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 .
Claim Objections
Claims 1 and 5 are objected to as failing to clearly identify a statutory class of invention. Claim 1 recites “The action of training artificial intelligence…” and claim 5 recites “Using a cloud computing or remote computing…,” which are drafted as statements of activity rather than clearly as a process, machine, manufacture, or composition of matter. Accordingly, it is unclear from the claim language what statutory class is being invoked. For purposes of examination, claims 1 and 5 are interpreted as method claims.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1-5 are rejected under 35 U.S.C. 112(a) as lacking adequate written description. The claims broadly recite training and using artificial intelligence to recognize sports splays, formations, audibles, defensive and offensive schemes, expected player actions (claim 1); interpreting commands from other devices or remote locations (claim 2); generating and modifying images, depictions, video, or animation of individualized expected actions using real-time data (claims 3-4); and using cloud or remote computing and GPS-based player data in connection with such functionality (claim 5).
The specification describes these features only in terms of intended results. For example, the specification states that the artificial intelligence algorithm is “trained to match commands… to plays in a playbook,” that it “should understand and interpret words,” and that it “should be able to relay pertinent information” and “learn what plays to suggest.” However, the specification does not describe the structure, process workflow, data representations, model configuration, training methodology, or any distinguishing technical characteristics sufficient to demonstrate possession of the broadly claimed artificial intelligence system.
The disclosure does not identify how commands are mapped to specific play updates, how individualized player views are generated and modified, how real-time opposition data is incorporated into model outputs, or how cloud/GPS data is processed to produce the claimed functionality. Instead, the specification describes desired outcomes and uses generalized references to “machine learning techniques like tensor flow and other machine learning packages” without further technical detail. Because the specification provides only high-level functional descriptions of what the system is intended to accomplish, and does not describe the claimed invention with sufficient identifying characteristics across its full scope, the disclosure does not reasonably convey to one of ordinary skill in the art that the inventor was in possession of the invention as now claimed. Accordingly, claims 1-5 lack adequate written description under 35 U.S.C. 112(a).
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 2 and 4 are rejected under 35 U.S.C. 112(b) as being indefinite for failing to particularly point out and distinctly claim the subject matter regarded as the invention.
Claim 2 recites “another similar device.” The term “similar” renders the claim indefinite because it is unclear what structural or functional characteristics define similarity in the context of the claimed invention. The claim does not specify whether the similarity relates to hardware configuration, artificial intelligence functionality, communication capability, role within the team, or some other attribute. The specification likewise does not an objective standard for determining what qualifies as a “similar” device. Accordingly, the scope is not reasonably certain.
Claim 4 recites “change the expected action of a player or a group of players.” The phrase “change the expected action” renders the claim indefinite because the claim does not define what constitutes an “expected action,” what baseline is used to determine that change has occurred, or what specific operation constitutes the “change.” It is unclear whether this limitation refers to modifying stored play data, updating a display representation, altering play instructions, or some other operation. As a result the boundaries of the claim are not reasonably certain.
Claim Rejections - 35 USC § 101
Claims 1, 2, 4, and 5 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
101 Subject Matter Eligibility Analysis
Step 1: Claims 1, 2, 4, and 5 are within the four statutory categories (a process, machine, manufacture or composition of matter).
Step 2A Prong One, Step 2A Prong Two, and Step 2B Analysis:
Step 2A Prong One asks if the claim recites a judicial exception (abstract idea, law of nature, or natural phenomenon). If the claim recites a judicial exception, analysis proceeds to Step 2A Prong Two, which asks if the claim recites additional elements that integrate the abstract idea into a practical application. If the claim does not integrate the judicial exception, analysis proceeds to Step 2B, which asks if the claim amounts to significantly more than the judicial exception. If the claim does not amount to significantly more than the judicial exception, the claim is not eligible subject matter under 35 U.S.C. 101.
None of the claims represent an improvement to technology.
Claims 1 and 5 are directed to a method consisting of a series of steps, meaning that it is directed to the statutory category of process. Claims 2 and 4 are directed to devices which are machines.
