DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 08/26/2024 and 02/26/2026 is/are compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Office Action Summary
Claim(s) 1, 7-8, and 14-15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Chang et al (US 2020/0193163 A1) in view of Choi et al (US 2018/0124423 A1).
Claim(s) 2-3, 9-10, and 16-17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Chang et al (US 2020/0193163 A1) in view of Choi et al (US 2018/0124423 A1), further in view of Lucey et al (US 2016/0260015 A1).
Claim(s) 4, 6, 11, 13, 18, and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Chang et al (US 2020/0193163 A1) in view of Choi et al (US 2018/0124423 A1), further in view of Richard et al (Weakly Supervised Action Learning with RNN based Fine-to-Coarse Modeling).
Claim(s) 5, 12, and 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Chang et al (US 2020/0193163 A1) in view of Choi et al (US 2018/0124423 A1) and Richard et al (Weakly Supervised Action Learning with RNN based Fine-to-Coarse Modeling), further in view of Hu et al (Direction-aware Spatial Context Features for Shadow Detection).
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.
Claim(s) 1, 7-8, and 14-15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Chang et al (US 2020/0193163 A1) in view of Choi et al (US 2018/0124423 A1).
Regarding claim(s) 1, 8, and 15, Chang teaches a computer system for predicting an action or an actor from multi-agent tracking data, the computer system comprising:
a memory having processor-readable instructions stored therein (Figure 54; and Paragraph [0449]: “a main memory 5404”); and
one or more processors configured to access the memory and execute the processor-readable instructions, which when executed by the one or more processors configures the one or more processors to perform a plurality of functions (Figure 54; and Paragraph [0449]: “[…] a processing device 5402 (e.g., one or more computer processors)”), including functions for:
receiving, by one or more processors, tracking data for an event from at least one of a device or a tracking system (Figure 1; Figure 2; Paragraph [0073]: “ The different technology layers or the technology stack 100 […] allow precise monitoring, analytics, and understanding of spatiotemporal data associated with an event, such as a sports event […] the technology stack may provide an analytic platform that may take spatiotemporal data (e.g., 3D motion capture “XYZ” data) from National Basketball Association (NBA) arenas or other sports arenas”; and Paragraph [0465]: “The spatiotemporal event data may comprise one or more pieces of raw coordinate data associated with each particular participant in the sporting event (e.g., at any time during the sporting event) […] the system is configured to track (e.g., and/or receive) substantially instantaneous (e.g., instantaneous) location data for each participant in the sporting event during the course of the event”);
generating, by the one or more processors, an input data set from the tracking data (Figure 2; Paragraph [0095] – Paragraph [0096]: “Spatiotemporal pattern recognition 208 is used to automatically identify relationships between physical and temporal patterns and various types of events […] basketball, one challenge is how to turn x, y, z positions of ten players and one ball at twenty-five frames per second into usable input for machine learning and pattern recognition algorithms. For patterns, one is trying to detect (e.g., pick & rolls) […] the motion of player one (P1) towards player two (P2), for at least T seconds, a rate of motion of at least V m/s for at least T seconds and at the projected point of intersection of paths A and B, and a separation distance less than D […]”; and Paragraph [0404] - Paragraph [0406]: “[…] the set of services and outputs may signify spatial-temporal positions of the players and sports accessories/objects such as a bat, ball, football, and the like […] a broadcast video feed may be aligned in time with another input feed, such as input from one or more motion tracking cameras, inputs from player tracking systems (such as wearable devices), and the like […] information of position and speed information for various items or elements, identification of basic events such as various types of shots and screens during a sporting event, and identification of complex events or a sequence of events such as various types of plays […] The machine learning tools and input feed alignment may allow automatic generation of content and information such as statistics, predictions, comparisons, and analysis”);
