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 .
Status of Claims
Claims 1, 4 , 7, 8, 11, 15 and 18 have been amended.
Claims 1 – 20 are currently pending.
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 therefore, subject to the conditions and requirements of this title.
This subject matter eligibility analysis follows the latest guidance for Patent Subject Matter Eligibility Guidance.
Claims 1 - 20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter.
Step 1:
Claims 1 – 7 are drawn to a method.
Claims 8 – 15 are drawn to a system and CRM.
Thus, initially, under Step 1 of the analysis, it is noted that the claims are directed towards eligible categories of subject matter.
Step 2A:
Prong 1: Does the Claim recite an Abstract idea, Law of Nature, or Natural Phenomenon?
Claims 15-20 are exemplary because they require substantially the same operative limitations of the remaining claims (reproduced below.) Examiner has underlined the claim limitations which recite the abstract idea, discussed in detail in the paragraphs that follow.
15. (Currently Amended) A system comprising:
a processor; and
a memory having programming instructions stored thereon, which, when executed by the processor, causes the system to perform operations comprising:
generating an interactive game space for predicting an outcome of an event during a live game;
receiving a plurality of video frames from a tracking system;
processing, during the live game by a preprocessing module, the plurality of video frames to generate tracking data by converting video frame data from the plurality of video frames into two-dimensional player positional information;
generating a game file including the tracking data and other data;
identifying a prediction event in the live game;
extracting current game context temporally up to the prediction event from the game file, wherein the current game context includes the tracking data, for the plurality of frames;
identifying pre-game player specific data for each player involved in the prediction event;
providing the pre-game specific data to a ratings model to generate pre- game ratings;
providing the tracking data and the pre-game ratings to a machine- learning model, trained to identify associations between the tracking data and the pre-game ratings and output a probability distribution for a set of possible outcomes for the prediction event;
receiving a proposed outcome from the set of possible outcomes for the prediction event from a user participating in the interactive game space;
determining that the proposed outcome was correct by comparing the proposed outcome to real-time event data for the live game; and
based on the determining, granting points to the user within the interactive game space.
The claims recite italicized limitations that fall within at least one of the groupings of abstract ideas enumerated in the 2019 PEG, namely, Certain Methods of Organizing Human Activity
More specifically, under this grouping, the italicized limitations represent fundamental economic principles or practices and managing interactions between people. For example, the italicized limitations are directed towards a prediction game wherein a player is awarded points based upon correct predictions based upon game data and probabilities of outcome occurring. A user may observe a video broadcast by watching it, analyze the video frames of the broadcast to visually determine two-dimensional player positions, record those positions via manual means on pen and paper, (i.e. a file) and identify events that can be predicted, apply pre game ratings to player data and output probability distribution of events that will likely occur and receive a wager upon them and determine points to award to a player based upon the outcome and the players prediction. The latter also falls under the grouping of managing interactions between people, i.e., rules for scoring a prediction-based game.)
Prong 2: Does the Claim recite additional elements that integrate the exception into a practical application of the exception?
Although the claims recite additional limitations, these limitations do not integrate the exception into a practical application of the exception. For example, the claims require additional limitations as follow, (emphasis added): processor, memory, and the use of machine learning models to analyze data.
These additional limitations do not represent an improvement to the functioning of a computer, or to any other technology or technical field, (MPEP 2106.05(a)). Nor do they apply the exception using a particular machine, (MPEP 2106.05(b)). Furthermore, they do not effect a transformation. (MPEP 2106.05(c)). Rather, these additional limitations amount to an instruction to “apply” the judicial exception using a computer as a tool to perform the abstract idea. Therefore, since the additional limitations, individually or in combination, are indistinguishable from a computer used as a tool to perform the abstract idea, the analysis continues to Step 2B, below.
Step 2B:
Under Step 2B, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because they amount to conventional and routine computer implementation and mere instructions for implementing the abstract idea on generic computing devices.
For example, as pointed out above, the claimed invention recites additional elements facilitating implementation of the abstract idea. Applicant has claimed computer processor, memory, and the use of machine learning models to analyze data. However, all of these elements viewed individually and as a whole, are indistinguishable from conventional computing elements known in the art. Therefore, the additional elements fail to supply additional elements that yield significantly more than the underlying abstract idea.
As the Alice court cautioned, citing Flook, patent eligibility cannot depend simply on the draftsman’s art. Here, amending the claims with generic computing elements does not (in this Examiner’s opinion), confer eligibility.
