DETAILED ACTION
Acknowledgements
This Office Action is in response to Applicant’s correspondence filed on 5/24/24.
The Examiner notes that citations to United States Patent Application Publication paragraphs are formatted as [####], #### representing the paragraph number.
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
Status of Claims
Claims 1-20 are currently pending.
Claims 1-20 are rejected as set forth below.
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 1-3, 9-10, 16-17 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter.
As per claims 1-3, 9-10, 16-17, the claimed invention is directed to an abstract idea without significantly more because:
Claims 1, 9, 16 recites: A server comprising: a data store storing non-transitory first computer instructions for instantiating a model adaptation engine (“MAE”); a processor configured to execute the first computer instructions and instantiate the MAE; wherein the MAE, when instantiated by the processor, instructs the server to perform MAE operations (“MAEO”) including: searching a database for fixture specific model (“FSM”) data (“FSMD”) pertinent to a given event-activity-fixture (“EAF”); obtaining generic model data (“GMD”) for a given event-activity pairing; and modifying the GMD with the FSMD to generate FSM adapted data (“FSMAD”) for the given EAF; and an FSMS bus coupling the processor with the data store.
Under Step 1 of the Section 101 analysis, the claim(s) is/are directed to a system and method, which are statutory categories of invention.
Under Step 2A Prong One of the 2019 Revised Patent Subject Matter Eligibility Guidance, the claimed invention as drafted includes language (see underlined language above) that recites an abstract idea of calculating a betting model by inputting fixture specific data into a mathematical model (a mental process such as a concept performed in the human mind, e.g. an observation, evaluation, judgment, opinion) but for the recitation of additional claim elements. That is, other than reciting generic computer components such as a data store, a processor, and a bus coupling the processor with the data store, nothing in the claim precludes the language from being practically performed in the mind. For example, a betting handicapper is capable of retrieving fixture specific data from the sports section of a newspaper and inputting the data into a betting model to calculate predicted odds by pen and paper.
A similar analysis can be applied to dependent claims 2-3, 10, 17, which further recite the abstract idea of calculating a betting model by inputting fixture specific data into a mathematical model.
Under Step 2A Prong Two of the 2019 Revised Patent Subject Matter Eligibility Guidance, the additional claim element(s), considered individually, do not apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception and in a manner that integrates the exception into a practical application of the exception. The additional claim elements(s) merely add the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. For example, the abstract idea is merely implemented on a computer comprised of generic computer components such as a data store, a processor, and a bus coupling the processor with the data store.
A similar analysis can be applied to dependent claims 2-3, 10, 17, which include additional claim elements that merely add the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. For example, the abstract idea is merely implemented on a computer comprised of generic computer components such as a data store, a processor, and a bus coupling the processor with the data store.
Under Step 2A Prong Two, the additional claim element(s), considered in combination, do not apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception and in a manner that integrates the exception into a practical application of the exception. The combination of elements is no more than the sum of their parts. Unlike the eligible claims in Diehr and Bascom, in which the elements limiting the exception taken together improve a technical field, the instant claim lacks an improvement to the functioning of a computer or to any other technology or technical field.
Under Step 2B, the additional claim element(s), considered individually and in combination, do not provide meaningful limitation(s) to transform the abstract idea into a patent eligible application of the abstract idea such that the claim(s) amounts to significantly more than the abstract idea itself for similar reasons outlined under Step 2A Prong Two.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1, 9, 16 is/are rejected under 35 U.S.C. 102(a)(2) as being anticipated by United States Patent Application Publication No. 20230162314 to Darcy.
