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 .
2. Applicant's submission filed on 17 August 2026 [hereinafter Response] has been entered, where:
Claims 1, 3, 8, 10, 15, and 17 have been amended.
Claims 2, 4, 6, 7, 9, 11, 13, 14, 16, 19, 20, 21, and 23 have been cancelled.
New claims 24-33 are presented for examination.
Claims 1, 3, 5, 8, 10, 12, 15, 17, 18, 22, and 24-33 are pending.
Claims 1, 3, 5, 8, 10, 12, 15, 17, 18, 22, and 24-33 are rejected.
Claim Rejections – 35 U.S.C. § 112
3. The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
4. Claims 3, 10, 17, 27, and 31 are rejected under 35 U.S.C. 112(b) as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor regards as the invention.
Claim 3, line 2, recites the limitation "the aligned data." There is insufficient antecedent basis for this limitation in the claim.
Claim 10, lines 2-3, recites the limitation "the aligned data." There is insufficient antecedent basis for this limitation in the claim.
Claim 17, line 2, recites the limitation "the aligned data." There is insufficient antecedent basis for this limitation in the claim.
Claim 27, line 2, recites the limitation "the aligned data." There is insufficient antecedent basis for this limitation in the claim.
Claim 31, line 2, recites the limitation "the aligned data." There is insufficient antecedent basis for this limitation in the claim.
Claim 1 Claim Rejections - 35 U.S.C. § 101
5. 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.
6. Claims 1, 3, 5, 8, 10, 12, 15, 17, 18, 22, and 24-33 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 “initializing . . . the player tracking data to an average position of each player in the plurality of events,” “filtering . . . the player tracking data to identify event frames corresponding to frames of tracking data in which an event occurs,” “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 using a Gaussian mixture model that parametrizes a distribution of the one or more player positions as a mixture of K Gaussians,” “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,” “assigning . . . a role to each player in the learned formation template,” and “generating, by the computing system, a graphical representation comprising one or more of the assigned roles of the player.” Each of these limitations of “normalizing,” “initializing,” “filtering,” ‘learning,” “assigning,” “clustering,” and “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). Also, the limitation of “learning . . . an optimal formation of one or more player positions using a Gaussian mixture model that parametrizes a distribution of the one or more player positions as a mixture of K Gaussians” is a limitation that recites a mathematical concept, ((MPEP § 2106.04(a)(2) subsection I), which an abstract idea. (MPEP § 2106.04(a)(2)).
Further, the claim recites more details or specifics of the abstract idea of “filtering,” “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 “assigning” “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 “assigning” includes the limitations of “applying a bipartite mapping between players and roles within a frame.” The limitation “applying a bipartite mapping,” 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 “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 “learning . . . an optimal formation template,” comprising “initializing a K-means algorithm using the average position of each player to convergence,” “initializing the Gaussian mixture model using cluster centers of a last iteration of the K-means algorithm,” “monitoring eigenvalues of one or more components of the Gaussian mixture model throughout an expectation-maximization process,” “determining whether an eigenvalue ratio corresponding to a component of the Gaussian mixture model is outside a threshold range of one or more acceptable values, the eigenvalue ratio being outside the threshold range indicating a component collapse of the Gaussian mixture model,” and “upon determining that the eigenvalue ratio is outside the threshold range of one or more acceptable values, running, in a next iteration of the expectation-maximization process, a Soft K-Means update comprising a mixture of Gaussians with spherical covariance instead of a full-covariance update so as to return the component within a solution space, thereby preventing the Gaussian mixture model from becoming trapped in a pathological solution and ensuring that a clustering output used to generate the optimal formation template is sensible,” and accordingly, are merely more specific to the abstract idea.
Moreover, the activities of “initializing a K-means algorithm, “initializing . . . one or more cluster centers,” “determining whether an eigenvalue ratio . . . is outside a threshold range of one or more acceptable values,” and “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 limitation of “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 “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 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 limitation of “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 a 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 “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 “initializing . . . the player tracking data to an average position of each player in the plurality of events,” “filtering . . . the player tracking data to identify event frames corresponding to frames of tracking data in which an event occurs,” “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 using a Gaussian mixture model that parametrizes a distribution of the one or more player positions as a mixture of K Gaussians,” “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,” “assigning . . . a role to each player in the learned formation template,” and “generating, by the computing system, a graphical representation comprising one or more of the assigned roles of the player.” Each of these limitations of “normalizing,” “initializing,” “filtering,” learning,” “assigning,” and “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). Also, the limitation of “learning . . . an optimal formation of one or more player positions using a Gaussian mixture model that parametrizes a distribution of the one or more player positions as a mixture of K Gaussians” is a limitation that recites a mathematical concept, ((MPEP § 2106.04(a)(2) subsection I), which an abstract idea. (MPEP § 2106.04(a)(2)).
