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 .
Election/Restrictions
Applicant’s election without traverse of claims 31-60 in the reply filed on 09/18/2025 is acknowledged. Claims 1-30 are drawn to generic claims. Therefore this office action will address claims 1-60.
Priority
The later-filed application must be an application for a patent for an invention which is also disclosed in the prior application (the parent or original nonprovisional application or provisional application). The disclosure of the invention in the parent application and in the later-filed application must be sufficient to comply with the requirements of 35 U.S.C. 112(a) or the first paragraph of pre-AIA 35 U.S.C. 112, except for the best mode requirement. See Transco Products, Inc. v. Performance Contracting, Inc., 38 F.3d 551, 32 USPQ2d 1077 (Fed. Cir. 1994).
The disclosure of the prior-filed application, Application No. 18354505, fails to provide adequate support or enablement in the manner provided by 35 U.S.C. 112(a) or pre-AIA 35 U.S.C. 112, first paragraph for one or more claims of this application. Applicant parent application 18354505 includes features regarding code review but does not indicate asset review as claimed. Therefore reviewing of assets beyond code, as found in example claim 5, is not clearly supported by the parent application. See paragraph [0390] of applicant’s original parent application disclosure. As per recommendation based on the review examiner did not find this for the review done by QA. Recommendations appear to be based on observed play. Therefore support for the current claims is unclear. Due to the length of applicant’s original parent disclosure applicant should cite to specific paragraphs for support.
Drawings
The drawings are objected to because Fig. 37, item 3708 text for output is missing the “t” at the end. Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. The figure or figure number of an amended drawing should not be labeled as “amended.” If a drawing figure is to be canceled, the appropriate figure must be removed from the replacement sheet, and where necessary, the remaining figures must be renumbered and appropriate changes made to the brief description of the several views of the drawings for consistency. Additional replacement sheets may be necessary to show the renumbering of the remaining figures. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
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 1-60 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a mental process without significantly more. As per step 1 examiner recognizes that the claims are directed towards systems or methods that include AI systems performing steps. Therefore step 1 is met. As per step 2A the claim(s) recite(s) “an AI-driven processor configured to analyze game assets, wherein the game assets comprise at least partially developed executable instructions or computer-readable files for presenting and allowing play of a game of chance on a gaming machine; the gaming machine including at least one of a monetary input device configured to receive a physical item associated with a monetary value and/or cashless wagering, a user interface, at least one processor configured to run the at least partially developed executable instructions or computer- readable files, a game display, and memory in communication with the processor; the AI-driven processor configured to identify defects within the game assets using algorithms after machine learning the requisite information necessary to process the game assets; the AI-driven processor further configured to generate recommendations for correcting identified defects, wherein the recommendations are based on the analysis of the game assets; and a media encoding and transcoding router configured to (i) change and/or consolidate input data types into a specific media format or file type and (ii) direct changed and/or consolidated data to a neural network in a transformer process for analysis.” as being directed towards an AI system using machine learning to detect defects in game assets and in the second species embodiment to provide a recommendation based on the analysis. Further machine steps include media encoding and transcoding for a neural network for analysis. Dependent claims include categorizing the machine learning into supervised, unsupervised, and reinforce. Additionally dependent claims include that game assets group consisting of: game graphics, game animations, game programming, game math, and game sound. See bolded sections for the mental process which reads on the process of identifying information from observed data and applying a subsequent action based on this review. In this case the mental step is to review assets to identify issues, or defects, and recommend a solution which is a task that can be performed mentally and is often done as a mental step. This is the mental process of quality review and is a task that is done in gaming to insure that a product is ready for release. Therefore the machine learning element is directed towards a mental step that can be performed by an individual. Regarding the machine learning examiner looks to see if the algorithm includes additional elements that would go beyond a mental process. In this case the machine learn appears to be a broad limitation indicating that machine learning is used without indication on what the algorithms are that would go beyond a mental process. Specifically no algorithms are included in the claims. Therefore the machine learning element does not provide more to the mental process. This judicial exception is not integrated into a practical application because the claims remain directed to the mental process of quality review. The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the machine learning elements are generically recited without sufficient detail as to provide more than the mental process. A practical application cannot be provided without more details on how the machine learning elements differ over a mental process. The additional elements are addressed in step 2B but examiner notes the final limitation regarding the transformation process for a neural network appears to be directed towards making the media elements readable by the system. This is not an additional step that would lend a practical application to the mental process but is instead an element of making the system able to perform the machine learning steps. Therefore this step does not lend more to the identified mental process and therefore does not provide a practical application.
