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, 11 and 20 have been amended.
Claims 1 – 20 are pending.
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.
Claim 1, 11 and 20 recites the limitation "the total wager amount" in the last limitation of each claim. There is insufficient antecedent basis for this limitation in the claim.
Claims 1 – 20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention. The claims have been amended to include the limitation of “and allocating, by the computer based on the real-time bet package, the total wager amount into one or more electronic accounts.” However, the claims make reference to “a plurality of electronic wagers” and “a wager request” Due to these being the only other references to “wagers” in the claims, it is not clear what wager amounts are being referred to by the “the total wager amount” being allocated to one or more electronic accounts. This new limitation renders the claims 1, 11 and 20 and by virtue of dependencies, all respective dependent claims indefinite as well.
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.
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 – 10 are drawn to a method.
Claims 11 – 19 are drawn to a system.
Claim 20 is drawn to a 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 11 - 19 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.
11. A system, comprising:
one or more processors; and
a memory storing executable instructions, the executable instructions when executed by the one or more processors cause the one or more processors to:
iteratively identify live odds for a horse racing event, wherein in each iteration the one or more processors:
monitor a plurality of electronic wagers submitted by a plurality of electronic devices corresponding to the horse racing event, each electronic wager identifying at least one horse and a monetary amount associated with a bet on the at least one horse;
execute a feed-forward neural network using monitored data to predict one or more missing data points to calculate, based on monitored data, live data comprising win odds, probables data comprising expected payouts for single event wagers, and will pays data comprising expected payouts for multi-race wagers;
calculate an implied probability of winning in one or more first pools of the horse racing event based on the calculated live data, probables data, and will pays data; and
calculate implied win probabilities in one or more second pools of the horse racing event for which odds or probables data is not available;
receive, from a client device, a wager request for the horse racing event, the wager request comprising one or more betting constraints;
generate a real-time bet package for the client device based on the live odds and the one or more betting constraints; and
transmit, to the client device, the real-time bet package for display;
and allocate, based on the real-time bet package, the total wager amount into one or more electronic accounts.
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 and Mental Processes
More specifically, under this grouping, the italicized limitations represent concepts performed in the human mind (including an observation, evaluation, judgment, opinion) For example, the italicized limitations are directed towards the upon the monitoring of data pertaining to wagers, calculating probabilities of winning, such as for horses in a race and displaying bet packages based upon desired wagering constraints and displaying the bet packages.
Prong 2: Does the Claim recite additional elements that integrate the exception in to 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): processors, memory, client devices and feed forward neural networks.
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 processors, memory, client devices and feed forward neural networks. 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, Applicants own specification establishes that these additional elements are generic:
[0111] Each client device 202 and server 206 may be deployed as and/or executed on any type and form of computing device, e.g., a computer, network device or appliance capable of communicating on any type and form of network and performing the operations described herein. FIGS. 2C and 2D depict block diagrams of a computing device 200 useful for practicing an embodiment of the client device 202 or a server 206. As shown in FIGS. 2C and 2D, each computing device 200 includes a central processing unit 221, and a main memory unit 222. As shown in FIG. 2C, a computing device 200 may include a storage device 228, a network interface 218, an input/output (I/O) controller 223, and optional I/O devices, such as display devices 224a- 224n, a keyboard 226, and a pointing device 227, such as a mouse. The storage device 228 may include, without limitation, an operating system 231,software 233, and a wager processing platform 220, which can implement any of the features of the wager processing server 110A described herein below in connection with FIG. 1. As shown in FIG. 2D, each computing device 200 may also include additional optional elements, such as a memory port 232, a bridge 270, one or more input/output devices 230a-230n (generally referred to using reference numeral230), and a cache memory 240 in communication with the central processing unit 221.
[0121] The computing device 200 can be any workstation, telephone, desktop computer, laptop or notebook computer, tablet, server, handheld computer, mobile telephone, smartphone or other portable telecommunications device, media playing device, a gaming system, mobile computing device, or any other type and/or form of computing, telecommunications or media device that is capable of communication. The computing device 200 has sufficient processor power and memory capacity to perform the operations described herein. In some embodiments, the computing device 200 may have different processors, operating systems, and input devices consistent with the device. Further detailed description of the techniques for rebalancing bet portfolios are described in greater detail herein below in connection with FIGS. 3, 4, 5, and 6, respectively.
