Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Continued Examination Under 37 CFR 1.114
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 December 23, 2025 has been entered.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitations are:
…identifying, by a spatial feature module of the computing system, coordinate data of each player of the one or more players from the event information… in claims 2, 11, and 20;
…generating, by the spatial feature module of the computing system, a heat map illustrating a pass origin and pass destination for each pass initiated by each player of the one or more players… in claims 2, 11, and 20;
…generating, by the spatial feature module of the computing system, as output, a plurality of factors that describe a spatial distribution of each player of the one or more players… in claims 3, and 12;
…identifying, by a playing style module of the computing system, each event of the plurality of events in the event information… in claims 4 and 13;
…for each event, portioning, by the playing style module of the computing system, the event into a plurality of possessions, wherein each possession comprises one or more touches of a ball… in claims 4 and 13;
…assigning, by a machine learning module associated with the playing style module, a touch value to each touch of the one or more touches, wherein the touch value represents a type of touch… in claims 5 and 14;
…aggregating, by the playing style module, each touch value to generate a weighted count for each player of the one or more players… in claims 5 and 14;
…generating, by the playing style module, a vector output describing a team's playing structure based on the weighted count for each player of the one or more players associated with the team… in claims 5 and 14;
…generating, by a movement chain module of the computing system, one or more possession motifs, each possession motif configured to break down sequences of player combinations into chains of consecutive player possessions… in claims 6 and 15;
…generating, by a machine learning module associated with a player chain module of the computing system, a feature vector representing each player's involvement in a team's possession based on the chains of consecutive player possessions and the one or more possession motifs… in claims 7 and 16;
…predicting, via the machine learning module associated with a possession value module of the computing system, a probability of a goal being scored based on the chains of the consecutive player possessions… in claims 8 and 17;
…generating, by a gaussian mixture module associated with a role prediction module of the computing system, the one or more clusters of players… in claims 9 and 18;
Regarding dependent claims 2, 11, and 20 and the above-noted three-prong test, the recited spatial feature module is a generic placeholder, which is used to identify coordinate data of each player of the one or more players from the event information is functional language, and there is no recitation of sufficient structure to perform the identifying.
Regarding dependent claims 3 and 12 and the above-noted three-prong test, the recited spatial feature module is a generic placeholder, which is used to generate as output, a plurality of factors that describe a spatial distribution of each player of the one or more players is functional language, and there is no recitation of sufficient structure to perform the generating.
Regarding dependent claims 4 and 13 and the above-noted three-prong test, the recited playing style module is a generic placeholder, which is used to, for each event, portion the event into a plurality of possessions, wherein each possession comprises one or more touches of a ball is functional language, and there is no recitation of sufficient structure to perform the portioning.
Regarding dependent claims 5 and 14 and the above-noted three-prong test, the recited machine learning module associated with the playing style module is a generic placeholder, which is used to assign a touch value to each touch of the one or more touches, wherein the touch value represents a type of touch is functional language, and there is no recitation of sufficient structure to perform the assigning.
Regarding dependent claims 5 and 14 and the above-noted three-prong test, the recited playing style module is a generic placeholder, which is used to aggregate each touch value to generate a weighted count for each player of the one or more players is functional language, and there is no recitation of sufficient structure to perform the aggregating.
Regarding dependent claims 5 and 14 and the above-noted three-prong test, the recited playing style module is a generic placeholder, which is used to generate a vector output describing a team's playing structure based on the weighted count for each player of the one or more players associated with the team is functional language, and there is no recitation of sufficient structure to perform the generating.
Regarding dependent claims 6 and 15 and the above-noted three-prong test, the recited movement chain module is a generic placeholder, which is used to generate one or more possession motifs, each possession motif configured to break down sequences of player combinations into chains of consecutive player possessions is functional language, and there is no recitation of sufficient structure to perform the generating.
Regarding dependent claims 7 and 16 and the above-noted three-prong test, the recited machine learning module associated with a player chain module is a generic placeholder, which is used to generate a feature vector representing each player's involvement in a team's possession based on the chains of consecutive player possessions and the one or more possession motifs is functional language, and there is no recitation of sufficient structure to perform the generating.
Regarding dependent claims 8 and 17 and the above-noted three-prong test, the recited machine learning module associated with a possession value module is a generic placeholder, which is used to predict a probability of a goal being scored based on the chains of the consecutive player possessions is functional language, and there is no recitation of sufficient structure to perform the predicting.
Regarding dependent claims 9 and 18 and the above-noted three-prong test, the recited gaussian mixture module associated with a role prediction module is a generic placeholder, which is used to generate the one or more clusters of players is functional language, and there is no recitation of sufficient structure to perform the generating.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
A review of the specification shows the following appears to be the corresponding structure described in the specification for the 35 U.S.C. 112(f) limitations:
Regarding the various “modules” recited in the claims above:
With reference to paragraph [0032], specification recites “Role prediction platform120 may include one or more software modules. The one or more software modules may be collections of code or instructions stored on a media (e.g., memory of organization computing system104) that represent a series of machine instructions (e.g., program code) that implements one or more algorithmic steps. Such machine instructions may be the actual computer code the processor of organization computing system104 interprets to implement the instructions or, alternatively, may be a higher level of coding of the instructions that is interpreted to obtain the actual computer code. The one or more software modules may also include one or more hardware components. One or more aspects of an example algorithm may be performed by the hardware components (e.g., circuitry) itself, rather as a result of the instructions”
Further, with reference to [0037], the specification recites “Figure 2 is a block diagram200illustrating role prediction platform120, according to example embodiments. Role prediction platform120 may include spatial feature module202, playing style module204, player chain module208, movement chain module210, possession value module212, passing/crossing risk module214, shooting features module216, and role prediction module218. Each of spatial feature module202, playing style module204, player chain module208, movement chain module210, possession value module212, passing/crossing risk module214, shooting features module216, and role prediction module218 may include one or more software modules. The one or more software modules may be collections of code or instructions stored on a media (e.g., memory of organization computing system104) that represent a series of machine instructions (e.g., program code) that implements one or more algorithmic steps. Such machine instructions may be the actual computer code the processor of organization computing system104 interprets to implement the instructions or, alternatively, may be a higher level of coding of the instructions that is interpreted to obtain the actual computer code”
Since the claims 2-9, 11-18, and 20 are interpreted under 35 U.S.C. 112(f), and applicant’s specification describes with reference to the recited “modules” that they may be “collections of code or instructions stored on a media (e.g., memory of organization computing system104) that represent a series of machine instructions (e.g., program code) that implements one or more algorithmic steps” and “One or more aspects of an example algorithm may be performed by the hardware components (e.g., circuitry) itself, rather as a result of the instructions”, or hardware circuits configured to perform the respective functions the various modules are being interpreted as any combination of software (i.e., a set of instructions, software modules, one or more programs) and hardware or hardware circuits capable of performing the claimed functions.
If applicant wishes to provide further explanation or dispute the examiner's
interpretation of the corresponding structure, applicant must identify the corresponding
structure with reference to the specification by page and line number, and to the
drawing, if any, by reference characters in response to this Office action.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Regarding Claim 1,
Claim 1 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 1 is directed to a method for generating a role summary associated with one or more players, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: The limitations:
“automatically transforming … the event information in a first format to generate a plurality of heat maps having a second format for each player of the one or more players, wherein the plurality of heat maps each identify one or more pass originations and one or more pass destinations based on the player motion data and the ball motion data”
“determine a number of factors corresponding to the one or more pass originations and the one or more pass destinations to identify in the plurality of heat maps”
“identifying …only the determined number of factors in the plurality of heat maps to output a spatial distribution for each player”
“generating …a context variable to pair with each player based on each output spatial distribution”
“encoding …each player and context variable as a one hot representation”
“identify …a potential role of each player based on each one hot representation”
“determining …a playing style of a team associated with each player using the event information”
“determining …a subset of paths associated with each player using the event information”
“determining …a player involvement using the event information and the subset of paths”
“generating …a first score corresponding to a value associated with the player involvement”
“generating …a second score associated with a passing ability of each player using the event information”
“determining …a shot style of each player using the event information”
“generating …one or more clusters of players using the potential role, the playing style, the subset of paths, the player involvement, the passing ability, and the shot style of each player, each cluster of players corresponding to a unique player role”
As drafted, under their broadest reasonable interpretations, cover mental processes, i.e., concepts performed in the human mind (including an observation, evaluation, judgement, opinion). The above limitations in the context of this claim correspond to mental processes, e.g., evaluation and judgement with assistance of pen and paper.
Step 2A Prong Two Analysis: The judicial exceptions are not integrated into a practical application. In particular, the claim recited additional elements that are mere instructions to apply an exception (See MPEP 2106.05(f)) and insignificant extra-solution activity (See MPEP 2106.05(g)).
The limitations:
various recitations of “by the computing system”
“providing, by the computing system, the plurality of heat maps to a machine- learning model trained to…”
“by the trained machine-learning model”
“providing, by the computing system, each one hot representation to a neural network trained to…”
As drafted, are additional elements that amount to no more than mere instructions to apply an exception for the abstract ideas. See MPEP 2106.05(f).
