DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 3/12/26 has been entered.
Response to Arguments
Response: Claim Objections
1. Applicants argue:
The applicant argues that the amendment to claim 11 has overcome the claim objection. (Remarks: page 7)
2. Examiner Response:
The examiner respectfully disagrees. The examiner notes that even with the recent amendment, the limitation of “a set of target values for the first live event” is still repeated twice, “the first request comprising a set of target values for the first live event: (i) a set of target values for the first live event”. Therefore, the claim objection is being maintained.
Response: 35 U.S.C. § 101
3. Applicants argue:
The applicant argues that with the recent amendment to the independent claims, the claims cannot be conducted in the human mind or with pencil and paper. The applicant also argues that although the claimed methods utilize math tools such as Monte-Carlo simulation, comparison to target values and convergence criteria, the claims do not recite a mathematical concept. (Remarks: pages 8-9)
4. Examiner Response:
The examiner respectfully disagrees. The examiner notes that in paragraph [0037] of the specification, it states that the parameters are adjusted using genetic algorithms “[0037] The model maintainer 130 can maintain, fine-tune, and adjust the parameters 175 of the model 177. For each live event or scenario, the model maintainer 130 can select and adjust the parameters 175 to reflect the current conditions and potential outcomes. The model maintainer 130 can define, manage, modify, update, or adjust the parameters 175. The model maintainer 130 can adjust threshold values that can determine changes in the model 177output, such as the shift in betting odds based on evolving game dynamics. The model maintainer 130 can modify, update, adjust, or fine-tune the regularization coefficients to prevent overfitting, and to ensure that the model remains robust and generalizable to different scenarios. The model maintainer 130 can manage, modify, update, or adjust the statistical model coefficients and probability distribution parameters, which can be fundamental to the stochastic nature of Monte Carlo modeling. The model maintainer 130 can manage, modify, update, or adjust the learning rates that can set dictate the speed at which the model 177 adapts to new data. The model maintainer 130 can implements time decay factors to adjust the significance of data over the course of a live event (e.g., diminishing or increasing relevance of certain information as the event progresses).”. In paragraph [0041] of the specification it states that the parameters can be adjusted using genetic algorithms “[0041] The parameter updater 145 can update the model 177 based on the set of target parameters/values in the odds request 173. To do so, the parameter updater 145 can iteratively generate candidate input parameters 175 based on the set of target values. The parameter updater 145 can adjust the parameters 175 that the model 177 uses to generate odds for the live events. The parameter updater 145 can update the model 177 based on the set of target values in the request. The parameter updater 145 can compare the received target values against the outputs of the model (odds values 179). The parameter updater 145 can use the target values to adjust the model parameters 175 until the model's generated odds (odd values 179) align with the target odds included in the request. The parameters 175 can be adjusted based on the results of the comparison. The parameters 175 can be adjusted based on a gradient descent where the parameters can be adjusted in the direction that reduces the mean squared error. The parameters 175 can be adjusted using genetic algorithms. The parameters 175 can be adjusted using a probabilistic model to guide the search for optimal parameters (e.g., Bayesian optimization). The model executor 140 can repeat the Monte Carlo simulations with the adjusted parameters, optimizing to achieve outputs that conform to the target values specified in the odds requests 173. The model executor 140 can continue an iterative process of simulation, comparison, and parameter adjustment until the model’s output closely aligns with the target output. The iterative process can involve analyzing the model 177 current output (e.g., odd values 179 generated using the current iteration of the parameters 175 for the model 177 and live event), comparing it to the target values, and then adjusting the parameters to reduce the difference between the two. The model executor 140 can determine a convergence criterion for the iterative process, such as a threshold for mean squared error or a maximum number of iterations. Once the convergence criteria are met, the model executor 140 can consider that the optimal parameters 175 are found.”. Using genetic algorithms to adjust the parameters (maintain) would fall in the “Mathematical Concept” grouping of abstract ideas, see MPEP 2106.04(a)(2).
5. Applicants argue:
The applicant argues that with the recent amendment to the claims, the claims integrate the abstract idea into a practical application by reciting a technical improvement. The applicant argues that the technology improves the operation of the modeling system and provides a more responsive and accurate live odds generation without blocking or delaying responses. (Remarks: pages 9-10)
6. Examiner Response:
The examiner respectfully disagrees. The examiner notes that even with the recent amendment, the claims are still not eligible under 35 U.S.C. 101. The examiner notes that the recent amendment that states “generating a set of candidate parameters for the Monte-Carlo model and the first live event” amounts to mere instructions to apply an exception, where it recites an idea of a solution. The limitation doesn’t indicate how the set of candidate parameters are related to the first odds. See MPEP 2106.05 (f) (1) Whether the claim recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished. The recitation of claim limitations that attempt to cover any solution to an identified problem with no restriction on how the result is accomplished and no description of the mechanism for accomplishing the result, does not integrate a judicial exception into a practical application or provide significantly more because this type of recitation is equivalent to the words "apply it".
Also, the limitation of “generating a set of outputs by executing at least one Monte-Carlo simulation using the Monte-Carlo model and the set of candidate parameters” amounts to mere instructions to apply an exception, where it recites an idea of a solution. The limitation doesn’t indicate how the set of outputs relate to the first odds of the first live event. See MPEP 2106.05 (f) (1) Whether the claim recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished. The recitation of claim limitations that attempt to cover any solution to an identified problem with no restriction on how the result is accomplished and no description of the mechanism for accomplishing the result, does not integrate a judicial exception into a practical application or provide significantly more because this type of recitation is equivalent to the words "apply it".
Also, the limitation of “determining whether a convergence criterion is met by comparing the set of outputs to the set of target values” doesn’t distinguish itself from being able to be conducted in the human mind or with pencil and paper. Therefore, under the broadest reasonable interpretation, this limitation is a process step that covers performance in the human mind or with the aid of pencil and paper. As such, this limitation falls within the “Mental Process” grouping of abstract ideas.
Also, the limitation of “and (i) terminating generation of the set of second parameters by selecting the set of candidate parameters as the set of second parameters for the Monte- Carlo model in response to the convergence criterion being met” amounts to mere instructions to apply an exception, where it recites an idea of a solution. The limitation doesn’t indicate what the convergence criterion is or how it relates to the set of parameters for the Monte Carlo model. See MPEP 2106.05 (f) (1) Whether the claim recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished. The recitation of claim limitations that attempt to cover any solution to an identified problem with no restriction on how the result is accomplished and no description of the mechanism for accomplishing the result, does not integrate a judicial exception into a practical application or provide significantly more because this type of recitation is equivalent to the words "apply it".
Also, the limitation of “or (ii) executing a subsequent iteration in response to the convergence criterion not being met” amounts to mere instructions to apply an exception, where it recites an idea of a solution. The limitation doesn’t indicate what the convergence criterion is. See MPEP 2106.05 (f) (1) Whether the claim recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished. The recitation of claim limitations that attempt to cover any solution to an identified problem with no restriction on how the result is accomplished and no description of the mechanism for accomplishing the result, does not integrate a judicial exception into a practical application or provide significantly more because this type of recitation is equivalent to the words "apply it".
