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 .
Continued Examination Under 37 CFR 1.114
2. 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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 6/12/2026 has been entered.
Response to Amendment
3. Applicant has amended the claims and argues the claims are eligible under 35 USC § 101 because they recite a specific technical data processing methodology that improves the functioning of the odds calculation system itself by providing a particular technical approach to real-time odds determination; and not merely applying machine learning to a new environment so the claims are eligible under 35 USC § 101. Further, Applicant asserts this is a specific technical process that constrains the claims to a particular implementation (Remarks 11 filed 6/25/2026). Examiner disagrees. Examiner deems the addition of “wherein the machine learning determines the probability and the set of odds by filtering a historical plays database on at least one parameter of the live event, performing correlations on the filtered data, extracting relevant correlations, and adjusting the set of odds based on a comparison of differences using the extracted correlations” (See Independent claims 1, 7, 10, and 14) as not providing a practical application but of introducing additional mathematical relationships which are an abstract mathematical concept. Consequently, because it is an abstract concept, it cannot be a practical application that improves the functioning of a computing system or technology. For the above reasons, the claims are ineligible under 35 USC § 101.
4. Examiner provides for Applicant’s consideration the recent decision in Ex Pate Desjardins (December 5, 2025). The decision analyzed eligibility in terms of whether the claims were directed to an improvement in the functioning of a computer, or an improvement to other technology or technical field under longstanding Federal Circuit precedent in Enfish, LLC v. Microsoft Corp., 822 F.3d 1327 (Fed. Cir. 2016) and McRO, Inc. v. Bandai Namco Games Am. Inc., 837 F.3d 1299 (Fed. Cir. 2016). See also MPEP §§ 2106.04(d)(l) and 2106.05(a). Based on this decision, the MPEP has been updated in-part to read:
The claimed invention was a method of training a machine learning model on a series of tasks. The Appeals Review Panel (ARP) overall credited benefits including reduced storage, reduced system complexity and streamlining, and preservation of performance attributes associated with earlier tasks during subsequent computational tasks as technological improvements that were disclosed in the patent application specification…In Step 2A Prong Two, the ARP then determined that the specification identified improvements as to how the machine learning model itself operates, including training a machine learning model to learn new tasks while protecting knowledge about previous tasks to overcome the problem of “catastrophic forgetting” encountered in continual learning systems. Importantly, the ARP evaluated the claims as a whole in discerning at least the limitation “adjust the first values of the plurality of parameters to optimize performance of the machine learning model on the second machine learning task while protecting performance of the machine learning model on the first machine learning task” reflected the improvement disclosed in the specification. Accordingly, the claims as a whole integrated what would otherwise be a judicial exception instead into a practical application at Step 2A Prong Two, and therefore the claims were deemed to be outside any specific, enumerated judicial exception. (MPEP 2106.04 (d)(III).
Applicant’s remarks that center on the machine learning steps be “special”, “technical”, “particular”, “concrete”, “particular approach to real-time odds determination” of “how” the machine learning operates (Remarks 11) is not the test. Applicant should instead focus on whether the claimed invention provides something on par with the “reduced storage, reduced system complexity and streamlining, and preservation of performance attributes associated with earlier tasks during subsequent computational tasks” as technological improvements if they were disclosed in the patent application specification to mitigate the problem of “catastrophic forgetting” by improving the technological computing environment.
5. Applicant has amended the claims to include reciting additional operations of the machine learning and directs his arguments to the amended claim language. Yet, Examiner finds these elements are taught by Shore and Chan as explained below. Thus, the rejections based on Shore, Alonso, and Chan are maintained.
Claim Rejections - 35 USC § 101
6. 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.
7. Claims 1, 3-7, 10-12, and 14-23 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to an abstract idea without significantly more.
8. Step 1
Claims 1, 3-7, 11-12, and 14-23 are directed to an apparatus/system or method meeting the requirements for Step 1.