As per claim(s) 1, 9, 16, Darcy teaches:
A server comprising: a data store storing non-transitory first computer instructions for instantiating a model adaptation engine (“MAE”); a processor configured to execute the first computer instructions and instantiate the MAE; an FSMS bus coupling the processor with the data store. ([0019], “The example data feed management system 200 of FIG. 2 includes the data feed architecture circuitry 210, which includes the odds generator circuitry 220 (e.g., also referred to as an odds factory) and conversion circuitry 230 to convert odds data generated by the odds generator circuitry 220 from market data provided by one or more data feed provider systems 205.”; [0039], “Thus, for example, any of the example odds generator circuitry 220, the example conversion circuitry 230 (including the example data feed management circuitry 235 and the configuration console 400), the example region data stores 250-254, and/or, more generally, the example architecture 200, could be implemented by processor circuitry, analog circuit(s), digital circuit(s), logic circuit(s), programmable processor(s), programmable microcontroller(s), graphics processing unit(s) (GPU(s)), digital signal processor(s) (DSP(s)), application specific integrated circuit(s) (ASIC(s)), programmable logic device(s) (PLD(s)), and/or field programmable logic device(s) (FPLD(s)) such as Field Programmable Gate Arrays (FPGAs).”)
wherein the MAE, when instantiated by the processor, instructs the server to perform MAE operations (“MAEO”) including: searching a database for fixture specific model (“FSM”) data (“FSMD”) pertinent to a given event-activity-fixture (“EAF”); obtaining generic model data (“GMD”) for a given event-activity pairing; and modifying the GMD with the FSMD to generate FSM adapted data (“FSMAD”) for the given EAF; ([0031], “In certain examples, a quantitative or “quant” model can be formed using the market data to facilitate in-play wagering on a sporting event (including an esports event, non-sports event, etc.). Such a quant model can also support player proposition bets and facilitate bet building, etc. The model can be dynamically updated during an event. For example, the odds generator circuitry 220 can build a model based on data related to passing yards for a certain football player in a game. The model can adjust as the game progresses, based on game state, time decay, etc. Updated model output can be provided regionally to update current wagers, update odds, facilitate new in-play wagers, etc.”; [0032]-[0034], “FIG. 3 depicts an example statistical model 300 generated for a National Football League (NFL) game. As shown in the example of FIG. 4, the example model 300 begins in a start state and advances over time during the game depending on a type of play, outcome of the play, etc. The example model 300 can form a tree of models or compound model and can trigger execution of a nested model depending on an outcome of an event being tracked in the game. At 402, the data feed provider 205 sends an update of market data to the odds generator circuitry 220. At 404, the odds generator circuitry 220 processes the data feed using one or more statistical, quantitative, and/or artificial intelligence models to, at 406, provide an odds/market data output to the conversion circuitry 230.”; [0021], “In operation, market data from one or more data provider(s) 205 is provided to the odds generator circuitry 220. For example, a data provider 205 may generate a data feed of Russian table tennis matches, and another data provider 205 may generate a data feed of Australian rules football games. The data feed provides information regarding the game and/or other event such as score, player statistics, etc. The data feed can include historical information, future prediction, in-play or live data from an ongoing event, etc.”)
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.
Claims 2-6, 10-13, 17-19 is/are rejected under 35 U.S.C. 103 as being unpatentable over United States Patent Application Publication No. 20230162314 to Darcy in view of United States Patent Application Publication No. 20240370889 to Back.
As per claim(s) 2, 17, Darcy does not explicitly teach, but Back teaches:
wherein the data store further stores non-transitory second computer instructions for instantiating a generic modeling engine (“GME”); wherein the processor is further configured to execute the second computer instructions and instantiate the GME; and wherein the GME, when instantiated by the processor, instructs the server to perform GME operations (“GMEO”) including: obtaining the GMD from a generic model database (“GMDB”). ([0045]-[0047], “FIG. 2 illustrates an example of system integration and processing. In this example, the system collects raw data 204 from the web 202 (such as from databases, news sites, financial exchanges, sports leagues, etc.). This raw data 204 is used by a predictive event outcomes model 206 executed in a cloud processing 208 environment, where the predictive event outcomes model can perform probability assignment for events that can occur in a given market. The result is simulated events data 210, which is a combination of many event simulations. FIG. 3 illustrates an example of baseball game simulations being modeled using the disclosed system. In this example, the system receives raw data from one or more sources, such as MAJOR LEAGUE BASEBALL™ (MLB) pitch data 302, MLB starting lineup and probable starters data 304, and MLB weather forecast data 306. These data 302, 304, 306 can come from a single source, multiple sources, or any combination thereof. the system inputs these data 302, 304, 306 into a baseball projections model 308, which is a sport-specific version of the predictive event outcomes model 206 illustrated in FIG. 2.”)