Regarding the Berkheimer decision, Applicant’s specification establishes that these additional elements are generic:
[0087] Figure 9A illustrates an architecture of computing system 900, according to example embodiments. System 900 may be representative of at least a portion of organization computing system 104. One or more components of system 900 may be in electrical communication with each other using a bus 905.System 900 may include a processing unit (CPU or processor) 910 and a system bus 905 that couples various system components including the system memory 915, such as read only memory (ROM) 920 and random access memory (RAM) 925, to processor 910.System 900 may include a cache of high-speed memory connected directly with, in close proximity to, or integrated as part of processor 910.System 900 may copy data from memory 915and/or storage device 930 to cache 912 for quick access by processor 910. In this way, cache 912 may provide a performance boost that avoids processor 910 delays while waiting for data. These and other modules may control or be configured to control processor 910 to perform various actions. Other system memory 915 may be available for use as well. Memory 915 may include multiple different types of memory with different performance characteristics. Processor 910 may include any general purpose processor and a hardware module or software module, such as service 1 932, service 2 934, and service 3 936 stored in storage device 930, configured to control processor 910 as well as a special-purpose processor where software instructions are incorporated into the actual processor design. Processor 910 may essentially be a completely self-contained computing system, containing multiple cores or processors, a bus, memory controller, cache, etc. A multi- core processor may be symmetric or asymmetric
Regarding the Berkheimer decision, Shwartz et al (US 2020/0267441) establishes that these additional elements are generic:
[0031] One or more user devices 150 facilitate end user interaction with various systems of the present disclosure. The user devices can be any type of computing device including, without limitation, a smartphone, tablet, OTT box, desktop computer, or smart TV. An example of a user device is shown in FIG. 16. The end user interacts with the other system components by sending a request to server 100 to view video content such as a news broadcast, live sporting event, or television program. The server responds with the requested content, and portions of the video feed can be customized to a specific user's preferences using the techniques further described below. In various embodiments, server 100 stores user profiles 102, which keeps track on user preference which may be based on user-specified interests (e.g., at the time of enrollment), user habits such as browsing history, and other user characteristics which may be learned over time using various techniques including known machine learning techniques. Server 100 also stores content that can be pushed to client devices periodically or sent to the client devices in response to a query for data. Background content 104 includes content created or captured by content creators 140 such as a background video feed as further described below. Additional content 109 includes data elements that can be used to augment the background video feed such as interactive data elements. The data elements can be extracted from the background video feed (e.g., isolating or highlighting footage of a specific player on a football team), generated (e.g., game/player stats) or a combination thereof.
Therefore, these elements fail to supply additional elements that yield significantly more than the underlying abstract idea. Thus, taken alone, the additional elements do not amount to significantly more than the above-identified judicial exception (the abstract idea).
Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation.
Moreover, the claims do not recite improvements to another technology or technical field. Nor, do the claims improve the functioning of the underlying computer itself -- they merely recite generic computing elements. Furthermore, they do not effect a transformation of a particular article to a different state or thing: the underlying computing elements remain the same.
Concerning preemption, the Federal Circuit has said in Ariosa Diagnostics, Inc., V. Sequenom, Inc., (Fed Cir. June 12, 2015):
The Supreme Court has made clear that the principle of preemption is the basis for the judicial exceptions to patentability. Alice, 134 S. Ct at 2354 (“We have described the concern that drives this exclusionary principal as one of pre-emption”). For this reason, questions on preemption are inherent in and resolved by the § 101 analysis. The concern is that “patent law not inhibit further discovery by improperly tying up the future use of these building blocks of human ingenuity.” Id. (internal quotations omitted). In other words, patent claims should not prevent the use of the basic building blocks of technology—abstract ideas, naturally occurring phenomena, and natural laws. While preemption may signal patent ineligible subject matter, the absence of complete preemption does not demonstrate patent eligibility. In this case, Sequenom’s attempt to limit the breadth of the claims by showing alternative uses of cffDNA outside of the scope of the claims does not change the conclusion that the claims are directed to patent ineligible subject matter. Where a patent’s claims are deemed only to disclose patent ineligible subject matter under the Mayo framework, as they are in this case, preemption concerns are fully addressed and made moot. (Emphasis added.)
For these reasons, it appears that the claims are not patent-eligible under 35 USC §101.
Claim Rejections - 35 USC § 103
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.
Claim(s) 1-13 and 15-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Srinivasan (US 2021/0134124) in view of Schwartz et al (US 2020/0234543) in view of Groll et al “Hybrid Machine Learning Forecasts for the UEFA EURO 2020” in view of Lucey et al (US 2018/0032858)
Regarding Claim 1, Srinivasan discloses a method (Fig. 3, Para. [0063]) comprising:
generating, by a computing system, an interactive game space for predicting an outcome of an event during a live game (Srinivasan discloses a system block diagram for the operation of the gaming engine using dynamic real-time pricing and trading, Para. [0063]; the gaming engine 330 is configured to predict the probability of a betting scenario 340 occurring, Para. [0081]);
identifying, by the computing system, a prediction event in the live game (Srinivasan discloses the betting scenarios 340 can be automatically generated or selected from a database by a gaming engine 330, Para. [0064]);
identifying, by the computing system, pre-game player specific data for each player involved in the prediction event (Srinivasan discloses when generating or selecting betting scenarios 340, the gaming engine 330 can consider the historical statistics of the players in the live sporting event, Para. -[0077]);
receiving, by the computing system, a proposed outcome from the set of possible outcomes for the prediction event from the user participating in the interactive game space (Srinivasan discloses a user can place a wager on the generated betting scenario at the displayed price using the bet selection interface 220, Para. [0060));
determining, by the computing system, that the proposed outcome was correct by comparing the proposed outcome to real-time event data for the live game; (Srinivasan discloses the proposed outcomes are compared to real-time event data to determine correctness of the wager, Para [0052],[0159])
based on the determining, granting, by the computing system, points to the user within the interactive game space (Srinivasan discloses the gaming engine 330 may be configured to choose from various betting scenario 340 frameworks and assign specific variables [e.g., players, point values, and time period, among others] such that specific betting scenarios 340, including initial prices, can be generated for each game, Para. [0072]).
