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 .
Response to Amendment
This office action is in response to amendments filed on 01/20/2026.
Terminal Disclaimer
Examiner recognizes that terminal disclaimer filed on 01/20/2026 was approved on 02/04/2026. Therefore the previous double patenting rejection is moot.
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 1-30 are rejected under 35 U.S.C. 101 because the claimed invention is directed to organizing human activity in the form of fundamental economic activities directed to hedging (wagering games are recognized as a form of hedging) including financial obligations and a mental step without significantly more. As per step 1 examiner recognizes that the claims are directed towards methods or gaming system comprising sufficient hardware elements performing the steps and therefore step 1 is met. As per step 2A the claim(s) recite(s) “prior to regulatory approval at least partially developing executable instructions or computer readable files related to animation elements for a game of chance using an animation-based artificial intelligence game design system based upon machine learning training including analyzing past game performance including at least one of coin-in data, win per unit per day relative to a house average, win per unit per day relative to a zone or bank average, occupancy rates, hold percentage, wager size distributions, play rates, feature trigger frequency, bonus round participation, volatility metrics, payout distributions, session duration, time-of-day performance metrics, composite metrics, or historical performance trends derived therefrom; developing executable instructions or computer-readable files related to animation elements for the game of chance by creating at least one of primary character animations, background animations, reel animations, symbol animations, bonus or feature animations, user interface animations, attract or presentation animations, animation timing characteristics, animation motion characteristics, keyframe placement data, keyframe interpolation data, animation transition data, synchronization data between animation states and game events, or animation sequences generated or modified based on machine-learning output;; 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 and select an output with a higher probability variable; and utilizing the at least partially developed executable instructions or computer readable files to present and allow play of the game of chance for 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 for running the at least partially developed executable instructions or computer readable files related to the animation elements for the game of chance, a game display and memory in communication with the at least one processor and/or an online or mobile gaming platform, wherein the online or mobile gaming platform comprises a computing device, remote server, or cloud-based system that executes the at least partially developed and/or executable instructions to present and allow remote gameplay” directed towards a method of machine learning for developing animations (see list above in claim), prior to regulatory approval, based on past game performance (see list above in claim) and to encode and transcode the input data type to a different media format and using a specific neural network perform a transformer process and select an output with a higher probability variable. Further the method is for a gaming machine comprising wagering game elements to carry out a wagering game. Further dependent claims clarify the machine learning to be supervised, unsupervised, or reinforced and what data is used and what elements of the game are modified. As per the hedging elements examiner recognizes the method is regarding the configuring or running of a game for the purpose of wagering which includes the element of hedging wherein a monetary value is put at risk for the purpose of financial gain. Specifically the games are directed towards managing a wagering game. As per the mental steps examiner recognizes that applicant cites development of the game using a machine-learning system without clarification on how the machine-learning functions beyond broad claims and which would therefore read on a mental process. Examiner recognizes that humans are able to develop and determine what animations to provide based on the mental step of analyzing known data. This would include past game performance data which allows the gaming development community to adapt such as to provide new games designed to meet trends or needs observed based on the function of the current system. This is the action of learning which is a mental step. Specifically examiner recognizes this is the use of a machine-learning algorithm to the perform the mental function of modifying or developing a game based on observation which is a step that can be performed by an individual. No steps are recited beyond generic automation steps that clarify the features away from the mental step or provide significantly more. As per amended language examiner notes this is a broad recitation of observation of what data is used to train the machine learning model without specific details and therefore is still directed towards claiming a mental step for developing a game based on observation. Specifically details of the model are sparse and non-specific and therefore fail to provide significantly more than claiming the mental concept of observing a game and updating the game to appeal to more players. As per amended language regarding what animations are developed by the learning model this goes towards the mental concept of a developers actions without more details on how the model uses the data to update the animation. For example specific mathematical functions of adjusting certain parameters based on learned data. As for regulatory approval this is a known concept legally required of gaming and therefore would go towards normal development. Additionally not steps are recited on how this is performed but merely that the process occurs prior to the approval. This judicial exception is not integrated into a practical application because remains directed towards a wagering game management and the mental step of developing games. 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 is broadly recited as to not go beyond steps that can be performed by an individual which would include dependent claim features. The actions of supervised, unsupervised, or reinforced training are actions that can be applied to learning for an individual and therefore the recitation of these methods with more details would not go beyond an individual learning what needs to be modified for a game.
