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 .
Continued Examination
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 11/05/25 has been entered.
Claim Status
Claims 1-22, 24-27, and 29-30 are pending. Claims 1 and 16 have been amended and claims 23 and 28 have been previously cancelled.
Terminal Disclaimer
The terminal disclaimer filed on 11/5/2025 disclaiming the statutory term which would extend beyond the expiration of any patent grand
Claim Objections
Claims 5-6 are objected to because of the following informalities:
Claim 5 recites the limitations “for a the game of chance” in line 3. The claims contain a grammatical error and should be amended to “for the game of chance”. Appropriate correction is required.
Claim 6 recites the limitations “for a the game of chance” in lines 3-4. The claims contain a grammatical error and should be amended to “for the game of chance”. Appropriate correction is required.
Claim 10 recites the limitations “for a the game of chance” in line 3. The claims contain a grammatical error and should be amended to “for the game of chance”. Appropriate correction is required.
Claim 15 recites the limitations “for a the game of chance” in line 3. The claims contain a grammatical error and should be amended to “for the game of chance”. Appropriate correction is required.
Response to Arguments
Applicant's arguments filed 11/5/25 have been fully considered but they are not persuasive. The Applicant’s representative asserts that the claims have been amended to recite “wherein the artificial intelligence quality assurance game design system utilized code review and/or computer-readable file review, to simulate player behavior and interaction with the game of chance to identify bugs and/or glitches” which involves “an active, algorithmic testing and file-analysis process” that is not merely a desired result but how the AI quality-assurance game-design system functions to achieve its objective of identifying problems with the game of chance (see Remarks, pg. 10-13). The Applicant’s representative asserts to portions of the Specification that are purported to show that to one of ordinary skill in the art to show that the inventor actually invented the invention as claimed (see Remarks, pg. 10-13). Furthermore, the Applicant’s representative asserts that Figs. 38-41 analyzes the computer-readable files associated with the game’s visual and audio components by the Media Encoding and Transcoding Router that processes heterogeneous file types through the same transformer based pipeline used for code analysis which enables the system to detect visual, timing or synchronization defects as part of the same automated QA routine (see Remarks, pg. 11-12). Specifically, the Applicant’s representative argues that the claimed subject matter of the artificial intelligence quality assurance game-design system to identify bugs and/or glitches by utilizing code review and/or computer readable file review would be apparent to a skilled artisan that the inventor had possession of the claimed invention. The Examiner respectfully disagrees. The amended claims recites an artificial intelligence quality assurance game design system which utilizes “code review and/or computer-readable file review, to simulate player behavior and interaction with the game of chance” to identify bugs and/or glitches. This limitation encompasses the broad genus of “code review” and “computer-readable file review” that is directed to functional language of a desired result (e.g., identifying bugs and/or glitches) by the “artificial intelligence quality assurance game design system”. The critical inquiry is whether the disclosure of the application reasonably conveys to those skilled in the art had possession of the claimed subject matter as of the filing date by a determination of whether the specification discloses the computer and the algorithm (e.g., necessary steps and/or flow charts that perform the claimed function such that one of ordinary skill in the art can reasonably conclude that the inventor possessed the claimed subject matter (see MPEP 2161.01). An algorithm is defined, for example, as a finite sequence of steps for solving a logical or mathematical problem or performing a task (see MPEP 2161.01 – citing Microsoft Computer Dictionary (5th Ed., 2002). Stated differently, it is not enough that one skill in the art could write a program to achieve the claimed function because the specification must explain how the inventor intends to achieve the claimed function to satisfy the written description requirement (see MPEP 2106.01 - Vasudevan Software, Inc. v. MicroStrategy, Inc., 782 F.3d 671, 681-683, 114 USPQ2d 1349, 1356, 1357 (Fed. Cir. 2015)). A review of the cited portions in the Specification by the Applicant’s representative to support “code review” and “computer-readable file review” to identify bugs and/or glitches are discussed in the sections below.