9. Step 2A Prong 1
In independent Claim 1 (and similarly for Claims 7, 10, and 14), the following italicized steps recite abstract rules for playing a game which is a certain form of human activity:
Claim 1
A system, comprising:
at least one processor; and
at least one memory having instructions stored thereon executed by the at least one processor to:
receive and send, to an application, at least one pre-event information regarding a live event;
define, with machine learning, at least one performance parameter;
determine, with machine learning, at least one state information regarding at least one performance parameter;
identify at least one plurality of possible future states of the live event that may occur between the first timing and the second timing;
identify a first timing and a second timing defining a timing interval associated with the at least one plurality of possible future states regarding the at least one performance parameter;
identify, with machine learning, at least one statistic regarding at least one type of outcome;
determine, with machine learning, in real time, a probability and a set of odds associated with the at least one type of outcome occurring between the first timing and the second timing;
wherein the machine learning determines the probability and the set of odds by filtering a historical plays database on at least one parameter of the live event, performing correlations on the filtered data, extracting relevant correlations, and adjusting the set of odds based on a comparison of differences using the extracted correlations;
receive at least one information request from the application,
send at least one information request to the application, wherein identification of the first timing and the second timing associated with the at least one plurality of future states, and
identification of the at least one statistic regarding the at least one type of outcome occurring between the first timing and the second timing are performed in parallel, and
display, on a user device, the set of odds, wherein the set of odds changes in real-time for the live event.
In light of Applicant’s Specification, users are able to view {bet on} the number of jump shots Kevin Durant attempts in the third quarter of the Brooklyn Nets vs. the Toronto Raptors event (Spec. [0016, 0090]) as an example of a pre-event information of a basketball game, performance information, state of the future performance parameter; as well as first and second timing. Additionally, these events generate statistical information such as average wager size (Spec. [0084]), and the probabilities/odds of a result in the event (Spec.[0088]). Following the rules of a game is similar to the abstract certain ways of organizing human activity in recent cases. See abstract rules for playing a game as in In re Smith, 815 F.3d 816, 818-19 (Fed. Cir. 2016) concluding that the claimed “method of conducting a wagering game” was directed to an abstract idea” and In re Marco Guldenaar Holding B.V., 911 F.3d 1157, 1161, 129 USPQ2d 1008, 1011 (Fed. Cir. 2018). There, the patentee claimed a method of playing a dice game including placing wagers on whether certain die faces will appear face up. 911 F.3d at 1160; 129 USPQ2d at 1011. The Federal Circuit determined that the claims were directed to the abstract idea of "rules for playing games", which the court characterized as a certain method of organizing human activity. 911 F.3d at 1160-61; 129 USPQ2d at 1011. See also Savvy Dog Sys. v. Pa. Coin, LLC, 2023-1073 (Fed. Cir. Mar. 21, 2024) which describes an electronic gaming method and system with a preview screen, specifically for a game called "Tic-Tac-Fruit." The Federal Circuit held that the claims directed to a skill-based element by allowing players to preview game setups before playing, was directed to an abstract idea.
Additionally, “wherein the machine learning determines the probability and the set of odds by filtering a historical plays database on at least one parameter of the live event, performing correlations on the filtered data, extracting relevant correlations, and adjusting the set of odds based on a comparison of differences using the extracted correlations” presents one or more mathematical relationships and in totality is an abstract mathematical concept.
Thus, each claim, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitations for following the rules of a game which is a certain method of organizing human activity and of containing mathematical relationships which is a mathematical concept. For analysis purposes these are merged into a single abstract idea of following the rules of a game.
10. Step 2A Prong II
The abstract idea is not integrated into a practical application. According to 2019 PEG, a consideration indicative of integration into a practical application includes improvements to the functioning of a computer or to any other technology or technical field (MPEP 2106.05(a)) or adding a specific limitation other than what is well-understood, routine, conventional activity, or adding unconventional steps that confine the claim to a particular application (a non-conventional and non-generic arrangement of various computer components for filtering Internet content, as discussed in BASCOM Global Internet v. AT&T Mobility LLC, 827 F.3d 1341, 1350-51, 119 USPQ2d 1236, 1243 (Fed. Cir. 2016) (MPEP § 2106.05(d)). Conversely, considerations not indicative of integration include adding words “apply it” (or equivalent) with the judicial exception or mere instructions to implement the abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. (MPEP 2106.05(f)); adding insignificant extra-solution activity (MPEP 2106.05(g)), or generally linking the use of the abstract idea to a particular technological environment or field of use (MPEP 2106.05(h)).
Here, a processor, memory, instructions, and display are recited so generically (no details whatsoever are provided other than in name only) that they represent no more than mere instructions to apply the judicial exception on a computer or user computing device. Applicant’s Specification does not disclose that the processor, memory, instructions, display or user computing device are directed to a technological solution to a technological problem that “overcome some sort of technical difficulty.” (citing ChargePoint, Inc. v. SemaConnect, Inc., 920 F.3d 759, 768 (Fed. Cir. 2019).
