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 .
DETAILED ACTION
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. The claims are directed to the abstract idea of mental processes and/ or certain methods of organizing human activity. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception as discussed below.
Step 1 of the 2019 Revised Patent Subject Matter
More specifically, regarding Step 1, of the 2019 Revised Patent Subject Matter Eligibility Guidance, the claims are directed to a machine, process, and/or an article of manufacturer, which are statutory categories of invention.
Step 2a – Prong 1 of the 2019 Revised Patent Subject Matter Eligibility Guidance
Next, the claims are analyzed to determine whether it is directed to a judicial exception.
Claims 1-20 describe a method and system for generating predictions in sporting events using a transformer-based neural network that processes "top-down" predictions (e.g., high-level contextual data like scores or market info) as feature vectors alongside detailed player/team data (e.g., from tracking devices) to output event predictions, with real-time updates and display.
The claims recite an abstract idea as it involves collecting data (top-down predictions and player/team features), processing it through a model (transformer-based neural network), and generating output predictions for a sporting event. This falls under "mathematical concepts" (mathematical calculations and relationships in neural network processing) and "mental processes" (concepts performed in the human mind, like analyzing game data to predict outcomes, albeit automated). Similar to SAP America, Inc. v. InvestPic, LLC (Fed. Cir. 2018), where statistical modeling for investment predictions was abstract, or Electric Power Group, LLC v. Alstom S.A. (Fed. Cir. 2016), involving data collection, analysis, and display in real-time systems. The use of "top-down" and "bottom-up" features is essentially hierarchical data fusion, a mathematical abstraction common in ML without specifying novel math.
Step 2a – Prong 2 of the 2019 Revised Patent Subject Matter Eligibility Guidance
The second prong of step 2a is the consideration if the claim limitations are directed to a practical application.
Limitations that are indicative of integration into a practical application:
-Improvements to the functioning of a computer, or to any other technology or technical field - see MPEP 2106.05(a)
-Applying or using a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition - see Vanda Memo
-Applying the judicial exception with, or by use of, a particular machine - see MPEP 2106.05(b)
-Effecting a transformation or reduction of a particular article to a different state or thing – see MPEP 2106.05(c)
-Applying or using the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception - see MPEP 2106.05(e) and Vanda Memo
Limitations that are not indicative of integration into a practical application:
-Adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea- see MPEP 2106.05(f)
-Adding insignificant extra-solution activity to the judicial exception - see MPEP 2106.05(g)
-Generally linking the use of the judicial exception to a particular technological environment or field of use - see MPEP 2106.05(h)
Claims 1-20 do not integrate the abstract idea into a practical application because the claims focus on the abstract prediction generation itself, not a specific technological improvement. The transformer-based neural network is recited at a high level (e.g., including standard components like embedding layers, encoder layers, and fully connected layers), without detailing how it improves computer functionality, data structures, or hardware efficiency. Elements like receiving data from tracking devices, real-time updates, accessing a data platform, and displaying predictions are generic data gathering and output steps, not transformative. They amount to "apply it on a computer" or "use generic sensors," which does not integrate (see Mayo Collaborative Services v. Prometheus Laboratories, Inc. (2012)). The claims do not solve a technological problem unique to computers or networks, such as improving transformer efficiency for sequential sports data. Instead, it's a business/application-specific use (sports predictions), akin to automating a manual process without technical advancement (see Intellectual Ventures I LLC v. Symantec Corp. (Fed. Cir. 2016)). Overall, the claims are directed to the abstract idea of data-driven prediction modeling, without a practical tie-in.
Step 2b of the 2019 Revised Patent Subject Matter Eligibility Guidance
Next, the claims as a whole are analyzed to determine whether any element, or combination of elements, is sufficient to ensure that the claim amounts to significantly more than the exception.
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because no element or combination of elements is sufficient to ensure any claim of the present application as a whole amounts to significantly more than one or more judicial exceptions, as described above.
The transformer-based neural network is well-understood, routine, and conventional in ML by the filing date (late 2023). Transformers were standard for sequence modeling, including in sports analytics (e.g., prior art cited below (Simpson et.), uses transformers for soccer event prediction). Data inputs (top-down predictions, tracking data) and outputs (updated predictions, display) use generic computing elements. Real-time querying is conventional data processing. As a whole, the claims append generic computer implementation to the abstract idea, without inventive programming or non-conventional use.
Consequently, consideration of each and every element of each and every claim, both individually and as an ordered combination, leads to the conclusion that the claims are not patent-eligible under 35 USC §101.
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 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.
Claim(s) 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Schnurr (US 2017/0061314 A1) in view of SIMPSON IAN (Cite no. 2 from 6/20/2024 IDS), (Simpson hereinafter).
1. Schnurr discloses a method of generating predictions for teams and players for each team associated with a sporting event [0041], [0045], the method comprising:
receiving one or more top down predictions related to the sporting event (receiving high-level contextual game data as inputs, such as score differential ('ScoreDiff'), quarter, down, yards to go, field position, which serve as top-down probabilistic context implying predictions like scoring likelihood; these are parsed and used as derivative statistics for model input), [0047]-[0049];
providing the top-down predictions as one or more top-down feature vectors to a computing system (game data parsed into a matrix of feature vectors/columns, including top-down contextual features like 'ScoreDiff' and 'FieldPos', provided to the computing system for model training and inference), [0042]-[0045];
receiving, by the computing system, a second set of feature vectors comprising data for one or more players associated with one or more respective teams and data for one or more teams associated with a sporting event (receiving player-specific data from tracking devices, such as positions, speeds, possessions, and actions, parsed into feature vectors; matrix includes team and player involvement data), [0042]-[0045];
inputting the one or more top-down feature vectors and second set of feature vectors into a neural network (inputs fed to neural network models for prediction), [0046]; and
generating, using the neural network, one or more predictions for the sporting event based on the one or more top-down feature vectors and the second set of feature vectors (generating predictions like run/pass probability or player targets based on fused features), [0052]-[0053].