As per step 2B examiner recognizes that additional elements are directed to conventional activities or extra solution activity. See below.
Limitation “utilizing the at least partially developed executable instructions or computer readable files to present and allow play of the game of chance for 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 for running the at least partially developed executable instructions or computer readable files related to the animation elements for the game of chance, a game display and memory in communication with the at least one processor and/or an online or mobile gaming platform, wherein the online or mobile gaming platform comprises a computing device, remote server, or cloud-based system that executes the at least partially developed and/or executable instructions to present and allow remote gameplay” and other associated hardware and computer steps. 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 conventional activities well-understood in the art. Singer et al. (US Pub. No. 2004/0192431 A1) see paragraphs [0003]-[0005] regarding conventional gaming machine hardware including means to deposit money, means to make a wager, and means to display a game. Therefore the hardware and animation features do not provide a practical application as being directed towards conventional features known in the wagering game art.
Limitations regarding encoding, transcoding, or routing date. See MPEP 2105(d) II. i. 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). The action of encoding data for transmission or routing data is a conventional activity within the electronic art and therefore does not provide a practical application under step 2B.
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) 1-30 is/are rejected under 35 U.S.C. 103 as being unpatentable over Keilwert et al. (US Pub. No. 2021/0043031 A1 hereinafter referred to as Keilwert) in view of Wang et al. (US Pub. No. 2023/0054130 A1 hereinafter referred to as Wang), Tunstall-Pedoe et al. (US Pub. No. 2023/0132072 A1 hereinafter referred to as Tunstall), and Nguyen (US Pub. No. 2009/0298576 A1).
As per claims 1 and 16, Keilwert teaches a gaming method and system (abstract) comprising: at least partially developing executable instructions or computer readable files related to animation elements for a game of chance using an animation-based artificial intelligence game design system based upon machine learning training including analyzing past game performance (abstract and paragraphs [0048]-[0049], [0060], [0063], and [0069]-[0072] teaches a system developed to use rules based on AI, such as learning models (paragraph [0049]) to modify a GUI animation (paragraph [0072]) in order to best fit a detected player profile based on historical data (paragraph [0060] learning) for a variety of inputs including game session information (paragraphs [0063] and [0069])) including at least one of coin-in data, win per unit per day relative to a house average, win per unit per day relative to a zone or bank average, occupancy rates, hold percentage, wager size distributions, play rates, feature trigger frequency, bonus round participation, volatility metrics, payout distributions, session duration, time-of-day performance metrics, composite metrics, or historical performance trends derived therefrom (paragraph [0069] see at least historical performances related to pace, or rate, of play of a player and results of the game which would include wins/losses which would read on using data related to payouts. While the focus is on a player this would be historical performance trends related to data associated with the gaming device itself.); developing executable instructions or computer-readable files related to animation elements for the game of chance by creating at least one of primary character animations, background animations, reel animations, symbol animations, bonus or feature animations, user interface animations, attract or presentation animations, animation timing characteristics, animation motion characteristics, keyframe placement data, keyframe interpolation data, animation transition data, synchronization data between animation states and game events, or animation sequences generated or modified based on machine-learning output (paragraphs [0071]-[0072] teaches modifying a user interface including animations and just a general modification of animations related to the game via the machine learning); and utilizing the at least partially developed executable instructions or computer readable files to present and allow play of the game of chance for a gaming machine (Figs. 6A-7B see game presented), 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 (paragraph [0029] bill, ticket, or coin acceptor for accepting value), a user interface (paragraph [0022]), at least one processor for running the at least partially developed executable instructions or computer readable files related to the animation elements for the game of chance (Fig. 1B, item 12 and paragraph [0003]), a game display (Fig. 1B, items 116 and 118) and memory (Fig. 1B, item 14) in communication with the at least one processor and/or an online or mobile gaming platform (paragraph [0052] includes functions for either the gaming device being the controller or a server in communication with a game device, such as a mobile device, being the controller for carrying out the game rule functions), wherein the online or mobile gaming platform comprises a computing device, remote server, or cloud-based system that executes the at least partially developed and/or executable instructions to present and allow remote gameplay (paragraph [0052] see remote server). Keilwert does not