordingly, is merely more specific to the abstract idea.
Still further, the claim recites the abstract idea of “[(h)] assigning” includes the limitations of “[(h.1.1)] determining a likelihood that each player is associated with the assigned role,” “[(h.1.2)] applying a bipartite mapping between players and roles within a frame,” and “[(h.1.3)] generating . . . aligned data that is aligned with the learned formation template and comprises a per-frame ordered role assignment of players within the frame.” Each of these limitations of “[(h.1.1)] determining a likelihood,” “[(h.1.2)] applying a bipartite mapping,” and “[(h.1.3)] generating . . . aligned data,” can practically be performed in the human mind, including, for example, observations, evaluations, judgments, and opinions, and thus are a mental process. (MPEP § 2106.04(a)(2) subsection III).
Also, the limitations include more details or specifics of the abstract idea of “[(g)] aligning . . . by identifying a distance . . . . ,” and accordingly, are merely more specific to the abstract idea. The claim also recites a “prediction engine,” which requires specific mathematical calculations (probability density functions) for learning by a prediction engine and therefore encompasses the abstract idea of a mathematical concept, (MPEP § 2106.04(a)(2) sub I).
The claim also recites more details or specifics to the abstract idea of “[(f)] learning . . . an optimal formation template,” comprising “[(f.1)] initializing a K-means algorithm using the average position of each player to convergence,” “[(f.2)] initializing, via a machine learning model, one or more cluster centers of the K-means algorithm,” “[(f.3)] determining whether an eigenvalue ratio corresponding to a component or a parameter of the machine learning model is outside a threshold range of one or more acceptable values,” and “[(f.4)] upon determining that the eigenvalue ratio is outside the threshold range of one or more acceptable values, resetting the component or the parameter of the machine learning model,” and accordingly, are merely more specific to the abstract idea. Moreover, the activities of “[(f.1)] initializing a K-means algorithm, “[(f.2)] initializing . . . one or more cluster centers,” “[(f.3)] determining whether an eigenvalue ratio . . . is outside a threshold range of one or more acceptable values,” and “[(f.4)] resetting the component or the parameter of the machine learning model,” are limitations that can practically be performed in the human mind, including, for example, observations, evaluations, judgments, and opinions, and thus recite a mental process. (MPEP § 2106.04(a)(2) subsection III). Thus, claim 8 recites an abstract idea.
Under Step 2A Prong Two, the abstract idea of claim 1 is not integrated into a practical application, because additional elements beyond the identified judicial exception recited in the claim include a computing system, an application server of the computer system, and a client device, which are generic computer components used to implement the abstract idea, (MPEP § 2106.05(f)), and cannot integrate the abstract idea into a practical application. Also, the claim recites a “prediction engine of the computing system,” and “a machine learning model,” which are recited at a high-level of generality, and accordingly, are generic computer components used to implement the abstract idea, (MPEP § 2106.05(f)), that does not serve to integrate the abstract idea into a practical application.
Also, the limitations of “[(a)] receiving, via a client device, a request to identify a team’s formation and role assignment,” and “[(b)] retrieving . . . player tracking data for a plurality of players across a plurality of events, the player tracking data comprising coordinates of player positions during each event,” is an insignificant extra-solution activity of storing and retrieving information in memory. (MPEP § 2106.05(d) subsection II.iv), that does not integrate the abstract idea into a practical application.
The limitation of “[(k)] transmitting the graphical representation for display on the client device” is a post-processing, insignificant extra-solution activity of a result output, (MPEP § 2106.05(g)), that does not amount to significantly more than the abstract idea. Therefore, claim 8 is directed to the abstract idea.
Finally, under Step 2B, the additional limitations, taken alone or in combination, do not represent significantly more than the abstract idea itself. The additional limitations include a computing system, an application server of the computer system, and a client device, which is are generic computer components used to implement the abstract idea, merely using a computer as a tool to perform an abstract idea, (MPEP § 2106.05(f)), and do not amount to significantly more than the abstract idea. Also, the claim recites a “prediction engine of the computing system,” and “a machine learning model,” which are recited at a high-level of generality, and accordingly, are generic computer components used to implement the abstract idea, (MPEP § 2106.05(f)), that does not amount to significantly more than the abstract idea.