Also, the examiner has found online reference Modeling In-Match Sports Dynamics Using the Evolving Probability Method, written by Sarcevic et al. as showing evidence that parameters of a Monte Carlo model are updated in real time while generating odds. This can be seen on Pg. 12 1st – 2nd paragraph, “To predict these values, and based on methodology introduced in, etc.” and Pg. 13, sec. 2.7.1 Group Profiling, 2nd paragraph, “In an ideal scenario, enough historical data to construct a profile of team performance over a longer time for each team would be available. Then, these data can be used to calculate the likelihood of winning the point on own serve, the parameters, etc.”.
Further, the claim recites the additional elements of a processor and memory. The processor and memory are recited at a high level of generality such that it amounts no more than mere instructions to apply the exception using a computer and/or a generic computer component. Accordingly, these additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
Response: 35 U.S.C. § 103
7. The examiner’s response regarding the applicant’s arguments to the newly added limitations are shown below.
Claim Objections
Claim 11 is objected to because of the following informalities: In claim 11 the limitation
of “the first request comprising a set of target values for the first live event” is repeated twice. The examiner recommends deleting one of the limitations. Appropriate correction is required.
Claim Rejections - 35 USC § 101
9. 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, 5-11 and 15-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Under the broadest reasonable interpretation, the claims cover performance of the limitation in the mind or by pencil and paper and as a mathematical concept.
Claims 1 and 11
Regarding step 1, claims 1 and 11 are directed towards a method and a system, which has the claims fall within the eligible statutory categories of processes, machines, manufactures and composition of matter under 35 U.S.C. 101.
Regarding step 2A, prong 1, claim 11 recites “maintaining a Monte-Carlo model configured to generate odds data for one or more live events, wherein the Monte-Carlo model is configured using a first set of parameters”. In paragraph [0037] of the specification it states that the model maintainer can adjust the parameters of the model. In paragraph [0041] of the specification it states that the parameters can be adjusted using genetic algorithms. The maintaining of the Monte-Carlo model is using genetic algorithms. Therefore, under MPEP 2106.04(a)(2), this limitation covers a mathematical concept, which falls in the “Mathematical Concept” grouping of abstract ideas.
Claim 11 recites “determining that the first request comprises the flag indicating that the deep learning model is to be updated according to the set of target values”. This limitation doesn’t distinguish itself from being able to be conducted in the human mind or with pencil and paper. Therefore, under the broadest reasonable interpretation, this limitation is a process step that covers performance in the human mind or with the aid of pencil and paper. As such, this limitation falls within the “Mental Process” grouping of abstract ideas.
Claim 11 recites “determining whether a convergence criterion is met by comparing the set of outputs to the set of target values” doesn’t distinguish itself from being able to be conducted in the human mind or with pencil and paper. Therefore, under the broadest reasonable interpretation, this limitation is a process step that covers performance in the human mind or with the aid of pencil and paper. As such, this limitation falls within the “Mental Process” grouping of abstract ideas.
Regarding step 2A, prong 2, the limitation of “receiving, by the one or more processors, a request to generate odds values for a first live event, the first request comprising a set of target values for the first live event, (i) a set of target values for the first live event, and (ii) a flag indicating that the deep learning model is to be updated according to the set of target values” amounts to insignificant extra-solution activity of receiving data i.e. pre-solution activity of gathering data for use in the claimed process, see MPEP 2106.05(g).
Also, the limitation of “generating, by the one or more processors, using the deep learning model and a set of first parameters for the first live event, first odds for the first live event in response to the request” amounts to mere instructions to apply an exception, where it recites an idea of a solution. The limitation doesn’t indicate what the first odds are and how they’re related to the set of first parameters. See MPEP 2106.05 (f) (1) Whether the claim recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished. The recitation of claim limitations that attempt to cover any solution to an identified problem with no restriction on how the result is accomplished and no description of the mechanism for accomplishing the result, does not integrate a judicial exception into a practical application or provide significantly more because this type of recitation is equivalent to the words "apply it".
Also, the limitation of “concurrent with generating the first odds for the first live event, and based on determining that the first request comprises the flag, iteratively generating, by the one or more processors, a set of second parameters for the Monte-Carlo model” amounts to mere instructions to apply an exception, where it recites an idea of a solution. The limitation doesn’t indicate what the set of second parameters are. See MPEP 2106.05 (f) (1) Whether the claim recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished. The recitation of claim limitations that attempt to cover any solution to an identified problem with no restriction on how the result is accomplished and no description of the mechanism for accomplishing the result, does not integrate a judicial exception into a practical application or provide significantly more because this type of recitation is equivalent to the words "apply it".
Also, the limitation of “generating a set of candidate parameters for the Monte-Carlo model and the first live event” amounts to mere instructions to apply an exception, where it recites an idea of a solution. The limitation doesn’t indicate how the set of candidate parameters are related to the first odds. See MPEP 2106.05 (f) (1) Whether the claim recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished. The recitation of claim limitations that attempt to cover any solution to an identified problem with no restriction on how the result is accomplished and no description of the mechanism for accomplishing the result, does not integrate a judicial exception into a practical application or provide significantly more because this type of recitation is equivalent to the words "apply it".
Also, the limitation of “generating a set of outputs by executing at least one Monte-Carlo simulation using the Monte-Carlo model and the set of candidate parameters” amounts to mere instructions to apply an exception, where it recites an idea of a solution. The limitation doesn’t indicate how the set of outputs relate to the first odds of the first live event. See MPEP 2106.05 (f) (1) Whether the claim recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished. The recitation of claim limitations that attempt to cover any solution to an identified problem with no restriction on how the result is accomplished and no description of the mechanism for accomplishing the result, does not integrate a judicial exception into a practical application or provide significantly more because this type of recitation is equivalent to the words "apply it".
Also, the limitation of “and (i) terminating generation of the set of second parameters by selecting the set of candidate parameters as the set of second parameters for the Monte- Carlo model in response to the convergence criterion being met” amounts to mere instructions to apply an exception, where it recites an idea of a solution. The limitation doesn’t indicate what the convergence criterion is or how it relates to the set of parameters for the Monte Carlo model. See MPEP 2106.05 (f) (1) Whether the claim recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished. The recitation of claim limitations that attempt to cover any solution to an identified problem with no restriction on how the result is accomplished and no description of the mechanism for accomplishing the result, does not integrate a judicial exception into a practical application or provide significantly more because this type of recitation is equivalent to the words "apply it".
Also, the limitation of “or (ii) executing a subsequent iteration in response to the convergence criterion not being met” amounts to mere instructions to apply an exception, where it recites an idea of a solution. The limitation doesn’t indicate what the convergence criterion is. See MPEP 2106.05 (f) (1) Whether the claim recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished. The recitation of claim limitations that attempt to cover any solution to an identified problem with no restriction on how the result is accomplished and no description of the mechanism for accomplishing the result, does not integrate a judicial exception into a practical application or provide significantly more because this type of recitation is equivalent to the words "apply it".
Also, the limitation of “receiving, by the one or more processors a second request to generate odds for the first live event” amounts to insignificant extra-solution activity of receiving data i.e. pre-solution activity of gathering data for use in the claimed process, see MPEP 2106.05(g).