teach a method comprising 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 and select an output with a higher probability variable nor the development occurring prior to regulatory approval. However, Wang teaches a system using machine-learning or training to modify a animation or video (abstract) comprising a media encoding (paragraph [0051] video is encoded) and transcoding (Fig. 4, items 402-404 and paragraph [0071]) router to (i) change input data type to a different media format or consolidate the input data type to a specific file type (paragraphs [0097]-[0098] such as switching formats based on which would provide the highest performance) wherein the learning process comprise determining a highest probability of accuracy (Fig. 7 and paragraphs [0058]-[0059]) wherein the system can be used for gaming (paragraph [0028] see gaming console) wherein the process involves sending and receiving data from a server to a client device (Fig. 1 and paragraphs [0028] and [0034]) and Tunstall teaches a machine-learning environment (abstract and paragraphs [0250]-[0252]) comprising a neural network (paragraphs [0271] and [0952]) wherein information is encoded and decoded in order to assign the information to neural nodes (paragraphs [0953]-[0955], [0957], and [0959]-[0962]) wherein the node used is based on a probability of matching (paragraphs [0260] and [0262]-[0264]). In addition, Nguyen teaches a gaming method of submitting new wagering game packages for regulatory approval (abstract) in order to meet legal obligations for new game packages (paragraph [0015]). 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 Keilwert with Wang, Tunstall, and Nguyen, since Keilwert is modifiable to use various machine-learning techniques in order to transcode the data for transmission since by using a transcoding techniques the amount of data used for transmitting and storage can be reduced (Wang paragraph [0002]) which would include animation effects as taught by Keilwert, paragraph [0072], and to include the teachings of Tunstall regarding transforming the data for the machine-learning process since this allows for the data to be easily read and used by the machine-learning process in order to assign the data to the highest probability matching node which allows for more efficient and accurate processing. As per regulatory approval, as shown by paragraph [0015] of Nguyen, this is a legal requirement in most, if not all, districts where gaming occurs and therefore having the newly developed or updated game elements approved by a regulatory authority would have been obvious since this is a legal requirement.
As per claims 2 and 17, Keilwert teaches a method and system wherein the animation-based artificial intelligence game design system includes utilizing at least partially supervised machine learning training to at least partially develop graphical elements for a game of chance (paragraph [0049] teaches supervised or unsupervised learning and paragraph [0072] effects animations).
As per claims 3 and 18, Keilwert teaches a method and system wherein the animation-based artificial intelligence game design system includes at least an animation-based game design system module utilizing at least partially supervised machine learning training to at least partially develop executable instructions or computer readable files (paragraph [0049] teaches supervised or unsupervised learning and paragraph [0072] lists various changes).
As per claims 4 and 19, Keilwert teaches a method and system wherein the animation-based artificial intelligence game design system includes at least a past game performance analytics module utilizing at least partially supervised machine learning training (paragraphs [0060]-[0061] prior known data is used regarding the parameters which would include game performance and paragraph [0063] player records are used including for past games).
As per claims 5 and 20, Keilwert teaches a method and system further comprising the at least partially developing executable instructions or computer readable files related to animation elements for a game of chance including developing a graphic of at least one of a game character, a reel set, game symbols, free symbols, a game background, a game logo, a game progressive box, a game credit bar, a secondary game character, or a game persistence graphic (paragraph [0072] see symbols for example).
As per claims 6 and 21, Keilwert teaches a method and system wherein the animation-based artificial intelligence game design system includes utilizing at least partially unsupervised machine learning training to at least partially develop animation elements for a game of chance (paragraph [0049] teaches supervised or unsupervised learning and paragraph [0072] effects animations).
As per claims 7 and 22, Keilwert teaches a method and system wherein the animation-based artificial intelligence game design system includes at least an animation-based artificial intelligence game design system module utilizing at least partially unsupervised machine learning training to at least partially develop executable instructions or computer readable files (paragraph [0049] teaches supervised or unsupervised learning and paragraph [0072] lists various changes).
As per claims 8 and 23, Keilwert teaches a method and system wherein the animation-based artificial intelligence game design system includes at least an animation-based game design system module utilizing at least partially unsupervised machine learning training to at least partially develop executable instructions or computer readable files (paragraph [0049] teaches supervised or unsupervised learning and paragraph [0072] lists various changes).