Srinivasan fails to disclose:
receiving a plurality of video frames from a tracking system;
processing, during the live game by a preprocessinq module, the plurality of video frames to generate tracking data by converting video frame data from the plurality of video frames into two-dimensional player positional information;
generating, by the computing system, a game file including the tracking data and other data;
extracting current game context temporally up to the prediction event from the game file, wherein the current game context includes the tracking data, for the plurality of frames;
providing, by the computing systems, the pre-game player specific data to ratings model to generate pre-game ratings
providing, by the computing system, the tracking data, and the pre-game ratings to a machine-learning model, trained to identify associations between the tracking data, and the pre-game ratings output a probability distribution for a set of possible outcomes for the prediction event;
However in a similar field of endeavor, Schwartz discloses a system that utilizes machine learning models to identify associations between pre-game/pre-play player data and game event data to determine probabilities and/or odds that are communicated to the user wagering on the game (Schwartz 0036, 0094, 0112). Schwartz teaches the utilization of pregame player data such as player position of the quarterback in a detected play formation (Schwartz 0112). As can be seen in Fig 9, Schwarz discloses the determination and displaying of a probability distribution wherein the user can visualize the probabilities of the next play event (Schwartz 0161, Fig 9, #908-1 - #908-10).
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It would be obvious to one of ordinary skill in the art, at the time of filing, to modify Srinivasan in view Schwartz to utilize a known technique to improved similar devices in the same way such as by using machine learning models to calculate and determine a probability distribution that is conveyed to the user. This would be beneficial as machine leaning techniques are able to process large amounts of data quickly and can aid the user in selecting the next event to wager upon based upon the pre-game player data and game data.
However in a similar field of endeavor, Groll discloses a system that utilizes a computing system that utilizes pre-game player specific data to generate pre-game ratings (Groll page 8, par 1; page 12:3 “3.5 Plus-Minus player ratings”), applying the pre-game ratings to a machine learning model (Groll page 9, Section 3 “Hybrid Machine learning models”; page 13 – 14, Section 3.6) to obtain probabilities of outcomes (Groll pages 14 - 15, par 3, Section “4 Model performance”) wherein the system generated and determines the probabilities of a set of outcomes for teams reaching various stages of the UEFA tournament (Groll page 19, par 1 – page 20 par 1, Table 9).
It would be obvious to one of ordinary skill in the art, at the time of filing, to modify Srinivasan, Schwartz in view of Groll to use a known technique to improve similar devices in the same way by utilizing pre-game player data to determine pre-game player ratings that are used in a ML model to determine probabilities of certain outcomes occurring. This would aid the bookmaker in determine the most likely outcome for teams that are favored to win and allow them to offer odds to bettors that are even and not likely suffering a crippling loss in the event that a user wins a bet.
Lucey et al teaches a game system that monitors and tracks a game being played wherein the system receives a plurality of video frames, processes the frames to determine player positions (x,y coordinate) and generates a game file that includes the tracking data and other data such as object positions and processes the data to determine game context information leading up to a predicted game event) (Lucey 0030, 0031, 0034, 0044)
It would be obvious to one of ordinary skill in the art, at the time of filing, to modify Srinivasan in view of Lucey to utilize a known technique to modify similar systems in the same way by analyzing video frames, processing the frames to determine player positions and generate a game file comprising tracking data to determine game context. This would be beneficial as Lucey teaches the system would lead to a system that more accurately predict outcomes for a given play (Lucey 0007)
Regarding Claim 2, Srinivasan discloses the method of claim 1, wherein the probability distribution comprises a likelihood for each outcome of the set of possible outcomes occurring (Srinivasan discloses the gaming engine 330 is configured to predict the probability of a betting scenario 340 occurring, and thus be able to set a price for that betting scenario 340, based on, for example, the sports data 310 received from a third-party provider, sports data 310 received from users of an application including the gaming engine 330, and historical data, Para. [0081]).,
Regarding Claim 3, Srinivasan discloses the method of claim 1, wherein identifying, by the computing system, the pre-game player specific data for each player involved in the prediction event comprises:
identifying historical event data for each player involved in the live game (Srinivasan discloses when generating or selecting betting scenarios 340, the gaming engine 330 can consider the historical statistics of the players in the live sporting event, Para. [0077]); and
generating the pre-game player specific data based on the historical event data for each player involved in the live game (Srinivasan discloses a user can build a portfolio or a team by selecting a player to reach a fantasy target at each position on a traditional sports team roster. The user can select the number of shares for each player that the player will reach his fantasy target. The user, in another variation, may select as few as two players on a team. As the games are progressing, the prices of the betting scenarios change as calculated by the gaming engine taking into account sports data and historical data and predicting the likelihood of each betting scenario occurring, Para. [0178]).
Regarding Claim 4, Srinivasan discloses the method of claim 1, further comprising:
identifying, by the computing system, a second prediction event in the live game (Srinivasan discloses the application interface 500 can display multi-sided betting scenarios of varying sizes, Para. [0149]);
receiving, by the computing system, updated event data for the live game up to the second prediction event (Srinivasan discloses the gaming engine 330 can generate or select betting scenarios 340 using sports data 310 received, such as from third party providers, over a network [e.g., internet] as well as user gaming activity data. The sports data 310 can include, for example, statistical information about the most recent play, Para. [0064]);
generating, by the computing system, a second probability distribution for a second set of possible outcomes for the second prediction event based on the updated event data and the pre-game player specific data (Srinivasan discloses the gaming engine 330 is configured to predict the probability of a betting scenario 340 occurring, and thus be able to set a price for that betting scenario 340, based on, for example, the sports data 310 received from a third-party provider, sports data 310 received from users of an application including the gaming engine 330, and historical data, Para. [0081]);
presenting, by the computing system, the second probability distribution to the user participating in the interactive game space (Srinivasan discloses the user device 130 may have an interface that can display a betting scenario and the price of the betting scenario generated by a gaming engine, Para. [0056]);
receiving, by the computing system, a second proposed outcome for the second prediction event from the user participating in the interactive game space (Srinivasan discloses a user can place a wager on the generated betting scenario at the displayed price using the bet selection interface 220, Para. [0060]); and
determining, by the computing system, that the proposed outcome was incorrect (Srinivasan discloses the gaming engine 330 may determine that User B seated directly behind the goal post would be able to immediately determine whether a field goal attempt was converted. As such, the gaming engine 330 would be able to determine that a Buy signal or input from these respective users when there is a potential touchdown or a field goal attempt being made towards that user is likely to be extremely accurate. Once the gaming engine 330 has determined how much weight to accord to inputs from in-stadium users, it may predict whether the betting scenario may have occurred or not occurred, and it may then adjust the price of the betting scenario based on such prediction. Para. [0124)).