Also, the limitation of “generating, by the one or more processors, using the Monte-Carlo model and a set of first parameters for the first live event and prior to updating the model, first odds for the first live event in response to the request” amounts to mere instructions to apply an exception, where it recites an idea of a solution. The limitation doesn’t indicate what the second odds are and how they’re related to the set of second parameters. See MPEP 2106.05 (f) (1) Whether the claim recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished. The recitation of claim limitations that attempt to cover any solution to an identified problem with no restriction on how the result is accomplished and no description of the mechanism for accomplishing the result, does not integrate a judicial exception into a practical application or provide significantly more because this type of recitation is equivalent to the words "apply it".
Also, the claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional element of the processor that applies a judicial exception, such as an abstract idea, by use of conventional computer functions does not qualify as a particular machine, see MPEP 2106.05(b) 1. It is important to note that a general purpose computer that applies a judicial exception, such as an abstract idea, by use of conventional computer functions does not qualify as a particular machine. Ultramercial, Inc. v. Hulu, LLC, 772 F.3d 709, 716-17, 112 USPQ2d 1750, 1755-56 (Fed. Cir. 2014). See also TLI Communications LLC v. AV Automotive LLC, 823 F.3d 607, 613, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016) (mere recitation of concrete or tangible components is not an inventive concept); Eon Corp. IP Holdings LLC v. AT&T Mobility LLC, 785 F.3d 616, 623, 114 USPQ2d 1711, 1715 (Fed. Cir. 2015) (noting that Alappat’s rationale that an otherwise ineligible algorithm or software could be made patent-eligible by merely adding a generic computer to the claim was superseded by the Supreme Court’s Bilski and Alice Corp. decisions).
Further, the claim recites the additional elements of a processor and memory. The processor and memory are recited at a high level of generality such that it amounts no more than mere instructions to apply the exception using a computer and/or a generic computer component. Accordingly, these additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
Regarding Step 2B, the limitations of “receiving, by the one or more processors, a request to generate odds values for a first live event, the first request comprising a set of target values for the first live event, (i) a set of target values for the first live event, and (ii) a flag indicating that the deep learning model is to be updated according to the set of target values” and “receiving, by the one or more processors a second request to generate odds for the first live event” are also shown to reflect the court decisions of Versata Dev. Group, Inc. v. SAP Am., Inc. iv. Storing and retrieving information in memory, shown in MPEP 2106.05(d) (II).
Also, the limitation of “generating, by the one or more processors, using the deep learning model and a set of first parameters for the first live event, first odds for the first live event in response to the request” amounts to mere instructions to apply an exception, where it recites an idea of a solution. The limitation doesn’t indicate what the first odds are and how they’re related to the set of first parameters. See MPEP 2106.05 (f) (1) Whether the claim recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished. The recitation of claim limitations that attempt to cover any solution to an identified problem with no restriction on how the result is accomplished and no description of the mechanism for accomplishing the result, does not integrate a judicial exception into a practical application or provide significantly more because this type of recitation is equivalent to the words "apply it".
Also, the limitation of “concurrent with generating the first odds for the first live event, and based on determining that the first request comprises the flag, iteratively generating, by the one or more processors, a set of second parameters for the Monte-Carlo model” amounts to mere instructions to apply an exception, where it recites an idea of a solution. The limitation doesn’t indicate what the set of second parameters are. See MPEP 2106.05 (f) (1) Whether the claim recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished. The recitation of claim limitations that attempt to cover any solution to an identified problem with no restriction on how the result is accomplished and no description of the mechanism for accomplishing the result, does not integrate a judicial exception into a practical application or provide significantly more because this type of recitation is equivalent to the words "apply it".
Also, the limitation of “generating a set of candidate parameters for the Monte-Carlo model and the first live event” amounts to mere instructions to apply an exception, where it recites an idea of a solution. The limitation doesn’t indicate how the set of candidate parameters are related to the first odds. See MPEP 2106.05 (f) (1) Whether the claim recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished. The recitation of claim limitations that attempt to cover any solution to an identified problem with no restriction on how the result is accomplished and no description of the mechanism for accomplishing the result, does not integrate a judicial exception into a practical application or provide significantly more because this type of recitation is equivalent to the words "apply it".
Also, the limitation of “generating a set of outputs by executing at least one Monte-Carlo simulation using the Monte-Carlo model and the set of candidate parameters” amounts to mere instructions to apply an exception, where it recites an idea of a solution. The limitation doesn’t indicate how the set of outputs relate to the first odds of the first live event. See MPEP 2106.05 (f) (1) Whether the claim recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished. The recitation of claim limitations that attempt to cover any solution to an identified problem with no restriction on how the result is accomplished and no description of the mechanism for accomplishing the result, does not integrate a judicial exception into a practical application or provide significantly more because this type of recitation is equivalent to the words "apply it".
Also, the limitation of “and (i) terminating generation of the set of second parameters by selecting the set of candidate parameters as the set of second parameters for the Monte- Carlo model in response to the convergence criterion being met” amounts to mere instructions to apply an exception, where it recites an idea of a solution. The limitation doesn’t indicate what the convergence criterion is or how it relates to the set of parameters for the Monte Carlo model. See MPEP 2106.05 (f) (1) Whether the claim recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished. The recitation of claim limitations that attempt to cover any solution to an identified problem with no restriction on how the result is accomplished and no description of the mechanism for accomplishing the result, does not integrate a judicial exception into a practical application or provide significantly more because this type of recitation is equivalent to the words "apply it".
Also, the limitation of “or (ii) executing a subsequent iteration in response to the convergence criterion not being met” amounts to mere instructions to apply an exception, where it recites an idea of a solution. The limitation doesn’t indicate what the convergence criterion is. See MPEP 2106.05 (f) (1) Whether the claim recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished. The recitation of claim limitations that attempt to cover any solution to an identified problem with no restriction on how the result is accomplished and no description of the mechanism for accomplishing the result, does not integrate a judicial exception into a practical application or provide significantly more because this type of recitation is equivalent to the words "apply it".
Also, the limitation of “generating, by the one or more processors, using the Monte-Carlo model and a set of first parameters for the first live event and prior to updating the model, first odds for the first live event in response to the request” amounts to mere instructions to apply an exception, where it recites an idea of a solution. The limitation doesn’t indicate what the second odds are and how they’re related to the set of second parameters. See MPEP 2106.05 (f) (1) Whether the claim recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished. The recitation of claim limitations that attempt to cover any solution to an identified problem with no restriction on how the result is accomplished and no description of the mechanism for accomplishing the result, does not integrate a judicial exception into a practical application or provide significantly more because this type of recitation is equivalent to the words "apply it".
Further, the claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional element of the processor that applies a judicial exception, such as an abstract idea, by use of conventional computer functions does not qualify as a particular machine, see MPEP 2106.05(b) 1. It is important to note that a general purpose computer that applies a judicial exception, such as an abstract idea, by use of conventional computer functions does not qualify as a particular machine. Ultramercial, Inc. v. Hulu, LLC, 772 F.3d 709, 716-17, 112 USPQ2d 1750, 1755-56 (Fed. Cir. 2014). See also TLI Communications LLC v. AV Automotive LLC, 823 F.3d 607, 613, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016) (mere recitation of concrete or tangible components is not an inventive concept); Eon Corp. IP Holdings LLC v. AT&T Mobility LLC, 785 F.3d 616, 623, 114 USPQ2d 1711, 1715 (Fed. Cir. 2015) (noting that Alappat’s rationale that an otherwise ineligible algorithm or software could be made patent-eligible by merely adding a generic computer to the claim was superseded by the Supreme Court’s Bilski and Alice Corp. decisions).