The limitations:
“capturing, by a tracking system, tracking data associated with the one or more players, wherein the tracking data comprises event information comprising player motion data and ball motion data during one or more events, and wherein the tracking system is in electronic communication with a computing system”
“transmitting by the computing system, a graphical user interface to a display of a user device, the graphical user interface including a text description of the unique player role associated with each cluster of players and players associated with each unique player role”
As drafted, are additional elements that amount to no more than insignificant extra-solution activity. See MPEP 2106.05(g).
Therefore, the additional elements do not integrate the abstract ideas into a practical application.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, all of the additional elements are “mere instructions to apply” and “insignificant extra-solution activity”. Furter, the capturing and transmitting limitations recite the well-understood, routine, and conventional activity of receiving or transmitting data over a network. MPEP 2106.05(d)(II); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network). Mere instructions to apply an exception, and insignificant extra-solution activity cannot provide an inventive concept. The claim is not patent eligible.
Regarding Claim 2,
Claim 2 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 2 is directed to a method for generating a role summary associated with one or more players, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: The limitations:
“identifying …coordinate data of each player of the one or more players from the event information”
“generating …a heat map illustrating a pass origin and pass destination for each pass initiated by each player of the one or more players”
As drafted, under their broadest reasonable interpretations, cover mental processes, i.e., concepts performed in the human mind (including an observation, evaluation, judgement, opinion). The above limitations in the context of this claim correspond to mental processes, e.g., evaluation and judgement with assistance of pen and paper
Step 2A Prong Two Analysis: The judicial exceptions are not integrated into a practical application. In particular, the claim recited additional elements that are mere instructions to apply an exception (See MPEP 2106.05(f)).
The limitations:
“by a spatial feature module of the computing system”
“by the spatial feature module of the computing system”
As drafted, are additional elements that amount to no more than mere instructions to apply an exception for the abstract ideas. See MPEP 2106.05(f).
Therefore, the additional elements do not integrate the abstract ideas into a practical application.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, all of the additional elements are “mere instructions to apply”. Mere instructions to apply an exception cannot provide an inventive concept. The claim is not patent eligible.
Regarding Claim 3,
Claim 3 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 3 is directed to a method for generating a role summary associated with one or more players, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: The limitations:
“generating …as output, a plurality of factors that describe the spatial distribution of each player of the one or more players.”
As drafted, under the broadest reasonable interpretation, covers a mental process, i.e., concepts performed in the human mind (including an observation, evaluation, judgement, opinion). The above limitations in the context of this claim correspond to mental processes, e.g., evaluation and judgement with assistance of pen and paper.
Step 2A Prong Two Analysis: The judicial exceptions are not integrated into a practical application. In particular, the claim recited additional elements that are mere instructions to apply an exception (See MPEP 2106.05(f)).
The limitations:
“by the spatial feature module of the computing system”
As drafted, is an additional element that amounts to no more than mere instructions to apply an exception for the abstract ideas. See MPEP 2106.05(f).
Therefore, the additional elements do not integrate the abstract ideas into a practical application.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, all of the additional elements are “mere instructions to apply”. Mere instructions to apply an exception cannot provide an inventive concept. The claim is not patent eligible.
Regarding Claim 4,
Claim 4 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 4 is directed to a method for generating a role summary associated with one or more players, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: The limitations:
“identifying …each event of the one or more events in the event information”
“for each event, portioning …each event into a plurality of possessions, wherein each possession comprises one or more touches of a ball.”
As drafted, under their broadest reasonable interpretations, cover mental processes, i.e., concepts performed in the human mind (including an observation, evaluation, judgement, opinion). The above limitations in the context of this claim correspond to mental processes, e.g., evaluation and judgement with assistance of pen and paper.
Step 2A Prong Two Analysis: The judicial exceptions are not integrated into a practical application. In particular, the claim recited additional elements that are mere instructions to apply an exception (See MPEP 2106.05(f)).
The limitations:
“by a playing style module of the computing system”
“by the playing style module of the computing system”
As drafted, are additional elements that amount to no more than mere instructions to apply an exception for the abstract ideas. See MPEP 2106.05(f).
Therefore, the additional elements do not integrate the abstract ideas into a practical application.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, all of the additional elements are “mere instructions to apply”. Mere instructions to apply an exception cannot provide an inventive concept. The claim is not patent eligible.
Regarding Claim 5,
Claim 5 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 5 is directed to a method for generating a role summary associated with one or more players, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: The limitations:
“assigning …a touch value to each touch of the one or more touches, wherein the touch value represents a type of touch”
“aggregating …each touch value to generate a weighted count for each player of the one or more players”
“generating …a vector output describing a team's playing structure based on the weighted count for each player of the one or more players associated with the team.”
As drafted, under their broadest reasonable interpretations, cover mental processes, i.e., concepts performed in the human mind (including an observation, evaluation, judgement, opinion). The above limitations in the context of this claim correspond to mental processes, e.g., evaluation and judgement with assistance of pen and paper.
Step 2A Prong Two Analysis: The judicial exceptions are not integrated into a practical application. In particular, the claim recited additional elements that are mere instructions to apply an exception (See MPEP 2106.05(f)).
The limitations:
“by a machine learning module associated with the playing style module”
“by the playing style module”
As drafted, are additional elements that amount to no more than mere instructions to apply an exception for the abstract ideas. See MPEP 2106.05(f).
Therefore, the additional elements do not integrate the abstract ideas into a practical application.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, all of the additional elements are “mere instructions to apply”. Mere instructions to apply an exception cannot provide an inventive concept. The claim is not patent eligible.
Regarding Claim 6,
Claim 6 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 6 is directed to a method for generating a role summary associated with one or more players, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: The limitations:
“generating …one or more possession motifs, each possession motif configured to break down sequences of player combinations into chains of consecutive player possessions”
As drafted, under the broadest reasonable interpretation, covers a mental process, i.e., concepts performed in the human mind (including an observation, evaluation, judgement, opinion). The above limitations in the context of this claim correspond to mental processes, e.g., evaluation and judgement with assistance of pen and paper.
Step 2A Prong Two Analysis: The judicial exceptions are not integrated into a practical application. In particular, the claim recited additional elements that are mere instructions to apply an exception (See MPEP 2106.05(f)).
The limitations:
“by a movement chain module of the computing system”
As drafted, are additional elements that amount to no more than mere instructions to apply an exception for the abstract ideas. See MPEP 2106.05(f).
Therefore, the additional elements do not integrate the abstract ideas into a practical application.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, all of the additional elements are “mere instructions to apply”. Mere instructions to apply an exception cannot provide an inventive concept. The claim is not patent eligible.
Regarding Claim 7,
Claim 7 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 7 is directed to a method for generating a role summary associated with one or more players, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: The limitations:
“generating …a feature vector representing each player's involvement in a team's possession based on the chains of consecutive player possessions and the one or more possession motifs”
As drafted, under the broadest reasonable interpretation, covers a mental process, i.e., concepts performed in the human mind (including an observation, evaluation, judgement, opinion). The above limitations in the context of this claim correspond to mental processes, e.g., evaluation and judgement with assistance of pen and paper.
Step 2A Prong Two Analysis: The judicial exceptions are not integrated into a practical application. In particular, the claim recited additional elements that are mere instructions to apply an exception (See MPEP 2106.05(f)).
The limitations:
“by a machine learning module associated with a player chain module of the computing system”
As drafted, are additional elements that amount to no more than mere instructions to apply an exception for the abstract ideas. See MPEP 2106.05(f).
Therefore, the additional elements do not integrate the abstract ideas into a practical application.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, all of the additional elements are “mere instructions to apply”. Mere instructions to apply an exception cannot provide an inventive concept. The claim is not patent eligible.
Regarding Claim 8,
Claim 8 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 8 is directed to a method for generating a role summary associated with one or more players, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: The limitations:
“predicting …a probability of a goal being scored based on the chains of the consecutive player possessions”
As drafted, under the broadest reasonable interpretation, covers a mental process, i.e., concepts performed in the human mind (including an observation, evaluation, judgement, opinion). The above limitations in the context of this claim correspond to mental processes, e.g., evaluation and judgement with assistance of pen and paper.
Step 2A Prong Two Analysis: The judicial exceptions are not integrated into a practical application. In particular, the claim recited additional elements that are mere instructions to apply an exception (See MPEP 2106.05(f)).
The limitations:
“via the machine learning module associated with a possession value module of the computing system”
As drafted, are additional elements that amount to no more than mere instructions to apply an exception for the abstract ideas. See MPEP 2106.05(f).
Therefore, the additional elements do not integrate the abstract ideas into a practical application.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, all of the additional elements are “mere instructions to apply”. Mere instructions to apply an exception cannot provide an inventive concept. The claim is not patent eligible.
Regarding Claim 9,
Claim 9 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 9 is directed to a method for generating a role summary associated with one or more players, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: The limitations:
“generating …the one or more clusters of players”
As drafted, under the broadest reasonable interpretation, covers a mental process, i.e., concepts performed in the human mind (including an observation, evaluation, judgement, opinion). The above limitations in the context of this claim correspond to mental processes, e.g., evaluation and judgement with assistance of pen and paper
Step 2A Prong Two Analysis: The judicial exceptions are not integrated into a practical application. In particular, the claim recited additional elements that are mere instructions to apply an exception (See MPEP 2106.05(f)).