inputting, by the one or more processors, the input data set into a prediction engine to generate at least one of one or more actions or one or more actors (Figure 47; Figure 56; Paragraph [0283]: “[…] The machine learning workflow 4700 includes a machine learning algorithm 4702 that may produce live and automatic machine learning (ML) classification output 4704 […] based on live spatiotemporal data 4710 […] a machine learning (ML) algorithm 4720 […] which may be rerun on the corresponding segments of data, to produce a time-delayed classification output 4724 with improved classification accuracy of neighboring events […]”; Paragraph [0477]: “Spatiotemporal Event Analysis Module 5600, the interactive game system 5300 is configured to: (1) determine spatiotemporal event data for each of a plurality of available players during a sporting event; (2) analyze the spatiotemporal event data to identify individual spatiotemporal events that occur during the sporting event; (3) determine which of a plurality of players participating in the sporting event are involved in each individual spatiotemporal event […]”; and Paragraph [0481] – Paragraph [0484]: “[…] (4) a particular action by one or more players during the sporting event (e.g., a pass, an attempted shot, a scored shot, an assist, a dribble, a tackle […] a defensed pass, and/or any other suitable potential action which a player may make during the course of any suitable sporting event)”);
inputting, by the one or more processors, at least one of the one or more actions or the one or more actors (Figure 47; Figure 56; Paragraph [0477]: “Spatiotemporal Event Analysis Module 5600, the interactive game system 5300 is configured to: (1) determine spatiotemporal event data for each of a plurality of available players during a sporting event; (2) analyze the spatiotemporal event data to identify individual spatiotemporal events that occur during the sporting event; (3) determine which of a plurality of players participating in the sporting event are involved in each individual spatiotemporal event […]”; and Paragraph [0481] – Paragraph [0484]: “[…] (4) a particular action by one or more players during the sporting event (e.g., a pass, an attempted shot, a scored shot, an assist, a dribble, a tackle […] a defensed pass, and/or any other suitable potential action which a player may make during the course of any suitable sporting event)”); and
generating, by the one or more processors, a graphical representation of at least one of the action prediction or the actor prediction (Figure 31 – Figure 38; Paragraph [0076]: “The visualizations layer 108 may allow dynamic visualizations of patterns and analytics developed from the data obtained from the real-time event. The visualizations may be presented in the form of a scatter rank, shot comparisons, a clip view, and many others. The visualizations layer 108 may use various types of visualizations and graphical tools for creating visual depictions. The visuals may include various types of interactive charts, graphs, diagrams, comparative analytical graphs, and the like”; Paragraph [0173]: “FIGS. 31-38 show examples of DataFX visualizations. The visualization of FIG. 31 requires court position to be solved in order to lay down grid, player “puddles”. Shot arc also requires backboard/hoop solution. In FIG. 32, Voronoi tessellation, heat map, shot and rebound arcs all require the camera pose solution. The highlight of the player uses rotoscoping. In FIG. 33, in addition to the above, players are rotoscoped for highlighting. FIGS. 34-38 show additional visualizations that are based on the use of the methods and systems disclosed herein”; Paragraph [0174]: “DataFX (video augmented with data-driven special effects) may be provided for pre-, during, or post-game viewing, for analytic and entertainment purposes […] DataFX can include use of a Voronoi overlay on court, a Grid overlay on court, a Heat map overlay on court, a Waterfall effect showing likely trajectories of the ball after a missed field goal attempt, a Spray effect on a shot, showing likely trajectories of the shot to the hoop, Circles and glows around highlighted players, Statistics and visual cues over or around players, Arrows and other markings denoting play actions, Calculation overlays on court, and effects showing each variable taken into account”).