Claim 1
Regarding step 2A, prong 1, The limitations of claim 1 recite the same substantive limitations of claim 11 above and are rejected using the same teachings.
Regarding step 2A, prong 2, the claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional element of the processor that applies a judicial exception, such as an abstract idea, by use of conventional computer functions does not qualify as a particular machine, see MPEP 2106.05(b) 1. It is important to note that a general purpose computer that applies a judicial exception, such as an abstract idea, by use of conventional computer functions does not qualify as a particular machine. Ultramercial, Inc. v. Hulu, LLC, 772 F.3d 709, 716-17, 112 USPQ2d 1750, 1755-56 (Fed. Cir. 2014). See also TLI Communications LLC v. AV Automotive LLC, 823 F.3d 607, 613, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016) (mere recitation of concrete or tangible components is not an inventive concept); Eon Corp. IP Holdings LLC v. AT&T Mobility LLC, 785 F.3d 616, 623, 114 USPQ2d 1711, 1715 (Fed. Cir. 2015) (noting that Alappat’s rationale that an otherwise ineligible algorithm or software could be made patent-eligible by merely adding a generic computer to the claim was superseded by the Supreme Court’s Bilski and Alice Corp. decisions).
Further, the claim recites the additional elements of a processor and memory. The processor and memory are recited at a high level of generality such that it amounts no more than mere instructions to apply the exception using a computer and/or a generic computer component. Accordingly, these additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
The limitations of claim 1 recite the same substantive limitations of claim 11 above and are rejected using the same teachings.
Regarding Step 2B, the claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional element of the processor that applies a judicial exception, such as an abstract idea, by use of conventional computer functions does not qualify as a particular machine, see MPEP 2106.05(b) 1. It is important to note that a general purpose computer that applies a judicial exception, such as an abstract idea, by use of conventional computer functions does not qualify as a particular machine. Ultramercial, Inc. v. Hulu, LLC, 772 F.3d 709, 716-17, 112 USPQ2d 1750, 1755-56 (Fed. Cir. 2014). See also TLI Communications LLC v. AV Automotive LLC, 823 F.3d 607, 613, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016) (mere recitation of concrete or tangible components is not an inventive concept); Eon Corp. IP Holdings LLC v. AT&T Mobility LLC, 785 F.3d 616, 623, 114 USPQ2d 1711, 1715 (Fed. Cir. 2015) (noting that Alappat’s rationale that an otherwise ineligible algorithm or software could be made patent-eligible by merely adding a generic computer to the claim was superseded by the Supreme Court’s Bilski and Alice Corp. decisions).
The limitations of claim 1 recite the same substantive limitations of claim 11 above and are rejected using the same teachings.
Claims 5 and 15
Dependent claims 5 and 15 recites “wherein the one or more processors are further configured to store the set of second parameters in a database in association with an identifier of the first live event.”. This limitation amounts to insignificant extra-solution activity of receiving data i.e. pre-solution activity of gathering data for use in the claimed process, see MPEP 2106.05(g). This limitation is also shown to reflect the court decisions of Versata Dev. Group, Inc. v. SAP Am., Inc. iv. Storing and retrieving information in memory, shown in MPEP 2106.05(d) (II).
Also, the claim recites the additional elements of a processor. The processor is recited at a high level of generality such that it amounts no more than mere instructions to apply the exception using a computer and/or a generic computer component. Accordingly, the additional element do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
Claims 6 and 16
Dependent claims 6 and 16 recites “retrieve the set of second parameters from the database”. This limitation amounts to insignificant extra-solution activity of receiving data i.e. pre-solution activity of gathering data for use in the claimed process, see MPEP 2106.05(g). This limitation is also shown to reflect the court decisions of Versata Dev. Group, Inc. v. SAP Am., Inc. iv. Storing and retrieving information in memory, shown in MPEP 2106.05(d) (II).
Dependent claims 6 and 16 recites “and execute the Monte-Carlo model using the set of second parameters to generate second odds for the first live event”. This limitation amounts to mere instructions to apply an exception, where it recites an idea of a solution. The limitation doesn’t indicate what the set of second parameters are. See MPEP 2106.05 (f) (1) Whether the claim recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished. The recitation of claim limitations that attempt to cover any solution to an identified problem with no restriction on how the result is accomplished and no description of the mechanism for accomplishing the result, does not integrate a judicial exception into a practical application or provide significantly more because this type of recitation is equivalent to the words "apply it".
Also, the claim recites the additional elements of a processor. The processor is recited at a high level of generality such that it amounts no more than mere instructions to apply the exception using a computer and/or a generic computer component. Accordingly, the additional element do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
Claims 7 and 17
Dependent claims 7 and 17 recites “wherein the one or more processors are further configured to update the Monte-Carlo model by iteratively generating candidate second parameters based on the set of target values”. This limitation amounts to mere instructions to apply an exception, where it recites an idea of a solution. The limitation doesn’t indicate what the candidate second parameters are and how they’re associated with the update of the deep learning model. See MPEP 2106.05 (f) (1) Whether the claim recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished. The recitation of claim limitations that attempt to cover any solution to an identified problem with no restriction on how the result is accomplished and no description of the mechanism for accomplishing the result, does not integrate a judicial exception into a practical application or provide significantly more because this type of recitation is equivalent to the words "apply it".
Also, the claim recites the additional elements of a processor. The processor is recited at a high level of generality such that it amounts no more than mere instructions to apply the exception using a computer and/or a generic computer component. Accordingly, the additional element do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
Claims 8 and 18
Dependent claims 8 and 18 recites “store a plurality of sets of first parameters for the Monte-Carlo model in a database”. This limitation amounts to insignificant extra-solution activity of receiving data i.e. pre-solution activity of gathering data for use in the claimed process, see MPEP 2106.05(g). This limitation is also shown to reflect the court decisions of Versata Dev. Group, Inc. v. SAP Am., Inc. iv. Storing and retrieving information in memory, shown in MPEP 2106.05(d) (II).
Dependent claims 8 and 18 recites “the plurality of sets of first parameters including the set of first parameters for the first live event”. Under the broadest reasonable interpretation, this limitation is a process step that covers performance in the human mind or with the aid of pencil and paper. As such, this limitation falls within the “Mental Process” grouping of abstract ideas.
Dependent claims 8 and 18 recites “and generate the first odds further based on an aggregation of the plurality of sets of first parameters”. This limitation amounts to mere instructions to apply an exception, where it recites an idea of a solution. The limitation doesn’t indicate what the plurality of sets of first parameters are and how they’re associated with the first odds. See MPEP 2106.05 (f) (1) Whether the claim recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished. The recitation of claim limitations that attempt to cover any solution to an identified problem with no restriction on how the result is accomplished and no description of the mechanism for accomplishing the result, does not integrate a judicial exception into a practical application or provide significantly more because this type of recitation is equivalent to the words "apply it".