The limitations:
“by a gaussian mixture module associated with a role prediction module of the computing system”
As drafted, is an additional element that amounts to no more than mere instructions to apply an exception for the abstract ideas. See MPEP 2106.05(f).
Therefore, the additional elements do not integrate the abstract ideas into a practical application.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, all of the additional elements are “mere instructions to apply”. Mere instructions to apply an exception cannot provide an inventive concept. The claim is not patent eligible.
Regarding Claim 10,
Claim 10 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 10 is directed to a non-transitory computer readable medium comprising one or more sequences of instructions, which is directed to a machine, one of the statutory categories.
Step 2A Prong One Analysis: The limitations:
“automatically transforming … the event information in a first format to generate a plurality of heat maps having a second format for each player of the one or more players, wherein the plurality of heat maps each identify one or more pass originations and one or more pass destinations based on the player motion data and the ball motion data”
“determine a number of factors corresponding to the one or more pass originations and the one or more pass destinations to identify in the plurality of heat maps”
“identifying …only the determined number of factors in the plurality of heat maps to output a spatial distribution for each player”
“generating …a context variable to pair with each player based on each output spatial distribution”
“encoding …each player and context variable as a one hot representation”
“identify …a potential role of each player based on each one hot representation”
“determining …a playing style of a team associated with each player using the event information”
“determining …a subset of paths associated with each player using the event information”
“determining …a player involvement using the event information and the subset of paths”
“generating …a first score corresponding to a value associated with the player involvement”
“generating …a second score associated with a passing ability of each player using the event information”
“determining …a shot style of each player using the event information”
“generating …one or more clusters of players using the potential role, the playing style, the subset of paths, the player involvement, the passing ability, and the shot style of each player, each cluster of players corresponding to a unique player role”
As drafted, under their broadest reasonable interpretations, cover mental processes, i.e., concepts performed in the human mind (including an observation, evaluation, judgement, opinion). The above limitations in the context of this claim correspond to mental processes, e.g., evaluation and judgement with assistance of pen and paper.
Step 2A Prong Two Analysis: The judicial exceptions are not integrated into a practical application. In particular, the claim recited additional elements that are mere instructions to apply an exception (See MPEP 2106.05(f)) and insignificant extra-solution activity (See MPEP 2106.05(g)).
The limitations:
“A non-transitory computer readable medium comprising one or more sequences of instructions, which, when executed by one or more processors, causes a computing system to perform operations”
various recitations of “by the computing system”
“providing, by the computing system, the plurality of heat maps to a machine- learning model trained to…”
“by the trained machine-learning model”
“providing, by the computing system, each one hot representation to a neural network trained to…”
As drafted, are additional elements that amount to no more than mere instructions to apply an exception for the abstract ideas. See MPEP 2106.05(f).
The limitations:
“capturing, by a tracking system, tracking data associated with the one or more players, wherein the tracking data comprises event information comprising player motion data and ball motion data during one or more events, and wherein the tracking system is in electronic communication with a computing system”
“transmitting by the computing system, a graphical user interface to a display of a user device, the graphical user interface including a text description of the unique player role associated with each cluster of players and players associated with each unique player role”
As drafted, are additional elements that amount to no more than insignificant extra-solution activity. See MPEP 2106.05(g).
Therefore, the additional elements do not integrate the abstract ideas into a practical application.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, all of the additional elements are “mere instructions to apply” and “insignificant extra-solution activity”. Furter, the capturing and transmitting limitations recite the well-understood, routine, and conventional activity of receiving or transmitting data over a network. MPEP 2106.05(d)(II); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network). Mere instructions to apply an exception, and insignificant extra-solution activity cannot provide an inventive concept. The claim is not patent eligible.
Regarding Claim 11,
Claim 11 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 11 is directed to a non-transitory computer readable medium comprising one or more sequences of instructions, which is directed to a machine, one of the statutory categories.
Step 2A Prong One Analysis: The limitations:
“identifying …coordinate data of each player of the one or more players from the event information”
“generating …a heat map illustrating a pass origin and pass destination for each pass initiated by each player of the one or more players”
As drafted, under their broadest reasonable interpretations, cover mental processes, i.e., concepts performed in the human mind (including an observation, evaluation, judgement, opinion). The above limitations in the context of this claim correspond to mental processes, e.g., evaluation and judgement with assistance of pen and paper.
Step 2A Prong Two Analysis: The judicial exceptions are not integrated into a practical application. In particular, the claim recited additional elements that are mere instructions to apply an exception (See MPEP 2106.05(f)).
The limitations:
“by a spatial feature module of the computing system”
“by the spatial feature module of the computing system”
As drafted, are additional elements that amount to no more than mere instructions to apply an exception for the abstract ideas. See MPEP 2106.05(f).
Therefore, the additional elements do not integrate the abstract ideas into a practical application.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, all of the additional elements are “mere instructions to apply”. Mere instructions to apply an exception cannot provide an inventive concept. The claim is not patent eligible.
Regarding Claim 12,
Claim 12 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 12 is directed to a non-transitory computer readable medium comprising one or more sequences of instructions, which is directed to a machine, one of the statutory categories.
Step 2A Prong One Analysis: The limitations:
“generating …as output, a plurality of factors that describe the spatial distribution of each player of the one or more players”
As drafted, under their broadest reasonable interpretations, cover mental processes, i.e., concepts performed in the human mind (including an observation, evaluation, judgement, opinion). The above limitations in the context of this claim correspond to mental processes, e.g., evaluation and judgement with assistance of pen and paper.
Step 2A Prong Two Analysis: The judicial exceptions are not integrated into a practical application. In particular, the claim recited additional elements that are mere instructions to apply an exception (See MPEP 2106.05(f)).
The limitations:
“by the spatial feature module of the computing system”
As drafted, is an additional element that amounts to no more than mere instructions to apply an exception for the abstract ideas. See MPEP 2106.05(f).
Therefore, the additional elements do not integrate the abstract ideas into a practical application.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, all of the additional elements are “mere instructions to apply”. Mere instructions to apply an exception cannot provide an inventive concept. The claim is not patent eligible.
Regarding Claim 13,
Claim 13 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 13 is directed to a non-transitory computer readable medium comprising one or more sequences of instructions, which is directed to a machine, one of the statutory categories.
Step 2A Prong One Analysis: The limitations:
“identifying …each event of the one or more events in the event information”
“for each event, portioning …each event into a plurality of possessions, wherein each possession comprises one or more touches of a ball.”
As drafted, under their broadest reasonable interpretations, cover mental processes, i.e., concepts performed in the human mind (including an observation, evaluation, judgement, opinion). The above limitations in the context of this claim correspond to mental processes, e.g., evaluation and judgement with assistance of pen and paper.
Step 2A Prong Two Analysis: The judicial exceptions are not integrated into a practical application. In particular, the claim recited additional elements that are mere instructions to apply an exception (See MPEP 2106.05(f)).
The limitations:
“by a playing style module of the computing system”
“by the playing style module of the computing system”
As drafted, are additional elements that amount to no more than mere instructions to apply an exception for the abstract ideas. See MPEP 2106.05(f).
Therefore, the additional elements do not integrate the abstract ideas into a practical application.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, all of the additional elements are “mere instructions to apply”. Mere instructions to apply an exception cannot provide an inventive concept. The claim is not patent eligible.
Regarding Claim 14,
Claim 14 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 14 is directed to a non-transitory computer readable medium comprising one or more sequences of instructions, which is directed to a machine, one of the statutory categories.
Step 2A Prong One Analysis: The limitations:
“assigning …a touch value to each touch of the one or more touches, wherein the touch value represents a type of touch”
“aggregating …each touch value to generate a weighted count for each player of the one or more players”
“generating …a vector output describing a team's playing structure based on the weighted count for each player of the one or more players associated with the team.”
As drafted, under their broadest reasonable interpretations, cover mental processes, i.e., concepts performed in the human mind (including an observation, evaluation, judgement, opinion). The above limitations in the context of this claim correspond to mental processes, e.g., evaluation and judgement with assistance of pen and paper.
Step 2A Prong Two Analysis: The judicial exceptions are not integrated into a practical application. In particular, the claim recited additional elements that are mere instructions to apply an exception (See MPEP 2106.05(f)).
The limitations:
“by a machine learning module associated with the playing style module”
“by the playing style module”
As drafted, are additional elements that amount to no more than mere instructions to apply an exception for the abstract ideas. See MPEP 2106.05(f).
Therefore, the additional elements do not integrate the abstract ideas into a practical application.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, all of the additional elements are “mere instructions to apply”. Mere instructions to apply an exception cannot provide an inventive concept. The claim is not patent eligible.
Regarding Claim 15,
Claim 15 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 15 is directed to a non-transitory computer readable medium comprising one or more sequences of instructions, which is directed to a machine, one of the statutory categories.
