DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Claim 1 recites a method for generating dynamic, user-specific sports betting simulations. The limitation of applying a machine learning model to a first historical outcome and a second historical outcome to generate a predictive outcome responsive to a user query, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is, other than reciting “machine learning model,” nothing in the claim element precludes the step from practically being performed in the mind. For example, but for the “machine learning model” language, “applying” in the context of this claim encompasses the user mentally thinking about historical sports outcomes and making a mental prediction about the future. Similarly, the limitations of: applying and determining are processes that, under their broadest reasonable interpretation, covers performance of the limitation in the mind. The same interpretation is applied to the remaining steps in claim 1. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea. This judicial exception is not integrated into a practical application. In particular, the claim only recites one additional element – machine learning model. The machine learning model is recited at a high-level of generality (i.e., as a generic processor implementing a step) such that it amounts no more than mere instructions to apply the exception using a generic computer component. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using machine learning model amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claim is not patent eligible. Similar reasoning is applied to claims 2-20.
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.
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) 1-9 and 11-19 are rejected under 35 U.S.C. 103 as being unpatentable over US Publication No. 2022/0157114 A1 to Huke et al. (hereinafter “Huke”) in view of US Publication No. 2018/0052928 A1 to Liu et al. (hereinafter “Liu”).
Concerning claim 1, Huke discloses a method for generating dynamic, user-specific sports betting simulations (Abstract), the method comprising:
applying a machine learning model to a first historical outcome and a second historical outcome to generate a predictive outcome responsive to a user query (paragraphs [0004], [0072]-[0078] – machine learning model is applied to multiple historical outcomes to generate a predictive outcome),
applying the machine learning model to a real-time event and the predictive outcome to generate an updated predictive outcome, the real-time event obtained by parsing the real-time event from a real-time event feed, the real-time event parsed from the real-time event feed (paragraphs [0004], [0072]-[0078] – machine learning is applied to a real-time event obtained from a feed); and
determining a prediction error indicative of a difference between the predictive outcome and the updated predictive outcome (paragraphs [0004], [0072]-[0078] – prediction error indicates a difference between predictive outcome and updated predictive outcome).
Huke lacks specifically disclosing, however, Liu discloses the first historical outcome and the second historical outcome selected based on a first keyword vector and a second keyword vector obtained by parsing the user query; the real-time event feed based on the first keyword vector and the second keyword vector (paragraphs [0062], [0078]-[0082], [0092]-[0100] – user query is parsed and converted into a semantic vectors and historical records are selected based on vector similarity).It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the use of multiple vectors as disclosed by Liu in the system of Huke in order to accurately predict an outcome.
Concerning claims 2 and 12, Huke discloses wherein the first historical outcome and the second historical outcome are obtained by searching a historical database, the historical database being communicatively coupled to the machine learning model, and wherein the first historical outcome and the second historical outcome are categorized by at least one of an event type, a player, and a sport-specific factor (paragraphs [0004], [0072]-[0078] – historical database has sport specific factors). Huke lacks specifically disclosing, however, Liu discloses searching a historical database using the first keyword vector and the second keyword vector (paragraphs [0062], [0078]-[0082], [0092]-[0100] – user query is parsed and converted into a semantic vectors and historical records are selected based on vector similarity).It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the use of multiple vectors as disclosed by Liu in the system of Huke in order to accurately predict an outcome.
Concerning claims 3 and 13, Huke discloses further comprising: providing the updated predictive outcome in response to the prediction error satisfying a prediction error threshold, wherein the prediction error is calculated in real-time and wherein satisfying the prediction error threshold prompts a response to the user query using the updated predictive outcome, and wherein the prediction error threshold is set based on user criteria (paragraphs [0004], [0072]-[0078] – predictive outcome exceeds an error threshold).