Also, the claim recites the additional elements of a processor. The processor is recited at a high level of generality such that it amounts no more than mere instructions to apply the exception using a computer and/or a generic computer component. Accordingly, the additional element do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
Claims 9 and 19
Dependent claims 9 and 19 recites “wherein the one or more processors are further configured to generate the first odds for the first live event and initiate updating the Monte-Carlo model in parallel.”. This limitation amounts to mere instructions to apply an exception, where it recites an idea of a solution. See MPEP 2106.05 (f) (1) Whether the claim recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished. The recitation of claim limitations that attempt to cover any solution to an identified problem with no restriction on how the result is accomplished and no description of the mechanism for accomplishing the result, does not integrate a judicial exception into a practical application or provide significantly more because this type of recitation is equivalent to the words "apply it".
Also, the claim recites the additional elements of a processor. The processor is recited at a high level of generality such that it amounts no more than mere instructions to apply the exception using a computer and/or a generic computer component. Accordingly, the additional element do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
Claims 10 and 20
Dependent claims 10 and 20 recites “wherein the one or more processors are further configured to provide an indication to a second computing system that the Monte-Carlo model has been updated based on the set of target values”. This limitation amounts to insignificant extra-solution activity, data gathering, see MPEP 2106.05(g). This limitation (wherein the one or more processors are further configured to provide) also amounts to mere instructions to apply an exception, where a generic computer is applying the instruction (, see MPEP 2106.05(f) (2) Whether the claim invokes computers or other machinery merely as a tool to perform an existing process. Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more.
Also, the claim recites the additional elements of a processor. The processor is recited at a high level of generality such that it amounts no more than mere instructions to apply the exception using a computer and/or a generic computer component. Accordingly, the additional element do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
Claims 1, 5-11 and 15-20 are therefore not drawn to eligible subject matter as they are directed to an abstract idea without significantly more.
Claim Rejections - 35 USC § 103
10. The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness
rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35
U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 1, 5-11 and 15-20 is/are rejected under 35 U.S.C. 103 as being unpatentable
over Schwartz et al. (U.S. PGPub 2020/0236288) in view of online reference Modeling In-Match Sports Dynamics Using the Evolving Probability Method, written by Sarcevic et al.
With respect to claim 1, Schwartz et al. discloses “A system” as [Schwartz et al. (paragraph [0019] “The invention can be implemented in numerous ways, including as a process; an apparatus; a system”)];
“one or more processors coupled to non-transitory memory” as [Schwartz et al. (paragraph [0019] “The invention can be implemented in numerous ways, including as a process; an apparatus; a system; a composition of matter; a computer program product embodied on a computer readable storage medium; and/or a processor, such as a processor configured to execute instructions stored on and/or provided by a memory coupled to the processor.”)];
“receive a first request to generate odds values for a first live event, the first request comprising a set of target values for the first live event” as [Schwartz et al. (paragraph [0041] “Accordingly, the API server 260 facilitates communication between various components of the system 48, one or more user devices 700, and a master statistics database 270 in order to provide various features and services of the present disclosure (e.g., a stream of the game, a request for statistics, placing a wager on a play, etc.). Communication between the API server 260 and the one or more user devices 700 includes providing streaming data 280 and/or direct data 282 to each respective user device 700 through the communications network 106, as well as receiving various requests 284 from each respective user device.”, The examiner considers the request for statistics to be the target values, since the target values can be statistical benchmarks that can include historical and current season statistics)];
While Schwartz et al. teaches receiving a request to generate odds values for a first live event and updating the model based on the set of target values in the request, see paragraphs [0023] and [0039] of the Schwartz et al. reference, Schwartz et al. does not explicitly disclose “maintain a Monte-Carlo model configured to generate odds data for one or more live events, wherein the Monte-Carlo is configured using a first set of parameters; the first request comprising (ii) a flag indicating that the Monte-Carlo model is to be updated using the set of target values; determine that the first request comprises the flag indicating that the Monte-Carlo model is to be updated using the set of target values; generate, using the Monte Carlo model and a set of first parameters for the first live event, first odds for the first live event in response to the first request; concurrent with generating the first odds for the first live event, and based on determining that the first request comprises the flag, iteratively generate a set of second parameters for the Monte-Carlo model, wherein each iteration comprises: generating a set of candidate parameters for the Monte-Carlo model and the first live event, generating a set of outputs by executing at least one Monte-Carlo simulation using the Monte-Carlo model and the set of candidate parameters, determining whether a convergence criterion is met by comparing the set of outputs to the set of target values, and(i) terminating generation of the set of second parameters by selecting the set of candidate parameters as the set of second parameters for the Monte- Carlo model in response to the convergence criterion being met, or (ii) executing a subsequent iteration in response to the convergence criterion not being met; receive a second request to generate odds for the first live event; and generate, using the set of second parameters for the Monte-Carlo model and the first live event, second odds for the first live event in response to the second request”
Sarcevic et al. discloses “maintain a Monte-Carlo model configured to generate odds data for one or more live events, wherein the Monte-Carlo is configured using a first set of parameters” as [Sarcevic et al. (Pg. 12, 1st – 2nd paragraph, “To predict these values, and based on methodology introduced in in the previous sections, an approach that traverses through a match tree while updating the transition probabilities after each transition using the Equations (7) and (8) is proposed. This Monte Carlo simulation method that incorporates the proposed combined formulation will be called the evolving probability method… The exact method is as follows: one single simulation generates one match tree that represents one possible flow of the match. From that tree, it is easy to calculate the handicap and the total number of points played in such a simulated match. The simulation of the same match can then be repeated numerous times, and ultimately, the results of all simulations of the match are combined to estimate the handicap and the total number of points that will be played in the match. This method will be formalized through a procedure called The evolving probability method (see procedure The evolving probability method in the Appendix A).”, Sarcevic et al. Pg. 13, sec. 2.7.1 Group Profiling, 2nd paragraph, “In an ideal scenario, enough historical data to construct a profile of team performance over a longer time for each team would be available. Then, these data can be used to calculate the likelihood of winning the point on own serve, the parameters of the short-term momentum for each team, as well as the rough estimate of the expected number of total points used to calculate the long-term momentum. These parameters can then be used as input parameters of the proposed evolving probability method to estimate the handicap and the total number of points that will be played between two teams.”)];
“the first request comprising (ii) a flag indicating that the Monte-Carlo model is to be updated using the set of target values” as [Sarcevic et al. (Pg. 11, 1st paragraph, “Figure 4 shows a match tree that incorporates the proposed combined Formulas (7) and (8). The figure shows the changes of serve probabilities throughout the match for both teams. In order to demonstrate the example, a match with the following characteristics is selected, etc.”, Fig. 4)];
“determine that the first request comprises the flag indicating that the Monte-Carlo model is to be updated using the set of target values” as [Sarcevic et al. (Pg. 11, 1st paragraph, “Figure 4 shows a match tree that incorporates the proposed combined Formulas (7) and (8). The figure shows the changes of serve probabilities throughout the match for both teams. In order to demonstrate the example, a match with the following characteristics is selected, etc.”, Fig. 4)];
“generate, using the Monte Carlo model and a set of first parameters for the first live event, first odds for the first live event in response to the first request” as [Sarcevic et al. (Pg. 12, sec. 2.7 Validation Dataset, 1st paragraph “In order to evaluate and validate the proposed evolving probability method on real-life data, a dataset pertaining to a number of volleyball matches was collected from the MarathonBet betting house (https://www.marathonbet.com, accessed on 1 May 2021). This dataset contains data about matches played between April 2016 and October 2017. More importantly, this dataset contains in-play changes in the score and the corresponding betting odds made by the betting house itself. For each match score, expected values and odds are stored for three different bet types— match win, expected total points and expected handicap. Odds on winning the match before the match started (so-called prematch odds) are also available in this dataset. The dataset is publicly available [57]. Used attributes of the dataset are described in Table 1.”, Sarcevic et al. Pg. 13, sec. 2.7.1 Group Profiling, 2nd paragraph, “In an ideal scenario, enough historical data to construct a profile of team performance over a longer time for each team would be available. Then, these data can be used to calculate the likelihood of winning the point on own serve, the parameters of the short-term momentum for each team, as well as the rough estimate of the expected number of total points used to calculate the long-term momentum. These parameters can then be used as input parameters of the proposed evolving probability method to estimate the handicap and the total number of points that will be played between two teams.”)];