Similar to the disclosure in Savvy Dog, here, Applicant’s specification never suggests that the processor is improved from a technical perspective, or that it would operate differently than it otherwise could but instead the disclosed processor is merely receiving various metadata which form the basis of the {wagering} game e.g., the game, performance parameter, future state, timing, outcome, probability, odds, and information request. According to Applicant, “[f]urther, many of the embodiments described herein are described in terms of sequences of actions to be performed by, for example, elements of a computing device. It should be recognized by those skilled in the art that specific circuits can perform the various sequence of actions described herein (e.g., application specific integrated circuits (ASICs)) and/or by program instructions executed by at least one processor. Additionally, the sequence of actions described herein can be embodied entirely within any form of computer-readable storage medium such that execution of the sequence of actions enables the processor to perform the functionality described herein.” (Spec. [0014]).
As to display on a user device as claimed, Applicant does not improve the displays itself but relies on off-the-shelf technologies where “[f]or example, the game information, such as live video footage of a sporting event, may be displayed to the user on a television or computer monitor, radio, mobile phone, or other output devices. In some embodiments, the information may be displayed on a device capable of receiving user inputs, such as a mobile device 108, smartphone, touch-sensitive display device, etc.” (Spec. [0090]).
Consequently, these devices and programming are viewed as nothing more than an
attempt to generally link the use of the judicial exception to the technological environment of a computer or as a means to automate the steps. It should be noted that because the courts have made it clear that mere physicality or tangibility of an additional element or elements is not a relevant consideration in the eligibility analysis, the physical nature of these computer components does not affect this analysis. See MPEP 2106.05(I) for more information on this point, including explanations from judicial decisions including Alice Corp. Pty. Ltd. v. CLS Bank Int'l, 573 U.S. 208, 224-26 (2014).
What remains after the abstract italicized sections of Claim 1 and the processor and memory tools are extra-solution activity where:
receive and send, to a wagering application, at least one pre-event information regarding a live event; and
receive at least one information request from the application
send at least one information request to the application, wherein identification of the first timing and the second timing associated with the at least one plurality of future states, and
display, on a user device, the set of odds, wherein the set of odds changes in real-time for the live event.
These activities involve one or more of receiving or transmitting data over a network (MPEP 2106.05(d)(II)(i)), performing repetitive calculations (MPEP 2106.05(d)(II)(ii), electronic recordkeeping (MPEP 2106.05(d)(II)(iii) and arranging a hierarchy of groups/sorting information (MPEP 2106.05(d)(II)(vi) as well as displaying of content (The Court have held similar claims ineligible in Electric Power Group (In Electric Power Group, the Court found patent ineligible certain claims for "monitoring of an electric power grid by collecting data from multiple data sources, analyzing the data, and displaying the results." Elec. Power Grp., LLC v. Alstom S.A., 830 F.3d 1350, 1351 (Fed. Cir. 2016), See also a graphical user interface for electric trading in Trading Technologies International v. IBG LLC, 921 F.3d 1084 (Fed. Cir. 2019)). As to the identification of statistics and timings of future states, Examiner deems these to be akin to the creation of shadow prices in Alice.
Even when the limitations are viewed in combination, the additional elements in this claim do no more than automate the steps needed to be performed, using the one of more computer components as tools. While this type of automation is an improvement in a general sense as opposed to performance manually, there is no change to the computers and other technology that are recited in the claim as automating the abstract ideas, and thus this claim cannot improve computer functionality or other technology. See, e.g., Trading Technologies Int’l v. IBG, Inc., 921 F.3d 1084, 1093 (Fed. Cir. 2019) (using a computer to provide a trader with more information to facilitate market trades improved the business process of market trading, but not the computer) and the cases discussed in MPEP 2106.05(a)(I), particularly FairWarning IP, LLC v. Latric Sys., 839 F.3d 1089, 1095 (Fed. Cir. 2016) (accelerating a process of analyzing audit log data is not an improvement when the increased speed comes solely from the capabilities of a general-purpose computer) and Credit Acceptance Corp. v. Westlake Services, 859 F.3d 1044, 1055 (Fed. Cir. 2017) (using a generic computer to automate a process of applying to finance a purchase is not an improvement to the computer’s functionality).
Accordingly, each claim, as a whole, does not integrate the recited judicial exception into a practical application and the claim is directed to the judicial exception. Thus, Claim 1, and similarly, Claims 7, 10, and 14, lack the eligibility requirements of Step 2 Prong II.