Also, the limitations of “[(a)] receiving, via a client device, a request to identify a team’s formation and role assignment,” and “[(b)] retrieving . . . player tracking data for a plurality of players across a plurality of events, the player tracking data comprising coordinates of player positions during each event,” are well-understood, routine, and conventional activity of storing and retrieving information in memory, (MPEP § 2106.05(d) subsection II.iv), which is not significantly more than the abstract idea itself.
The limitation of “[(k)] transmitting the graphical representation for display on the client device” is a well-understood, routine, and conventional activity of transmitting or receiving data over a network, (MPEP § 2106.05(d) sub II.i), that does not amount to significantly more than the abstract idea. Therefore, claim 8 is subject-matter ineligible.
Claim 15 recites a system, which is a “machine” and thus one of the statutory categories of patentable subject matter. (35 U.S.C. § 101).
However, under Step 2A Prong One, the claim recites the limitations of “[(c)] normalizing, by a pre-processing agent of the computing system, the coordinates of one or more player positions of the player tracking data,” “[(d)] initializing . . . the player tracking data to an average position of each player in the plurality of events,” “[(e)] filtering . . . the player tracking data to identify event frames corresponding to frames of tracking data in which an event occurs,” “[(f)] learning, by a prediction engine of the computing system and using the reduced set of player tracking data, an optimal formation of one or more player positions,” “[(g)] aligning . . . the optimal formation of player positions to a global template by identifying a distance between each distribution in the optimal formation and each distribution in the global template to generate a learned formation template,” “[(h)] assigning . . . a role to each player in the learned formation template,” “[(i)] clustering, by the computing system, the aligned data to identify new formations,” and “[(j)] generating a graphical representation of a structured representation of a team's formation across the subset of games for the defined context.” Each of these limitations of “[(c)] normalizing,” “[(d)] initializing,” “[(e)] filtering,” [(f)] learning,” “[(h)] assigning,” “[(i)] clustering,” and “[(j)] generating,” can practically be performed in the human mind, including, for example, observations, evaluations, judgments, and opinions, and thus recite a mental process. (MPEP § 2106.04(a)(2) subsection III).
Further, the claim recites more details or specifics of the abstract idea of “[(e)] filtering,” “[(e.1)] wherein filtering the player tracking data based on the event frames produces a reduced set of player tracking data,” and accordingly, is merely more specific to the abstract idea.
The claim also recites more details or specifics to the abstract idea of “[(h)] assigning” “[(h.1)] wherein the role assigned to each player is restricted such that only one player occupies said role within an event frame,” and accordingly, is merely more specific to the abstract idea.
Still further, the claim recites the abstract idea of “[(h)] assigning” includes the limitations of “[(h.1.1)] determining a likelihood that each player is associated with the assigned role,” “[(h.1.2)] applying a bipartite mapping between players and roles within a frame,” and “[(h.1.3)] generating . . . aligned data that is aligned with the learned formation template and comprises a per-frame ordered role assignment of players within the frame.” Each of these limitations of “[(h.1.1)] determining a likelihood,” “[(h.1.2)] applying a bipartite mapping,” and “[(h.1.3)] generating . . . aligned data,” can practically be performed in the human mind, including, for example, observations, evaluations, judgments, and opinions, and thus are a mental process. (MPEP § 2106.04(a)(2) subsection III).
Also, the limitations include more details or specifics of the abstract idea of “[(g)] aligning . . . by identifying a distance . . . . ,” and accordingly, are merely more specific to the abstract idea. The claim also recites a “prediction engine,” which requires specific mathematical calculations (probability density functions) for learning by a prediction engine and therefore encompasses the abstract idea of a mathematical concept, (MPEP § 2106.04(a)(2) sub I).