As per claims 9 and 24, Keilwert teaches a method and system wherein the animation-based artificial intelligence game design system includes at least a past game performance analytics module utilizing at least partially unsupervised machine learning training (paragraphs [0060]-[0061] prior known data is used regarding the parameters which would include game performance and paragraph [0063] player records are used including for past games).
As per claims 10 and 25, Keilwert teaches a method and system further comprising the at least partially developing executable instructions or computer readable files related to animation elements for a game of chance utilizing at least partially unsupervised machine learning training and including developing a graphic of at least one of a game character, a reel set, game symbols, free symbols, a game background, a game logo, a game progressive box, a game credit bar, a secondary game character, or a game persistence graphic (paragraph [0049] and paragraph [0072] see symbols for example).
As per claims 11 and 26, Keilwert teaches a method and system wherein the animation-based artificial intelligence game design system includes utilizing at least partially supervised machine learning training to at least partially develop animation elements for a game of chance (paragraph [0049] teaches supervised or unsupervised learning and paragraph [0072] effects animations). Keilwert does not specifically teach partially reinforced machine learning training. However, Wang teaches learning wherein reinforcement or accuracy determination is used to discard results if the results do not meet a certain threshold in order to train the model (paragraphs [0058]-[0060]). 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 Keilwert with Wang and Tunstall, since by including a reinforced learning model training method the system is able to better train the system for accuracy by identifying when solutions are inaccurate and reinforcing the system towards greater accuracy.
As per claims 12 and 27, Keilwert teaches a method and system wherein the animation-based artificial intelligence game design system includes at least an animation-based artificial intelligence game design system module utilizing at least partially reinforced machine learning training to at least partially develop executable instructions or computer readable files (paragraph [0049] teaches supervised or unsupervised learning and paragraph [0072] lists various changes). Keilwert does not specifically teach partially reinforced machine learning training. However, Wang teaches learning wherein reinforcement or accuracy determination is used to discard results if the results do not meet a certain threshold in order to train the model (paragraphs [0058]-[0060]). 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 Keilwert with Wang and Tunstall, since by including a reinforced learning model training method the system is able to better train the system for accuracy by identifying when solutions are inaccurate and reinforcing the system towards greater accuracy.
As per claims 13 and 28, Keilwert teaches a method and system wherein the animation-based artificial intelligence game design system includes at least an animation-based game design system module utilizing at least partially supervised machine learning training to at least partially develop executable instructions or computer readable files (paragraph [0049] teaches supervised or unsupervised learning and paragraph [0072] lists various changes). Keilwert does not specifically teach partially reinforced machine learning training. However, Wang teaches learning wherein reinforcement or accuracy determination is used to discard results if the results do not meet a certain threshold in order to train the model (paragraphs [0058]-[0060]). 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 Keilwert with Wang and Tunstall, since by including a reinforced learning model training method the system is able to better train the system for accuracy by identifying when solutions are inaccurate and reinforcing the system towards greater accuracy.
As per claims 14 and 29, Keilwert teaches a method and system wherein the animation-based artificial intelligence game design system includes at least a past game performance analytics module utilizing at least partially supervised machine learning training (paragraphs [0060]-[0061] prior known data is used regarding the parameters which would include game performance and paragraph [0063] player records are used including for past games). Keilwert does not specifically teach partially reinforced machine learning training. However, Wang teaches learning wherein reinforcement or accuracy determination is used to discard results if the results do not meet a certain threshold in order to train the model (paragraphs [0058]-[0060]). 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 Keilwert with Wang and Tunstall, since by including a reinforced learning model training method the system is able to better train the system for accuracy by identifying when solutions are inaccurate and reinforcing the system towards greater accuracy.
As per claims 15 and 30, Keilwert teaches a method and system further comprising the at least partially developing executable instructions or computer readable files related to animation elements for a game of chance including developing a graphic of at least one of a game character, a reel set, game symbols, free symbols, a game background, a game logo, a game progressive box, a game credit bar, a secondary game character, or a game persistence graphic (paragraph [0072] see symbols for example). Keilwert does not specifically teach partially reinforced machine learning training. However, Wang teaches learning wherein reinforcement or accuracy determination is used to discard results if the results do not meet a certain threshold in order to train the model (paragraphs [0058]-[0060]). 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 Keilwert with Wang and Tunstall, since by including a reinforced learning model training method the system is able to better train the system for accuracy by identifying when solutions are inaccurate and reinforcing the system towards greater accuracy.
