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 Under 37 CFR 1.114
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 1/22/2026 has been entered.
Status of Claims
Claims 1, 17 and 20 have been amended.
Claim 21 has been newly added.
Claims 3- 5, 8 and 19 have been canceled.
Claims 1, 2, 6, 7, 9 – 18, 20 and 21 are pending
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.
This subject matter eligibility analysis follows the latest guidance for Patent Subject Matter Eligibility Guidance.
Claims 1, 2, 6, 7, 9 – 18, 20 and 21 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter.
Step 1:
Claims 1, 2, 6, 7, 9 – 16, 20 and 21 are drawn to a system.
Claims 17 and 18 are drawn to a method.
Thus, initially, under Step 1 of the analysis, it is noted that the claims are directed towards eligible categories of subject matter.
Step 2A:
Prong 1: Does the Claim recite an Abstract idea, Law of Nature, or Natural Phenomenon?
Claims 1, 2, 6, 7, 9 – 16, 20 and 21 are exemplary because they require substantially the same operative limitations of the remaining claims (reproduced below.) Examiner has underlined the claim limitations which recite the abstract idea, discussed in detail in the paragraphs that follow.
1. (Currently Amended) One or more computing devices, comprising one or more processors, configured to:
determine, for a fantasy sports contest, base contest parameters associated with a plurality of fantasy sports players and a predicted outcome associated with the one or more fantasy sports players, wherein the plurality of base contest parameters and the predicted outcome are determined based on real-time performance metrics associated with the plurality of fantasy sports players and a plurality of selected outcome predictions associated with the fantasy sports players received from a plurality of client devices, and the predicted outcome comprises a performance threshold;
calculate, by a machine learning model, an expected distribution of the base contest parameters across a plurality of outcomes associated with the predicted outcome;
determine a first modified performance threshold and a second modified performance threshold of the plurality of outcomes based on the expected distribution of the base contest parameters, wherein the first modified performance threshold is higher than the performance threshold, the second modified performance threshold is lower than the performance threshold, and a plurality of selected modified outcome predictions associated with the fantasy sports players received from the plurality of client devices;
predict one or more adjusted contest parameters associated with each of the first modified performance threshold and the second modified performance threshold;
display, on a client device, a user interface comprising one or more selectable options, wherein the one or more selectable options comprise at least one first modifier and at least one second modifier, wherein the at least one first modifier comprises the first modified performance threshold and the one or more adjusted contest parameters, and the at least one second modifier comprises the second modified performance threshold and the one or more adjusted contest parameters;
receive a selection comprising an indication of the one or more fantasy sports players and the predicted outcome from the client device;
receive, from the participant, a selection of the one or more selectable options, wherein the selection indicates at least one of the at least one first modifier and the at least one second modifier from the client device; and
transmit an award to the participant based on the at least one first modifier or the at least one second modifier, the one or more adjusted contest parameters, and an outcome associated with the selection, wherein the award is determined based on a payout ratio associated with the selection.
The claims recite italicized limitations that fall within at least one of the groupings of abstract ideas enumerated in the 2019 PEG, namely, Certain Methods of Organizing Human Activity.
More specifically, under this grouping, the italicized limitations represent fundamental economic principles and managing interactions between people. For example, the italicized limitations are directed towards the rules for implementing a fantasy sports game based upon adjusted game parameters wherein users are awarded payouts determined by a payout ratio. This falls under the grouping of managing interactions between people, (i.e., following game rules.) and fundamental economic principles.
Prong 2: Does the Claim recite additional elements that integrate the exception in to a practical application of the exception?
Although the claims recite additional limitations, these limitations do not integrate the exception into a practical application of the exception. For example, the claims require additional limitations as follow, (emphasis added): determinations by a machine learning model…, computing devices, processors and memory.
These additional limitations do not represent an improvement to the functioning of a computer, or to any other technology or technical field, (MPEP 2106.05(a)). Nor do they apply the exception using a particular machine, (MPEP 2106.05(b)). Furthermore, they do not effect a transformation. (MPEP 2106.05(c)). Rather, these additional limitations amount to an instruction to “apply” the judicial exception using a computer as a tool to perform the abstract idea. Therefore, since the additional limitations, individually or in combination, are indistinguishable from a computer used as a tool to perform the abstract idea, the analysis continues to Step 2B, below.
Step 2B:
Under Step 2B, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because they amount to conventional and routine computer implementation and mere instructions for implementing the abstract idea on generic computing devices.
For example, as pointed out above, the claimed invention recites additional elements facilitating implementation of the abstract idea. Applicant has claim computing devices, processors and memory. However, all of these elements viewed individually and as a whole, are indistinguishable from conventional computing elements known in the art. Therefore, the additional elements fail to supply additional elements that yield significantly more than the underlying abstract idea.
As the Alice court cautioned, citing Flook, patent eligibility cannot depend simply on the draftsman’s art. Here, amending the claims with generic computing elements does not (in this Examiner’s opinion), confer eligibility.