Chang fails to teach to inputting, by the one or more processors, at least one of the one or more actions or the one or more actors into a refinement module configured to generate at least one of an action prediction or an actor prediction. However, Choi teaches to inputting, by the one or more processors, at least one of the one or more actions or the one or more actors into a refinement module configured to generate at least one of an action prediction or an actor prediction (Figure 2; Figure 5; Figure 7; Paragraph [0017]: “the present embodiments generate a diverse set of hypothetical predictions and ranks and refines those hypothetical predictions in an iterative fashion”; Paragraph [0042]: “The prediction samples 501 generated by block 202 are provided as input to a feature pooling block 502 and to an RNN decoder 506 […]”; Paragraph [0043]: “The feature pooling block 502 provides its output to the RNN decoder 506, which processes the feature pooling output and the prediction samples, providing its output to scoring block 508, which scores the samples and tracks accumulated rewards, and to regression block 510, which provides the regression vector […] as feedback that is combined with input 501 for the next iteration”; Paragraph [0044]: “The RNN decoder 506 in the ranking and refinement block 204 makes use of information about the past motion context of individual agents, the semantic scene context, and the interaction between multiple agents to provide hidden representations that can score and refine the prediction […]”; and Paragraph [0057]: “the prediction sample module 710 generates sets of such predictions for the ranking/refinement module 712 to work with, ultimately producing one or more predictions that represent the most likely future trajectories for agents, taking into account the presence and likely actions of other agents in the scene”).
Therefore, it would have been obvious to one of ordinary skill in the art to combine the system of Chang with the refinement architecture of Choi before the effective filing date of the claimed invention. The motivation for this combination of references would have been to iteratively refine the prediction samples to generate a more accurate prediction, as taught by Choi, thereby improving the accuracy of the action and actor predictions generated from Chang’s spatiotemporal event analysis. This motivation for the combination of Chang and Choi is/are supported by KSR exemplary rationale (G) Some teaching, suggestion, or motivation in the prior art that would have led one of ordinary skill to modify the prior art reference or to combine prior art reference teachings to arrive at the claimed invention. MPEP 2141 (III).
Regarding claim(s) 7 and 14, Chang as modified by Choi teaches the computer-implemented method of claim 1, where Chang teaches wherein the tracking data includes one or more frames that include a set of actors and a set of corresponding trajectories (Paragraph [0406]: “the set of services and outputs may represent spatial-temporal alignments of the inputs such as the video feeds, etc. For example, a broadcast video feed may be aligned in time with another input feed, such as input from one or more motion tracking cameras, inputs from player tracking systems (such as wearable devices) […] The machine understanding may include various levels of semantic identification, as well as information of position and speed information for various items or elements”; Paragraph [0172]: “[…] instead of manually tracing a player's trajectory and increasing the shot probability in each frame as the player gets closer to the ball […]”; and Paragraph [0174]: “[…] a Waterfall effect showing likely trajectories of the ball after a missed field goal attempt, a Spray effect on a shot, showing likely trajectories of the shot to the hoop, Circles and glows around highlighted players, Statistics and visual cues over or around players, Arrows and other markings denoting play actions […]”).
Claim(s) 2-3, 9-10, and 16-17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Chang et al (US 2020/0193163 A1) in view of Choi et al (US 2018/0124423 A1), further in view of Lucey et al (US 2016/0260015 A1).
Regarding claim(s) 2, 9, and 16, Chang as modified by Choi teaches the computer-implemented method of claim 1, but do not specifically teach wherein generating the input data set includes parsing the tracking data to generate a matrix representation of the tracking data. However, Lucey teaches wherein generating the input data set includes parsing the tracking data to generate a matrix representation of the tracking data (Paragraph [0049]: “may “clean” the tracking data to compensate for such missed or false detections prior to assigning player roles […] tracking data by converting the raw tracking data into a representation based on shape basis and trajectory basis”; Paragraph [0056]: “The tracking all players across time may be expressed as a vector of ordered player locations […] at each time instant. Although the particular ordering of players may be arbitrary, the ordering is consistent across time”; and Paragraph [0060]: “[…] Accordingly, the time-varying structure matrix S includes 2FP parameters […] As a result, the complete structure matrix may be represented by Equation 3 below […] Alternatively, the complete structure matrix may be represented by Equation 4 below […]”).