Step 2A Prong One Analysis: The limitations:
“generating …one or more possession motifs, each possession motif configured to break down sequences of player combinations into chains of consecutive player possessions”
As drafted, under their broadest reasonable interpretations, cover mental processes, i.e., concepts performed in the human mind (including an observation, evaluation, judgement, opinion). The above limitations in the context of this claim correspond to mental processes, e.g., evaluation and judgement with assistance of pen and paper.
Step 2A Prong Two Analysis: The judicial exceptions are not integrated into a practical application. In particular, the claim recited additional elements that are mere instructions to apply an exception (See MPEP 2106.05(f)).
The limitations:
“by a movement chain module of the computing system”
As drafted, are additional elements that amount to no more than mere instructions to apply an exception for the abstract ideas. See MPEP 2106.05(f).
Therefore, the additional elements do not integrate the abstract ideas into a practical application.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, all of the additional elements are “mere instructions to apply”. Mere instructions to apply an exception cannot provide an inventive concept. The claim is not patent eligible.
Regarding Claim 16,
Claim 16 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 16 is directed to a non-transitory computer readable medium comprising one or more sequences of instructions, which is directed to a machine, one of the statutory categories.
Step 2A Prong One Analysis: The limitations:
“generating …a feature vector representing each player's involvement in a team's possession based on the chains of consecutive player possessions and the one or more possession motifs”
As drafted, under their broadest reasonable interpretations, cover mental processes, i.e., concepts performed in the human mind (including an observation, evaluation, judgement, opinion). The above limitations in the context of this claim correspond to mental processes, e.g., evaluation and judgement with assistance of pen and paper.
Step 2A Prong Two Analysis: The judicial exceptions are not integrated into a practical application. In particular, the claim recited additional elements that are mere instructions to apply an exception (See MPEP 2106.05(f)).
The limitations:
“by a machine learning module associated with a player chain module of the computing system”
As drafted, are additional elements that amount to no more than mere instructions to apply an exception for the abstract ideas. See MPEP 2106.05(f).
Therefore, the additional elements do not integrate the abstract ideas into a practical application.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, all of the additional elements are “mere instructions to apply”. Mere instructions to apply an exception cannot provide an inventive concept. The claim is not patent eligible.
Regarding Claim 17,
Claim 17 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 17 is directed to a non-transitory computer readable medium comprising one or more sequences of instructions, which is directed to a machine, one of the statutory categories.
Step 2A Prong One Analysis: The limitations:
“predicting …a probability of a goal being scored based on the chains of the consecutive player possessions”
As drafted, under their broadest reasonable interpretations, cover mental processes, i.e., concepts performed in the human mind (including an observation, evaluation, judgement, opinion). The above limitations in the context of this claim correspond to mental processes, e.g., evaluation and judgement with assistance of pen and paper.
Step 2A Prong Two Analysis: The judicial exceptions are not integrated into a practical application. In particular, the claim recited additional elements that are mere instructions to apply an exception (See MPEP 2106.05(f)).
The limitations:
“via a second machine learning module associated with a possession value module of the computing system”
As drafted, are additional elements that amount to no more than mere instructions to apply an exception for the abstract ideas. See MPEP 2106.05(f).
Therefore, the additional elements do not integrate the abstract ideas into a practical application.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, all of the additional elements are “mere instructions to apply”. Mere instructions to apply an exception cannot provide an inventive concept. The claim is not patent eligible.
Regarding Claim 18,
Claim 18 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 18 is directed to a non-transitory computer readable medium comprising one or more sequences of instructions, which is directed to a machine, one of the statutory categories.
Step 2A Prong One Analysis: The limitations:
“generating… the one or more clusters of players”
As drafted, under their broadest reasonable interpretations, cover mental processes, i.e., concepts performed in the human mind (including an observation, evaluation, judgement, opinion). The above limitations in the context of this claim correspond to mental processes, e.g., evaluation and judgement with assistance of pen and paper.
Step 2A Prong Two Analysis: The judicial exceptions are not integrated into a practical application. In particular, the claim recited additional elements that are mere instructions to apply an exception (See MPEP 2106.05(f)).
The limitations:
“by a gaussian mixture module associated with a role prediction module of the computing system”
As drafted, is an additional element that amounts to no more than mere instructions to apply an exception for the abstract ideas. See MPEP 2106.05(f).
Therefore, the additional elements do not integrate the abstract ideas into a practical application.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, all of the additional elements are “mere instructions to apply”. Mere instructions to apply an exception cannot provide an inventive concept. The claim is not patent eligible.
Regarding Claim 19,
Claim 19 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 19 is directed to a system, comprising: one or more processors; and a memory having programming instructions stored thereon, which is directed to a machine, one of the statutory categories.
Step 2A Prong One Analysis: The limitations:
“automatically transforming … the event information in a first format to generate a plurality of heat maps having a second format for each player of the one or more players, wherein the plurality of heat maps each identify one or more pass originations and one or more pass destinations based on the player motion data and the ball motion data”
“determine a number of factors corresponding to the one or more pass originations and the one or more pass destinations to identify in the plurality of heat maps”
“identifying …only the determined number of factors in the plurality of heat maps to output a spatial distribution for each player”
“generating …a context variable to pair with each player based on each output spatial distribution”
“encoding …each player and context variable as a one hot representation”
“identify …a potential role of each player based on each one hot representation”
“determining …a playing style of a team associated with each player using the event information”
“determining …a subset of paths associated with each player using the event information”
“determining …a player involvement using the event information and the subset of paths”
“generating …a first score corresponding to a value associated with the player involvement”
“generating …a second score associated with a passing ability of each player using the event information”
“determining …a shot style of each player using the event information”
“generating …one or more clusters of players using the potential role, the playing style, the subset of paths, the player involvement, the passing ability, and the shot style of each player, each cluster of players corresponding to a unique player role”
As drafted, under their broadest reasonable interpretations, cover mental processes, i.e., concepts performed in the human mind (including an observation, evaluation, judgement, opinion). The above limitations in the context of this claim correspond to mental processes, e.g., evaluation and judgement with assistance of pen and paper.
Step 2A Prong Two Analysis: The judicial exceptions are not integrated into a practical application. In particular, the claim recited additional elements that are mere instructions to apply an exception (See MPEP 2106.05(f)) and insignificant extra-solution activity (See MPEP 2106.05(g)).
The limitations:
“A computing system, comprising: one or more processors; and a memory having programming instructions stored thereon, which, when executed by the one or more processors, causes the computing system to perform operations”
various recitations of “by the computing system”
“providing, by the computing system, the plurality of heat maps to a machine- learning model trained to…”
“by the trained machine-learning model”
“providing, by the computing system, each one hot representation to a neural network trained to…”
As drafted, are additional elements that amount to no more than mere instructions to apply an exception for the abstract ideas. See MPEP 2106.05(f).
The limitations:
“capturing, by a tracking system, tracking data associated with the one or more players, wherein the tracking data comprises event information comprising player motion data and ball motion data during one or more events, and wherein the tracking system is in electronic communication with the computing system”
“transmitting by the computing system, a graphical user interface to a display of a user device, the graphical user interface including a text description of the unique player role associated with each cluster of players and players associated with each unique player role”
As drafted, are additional elements that amount to no more than insignificant extra-solution activity. See MPEP 2106.05(g).
Therefore, the additional elements do not integrate the abstract ideas into a practical application.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, all of the additional elements are “mere instructions to apply” and “insignificant extra-solution activity”. Furter, the capturing and transmitting limitations recite the well-understood, routine, and conventional activity of receiving or transmitting data over a network. MPEP 2106.05(d)(II); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network). Mere instructions to apply an exception, and insignificant extra-solution activity cannot provide an inventive concept. The claim is not patent eligible.
Regarding Claim 20,
Claim 20 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 20 is directed to a system, comprising: one or more processors; and a memory having programming instructions stored thereon, which is directed to a machine, one of the statutory categories.
Step 2A Prong One Analysis: The limitations:
“identifying …coordinate data of each player of the one or more players from the event information”
“generating …a heat map illustrating a pass origin and pass destination for each pass initiated by each player of the one or more players”
As drafted, under their broadest reasonable interpretations, cover mental processes, i.e., concepts performed in the human mind (including an observation, evaluation, judgement, opinion). The above limitations in the context of this claim correspond to mental processes, e.g., evaluation and judgement with assistance of pen and paper.
Step 2A Prong Two Analysis: The judicial exceptions are not integrated into a practical application. In particular, the claim recited additional elements that are mere instructions to apply an exception (See MPEP 2106.05(f)).
The limitations:
“by a spatial feature module of the computing system”
“by the spatial feature module of the computing system”
As drafted, are additional elements that amount to no more than mere instructions to apply an exception for the abstract ideas. See MPEP 2106.05(f).
Therefore, the additional elements do not integrate the abstract ideas into a practical application.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, all of the additional elements are “mere instructions to apply”. Mere instructions to apply an exception cannot provide an inventive concept. The claim is not patent eligible.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-8, 10-17, and 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over Lucey et al. (U.S. Patent Publication No. 2018/0032858) (“Lucey”) in view of Kerr et al. (Using machine learning to draw inferences from pass location data in soccer) (“Kerr”) in further view of Sicilia et al. (DeepHoops: Evaluating Micro-Actions in Basketball Using Deep Feature Representations of Spatio-Temporal Data) (“Sicilia”).
