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 non-statutory subject matter.
Step 1:
Claims 1-14 are drawn to a system (machine).
Claims 15-19 are drawn to a non-transitory computer readable medium (machine).
Claim 20 is drawn to a method (process).
Thus, initially, under Step 1 of the analysis, it is noted that the claims are directed towards eligible categories of subject matter.
Step 2A:
However, under Step 2A, the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea). The claims are directed to the abstract idea of a mental process.
Let us begin by considering the requirements of each independent claim:
Thus, let us take Claim 1 as exemplary:
A system comprising:
at least one storage device configured to store data that represents at least one image of a roulette wheel spin; and
circuitry configured to:
rotate at least a portion of the image represented by the data to align with a reference position associated with a roulette wheel;
identify one or more attributes of the roulette wheel spin based at least in part on the image by implementing an artificial intelligence (AI) model comprising an object detection model (mental process: concepts performed in the human mind including an observation, evaluation, judgment, opinion; an observation could include visually identifying attributes of a roulette spin);
predict, via the object detection model, which slot of the roulette wheel catches a roulette ball during the roulette wheel spin based at least in part on the attributes (mental process: concepts performed in the human mind including an observation, evaluation, judgment, opinion; an evaluation could include a prediction of a roulette outcome); and
account for a number corresponding to the slot of the roulette wheel in a wagering game application (mental process: concepts performed in the human mind including an observation, evaluation, judgment, opinion; an evaluation could include identifying where a roulette ball lands).
Under broadest reasonable interpretation, independent claims 1, 15, and 20 are directed to a mental process, aside from the reference to generic computer components (e.g. a storage device, circuitry, and an object detection model of an artificial intelligence model).
The second prong of Step 2A, ask whether the claims recite additional elements that would integrate the abstract idea into a practical application. Here, the abstract idea is not integrated into a practical application. Claim 1 recites the additional elements of a storage device, circuitry, an object detection model of an artificial intelligence model, and rotating a portion of the image. The storage device, circuitry, and object detection model of an artificial intelligence model are recited at a high level of generality (i.e. generic computer components performing generic functions like storing and processing data) and do not add any meaningful limitation to the abstract idea because it amounts to simply invoking a computer as a tool to perform an existing process in their ordinary capacity and/or generally linking the abstract idea to a technological environment. In other words, the claims invoke the storage device, circuitry, and object detection model of an artificial intelligence model merely as tools to execute the abstract idea without adding any meaningful limitation to the abstract idea. Further, the step of rotating at least a portion of the image is considered insignificant extra-solution activity related to data (image) gathering and manipulation and does not add any meaningful limitation to the mental process steps.
Step 2B:
Step 2B asks whether a claimed invention which fails Step 2A contains an inventive concept, i.e. significantly more. Independent claim 1 does not include additional elements, when considered individually and in combination, that amount to significantly more than the abstract idea. As discussed above with respect to the integration of the abstract idea into a practical application, the storage device, circuitry, and object detection model of an artificial intelligence model are recited at a high level of generality (i.e. as generic components performing generic functions like storing and processing data) and simply amount to implementing the abstract idea using a generic computer. The additional elements that were considered insignificant pre-solution or extra-solution activity have been re-analyzed and do not amount to anything more than what is well-understood, routine and conventional (see MPEP 2106.05(d), Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network); but see DDR Holdings, LLC v. Hotels.com, L.P., 773 F.3d 1245, 1258, 113 USPQ2d 1097, 1106 (Fed. Cir. 2014) ("Unlike the claims in Ultramercial, the claims at issue here specify how interactions with the Internet are manipulated to yield a desired result--a result that overrides the routine and conventional sequence of events ordinarily triggered by the click of a hyperlink AND Storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93).
The combination of additional elements adds nothing that is not already present when considered separately. Therefore, the claims recite an abstract idea without significantly more.
Dependent claims
Claims 2-14 and 16-19 inherit the same abstract idea as claims 1, 15, and 20.
Claims 2-14 and 16-19 recite further additional element limitations related to mental processes (i.e. steps of detecting, training, determining, calculating, scoring, etc., along with various steps related to manipulating images). These additional elements, under their BRI, fall within the mental processes grouping(s) of abstract ideas and do not add any meaningful limitation to the abstract idea and do not amount to anything more than what is well-understood, routine and conventional, as would flow naturally from the similar recitations discussed above.
Claim Rejections - 35 USC § 103
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 (i.e., changing from AIA to pre-AIA ) 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.
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim(s) 1-4, 9, 10, 15-18, and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Pearce (US 9,717,980 B2) in view of Quinn et al. (US 2023/0196874 A1).