One of ordinary skill in the art would have recognized that applying the known technique of Back to the known invention of Darcy would have yielded predictable results and resulted in an improved invention. It would have been recognized that the application of the technique would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such modeling features into a similar invention. Further, it would have been recognized by those of ordinary skill in the art that modifying the invention so wherein the data store further stores non-transitory second computer instructions for instantiating a generic modeling engine (“GME”); wherein the processor is further configured to execute the second computer instructions and instantiate the GME; and wherein the GME, when instantiated by the processor, instructs the server to perform GME operations (“GMEO”) including: obtaining the GMD from a generic model database (“GMDB”), results in an improved invention because applying said technique leverages the use of generic models, which are faster and less costly to develop versus purely specialized models, thus improving the overall efficiency of the invention.
As per claim(s) 3, 10, 17, Darcy teaches:
wherein the GMDB includes non-transitory generic models identifiable based on at least one of an event identifier and an activity identifier. ([0031])
As per claim(s) 4, 11, 18, Darcy teaches:
wherein at least one of the generic models has been refined using a supervised artificial intelligence and machine learning (“AI/ML”); wherein the AI/ML has been initially trained based on past instances of an event-activity pairing; wherein the AI/ML has been refined based on additional event-activity pairings; ([0029], “In certain examples, odds can be represented using a model, such as a machine learning model, a data structure, and/or other representation. Market data can be provided to a model, and odds can be generated from the model, for example. In certain examples, one or more machine learning models can generate predictions in real time based on data from the data feed(s), regional update(s) from the data store(s) 250-254, etc. Factors such as player performance, weather, fan sentiment, timing, etc., can factor into a prediction of an outcome or an occurrence such as a final score, a margin of victory, an occurrence of an event in a game, etc. In certain examples, regional and/or other locational restrictions, regulations, or rules on odds, betting, market data, etc., can be applied as weighting on nodes and/or connections of a model, etc. In certain examples, one or more historical data feeds can be used to train a model, test a model, validate a model, etc. One or more current data feeds 205 can be used to provide feedback to retrain and/or otherwise update a model to be deployed to the odds generator circuitry 220, for example. Data feeds from a plurality of provider systems 205 can be used to help ensure robust, reliable model(s) trained and tested from multiple sources before being deployed for use, for example.”)
Back teaches:
wherein the GME further performs the GMEO of obtaining the GMD from the GMDB by utilizing the AI/ML to identify, on a Cloud storage device, data pertinent to one or more event-activity pairings that are substantially similar to the given event-activity pairing. ([0045]-[0047])
As per claim(s) 5, 12, Darcy teaches:
wherein the GMEO further include: receiving event-activity-fixture data (“EAFD”) from at least one of a historical EAF database (“HEAFDB”) and an EAF database (“EAFDB”), and further obtaining the generic model based on at least one of HEAF data (“HEAFD”) received from the HEAFDB and RTEAF data (“RTEAFD”) received from the EAFDB; ([0021]; [0032], “FIG. 3 depicts an example statistical model 300 generated for a National Football League (NFL) game. As shown in the example of FIG. 4, the example model 300 begins in a start state and advances over time during the game depending on a type of play, outcome of the play, etc. The example model 300 can form a tree of models or compound model and can trigger execution of a nested model depending on an outcome of an event being tracked in the game. As such, market data and associated odds evolve during the game for in-play wager opportunities. The model(s) 300 can be generated by the odds generator circuitry 220 and provided to the data feed management circuitry 235 for output to the region data store(s) 250-254 and associated region user system(s) 260-264, for example.”)