Determination of whether “Code Review” is sufficiently described in the Specification
The Applicant’s representative asserts that the Specification indicates to one of ordinary skill in the art at the time of filing the application that they had possession of the genus of “code review” to identify bugs and/or glitches by the artificial intelligence quality assurance game design system. The Applicant’s representative asserts that support for the claimed invention may be found by paragraphs [0355], [0358], [0382]-[0383], [0390]-[0400], and [0404] that state the one skilled in the art would recognize that the artificial-intelligence-based software can be used to review source code, compiled code, and to analyze associated computer-readable files (see Remarks, pg. 11). The Examiner respectfully disagrees. It is insufficient that one of ordinary skill in the art could write a program to achieve the claimed function but the specification must explain how the inventor intends to achieve the claimed function to satisfy the written description requirement (see MPEP 2161.01). A review of the cited portions do not identify the necessary steps in sufficient detail that would indicate how the inventor intends to achieve the claimed function in order to satisfy the written description requirement. With respect to the closest support for “code review”, the Specification states:
[0390] - The specialized AI game design systems according to the embodiments of the present invention can be used to test games to identify potential bugs or issues that may affect the quality of the game. This can be done through code review and automated testing, where the specialized AI game design systems simulate player behavior and interaction with the game to identify problems, or through manual or automatic testing, where the specialized AI game design systems assist human testers by identifying potential issues and areas for improvement. By using specialized AI Quality Assurance (QA) game design systems for game testing, game designers can identify and fix potential issues before they are submitted to a gaming laboratory or regulated jurisdiction.
The Examiner has determined that this does not adequately support the breadth of the genus claimed to identify bugs, glitches, or issues using “code review” using the specialized game design system that would indicate to one of ordinary skill in the art how the inventor intended to achieve the claimed function to “at least partially developing executable instructions or computer readable files related to elements for a game of chance for a gaming machine…to simulate player behavior and interaction with the game of chance to identify bugs and/or glitches” to show possession of the claimed AI functionality. Specifically, this determination is based on the lack of disclosure as to the particular algorithms that would show how inventor intended the specialized AI quality assurance game design system to accomplished the task of code review to simulate the player behavior and interactions with the game of change to identify the bugs and/or glitches. For at least these reasons, the Applicant’s argument that using an “artificial intelligence game design system based upon machine learning training including analyzing past game performance wherein the artificial intelligence quality assurance game design system utilized code review and/or computer-readable file review, to simulate player behavior and interaction with the game of chance to identify bugs and/or glitches;” is not adequately supported to satisfy the written description requirement.
Determination of whether “Computer-Readable File Review” is sufficiently described in the Specification
The Applicant’s representative asserts that in addition to review source or compiled code and/or computer readable files, the quality assurance system analyzes the computer-readable files associated with the game’s visual and audio components. Specifically, the Applicant’s representative asserts that as shown in Figs. 38-41 and paragraph [0424]-[0430], the “Media Encoding and Transcoding Router processes heterogeneous file types – graphic, animation, and sound assets through the same transformer-based pipeline used for code analysis” allows the system to detect visual, timing, or synchronization defects as part of the same automated QA routine. Accordingly, the claimed “code review” as noted by the Applicant’s representative encompasses review of all executable and media files defining the game of chance. As noted above, and incorporated herein, the Specification is not found to adequately support the claimed “code review” and therefore is not adequate to encompass review for all executable and media files defining the game of game. Furthermore, the cited portions of Figs. 38-41 and paragraphs [0424]-[0430] are not found to adequately describe how the inventor intended to achieve the claimed function “(i) change input data type to a different media format or consolidate the input data type to a specific file type” and “(ii) direct the changed or consolidated input data type to a specific neural network in a transformer process” in sufficient detail that one of ordinary skill in the art would understand how the inventor intended for the artificial general intelligence model and universal translator of Fig. 41 to perform the specialized process that transform the heterogenous file types to i) change input data type to a different media format or consolidate the input data type to a specific file type and ii) direct the changed or consolidated input data type to a specific neural network in a transformer process”. For instance, while the Drawings and the Specification at first appear to provide: i) a flow chart 3900 of Fig. 39; ii) flow chart 4000 relating to the AI large language model human to machine to human translation process of the specialized game design system of Fig. 40; and iii) a flow chart 4100 relating to AI large language model human to machine to human translation process/”artificial general intelligence foundational model and universal translator of Fig. 41, they do not sufficiently describe how the inventor intended to identify bugs and/or glitches by utilizing “code review” and/or “computer readable file review” as claimed. The relevant portions that were reviewed as provided below:
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The relevant portions of the Specification corresponding to Fig. 38 were found to be [0424]-[0426]:
[0424] Fig. 38 illustrates an exemplary flow chart 3800 relating to the AI game performance prediction process of the specialized AI game design system. The flow chart 3800 includes seeking data 3802 which moves to an input block 3704 and then to correlate performance variable high/low block 3706 and proceeds to the predict high performance variables or characteristics 3708 which may generate a full game of at least a portion of a game 3810. Past game performance or datasets will include differences in type, scope, complexity, age, human generated, human recalled, computer generated, etc., it is desirable to correlate the same for best results in conjunction with the specialized AI game design systems. That is, in many instances the performance data may be measured differently by individual casinos. For example, some casinos have in-depth reports while others record limited metrics like, “win per unit vs. house average win per unit” or “win per unit zone vs. zone average win per unit” or like “coin-in per unit vs. house average coin in.” In some instances, the performance data may need to be estimated and may utilize human judgment or human recollection. Accordingly, depending on the past performance data, it may be necessary to enter or input dissimilar data in a manner that is useful to the system. For instance, in one embodiment, past performance data may be entered uses tiered scales based on importance (e.g., the important “win per unit vs. house” on a scale from 100 (top performer) to -100 (worst performer) whereas the less important “machine occupancy percentage” may be rated on a scale from37 (top performer) to -37 (worst performer)).
[0425] Following the generation of a full game of at least a portion of a game, the system may run one or more game play scenarios 3812 which may include large scale simulations after which, game scenario optimization 3814 may take place. Following game scenario optimization 3814, performance threshold checks 3816 may be made and if necessary, variables and/or other game mechanics may be revised 3818 at which point the game review will loop back and start the process again or in the alternative output a full game of at least a portion of a game 3820.
[0426] This game performance prediction algorithm allows the specialized AI game design system algorithm may call out to the game design algorithm to discretely and/or continually goal seek predetermined performance variables which are most likely positive performance variables and/or game mechanics.
A review of the disclosure of Fig. 38, does not adequately describe how each of the components indicated on the flow chart are intended to achieve the claimed functionality to identify bugs and/or glitches. The Examiner finds that while the Specification desires the result for the specialized AI game design system algorithm to call out the game design algorithm to discretely and/or continually goal seek predetermined performance variables which are most likely positive performance variables and/or game mechanics it does not provide sufficient detail as to the individual AI algorithms or the specialized AI game design system algorithm that would indicate to one of ordinary skill in the art how the inventor achieved the claimed function. For at least this reasons, the AI game prediction process of the specialized AI game design system is not sufficiently described to adequately show that the inventor had possession of the specialized AI game design system of Fig. 38.
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The relevant portions of the Specification were found to be:
[0427] Fig. 39 illustrates an exemplary flow chart 3900 relating to the AI game generation process of the specialized AI game design system. The flow chart 3900 includes input parameters 3902 flowing to image block 3904, motion block 3906, dimension block 3908, sound block 3910, text block 3912, math block 3914, game mechanics block 3916, and test block 3918. At this point, revisions may take place 3922 and either loop back to the image block 3904 or are output at block 3924. Individual AI algorithms of the specialized game design system represent a number of game aspects including image block 3904, motion block 3906, dimension block 3908, sound block 3910, text block 3912, math block 3914, game mechanics block 3916, and test block 3918 or others that may feed forward and back on each other to perform small-world network optimizations as the computation occurs.
A review of the disclosure of Fig. 39, merely indicates and names the blocks but does not provide sufficient detail and/or any steps performed by these steps that would indicate the particular algorithm and/or individual algorithms used to adequately describe how the inventor intended to achieve the claimed function to identify bugs and/or glitches by the specialized AI game design system. As indicated above, the Specification indicates the desire for the individual AI algorithms of the AI game design system to perform an AI game generation process but does not provide sufficient details as to how the AI game generation process and/or how the AI algorithms feed forward and back on each other to perform optimizations that would identify bugs and/or glitches. For at least these reasons, the Examiner does not find a particular algorithm and/or the necessary steps disclosed as to how the inventor intended to achieve the claimed function to show possession of the specialized AI game design system described in Fig. 39.