Regarding Claim 5, Srinivasan discloses the method of claim 1, further comprising: receiving, by the computing system, a second proposed outcome for the prediction event from a second user participating in the interactive game space (Srinivasan discloses similarly, if the gaming engine determines that-a second user is seated behind the outfield, the gaming engine may predict that the second user is not going to have as advantageous a vantage point as the first user. Accordingly, the gaming engine may be configured to weigh the input from the first user who is seated in close proximity to first base as significantly higher than the second user who is seated in the outfield, Para. [(0128)), wherein the second user is not presented with the probability distribution (a user's social circle may be determined from that user's social media accounts, their uploaded contact lists, referrals, and the like. Additionally, the gaming engine can monitor a user's social media such as Twitter to determine if that user or set of that user's followers are able to know gaming outcomes, or are reacting to gaming outcomes before the gaming engine receives data from a sports data provider or broadcast feed, Para. [0130}).
Regarding Claim 6, Srinivasan discloses the method of claim's, wherein the user has a first type of membership account and the second user has a second type of membership account (Srinivasan discloses the gaming engine 330 may determine that User B seated directly behind the goal-post would be able to immediately determine whether a field goal attempt was converted. As such, the gaming engine 330 would be able to determine that a Buy signal or input from these respective users when there is a potential touchdown or a field goal attempt being made towards that user is likely to. be extremely accurate and, accordingly, the gaming engine may assign a weight to this input that is greater than weight it assigns to other in-stadium users or non-in-stadium users, Para. [0124]).
Regarding claim 7, generating, by the computing system, a match context by combining the current game context and the pre-qame ratings (Combination of Srinivasan and Groll wherein Groll teaches the specific use of combining all types of data such as pre-game rating and various current game/tournament context to derive further data describing a tournament context) (Groll page 7, “3 Hybrid machine learning models”)
Regarding Claim 8, Srinivasan discloses a non-transitory computer readable medium comprising one or more sequences of instructions, which, when executed by a processor (Srinivasan discloses In a processing circuit, as used herein, each function is performed either by hardware configured, i.e., hard-wired, to perform that function, or by more general-purpose hardware, such as a CPU, configured to execute instructions stored in a non-transitory storage medium, Para. [0245]), causes a computing system to perform operations comprising:
generating, by a computing system, an interactive game space for predicting an outcome of an event during a live game (Srinivasan discloses a system block diagram for the operation of the gaming engine using dynamic real-time pricing and trading, Para. [0063]; the gaming engine 330 is configured to predict the probability of a betting scenario 340 occurring, Para. [0081]);
identifying, by the computing system, a prediction event in the live game (Srinivasan discloses the betting scenarios 340 can be automatically generated or selected from a database by a gaming engine 330, Para. [0064]);
identifying, by the computing system, pre-game player specific data for each player involved in the prediction event (Srinivasan discloses when generating or selecting betting scenarios 340, the gaming engine 330 can consider the historical statistics of the players in the live sporting event, Para. -[0077]);
receiving, by the computing system, a proposed outcome from the set of possible outcomes for the prediction event from the user participating in the interactive game space (Srinivasan discloses a user can place a wager on the generated betting scenario at the displayed price using the bet selection interface 220, Para. [0060));
determining, by the computing system, that the proposed outcome was correct by comparing the proposed outcome to real-time event data for the live game; (Srinivasan discloses the proposed outcomes are compared to real-time event data to determine correctness of the wager, Para [0052],[0159])
based on the determining, granting, by the computing system, points to the user within the interactive game space (Srinivasan discloses the gaming engine 330 may be configured to choose from various betting scenario 340 frameworks and assign specific variables [e.g., players, point values, and time period, among others] such that specific betting scenarios 340, including initial prices, can be generated for each game, Para. [0072]).
Srinivasan fails to disclose:
receiving a plurality of video frames from a tracking system;
processing, during the live game by a preprocessinq module, the plurality of video frames to generate tracking data by converting video frame data from the plurality of video frames into two-dimensional player positional information;
generating, by the computing system, a game file including the tracking data and other data;
extracting current game context temporally up to the prediction event from the game file, wherein the current game context includes the tracking data, for the plurality of frames;
providing, by the computing systems, the pre-game player specific data to ratings model to generate pre-game ratings
providing, by the computing system, the tracking data, and the pre-game ratings to a machine-learning model, trained to identify associations between the tracking data, and the pre-game ratings output a probability distribution for a set of possible outcomes for the prediction event;
However in a similar field of endeavor, Schwartz discloses a system that utilizes machine learning models to identify associations between pre-game/pre-play player data and game event data to determine probabilities and/or odds that are communicated to the user wagering on the game (Schwartz 0036, 0094, 0112). Schwartz teaches the utilization of pregame player data such as player position of the quarterback in a detected play formation (Schwartz 0112). As can be seen in Fig 9, Schwarz discloses the determination and displaying of a probability distribution wherein the user can visualize the probabilities of the next play event (Schwartz 0161, Fig 9, #908-1 - #908-10).