As per claim(s) 6, 13, 19, Darcy teaches:
wherein the data store further stores non-transitory third computer instructions for instantiating an EAF data engine (“EAFDE”); wherein the processor is further configured to execute the third computer instructions and instantiate the EAFDE; wherein the EAFDE, when instantiated by the processor, instructs the server to perform EAFDE operations (“EAFDEO”) including: receiving the RTEAFD from a real-time data server (“RTDS”); and providing at least one of the HEAFD and the RTEAFD to the MAE; ([0021])
Back teaches:
requesting the HEAFD from the HEAF database; ([0025], “The system described herein can also be used for the purposes of probability assignment in fantasy sports and betting. In order to make such predictions, the system collects “raw data” (i.e., data which has not been manipulated) about a given event. For a sporting example, the sports data information can be sourced in a raw format from accredited official sources (e.g., directly from the sports league, directly from a reporter, directly from game officials, etc.). Such data can be granular detail from the event (e.g., “Play by play”). The data received can be stored within a database, and may require normalization, organization, and/or cleaning for re-use. An Application Programming Interface (API) may be established with the database to allow recall of information from the database. When making predictions of future phases, the system can use the historical data stored within the database.”)
Claims 7-8, 14-15, 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over United States Patent Application Publication No. 20230162314 to Darcy in view of United States Patent Application Publication No. 20240370889 to Back, and further in view of United States Patent No. 11455536 to Thomas.
As per claim(s) 7, 14, 20, Darcy as modified does not explicitly teach, but Thomas teaches:
wherein the data store further stores non-transitory fourth computer instructions for instantiating an FSM leveling engine (“FSMLE”); wherein the processor is further configured to execute the fourth computer instructions and instantiate the FSMLE; wherein the FSMLE, when instantiated by the processor, instructs the server to perform FSMLE operations (“FSMLEO”) including: receiving the FSMAD from the MAE; leveling the FSMAD received from the MAE in a relational database; and outputting leveled FSM data (“FSMLD”) for storage in an FSM level database (“FSMLDB”); (col 3 lines 11-27, “To provide such a capability, the system may create a predicted odds machine learning model and a risk management machine learning model. To create the predicted odds machine learning model, the system may perform a specialized process that uses an ensemble machine learning training algorithm. In some embodiments, the system may use predictive variables associated with past events and historical odds for outcomes of the past events, as input, to train the predicted odds machine learning model. The training of the predicted odds machine learning model may include an ensemble training algorithm that includes training multiple linear regression models by implementing forward stepwise variable selection using the predictive variables, bootstrap aggregation, and objective function noise injection. The system may then determine an average model from the multiple linear regressions models and adjust the average model on per-venue basis.”)
One of ordinary skill in the art would have recognized that applying the known technique of Thomas to the known invention of Darcy as modified would have yielded predictable results and resulted in an improved invention. It would have been recognized that the application of the technique would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such model leveling features into a similar invention. Further, it would have been recognized by those of ordinary skill in the art that modifying the invention so wherein the data store further stores non-transitory fourth computer instructions for instantiating an FSM leveling engine (“FSMLE”); wherein the processor is further configured to execute the fourth computer instructions and instantiate the FSMLE; wherein the FSMLE, when instantiated by the processor, instructs the server to perform FSMLE operations (“FSMLEO”) including: receiving the FSMAD from the MAE; leveling the FSMAD received from the MAE in a relational database; and outputting leveled FSM data (“FSMLD”) for storage in an FSM level database (“FSMLDB”), results in an improved invention because applying said technique leverages multiple models to achieve more accurate predictions, thus improving the overall accuracy of the invention.
As per claim(s) 8, 15, 20, Darcy teaches:
wherein the model is utilized by a simulation server during a real time EAF to simulate one or more outcomes of the real-time EAF; and wherein the one or more outcomes are utilized by an EAF pricing server to determine one or more betting lines for the event; ([0031])
Thomas teaches:
the FSMLD stored in the FSMLDB; (col 3 lines 11-27)
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
United States Patent Application Publication No. 20170140605 to Lewski discloses an invention wherein data containing user-defined terms for a proposition bet are received at a first network connected device, wherein the user-defined terms predict an outcome for the proposition bet. The user-defined terms may take the form of a performance condition for a proposition bet customized by a user; wherein the performance condition predicts an outcome for the proposition bet. A database is queried to retrieve historical data associated with the user defined terms for the proposition bet. The historical data is processed to generate odds that the user defined terms correctly predict the outcome for the proposition bet. The odds are transmitted to a second network connected device for acceptance or rejection by the user.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JAY HUANG whose telephone number is (408)918-9799. The examiner can normally be reached 9:00a - 5:30p PT.
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/JAY HUANG/Primary Examiner, Art Unit 3619