The claim also recites more details or specifics to the abstract idea of “[(f)] learning . . . an optimal formation template,” comprising “[(f.1)] initializing a K-means algorithm using the average position of each player to convergence,” “[(f.2)] initializing, via a machine learning model, one or more cluster centers of the K-means algorithm,” “[(f.3)] determining whether an eigenvalue ratio corresponding to a component or a parameter of the machine learning model is outside a threshold range of one or more acceptable values,” and “[(f.4)] upon determining that the eigenvalue ratio is outside the threshold range of one or more acceptable values, resetting the component or the parameter of the machine learning model,” and accordingly, are merely more specific to the abstract idea. Moreover, the activities of “[(f.1)] initializing a K-means algorithm, “[(f.2)] initializing . . . one or more cluster centers,” “[(f.3)] determining whether an eigenvalue ratio . . . is outside a threshold range of one or more acceptable values,” and “[(f.4)] resetting the component or the parameter of the machine learning model,” are limitations that can practically be performed in the human mind, including, for example, observations, evaluations, judgments, and opinions, and thus recite a mental process. (MPEP § 2106.04(a)(2) subsection III). Thus, claim 15 recites an abstract idea.
Under Step 2A Prong Two, the abstract idea of claim 1 is not integrated into a practical application, because additional elements beyond the identified judicial exception recited in the claim include “an application server,” “a preprocessing agent,” “a processor,” “a memory,” and “a client device,” which are generic computer components used to implement the abstract idea, (MPEP § 2106.05(f)), and cannot integrate the abstract idea into a practical application. Also, the claim recites a “prediction engine of the computing system” and a “machine learning model,” which are recited at a high-level of generality, and accordingly, are generic computer components used to implement the abstract idea, (MPEP § 2106.05(f)), that does not serve to integrate the abstract idea into a practical application.
Also, the limitations of “[(a)] receiving, via a client device, a request to identify a team’s formation and role assignment,” and “[(b)] retrieving . . . player tracking data a plurality of players across a plurality of events, the player tracking data comprising coordinates of player positions during each event,” is an insignificant extra-solution activity of storing and retrieving information in memory. (MPEP § 2106.05(d) subsection II.iv), that does not integrate the abstract idea into a practical application. The limitation of “transmitting the graphical representation for display on the client device” is a post-processing, insignificant extra-solution activity of a result output, (MPEP § 2106.05(g)), that does not amount to significantly more than the abstract idea. Therefore, claim 15 is directed to the abstract idea.
Finally, under Step 2B, the additional limitations, taken alone or in combination, do not represent significantly more than the abstract idea itself. The additional limitations include “an application server,” “a preprocessing agent,” “a processor,” “a memory,” and “a client device,” which is are generic computer components used to implement the abstract idea, merely using a computer as a tool to perform an abstract idea, (MPEP § 2106.05(f)), and do not amount to significantly more than the abstract idea. Also, the claim recites a “prediction engine of the computing system” and a “machine learning model,” which is recited at a high-level of generality, and accordingly, is a generic computer component used to implement the abstract idea, (MPEP § 2106.05(f)), that does not amount to significantly more than the abstract idea.
Also, the limitations of “[(a)] receiving, via a client device, a request to identify a team’s formation and role assignment,” and “[(b)] retrieving . . . player tracking data for a plurality of players across a plurality of events, the player tracking data comprising coordinates of player positions during each event,” is well-understood, routine, and conventional activity of storing and retrieving information in memory, (MPEP § 2106.05(d) subsection II.iv), which is not significantly more than the abstract idea itself. The limitation of “[(j)] transmitting the graphical representation for display on the client device” is a well-understood, routine, and conventional activity of transmitting or receiving data over a network, (MPEP § 2106.05(d) sub II.i), that does not amount to significantly more than the abstract idea. Therefore, claim 15 is subject-matter ineligible.
Claims 3, 10, and 17 depend directly or indirectly from claims 1, 8, and 15, respectively. These claims merely recite more details or specifics of the abstract idea of “[(f)] learning player distribution and role assignments,” (claims 3, 10, and 17: “wherein the clustering comprises a flat or hierarchical clustering algorithm”; and accordingly, are merely more specific to the abstract idea. The additional elements of the claim do not serve to integrate the abstract idea into integrated into a practical application, (see MPEP § 2106.04(d)), nor do the additional elements amount to significantly more than the abstract idea, (MPEP § 2106.05 sub I; see also MPEP § 2106.05(a) – (h)), and thus, the claim recites no more than the abstract idea. Thus, claims 3, 10, and 17 are subject-matter ineligible.