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It would be obvious to one of ordinary skill in the art, at the time of filing, to modify Srinivasan in view Schwartz to utilize a known technique to improved similar devices in the same way such as by using machine learning models to calculate and determine a probability distribution that is conveyed to the user. This would be beneficial as machine leaning techniques are able to process large amounts of data quickly and can aid the user in selecting the next event to wager upon based upon the pre-game player data and game data.
However in a similar field of endeavor, Groll discloses a system that utilizes a computing system that utilizes pre-game player specific data to generate pre-game ratings (Groll page 8, par 1; page 12:3 “3.5 Plus-Minus player ratings”), applying the pre-game ratings to a machine learning model (Groll page 9, Section 3 “Hybrid Machine learning models”; page 13 – 14, Section 3.6) to obtain probabilities of outcomes (Groll pages 14 - 15, par 3, Section “4 Model performance”) wherein the system generated and determines the probabilities of a set of outcomes for teams reaching various stages of the UEFA tournament (Groll page 19, par 1 – page 20 par 1, Table 9).
It would be obvious to one of ordinary skill in the art, at the time of filing, to modify Srinivasan, Schwartz in view of Groll to use a known technique to improve similar devices in the same way by utilizing pre-game player data to determine pre-game player ratings that are used in a ML model to determine probabilities of certain outcomes occurring. This would aid the bookmaker in determine the most likely outcome for teams that are favored to win and allow them to offer odds to bettors that are even and not likely suffering a crippling loss in the event that a user wins a bet.
Lucey et al teaches a game system that monitors and tracks a game being played wherein the system receives a plurality of video frames, processes the frames to determine player positions (x,y coordinate) and generates a game file that includes the tracking data and other data such as object positions and processes the data to determine game context information leading up to a predicted game event) (Lucey 0030, 0031, 0034, 0044)
It would be obvious to one of ordinary skill in the art, at the time of filing, to modify Srinivasan in view of Lucey to utilize a known technique to modify similar systems in the same way by analyzing video frames, processing the frames to determine player positions and generate a game file comprising tracking data to determine game context. This would be beneficial as Lucey teaches the system would lead to a system that more accurately predict outcomes for a given play (Lucey 0007)
Regarding Claim 9, Srinivasan discloses the non-transitory computer readable medium of claim 8, wherein the probability distribution comprises a likelihood for each outcome of the set of possible outcomes occurring (Srinivasan discloses the gaming engine 330 is configured to predict the probability of a betting scenario 340 occurring, and thus be able to set a price for that betting scenario 340, based on, for example, the sports data 310 received from a third-party provider, sports data 310 received from users of an application including the gaming engine 330, and historical data, Para. [0081]).
Regarding Claim 10, Srinivasan discloses the non-transitory computer readable medium of claim 8, wherein identifying, by the computing system, the pre-game player specific data for each player involved in the prediction event comprises:
identifying historical event data for each player involved in the live game (Srinivasan discloses when generating or selecting betting scenarios 340, the gaming engine 330 can consider the historical statistics of the players in the live sporting event, Para. [(0077]); and
generating the pre-game player specific data based on the historical event data for each player involved in the live game (Srinivasan discloses a user can build a portfolio or a team by selecting a player to reach a fantasy target at each position on a traditional sports team roster. The user can select the number of shares for each player that the player will reach his fantasy target. The user, in another variation, may select as few as two players on a team. As the games are progressing, the prices of the betting scenarios change as calculated by the gaming engine taking into account sports data and historical data and predicting the likelihood of each betting scenario occurring, Para. {0178}).
Regarding Claim 11, Srinivasan discloses the non-transitory computer readable medium of claim 8, further comprising:
identifying, by the computing system, a second prediction event in the live game (Srinivasan discloses the application interface 500 can display multi-sided betting scenarios of varying sizes, Para. [0149]);
receiving, by the computing system, updated event data for the live game up to the second prediction event (Srinivasan discloses the gaming engine 330 can generate or select betting scenarios 340 using sports data 310 received, such as from third party providers, over a network [e.g., internet] as well as user gaming activity data. The sports data 310 can include, for example, statistical information about the most recent play, Para. [0064]);
generating, by the computing system, a second probability distribution for a second set of possible outcomes for the second prediction event based on the updated event data and the pre-game player specific data (Srinivasan discloses the gaming engine 330 is configured to predict the probability of a betting scenario 340 occurring, and thus be able to set a price for that betting scenario 340, based on, for example, the sports data 310 received from a third-party provider, sports data 310 received from users of an application including the gaming engine 330, and historical data, Para. [0081]);
presenting, by the computing system, the second probability distribution to the user participating in the interactive game space (Srinivasan discloses the user device 130 may have an interface that can display a betting scenario and the price of the betting scenario generated by a gaming engine, Para. [0056]);
receiving, by the computing system, a second proposed outcome for the second prediction event from the user participating in the interactive game space (Srinivasan discloses a user can place a wager on the generated betting scenario at the displayed price using the bet selection interface 220, Para. [(0060]); and
determining, by the computing system, that the proposed outcome was incorrect (Srinivasan discloses the gaming engine 330 may determine that User B seated directly behind the goal post would be able to immediately determine whether a field goal attempt was converted. As such, the gaming engine 330 would be able to determine that a Buy signal or input from these respective users when there is a potential touchdown or a field goal attempt being made towards that user is likely to be extremely accurate. Once the gaming engine 330 has determined how much weight to accord to inputs from in-stadium users, it may predict whether the betting scenario may have occurred or not occurred, and it may then adjust the price of the betting scenario based on such prediction, Para. [0124)).