Regarding claim 1, the following claim elements are abstract ideas:
to recognize sports plays, formations, audibles, defensive and offensive schemes and expected actions while playing team sports (This is an abstract idea of a mental process. The limitation recites the recognition and classification of plays, formations, audibles, schemes, and expected actions. A person could observe play positioning, listen to play calls, consult a playbook, and mentally determine the corresponding scheme and expected player roles. Such observation, pattern recognition, and classification based on experience and judgement can be practically performed in the human mind or with the aid of pen and paper. Accordingly, this limitation falls within the mental process grouping of abstract ideas. See MPEP 2106.04(a)(2)(III).)
The following claim elements are additional elements which, taken alone or in combination with the other elements, do not integrate the judicial exception into a practical application nor amount to significantly more than the judicial exception:
The action of training artificial intelligence using software packages or any machine learning techniques (This limitation amounts to mere instructions to apply the abstract idea using generic computer implementation. The claim recites training artificial intelligence at a high level without specifying a particular technical improvement or specialized implementation. Such recitation simply instructs the practitioner to perform the abstract idea using conventional machine learning tools and therefore does not provide a meaningful limitation. See MPEP 2106.05(f).)
Regarding claim 2, the following claim elements are abstract ideas:
to interpret commands for plays and formations associated with teams sports from another similar device or remote location (This is an abstract idea of a mental process. The limitation recites interpreting commands and determining the corresponding play or formation. A person could hear a command such as “WR1 go route,” recall the associated play from memory or a playbook, a mentally determine the corresponding formation and expected play actions. This act of interpreting and mapping commands to known plays involves observation, recall, and judgement, which can be practically performed in the human mind or with the aid of pen and paper. Accordingly, this limitation falls within the mental process grouping of abstract ideas.),
The following claim elements are additional elements which, taken alone or in combination with the other elements, do not integrate the judicial exception into a practical application nor amount to significantly more than the judicial exception:
A device that uses artificial intelligence trained (This limitation recites generic computer components performing the abstract idea. The recitation of a “device” and “artificial intelligence” is stated at a high level and merely implements the abstract idea using conventional computing technology. Such recitation amounts to insignificant extra-solution activity and generic computer implementation, and does not provide a meaningful limitation.)
Regarding claim 4, the following claim elements are abstract ideas:
to change the expected action of a player or group of players, in a team sports play generated image, video or animation… and depict the changes in a new image, video or animation (This is an abstract idea of a mental process. The limitation recites deciding to modify a player’s expected action and updating the corresponding play depiction. A person could determine that a receiver’s route should be changed, mentally revise the expected play action, and redraw or describe the updated play on a whiteboard or play sheet. Such decision-making and revision of play instructions based on observation and judgement can be practically performed in the human mind or with pen and paper. Accordingly, this limitation falls within the mental process grouping of abstract ideas.).
The following claim elements are additional elements which, taken alone or in combination with the other elements, do not integrate the judicial exception into a practical application nor amount to significantly more than the judicial exception:
A wearable device using artificial intelligence or some other coded means (This limitation merely recites generic computer implementation of the abstract idea. The “wearable device” is described at a high level and performs its conventional function of processing and displaying information. The recitation of “artificial intelligence or some other coded means” does not specify any particular technical improvement or specialized implementation, and instead broadly instructs that the abstract idea be carried out using conventional computing tools. Accordingly, this limitation amounts to insignificant extra-solution activity and generic computer implementation, and does not provide a meaningful limitation.)
that is generated on a wearable device worn by individual players (This limitation merely recites displaying the result of the abstract idea on a generic device. Generating or displaying information on a wearable device amounts to presenting data output and constitutes insignificant extra-solution activity. The wearable device is recited at a high level and performs is conventional function of displaying information.)