11. Step 2B
According to the 2019 PEG, in addition to the considerations discussed in Step 2A, an additional consideration indicative of an inventive concept (aka “significantly more”) is the addition of a specific limitation other than what is well-understood, routine, conventional activity in the field (MPEP 2106.05(d)). Conversely, an additional consideration not indicative of an inventive concept is simply appending well-understood, conventional activities previously known to the industry, specified at a high level of generality, to the abstract idea (MPEP 2106.05(d) and Berkheimer Memo, April 20, 2018). Thus, the additional elements evaluated under Step 2A are re-evaluated in Step 2B to determine if they are more than what is well-understood, routine, conventional activity in the field.
Here, the identified portions of the claim wherein:
receive and send, to a wagering application, at least one pre-event information regarding a live event; and receive at least one information request from the application
send at least one information request to the application, and
display, on a user device, the set of odds, wherein the set of odds changes in real-time for the live event.
are all determined to be well-understood activities evidencing a combination of receiving or transmitting data over a network (MPEP 2106.05(d)(II)(i)), performing repetitive calculations (MPEP 2106.05(d)(II)(ii), electronic recordkeeping (MPEP 2106.05(d)(II)(iii) and arranging a hierarchy of groups/sorting information (MPEP 2106.05(d)(II)(vi) and of displaying as in Electric Power Group. Thus, Claim 1, and similarly, Claims 7, 10 and 14, do not recite additional elements, individually or in combination, that amount to significantly more than the abstract idea. Thus, Claims 1, 7, 10, and 14 are ineligible.
12. Dependent Claims 3-7, 11-12, and 14-23
Claims 3 and 18-21 is extra-solution tools and data gathering/processing. Claims 4-6, 17, and 22-23 recite more abstract rules and particulars of the wagering and definitions of elements. Claims 11-12 and 15-16 are additional abstract details of the abstract machine learning operations. Thus, none of the claims supply a practical application or inventive concept sufficient to transform the nature of the claim into a patent-eligible application.
Claim Rejections - 35 USC § 103
13. 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.
14. 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.
15. The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied 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.
16. Claims 1, 3-7, 10-12, and 14-23 is/are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Pat. Pub. No. 2015/0287285 to Shore in view of U.S. Pat. Pub. No. 2015/0149837 to Alonso and U.S. Pat. Pub. No. 2019/0354765 to Chan.
In Reference to Claims 1, 4-7, 10-12, 14, and 17
Shore discloses a system and method {betting exchange system, wagering system}, comprising:
at least one processor (Fig. 1 processor 138); and
at least one memory having instructions stored thereon (Fig. 1 memory 136) executed by the at least one processor to:
receive and send, to an {wagering} application, at least one pre-event information regarding a live event (Figs. 3 and 4, micro-event information such as a player swinging a bat in the context of a particular inning of a baseball game [0086] is received by the wagering system (Figs. 1 and 2) followed by a user placing a micro-bet with respect to the micro-event [0088] of Fig. 3, it is noted the baseball game is a real live game ([0024, 0092]);
define at least one performance parameter (Fig. 6 pass, see also ball or strike [0089] or any other parameter for a portion of a game [0089]);
determine at least one state information regarding the performance parameter (Fig. 6 incomplete pass {system variable/system metric} and historical information such as a team history [0069], see also historical averages [0091], histories [0091] {historical information});
identify at least one plurality of possible future states of the live event that may occur between the first timing and the second timing (Fig. 6 desctibes a plurality of 1st down, turnover, touchdown [0103] which may occur as micro events as part of a macro event. Shore defines macro-events and micro-events such that “Micro-events with micro-outcomes can occur several or more times, for example, during an overall sporting macro-event, or game or be calculated at the conclusion of segments of the macro event (e.g., quarters, halves, etc.) or the entire macro event.” [0089], these activities happen in the context of whether the sport depends on time, or events such as innings. Examiner construes the first timing and second timing the same as Shore’s macro event (first timing such as a baseball game) and second timing (such as an inning in the game) See also [0106]);
identify at least one first timing and second timing interval associated with the plurality of possible future states regarding the performance parameter (Examiner construes this in light of the specification as the beginning and end or result of a play. Here, Shore discloses that micro-betting can occur over segments of a macro-event such as “quarters or halves” – which have first and second timings, for example, a baseball game and innings {timing intervals} in the game) or for each swing of a baseball bat by a baseball player resulting in a micro-outcome – which has a first timing of entering the batter’s box to a second timing interval of each “a strike, a ball, or a walk” outcomes [0089]);
identify at least one statistic regarding at least one type of outcome (view statistics related to the event or outcome such as team history and official statistics tracking micro-outcomes [0011, 0033, 0069; 0090, 0091])
determine in real-time at least one of a probability and a set of odds associated with the type of outcome occurring between the at least one of the at least one first timing and the second timing (Fig. 6 Odds are determined with at least one member in the set for the micro-event and micro-outcome);
display information regarding at least one opportunity for placement of at least one back bet (that the event will occur) bet (Fig. 6); and
receive at least one information request from the {wagering} application (Fig. 6 receiving the wager input screen of available micro-wagers),
send at least one information request to the {wagering} application (Fig. 6 Submit),
receiving at least one {back bet} wager selection and the at least one wager confirmation application (the system receives the wager submission and remits a wager confirmation ([0032, 0088]), and
display, on a user device, the set of odds, wherein the set of odds changes in real-time for the live event (remote computing device 210 Fig. 4, odds which are updated [0092] and as long as the game is being played the odds are being updated [0093], Fig. 6, odds changes, and in real-time ([0024, 0092]).