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The relevant portions of the Specification that discuss Fig. 40 are found in the following paragraphs:
[0428] Fig. 40 illustrates an exemplar flow chart 4000 relating to the AI large language model human to machine to human translation process of the specialized AI game design system. The flow chart 4000 includes input parameters 4002 flowing to tokenization block 4004 to embedding block 4006 to positional encoding block 4008 to transformer 1 (attention > feedforward) block 4010 to transformer 2 (attention > feedforward) block 4012 to transformer…n (attention > feedforward) block 4014 to softmax block 4016 and finally to an output block 4018.
A review of the cited portions of the Specification indicates that flow chart is an exemplary embodiment of the AI large language model human to machine to human translation process. However, the Specification does not indicate and provide sufficient detail as to any of the steps of the large language model human to machine to human translation process is performed nor how it achieves identifying bugs and/or glitches in the game design system. It follows that the claimed invention is not adequately described to satisfy the written description requirement because it must show how the inventor intends to achieve the claimed function to satisfy the written description requirement. In the instant application, the relevant portions of the Specification indicate a desired result to be achieved by the specialized AI game design system but does not indicate how the inventor intended to achieve the computer-implemented function using the AI game design system. For at least this reasons, the Examiner does not find that the AI large language human to machine to human translation process of the specialized AI game design system achieves the claimed function.
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The relevant portions of the Specification that discuss Fig. 41 are found in the following paragraphs:
[0429] Fig. 41 illustrates an exemplar flow chart 4100 relating to the AI large language model human to machine to human translation process of a full specialized AI game design system. This artificial general intelligence foundational model and universal translator illustrated in the flow chart 4100 may be considered as an optimal full specialized AI game design system as it may include virtually all individual specialized AI game design systems described herein and others, all within one full specialized AI game design system. This system may provide a limited number of artificial neural networks or a great many to achieve the deep learning results desired. Those skilled in the art will recognize that while the full specialized AI game design system illustrated may handle all processes and systems described herein, it may have similar utility for any one or more of the individual specialized AI game design systems or specialized AI game design system modules or specialized AI game design system components.
[0430] The artificial general intelligence foundational model and universal translator illustrated in Fig. 41 includes a trigger and input step 4102 wherein the initial system trigger may originate from a human or machine or combination thereof and can be given instructions for how many loops to run and may include a wait function enabling it to sit in a state of stasis until such events occur that trigger computation to commence. In addition, trigger and input step 4102 may make take input from itself and can be of any media format including individual binary bits and quantum qubits, text, image, video, graphics, performance, audio, variables, or collections of these individual elements consolidated into entire files such as spreadsheets, graphics, word documents, etc. During the seek data step 4104, transformer(s) may send specific seek instructions to assist in fine-tuning its parameters, model, or gathering/generating more data and seek responses can be in the format of specific data elements, a reward or outcome variable or a specific goal to seek against. Step 4106 includes a media encoding and transcoding router where the media encoder and transcoder understands the input data type and may change the data type to a different media format or consolidate into a specific file type. The router then directs the individual data elements or file types to a specific neural network 4108a, 4108b to 4108n in the transformer process. Within the transforms, certain neural network guardrails may exist such that the system can remove itself from a continuous loop whereby its efficiency value reaches diminishing returns. Each neuron may attach or disconnect itself to other neurons in the same network, moving closer or further away based on the data it is trained against and each. Neural Networks 4108a, 4108b and 4108n may move closer or further away from one another, attaching and disconnecting as required. Each step in the overall process may be supervised and/or unsupervised and reviewed by another function, neural network, or entire system to improve accuracy against the desired outcome or input mechanism based on probabilities of each potential output, whereby the system selects the element with the highest probability value. Step 4114 includes media output which includes a probability variable whereby the system selects the output with the highest probability variable. The media output may be in any binary bit, quantum qubit, pixel, voxel, signal, variable, audio, video, math, code, graphic, file, etc. The artificial general intelligence foundational model and universal translator illustrated in Fig. 41 may be considered a master algorithm that inputs, reads, understands any input format, translates it to any other input format, does its own goal-seeking and reward behavior, gathers or creates training data in real-time, institutes its own guardrails and optimization networks, and “learns” by feeding its output back into its input mechanism. This method looks at each process step the algorithm takes and ensures that it is correct based on the input and output of the computational engine(s). It may review each small step, small collections of steps, and overall process steps iteratively until it gets it correct based on its own goal-seeking behavior. The full specialized AI game design system engine requires this because of the various media formats and inputs/outputs required for the game design process. Although a very high level of accuracy is generally considered a requirement or goal for autonomous systems, this level of accuracy may be lower for the embodiments of the specialized AI game design systems described and still achieve the improved and/or desired results.