Regarding claim 5, the following claim elements are abstract ideas:
to interpret and relay expected player actions to wearable devices in the form of images, videos or animations during team sports (This is an abstract idea of a mental process. The limitation recites interpreting expected player actions and communicating those actions in a presentational form. A person could observe a play, determine each player’s expected action, and verbally communicate or draw those actions on a whiteboard or play sheet for players to follow. Such interpretation and communication of instructions based on observation and judgement can be practically performed in the human mind or with pen and paper. Accordingly, this limitation falls within the mental process grouping of abstract ideas.)
to collect or interpret individual player-based GPS location actions and sports statistics (This is an abstract idea of a mental process. The limitation recites observing player location information and interpreting actions and statistics based on that information. A person could watch players on the field, note their positions and movements, record performance statistics such as distance run or successful plays, and evaluate those statistics through observation and simple calculation. Such data gathering, analysis, and interpretation based on observation, comparison, and judgement can be practically performed in the human mind or with the aid of pen and paper. Accordingly, the limitation falls within the mental process grouping of abstract ideas.).
The following claim elements are additional elements which, taken alone or in combination with the other elements, do not integrate the judicial exception into a practical application nor amount to significantly more than the judicial exception:
Using cloud computing or remote computing (This is a high-level recitation of generic computer components for performing the abstract idea. The limitation merely instructs that the abstract idea be carried out using cloud or remote computing, without specifying any particular technical implementation or improvement. Accordingly, this amounts to insignificant extra solution activity and does not provide a meaningful limitation. See MPEP 2106.05(f) and MPEP 2106.05(g).)
using artificial intelligence (This a high-level recitation of generic computer components for performing the abstract idea. The limitation merely instructs that the abstract idea be carried out using artificial intelligence without specifying any particular technical implementation. Accordingly, this amounts to insignificant extra-solution activity and does not provide a meaningful limitation.)
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim 2 is rejected under 35 U.S.C. 102(a)(2)as being anticipated by Adams (Pub. No.: US 20230256348 A1 (Filed: April 20, 2023)).
Regarding claim 2, Adams discloses the following limitation:
A device that uses artificial intelligence trained to interpret commands for plays and formations associated with teams sports from another similar device or remote location (Adams, paragraph [0042] “In an embodiment, the provided system and/or method may receive input data from the one or more users, from one or more third-parties or from a combination of both.” [0042] “ Network adapters 108 may also be coupled to the system to enable the data processing system to become coupled to other data processing systems or remote printers or storage devices through intervening private or public networks.” [0062] “ In an embodiment, the provided system and/or method may receive input data from the one or more users, from one or more third-parties or from a combination of both.” [0069] “ In order to carry on the multi-aspect analysis, AI is used to conduct deep learning and to enable automatic machine learning to parse through the large volumes of input data, such as text (like soccer strategies), images (like video) and verbal information, to discover insightful patterns underneath the data that is otherwise unseen-able. AI, a mimic human thinking on an expedited basis, when parsing through the sports performance input data, makes sense of each data points and draws connections between the meaning of data points and then making correlations among data points, and ultimately provides pseudo-cognitive information, in which case, the program algorithms and instructions of the certain embodiments set forth the framework for the pseudo-cognitive information, and the AI therein learns from the sample data within the framework to draw learned experience and then applies the learned experience to infer conclusions from a new set of input data.” [0093] “Second, a forward prediction model is created using the tracking data over the samples (i.e., training data), updating the edges of the graphs to reflect the relationships among the nodes. These edges store functions that reflect the dynamics of the system, which are updated as the neural network learns the behavior of the system itself through observation (i.e., parsing through the samples).” – Adam teaches a device employing artificial intelligence and machine learning that is trained using tracking data and training samples to learn system behavior. The reference teaches deep learning and automatic machine learning used to parse strategy-related input data, including strategy and verbal information. Under the broadest reasonable interpretation, parsing and learning from strategy data teaches interpreting commands related to plays and formations in a team sports context. Adams further teaches coupling to other data processing systems via network adapters and receiving input data from users or third parties, thereby teaching receiving play-related commands from remote systems or other devices. Accordingly, Adams teaches a device using trained artificial intelligence to interpret plays and formation-related commands received from remote or networked sources in a team sports environment.),
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1 and 3-5 are rejected under the 35 U.S.C. 103 as being unpatentable over Adams (Pub. No.: US 20230256348 A1 (Filed: April 20, 2023)) in view of Genova (Pub. No.: US 20190091545 A1 (Published: 2018)).