Regarding Claim 12, Srinivasan discloses the non-transitory computer readable medium of claim 8, further comprising:
receiving, by the computing system, a second proposed outcome for the prediction event from a second user participating in the interactive game space (Srinivasan discloses similarly, if the gaming engine determines that a second user is seated behind the outfield, the gaming engine may predict that the second user is not going to have as advantageous a vantage point as the first user. Accordingly, the gaming engine may be configured to _ weigh the input from the first user who is seated in close proximity to first base as significantly higher than the second user who is seated in the outfield, Para. [0128]), wherein the second user is not presented with the probability distribution (a user's social circle may be determined from that user's social media accounts, their uploaded contact lists, referrals, and the like. Additionally, the gaming engine can monitor a user's social media such as Twitter to determine if that user or set of that user's followers are able to know gaming outcomes, or are reacting to gaming outcomes before the gaming engine receives data from a sports data provider or broadcast feed, Para. [0130]).
Regarding Claim 13, Srinivasan discloses the non-transitory computer readable medium of claim 12, wherein the user has a first type of membership account and the second user has a second type of membership account (Srinivasan discloses the gaming engine 330 may determine that User B seated directly behind the goal post would be able to immediately determine whether a field goal attempt was converted. As such, the gaming engine 330 would be able to determine that a Buy signal or input from these respective users when there is a potential touchdown or a field goal attempt being made towards that user is likely to be extremely accurate and, accordingly, the gaming engine may assign a weight to this input that is greater than weight it assigns to other in-stadium users or non-in-stadium users, Para. [0124]).
Regarding Claim 15, Srinivasan discloses a system (Figs. 1 and 4) comprising:
a processor; and
a memory having programming instructions stored thereon, which, when executed by the processor, causes the system to perform operations (Srinivasan discloses the server 110 can include a processor and memory storing instructions, where the processor executes the instructions to implement the gaming engine for managing the gaming event, Para. [0058]) comprising:
generating an interactive game space for predicting an outcome of an event during a live game; identifying a prediction event in the live game (Srinivasan discloses the gaming engine 330 can generate or select betting scenarios 340 using sports data 310 received, such as from third party providers, over a network [e.g., internet] as well as user gaming activity data. The sports data 310 can include, for example, statistical information about the most recent play, Para. [0064]);
receiving event data for the live game up to the prediction event (Srinivasan discloses the gaming engine 330 can generate or select betting scenarios 340 using sports data 310 received, such as from third party providers, over a network [e.g., internet] as well as user gaming activity data. The sports data 310 can include, for example, statistical information about the most recent play, Para, [0064]);
identifying pre-game player specific data for each player involved in the prediction event (Srinivasan discloses when generating or selecting betting scenarios 340, the gaming engine 330 can consider the historical statistics of the players in the live sporting event, Para. [0077]);
presenting the probability distribution for the set of possible outcomes for the prediction event to a user participating in the interactive game space (Srinivasan discloses the user device 130 may have an interface that can display a betting scenario and the price of the betting scenario generated by a gaming engine, Para. [0056]);
receiving a proposed outcome from the set of possible outcomes for the prediction event from the user participating in the interactive game space (Srinivasan discloses a user can place a wager on the generated betting scenario at the displayed price using the bet selection interface 220, Para. [0060]);
determining, by the computing system, that the proposed outcome was correct by comparing the proposed outcome to real-time event data for the live game; (Srinivasan discloses the proposed outcomes are compared to real-time event data to determine correctness of the wager, Para [0052],[0159])
based on the determining, granting points to the user within the interactive game space (Srinivasan discloses the gaming engine 330 may be configured to choose from various betting scenario 340 frameworks and assign specific variables (e.g., players, point values, and time period, among others) such that specific betting scenarios 340, including initial prices, can be generated for each game, Para. [0072]).
Srinivasan fails to disclose:
receiving a plurality of video frames from a tracking system;
processing, during the live game by a preprocessinq module, the plurality of video frames to generate tracking data by converting video frame data from the plurality of video frames into two-dimensional player positional information;
generating, by the computing system, a game file including the tracking data and other data;
extracting current game context temporally up to the prediction event from the game file, wherein the current game context includes the tracking data, for the plurality of frames;
providing, by the computing systems, the pre-game player specific data to ratings model to generate pre-game ratings
providing, by the computing system, the tracking data, and the pre-game ratings to a machine-learning model, trained to identify associations between the tracking data, and the pre-game ratings output a probability distribution for a set of possible outcomes for the prediction event;
However in a similar field of endeavor, Schwartz discloses a system that utilizes machine learning models to identify associations between pre-game/pre-play player data and game event data to determine probabilities and/or odds that are communicated to the user wagering on the game (Schwartz 0036, 0094, 0112). Schwartz teaches the utilization of pregame player data such as player position of the quarterback in a detected play formation (Schwartz 0112). As can be seen in Fig 9, Schwarz discloses the determination and displaying of a probability distribution wherein the user can visualize the probabilities of the next play event (Schwartz 0161, Fig 9, #908-1 - #908-10).