With respect to Fig. 41, the Specification indicates that it illustrates the artificial generation intelligence foundational model may be considered a master algorithm. However, the relevant portions of the Specification do not adequately describe the steps of the general ‘master algorithm’ such as 4102, 4104, 4106, 4108(a)-(n), 4114 with sufficient detail that would indicate to one of ordinary skill in the art that the inventor possess the invention as claimed. For instance, Fig. 41 discloses that each neural network provides a “step-by-step process supervision” that is performed by the Media Encoding and Transcoding Router to perform a transformer process but fails to adequately describe and/or provide any of the particular and/or necessary steps that are performed by the Media Encoding and Transcoding Router. In fact, a review of the Specification is silent as to any steps taken at Steps 4112(a)-(n) that would indicate to one of ordinary skill in the art how the inventor intended to identify bugs and/or glitches that would satisfy the written description requirement. It follows, that similar to the artificial generation intelligence foundational model, the scope of the transformer process carried out by the Media Encoding and Transcoding Router is not adequately described. For instance, as acknowledged by the Applicant’s representative, the “computer-readable file review” is the same QA process as the transformer process carried out by the Media Encoding and Transcoding Router (see Remarks, pg. 11-12). However, as noted above, the transformer process as indicated by the lack of disclosure of Steps 4112(a)-(n) provides a incomplete picture as to the transformer process. In particular the Specification lacks sufficient detail as to the necessary steps of the master algorithm. It follows that the transformer process of the Media Encoding and Transformer Router is also not adequately describe because it is the same QA process that is directed to “computer-readable file review” which has not been found to not satisfy the written description requirement. For at least these reasons, the Applicant’s argument is not persuasive and the rejection under 35 USC 112(a) been maintained below.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1-7, 9-12, 14-22, and 24-27, and 29-30 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claims contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention. Specifically, the claims are directed to computer-implemented functions to “at least partially developing executable instructions or computer readable files related to elements for a game of chance for a gaming machine using an artificial intelligence quality assurance design system based upon machine learning training including analyzing past performance wherein the artificial intelligence quality assurance game design system utilizes code review and/or computer-readable file review, to simulate player behavior and interaction with the game of chance to identify bugs and/or glitches” and “utilizing a media encoding and transcoding router to (i) change input data type to a different media format or consolidate the input data type to a specific file type and (ii) direct the changed or consolidated input data type to a specific neural network in a transformer process” are not adequately described to show possession of the claimed subject matter. Specifically, the Specification does not support the scope of the genus claimed to develop executable instructions or computer related files to elements for a game of chance to identify bugs and glitches related to quality assurance elements of the game of chance by code review and/or computer-readable file review because it merely provides a generic statements that specify the desired result by “using an artificial intelligence quality assurance design system”. Moreover, the subject matter claimed an artificial intelligence quality assurance design system is not adequately supported it does not adequately describe the interrelationship between the hardware and software in sufficient detail that would demonstrate to one of ordinary skill in the art that the inventor possessed the claimed invention. As artificial intelligence quality assurance design systems are an emerging technology that is complex and unpredictable, in order to satisfy the written description requirement the algorithm or steps/procedure taken to perform the function must be described with sufficient detail so that one of ordinary skill in the art would understand how the inventor intended the function to be performed. It is not enough that one of ordinary skill in the art would know how to program the claimed invention but how the inventor intends to achieve the claimed function. A review of the Specification resulted in the determination that the Specification does not adequately describe with sufficient