Further, the claim recites more details or specifics of the abstract idea of “filtering,” “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 “assigning” “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 “assigning” includes the limitations of “applying a bipartite mapping between players and roles within a frame,” The limitation “applying a bipartite mapping,” 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 “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 “learning . . . an optimal formation template,” comprising “initializing a K-means algorithm using the average position of each player to convergence,” “initializing the Gaussian mixture model using cluster centers of a last iteration of the K-means algorithm,” “monitoring eigenvalues of one or more components of the Gaussian mixture model throughout an expectation-maximization process,” “determining whether an eigenvalue ratio corresponding to a component of the Gaussian mixture model is outside a threshold range of one or more acceptable values, the eigenvalue ratio being outside the threshold range indicating a component collapse of the Gaussian mixture model,” and “upon determining that the eigenvalue ratio is outside the threshold range of one or more acceptable values, running, in a next iteration of the expectation-maximization process, a Soft K-Means update comprising a mixture of Gaussians with spherical covariance instead of a full-covariance update so as to return the component within a solution space, thereby preventing the Gaussian mixture model from becoming trapped in a pathological solution and ensuring that a clustering output used to generate the optimal formation template is sensible,” and accordingly, are merely more specific to the abstract idea.
Moreover, the activities of “initializing a K-means algorithm, “initializing . . . one or more cluster centers,” “determining whether an eigenvalue ratio . . . is outside a threshold range of one or more acceptable values,” and “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 “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 “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 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 limitation of “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 a 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 “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 “initializing . . . the player tracking data to an average position of each player in the plurality of events,” “filtering . . . the player tracking data to identify event frames corresponding to frames of tracking data in which an event occurs,” “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 using a Gaussian mixture model that parametrizes a distribution of the one or more player positions as a mixture of K Gaussians,” “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,” “assigning . . . a role to each player in the learned formation template,” and “generating, by the computing system, a graphical representation comprising one or more of the assigned roles of the player.” Each of these limitations of “normalizing,” “initializing,” “filtering,” learning,” and “assigning,” 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). Also, the limitation of “learning . . . an optimal formation of one or more player positions using a Gaussian mixture model that parametrizes a distribution of the one or more player positions as a mixture of K Gaussians” is a limitation that recites a mathematical concept, ((MPEP § 2106.04(a)(2) subsection I), which an abstract idea. (MPEP § 2106.04(a)(2)).
Further, the claim recites more details or specifics of the abstract idea of “filtering,” “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 “assigning” “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 “assigning” includes the limitations of “applying a bipartite mapping between players and roles within a frame,” The limitation of “applying a bipartite mapping,” 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 “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 “learning . . . an optimal formation template,” comprising “initializing a K-means algorithm using the average position of each player to convergence,” “initializing the Gaussian mixture model using cluster centers of a last iteration of the K-means algorithm,” “monitoring eigenvalues of one or more components of the Gaussian mixture model throughout an expectation-maximization process,” “determining whether an eigenvalue ratio corresponding to a component is outside a threshold range of one or more acceptable values, the eigenvalue ratio being outside the threshold range indicating a component collapse of the Gaussian mixture model,” and “upon determining that the eigenvalue ratio is outside the threshold range of one or more acceptable values, running, in a next iteration of the expectation-maximization process, a Soft K-Means update comprising a mixture of Gaussians with spherical covariance instead of a full-covariance update so as to return the component within a solution space, thereby preventing the Gaussian mixture model from becoming trapped in a pathological solution and ensuring that a clustering output used to generate the optimal formation template is sensible,” and accordingly, are merely more specific to the abstract idea.
Moreover, the activities of “initializing a K-means algorithm, “initializing . . . one or more cluster centers,” “determining whether an eigenvalue ratio . . . is outside a threshold range of one or more acceptable values,” and “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 “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 limitation of “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 a 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 “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 further recite “clustering, by the computing system, the aligned data to identify new formations, 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 further recite normalizing, by the computing system, the player tracking data so that all players in the player tracking data are attacking from left to right.” The activity of “normalizing” 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). 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 18 depends directly or indirectly from claim 15. The claim recites more details or specifics of the additional element of “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 24 depends directly or indirectly from claim 1. Claim 28 depends directly or indirectly from claim 8. Claim 32 depends directly or indirectly from claim 15. The claims further recite “receiving, via the client device, a request to identify a team's formation and role assignment,” which 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. Also, the activity of “receiving” is a 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. Therefore, claims 24, 28, and 32 are subject-matter ineligible.