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It would be obvious to one of ordinary skill in the art, at the time of filing, to modify Srinivasan in view Schwartz to utilize a known technique to improved similar devices in the same way such as by using machine learning models to calculate and determine a probability distribution that is conveyed to the user. This would be beneficial as machine leaning techniques are able to process large amounts of data quickly and can aid the user in selecting the next event to wager upon based upon the pre-game player data and game data.
However in a similar field of endeavor, Groll discloses a system that utilizes a computing system that utilizes pre-game player specific data to generate pre-game ratings (Groll page 8, par 1; page 12:3 “3.5 Plus-Minus player ratings”), applying the pre-game ratings to a machine learning model (Groll page 9, Section 3 “Hybrid Machine learning models”; page 13 – 14, Section 3.6) to obtain probabilities of outcomes (Groll pages 14 - 15, par 3, Section “4 Model performance”) wherein the system generated and determines the probabilities of a set of outcomes for teams reaching various stages of the UEFA tournament (Groll page 19, par 1 – page 20 par 1, Table 9).
It would be obvious to one of ordinary skill in the art, at the time of filing, to modify Srinivasan, Schwartz in view of Groll to use a known technique to improve similar devices in the same way by utilizing pre-game player data to determine pre-game player ratings that are used in a ML model to determine probabilities of certain outcomes occurring. This would aid the bookmaker in determine the most likely outcome for teams that are favored to win and allow them to offer odds to bettors that are even and not likely suffering a crippling loss in the event that a user wins a bet.
Lucey et al teaches a game system that monitors and tracks a game being played wherein the system receives a plurality of video frames, processes the frames to determine player positions (x,y coordinate) and generates a game file that includes the tracking data and other data such as object positions and processes the data to determine game context information leading up to a predicted game event) (Lucey 0030, 0031, 0034, 0044)
It would be obvious to one of ordinary skill in the art, at the time of filing, to modify Srinivasan in view of Lucey to utilize a known technique to modify similar systems in the same way by analyzing video frames, processing the frames to determine player positions and generate a game file comprising tracking data to determine game context. This would be beneficial as Lucey teaches the system would lead to a system that more accurately predict outcomes for a given play (Lucey 0007)
Regarding Claim 16, Srinivasan discloses the system of claim 15, wherein the probability distribution comprises a likelihood for each outcome of the set of possible outcomes occurring (Srinivasan discloses the gaming engine 330 is configured to predict the probability of a betting scenario 340 occurring, and thus be able to set a price for that betting scenario 340, based on, for example, the sports data 310 received from a third-party provider, sports data 310 received from users of an application including the gaming engine 330, and historical data, Para. [0081]).
Regarding Claim 17, Srinivasan discloses the system of claim 15, wherein identifying the pre-game player specific data for each player involved in the prediction event comprises:
identifying historical event data for each player involved in the live game (Srinivasan discloses when generating or selecting betting scenarios 340, the gaming engine 330 can consider the historical statistics of the players in the live sporting event, Para. [0077]); and
generating the pre-game player specific data based on the historical event data for each player involved in the live game (Srinivasan discloses a user can build a portfolio or a team by selecting a player to reach a fantasy target at each position on a traditional sports team roster. The user can select the number of shares for each player that the player will reach his fantasy target. The user, in another variation, may select as few as two players on a team. As the games are progressing, the prices of the betting scenarios change as calculated by the gaming engine taking into account sports data and historical data and predicting the likelihood of each betting scenario occurring, Para. [0178)}).
Regarding Claim 18, Srinivasan discloses the system of claim 15, wherein the operations further comprise: identifying a second prediction event in the live game (Srinivasan discloses the application interface 500 can display multi-sided betting scenarios of varying sizes, Para. [0149]);
receiving updated event data for the live game up to the second prediction event (Srinivasan discloses the gaming engine 330 can generate or select betting scenarios 340 using sports data 310 received, such as from third party providers, over a network [e.g., internet] as well as user gaming activity data. The sports data 310 can include, for example, statistical information about the most recent play, Para. [0064]);
generating a second probability distribution for a second set of possible outcomes for the second prediction event based on the updated event data and the pre-game player specific data (Srinivasan discloses the gaming engine 330 is configured to predict the probability of a betting scenario 340 occurring, and thus be able to set a price for that betting scenario 340, based on, for example, the sports data 310 received from a third-party provider, sports data 310 received from users of an application including the gaming engine 330, and historical data, Para. [(0081]);
presenting the second probability distribution to the user participating in the interactive game space (Srinivasan discloses the user device 130 may have an interface that can display a betting scenario and the price of the betting scenario generated by a gaming engine, Para. [0056]);
receiving a second proposed outcome for the second prediction event from the user participating in the interactive game space (Srinivasan discloses a user can place a wager on the generated betting scenario at the displayed price using the bet selection interface 220, Para. [0060]); and
determining that the proposed outcome was incorrect (Srinivasan discloses the gaming engine 330 may determine that User B seated directly behind the goal post would be able to immediately determine whether a field goal attempt was converted. As such, the gaming engine 330 would be able to determine that a Buy signal or input from these respective users when there is a potential touchdown or a field goal attempt being made towards that user is likely to be extremely accurate. Once the gaming engine 330 has determined how much weight to accord to inputs from in-stadium users, it may predict whether the betting scenario may have occurred or not occurred, and it may then adjust the price of the betting scenario based on such prediction, Para. [0124]).