Regarding claim 1, Lucey teaches a method for generating a role summary associated with one or more players (Lucey [0052] “Role information for players (e.g., point guard, shooting guard, center) can also be either inferred from the positional tracking data or entered separately.” Lucey provides roles associated with players.), comprising: capturing, by a tracking system, tracking data associated with the one or more players (Lucey [0030] “A general overview of the context of the system is described with respect to FIG. 1, in accordance with an embodiment. At a sporting event taking place at a venue 110, a tracking system 120 records the motions of all players on the playing surface, as well as any other objects of relevance (e.g., the ball, the referees, etc.).” Lucey provides tracking system 120 to track the motions of all players on the playing surface, as well as any other objects of relevance, corresponding to a tracking system to track data associated with players.), wherein the tracking data comprises event information comprising player motion data and ball motion data during one or more events (Lucey [0030] “Tracking system 120 stores at least player identity and positional information (e.g., (x,y) position) for all players and objects on the playing surface for each frame in a game file 140.” Lucey provides tracking player and ball motion data during a game, corresponding to an event.), and wherein the tracking system is in electronic communication with a computing system (Lucey [0030] “Tracking system 120 can be an optically-based system using, for example, a plurality of fixed cameras. Alternatively, tracking system 120 can be a radio-based system using, for example, RFID tags worn by players or embedded in objects to be tracked, or tracking system 120 can be another type of system that tracks moving objects. Preferably, tracking system 120 samples and records at a high frame rate (e.g., 25 frames per second) so as to minimize quantization, enabling expert humans to select the onset and offset of plays at precise times (i.e., frame-level), as well as particular players of interest. Tracking system 120 stores at least player identity and positional information (e.g., (x,y) position) for all players and objects on the playing surface for each frame in a game file 140” Lucey provides tracking system 120, which records tracking data and stores player identity and event information in game file 140, corresponding to the tracking system is in electronic communication with a computing system.) …determining, by the computing system, a playing style of a team associated with each player using the event information (Lucey [0044] “Thus, embodiments of the present system utilize information regarding the trajectories of the ball and the players, as well as game events and contexts, to create a hash-table, effectively learning a “playbook” of representative plays for a team or player's behavior.”; [0053] “Thus, embodiments of the system find examples which are similar to the situation of interest—whether that be finding players who have similar characteristics or teams who play in a similar manner.”; [0060] “As discussed above, one technique for learning a single template uses a player's role (e.g., point guard, power forward, etc.) A general role-based alignment method is described with respect to FIG. 11.” Lucey provides determining team playing styles and player role information corresponding to determining by the computing system a playing style of a team associated with each player using the event information.); determining, by the computing system, a subset of paths associated with each player using the event information (Lucey [0079] “The heat-map associated with each agent (A-E=first team, F-J=second team) is unimodal when compared to the ball trajectory, which is multimodal. Thus, even when player motions may take a similar path, the ball has multiple distinctive paths it can take.”; [0052] “Role information for players (e.g., point guard, shooting guard, center) can also be either inferred from the positional tracking data or entered separately.” Lucey provides role information from player tracking including player path association corresponding to determining, by the computing system, a subset of paths associated with each player using the event information.); determining, by the computing system, a player involvement using the event information and the subset of paths (Lucey [0030] “Preferably, tracking system 120 samples and records at a high frame rate (e.g., 25 frames per second) so as to minimize quantization, enabling expert humans to select the onset and offset of plays at precise times (i.e., frame-level), as well as particular players of interest. Tracking system 120 stores at least player identity and positional information (e.g., (x,y) position) for all players and objects on the playing surface for each frame in a game file 140.”; [0052] “The game events and contexts in the database for a play may be inferred directly from the raw positional tracking data (e.g., a made or missed basket), or may be manually entered. Role information for players (e.g., point guard, shooting guard, center) can also be either inferred from the positional tracking data or entered separately.” Lucey provides player tracking and role information from plays made during a game, corresponding to determining, by the computing system, a player involvement using the event information and the subset of paths.); generating, by the computing system, a first score corresponding to a value associated with the player involvement (Lucey [0044] “However, the semantics of a play are more accurately captured by using additional information, such as information about the players (e.g., identity, trajectory, etc.) and events (pass, dribble, shot, etc.), as well as contextual information (e.g., if team is winning or losing, how much time remaining, etc.)… Other predicted values can also be chosen for performance variables, such as probability of making a pass, probability of shooting, probability of moving in a certain direction/trajectory, or the probability of fatigue/injury of a player.” Lucey provides generating probabilities for probabilities for players including probability of shooting, corresponding to generating, by the computing system, a first score corresponding to a value associated with the player involvement.); generating, by the computing system, a second score associated with a passing ability of each player using the event information (Lucey [0044] “However, the semantics of a play are more accurately captured by using additional information, such as information about the players (e.g., identity, trajectory, etc.) and events (pass, dribble, shot, etc.), as well as contextual information (e.g., if team is winning or losing, how much time remaining, etc.)… Other predicted values can also be chosen for performance variables, such as probability of making a pass, probability of shooting, probability of moving in a certain direction/trajectory, or the probability of fatigue/injury of a player.” Lucey provides generating probabilities for players including probability of making a pass, corresponding to generating, by the computing system, a second score associated with passing ability of each of the one or more players and based on the event information.); determining, by the computing system, a shot style of each player using the event information (Lucey [0044] “However, the semantics of a play are more accurately captured by using additional information, such as information about the players (e.g., identity, trajectory, etc.) and events (pass, dribble, shot, etc.), as well as contextual information (e.g., if team is winning or losing, how much time remaining, etc.). Thus, embodiments of the present system utilize information regarding the trajectories of the ball and the players, as well as game events and contexts, to create a hash-table, effectively learning a “playbook” of representative plays for a team or player's behavior.”; [0052] “Role information for players (e.g., point guard, shooting guard, center) can also be either inferred from the positional tracking data or entered separately. In embodiments of the system, a model for the database can then be trained by crafting features which encode game specific information based on the positional and game data (e.g., distance from basket/goal, distance from defenders, particular events, etc.), and then calculating a prediction value (between 0 and 1) with respect to a classification metric (e.g., expected point value).” Lucey provides tracking player identity, trajectory, and shots in addition to contextual information to determine role information from players, corresponding to determining, by the computing system, a shot style of each player using the event information.); generating, by the computing system, one or more clusters of players using the potential role, the playing style, the subset of paths, the player involvement, the passing ability, and the shot style of each player, each cluster of players corresponding to a unique player role (Lucey [0039] “At step 430, a value of K is chosen and a clustering algorithm (e.g., K-means, agglomerative clustering, affinity propagation) is used to assign each play of the database to one of K plays. The total reconstruction error is measured for the K clusters.”; [0049] “In another embodiment of the system, a top-down approach to learning the decision tree is used. An example of the top-down approach is described with respect to FIG. 5. At step 510, all the plays are aligned to the set of templates. From this initial set of templates, at step 520 the plays are assigned to a set of K groups (clusters), using all ball and player information, forming Layer 1 of the decision tree. Back propagation is then used at step 530 to prune out unimportant players and divide each cluster into sub-clusters (Layer 2). The approach continues at step 540 until the leaves of the tree represent a dictionary of plays which are predictive of a particular task—i.e., goal-scoring (Layer 3).” Lucey provides clustering the game information including player and ball motion, where each cluster represents a particular play that is predictive of a particular task, such as scoring a goal, corresponding to generating clusters of players using player and ball information to determine player roles.); and transmitting, by the computing system, a graphical user interface to a display of a user device, the graphical user interface including a text description of the unique player role associated with each cluster of players and players associated with each unique player role (Lucey [0029] “Embodiments of the present system process large amounts of sports-related tracking data in an efficient manner, enabling the querying and retrieval of statistically similar sports plays and the generation of analytical statistical predictions for player and team behavior through an interactive visual interface.”; [0054] “Embodiments of the system use the plays in the hash-table/playbook that were learned through the distributive clustering processes described above.”; [0055] “The play is initially either loaded from the database (for example, as a result of a user search through a search panel 930 and/or a Game Select button 940), or drawn by the user using a touchscreen, stylus, keyboard or other input device (for example, after a user's selection of a Draw Play option 950)”; [0060] “As discussed above, one technique for learning a single template uses a player's role (e.g., point guard, power forward, etc.)” Lucey provides clustering to determine play information associated with players including role information and plays, which may be accessed by a user input device using a touchscreen, stylus, keyboard, etc., corresponding to transmitting a graphical user interface to a display of a user device including text and role information based on the results of clustering.).