Claim 5 depends from claim 1. Claim 12 depends from claim 8. Claim 22 depends from claim 15. The claims recite more details or specifics to the abstract idea of [(c)] normalizing,” (claims 5, 12, and 22: normalizing, by the computing system, the player tracking data so that all players in the player tracking data are attacking from left to right”), and accordingly, are merely more specific to the abstract idea. The additional elements of the claim do not serve to integrate the abstract idea into integrated into a practical application, (see MPEP § 2106.04(d)), nor do the additional elements amount to significantly more than the abstract idea, (MPEP § 2106.05 sub I; see also MPEP § 2106.05(a) – (h)), and thus, the claim recites no more than the abstract idea. Thus, claims 5, 12, and 22 are subject-matter ineligible.
Claim 6 depends from claim 1. Claim 13 depends from claim 8. The claims further recite “[(l)] parametrizing the distribution of the one or more player positions comprises using a mixture of K Gaussians to identify the optimal formation template.” The activity of [(l)] parameterizing” is a limitation that can practically be performed in the human mind, including, for example, observations, evaluations, judgments, and opinions, and thus recite a mental process. (MPEP § 2106.04(a)(2) subsection III). The additional elements of the claim do not serve to integrate the abstract idea into integrated into a practical application, (see MPEP § 2106.04(d)), nor do the additional elements amount to significantly more than the abstract idea, (MPEP § 2106.05 sub I; see also MPEP § 2106.05(a) – (h)), and thus, the claims recite no more than the abstract idea. Thus, claims 6 and 13 are subject-matter ineligible.
Claim 7 depends from claim 1. Claim 14 depends from claim 8. Claim 20 depends from claim 15. The claim recites more details or specifics to the abstract idea of “[(f)] learning,” (claims 7, 14, and 20: the machine learning model includes a Gaussian mixture model that maximizes the likelihood of each player of the plurality of players being in their average position based on the player tracking data”), and accordingly, are merely more specific to the abstract idea. The additional elements of the claim do not serve to integrate the abstract idea into integrated into a practical application, (see MPEP § 2106.04(d)), nor do the additional elements amount to significantly more than the abstract idea, (MPEP § 2106.05 sub I; see also MPEP § 2106.05(a) – (h)), and thus, the claims recite no more than the abstract idea. Thus, claims 7, 14, and 20 are subject-matter ineligible.
Claim 18 depends directly or indirectly from claim 15. The claim recites more details or specifics of the additional element of “[(b)] retrieving . . . player tracking data,” “wherein the plurality of events corresponds to an in-game situation”, and accordingly, is merely more specific to the additional element. None of the claims include an additional element that integrate the abstract idea into a practical application because the claims do not impose any meaningful limits on practicing the abstract idea. Also, there are no additional elements set out that amount to an inventive concept (also known as “significantly more”) than the recited judicial exception. The additional elements of the claim do not serve to integrate the abstract idea into integrated into a practical application, (see MPEP § 2106.04(d)), nor do the additional elements amount to significantly more than the abstract idea, (MPEP § 2106.05 sub I; see also MPEP § 2106.05(a) – (h)), and thus, the claim recites no more than the abstract idea. Thus, claim 18 is subject-matter ineligible.
Claim 21 depends from claim 1. The claim recites more details or specifics of the abstract idea of “[(f)] learning,” “wherein learning the optimal player formation template comprises parametrizing, in a Gaussian mixture model, a distribution of the one or more player positions as a mixture of K Gaussians,” and accordingly, is merely more specific to the abstract idea. The additional elements of the claim do not serve to integrate the abstract idea into integrated into a practical application, (see MPEP § 2106.04(d)), nor do the additional elements amount to significantly more than the abstract idea, (MPEP § 2106.05 sub I; see also MPEP § 2106.05(a) – (h)), and thus, the claims recite no more than the abstract idea. Thus, claim 21 is subject-matter ineligible.