As per step 2B examiner recognizes that additional elements are directed to conventional activities or extra solution activity. See below.
Limitation “gaming machine including at least one of a monetary input device configured to receive a physical item associated with a monetary value and/or cashless wagering, a user interface, at least one processor configured to run the at least partially developed executable instructions or computer- readable files, a game display, and memory in communication with the processor” and “a media encoding and transcoding router configured to (i) change and/or consolidate input data types into a specific media format or file type and (ii) direct the changed and/or consolidated data to a neural network in a transformer process for analysis.” and other hardware elements. The hardware elements are commonly found in the gaming art related to electronic slot machines or wagering terminals and therefore are no more than a generic recitation of computer hardware elements including network elements and therefore does not provide a practical application that amounts to more than the identified abstract idea. This includes the recitation of memory, processors, and displaying steps which are generically found in electronic gaming machine including the elements accepting wagers for the purpose of presenting an outcome and payout for the results. See US 6186894 B1 at col. 5, lines 25-38 regarding video slot reels including displaying outcomes and that the activity of spinning and producing random outcomes from a wagering game are convention activities well-understood in the art. See Acres (US Pub. No. 2012/0172107 A1) teaches within the electronic gaming art the use of a random number generator to determine numbers for specific reel stop positions in order to determine an outcome which is evaluated if it is a winning combination of symbols appearing on a played payline (paragraph [0073]). Du et al. (US Pub. No. 2023/0196117 A1) broadly discussed conventional machine learning such as supervised and unsupervised (paragraph [0003]). Han et al. (US Pub. No. 2023/0169075 A1) broadly discusses transformer neural network of the conventional technique in order to create natural language query and scheme to translate information for use by the system (paragraphs [0088]-[0090]). Therefore the additional elements cited do not appear to go beyond conventional features related to the gaming art or to the machine learning art. Therefore the hardware features does not provide a practical application.
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) 1-60 is/are rejected under 35 U.S.C. 103 as being unpatentable over Sobran et al. (US Pub. No. 2019/0347188 A1 hereinafter referred to as Sobran) in view of Russ et al. (US Pub. No. 2023/0377421 A1 hereinafter referred to as Russ).
As per claims 1 and 21, Sobran teaches a system and method for automated defect identification of software (abstract software product is reviewed to determine defects), comprising: an AI-driven processor (Fig. 2, item 201 and paragraph [0021]) configured to analyze assets, wherein the software comprise at least partially developed executable instructions or computer-readable files for presenting and allowing software functions of the machine (paragraphs [0005], [0041], and [0051]-[0052] potential software errors are identified based on a trained machine learning module using previous detected error reports); the AI-driven processor configured to identify defects within the assets using algorithms after machine learning the requisite information necessary to process the assets (paragraphs [0005], [0041], and [0051]-[0052] potential software errors are identified based on a trained machine learning module using previous detected error reports); and a media encoding and transcoding router configured to (i) change and/or consolidate input data types into a specific media format or file type and (ii) direct the changed and/or consolidated data to the system in a transformer process for analysis (paragraphs [0005] and [0041]-[0043] input data is transformed via vectorization to be used in the system to identify errors). Sobran does not teach a slot machine game software analyze game assets wherein the gaming machine including at least one of a monetary input device configured to receive a physical item associated with a monetary value and/or cashless wagering, a user interface, at least one processor configured to run the at least partially developed executable instructions or computer- readable files, a game display, and memory in communication with the processor and a specific mention of a neural network. However, Russ teaches a gaming system (abstract) for a slot game (paragraph [0253] see slot reels) comprising artificial intelligence in the form of machine learning to modify game function to adapt to new user information (Fig. 5 and paragraph [0006]) wherein the artificial intelligence system comprises the use of a neural network which is a machine learning network (paragraph [0115]) and the gaming machine comprises at least one of a monetary input device configured to receive a physical item associated with a monetary value (Fig. 3, item 340) and/or cashless wagering, a user interface (Fig, item 316 and paragraph [0080]), at least one processor configured to run the at least partially developed executable instructions or computer-readable files (Fig. 3, item 304), a game display (paragraph [0253]), and memory in communication with the processor (Fig. 3, item 308). Hence, it would have been obvious to one of ordinary skill in the art at the time of filing to have combined the teachings of Sobran with Russ, since the gaming art includes the action of developing software for the purpose of gaming with Sobran showing that is desirable to detect errors in software development before release and to ease the amount of resources needed for this task (paragraph [0050] of Sobran) the system of Russ can be more cheaply implemented and as show by Russ the inclusion of machine learning to develop and maintain a game is known and desirable as a way to adapt and reduce resources. As per the use of a neural network as shown by Russ the machine learning can be considered as a form of neural network and therefore using a machine learning algorithm with explicit neural network setup would be obvious in the art as this is a form of machine learning. As per claim 21 a slot game is a game of chance.