Regarding claim 1, Adam in view of Genova teaches the following limitation:
The action of training artificial intelligence using software packages or any machine learning techniques to recognize sports plays, formations, audibles, defensive and offensive schemes and expected actions while playing team sports (Adams, paragraph [0051] “Since static historical data (such as goals scored, shots on goals, passing percentages and the like) only provide a limited set of information to sports teams and other users, static historical data is not the critical part of certain embodiments, and thus may not be stored in the database(s) 118 and be applied in computer program(s) 106 and the algorithms therein. Thus, the provided method and/or system primarily use the holistic and non-static analysis of large amounts of data input for at least some of the programmed and computerized analysis… Examples of predictive analyses…” [0053] “Evaluating the offensive/defensive patterns of individuals or groups of players vis-à-vis the movement of the ball and/or location on the field of play” [0057] “Likewise, the same predictive performance analyses may be applied in other team sports such as American Football, Rugby, Ice Hockey, Water Polo, Lacrosse, etc.” [0069] “In order to carry on the multi-aspect analysis, AI is used to conduct deep learning and to enable automatic machine learning to parse through the large volumes of input data, such as text (like soccer strategies), images (like video) and verbal information, to discover insightful patterns underneath the data that is otherwise unseen-able.” [0093] “Second, a forward prediction model is created using the tracking data over the samples (i.e., training data), updating the edges of the graphs to reflect the relationships among the nodes. These edges store functions that reflect the dynamics of the system, which are updated as the neural network learns the behavior of the system itself through observation (i.e., parsing through the samples). To infer extracted properties of the system, this sample implementation of the provided system and/or method uses these functions (associated with each edge of the GNs) as a basis for its estimations.” Genova, paragraph [0037] “suppose that the data packet includes an audible option for the selected play. If the user of the wearable device 202J is the quarterback and the quarterback determines to audible, or to change to an alternative play, the wearable device 202J may transmit a data packet to the other wearable devices 202 with the selection of the audible or alternative play.”- Under the broadest reasonable interpretation, training artificial intelligence to recognize sports plays and formations encompasses training a neural network using tracking data to learn coordinated player relationships and system dynamics. Adams teaches creating a prediction model using training data and updating learned graph relationships as neural network learns system behavior. The reference further teaches using deep learning and automatic machine learning to parse strategy, video, and tracking data to discover patterns. It also expressly teaches evaluating offensive and defensive pattens of players based on movement and field location, which corresponds to offensive and defensive schemes. Genova teaches that a selected play may include an audible option and that a quarterback may determine an audible, i.e., change to an alternative play, and transmit that selection to other wearable devices. Accordingly, Genova teaches structured play changes and alternative play selections during team sports gameplay, including audibles.).
Accordingly, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, having a combination of Adams and Genova before them, to incorporate the audible play-selection functionality of the wearable device system into the machine learning based sports pattern analysis system of Adams. One would have been motivated to make such a combination in order to enable a trained artificial intelligence system that learns offensive and defensive patterns from tracking data to recognize and process real-time play changes, including audibles and alternative plays, during gameplay. This would allow the machine learning system not only to evaluate learned formations and schemes from historical data, but also to identify and respond to structured play modifications as they occur, thereby improving strategic analysis, communication, and execution in team sports environments.