Regarding claims 1, 15, and 20, Pearce discloses a system comprising:
at least one storage device configured to store data that represents at least one image of a roulette wheel spin (see col. 2, lines 29-30, The image processor may be configured to generate video image data from the formed image); and
circuitry configured to:
rotate at least a portion of the image represented by the data to align with a reference position associated with a roulette wheel (see col. 2, lines 31-36, The image processor may be further configured to sample different data portions of the generated video image data, each sampled data portion corresponding to a respective image portion of the formed image, and each image portion being associated with a corresponding region of the ball play volume);
identify one or more attributes of the roulette wheel spin based at least in part on the image by implementing an artificial intelligence (AI) model comprising an object detection model (see col. 2, lines 43-50, The image processor may be further configured to determine an angular velocity and/or a deceleration of a ball within the ball play volume by comparing a first data portion of the video image data sampled at different times, the first data portion corresponding to a respective first image portion associated with a corresponding region of the ball play volume in which the ball track of the roulette wheel is located);
predict, via the object detection model, which slot of the roulette wheel catches a roulette ball during the roulette wheel spin based at least in part on the attributes (see col. 2, lines 61-65, Thus, the image processor may be even further configured to determine a drop zone of the ball by determining an angular position of the ball with respect to the cylinder of the roulette wheel, when during its deceleration the ball leaves the ball track of the roulette wheel); and
account for a number corresponding to the slot of the roulette wheel in a wagering game application (see col. 3, lines 13-20, The image processor may be further configured to determine a ball pocket of the roulette wheel in which a ball is resting by comparing a third data portion of the video image data sampled at different times, the third data portion corresponding to a respective third image portion associated with a corresponding region of the ball play volume in which the ball pockets of the roulette wheel are located).
However, Pearce does not explicitly disclose that the identifying step occurs by implementing an artificial intelligence (AI) model comprising an object detection model.
Quinn teaches a gaming activity monitoring system which can identify gaming objects by implementing an artificial intelligence (AI) model comprising an object detection model (see par. [0092], The upstream computing device may be configured to process the received image data to detect a game object and an image region corresponding to the detected game object. The upstream computing device may be configured to process the image region corresponding to the identified game object to determine a game object attribute; also see par. [0104], The object detection module 123 may comprise a game object detection neural network 151 trained to process images of the gaming table and detect game objects placed on the gaming table). It would have been obvious to one of ordinary skill in the art to combine the system of Pearce with the AI model of Quinn so that data regarding gaming activities may facilitate data analytics to improve operations and management of the gaming venue (see Quinn, par. [0003]).
Regarding claims 2 and 16, Pearce discloses wherein the circuitry is further configured to detect the slot into which the roulette ball lands as part of the roulette wheel spin via the object detection model (see col. 3, lines 13-20, The image processor may be further configured to determine a ball pocket of the roulette wheel in which a ball is resting by comparing a third data portion of the video image data sampled at different times, the third data portion corresponding to a respective third image portion associated with a corresponding region of the ball play volume in which the ball pockets of the roulette wheel are located).
Regarding claims 3 and 17, Pearce discloses wherein the circuitry is further configured to detect the slot into which the roulette ball lands while the roulette wheel is spinning (see col. 3, lines 13-20, The image processor may be further configured to determine a ball pocket of the roulette wheel in which a ball is resting by comparing a third data portion of the video image data sampled at different times, the third data portion corresponding to a respective third image portion associated with a corresponding region of the ball play volume in which the ball pockets of the roulette wheel are located).
Regarding claims 4 and 18, Quinn teaches wherein the object detection model is trained by training data comprising at least one of: images of roulette wheel spins captured at different angles over roulette wheels; images of roulette wheel spins in which roulette balls land in different slots; or images of roulette wheel spins in which roulette balls caught in different slots are rotated to different positions around roulette wheels (see par. [0104], The object detection module 123 may also be trained to determine a region or zone of the gaming table where the game object is or can be detected).
Regarding claim 9, Pearce discloses wherein the circuitry is further configured to crop the portion of the image around the number corresponding to the slot; and identify the number corresponding to the slot based at least in part on the cropped portion of the image (see col. 5, lines 33-39, The method may further comprise the step of comparing a third data portion of the video image data sampled at different times, the third data portion corresponding to a respective third image portion associated with a corresponding region of the ball play volume in which the ball pockets of the roulette wheel are located, so as to determine a ball pocket of the roulette wheel in which a ball is resting).
Regarding claim 10, Pearce discloses wherein the storage device is further configured to store a video of the roulette wheel spin; and the circuitry is further configured to: convert the video into multiple still images of the roulette wheel spin; and store the multiple still images in the storage device for use in predicting which slot catches the roulette ball during the roulette wheel spin (see col. 2, lines 31-36, The image processor may be further configured to sample different data portions of the generated video image data, each sampled data portion corresponding to a respective image portion of the formed image, and each image portion being associated with a corresponding region of the ball play volume; also see col. 2, lines 61-65, Thus, the image processor may be even further configured to determine a drop zone of the ball by determining an angular position of the ball with respect to the cylinder of the roulette wheel, when during its deceleration the ball leaves the ball track of the roulette wheel).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
JP4549311B2 (machine translation provided)
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/ALLEN CHAN/Primary Examiner, Art Unit 3715 6/23/2026
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