As per claims 6, 16 and 26, Sobran teaches an automated defect identification of software system and method (abstract software product is reviewed to determine defects), comprising: an AI-driven automated defect identification engine configured to access assets (paragraphs [0005], [0041], and [0051]-[0052] potential software errors are identified based on a trained machine learning module using previous detected error reports), wherein the assets include executable instructions or computer-readable files for operating software on a machine (paragraphs [0005], [0041], and [0051]-[0052] potential software errors are identified based on a trained machine learning module using previous detected error reports), the AI-driven automated defect identification engine configured to analyze the assets and identify defects and/or inconsistencies using algorithms (paragraphs [0005], [0041], and [0051]-[0052] potential software errors are identified based on a trained machine learning module using previous detected error reports), based on prior machine learning of the requisite information needed to evaluate the assets (paragraphs [0005], [0041], and [0051]-[0052] potential software errors are identified based on a trained machine learning module using previous detected error reports); a media encoding and transcoding router configured to reformat and/or consolidate data from the assets into specific media types and route reformatted and/or consolidated data to appropriate a transformer process for analysis (paragraphs [0005] and [0041]-[0043] input data is transformed via vectorization to be used in the system to identify errors); and a logging module configured to store and report identified defects and/or inconsistencies for review and/or further action (paragraph [0048] log report is provided to developers). Sobran does not teach a slot machine game software analyze game assets wherein the gaming machine including at least one of a monetary input device configured to receive a physical item associated with a monetary value and/or cashless wagering, a user interface, at least one processor configured to run the at least partially developed executable instructions or computer- readable files, a game display, and memory in communication with the processor and a specific mention of a neural network. However, Russ teaches a gaming system (abstract) for a slot game (paragraph [0253] see slot reels) comprising artificial intelligence in the form of machine learning to modify game function to adapt to new user information (Fig. 5 and paragraph [0006]) wherein the artificial intelligence system comprises the use of a neural network which is a machine learning network (paragraph [0115]) and the gaming machine comprises at least one of a monetary input device configured to receive a physical item associated with a monetary value (Fig. 3, item 340) and/or cashless wagering, a user interface (Fig, item 316 and paragraph [0080]), at least one processor configured to run the at least partially developed executable instructions or computer-readable files (Fig. 3, item 304), a game display (paragraph [0253]), and memory in communication with the processor (Fig. 3, item 308). Hence, it would have been obvious to one of ordinary skill in the art at the time of filing to have combined the teachings of Sobran with Russ, since the gaming art includes the action of developing software for the purpose of gaming with Sobran showing that is desirable to detect errors in software development before release and to ease the amount of resources needed for this task (paragraph [0050] of Sobran) the system of Russ can be more cheaply implemented and as show by Russ the inclusion of machine learning to develop and maintain a game is known and desirable as a way to adapt and reduce resources. As per the use of a neural network as shown by Russ the machine learning can be considered as a form of neural network and therefore using a machine learning algorithm with explicit neural network setup would be obvious in the art as this is a form of machine learning. As per claim 26 a slot game is a game of chance.