Lucey fails to teach …automatically transforming, by the computing system, the event information in a first format to generate a plurality of heat maps having a second format for each player of the one or more players, wherein the plurality of heat maps each identify one or more pass originations and one or more pass destinations based on the player motion data and the ball motion data; providing, by the computing system, the plurality of heat maps to a machine- learning model trained to determine a number of factors corresponding to the one or more pass originations and the one or more pass destinations to identify in the plurality of heat maps identifying, by the trained machine-learning model, only the determined number of factors in the plurality of heat maps to output a spatial distribution for each player: generating, by the computing system, a context variable to pair with each player based on each output spatial distribution; encoding, by the computing system, each player and context variable as a one hot representation; providing, by the computing system, each one hot representation to a neural network trained to identify a potential role of each player based on each one hot representation…
However, Kerr teaches automatically transforming, by the computing system, the event information in a first format to generate a plurality of heat maps having a second format for each player of the one or more players (Kerr Section 3 The Data “The data are hand-labeled annotations of each ball-event that took place during the course of a match, for example, each pass, tackle, shot, etc. A ball-event is recorded any time a player makes a play on the ball, apart from dribbling. The dataset also includes additional information for each ball-event such as the location, the player involved, and the outcome.”; Section 4.1 Pass Heatmaps “For a given set of attempted passes, we counted how many passes originated from each zone in our discretized pitch and normalized by the total number of passes to produce a heatmap of the origins of passes. The heatmap can be represented by a 3 × 6 matrix of frequency values… In Fig. 3, we have plotted several heatmaps.” Kerr provides transforming event information, such as passes from players during a soccer game, into a plurality of heat maps.), wherein the plurality of heat maps each identify one or more pass originations and one or more pass destinations based on the player motion data and the ball motion data (Kerr Section 4.1 Pass Heatmaps “For a given set of attempted passes, we counted how many passes originated from each zone in our discretized pitch and normalized by the total number of passes to produce a heatmap of the origins of passes. The heatmap can be represented by a 3 × 6 matrix of frequency values… the second experiment highlighted in this paper includes pass destination”; Section 5.1 “Origin-Destination grid: Percentage of passes in a pos session from one zone to another zone” Kerr provides heatmaps of passes made by players during a soccer game, including, origin and dentitions.); providing, by the computing system, the plurality of heat maps to a machine-learning model trained to determine a number of factors corresponding to the one or more pass originations and the one or more pass destinations to identify in the plurality of heat maps (Kerr Section 4.1 Pass Heatmaps “We use the heatmaps as the basis for the visualization and classification experiments we present in this section”; Section 4.3 Team Classification “To determine whether the heatmaps were representative of a team’s passing style, we conducted the following classification experiment: …Each heatmap is treated as an example, and its respective label is the team that attempted the passes. To use the heatmaps as features, we flatten the 3 × 6matrix into a single vector of values, creating a model that has feature dimensionality of 18. After constructing the heatmaps, we take a 70–30 training/test split stratified by team, thus ensuring that the class balance is equal in both the training set and test set. We then construct a K-nearest neighbor (K-NN) classifier to perform the classification task… After choosing a value for K, we classify each example in the test set and calculate the overall accuracy” Kerr provides a K-nearest neighbor classifier to determine a team’s passing style, corresponding to determining a number of factors corresponding to the pass origins and destinations, by providing heat maps to the trained classifier.); identifying, by the trained machine-learning model, only the determined number of factors in the plurality of heat maps to output a spatial distribution for each player (Kerr Figure 8 “Average pass shot value (APSV) for all players with more than 200 passes in the 2012–13 La Liga season”; Section 5.4 Player Rankings by Shot Prediction Models “We took every completed pass in the La Liga 2012–13, and using our model computed an estimate of its relative importance for generating a shot. This importance, called the pass shot value (PSV), is computed for a pass p as PSV(p), where wop and wdp are the model weights for the origin and destination of p, respectively, and wodp, is the model weight for the pair of the origin and destination of p. For example, a pass from zone 3 to zone 4 would have a PSV of the sum of the model weight for an origin in zone 3, the weight for the destination in zone 4, and the weight of the pair of having an origin of 3 and a destination of 4.We then computed the average pass shot value (APSV) for all players in La Liga who had over 200 completed passes in the 2012–13 season… We plot the APSV for these players in Fig. 8, which shows how our model would rank each player by their average tendency to complete passes that are conducive to leading to a shot.”; Section 6 Conclusion “Using a KNN model, we were able to identify teams from the heatmaps of their pass-origin locations with 87% accuracy in a 20-way classification task.” Kerr provides performing classification using a trained classifier using heat maps as input to determine average pass shot value (APSV) for all players in La Liga who had over 200 completed passes in the 2012–13 season, corresponding to identifying heatmap factors by a trained model and outputting a spatial distribution for each player, which is shown in Figure 8.);
Lucey and Kerr are both considered to be analogous to the claimed invention because they are in the same field of artificial intelligence and more specifically sports analysis. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Lucey with the above teachings of Kerr. Doing so would help provide interesting and sometimes nonobvious insights (Kerr Section 6 Conclusion “We believe that our results show that appropriate analyses of pass-event data in soccer can provide interesting and sometimes nonobvious insights.”).
Further, Sicilia teaches generating, by the computing system, a context variable to pair with each player based on each output spatial distribution (Sicilia Section 3.1 Description and Processing of Spatio-Temporal Data “Possessions: Our dataset consists of a sample of more than 134,000 team possessions of interest. We define a possession i as a sequence of n moments (x(i) t )n t=1 where each moment is a 24 dimensional vector, i.e., x(i) t ∈ R24. The first 20 elements capture the court location of the 10 players via (x,y)-coordinates, the next three represent the court location and height of the ball via (x,y,z) coordinates, while the last element represents the current value of the shot-clock. Each of these moments are well-annotated with event descriptions when relevant (e.g., a pass occurred during moment (x(i) τ )).” Sicilia generating player and ball locations using coordinates and annotated event descriptions (e.g., a pass occurred during moment (x(i) τ), corresponding generating context variables to pair with players based on spatial distributions.); encoding, by the computing system, each player and context variable as a one hot representation (Sicilia Section 3.1 Description and Processing of Spatio-Temporal Data “Possessions: Our dataset consists of a sample of more than 134,000 team possessions of interest. We define a possession i as a sequence of n moments (x(i) t )n t=1 where each moment is a 24 dimensional vector, i.e., x(i) t ∈ R24. The first 20 elements capture the court location of the 10 players via (x,y)-coordinates, the next three represent the court location and height of the ball via (x,y,z) coordinates, while the last element represents the current value of the shot-clock. Each of these moments are well-annotated with event descriptions when relevant (e.g., a pass occurred during moment (x(i) τ )). Additionally, each possession i maintains information about the 5 offensive players {s (i) j {s (i) j }10 }5 j=1 and 5 defensive players j=6 on the court via one-hot encoding.” Silica encoding players and ball locations and event descriptions corresponding to the context variables as one-hot encoding, corresponding to a one hot representation.); providing, by the computing system, each one hot representation to a neural network trained to identify a potential role of each player based on each one hot representation (Sicilia Section 3.3 The DeepHoops Architecture “Stacked LSTM Network: The LSTM network is a common, effective neural network for processing sequential inputs. Each layer of the LSTM has a number of LSTM cells (in our implementation, we have 32 for each of the 3 layers)… Player Representation: As aforementioned, we jointly learn an embedding of individual players with in our end-to-end architecture. …If we form the tensor A, then the one-hot encoding of the jth players (i) j can be used to extract that player’s representation via the matrix multiplication As (i) j . As discussed, the extracted player representations are concatenated to the final state h(T) of the last layer of the LSTM and fed through an additional dense layer before classification… Players with similar contributions to the distribution of terminal actions are then expected to be close in the corresponding latent space.” Sicilia provides providing the one-hot encoding to an LSTM neural network to learn an embedding for individual players, corresponding to providing the one-hot encoding a neural network and identifying a potential role based on the one-hot representation.);
Lucey, Kerr and Sicilia are all considered to be analogous to the claimed invention because they are in the same field of artificial intelligence and more specifically sports analysis. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Lucey in view of Kerr with the above teachings of Sicilia. Doing so would help provide evaluations for actions that have traditionally been challenging to evaluate, such as how a standard pass differs from an assist (Sicilia Section 5 Conclusions and Discussion “We further showcase how DeepHoops can be used to evaluate micro-actions that have traditionally been challenging to evaluate (e.g., how a standard pass differs from an assist).”).
Regarding claim 2, Lucey in view of Kerr in further view of Sicilia teaches the method of claim 1, wherein Lucey teaches further comprising: identifying by a spatial feature module of the computing system, coordinate data of each player of the one or more players from the event information (Lucey [0030] “Tracking system 120 stores at least player identity and positional information (e.g., (x,y) position) for all players and objects on the playing surface for each frame in a game file 140.”; [0031] “A computing device 190 runs an interactive sports analytics interface and is communicatively connected to the play database server 180.” Lucey provides a computing system corresponding to a module, and including player tracking using (x,y) positioning ,corresponding to identifying by a spatial feature module of the computing system, coordinate data of each player of the one or more players from the event information.), but fails to teach and generating, by the spatial feature module of the computing system, a heat map illustrating a pass origin and pass destination for each pass initiated by each player of the one or more players.