Claim 25 depends directly or indirectly from claim 1. Claim 29 depends directly or indirectly from claim 8. Claim 33 depends directly or indirectly from claim 15. The claims further recite “normalizing, by the computing system, the coordinates of one or more player positions of the player tracking data.” The activity of “normalizing” contain limitations that can practically be performed in the human mind, including, for example, observations, evaluations, judgments, and opinions, and accordingly, are a mental process, (MPEP § 2106.04(a)(2) sub III), which is one of the groupings of abstract ideas. (MPEP § 2106.04(a)(2)). 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. Therefore, claims 25, 29, and 33 are subject-matter ineligible.
Claim 26 depends directly or indirectly from claim 1. Claim 30 depends directly or indirectly from claim 8. The claims further recite “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.” The activity of “generating . . . aligned data” has a broadest reasonable interpretation of converting information from one format to another, and accordingly, is a limitation that can practically be performed in the human mind, including, for example, observations, evaluations, judgments, and opinions, and is a mental process, (MPEP § 2106.04(a)(2) sub III), which is one of the groupings of abstract ideas. (MPEP § 2106.04(a)(2)). 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. Therefore, claims 26 and 30 are subject-matter ineligible.
Claim 27 depends directly or indirectly from claim 1. Claim 31 depends directly or indirectly from claim 8. The claims further recite “clustering, by the computing system, the aligned data to identify new formations.” The actvitiy of “clustering” is a limitation that can practically be performed in the human mind, including, for example, observations, evaluations, judgments, and opinions, and is a mental process, (MPEP § 2106.04(a)(2) sub III), which is one of the groupings of abstract ideas. (MPEP § 2106.04(a)(2)). 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. Therefore, claims 27 and 31 are subject-matter ineligible.
Response to Arguments
7. Examiner has fully considered Applicant’s arguments, and responds below accordingly.
Rejection under Section 101
8. Under Step 2A Prong One, Applicant submits, inter alia, that the “limitations do not merely require a person to observe player positions and mentally assign players to roles. Rather, claim 1 requires a computing system to execute a particular formation-discovery process using machine-generated player-tracking data. The claimed process initializes a Gaussian mixture model using the output of a converged K-means algorithm, monitors eigenvalues of the Gaussian mixture model during an expectation-maximization process, detects component collapse based on an eigenvalue ratio, and returns the collapsed component within a solution space, preventing the Gaussian mixture model from becoming trapped in a pathological solution. The monitoring and conditional modification of an iterative Gaussian mixture model cannot practically be performed in the human mind.
Accordingly, the claims do not merely recite observations, evaluations, judgments, or opinions that can practically be performed in the human mind. Nor do the claims merely claim a mathematical result. The claims require a specific computer-implemented process that detects and corrects operation of a particular machine-learning model. Thus, Applicant respectfully requests that the Section 101 rejections be withdrawn at least on these grounds.” (Response at pp. 16-17).
Examiner Response:
Examiner respectfully submits that the claims are directed to an abstract idea, as set out above in detail. Under Step 2A Prong One, the rejection identifies the judicial exception (for example, an abstract idea) by referring to what is recited (that is, set forth or described) in the claim and explains why it is considered an exception. Specifically, where the claim is directed to an abstract idea, the rejection identifies the abstract idea as it is recited (i.e., set forth or described) in the claim and explains why it is an abstract idea. (see MPEP § 2106.07(a)).
The first prong (Step 2A, Prong One) is a determination of whether a claim recites (i.e., sets forth or describes) a judicial exception. A claim ‘‘recites’’ a judicial exception when the judicial exception is ‘‘set forth’’ or ‘‘described’’ in the claim. If the claim does recite a judicial exception, the eligibility analysis continues to the second prong of Step 2A. (see MPEP § 2106.04, subsection II.A.1).
The rejection identifies the judicial exception (e.g., an abstract idea) by referring to what is recited (that is, set forth or described) in the claim and explains why it is considered an exception. Specifically, the claims are directed to an abstract idea, the rejection identifies the abstract idea as it is recited (i.e., set forth or described) in the claim and explains why it is an abstract idea. (see MPEP § 2106.07(a)).
For example, as set out above in detail, limitations of “normalizing,” “initializing,” “filtering,” learning,” “assigning,” “clustering,” and “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). Also, the limitation of “learning . . . an optimal formation of one or more player positions using a Gaussian mixture model that parametrizes a distribution of the one or more player positions as a mixture of K Gaussians” is a limitation that recites a mathematical concept, ((MPEP § 2106.04(a)(2) subsection I), which an abstract idea. (MPEP § 2106.04(a)(2)).
Accordingly, per the subject-matter eligibility analysis of the MPEP guidance, the claims are directed to an abstract idea.