As per claim 11, Sobran teaches an automated defect identification of software system (abstract software product is reviewed to determine defects), comprising: an artificial intelligence engine configured to access and analyze at least partially developed executable instructions or computer-readable files for software (paragraphs [0005], [0041], and [0051]-[0052] potential software errors are identified based on a trained machine learning module using previous detected error reports); the artificial intelligence engine being operable to identify defects and/or inconsistencies in the assets using algorithms trained via machine learning to process the software development files (paragraphs [0005], [0041], and [0051]-[0052] potential software errors are identified based on a trained machine learning module using previous detected error reports); and a media encoding and transcoding router configured to (i) convert and/or consolidate input data types into specified formats and (ii) route converted and/or consolidated data to a transformer process (paragraphs [0005] and [0041]-[0043] input data is transformed via vectorization to be used in the system to identify errors), enabling analysis and error detection and/or further action (paragraph [0048] log report is provided to developers). Sobran does not teach a slot machine game software analyze game assets wherein the gaming machine including at least one of a monetary input device configured to receive a physical item associated with a monetary value and/or cashless wagering, a user interface, at least one processor configured to run the at least partially developed executable instructions or computer- readable files, a game display, and memory in communication with the processor and a specific mention of a neural network. However, Russ teaches a gaming system (abstract) for a slot game (paragraph [0253] see slot reels) comprising artificial intelligence in the form of machine learning to modify game function to adapt to new user information (Fig. 5 and paragraph [0006]) wherein the artificial intelligence system comprises the use of a neural network which is a machine learning network (paragraph [0115]) and the gaming machine comprises at least one of a monetary input device configured to receive a physical item associated with a monetary value (Fig. 3, item 340) and/or cashless wagering, a user interface (Fig, item 316 and paragraph [0080]), at least one processor configured to run the at least partially developed executable instructions or computer-readable files (Fig. 3, item 304), a game display (paragraph [0253]), and memory in communication with the processor (Fig. 3, item 308). Hence, it would have been obvious to one of ordinary skill in the art at the time of filing to have combined the teachings of Sobran with Russ, since the gaming art includes the action of developing software for the purpose of gaming with Sobran showing that is desirable to detect errors in software development before release and to ease the amount of resources needed for this task (paragraph [0050] of Sobran) the system of Russ can be more cheaply implemented and as show by Russ the inclusion of machine learning to develop and maintain a game is known and desirable as a way to adapt and reduce resources. As per the use of a neural network as shown by Russ the machine learning can be considered as a form of neural network and therefore using a machine learning algorithm with explicit neural network setup would be obvious in the art as this is a form of machine learning.
As per claims 31 and 51, Sobran teaches a system and method for automated defect identification of software (abstract software product is reviewed to determine defects), comprising: an AI-driven processor (Fig. 2, item 201 and paragraph [0021]) configured to analyze assets, wherein the software comprise at least partially developed executable instructions or computer-readable files for presenting and allowing software functions of the machine (paragraphs [0005], [0041], and [0051]-[0052] potential software errors are identified based on a trained machine learning module using previous detected error reports); the AI-driven processor configured to identify defects within the assets using algorithms after machine learning the requisite information necessary to process the assets (paragraphs [0005], [0041], and [0051]-[0052] potential software errors are identified based on a trained machine learning module using previous detected error reports); the AI-driven processor further configured to generate recommendations for correcting identified defects, wherein the recommendations are based on the analysis of the assets (paragraphs [0040] closest previous error log is provided which includes recommendations to “crowd sourced data (e.g., forums, published documentations, real time build logs) with to the minute knowledge of changes in these landscapes whether they be public or proprietary to ultimately provide an immediate fix based on identifying a vectorized negative log report that is closest in distance to the vectorized log report of a recent build log”); and a media encoding and transcoding router configured to (i) change and/or consolidate input data types into a specific media format or file type and (ii) direct the changed and/or consolidated data to the system in a transformer process for analysis (paragraphs [0005] and [0041]-[0043] input data is transformed via vectorization to be used in the system to identify errors). Sobran does not teach a slot machine game software analyze game assets wherein the gaming machine including at least one of a monetary input device configured to receive a physical item associated with a monetary value and/or cashless wagering, a user interface, at least one processor configured to run the at least partially developed executable instructions or computer- readable files, a game display, and memory in communication with the processor and a specific mention of a neural network. However, Russ teaches a gaming system (abstract) for a slot game (paragraph [0253] see slot reels) comprising artificial intelligence in the form of machine learning to modify game function to adapt to new user information (Fig. 5 and paragraph [0006]) wherein the artificial