Therefore, it would have been obvious to one of ordinary skill in the art to combine Chang, Choi, and Lucey before the effective filing date of the claimed invention. The motivation for this combination of references would have been to improve the representation of tracking data by cleaning and converting the raw tracking data into a structured matrix representation while exploiting the spatial and temporal regularity exhibited in the motion data, thereby improving the organization and subsequent processing of the tracking data. This motivation for the combination of Chang, Choi, and Lucey is/are supported by KSR exemplary rationale (G) Some teaching, suggestion, or motivation in the prior art that would have led one of ordinary skill to modify the prior art reference or to combine prior art reference teachings to arrive at the claimed invention. MPEP 2141 (III).
Regarding claim(s) 3, 10, and 17, Chang as modified by Choi and Lucey teaches the computer-implemented method of claim 2, where Lucey teaches wherein the matrix representation represents one or more trajectories of the one or more actors over a duration of the tracking data (Paragraph [0049]: “[…] converting the raw tracking data into a representation based on shape basis and trajectory basis”; and Paragraph [0060]: “[…] the time-varying structure may be represented by modeling the representation in the trajectory subspace, as a linear combination of trajectory basis vectors[…] and A is a 2P×Kt matrix of trajectory coefficients”).
Claim(s) 4, 6, 11, 13, 18, and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Chang et al (US 2020/0193163 A1) in view of Choi et al (US 2018/0124423 A1), further in view of Richard et al (Weakly Supervised Action Learning with RNN based Fine-to-Coarse Modeling).
Regarding claim(s) 4, 11, and 18, Chang as modified by Choi teaches the computer-implemented method of claim 1, where Chang teaches wherein the prediction engine includes an action-actor-attention network configured to generate (Figure 47; Figure 56; Paragraph [0283]: “[…] The machine learning workflow 4700 includes a machine learning algorithm 4702 that may produce live and automatic machine learning (ML) classification output 4704 […] based on live spatiotemporal data 4710 […] a machine learning (ML) algorithm 4720 […] which may be rerun on the corresponding segments of data, to produce a time-delayed classification output 4724 with improved classification accuracy of neighboring events […]”; Paragraph [0477]: “the interactive game system 5300 is configured to […] determine which of a plurality of players participating in the sporting event are involved in each individual spatiotemporal event […]”; Paragraph [0481]: “[…] (4) a particular action by one or more players during the sporting event (e.g., a pass, an attempted shot, a scored shot, an assist, a dribble, a tackle […] a defensed pass, and/or any other suitable potential action which a player may make during the course of any suitable sporting event)”; and Paragraph [0484]: “The system may […] determine which of the plurality of available players are involved in each of the one or more spatiotemporal events [...]”).
Chang fails to teach wherein Richard teaches wherein (Page 754, Right Col., Last Paragraph: “The usage of subactions allows to distribute heterogeneous information of one action class over many subclasses and to capture characteristics such as the length of the overall action class”; and Page 756, Right Col., 2nd Paragraph: “To efficiently capture those characteristics, we propose to model each action as a sequential combination of subactions. Therefore, for each action class a, a set of subactions […] The number Ka is initially estimated by a heuristic and refined during the optimization process. Practically, this means that we subdivide the original long action classes into a set of smaller subactions”).
Therefore, it would have been obvious to one of ordinary skill in the art to combine Chang, Choi, and Richard before the effective filing date of the claimed invention. The motivation for this combination of references would have been to efficiently capture the characteristics of an action by modeling each action as a sequential combination of subactions, thereby distributing heterogeneous information of one action class over many subclasses. This motivation for the combination of Chang, Choi, and Richard is/are supported by KSR exemplary rationale (G) Some teaching, suggestion, or motivation in the prior art that would have led one of ordinary skill to modify the prior art reference or to combine prior art reference teachings to arrive at the claimed invention. MPEP 2141 (III).