“concurrent with generating the first odds for the first live event, and based on determining that the first request comprises the flag, iteratively generate a set of second parameters for the Monte-Carlo model” as [Sarcevic et al. (Pg. 11, 1st paragraph, “Figure 4 shows a match tree that incorporates the proposed combined Formulas (7) and (8). The figure shows the changes of serve probabilities throughout the match for both teams. In order to demonstrate the example, a match with the following characteristics is selected, etc.”, Sarcevic et al. Pg. 12, 1st – 2nd paragraph, “To predict these values, and based on methodology introduced in in the previous sections, an approach that traverses through a match tree while updating the transition probabilities after each transition using the Equations (7) and (8) is proposed. This Monte Carlo simulation method that incorporates the proposed combined formulation will be called the evolving probability method… The exact method is as follows: one single simulation generates one match tree that represents one possible flow of the match. From that tree, it is easy to calculate the handicap and the total number of points played in such a simulated match. The simulation of the same match can then be repeated numerous times, and ultimately, the results of all simulations of the match are combined to estimate the handicap and the total number of points that will be played in the match. This method will be formalized through a procedure called The evolving probability method (see procedure The evolving probability method in the Appendix A).”, Fig. 4)];
“generate a set of second parameters for the Monte-Carlo model, wherein each iteration comprises: generating a set of candidate parameters for the Monte-Carlo model and the first live event” as [Sarcevic et al. (Pg. 11, 1st paragraph, “Figure 4 shows a match tree that incorporates the proposed combined Formulas (7) and (8). The figure shows the changes of serve probabilities throughout the match for both teams. In order to demonstrate the example, a match with the following characteristics is selected, etc.”, Sarcevic et al. Pg. 12, 1st – 2nd paragraph, “To predict these values, and based on methodology introduced in in the previous sections, an approach that traverses through a match tree while updating the transition probabilities after each transition using the Equations (7) and (8) is proposed. This Monte Carlo simulation method that incorporates the proposed combined formulation will be called the evolving probability method… The exact method is as follows: one single simulation generates one match tree that represents one possible flow of the match. From that tree, it is easy to calculate the handicap and the total number of points played in such a simulated match. The simulation of the same match can then be repeated numerous times, and ultimately, the results of all simulations of the match are combined to estimate the handicap and the total number of points that will be played in the match. This method will be formalized through a procedure called The evolving probability method (see procedure The evolving probability method in the Appendix A).”, Fig. 4)];
“generating a set of outputs by executing at least one Monte-Carlo simulation using the Monte-Carlo model and the set of candidate parameters” as [Sarcevic et al. (Pg. 11, 1st paragraph, “Figure 4 shows a match tree that incorporates the proposed combined Formulas (7) and (8). The figure shows the changes of serve probabilities throughout the match for both teams. In order to demonstrate the example, a match with the following characteristics is selected, etc.”, Fig. 4)];
“determining whether a convergence criterion is met by comparing the set of outputs to the set of target values” as [Sarcevic et al. (Pg. 18, sec. 4 Discussion, 1st paragraph, “When predicting the total number of points that will be played in those groups of matches, the simulation error when using the approach that combines short- and long-term sports momentums is on average 20% smaller compared to the iid approach. Even greater improvement is evident when using the evolving probability approach to predict the handicap. The simulation error, in this case, is on average 25% smaller compared to the iid approach.”, The examiner considers determining the simulation error to be the convergence criterion, since the simulation error is showing how the simulation results are different from the actual results. In paragraph [0041] of the specification it states “The model executor 140 can determine a convergence criterion for the iterative process, such as a threshold for mean squared error or a maximum number of iterations.”)];
“and (i) terminating generation of the set of second parameters by selecting the set of candidate parameters as the set of second parameters for the Monte- Carlo model in response to the convergence criterion being met, or (ii) executing a subsequent iteration in response to the convergence criterion not being met” as [Sarcevic et al. (Pg. 11, 1st paragraph, “Figure 4 shows a match tree that incorporates the proposed combined Formulas (7) and (8). The figure shows the changes of serve probabilities throughout the match for both teams. In order to demonstrate the example, a match with the following characteristics is selected, etc.”, Sarcevic et al. Pg. 12, 1st – 2nd paragraph, “To predict these values, and based on methodology introduced in in the previous sections, an approach that traverses through a match tree while updating the transition probabilities after each transition using the Equations (7) and (8) is proposed. This Monte Carlo simulation method that incorporates the proposed combined formulation will be called the evolving probability method… The exact method is as follows: one single simulation generates one match tree that represents one possible flow of the match. From that tree, it is easy to calculate the handicap and the total number of points played in such a simulated match. The simulation of the same match can then be repeated numerous times, and ultimately, the results of all simulations of the match are combined to estimate the handicap and the total number of points that will be played in the match. This method will be formalized through a procedure called The evolving probability method (see procedure The evolving probability method in the Appendix A).”, Fig. 4)];
“receive a second request to generate odds for the first live event” as [Sarcevic et al. (Pg. 12, sec. 2.7 Validation Dataset, 1st paragraph “In order to evaluate and validate the proposed evolving probability method on real-life data, a dataset pertaining to a number of volleyball matches was collected from the MarathonBet betting house (https://www.marathonbet.com, accessed on 1 May 2021). This dataset contains data about matches played between April 2016 and October 2017. More importantly, this dataset contains in-play changes in the score and the corresponding betting odds made by the betting house itself. For each match score, expected values and odds are stored for three different bet types— match win, expected total points and expected handicap. Odds on winning the match before the match started (so-called prematch odds) are also available in this dataset. The dataset is publicly available [57]. Used attributes of the dataset are described in Table 1.”, Sarcevic et al. Pg. 13, sec. 2.7.1 Group Profiling, 2nd paragraph, “In an ideal scenario, enough historical data to construct a profile of team performance over a longer time for each team would be available. Then, these data can be used to calculate the likelihood of winning the point on own serve, the parameters of the short-term momentum for each team, as well as the rough estimate of the expected number of total points used to calculate the long-term momentum. These parameters can then be used as input parameters of the proposed evolving probability method to estimate the handicap and the total number of points that will be played between two teams.”)];
“and generate, using the set of second parameters for the Monte-Carlo model and the first live event, second odds for the first live event in response to the second request” as [Sarcevic et al. (Pg. 12, sec. 2.7 Validation Dataset, 1st paragraph “In order to evaluate and validate the proposed evolving probability method on real-life data, a dataset pertaining to a number of volleyball matches was collected from the MarathonBet betting house (https://www.marathonbet.com, accessed on 1 May 2021). This dataset contains data about matches played between April 2016 and October 2017. More importantly, this dataset contains in-play changes in the score and the corresponding betting odds made by the betting house itself. For each match score, expected values and odds are stored for three different bet types— match win, expected total points and expected handicap. Odds on winning the match before the match started (so-called prematch odds) are also available in this dataset. The dataset is publicly available [57]. Used attributes of the dataset are described in Table 1.”, Sarcevic et al. Pg. 13, sec. 2.7.1 Group Profiling, 2nd paragraph, “In an ideal scenario, enough historical data to construct a profile of team performance over a longer time for each team would be available. Then, these data can be used to calculate the likelihood of winning the point on own serve, the parameters of the short-term momentum for each team, as well as the rough estimate of the expected number of total points used to calculate the long-term momentum. These parameters can then be used as input parameters of the proposed evolving probability method to estimate the handicap and the total number of points that will be played between two teams.”)];
Schwartz et al. and Sarcevic are analogous art because they are from the same
field endeavor of analyzing a live event with betting odds.