Regarding the Berkheimer decision, Khashei establishes that these additional elements are generic:
In recent years, more hybrid forecasting models have been proposed using feed-forward neural networks and applied in many areas with good prediction performance. Yu et al. (2005) proposed a novel nonlinear ensemble forecasting model integrating generalized linear auto regression (GLAR) with back-propagation neural networks (BPNNs) in order to obtain accurate prediction in foreign exchange market. (Faruk (2010) proposed a hybrid approach for water quality time series prediction which consists of an ARIMA methodology and feed-forward, back propagation network structure with an optimized conjugated training algorithm. (Khashei 1277, par 2)
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) 1-8, 10-18 and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Reetz (US 2019/0221080) in view of Hood (US 2021/0049726) in view of Khashei et al “Hybridization of the probabilistic neural networks with feed-forward neural networks for forecasting” (2012).
As per claim 1, Reetz discloses:
iteratively identifying, by a computer system including one or more processors,(Reetz Fig 13) live odds[Fig. 3, element 308, Real-lime updates} for a horse racing event, wherein each iteration includes: (Reetz Fig.2 and 4)
monitoring a plurality of electronic wagers submitted by a plurality of electronic devices corresponding to the horse racing event,( Reetz Fig. 4) each electronic wager identifying at least one horse (Reetz Fig. 4, Horse #1, 2, etc.) and a monetary amount associated with a bet on the at least one horse; Reetz Fig. 4, wager budget 100)
calculating, based on monitored data, live data comprising win odds, probables data comprising expected payouts for single event wagers, and will pays data comprising expected payouts for multi-race wagers; (Reetz Fig 4)
calculating an implied probability of winning in one or more first pools of the horse racing event based on the calculated live data, probables data, and will pays data; and [Reetz discloses using proprietary algorithms, TrackAdvantage may compute the probability that any of 3 possible wagers will cash, the expected Return on Investment (ROI) for each, and devises a wagering strategy that provides a reasoned balance between the likelihood of success and the wagers' ROI, all within the budget specified by the user. Further, as the pools change, TrackAdvantage keeps pace, adjusting the wagering strategy, as required, on a real-time basis: (Reetz 0034, 0052- 0053 and 0062)
receiving, by the computer system, from a client device, a wager request for the horse racing event, the wager request comprising one or more betting constraints (Reetz discloses receiving a wagering, budget associated with the pari-mutuel event from a user device associated with a user account; Fig.6 step 602). The User specified wagering budget is a constraint that the system must satisfy to thereby suggest a wagering strategy to the user ) (Reetz 0062 – 0064),
generating, by the computer system, a real-time bet package for the client device based on the live odds and one or more betting constraints [Reetz discloses The processing device configured for generating the optimized wagering strategy based on each of the wagering budget, wagering data and the prediction data; Para 0043- the method 300 may include generating, using the processing device, a betting/wagering strategy. For example, using proprietary algorithms, TrackAdvantage may compute the probability that any of 13 possible wagers will cash, the expected Return on Investment (ROI) for each, and devises a wagering strategy that provides a reasoned balance between the likelihood of success and the wagers' ROI, all within the budget specified by the user. Further, as the pools change, TrackAdvantage keeps pace, adjusting the wagering strategy, as required, on a real-time basis;) (Reetz Fig. 6, step 608 abstract, 0063 – 0064)
transmitting, by the computer to the client device, the real-time bet package for display [Reetz discloses transmitting an optimizing wagering strategy to the user device ... the user device may be configured for presenting the optimized wagering strategy;) (Reetz abstract 0064, Fig.6 step 610
Reetz fails to disclose:
calculating implied win probabilities in one or more second pools of the horse racing event for which odds or probables data is not available;
executing a feed-forward neural network using monitored data to predict one or more missing data points to calculate
However, in a similar field of endeavor, Hood discloses the determination of implied win probabilities in that Hood receives a plurality of stored wagers for a user account and generating an expected value of the portfolio of wagers based on the real-time winning probability of the plurality of stored wagers which implies that the odds or probables data is not available yet. (Hood 005, 0024).
It would be obvious to one of ordinary skill in the art, at the time of filing, to modify Reetz in view of Hood to determine implied probable data that for horses apart of a second pool for which the odds are not yet determined. A system that is able to determine win probabilities as soon as they possibly can will be of great interest to players as this would ensure that they have the most complete and update financial information before they make a financial commitment such as a large wager or bet.