detail any of the individual algorithms indicated in Figs. 37-40 that would indicate the necessary steps of the artificial intelligence quality assurance design system based upon machine learning. Similarly, the Specification does not adequately describe how the inventor intends to achieve the computer-implemented function by the system to identify bugs and/or glitches. For instance, Fig. 41 is disclosed as an artificial intelligence foundational model that serves as a master algorithm including a media encoding and transcoding router for a transformer process. However, a review of the Specification fails to provide sufficient disclosure as to i) the necessary steps that are performed by the master algorithm of Fig. 41, individual AI algorithms of Figs. 38-40 or interaction between AI algorithms of Fig. 39; ii) does not provide details as to ascertainable standards and/or techniques to train the game design system, how the neural networks are trained to perform the AI implemented task, and/or any disclosure of Steps 4112(a)-(n) (e.g., Step-By-Step Process Supervision) performed by the Media Encoding and Transcoding Router during the foundational model (see Figs. 41, Specification, [0423]-[0430]).
Moreover, the computer-implemented functions of the Media encoding and Transcoding router are not adequately describe in sufficient detail. For instance, the claim recites the computer-implemented function to change input data type to a different media format or consolidate the input data type to a specific file type. Although the Specification discloses that the input into the media encoding and transcoding router may be any media formats such as binary bits, text, image, video, graphics, performance, audio, variables, or collections of these individual elements consolidated into entire files such as spreadsheets, graphics, words documents, it fails to adequately describe how the router in the transformer process changes the input data type to a different media format or consolidate the input to a specific file type. Specifically, the Specification does not adequately describe the encoding and transcoding process as to how the inventor intends to change the input data to a different specific file type. Furthermore, the Specification fails to adequately describe how to identify “bugs and glitches” in the game of chance using the master algorithm of Fig. 41 with sufficient detail that it amounts to a desired result as opposed to an adequately described algorithm and/or necessary steps to show to one of ordinary in the art that the inventor had possession of the invention. In particular, the lack of sufficiency related to the interrelationship and interdependence of the computer hardware and software (e.g., the artificial intelligence quality assurance design system and the algorithm/necessary steps that are used that would indicate that the inventor possessed the system to “identify the bugs and glitches” related to quality assurance elements and/or how the developing of executable instructions and/or computer readable files related to the game of chance is achieved that would satisfy the written description requirement. For at least these reasons, claims 1-7, 9-12, 14-22, and 24-27, and 29-30are rejected under 35 USC 112(a) for failing to satisfy the written description requirement of the claimed invention.
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-7, 9-12, 14, 16-22, and 24-27 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention. Claims 1 and 16 recites the limitation "wherein executable instructions of computer readable files related to quality assurance elements for the game of chance identify bugs or glitches in software" in lines 19-20. The limitations “wherein executable instructions of computer readable files related” renders the claim unclear as to whether they are the same and/or different as the “at least partially developing executable instructions or computer readable files related to elements for a game of chance” previously recited by the respective independent Claims 1 and 16. Dependent Claims 2-7, 9-12, 14, 17-22, 24-27, and 29-30 are dependent upon independent Claim 1 and are found to be indefinite for substantially the same reasons.
Claim 10 recites the limitation “any of claims 1 or 6 to 9” which renders the claim unclear as it is dependent upon a cancelled claims.
Claim 15 recites the limitation “any of claims 1 or 11 to 14” which renders the claim unclear as it is dependent upon a cancelled claims.
Claim 25 recites the limitation “any of claims 16 or 21-24” which renders the claim unclear as it is are dependent upon a cancelled claims.
Claim 30 recites the limitation “any of claims 16 or 26-29” which renders the claim unclear as it is are dependent upon a cancelled claims.
Conclusion
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/RYAN HSU/EXAMINER, Art Unit 3715