Before the effective filing date of the invention, it would have been obvious to a person
of ordinary skill in the art to modify the teachings of Schwartz et al. of receiving a request to generate odds values for a first live event and updating the model based on the set of target values in the request by incorporating maintain a Monte-Carlo model configured to generate odds data for one or more live events, wherein the Monte-Carlo is configured using a first set of parameters; the first request comprising (ii) a flag indicating that the Monte-Carlo model is to be updated using the set of target values; determine that the first request comprises the flag indicating that the Monte-Carlo model is to be updated using the set of target values; generate, using the Monte Carlo model and a set of first parameters for the first live event, first odds for the first live event in response to the first request; concurrent with generating the first odds for the first live event, and based on determining that the first request comprises the flag, iteratively generate a set of second parameters for the Monte-Carlo model, wherein each iteration comprises: generating a set of candidate parameters for the Monte-Carlo model and the first live event, generating a set of outputs by executing at least one Monte-Carlo simulation using the Monte-Carlo model and the set of candidate parameters, determining whether a convergence criterion is met by comparing the set of outputs to the set of target values, and(i) terminating generation of the set of second parameters by selecting the set of candidate parameters as the set of second parameters for the Monte- Carlo model in response to the convergence criterion being met, or (ii) executing a subsequent iteration in response to the convergence criterion not being met; receive a second request to generate odds for the first live event; and generate, using the set of second parameters for the Monte-Carlo model and the first live event, second odds for the first live event in response to the second request as taught by Sarcevic et al. for the purpose of modeling sports with a strongly defined structure and a rigid scoring system.
Schwartz et al. in view of Sarcevic et al. teaches maintain a Monte-Carlo model configured to generate odds data for one or more live events, wherein the Monte-Carlo is configured using a first set of parameters; the first request comprising (ii) a flag indicating that the Monte-Carlo model is to be updated using the set of target values; determine that the first request comprises the flag indicating that the Monte-Carlo model is to be updated using the set of target values; generate, using the Monte Carlo model and a set of first parameters for the first live event, first odds for the first live event in response to the first request; concurrent with generating the first odds for the first live event, and based on determining that the first request comprises the flag, iteratively generate a set of second parameters for the Monte-Carlo model, wherein each iteration comprises: generating a set of candidate parameters for the Monte-Carlo model and the first live event, generating a set of outputs by executing at least one Monte-Carlo simulation using the Monte-Carlo model and the set of candidate parameters, determining whether a convergence criterion is met by comparing the set of outputs to the set of target values, and(i) terminating generation of the set of second parameters by selecting the set of candidate parameters as the set of second parameters for the Monte- Carlo model in response to the convergence criterion being met, or (ii) executing a subsequent iteration in response to the convergence criterion not being met; receive a second request to generate odds for the first live event; and generate, using the set of second parameters for the Monte-Carlo model and the first live event, second odds for the first live event in response to the second request.
The motivation for doing so would have been because Sarcevic et al. teaches by modeling sports with a strongly defined structure and a rigid scoring system, the ability to analyze the performance of players or study field scenarios that may arise under different circumstances, can be accomplished. This allows players to be analyzed at different stages of a match (Sarcevic et al., Abstract, Pg. 18, sec. 4 Discussion, 1st paragraph, “In this paper, a common approach of modeling Markovian sports which relies on, etc.”).
With respect to claim 5, the combination of Schwartz et al. and Sarcevic et al. discloses the system of claim 1 above, and Schwartz et al. further discloses “wherein the one or more processors are further configured to store the set of second parameters in a database in association with an identifier of the first live event.” as [Schwartz et al. (paragraph [0023] “Statistics system 500 stores and/or generates various statistics for use in predicting an outcome at a competition such as a live sports event, providing odds for wagering on various circumstances or outcomes in the sports event, and other similar activities.”, Schwartz et al. paragraph [0081] “Statistics system 500 stores and determines various statistics in accordance with the present disclosure. The statistics system 500 includes one or more processing units (CPUs) 574, peripherals interface 570, memory controller 588, a network or other communications interface 584, a memory 502 (e.g., random access memory), a user interface 578, the user interface 578 including a display 582 and an input 580 (e.g., a keyboard, a keypad, a touch screen, etc.), input/output (I/O) subsystem 566, one or more communication buses 513 for interconnecting the aforementioned components, and a power supply system 576 for powering the aforementioned components.”, Fig. 5)];
With respect to claim 6, the combination of Schwartz et al. and Sarcevic et al. discloses the system of claim 5 above, and Schwartz et al. further discloses “retrieve the set of second parameters from the database” as [Schwartz et al. (paragraph [0035] “In some embodiments, machine learning engine 210 receives data from various sources of the present disclosure in order to predict a future outcome at a live sporting event and generate statistics for analysis and use.”, Fig. 2A)];
Sarcevic et al. discloses “and execute the Monte-Carlo model using the set of second parameters to generate second odds for the first live event” as [Sarcevic et al. (Pg. 12, 1st – 2nd paragraph, “To predict these values, and based on methodology introduced in in the previous sections, an approach that traverses through a match tree while updating the transition probabilities after each transition using the Equations (7) and (8) is proposed. This Monte Carlo simulation method that incorporates the proposed combined formulation will be called the evolving probability method… The exact method is as follows: one single simulation generates one match tree that represents one possible flow of the match. From that tree, it is easy to calculate the handicap and the total number of points played in such a simulated match. The simulation of the same match can then be repeated numerous times, and ultimately, the results of all simulations of the match are combined to estimate the handicap and the total number of points that will be played in the match. This method will be formalized through a procedure called The evolving probability method (see procedure The evolving probability method in the Appendix A).”)];
With respect to claim 7, the combination of Schwartz et al. and Sarcevic et al. discloses the system of claim 1 above, and Sarcevic et al. further discloses “wherein the one or more processors are further configured to update the Monte-Carlo model by iteratively generating candidate second parameters based on the set of target values” as [Sarcevic et al. (Pg. 12, 1st – 2nd paragraph, “To predict these values, and based on methodology introduced in in the previous sections, an approach that traverses through a match tree while updating the transition probabilities after each transition using the Equations (7) and (8) is proposed. This Monte Carlo simulation method that incorporates the proposed combined formulation will be called the evolving probability method… The exact method is as follows: one single simulation generates one match tree that represents one possible flow of the match. From that tree, it is easy to calculate the handicap and the total number of points played in such a simulated match. The simulation of the same match can then be repeated numerous times, and ultimately, the results of all simulations of the match are combined to estimate the handicap and the total number of points that will be played in the match. This method will be formalized through a procedure called The evolving probability method (see procedure The evolving probability method in the Appendix A).”)];