Regarding claim 3, Adams in view of Genova teaches the following limitation:
A wearable device that uses artificial intelligence to generate images and depictions of expected actions, based on commands from a responsible party or actions learned from machine learning in response to opposition actions using real time in game data, to relay to the players individually while playing team sports (Adams, paragraph [0069] “In order to carry on the multi-aspect analysis, AI is used to conduct deep learning and to enable automatic machine learning to parse through the large volumes of input data, such as text (like soccer strategies), images (like video) and verbal information, to discover insightful patterns underneath the data that is otherwise unseen-able.” [0093] “ Second, a forward prediction model is created using the tracking data over the samples (i.e., training data), updating the edges of the graphs to reflect the relationships among the nodes. These edges store functions that reflect the dynamics of the system, which are updated as the neural network learns the behavior of the system itself through observation (i.e., parsing through the samples).” [0098] “Given a known starting state, such as team X lined up against team Y in a common attacking scenario, the control model, after learning from provided sample data (i.e., training data), outputs an optimized action sequence to maximize the reward function. The reward function encodes the optimized patterns of play.” [0100] “A visualization would be created with positive values in one color (e.g., green) and negative values in another color (e.g., red) to show what areas are controlled by each team and to what degree, see 1040. This visualization is called a heatmap to account for players force potential, or their danger level. A heatmap would be a powerful analysis tool as it is visually (and thus intuitively) representative of the balance of power on the pitch at a given moment of time of a game or training practice” [0062] “ In an embodiment, the provided system and/or method may receive input data from the one or more users, from one or more third-parties or from a combination of both.” Genova, paragraph [0039] “The wearable device 202 may include a display 302… The display 302 may be capable of displaying text, an image, an animated image, or a video to the user. For example, the display 302 may present to a player user an image of a play to be performed. In some cases, the display 302 may present an animated image or a video illustrating the play the users to perform. In some embodiments, different wearable devices 202 may present different images, or other output, to different users based on their role on the team.” – Adams teaches artificial intelligence and machine-learning models trained on tracking data and match-event data, where the neural network learns system behavior and generates predictive outputs. The reference further teaches creating a representation of the “balance of power between the two teams at a given moment of time” and highlighting “where promising attacking play is likely to occur,” which under BRI, constitutes depictions of expected actions generated in response to opposition interactions using real-time in-game data. The system also receives input from users, which corresponds to commands from a responsible party. Genova teaches a wearable device with a display capable of presenting images, animated images, and video to individual players, including images of plays to be performed, and further teaches that different wearable devices present different images to different players based on their roles. This satisfies relaying depictions individually to players during team-sports gameplay. Accordingly, the combined references teach a wearable device that uses AI to generate depictions of expected actions based on commands or learned behavior in response to opposition actions using real-time in-game data, and to relay those depictions individually to players while playing team sports.),
Regarding claim 4, Adam in view of Genova teaches the following limitation:
A wearable device using artificial intelligence or some other coded means to change the expected action of a player or group of players, in a team sports play generated image, video or animation, that is generated on a wearable device worn by individual players, and depict the changes in a new image, video or animation (Adam, paragraph [0065] “The software aspect of the provided system and/or method may also employ artificial intelligence (AI) because, inter alia, analyzing a large volume of sports performance input data for multiple teams and/or players over a sufficiently long period of time for the purpose of identifying the team/player habits requires deep understanding the patterns and intricacies hid underneath the input data, and thus discovery power of AI is tapped to fulfil the purpose.” [0092] “Likewise, a control model could be used to create optimal or improved patterns of play, based upon, inter alia, maximizing disruption of the opposition's offensive or defensive play routines to create better defense or attacking patterns. For these models, the ball, players and other objectives (such as goal area) are represented as nodes in the GN connected by edges.” [0093] “Second, a forward prediction model is created using the tracking data over the samples (i.e., training data), updating the edges of the graphs to reflect the relationships among the nodes. These edges store functions that reflect the dynamics of the system, which are updated as the neural network learns the behavior of the system itself through observation (i.e., parsing through the samples)” Genova, paragraph [0039] “The display 302 may be capable of displaying text, an image, an animated image, or a video to the user. For example, the display 302 may present to a player user an image of a play to be performed. In some cases, the display 302 may present an animated image or a video illustrating the play the users to perform. In some embodiments, different wearable devices 202 may present different images, or other output, to different users based on their role on the team.” [0037] “ suppose that the data packet includes an audible option for the selected play. If the user of the wearable device 202J is the quarterback and the quarterback determines to audible, or to change to an alternative play, the wearable device 202J may transmit a data packet to the other wearable devices 202 with the selection of the audible or alternative play.” – Adams teaches a system that employs artificial intelligence and neural network models trained on gameplay tracking data. The reference further teaches that a control model may create “optimal or improved patterns of play” by modifying play behavior in response to opposition routines, thereby teaching changing expected actions of players or groups of players through AI or coded logic. Genova teaches a wearable device including a display capable of presenting an image, animation, or video of a play to be performed by a player. The reference further teaches that a quarterback may select an audible or alternative play, which constitutes a change to the selected play, and that the wearable device transmits the alternative play selection to other wearable devices. Because the wearable device presents an image of the play to be performed, presenting the alternative play necessarily results in depicting the changed play in a new image, animation, or video on wearable devices worn by individual players. Accordingly, the combined references teach a wearable device using AI to change expected actions of players within a team sports play depiction and to depict those changed actions in a new image, video, or animation presented individually to players.).