Schnurr does not explicitly disclose that the neural network is transformer-based. However, Simpson teaches inputting fused top-down (contextual) and bottom-up (event-specific) feature vectors into a transformer-based neural network (Section 3.2: "The Seq2Event model comprises seven main stages, with RNN and Transformer variants... An embedding layer is used to embed the actions and a dense layer is used to transform the continuous variables"; inputs include contextual features like score advantage and time, combined with event actions and locations) and generating predictions (Section 3.2: "The softmax of the four action logits is taken, yielding next action prediction probabilities"). It would have been obvious to a person of ordinary skilled in the art to modify Schnurr with Simpson and would have been motivated to do so because transformers excel at long-range contexts in time-series data like sport plays as it is quicker to train, see section 4.1.
2. Schnurr and Simpson disclose the method of claim 1, wherein Schnurr further discloses the one or more top-down predictions are one or more of a neural network output, market information, or game context information, [0046].
3. Schnurr and Simpson disclose the method of claim 1, wherein Schnurr further discloses the top-down predictions comprise a first top-down prediction based on pre-game data and a second top-down prediction based on in-play data (pre-game historical data from prior seasons for training, including contextual features, [0041]-[0045] and in-play real-time data updates contextual features like current score and clock for ongoing predictions, [0052]-[0053]).
4. Schnurr and Simpson disclose the method of claim 1, wherein Schnurr further discloses causing the one or more predictions for the sporting event to be displayed on a display device [0038], [0058], [0068].
5. Schnurr and Simpson disclose the method of claim 1, wherein Simpson further discloses the transformer-based neural network further includes: a set of embedding layers; transformer encoder layers; and fully connected layers (Section 3.2: "An embedding layer is used to embed the actions... RNN/Transformer Component, processing sequences (e.g., Transformer with multi-head attention)... A dense layer with rectified linear unit (ReLU) activation function is applied... The final layer splits into a vector of action logits... and x,y coordinates"; fully connected dense layers post-encoder).
6. Schnurr and Simpson disclose the method of claim 1, wherein Schnurr further discloses the data for one or more players comprises actions of one or more agents on a playing surface received from a tracking device (player actions and positions from tracking devices like RFID sensors and cameras), [0032]-[0034].
7. Schnurr and Simpson disclose the method of claim 1, wherein Schnurr further discloses receiving, from a tracking device, updated data for the one or more players or teams; providing the updated data to the transformer-based neural network; and generating an updated one or more predictions for the sporting event based on the updated data (real-time updates from trackers uploaded and used to update models/predictions), [0033]-[0034], [0054].
8. Schnurr and Simpson disclose the method of claim 1, wherein Schnurr further discloses accessing, using a trigger processing step, a data platform at a set interval to determine when the sporting event occurs; and creating, using a feature creator processing step, the second set of feature vectors by querying data from the data platform (accessing live data platforms/APIs at intervals (e.g., every second) during the event, computing derivative features into vectors for model querying), [0052]-[0053], [0076].
9-16. Schnurr and Simpson disclose a system for generating predictions for teams and players for each team associated with a sporting event, the system comprising: a non-transitory computer readable medium configured to store processor-readable instructions; and a processor operatively connected to the non-transitory computer readable medium, and configured to execute the instructions to perform operations comprising: receiving one or more top-down predictions related to the sporting event; providing the top-down predictions as one or more top-down feature vectors to a computing system; receiving, by the computing system, a second set of feature vectors comprising data for one or more players associated with one or more respective teams and data for one or more teams associated with a sporting event; inputting the one or more top-down feature vectors and second set of feature vectors into a transformer-based neural network; and generating, using the transformer-based neural network, one or more predictions for the sporting event based on the one or more top-down feature vectors and the second set of feature vectors as similarly discussed above.
17-20. Schnurr and Simpson disclose a non-transitory computer readable medium configured to store processor-readable instructions, wherein when executed by a processor, the instructions perform operations comprising: receiving one or more top-down predictions related to the sporting event; providing the top-down predictions as one or more top-down feature vectors to a computing system; receiving, by the computing system, a second set of feature vectors comprising data for one or more players associated with one or more respective teams and data for one or more teams associated with a sporting event; inputting the one or more top-down feature vectors and second set of feature vectors into a transformer-based neural network; and generating, using the transformer-based neural network, one or more predictions for the sporting event based on the one or more top-down feature vectors and the second set of feature vectors as similarly discussed above.
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Please see attached USPTO form PTO-892.
Filing of New or Amended Claims
The examiner has the initial burden of presenting evidence or reasoning to explain why persons skilled in the art would not recognize in the original disclosure a description of the invention defined by the claims. See Wertheim, 541 F.2d at 263, 191 USPQ at 97 (“[T]he PTO has the initial burden of presenting evidence or reasons why persons skilled in the art would not recognize in the disclosure a description of the invention defined by the claims.”). However, when filing an amendment an applicant should show support in the original disclosure for new or amended claims. See MPEP § 714.02 and § 2163.06 (“Applicant should specifically point out the support for any amendments made to the disclosure.”). Please see MPEP 2163 (II) 3. (b)
Correspondence
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SENG H LIM whose telephone number is (571)270-3301. The examiner can normally be reached Monday-Friday (9-5).
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, David L. Lewis can be reached at (571) 272-7673. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/Seng H Lim/Primary Examiner, Art Unit 3715