Response to Arguments
5. Examiner has fully considered Applicant’s arguments, and responds below accordingly.
Rejection under Section 101
6. Applicant’s exemplar claim 1 recites:
Claim 1 recites:
* * *
[(a)] receiving, via a client device, a request to identify a team’s formation and role assignment;
[(b)] retrieving, via an application server of a computing system, player tracking data for a plurality of players across a plurality of events, the player tracking data comprising coordinates of player positions during each event;
[(c)] normalizing, by a pre-processing agent of the computing system, the coordinates of one or more player positions of the player tracking data;
[(d)] initializing, by a pre-processing agent of the computing system, the player tracking data to an average position of each player in the plurality of events;
[(e)] filtering, by the computing system, the player tracking data to identify event frames corresponding to frames of tracking data in which an event occurs,
[(e.1)] wherein filtering the player tracking data based on the event frames produces a reduced set of player tracking data;
[(f)] learning, by a prediction engine of the computing system and using reduced set of player tracking data, an optimal formation template of player positions, the learning comprising:
[(f.1)] initializing a K-means algorithm using the average position of each player to convergence;
[(f.2)] initializing, via a machine learning model, one or more cluster centers of the K-means algorithm;
[(f.3)] determining whether an eigenvalue ratio corresponding to a component or a parameter of the machine learning model is outside a threshold range of one or more acceptable values; and
[(f.4)] upon determining that the eigenvalue ratio is outside the threshold range of one or more acceptable values, resetting the component or the parameter of the machine learning model;
[(g)] aligning, by the computing system, the optimal formation template of player positions to a global template by identifying a distance between each distribution in the optimal formation template and each distribution in the global template to generate a learned formation template;
[(h)] assigning, by the computing system, a role to each player in the learned formation template,
[(h.1)] wherein the role assigned to each player is restricted such that only one player occupies said role within an event frame, the assigning comprising:
[(h.1.1)] determining a likelihood that each player is associated with the assigned role;
[(h.1.2)] applying a bipartite mapping between players and roles within a frame; and
[(h.1.3)] generating, based on the bipartite mapping and by the computing system, aligned data that is aligned with the learned formation template and comprises a per-frame ordered role assignment of players within the frame for the identified event frames;
[(h)] clustering, by the computing system, the aligned data to identify new formations;
[(i)] generating, by the computing system, a graphical representation comprising one or more of the assigned roles of the players and the new formations; and
[(j)] transmitting, by the application server of the computing system, the graphical representation for display on the client device.
(Claim 1 (emphasis by Applicant)).
7. Applicant submits that “By analogy, independent claims 1, 8, and 15 relate closely to the principles of Desjardins that were identified in the Memorandum 1. The pending claims recite a method in which a team's formation of a positioning of team's players can be ascertained in a limited information environment. For example, a "prediction engine" uses the "reduced set of player tracking data," improving the "run-time" for predicting the formation or positioning of a team's players to allow for near real-time identification and production of formation templates. The prediction engine is thus able to take a process that would normally take upwards of 20 minutes, and execute the process in under 10 seconds. (See Paragraph 69 of the Specification As-Published).” (Response at p. 17).
Applicant also submits that “independent claims 1, 8, and 15 recite a ‘prediction engine’ that learns an optimal formation template using ‘a K-means algorithm’ and a ‘machine learning model.’ Use of the ‘K-means algorithm’ produces better results than conventional approaches. (See Paragraph 49 of the Specification As-Published). The usage of the machine learning model employs clustering after the last iteration of the K-means algorithm to better capture the shape of each player role. (See Paragraph 49 of the Specification As-Published). Additionally, the prediction engine ‘determin[es] whether an eigenvalue ratio corresponding to a component or a parameter of the machine learning model is outside a threshold range of one or more acceptable values," in order to prevent collapse and non-sensible clustering when using the machine learning model. This is akin to the improvements stated in Memorandum 1 and specifically, the issue of improving the functioning of the machine learning model.” (Response at p. 18).
Still further, “Applicant submits that the USPTO Memorandum, Subject Matter Eligibility Guidance under 35 U.S.C §101, August 4, 2025, (‘Memorandum 2’) states, ‘The analysis in Step 2A Prong Two considers the claim as a whole. The way in which the additional elements use or interact with the exception may integrate the judicial exception into a practical application. Accordingly, the additional limitations should not be evaluated in a vacuum, completely separate from the recited judicial exception. Instead, the analysis should take into consideration all the claim limitations and how these limitations interact and impact each other when evaluating whether the exception is integrated into a practical application." (Emphasis Added).” (Response at p.18).