Regarding claim 5, Adams in view of Genova teaches the following limitations:
Using cloud computing or remote computing to interpret and relay expected player actions to wearable devices in the form of images, videos or animations during team sports and using artificial intelligence to collect or interpret individual player-based GPS location actions and sports statistics (Adams, paragraph [0019] “Provided is a computer-implemented method of processing and analyzing player tracking data to optimize team strategy and infer more meaningful statistics.” [0042] “Network adapters 108 may also be coupled to the system to enable the data processing system to become coupled to other data processing systems or remote printers or storage devices through intervening private or public networks. Modems, cable modem and Ethernet cards are just a few of the currently available types of network adapters 108. Network adapters 108 may also be coupled to internet 122 and/or cloud 124 to access remote computer resources.” [0069] “In order to carry on the multi-aspect analysis, AI is used to conduct deep learning and to enable automatic machine learning to parse through the large volumes of input data, such as text (like soccer strategies), images (like video) and verbal information, to discover insightful patterns underneath the data that is otherwise unseen-able.” [0093] “Second, a forward prediction model is created using the tracking data over the samples (i.e., training data), updating the edges of the graphs to reflect the relationships among the nodes. These edges store functions that reflect the dynamics of the system, which are updated as the neural network learns the behavior of the system itself through observation (i.e., parsing through the samples).” [0092] “ For example, forward predictive models could simulate possible match outcomes given expected lineups… For these models, the ball, players and other objectives (such as goal area) are represented as nodes in the GN connected by edges. Each node may contain information about the specific object (ball, player, etc.) such as position and velocity while the graph as a whole may contain system-wide information (such as match time, passes, dribbles, tackles, etc.).” Genova, paragraph [0039] “ The wearable device 202 may include a display 302… The display 302 may be capable of displaying text, an image, an animated image, or a video to the user. For example, the display 302 may present to a player user an image of a play to be performed. In some cases, the display 302 may present an animated image or a video illustrating the play the users to perform. In some embodiments, different wearable devices 202 may present different images, or other output, to different users based on their role on the team.” – Adam teaches that the system may access remote computer resources through network adapters coupled to private or public networks, including internet and cloud. Under BRI, this corresponds to cloud computing or remote computing used to process and interpret sports data. The reference further teaches using artificial intelligence and neural-network-based models to parse large volumes of input data and to create forward prediction models updated as the neural network learns system behavior. It also teaches that player nodes include position and velocity information and that the system maintains sports statistics such as passes, dribbles, and tackles. Additionally, the reference teaches forward predictive models that simulate match outcomes given expected lineups, which corresponds to interpreting expected player actions based on individual player-based location and sports statistics. Genova teaches wearable devices that include displays capable of presenting text, images, animated images, or video to users, including images of plays to be performed, and that wearable devices may present different images to different users based on their roles on the team. Under BRI, this corresponds to relaying expected player actions fin form of images, videos, or animations to wearable devices worn by individual players during team sports. Accordingly, the combined references teach using cloud or remote computing to interpret expected player actions and using AI to collect or interpret individual player-based location actions and sport statistics, and relaying those expected actions to wearable devices in the form of images, videos, or animations during team sports.).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Daravanh Phakousonh whose telephone number is (571)272-6324. The examiner can normally be reached Mon - Thurs 7 AM - 5 PM, Every other Friday 7 AM - 4PM.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Li B Zhen can be reached at 571-272-3768. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/Daravanh Phakousonh/Examiner, Art Unit 2121
/Li B. Zhen/Supervisory Patent Examiner, Art Unit 2121