However in a similar field of endeavor wherein data is being forecasted or predicted, Khashei discloses the use of feed-forward neural networks being used to predict missing data when the data is not available. Khashei teaches “Feed-forward neural networks (FFNNs) are among the most important neural networks that can be applied to a wide range of forecasting problems with a high degree of accuracy. Several large-scale forecasting competitions with a large number of commonly used time series forecasting models conclude that combining forecasts from more than one model often leads to improved performance, especially when the models in the ensemble are quite different. In the literature, several hybrid models have been proposed by combining different time series models together. In this paper, in contrast of the traditional hybrid models, a novel hybridization of the feed-forward neural networks (FFNNs) is proposed using the probabilistic neural networks (PNNs) in order to yield more accurate results than traditional feed-forward neural networks. In the proposed model, the estimated values of the FFNN models are modified based on the distinguished trend of their residuals and optimum step length, which are respectively yield from a probabilistic neural network and a mathematical programming model. Empirical results with three well-known real data sets indicate that the proposed model can be an effective way in order to construct a more accurate hybrid model than FFNN models. Therefore, it can be applied as an appropriate alternative model for forecasting tasks, especially when higher forecasting accuracy is needed.” (Khashei Abstract).
It would be obvious to one of ordinary skill in the art, at the time of filing, to modify Reetz in view of Khashei to use a known technique to utilize a feed-forward network to predict or forecast data since Khashei teaches “Feed-forward neural networks (FFNNs) are among the most important and widely used forms of neural networks for time series modeling and forecasting. One significant advantage of the feed-forward neural networks over other classes of nonlinear models is that they are universal approximators that can approximate a large class of functions with a high degree of accuracy. Their power comes from the parallel processing of the information from the data. No prior assumption of the model form is required in the model building process. Instead, the network model is largely determined by the characteristics of the data. ) (Khashei 1278, par 6)
As per claim 2, wherein the one or more betting constraints include a payout constraint specifying a minimum payout amount. (Reetz discloses betting constraints that include a minimum payout or ROI) (Reetz 0048)
As per claim 3, wherein the one or more betting constraints include a payout constraint specifying an expected return. (Reetz discloses betting constraints that include an expected return or ROI) (Reetz 0048)
As per claim 4, wherein the one or more betting constraints include a probability of payout constraint specifying a minimum payout amount. (Reetz discloses betting constraints that include a probability of cashing ) (Reetz 0051)
As per claim 5, wherein the one or more betting constraints include a winning likelihood constraint specifying one or more expected wining probabilities. (Reetz discloses betting constraints that include a probability or likelihood of cashing ) (Reetz 0051)
As per claim 6, wherein generating the real-time bet package includes: optimizing, by the computer system, one or more betting strategies subject to the one or more betting constraints; and generating, by the computer system, the real-time bet package based on the optimized one or more betting strategies. (Reetz discloses the optimizing of the strategies according to specified constraints continuously as post time approaches and updated the strategies and further offering the updated strategies to the user) (Reetz 0044-0054, 0083)
As per claim 7, wherein optimizing the one or more betting strategies includes: calculating, by the computer system using a machine learning model and the live odds, a plurality of outcome predictions of a plurality of betting strategies; and selecting, by the computer system, one or more betting strategies from the plurality of betting strategies with corresponding outcome predictions satisfying the one or more betting constraints. (Reetz discloses the optimizing of the strategies according to specified constraints such as live odds continuously as post time approaches and updated the strategies and further offering the updated strategies to the user (Reetz 0044-0054, 0050, 0075, 0082, 0083). Reetz further discloses the system may be implemented by use of machine learning program module (Reetz 0143, 0146)
As per claim 8, wherein a type of the one or more betting strategies is selected by the client device. (Reetz discloses a client selecting one of the proposed betting strategies) (Reetz 0052)
As per claim 10, detecting, by the computer system, a change in the live odds; calculating, by the computer system, an updated real-time bet package based on the change in the live odds and the one or more betting constraints; and transmitting, by the computer system to the client device, an indication of the updated real- time bet package. . (Reetz discloses the optimizing of the strategies according to specified constraints such as live odds continuously as post time approaches and updated the strategies and further offering the updated strategies to the user (Reetz 0044-0054, 0050, 0075, 0082, 0083).
Dependent claim(s) 11 and 20 is/are made obvious by the combination of Reetz and Hood based on the same analysis set forth for claim(s) 1, which are similar in claim scope.
Dependent claim(s) 12 - 18 is/are made obvious by the combination of Reetz and Hood based on the same analysis set forth for claim(s) 2 - 8, which are similar in claim scope.
Claim(s) 9 and 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Reetz (US 2019/0221080) in view of Mendell (US 11,451,878).
As per claim 9, Reetz fails to disclose:
wherein the machine learning model includes, for each betting strategy of a plurality of betting strategies, a corresponding decision tree model.