Response to Arguments
Applicant's arguments filed 01/20/2026 have been fully considered but they are not persuasive. Applicant argues that amended claims overcome the previous 101 rejection. Regarding the use of the development prior to regulatory approval examiner notes that game development, as shown by Nguyen cited above, requires regulatory approval for game packages which are developed in order to meet legal requirements. Wagering games are a highly regulated field of gaming and therefore the inclusion of this step would not go towards a practical improvement since regulatory approval was required previously and would be required even with machine learning game development. Applicant additionally does not include specific steps that may represent an improvement regarding gaining regulatory approval and therefore this feature is not persuasive as overcoming the previous 101. As per the specific data collected this appears to go towards a general mental step of observation without additional details on how the data is transformed by the training model into useful data for the development of animations. Specifically steps such as mathematical formulas regarding transforming collected data and then how that transformation is applied to modify specific parameters of animation. As currently written it appears to still read on the machine learning claiming the general process of game development based on feedback without details on how the machine learning algorithm performs this process beyond generic claims on what data is used. Therefore without additional clarification the 101 rejection is maintained. Regarding the argued machine elements these are addressed as conventional under step 2B. Inclusion of machine elements that are conventional in nature do not overcome a 101 rejection. Applicant argues the router is unconventional without details on how AI systems do not transform data is a similar way. Clarification is requested including potential amendments to clarify this feature. As for the argued practical improvement examiner notes that the use of automation without details on the specific computer steps that go beyond a mental step do not overcome 101 and therefore the use of machine learning to perform the action of game development in of itself does not overcome the rejection. Additional details are required including specific claims regarding algorithms that would not be reasonably performed in the mind.
Regarding the prior art rejection see above for amended features. Specifically applicant includes a long series of potential data to train the model and what animations are modified. Examiner notes that in that list paragraph [0069] at least teaches the play rate of a player which is part of the overall historical information related to play of the machine. As per animation the GUI is modified and the statement includes general animations which would read on the prior art. Therefore the amendments do not overcome the previous rejection.
In response to applicant's argument that AI elements of the secondary art is nonanalogous art with the primary art because the primary art focuses on a different feature, it has been held that a prior art reference must either be in the field of the inventor’s endeavor or, if not, then be reasonably pertinent to the particular problem with which the inventor was concerned, in order to be relied upon as a basis for rejection of the claimed invention. See In re Oetiker, 977 F.2d 1443, 24 USPQ2d 1443 (Fed. Cir. 1992). In this case, the use of machine learning or neural networks is the feature that is relied upon and not what the machine learning is used for in the secondary art. Specifically common features use in machine learning is what is taught by the prior art and how it would have been obvious to combine with the primary art since the primary art is additionally focused on using machine learning. Therefore this argument is not persuasive.
In response to applicant's arguments against the references individually, one cannot show nonobviousness by attacking references individually where the rejections are based on combinations of references. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981); In re Merck & Co., 800 F.2d 1091, 231 USPQ 375 (Fed. Cir. 1986). Applicant arguments regarding Wang appear to focus on the limitation as a whole and not the portions taught by Wang which render obvious, in combination, the claim as a whole. Applicant should address the combination and not individual references.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Sheth et al. (US Pub. No. 2023/0027573 A1) teaches a system using machine learning to develop GUIs for ATMs using "the system may receive an indication that a user is accessing an ATM, receive, from the ATM, average session duration data over a predetermined period, generate, using a machine learning model, a busyness score for the ATM based on the average session duration data over the predetermined period, and determine whether the busyness score for the ATM exceeds a busyness score threshold" (paragraph [0004]).
Idris et al. (US Pub. No. 2021/0192884 A1) teaches a gaming system wherein "the AI system may play different wagering games in a simulated or real-time environment, and collect data regarding the individual game elements and/or characteristics and generate recommendation score values. The AI system can then recommend games to players based on the player's-historical play or other data. Recommendation score values relating to aspects of wagering games, such as theme, volatility, character design, art style, type of animations, win presentations, etc., may also be used to aid in game design. In some embodiments, the AI system may design new games automatically based on the recommendation score values for different user interface elements, to appeal to different player demographics." (paragraph [0021]).
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JUSTIN L MYHR whose telephone number is (571)270-7847. The examiner can normally be reached 10AM-6PM.
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/JUSTIN L MYHR/ Primary Examiner, Art Unit 3715