intelligence system comprises the use of a neural network which is a machine learning network (paragraph [0115]) and the gaming machine comprises at least one of a monetary input device configured to receive a physical item associated with a monetary value (Fig. 3, item 340) and/or cashless wagering, a user interface (Fig, item 316 and paragraph [0080]), at least one processor configured to run the at least partially developed executable instructions or computer-readable files (Fig. 3, item 304), a game display (paragraph [0253]), and memory in communication with the processor (Fig. 3, item 308). Hence, it would have been obvious to one of ordinary skill in the art at the time of filing to have combined the teachings of Sobran with Russ, since the gaming art includes the action of developing software for the purpose of gaming with Sobran showing that is desirable to detect errors in software development before release and to ease the amount of resources needed for this task (paragraph [0050] of Sobran) the system of Russ can be more cheaply implemented and as show by Russ the inclusion of machine learning to develop and maintain a game is known and desirable as a way to adapt and reduce resources. As per the use of a neural network as shown by Russ the machine learning can be considered as a form of neural network and therefore using a machine learning algorithm with explicit neural network setup would be obvious in the art as this is a form of machine learning. As per claim 21 a slot game is a game of chance.
As per claims 36, 46 and 56, Sobran teaches an automated defect identification of software system and method (abstract software product is reviewed to determine defects), comprising: an AI-driven automated defect identification engine configured to access assets (paragraphs [0005], [0041], and [0051]-[0052] potential software errors are identified based on a trained machine learning module using previous detected error reports), wherein the assets include executable instructions or computer-readable files for operating software on a machine (paragraphs [0005], [0041], and [0051]-[0052] potential software errors are identified based on a trained machine learning module using previous detected error reports), the AI-driven automated defect identification engine configured to analyze the assets and identify defects and/or inconsistencies using algorithms (paragraphs [0005], [0041], and [0051]-[0052] potential software errors are identified based on a trained machine learning module using previous detected error reports), based on prior machine learning of the requisite information needed to evaluate the assets (paragraphs [0005], [0041], and [0051]-[0052] potential software errors are identified based on a trained machine learning module using previous detected error reports); a media encoding and transcoding router configured to reformat and/or consolidate data from the assets into specific media types and route reformatted and/or consolidated data to appropriate a transformer process for analysis (paragraphs [0005] and [0041]-[0043] input data is transformed via vectorization to be used in the system to identify errors); the AI-driven processor further configured to generate recommendations for correcting identified defects, wherein the recommendations are based on the analysis of the assets (paragraphs [0040] closest previous error log is provided which includes recommendations to “crowd sourced data (e.g., forums, published documentations, real time build logs) with to the minute knowledge of changes in these landscapes whether they be public or proprietary to ultimately provide an immediate fix based on identifying a vectorized negative log report that is closest in distance to the vectorized log report of a recent build log”); and a logging module configured to store and report identified defects and/or inconsistencies for review and/or further action (paragraph [0048] log report is provided to developers). Sobran does not teach a slot machine game software analyze game assets wherein the gaming machine including at least one of a monetary input device configured to receive a physical item associated with a monetary value and/or cashless wagering, a user interface, at least one processor configured to run the at least partially developed executable instructions or computer- readable files, a game display, and memory in communication with the processor and a specific mention of a neural network. However, Russ teaches a gaming system (abstract) for a slot game (paragraph [0253] see slot reels) comprising artificial intelligence in the form of machine learning to modify game function to adapt to new user information (Fig. 5 and paragraph [0006]) wherein the artificial intelligence system comprises the use of a neural network which is a machine learning network (paragraph [0115]) and the gaming machine comprises at least one of a monetary input device configured to receive a physical item associated with a monetary value (Fig. 3, item 340) and/or cashless wagering, a user interface (Fig, item 316 and paragraph [0080]), at least one processor configured to run the at least partially developed executable instructions or computer-readable files (Fig. 3, item 304), a game display (paragraph [0253]), and memory in communication with the processor (Fig. 3, item 308). Hence, it would have been obvious to one of ordinary skill in the art at the time of filing to have combined the teachings of Sobran with Russ, since the gaming art includes the action of developing software for the purpose of gaming with Sobran showing that is desirable to detect errors in software development before release and to ease the amount of resources needed for this task (paragraph [0050] of Sobran) the system of Russ can be more cheaply implemented and as show by Russ the inclusion of machine learning to develop and maintain a game is known and desirable as a way to adapt and reduce resources. As per the use of a neural network as shown by Russ the machine learning can be considered as a form of neural network and therefore using a machine learning algorithm with explicit neural network setup would be obvious in the art as this is a form of machine learning. As per claim 26 a slot game is a game of chance.