In light of Applicant’s specification various modules may operate, process data, update, and transmit data in a parallel manner [0098] to provide appropriate and continuous wagers through the wagering network [0098]. Shore (Fig. 6, see also [0091-0093]) continuously presents plays which have first and second timings along with contemporaneously stated odds and parimutuel wager amounts ([0091-0093]) and Shore describes by example where micro-events and odds information are determined in parallel [(0091-0093]). If Applicant disagrees with the cited portion and interpretation of Shore, Applicant is directed to the teachings of Alonso for “wherein identification of the first timing and second timing interval associated with future states and identification of a statistic regarding an outcome occurring between the first and second timing are performed in parallel.” One of skill in the art would be aware of the teachings of Alonso.
Alonso teaches of collecting and displaying sporting event data based on real time data (Titl.). Tag ID and sensor data transmission as in Figs. 3A, 3B, 3C, 3D, and 3E show block diagrams of various different architectures that may be utilized in transmitting signals from one or more tags and sensors to one or more receivers of a receiver processing and analytics system in accordance with embodiments of the invention. In some embodiments, the depicted architectures may be used in connection with the receiver processing and analytics system 110 of FIG. 1. More than one of these architectures may be used together in a single system. Importantly, tag ID and sensor data in one architecture are processed in parallel (Fig. 3B). See also where “[o]ptionally, at block 2814, the filter and analytics engine 515 may process tag events in parallel by processing tag event data as a predefined unit of work. The filter and analytics engine 515 determines a unit of work or predetermined unit time of accumulated tag event data (e.g. one second). A unit of work may include all tag events for the unit of time for both the left and right shoulder tag for a player. The average time, for example two seconds, may require the next unit of work to include blink data events from the previous unit of work. (Fig. 28 [0419]).
The Supreme Court in KSR Int'l Co. v. Teleflex Inc., 550 U.S. 398, 415-421, 82 USPQ2d 1385, 1395-97 (2007) identified a number of rationales to support a conclusion of obviousness
(A) Combining prior art elements according to known methods to yield predictable results;
(B) Simple substitution of one known element for another to obtain predictable results;
(C) Use of known technique to improve similar devices (methods, or products) in the same way; and
(D) Applying a known technique to a known device (method, or product) ready for improvement to yield predictable results.
Here, it would require only routine skill in the art to modify or clarify the process of Shore with the parallel processing of Alonso to achieve the predictable result of accurately and quickly determine micro-event, statistical, and micro-outcome data more quickly than had the information been processed serially. The Courts have held that combining prior art elements according to known methods to yield predictable results to be indicia of obviousness.
Shore discloses the invention substantially as claimed to include identification of performance parameters, state information, statistic, probability, and odds yet the reference does not explicitly disclose use of {unsupervised} machine learning. According to Applicant, machine learning is an unsupervised algorithm is the absence of human input to define patterns ([0072]) and “wherein the machine learning determines the probability and the set of odds by filtering a historical plays database on at least one parameter of the live event, performing correlations on the filtered data, extracting relevant correlations, and adjusting the set of odds based on a comparison of differences using the extracted correlations. One of skill in the art would be aware of the teachings of Chan.