Regarding “improvements,” Applicant submits “’improvements to computer component or system performance based upon adjustments to parameters of a machine learning model associated with tasks or workstreams.’ As previously discussed and as presently amended, the present claims include an improvement to system performance based upon adjustments to machine learning parameters. For example, the pre-processing agent "[(c)] normaliz[es] the coordinates of one or more player positions of the player tracking data" and "[(d)] initializ[es] the coordinates of one or more players positions of the player tracking data to an average position of each player in the plurality of events." Normalization removes translational effects from the player tracking data and initialization of the normalized player tracking data minimizes data variance. (See Paragraphs 45 and 46 of the Specification As-Filed).”: (Response at p. 20).
Examiner’s Response:
Examiner submits that though the Specification may provide “sufficient details such that one of ordinary skill in the art would recognize the claimed invention as providing an improvement,” the claims do not appear to reflect the disclosed improvement.
Under Step 2A Prong Two, the rejections hereinabove identify any additional elements recited in the claim beyond the identified judicial exception (i.e., abstract idea); and evaluate the integration of the judicial exception into a practical application by explaining that the claim as a whole, looking at the additional elements individually and in combination, does not integrate the judicial exception into a practical application using the considerations set forth in MPEP §§ 2106.04(d), 2106.05(a)-(c) and (e)-(h).
“Integration” may be based on the improvements in the functioning of a computer or an improvement to any other technology or technical field. (MPEP § 2106.04(d)(1)). The evaluation requires, [i]n sum, that (1) the specification should be evaluated to determine if the disclosure provides sufficient details such that one of ordinary skill in the art would recognize the claimed invention as providing an improvement. Next, (2) if the specification sets forth such an improvement, the claim must be evaluated to ensure that the claim itself reflects the disclosed improvement.
By way of example to Desjardins, the MPEP provides under Step 2A Prong Two that “the [Desjardins] specification identified improvements as to how the machine learning model itself operates, including training a machine learning model to learn new tasks while protecting knowledge about previous tasks to overcome the problem of ‘catastrophic forgetting’ encountered in continual learning systems. Importantly, the [appeals review panel (ARP)] evaluated the claims as a whole in discerning at least the limitation ‘adjust the first values of the plurality of parameters to optimize performance of the machine learning model on the second machine learning task while protecting performance of the machine learning model on the first machine learning task’ reflected the improvement disclosed in the specification. Accordingly, the claims as a whole integrated what would otherwise be a judicial exception instead into a practical application at Step 2A Prong Two, and therefore the claims were deemed to be outside any specific, enumerated judicial exception (Step 2A: NO).” (MPEP § 2106.04(d) sub III; see “Advance Notice of Change to the MPEP in light of Ex Parte Desjardins” (05 December 2025) at p. 2) (emphasis added by Examiner)).
Turning to Applicant’s disclosure, team sport predictions are problematic because there “is an inherent permutation disorder in team sports, which increases the difficulty at which a system can predict a team's formation or a positioning of a team's players on a playing surface given limited information.” (Specification ¶ 0003).
In the pursuit of bringing order to chaos, the Specification is directed to learning a team formation from tracking data, then aligns that learned formation to a global template so player positions have a consistent ordering. It uses average player positions and a K-means/Gaussian mixture workflow to discover role distributions, rather than relying only on direct frame-by-frame assignment. (see Specification ¶¶ 0004, 0044-50, & 0026-27). In this respect, the
By way of example, in regards addressing the difficulties in team sport formation prediction, the disclosure refers to
(1) player role alignment and mapping an unstructured set of player trajectory to an ordered set, (see Specification ¶ 0035 (“Mathematically, the goal of role-alignment procedure may be to find the transformation . . . , which may map the unstructured set U of N player trajectories to an ordered set (i.e., a vector) of K role -trajectories R”)),
(2) adjusting learning when eigenvalue ratios indicate collapse or pathological behavior, (Specification ¶ 0048 (“If formation discovery module 124 determines that the eigenvalue ratio of any component becomes too large or too small, the next iteration may run a Soft K-Means . . . update instead of the full-covariance update”)),
(3) player tracking data may include event labels forming “event frames,” (Specification ¶ 0041 (“the player tracking data may further include single-frame event-labels (e.g., pass, shot, cross) in each frame of player tracking data. These frames may be referred to as "event frames." As shown, the initial player tracking data may be represented as a set U of N player trajectories. Each player trajectory itself may be an ordered set of positions Un . . . for an agent n . . . and a frame“)),
(4) normalizing raw position data relating to left-to-right to remove translational effects from data, (Specification ¶ 0043 (“pre-processing agent 116 may normalize the raw position data of the players. For example, pre-processing agent 116 may normalize the raw position data of the players so that all teams in the player tracking data are attacking from left to right and have zero mean in each frame. Such normalization may result in the removal of translational effects from the data”)).