Regarding claim(s) 6, 13, and 20, Chang as modified by Choi and Richard teaches the computer-implemented method of claim 4, wherein inputting the one or more actions or the one or more actors into a refinement module includes:
inputting, by the one or more processors, the set of sub-actions and the set of actors into the refinement module to generate the action prediction or the actor prediction (where Chang teaches in Paragraph [0095]: “Features that relate multiple actors in time are key components to the input […]”; Paragraph [0097]: “there is provided a library of such features involving multiple actors over space and time […] The library may include relationships between actors (e.g., players one through ten in basketball), relationships between the actors and other objects such as the ball […] projected locations based on predicted motion”; and Paragraph [0483] – Paragraph [0484]: “identify one or more spatiotemporal events during the sporting event […] to determine which of the plurality of available players are involved in each of the one or more spatiotemporal events […]”; and where Richard teaches in Page 754, Right Col., Last Paragraph: “The usage of subactions allows to distribute heterogeneous information of one action class over many subclasses and to capture characteristics such as the length of the overall action class”; and Page 756, Right Col., 2nd Paragraph: “To efficiently capture those characteristics, we propose to model each action as a sequential combination of subactions. Therefore, for each action class a, a set of subactions […] is defined […] The number Ka is initially estimated by a heuristic and refined during the optimization process. Practically, this means that we subdivide the original long action classes into a set of smaller subactions”; and where Choi teaches in Figure 2; Figure 5; Paragraph [0044]: “The RNN decoder 506 in the ranking and refinement block 204 makes use of information about the past motion context of individual agents, the semantic scene context, and the interaction between multiple agents to provide hidden representations that can score and refine the prediction […] The RNN decoder 506 therefore takes as input […] The embedding vector […] is shared as the initial hidden state of the RNN decoder 506 of the ranking and refinement block […]”; and Paragraph [0057]: “After training, the prediction sample module 710 generates sets of such predictions for the ranking/refinement module 712 to work with, ultimately producing one or more predictions that represent the most likely future trajectories for agents […]”).
Therefore, it would have been obvious to modify Chang’s action-and-actor prediction framework with Richard’s sub-action representation and to employ Choi’s ranking/refinement module such that the set of sub-actions and the set of actors are input into the refinement module to generate the action prediction or the actor prediction. This motivation for the combination of Chang, Choi, and Richard is/are supported by KSR exemplary rationale (G) Some teaching, suggestion, or motivation in the prior art that would have led one of ordinary skill to modify the prior art reference or to combine prior art reference teachings to arrive at the claimed invention. MPEP 2141 (III).
Claim(s) 5, 12, and 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Chang et al (US 2020/0193163 A1) in view of Choi et al (US 2018/0124423 A1) and Richard et al (Weakly Supervised Action Learning with RNN based Fine-to-Coarse Modeling), further in view of Hu et al (Direction-aware Spatial Context Features for Shadow Detection).
Regarding claim(s) 5, 12, and 19, Chang as modified by Choi and Richard teaches the computer-implemented method of claim 4, wherein the action-actor-attention network is configured (Where Chang teaches in Figure 47; Figure 56; Paragraph [0283]; Paragraph [0477]; Paragraph [0481]; Paragraph [0484]; Paragraph [0267]: “the output and cost function optimization may be at the highest level of state aggregation […] the machine learning model may be trained using an ensemble of active learning methods for multiclass classification including weighting of methods based on a confusion matrix and a cost function that may be used to optimize […]”; and where Richard teaches in Page 754, Right Col., Last Paragraph: “The usage of subactions allows to distribute heterogeneous information of one action class over many subclasses and to capture characteristics such as the length of the overall action class”; and Page 756, Right Col., 2nd Paragraph: “To efficiently capture those characteristics, we propose to model each action as a sequential combination of subactions. Therefore, for each action class a, a set of subactions […] is defined […] The number Ka is initially estimated by a heuristic and refined during the optimization process. Practically, this means that we subdivide the original long action classes into a set of smaller subactions”).