Chan teaches of interface navigation of video (Titl.) of a system of Fig. 1 which illustrates a technology stack 100 indicative of technology layers configured to execute a set of capabilities, in accordance with an embodiment of the present invention. The technology stack 100 may include a customization layer 102, an interaction layer 104, a visualizations layer 108, an analytics layer 110, a patterns layer 112, an events layer 114, and a data layer 118, without limitations. The different technology layers or the technology stack 100 may be referred to as an “Eagle” Stack 100 {unsupervised algorithm}, which should be understood to encompass the various layers to allow precise monitoring, analytics, and understanding of spatiotemporal data associated with an event, such as a sports event and the like. For example, the technology stack may provide an analytic platform that may take spatiotemporal data (e.g., 3D motion capture “XYZ” data) from National Basketball Association (NBA) arenas or other sports arenas and, after cleansing, may perform spatiotemporal pattern recognition to extract certain “events”. The extracted events may be, for example, (among many other possibilities) events that correspond to particular understandings of events within the overall sporting event, such as “pick and roll” or “blitz.” Such events may correspond to real events in a game, and may, in turn, be subject to various metrics, analytic tools, and visualizations around the events. Event recognition may be based on pattern recognition by machine learning, such as spatiotemporal pattern recognition, and in some cases, may be augmented, confirmed, or aided by human feedback.” ([0067]). See also “[t]he machine understanding may include various levels of semantic identification, as well as information of position and speed information for various items or elements, identification of basic events such as various types of shots and screens during a sporting event, and identification of complex events or a sequence of events such as various types of plays.” ([0418]).
“The machine learning tools and input feed alignment may allow automatic generation of content and information such as statistics, predictions, comparisons, and analysis. The machine learning tools may further allow to generate outputs based on a user query input such as to determine various predictive analytics for a particular team player in view of historical shots and screens in a particular context, determine possibilities of success and failures in particular zones and game scenarios conditioned to particular user inputs, and the like. The machine understanding tools may simulate entire aspects of real-life sporting events on a computer screen utilizing visualization and modeling examples.” ([0418]).
Further, Chan teaches of filtering a historical plays database on at least one parameter of the live event (filtering [0097, 0103, 0104, 0105, of events 0122, 0130] for historical plays and play types [0253, 0408, 0418], performing correlations on the filtered data, extracting relevant correlations (see “correlations” where “[i]n embodiments, machine-extracted information about events and contexts may be used to determine one or more patterns, such as by analyzing time series, correlations, and the like in the machine-extracted events and contexts. For example, tendencies of a team to follow running a certain play with a particular play may be determined by comparing instances of the two plays over time.” [0404]), and adjusting the set of odds based on a comparison of differences using the extracted correlations (“[t]he methods and systems disclosed herein may include methods and systems for real time adjustment of betting lines and/or additional betting option creation based on in-game contextual content.” [0351];
Chan provides this approach because “[a]rtificial Intelligence tools to perform analysis and interpretation of data retrieved from the real-time event such as a sports event so that the analyzed data results in insights that make sense out of the pulled big data from the real-time event.” [0296]).
It would require only routine skill in the art to rely on the machine learning and processes of Chan of filtering historical play data to enhance the capabilities of Shore so the analyzed data results in insights that make sense out of the pulled big data from the real-time event. (Chan [0296]). The Courts have held that the use of a known technique to improve similar devices (methods, or products) in the same way to be indicia of obviousness.
In Reference to Claims 3 and 18-23
Shore discloses a state of an event such as a “player currently at bat” {player position data} and of the position of a ball (strike or ball) resulting from a pitch [0089] but is silent as to the use of sensors. Chan further teaches data gathering through one of more sensors such as accelerometer and other motions sensors, image sensors or the like employed to service and aid machine learning [0408]). Chan further captures both 2D (x, y) [0145, 0222] and 3D (x, y, z) “XYZ” coordinate data ([0067]).
In Reference to Claims 15 and 16
Applicant discloses that supervised machine learning models may operate to perform analysis involving setting categories (Spec. 0070). Chan discloses the use of supervised machine learning models used for classification ([0192]) and semi-supervised models ([0243]).
Conclusion
17. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure is in the Notice of References Cited.
18. Any inquiry concerning this communication or earlier communications from the examiner should be directed to Paul A. D’Agostino whose telephone number is (571) 270-1992.
19. Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
20. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Kang Hu can be reached on (571) 270-1344. The fax phone number for the organization where this application or proceeding is assigned is 571-270-2992.
/PAUL A D'AGOSTINO/Primary Examiner, Art Unit 3715
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