With respect to claim 8, the combination of Schwartz et al. and Sarcevic et al. discloses the system of claim 1 above, and Sarcevic et al. further discloses “store a plurality of sets of first parameters for the Monte-Carlo model in a database” as [Sarcevic et al. (Pg. 12, sec. 2.7 Validation Dataset, 1st paragraph “In order to evaluate and validate the proposed evolving probability method on real-life data, a dataset pertaining to a number of volleyball matches was collected from the MarathonBet betting house (https://www.marathonbet.com, accessed on 1 May 2021). This dataset contains data about matches played between April 2016 and October 2017. More importantly, this dataset contains in-play changes in the score and the corresponding betting odds made by the betting house itself. For each match score, expected values and odds are stored for three different bet types— match win, expected total points and expected handicap. Odds on winning the match before the match started (so-called prematch odds) are also available in this dataset. The dataset is publicly available [57]. Used attributes of the dataset are described in Table 1.”)];
“the plurality of sets of first parameters including the set of first parameters for the first live event” as [Sarcevic et al. (Pg. 12, sec. 2.7 Validation Dataset, 1st paragraph “In order to evaluate and validate the proposed evolving probability method on real-life data, a dataset pertaining to a number of volleyball matches was collected from the MarathonBet betting house (https://www.marathonbet.com, accessed on 1 May 2021). This dataset contains data about matches played between April 2016 and October 2017. More importantly, this dataset contains in-play changes in the score and the corresponding betting odds made by the betting house itself. For each match score, expected values and odds are stored for three different bet types— match win, expected total points and expected handicap. Odds on winning the match before the match started (so-called prematch odds) are also available in this dataset. The dataset is publicly available [57]. Used attributes of the dataset are described in Table 1.”)];
“and generate the first odds further based on an aggregation of the plurality of sets of first parameters” as [Sarcevic et al. (Pg. 12, sec. 2.7 Validation Dataset, 1st paragraph “In order to evaluate and validate the proposed evolving probability method on real-life data, a dataset pertaining to a number of volleyball matches was collected from the MarathonBet betting house (https://www.marathonbet.com, accessed on 1 May 2021). This dataset contains data about matches played between April 2016 and October 2017. More importantly, this dataset contains in-play changes in the score and the corresponding betting odds made by the betting house itself. For each match score, expected values and odds are stored for three different bet types— match win, expected total points and expected handicap. Odds on winning the match before the match started (so-called prematch odds) are also available in this dataset. The dataset is publicly available [57]. Used attributes of the dataset are described in Table 1.”, Sarcevic et al. Pg. 13, sec. 2.7.1 Group Profiling, 2nd paragraph, “In an ideal scenario, enough historical data to construct a profile of team performance over a longer time for each team would be available. Then, these data can be used to calculate the likelihood of winning the point on own serve, the parameters of the short-term momentum for each team, as well as the rough estimate of the expected number of total points used to calculate the long-term momentum. These parameters can then be used as input parameters of the proposed evolving probability method to estimate the handicap and the total number of points that will be played between two teams.”)];
With respect to claim 9, the combination of Schwartz et al. and Sarcevic et al. discloses the system of claim 1 above, and Schwartz et al. further discloses “wherein the one or more processors are further configured to generate the first odds for the first live event” as [Schwartz et al. (paragraph [0024] “In various embodiments, system 48 includes odds management system 600 for managing odds and a plurality of user devices 700-1 to 700-R. Although odds management system 600 is shown external to processor 100, in some embodiments the odds management system is included in the processor. Odds management system 600 facilitates determining odds for outcomes in a sports event and managing various models related to predicting outcomes at the live event.”, Fig. 1)];
Sarcevic et al. discloses “and initiate updating the Monte-Carlo model in parallel.” as [Sarcevic et al. (Pg. 11, 1st paragraph, “Figure 4 shows a match tree that incorporates the proposed combined Formulas (7) and (8). The figure shows the changes of serve probabilities throughout the match for both teams. In order to demonstrate the example, a match with the following characteristics is selected, etc.”, Sarcevic et al. Pg. 12, 1st – 2nd paragraph, “To predict these values, and based on methodology introduced in in the previous sections, an approach that traverses through a match tree while updating the transition probabilities after each transition using the Equations (7) and (8) is proposed. This Monte Carlo simulation method that incorporates the proposed combined formulation will be called the evolving probability method… The exact method is as follows: one single simulation generates one match tree that represents one possible flow of the match. From that tree, it is easy to calculate the handicap and the total number of points played in such a simulated match. The simulation of the same match can then be repeated numerous times, and ultimately, the results of all simulations of the match are combined to estimate the handicap and the total number of points that will be played in the match. This method will be formalized through a procedure called The evolving probability method (see procedure The evolving probability method in the Appendix A).”, Fig. 4)];
With respect to claim 10, the combination of Schwartz et al. and Sarcevic et al. discloses the system of claim 1 above, and Schwartz et al. further discloses “wherein the one or more processors are further configured to provide an indication to a second computing system that the deep learning model has been updated based on the set of target values” as [Schwartz et al. (paragraph [0039] “Machine learning engine 210 communicates various odds and outputs of the various databases and models therein to an odds management system 600. In communicating with the machine learning engine 210, the odds management system 600 provides various wagers and predictive odds for future events at a sporting event to the user devices 700, while also updating these odds in real time to reflect current situations and statistics of a game.”, The examiner considers the odds management system to be the second computing system, since the odds management system is outside of the system and facilitates determining odds for outcomes in a sports event and managing various models related to predicting outcomes at the live event, Fig. 1)];
With respect to claim 11, Schwartz et al. discloses “A method” as [Schwartz et al. (paragraph [0095] “In some embodiments the elucidation of the formation class by formation classifier 212 is used as a covariate in statistical models that predict the outcome of a current live game (e.g., win/loss, point spread, etc.) as disclosed with respect to methods and features described with respect to FIG. 8.”)];
The other limitations of the claim recite the same substantive limitations as claim 1 above and are rejected using the same teachings.
With respect to claims 15-20, the claims recite the same substantive limitations as claims 5-10 above, and are rejected using the same teachings.
Conclusion
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/BERNARD E COTHRAN/Examiner, Art Unit 2188
/RYAN F PITARO/Supervisory Patent Examiner, Art Unit 2188
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