However, Kerr teaches generating, by the spatial feature module of the computing system, a heat map illustrating a pass origin and pass destination for each pass initiated by each player of the one or more players (Kerr Section 4.1 Pass Heatmaps “For a given set of attempted passes, we counted how many passes originated from each zone in our discretized pitch and normalized by the total number of passes to produce a heatmap of the origins of passes…indeed, the second experiment highlighted in this paper includes pass destination”; Section 5.1 Feature Extraction “We then constructed the feature vectors by concatenating all the values from each of the three grids. The origin and destination grids have one value per zone, so each accounts for 18 features, and the origin–destination grid has a value for each origin–destination pair, and thus is 18 × 18 = 324 features.” Kerr provides producing heat maps from passing data that include pass originations and destinations, corresponding to generating, by the spatial feature module of the computing system, a heat map illustrating a pass origin and pass destination for each pass initiated by each player of the one or more players.).
Lucey, Kerr, and Sicilia are all considered to be analogous to the claimed invention because they are in the same field of artificial intelligence and more specifically sports analysis. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Lucey in view of Kerr in further view of Sicilia with the above teachings of Kerr. Doing so would help provide interesting and sometimes nonobvious insights (Kerr Section 6 Conclusion “We believe that our results show that appropriate analyses of pass-event data in soccer can provide interesting and sometimes nonobvious insights.”).
Regarding claim 3, Lucey in view of Kerr in further view of Sicilia teaches the method of claim 2, further comprising: generating by the spatial feature module of the computing system, as output, a plurality of factors that describe the spatial distribution of each player of the one or more players (Lucey [0033] “In an embodiment, the alignment of plays is performed using multiple templates. An example of misalignment of tracking data is illustrated in FIG. 3. FIG. 3a indicates the locations of five basketball player positions in one quarter of a game. There is little distinctiveness to any of the players, particularly on the offensive end of the court. FIG. 3b illustrates player positions after aligning the plays based on their role (point guard, shooting guard, center, etc.) However, because the plays have been aligned to only single template, there is little variation shown between offensive and defensive positioning for each player role.”; [0079] “FIG. 18a illustrates the centroids of two cluster centers that have been applied on a test set of two 4 second plays. The heat-map of each player's position is shown in FIG. 18b.” Lucey provides player tracking including heat maps corresponding to generating as output, a plurality of factors that describe the spatial distribution of each player of the one or more players.).
It would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Lucey in view of Kerr in further view of Sicilia for the same reasons disclosed above in the rejection of claim 2.
Regarding claim 4, Lucey in view of Kerr in further view Sicilia teaches the method of claim 1, further comprising: identifying, by a playing style module of the computing system, each event of the one or more events in the event information (Lucey [0030] “At a sporting event taking place at a venue 110, a tracking system 120 records the motions of all players on the playing surface, as well as any other objects of relevance (e.g., the ball, the referees, etc.).”; [0052] “In addition to raw trajectory information, in embodiments of the system, the plays in the database are also associated with game event information and context information. The game events and contexts in the database for a play may be inferred directly from the raw positional tracking data (e.g., a made or missed basket), or may be manually entered.” Lucey provides identifying each event of the plurality of events in the event information); and for each event, portioning, by the playing style module of the computing system, each event into a plurality of possessions, wherein each possession comprises one or more touches of a ball (Lucey [0030] “At a sporting event taking place at a venue 110, a tracking system 120 records the motions of all players on the playing surface, as well as any other objects of relevance (e.g., the ball, the referees, etc.).”; [0063] “Unlike role-based alignment, which enforces a global alignment that is agnostic to particular game-states and contexts, the tree-based alignment used in embodiments of the present system enable all the data to be permuted to get in the same frame of reference for further clustering to occur. This permits capture of, e.g., possession states—i.e., which side of the basketball court both teams are on (e.g., left-hand-side vs right-hand-side).” Lucey provides player and ball tracking including possession states corresponding to and for each event, portioning the event into a plurality of possessions, wherein each possession comprises one or more touches of a ball.).
It would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Lucey in view of Kerr in further view of Sicilia for the same reasons disclosed above in the rejection of claim 1.
Regarding claim 5, Lucey in view of Kerr in further view of Sicilia teaches the method of claim 4, further comprising: assigning, by a machine learning module associated with the playing style module, a touch value to each touch of the one or more touches, wherein the touch value represents a type of touch (Kerr Section 5.1 Feature Extraction “We filtered out any possessions that did not contain at least three passes in order to remove regions of play where a team only had a few touches on the ball, since we hypothesized that these epochs of play are less likely to reveal useful strategic elements.”; Section 5.3 Classification Results “Our model that predicts when a possession will end in a shot has an area under the receiver operating characteristic (AUROC) of 0.785 and an F-score of 0.311… The next most important feature is a pass from the right side of the attacking third to zone 14, one of the ‘critical zones’ right in front of the opposing team's penalty box. This could be representative of passes, such as crosses, from the outside into the dangerous areas, which is one of the most identifiable offensive strategies in soccer.” Kerr provides values from a classifier from possessions/touches which predicts when a possession will end in a shot and can be representative of passes, such as crosses, from the outside into the dangerous areas corresponding to assigning, by a machine learning module associated with the playing style module, a touch value to each touch of the one or more touches, wherein the touch value represents a type of touch.); aggregating, by the playing style module, each touch value to generate a weighted count for each player of the one or more players (Kerr Section 4.1 Pass Heatmaps “For a given set of attempted passes, we counted how many passes originated from each zone in our discretized pitch and normalized by the total number of passes to produce a heatmap of the origins of passes. The heatmap can be represented by a 3 × 6 matrix of frequency values.”; Section 5.4 Player Rankings by Shot Prediction Models “In the previous section, we described how we trained a model relating a possession to the outcome of the possession ending in a shot. As a result, this model has a feature weight associated with a pass origin and destination for each zone on the pitch, as well as a weight for each origin–destination pair.” Kerr provides passing heatmap with aggregated touch values and a feature weight associated with a pass origin and destination for each zone on the pitch, corresponding to aggregating each touch value to generate a weighted count for each player of the one or more players); and generating, by the playing style module, a vector output describing a team's playing structure based on the weighted count for each player of the one or more players associated with the team (Kerr Section 5.1 Feature Extraction “After segmenting each game into a discrete sequence of observations, we extracted features from each of these observations to construct feature vectors …We then constructed the feature vectors by concatenating all the values from each of the three grids. The origin and destination grids have one value per zone, so each accounts for 18 features, and the origin–destination grid has a value for each origin–destination pair, and thus is 18 × 18 = 324 features. As a result, each possession was converted into a feature vector of length 360.”; Section 5.2 Method, Experiment Design, and Testing “Upon converting each possession in a game to a fixed-length feature vector, we then used these feature vectors to train models to relate passing strategy in a possession to shots taken.” Kerr provides feature vectors from players/passes and team passing strategy corresponding to generating, by the playing style module, a vector output describing a team's playing structure based on the weighted count for each player of the one or more players associated with the team.).
Lucey, Kerr, and Sicilia are all considered to be analogous to the claimed invention because they are in the same field of artificial intelligence and more specifically sports analysis. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Lucey in view of Kerr in further view of Sicilia with the above teachings of Kerr. Doing so would help provide interesting and sometimes nonobvious insights (Kerr Section 6 Conclusion “We believe that our results show that appropriate analyses of pass-event data in soccer can provide interesting and sometimes nonobvious insights.”).
Regarding claim 6, Lucey in view of Kerr in further view of Sicilia teaches the method of claim 1, further comprising: generating, by a movement chain module of the computing system, one or more possession motifs, each possession motif configured to break down sequences of player combinations into chains of consecutive player possessions (Lucey [0063] “Unlike role-based alignment, which enforces a global alignment that is agnostic to particular game-states and contexts, the tree-based alignment used in embodiments of the present system enable all the data to be permuted to get in the same frame of reference for further clustering to occur. This permits capture of, e.g., possession states—i.e., which side of the basketball court both teams are on (e.g., left-hand-side vs right-hand-side).” Lucey provides possession states corresponding to generating one or more possession motifs, each possession motif configured to break down sequences of player combinations into chains of consecutive player possessions.).
It would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Lucey in view of Kerr in further view of Sicilia for the same reasons disclosed above in the rejection of claim 1.
Regarding claim 7, Lucey in view of Kerr in further view of Sicilia teaches the method of claim 6, further comprising generating, by a machine learning module associated with a player chain module of the computing system, a feature vector representing each player's involvement in a team's possession based on the chains of consecutive player possessions and the one or more possession motifs (Kerr Section 5.1 Feature Extraction “After segmenting each game into a discrete sequence of observations, we extracted features from each of these observations to construct feature vectors. All of the features that we utilized are based on an abstract representation of passing strategy, which we call pass grids.” Kerr provides generating feature vectors representing team passing including pass grids, corresponding to generating a feature vector representing each player's involvement in a team's possession based on the chains of consecutive player possessions and the one or more possession motifs.).
Lucey, Kerr, and Sicilia are all considered to be analogous to the claimed invention because they are in the same field of artificial intelligence and more specifically sports analysis. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Lucey in view of Kerr in further view of Sicilia with the above teachings of Kerr. Doing so would help provide interesting and sometimes nonobvious insights (Kerr Section 6 Conclusion “We believe that our results show that appropriate analyses of pass-event data in soccer can provide interesting and sometimes nonobvious insights.”).