Regarding Claim 19, Srinivasan discloses the system of claim 15, wherein the operations further comprise: receiving a second proposed outcome for the prediction event from a second user participating in the interactive game space (Srinivasan discloses similarly, if the gaming engine determines that a second user is seated behind the outfield, the gaming engine may predict that the second user is not going to have as advantageous a vantage point as the first user. Accordingly, the gaming engine may be configured to weigh the input-from the first user who is seated in close proximity to first base as significantly higher than the second user who is seated in the outfield, Para. [0128]), wherein the second user is not presented with the probability distribution (a user's social circle may be determined from that user's social media accounts, their uploaded contact lists, referrals, and the like. Additionally, the gaming engine can monitor a user's social media such as Twitter to determine if that user or set of that user’s followers are able to know gaming outcomes, or are reacting to gaming outcomes before the gaming engine receives data from a sports data provider or broadcast feed, Para. [0130]).
Regarding Claim 20, Srinivasan discloses the system of claim 19, wherein the user has a first type of membership account and the second user has a second type of membership account (Srinivasan discloses the gaming engine 330 may determine that User B seated directly behind the goal post would be able to immediately determine whether a field goal attempt was converted. As such, the gaming engine 330 would be able to determine that a Buy signal or input from these respective users when there is a potential touchdown or a field goal attempt being made towards that user is likely to be extremely accurate and, accordingly, the gaming engine may assign a weight to this input that is greater than weight it assigns to other in-stadium users or non-in-stadium users, Para. [0124)).
Claim(s) 14 is/are rejected under 35 U.S.C. 103 as being unpatentable over to Srinivasan (US 2021/0134124) in view of Schwartz et al (US 2020/0234543) in view of Groll et al “Hybrid Machine Learning Forecasts for the UEFA EURO 2020” in view of Lucey et al (US 2018/0032858) in view of Gurpinar-Morgan et al (hereinafter, “Gurpinar ") (US 2021/0256265)
Regarding Claim 14, Srinivasan discloses the method of claim 1, but fails to explicitly disclose the method wherein the live game is a cricket match and the prediction event is a ball to be bowled from a bowler to a hitter.
However, Gurpinar is in the field of system and method dynamically predicting a shot type in sport (Gurpinar Para. [0002]) and teaches the method wherein the live game is a cricket match (game file 110 may further include ball-by-ball data for each shot in a cricket match, Para. [0029}) and the prediction event is a ball to be bowled from a bowler to a hitter (At step 502, organization computing system 104 may receive [or retrieve] ball-by-ball data for a given bowler from a given event, Para. [0056)).
It would have been obvious to one of ordinary skill in the art at the time of filing to modify Srinivasan to include a five-cricket match and prediction event is a ball to be bowled as taught by Gurpinar. The motivation being to predict the probabilities of where a specific batsman will hit a specific bowler and bowl type in a specific game-scenario (Gurpinar Para. [0023]).
Response to Arguments
Applicant’s arguments with respect to claim(s) 1-20 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. Please see above rejection in view of newly found prior art to Lucey that addresses the newly amended claim language.
Regarding the rejection of the claims under 35 U.S.C. 101, the Applicant argues:
It is difficult to imagine that the phrase "integrated into the practical application" could have any substantive meaning if the above claim language were not considered to be "integrated into the practical application." At least every emphasized limitation is integrated into the specific practical application including processing the plurality of video frames to generate tracking data by converting video frame data from the plurality of video frames into two-dimensional player positional information; generating a game file including the tracking data and other data, extracting current game context based on positional information for a plurality of frames, and utilizing a prediction model to predict outcomes. The claims very clearly "use the judicial exception in a manner that imposes a meaningful limit on the judicial exception [here, mental processes], such that the claim is more than a drafting effort designed to monopolize the judicial exception." (October Guidance, Pg. 11.)” (Remarks page 14). The Examiner respectfully disagrees and notes that that claims appear to be merely generally linking the judicial exception to a particular to a particular technological environment or field of use (MPEP 2106.05(h)). For example the newly added claim limitations barring the general linking to a video frame data and computing files that contain data, the claims can be accomplished by a human utilizing manual means such as powers of observation and pen and paper.
For example:
receiving a plurality of video frames from a tracking system; - A human can view or observe a video broadcast on a television that comprises video frame data.
processing, during the live game by a preprocessinq module, the plurality of video frames to generate tracking data by converting video frame data from the plurality of video frames into two-dimensional player positional information; - A human can analyze the video frame data to determine what video data corresponds (converts) to two-dimensional player positions by means of recording on with pen and paper x’s an o’s representing player positions in a game of football
generating a game file including the tracking data and other data;- A human can record the tracking data in a game “file” or record on paper
identifying a prediction event in the live game; - A human and identify a prediction event in the video broadcast by mere observation
extracting current game context temporally up to the prediction event from the game file, wherein the current game context includes the tracking data, for the plurality of frames; -A human can identify and record what the game’s context is on a paper record (game file) that includes tracking data
As can be seen above limitations newly added can be accomplished by a human utilizing manual means and merely link that exception to a particular technology or field.
Regarding the Applicant’s comparison to SME example #42, the additional limitations of processing video frame data to determine two-dimensional player positioning data and the generation of a game file comprising tracking data and extracting data from a game file to determine context does not meaningfully integrate the judicial exception into a practical application. The claims simply do not recite a particular technological mechanism or technique by which the result is achieved. For example, the claims do not recite an improvement to video processing, file processing, file management, automatic content recognition, optical content recognition, or other technological field other than the mere application of the prediction game steps to a technology or field.
The Examiner maintains the rejection.
Conclusion
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/RAW/Examiner, Art Unit 3715 9/27/2026
/KANG HU/Supervisory Patent Examiner, Art Unit 3715