Concerning claims 4 and 14, Huke discloses further comprising: initiating, in response to determining the real-time event, a first application programming interface call to a first external database to obtain a first statistic, the first statistic indicative of how the real-time event affects the predictive outcome, wherein the applying the machine learning model to the real-time event and the predictive outcome to generate the updated predictive outcome further comprises applying, in response to the first application programming interface call, the machine learning model to the first statistic received from the first external database and the predictive outcome to generate the updated predictive outcome (paragraphs [0004], [0072]-[0078] – api determines statistics and applying the machine learning to update the predictive outcome).
Concerning claims 5 and 15, Huke discloses further comprising: initiating, in response to determining the real-time event, a second application programming interface call to a second external database to obtain a second statistic, the second statistic indicative of how the real-time event affects the predictive outcome, wherein the applying the machine learning model to the real-time event and the predictive outcome to generate the updated predictive outcome further comprises applying, in response to the second application programming interface call, the machine learning model to the second statistic received from the second external database and the predictive outcome to generate the updated predictive outcome, and wherein the second statistic is weighed by the machine learning model more than the first statistic for generating the updated predictive outcome (paragraphs [0004], [0072]-[0078] – api determines statistics and applying the machine learning to update the predictive outcome).
Concerning claims 6 and 16, Huke discloses wherein the first application programming interface call is initiated prior to the second application programming interface call, wherein the second statistic is obtained from the second external database prior the obtaining the first statistic from the first external database, and wherein the machine learning model is applied the second statistic while waiting to obtain the first statistic from the first external database (paragraphs [0004], [0072]-[0078] – statistical information is determined from the external databases).
Concerning claims 7 and 17, Huke discloses further comprising: generating a user interface that provides an intermediate predictive outcome while waiting to obtain the first statistic from the first external database, the intermediate predictive outcome generated in response to generating the updated predictive outcome based on applying the machine learning model to the second statistic and the predictive outcome (paragraphs [0004], [0072]-[0078] – statistical information is determined from the external databases).
Concerning claims 8 and 18, Huke lacks specifically disclosing, however, Liu discloses further comprising: continuously determining additional real-time events by monitoring the real-time event feed using the first keyword vector and the second keyword vector obtained from the user query; and continuously applying, in response to determining the additional real-time events, the machine learning model to the additional real-time events and the predictive outcome to generate the updated predictive outcome (paragraphs [0062], [0078]-[0082], [0092]-[0100] – user query is parsed and converted into a semantic vectors and historical records are selected based on vector similarity). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the use of multiple vectors as disclosed by Liu in the system of Huke in order to accurately predict an outcome.
Concerning claims 9 and 19, Huke discloses wherein the continuously determining the additional real-time events and the continuously applying the machine learning model occur concurrently using parallel processing (paragraphs [0004], [0072]-[0078] – additional real time events and determined and machine learning is applied).
Concerning claim 11, see the rejection of claim 1.
Claim(s) 10 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Huke, Liu, and further in view of US Patent No. 11,100,753 B1 to Huke et al. (hereinafter “Huke ‘753”).
Concerning claims 10 and 20, Huke lacks specifically disclosing, however Huke ‘753 discloses wherein the machine learning model is configured to generate Monte Carlo simulations that are refined using linear programming to optimize prediction outcomes, the refining using linear programming including assigning probabilities to the Monte Carlo simulations, wherein the Monte Carlo simulations are based on at least one of user-defined bankroll, a preferred odd, and a risk tolerance (column 11, lines 48-67 – Monte Carlo simulations are generated using machine learning). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the use of Monte Carlo simulations as disclosed by Huke ‘753 in the system of Huke in order to accurately predict an outcome.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure is listed in the PTO-892.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MALINA D BLAISE whose telephone number is (571)270-3398. The examiner can normally be reached Mon. - Thurs. 7:00 am - 5:00 pm (PT).
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MALINA D. BLAISE
Primary Examiner
Art Unit 3715
/MALINA D. BLAISE/Primary Examiner, Art Unit 3715
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