Regarding claim 8, Lucey in view of Kerr in further view of Sicilia teaches the method of claim 7, further comprising: predicting, via the machine learning module associated with a possession value module of the computing system, a probability of a goal being scored based on the chains of the consecutive player possessions (Lucey [0044] “Thus, embodiments of the present system utilize information regarding the trajectories of the ball and the players, as well as game events and contexts, to create a hash-table, effectively learning a “playbook” of representative plays for a team or player's behavior. The playbook is learned by choosing a classification metric that is indicative of interesting or discriminative plays. Suitable classification metrics may include predicting the probability of scoring in soccer or basketball (e.g., expected point value (“EPV”), or expected goal value (“EGV”)” Lucey provides predicting a probability of a goal being scored in soccer based on the chains of the consecutive player possessions.).
It would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Lucey in view of Kerr in further view of Sicilia for the same reasons disclosed above in the rejection of claim 7.
Regarding claim 10, it is the non-transitory computer readable medium comprising one or more sequences of instructions embodiment of claim 1 with similar limitations to claim 1 and is rejected using the same reasoning found in the rejection of claim 1 above. Further, Lucey teaches a non-transitory computer readable medium comprising one or more sequences of instructions, which, when executed by one or more processors, causes a computing system to perform operations (Lucey [0031] “A computing device 190 runs an interactive sports analytics interface and is communicatively connected to the play database server 180.”; [0055] “The play is initially either loaded from the database (for example, as a result of a user search through a search panel 930 and/or a Game Select button 940), or drawn by the user using a touchscreen, stylus, keyboard or other input device (for example, after a user's selection of a Draw Play option 950).” Lucey provides a computing system with corresponding graphical user interface which may receive user input, corresponding to a non-transitory computer readable medium comprising one or more sequences of instructions, which, when executed by one or more processors, causes a computing system to perform operations.).
It would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Lucey in view of Kerr in further view of Sicilia for the same reasons disclosed above in the rejection of claim 1.
Regarding claim 11, the rejection of claim 10 is incorporated herein. Further, the limitations in this claim are taught by Lucey in view of Kerr in further view of Sicilia for the same reasons disclosed above in the rejection of claim 2.
Regarding claim 12, the rejection of claim 11 is incorporated herein. Further, the limitations in this claim are taught by Lucey in view of Kerr in further view of Sicilia for the same reasons disclosed above in the rejection of claim 3.
Regarding claim 13, the rejection of claim 10 is incorporated herein. Further, the limitations in this claim are taught by Lucey in view of Kerr in further view of Sicilia for the same reasons disclosed above in the rejection of claim 4.
Regarding claim 14, the rejection of claim 13 is incorporated herein. Further, the limitations in this claim are taught by Lucey in view of Kerr in further view of Sicilia for the same reasons disclosed above in the rejection of claim 5.
Regarding claim 15, the rejection of claim 10 is incorporated herein. Further, the limitations in this claim are taught by Lucey in view of Kerr in further view of Sicilia for the same reasons disclosed above in the rejection of claim 6.
Regarding claim 16, the rejection of claim 15 is incorporated herein. Further, the limitations in this claim are taught by Lucey in view of Kerr in further view of Sicilia for the same reasons disclosed above in the rejection of claim 7.
Regarding claim 17, the rejection of claim 16 is incorporated herein. Further, the limitations in this claim are taught by Lucey in view of Kerr in further view of Sicilia for the same reasons disclosed above in the rejection of claim 8.
Regarding claim 19, it is the system, comprising: one or more processors; and a memory having programming instructions stored thereon embodiment of claim 10 with similar limitations to claim 10 and is rejected using the same reasoning found in the rejection of claim 10 above.
Regarding claim 20, the rejection of claim 19 is incorporated herein. Further, the limitations in this claim are taught by Lucey in view of Kerr in further view of Sicilia for the same reasons disclosed above in the rejection of claim 2.
Claims 9 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Lucey et al. (U.S. Patent Publication No. 2018/0032858) (“Lucey”) in view of Kerr et al. (Using machine learning to draw inferences from pass location data in soccer) (“Kerr”) in further view of Sicilia et al. (DeepHoops: Evaluating Micro-Actions in Basketball Using Deep Feature Representations of Spatio-Temporal Data) (“Sicilia”) in further view of César Soto-Valero (A Gaussian mixture clustering model for characterizing football players using the EA Sports’ FIFA video game system, June 2017) (“César”).
Regarding claim 9, Lucey in view of Kerr in further view of Sicilia teaches the method of claim 1 as discussed above in the rejection of claim 1, but fails to teach further comprising generating, by a gaussian mixture module associated with a role prediction module of the computing system, the one or more clusters of players.
However, César teaches further comprising: generating, by a gaussian mixture module associated with a role prediction module of the computing system, the one or more clusters of players (César Method, Procedures, “Players’ roles were obtained via Gaussian mixture clustering, using the mclust R package, from these principal components.” César provides generating unique player roles corresponding to clusters using a Gaussian Mixture Model.).
Lucey, Kerr, Sicilia and César are all to be considered analogous to the claimed invention because they are the same field of artificial intelligence and more specifically predictive analytics associated with sporting events. Therefore, it would have been obvious to someone of ordinary skill in art before the effective filing date of the claimed invention to have modified Lucey in view of Kerr in further view of Sicilia with the above teachings of César. Doing so would allow for a model-based cluster method which enables characterizing professional players according to their role and performance criteria (César Method, Procedures, “The objective of this paper is to propose a model-based cluster method which enables characterizing professional football players according to their role and performance criteria.”).
Regarding claim 18, the rejection of claim 10 is incorporated herein. Further, the limitations in this claim are taught by Lucey in view of Kerr in further view of Sicilia and César for the same reasons disclosed above in the rejection of claim 9.
Response to Arguments
Regarding the rejection applied under 35 U.S.C. 101, Applicant firstly asserts that the claims do not recite abstract ideas (“Remarks”, Page 16). Applicant further asserts that the disclosed systems and methods improve the functioning of a computing device and the recited machine-learning model and neural network (“Remarks”, Page 16).
However, as discussed above in the 35 U.S.C. 101 rejection of claim 1 above, the claims recite at least an abstract idea. For example, “identifying …only the determined number of factors in the plurality of heat maps to output a spatial distribution for each player” can be mentally performed (e.g., evaluation and judgement) by simply identifying factors in heatmap data and generating a “spatial distribution” therefrom, which can be performed mentally by, for example, evaluating heatmap data and making a judgement therefrom. The use a “trained machine-learning model”, as recited in the claim, to perform the identification is simply a tool to perform an abstract idea (i.e., the identification), and consistent with MPEP 2106.05(f), amounts to no more than mere instructions to apply an exception for the abstract ideas. Therefore, the claims recite at least an abstract idea.
Applicant further asserts the present claims are similar to those of Desjardins, and particularly Applicant asserts that the “providing, by the computing system, the plurality of heat maps to a machine-learning model trained to determine a number of factors corresponding to the one or more pass originations and the one or more pass destinations to identify in the plurality of heat maps,” "identifying, by the trained machine-learning model, only the determined number of factors in the plurality of heat maps to output a spatial distribution for each player," and "generating, by the computing system, a context variable to pair with each player based on each output spatial distribution," limitations are analogous to the limitations of Desjardins.
However, Desjardins provided a specific training strategy that allows the recited model to preserve performance on earlier tasks even as it learns new ones. The current claims only use machine learning models at a high level to perform abstract ideas, and further, do not disclose a specific training strategy, as was done in Desjardins. Therefore, the present claims are not analogous to the claims of Desjardins.
Applicant further asserts that the claimed machine learning model is trained to determine a number of factors corresponding to the one or more pass originations and destinations and identifies only the number of factors in the plurality of heat maps to output a spatial distribution, which Applicant asserts improves performance and accuracy of the model (“Remarks”, Page 17). Applicant further asserts that a context variable and a player are encoded as a one hot representation, which is subsequently provided to a trained neural network, which Applicant asserts aims to prevent false suggestions in the model. (“Remarks”, Pages 17-18). Applicant therefore asserts that even if the claims do recite an abstract idea, any abstract idea is integrated into a practical application (“Remarks”, Page 18).
However, as discussed above in the 35 U.S.C. 101 rejection of claim 1 above, the determine a number of factors corresponding to the one or more pass originations and destinations and identify only the number of factors in the plurality of heat maps to output a spatial distribution limitations are abstract ideas, and the use of a trained machine learning model amounts to no more than mere instructions to apply an exception for the abstract ideas, as discussed above. Further, the encoding of a one-hot representation is also an abstract idea. Therefore, even if the claims did recite an improvement, it would be an improvement in the abstract ideas of determining a number of factors, outputting a spatial distribution, or encoding a one-hot representation. The MPEP notes that it is important to keep in mind that an improvement in the abstract idea itself is not an improvement in technology. MPEP 2106.05(a)(II). Therefore, the claims remain ineligible under 35 U.S.C. 101.
Regarding the rejection applied under 35 U.S.C. 103, Applicant’s arguments with respect to claims have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Conclusion
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/KURT NICHOLAS PRESSLY/Examiner, Art Unit 2125
/KAMRAN AFSHAR/Supervisory Patent Examiner, Art Unit 2125