As per claim 41, Sobran teaches an automated defect identification of software system (abstract software product is reviewed to determine defects), comprising: an artificial intelligence engine configured to access and analyze at least partially developed executable instructions or computer-readable files for software (paragraphs [0005], [0041], and [0051]-[0052] potential software errors are identified based on a trained machine learning module using previous detected error reports); the artificial intelligence engine being operable to identify defects and/or inconsistencies in the assets using algorithms trained via machine learning to process the software development files (paragraphs [0005], [0041], and [0051]-[0052] potential software errors are identified based on a trained machine learning module using previous detected error reports); the AI-driven processor further configured to generate recommendations for correcting identified defects, wherein the recommendations are based on the analysis of the assets (paragraphs [0040] closest previous error log is provided which includes recommendations to “crowd sourced data (e.g., forums, published documentations, real time build logs) with to the minute knowledge of changes in these landscapes whether they be public or proprietary to ultimately provide an immediate fix based on identifying a vectorized negative log report that is closest in distance to the vectorized log report of a recent build log”); and a media encoding and transcoding router configured to (i) convert and/or consolidate input data types into specified formats and (ii) route converted and/or consolidated data to a transformer process (paragraphs [0005] and [0041]-[0043] input data is transformed via vectorization to be used in the system to identify errors), enabling analysis and error detection and/or further action (paragraph [0048] log report is provided to developers). Sobran does not teach a slot machine game software analyze game assets wherein the gaming machine including at least one of a monetary input device configured to receive a physical item associated with a monetary value and/or cashless wagering, a user interface, at least one processor configured to run the at least partially developed executable instructions or computer- readable files, a game display, and memory in communication with the processor and a specific mention of a neural network. However, Russ teaches a gaming system (abstract) for a slot game (paragraph [0253] see slot reels) comprising artificial intelligence in the form of machine learning to modify game function to adapt to new user information (Fig. 5 and paragraph [0006]) wherein the artificial intelligence system comprises the use of a neural network which is a machine learning network (paragraph [0115]) and the gaming machine comprises at least one of a monetary input device configured to receive a physical item associated with a monetary value (Fig. 3, item 340) and/or cashless wagering, a user interface (Fig, item 316 and paragraph [0080]), at least one processor configured to run the at least partially developed executable instructions or computer-readable files (Fig. 3, item 304), a game display (paragraph [0253]), and memory in communication with the processor (Fig. 3, item 308). Hence, it would have been obvious to one of ordinary skill in the art at the time of filing to have combined the teachings of Sobran with Russ, since the gaming art includes the action of developing software for the purpose of gaming with Sobran showing that is desirable to detect errors in software development before release and to ease the amount of resources needed for this task (paragraph [0050] of Sobran) the system of Russ can be more cheaply implemented and as show by Russ the inclusion of machine learning to develop and maintain a game is known and desirable as a way to adapt and reduce resources. As per the use of a neural network as shown by Russ the machine learning can be considered as a form of neural network and therefore using a machine learning algorithm with explicit neural network setup would be obvious in the art as this is a form of machine learning.
As per claims 2, 7, 12, 17, 22, 27, 32, 37, 42, 47, 52, and 57, Sobran does not teach a system or method for automated defect identification of slot machine game software wherein the machine learning the requisite information necessary to process the game assets utilizes at least partially supervised machine learning. However, Russ teaches a machine learning method for modifying the function of a slot game (abstract and paragraph [0253]) wherein the machine learning is supervised (paragraph [0274]). Hence, it would have been obvious to one of ordinary skill in the art at the time of filing to have combined the teachings of Sobran with Russ, since the use of supervised machine learning allows for a human operator to better guide the system by identifying positive results in order to guide to a correct result in the future and reduce errors.