9. Under Step 2A Prong Two, refers to an improvement to the functioning of a computer or improves another technology or technical field. (Response at p. 17).
A problem identified by Applicant’s Specification relates to “pathological solutions.” In particular, Applicant submits “Gaussian mixture models are known to suffer from component collapse and may become trapped in pathological solutions, producing non-sensible clustering outputs that cannot be utilized. Specification at para. [0050]. The specification provides a particular technical solution: monitor eigenvalues of Gaussian mixture model components during the expectation-maximization process and, when an eigenvalue ratio indicates collapse, replace the next full-covariance update with a Soft K-Means update using spherical covariance. Specification at para. [0050]. The alternative update gradually returns erratic model parameters within the solution space and ensures a sensible clustering output. Specification at para. [0050].” (Response at p. 19).
In this respect, Applicant points to the instant claims as reflecting this improvement of the Specification, where:
learning, by a prediction engine of the computing system and using the reduced set of player tracking data, an optimal formation template of one or more player positions using a Gaussian mixture model that parametrizes a distribution of the one or more player positions as a mixture of K Gaussians, the learning comprising;
* * *
upon determining that the eigenvalue ratio is outside the threshold range of one or more acceptable values, running, in a next iteration of the expectation-maximization process, a Soft K-Means update comprising a mixture of Gaussians with spherical covariance instead of a full-covariance update so as to return the component within a solution space, thereby preventing the Gaussian mixture model from becoming trapped in a pathological solution and ensuring that a clustering output used to generate the optimal formation template is sensible;
* * *
“These limitations do not merely apply generic machine learning to player-position information. Rather, the limitations recite how the Gaussian mixture model itself operates during the expectation-maximization process. The result is a more stable formation-discovery model that avoids pathological solutions and produces sensible clustering output.” (Response at p. 20 (also referring to Ex parte Desjardins, Appeal 2024-000567 at 7-9)).
Examiner Response:
Examiner respectfully disagrees. Under Step 2A Prong Two, the rejection identifies 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)).
Generally, a Gaussian mixture model is a probabilistic model that represents data as a combination of multiple Gaussian distributions.
The additional elements of the claims 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.
As explained in MPEP 2106.04(d), subsection III, the Step 2A, ‘‘Prong Two analysis considers the claim as a whole. That is, the limitations containing the judicial exception as well as the additional elements in the claim besides the judicial exception need to be evaluated together to determine whether the claim integrates the judicial exception into a practical application.’’ (SME Guidance, 89 Fed. Reg. 137 at p. 58136 (17 July 2024)). As the claim stands, the selective use of “a Soft K-Means update” in avoiding a ”pathological solution” of the probabilistic model. That is, the claim recites an improvement to an abstract idea (mental process and/or mathematical concept), where any improvement to the GMM model merely improves the abstract idea itself.
Accordingly, the claims do not set out an improvement the functioning of a computer or an improvement to any other technology or technical field.
Conclusion
10. Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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.
11. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
(Ribes et al., “Active Learning of Object and Body Models with Time Constraints on a Humanoid Robot,” IEEE (2016)) teaches where a human subject provides a set of goals the robot should learn to reproduce with proficiency, guiding the exploration of the object it is interacting with. The applied active exploration strategy is similar to the one proposed by [4], where a probabilistic model is exploited in order to provide an estimate of expected reduction in the predictive distribution entropy. However, our models are based on Gaussian mixture models (GMM), which naturally support multimodal and multivariate predictive distributions, and also, in contrast to the classification nature of [4], our problem is a regression one. Similar in its modelization is the work by [28], as they use GMMs to learn the sensori-motor maps. Despite of that, they use an active learning exploration strategy based on the modeling of the prediction error, rather than an in formation based measurement.
(Wang et al., “A new perspective on positions in basketball players using cluster analysis,” Academia (2017)) teaches project aims to study and redefine the rules of basketball positions according to players’ statistics and on-court performances. We used all current NBA players’ statistics in 2015-2016 regular season, which include 14 variables that describe one’s offensive abilities, defensive abilities and general skills. We standardized the variables and then applied three clustering methods to conduct cluster analysis: Hierarchical clustering, K-means cluster ing and Gaussian Mixture Model (GMM) Clustering. Among them K-means clustering gave better results.
12. Any inquiry concerning this communication or earlier communications from the Examiner should be directed to KEVIN L. SMITH whose telephone number is (571) 272-5964. Normally, the Examiner is available on Monday-Thursday 0730-1730.
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If attempts to reach the Examiner by telephone are unsuccessful, the Examiner’s supervisor, KAKALI CHAKI can be reached on 571-272-3719. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/K.L.S./
Examiner, Art Unit 2122
/KAKALI CHAKI/Supervisory Patent Examiner, Art Unit 2122