Chang as modified by Choi and Richard fails to teach wherein Hu teaches wherein the action-actor-attention network is configured to optimize a weighted cross-entropy loss between the action prediction, the set of sub-actions, the set of actors, and the actor prediction (Equation (3); Equation (4); Equation (5); Abstract: “we first formulate the direction-aware attention mechanism in a spatial recurrent neural network (RNN) by introducing attention weights […] This design is developed into the DSC module and embedded in a CNN to learn DSC features at different levels. Moreover, a weighted cross entropy loss is designed to make the training more effective”; Page 7455, Left Col., 1st Paragraph: “The whole network is trained in an end-to-end manner with a weighted cross entropy loss […]”; and Page 7457, 3.2. Training and Testing Strategies, Loss Function, 1st Paragraph: “[…] if the loss function simply aims for overall accuracy, it will incline to match the non-shadow regions, which have far more pixels. Therefore, we use a weighted cross-entropy loss to optimize the whole network in the training process”).
Chang teaches a machine learning prediction framework configured to generate action predictions and actor predictions, and further teach optimizing a machine learning model using a weighted cost function during training, including cost function optimization and weighting for multiclass classification. Additionally, Richard teaches modeling each action as a sequence of sub-actions by defining a set of sub-actions for each action class, thereby teaching the generation and prediction of sub-action based on actions. Furthermore, Hu teaches attention-based neural network and teaches training the network using a weighted cross-entropy loss. In particular, Hu teaches that a weighted cross-entropy loss is designed to make the training more effective and that the whole network is optimized during training using the weighted cross-entropy loss.
Therefore, it would have been obvious to one of ordinary skill in the art to combine Chang, Choi, Richard and Hu before the effective filing date of the claimed invention. The motivation for this combination of references would have been to modify the machine learning prediction framework of Chang, as further modified by Richard, to employ the weighted cross-entropy loss taught by Hu in order to make the training more effective by optimizing the neural network using the weighted cross-entropy loss. This motivation for the combination of Chang, Choi, Richard and Hu is/are supported by KSR exemplary rationale (G) Some teaching, suggestion, or motivation in the prior art that would have led one of ordinary skill to modify the prior art reference or to combine prior art reference teachings to arrive at the claimed invention. MPEP 2141 (III).
Relevant Prior Art Directed to State of Art
Knittel (US 2019/0180149 A1) are relevant prior art not applied in the rejection(s) above. Knittel discloses a method of classifying an action or event using an artificial neural network, the method comprising: obtaining a first plurality of feature responses corresponding to point data in a first channel and a second plurality of feature responses corresponding to point data in a second channel, each of the first and second plurality of feature responses having associated temporal and spatial position values, the first and second plurality of feature responses relating to a plurality of objects; generating, using the artificial neural network, a third plurality of feature responses, each of the third plurality of feature responses being generated based on one of the first plurality of feature responses from the first channel and one of the second plurality of feature responses from the second channel, and a weighted combination of associated temporal and spatial position values of the corresponding one of the received first and second plurality of feature responses; and classifying an action or event relating to the plurality of objects using the artificial neural network based on the generated third plurality of feature responses.
Moyerman et al (US 2018/0005129 A1) are relevant prior art not applied in the rejection(s) above. Moyerman discloses a system for predictive action assessment in action sports, the system comprising: one or more processors; a memory including instructions that, when executed by the one or more processors, cause the one or more processors to perform operations to: identify a start point for an action in a data stream including a plurality of data sets corresponding to the action, the data stream collected from a sensor array; extract action performance features from the data stream subsequent to the start point; compare, in real time, the action performance features to a set of statistical models; select a label for the action based on the comparison; generate a likelihood of success for the action based on the comparison; and output the label for the action and the likelihood of success for display on a display device.
Conclusion
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/JONGBONG NAH/Examiner, Art Unit 2674
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