However in a similar field of endeavor, broadcasting live game events for user to wager upon, Mendell discloses the use of machine learning to rank content items or events based upon their attributes based upon machine leaning and decision trees (Mendell 41:33 – 40).
It would be obvious to one of ordinary skill in the art, at the time of filing, to modify Reetz in view of Mendell to utilize machine learning models with corresponding decision tree models to rank content items such as potential strategies for wagering to be presented to a user. This would be beneficial as it would assist a user is selecting the most personally advantageous betting strategy and maximize their profits if they pick a top ranked strategy.
Dependent claim(s) 19 is/are made obvious by the combination of Reetz, Hood and Mendell based on the same analysis set forth for claim(s) 9, which are similar in claim scope.
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 Khashei.
Applicant's arguments filed 10/16/2025 have been fully considered but they are not persuasive. Regarding the rejection of the claims under 35 U.S.C. 101, the Applicant argues that the claims are patent eligible because they are directed toward generating dynamic packages via real-time constrained optimization and automatic multi-account allocation-not mere bet placement. The Examiner respectfully disagrees and notes that while the claims utilize multiple different types of data that is monitored and computes probabilities, wherein even missing probability data is predicted, betting packages are managed and optimized, and funds are allocated to different “electronic” accounts, the claims are still directed towards at least the mental process of monitoring of data pertaining to wagers, calculating probabilities of winning, such as for horses in a race and displaying bet packages based upon desired wagering constraints to thereby enable a user to place a wager upon at least one bet package.
Applicant further states: The claims are not directed toward organizing human activity or mental process under the latest guidance from the Patent Office. Applicant reasons essentially that the claimed steps cannon be performed by the human mind and that they provide a “specific, technical sequence of operations executed entirely within a computer system having processors iteratively identifying live odds by monitoring electronic wagers, calculating live, probables, and will-pays data streams, computing implied probabilities across multiple pools, and using a feed-forward neural network to predict missing data points when certain odds are unavailable.” “These steps involve sophisticated computational models and data transformations that go far beyond what could be carried out mentally or with pen and paper, and they are implemented in a manner that is entirely machine-based. While the data inputs pertain to wagers in a horse race, the claims' core is directed to how a machine processes, predicts, and packages technical information in real time, not to managing or directing human betting behavior.” The Examiner respectfully disagrees and notes that the other than the generic usage of a machine learning such as utilizing a FFNN to perform a prediction of missing data, the Applicant has not shown how a human cannot perform the monitoring of submitted wagers, calculating of win odds, expected payouts, will pays, will pays, implied probabilities, making data predictions despite missing data to calculate implied probabilities, receiving of wager requests comprising wagering constraints, generating bet packages and displaying them, and allocating wager amounts to electronic accounts. A human utilizing pen and paper and the human mind can perform these steps. The Applicant has not shown persuasively beyond the mere allegation how these steps cannot be performed by a human utilizing a mental process and further has not shown what improvement is made to the underlying technology or functioning of a computer.
Applicant further states:
The claims are patent eligible because they integrate the alleged abstract idea into a practical application.
As discussed during the Examiner Interview, the claims are patent-eligible because they recite operating in a real-time, iterative loop that continually ingests live electronic wagers submitted by a plurality of electronic devices, recalculates implied probabilities for pools lacking public odds, and regenerates an optimized bet package until wagering closes.” The Examiner acknowledges that the claims may indeed be performing the above steps, however, the Examiner respectfully disagrees with the following assertation and reasoning of “ By tethering each calculation to current network conditions, the claims do more than merely store or display information. Instead, they actively adapt to incoming data and thereby reduce latency, fill data gaps, and automate controls.” This amounts to a mere allegation without any persuasive evidence from the specification supporting the “tethering to network conditions that enable the adaption the system to adapt to incoming data to reduce latency, automate controls, etc.
Finally the Applicant argues that essentially that the final step of allocating wager amount to electronic accounts “effectuates a tangible transformation that satisfies the practical application test of Step 2A, Prong II”. The Examiner respectfully disagrees. This allocation step is an accounting step or accounting operation that amount so data manipulation and not a physical transformation, such as the step of assigning a wager or portions thereof to one or multiple accounts. Adjusting account values, and tracking balances or moving money electronically is more akin to a manipulation of data and not a tangible transformation. The Examiner maintains the rejection.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/RAW/ Examiner, Art Unit 3715
1/21/2026
/KANG HU/ Supervisory Patent Examiner, Art Unit 3715