Though the disclosure may come within “leg 1” of MPEP § 2106.04(d)(1), the claim limitations, however, do not “reflect the disclosed improvement.”
For example, relating to “mapping,” the claim merely recites “[(h.1.2)] applying a bipartite mapping between players and roles within a frame,” but does not refer to “unordered” mapping to “an ordered set,” nor do the claims do not recite that further details of “[(b)] retrieving . . . player tracking data” including “unstructured set U of N player trajectories,” but simply coordinates of . . . player positions.” (see, e.g., claim 1, lines 4-6; cf. Specification ¶ 0035).
With regard to “eigenvalues,” the claim merely recites, inter alia, that the learning comprises:
* * *
[(f.3)] determining whether an eigenvalue ratio corresponding to a component or a parameter of the machine learning model is outside a threshold range of one or more acceptable values; and
[(f.4)] upon determining that the eigenvalue ratio is outside the threshold range of one or more acceptable values, resetting the component or the parameter of the machine learning model;
* * *
(claim 1, lines 23-28). That is, claim does not refer to addressing GMM component collapse, where if the eigenvalue ratio of any GMM component “becomes too large or too small, the next iteration may run a Soft K-Means . . . update instead of the full-covariance update.” (Specification ¶ 0048).
With regard to “event frames,” the claim recites, inter alia, that the event frame is restricted to a number of players, where:
* * *
[(h)] assigning, by the computing system, a role to each player in the learned formation template,
[(h.1)] wherein the role assigned to each player is restricted such that only one player occupies said role within an event frame, . . . :
* * *
(claim 1, lines 33-35). However, though the Specification recites “player tracking data may further include single-frame event-labels (e.g., pass, shot, cross) in each frame of player tracking data. These frames may be referred to as "event frames,” (Specification ¶ 0041), and 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, 20 USPQ2d 1057 (Fed. Cir. 1993).
Also, with regard to “normalizing,” the claim does not provide any details or specifics of how the normalization is conducted. The claim merely recites
* * *
“[(c)] normalizing , by a pre-processing agent of the computing system, the coordinates of one or more player positions of the player tracking data,
* * *
(claim 1, lines 7-8). The plain meaning of “normalizing” is to make (something) conform to or reduce (something) to a norm or standard; however, the claim is not so limited, particularly to normalization that “may result in the removal of translational effects from the data.” (Specification ¶ 0043).
In this regard, the claims do not “reflect the disclosed improvement(s).” (MPEP § 2106.04(d)(1)). Still further, a claim that integrates a judicial exception (that is, abstract idea) into a practical application of the exception will apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize or preempt the judicial exception. (2024 SME Guidance, 89 Fed. Reg. 137 at p. 58136 (17 July 2024)).
Accordingly, the pending claims are subject-matter ineligible, as set out above in detail.
Conclusion
8. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
(US Published Application 20160260015 to Lucey et al.) teaches players may be assigned to roles based on player detection data and a prototype formation, such as a formation selected from a code book. As play progresses, players may instantaneously swap roles. Such players may not necessarily be next to each other when the players swap roles. Roles change may be assumed to be infrequent, such that the spatial prior of a formation may be combined with the spatiotemporal prior of player inertia to track player identities and roles over time. Such identity and role assignments may be applied throughout a game or match and compared with similar data from other matches from a current or prior season. Such an approach may enhance game analysis both during a game or match and after completion of the game or match.
(Cohen et al., “Predictive Modeling and Statistical Analysis in Sports,” Pomona (2015)) teaches examining the effect of momentum in professional NBA basketball with respect to both players and teams. Momentum is defined via a set of enumerable conditions and its model is created by means of a variation of the same in house web scrape
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/K.L.S./
Examiner, Art Unit 2122
/KAKALI CHAKI/Supervisory Patent Examiner, Art Unit 2122
1 Examiner adds these limitation identifiers for the limited purpose of evaluating the claims for subject matter eligibility under MPEP § 2106, and not for the purpose of oversimplifying the claims.
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