As per claim 3, 8, 13, 18, 23, 28, 33, 38, 43, 48, 53, and 58, Sobran does not teach a system or method for automated defect identification of slot machine game software wherein the machine learning the requisite information necessary to process the game assets utilizes at least partially unsupervised machine learning. However, Russ teaches a machine learning method for modifying the function of a slot game (abstract and paragraph [0253]) wherein the machine learning is supervised (paragraph [0115]). Hence, it would have been obvious to one of ordinary skill in the art at the time of filing to have combined the teachings of Sobran with Russ, since the use of unsupervised machine learning this allows for fewer human operators to be involved and thereby reduce human resources used and therefore cost related to employing people.
As per claim 4, 9, 14, 19, 24, 29, 34, 39, 44, 49, 54, and 59, Sobran does not teach a system or method for automated defect identification of slot machine game software wherein the machine learning the requisite information necessary to process the game assets utilizes at least partially reinforced machine learning. However, Russ teaches a machine learning method for modifying the function of a slot game (abstract and paragraph [0253]) wherein the machine learning is supervised with reinforced decisions (paragraph [0274]). Hence, it would have been obvious to one of ordinary skill in the art at the time of filing to have combined the teachings of Sobran with Russ, since the use of reinforced machine learning allows for a human operator to better guide the system by identifying positive results in order to guide to a correct result in the future and reduce errors.
As per claim 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, and 60, Sobran teaches a system and method for automated defect identification of software wherein the assets are selected from the group consisting of: game graphics, game animations, programming (abstract see software), game math, and game sound. Sobran does not teach the software or other assets as being game related. However, Russ teaches a gaming system (abstract) for a slot game (paragraph [0253] see slot reels) comprising artificial intelligence in the form of machine learning to modify game function to adapt to new user information (Fig. 5 and paragraph [0006]) Hence, it would have been obvious to one of ordinary skill in the art at the time of filing to have combined the teachings of Sobran with Russ, since the gaming art includes the action of developing software for the purpose of gaming with Sobran showing that is desirable to detect errors in software development before release and to ease the amount of resources needed for this task (paragraph [0050] of Sobran) the system of Russ can be more cheaply implemented and as show by Russ the inclusion of machine learning to develop and maintain a game is known and desirable as a way to adapt and reduce resources.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
"How Artificial Intelligence (AI)Upends Game Development" from https://pixelplex.io/blog/ discusses the use of AI in game development.
"Finding and fixing bugs with deep learning" by Miltos Allamanis and Marc Brockschmidt discusses the use of AI techniques to detect bugs in code.
Senchenko et al. (US Pub. No. 2024/0330149 A1) teaches a quality analysis tool for visual-programming scripting languages uses machine learning to process changes from visual-programming environments with visual-programming scripting languages being used by game developers for various aspects of game logic.
Obando Chacon et al. (US Pub. No. 2023/0385042 A1) teaches a system comprising "trained machine learning model 530, 502 or AI-based module 530 which is configured to perform program source code linting analysis 532 to detect programming errors, bugs, stylistic errors, or suspicious constructs" (paragraph [0066]).
Hussain et al. (US Pub. No. 2023/0236944 A1) teaches a system for recommendations to handle errors in a computing system which includes machine learning with the error code and a success label used to train the system.
Cope et al. (US Pub. No. 2023/0236950 A1)"Using artificial intelligence or other methods, computing device 110 may read the actual source code related to a defect to determine if the source code looks problematic." paragraph [0045].
Jayaraman et al. (US Pub. No. 2021/0064361 A1) teaches a system for using AI to improve software development productivity wherein a neural network is trained to generate a software impact analyzer model.
Culibrk et al. (US Pub. No. 2020/0310948 A1) teaches the use of machine learning in quality testing for games.
Hauser (US Pub. No. 2019/0108001 A1) teaches a system for detecting and correcting errors in software, such as in source code, using machine learning.
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/JUSTIN L MYHR/Primary Examiner, Art Unit 3715 10/2/2025