Regarding the Berkheimer decision, Sloan et al (US 2013/0344964) establishes that these additional elements are generic:
[0028] The processor 210, the memory 220, the input device 230, the display 240, and the transceiver 250 may all provide conventional functionalities for the user device 130. For example, the processor 210 may execute the interface for the fantasy sports application. In another example, the processor 210 may execute a browser application, which then provides a user interface wherein the fantasy sports application is displayed and through which user actions are communicated. The transceiver 250 may exchange data through the network 120 with the host 110, in particular to receive data related to the fantasy sports application as well as the recommendations generated by the recommendation engine, as will be discussed in further detail below.
Dills (US 10,709,983) establishes that these additional elements are generic:
(28) In one or more implementations, the neural network models 244 may be a series of neural networks, one neural network for each user type classification. As discussed herein, the neural network models 244 are a type of feed-forward artificial neural network where individual neurons are tiled in such a way that the individual neurons respond to overlapping regions in a visual field. The architecture of the neural network models 244 may be in the object of existing well-known machine learning architectures such as AlexNet, GoogLeNet, or Visual Geometry Group models. In one or more implementations, each of the neural network models 244 consists of a stack of convolutional layers followed by a single fully connected layer. In this respect, the fully connected layer is the layer that maps the convolutional features to one of a plurality of training classes. The neural network models 244 can include a loss layer (e.g., softmax or hinge loss layer) to back propagate errors so that the neural network models 244 learns and adjusts its weights to better fit provided input data.(Dills 6:43-60)
Therefore, these elements fail to supply additional elements that yield significantly more than the underlying abstract idea. Thus, taken alone, the additional elements do not amount to significantly more than the above-identified judicial exception (the abstract idea).
Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation.
Moreover, the claims do not recite improvements to another technology or technical field. Nor, do the claims improve the functioning of the underlying computer itself -- they merely recite generic computing elements. Furthermore, they do not effect a transformation of a particular article to a different state or thing: the underlying computing elements remain the same.
Concerning preemption, the Federal Circuit has said in Ariosa Diagnostics, Inc., V. Sequenom, Inc., (Fed Cir. June 12, 2015):
The Supreme Court has made clear that the principle of preemption is the basis for the judicial exceptions to patentability. Alice, 134 S. Ct at 2354 (“We have described the concern that drives this exclusionary principal as one of pre-emption”). For this reason, questions on preemption are inherent in and resolved by the § 101 analysis. The concern is that “patent law not inhibit further discovery by improperly tying up the future use of these building blocks of human ingenuity.” Id. (internal quotations omitted). In other words, patent claims should not prevent the use of the basic building blocks of technology—abstract ideas, naturally occurring phenomena, and natural laws. While preemption may signal patent ineligible subject matter, the absence of complete preemption does not demonstrate patent eligibility. In this case, Sequenom’s attempt to limit the breadth of the claims by showing alternative uses of cffDNA outside of the scope of the claims does not change the conclusion that the claims are directed to patent ineligible subject matter. Where a patent’s claims are deemed only to disclose patent ineligible subject matter under the Mayo framework, as they are in this case, preemption concerns are fully addressed and made moot. (Emphasis added.)
For these reasons, it appears that the claims are not patent-eligible under 35 USC §101.
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.
Claim(s) 1, 2, 7, 9, 10, 12, 13, 15, 17 – 18 and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Bravo (US 2022/0387888) in view of Srinivasan (2021/0134124) in view of Warren (US 2021/0065516)
As per claim 1, Bravo discloses:
One or more computing devices, comprising one or more processors, configured to: (Bravo Fig 2, 3)
determine, for a fantasy sports contest, base contest parameters associated with a plurality of fantasy sports players and a predicted outcome associated with the one or more fantasy sports players…; (Bravo discloses the use of a fantasy sports contest wherein players choose players to fill out their lineups that they predict will achieve the most points over a period of time. The contest utilizes player statistics which are used to determine points (base parameters) that are associated with each player’s real-life performance) (Bravo 0043 – 0044)
receive, a selection comprising an indication of the one or more fantasy sports players and the predicted outcome from the client device; ( Bravo discloses a user creating their lineup (Bravo 0051) with the goal of predicting player performance in relation to an outcome) (Bravo 0046)
receive, from the participant, a selection of the one or more selectable options, wherein the selection indicates at least one of at least … modifier and the at … modifier from the client device and; transmit an award to the participant …the one or more adjusted contest parameters and an outcome associated with the selection…. (Bravo discloses receiving an indication of a modifier and the transmittal of an award such as points to the user based on the booster and the adjusted statistics associated with the player’s performance) (Bravo 0052, 0064, 0076 – 0078, 0089, 0090)
Bravo fails to disclose specifically:
wherein the plurality of base contest parameters and the predicted outcome are determined based on real-time performance metrics associated with the plurality of fantasy sports players and a plurality of selected outcome predictions associated with the fantasy sports players received from a plurality of client devices, and the predicted outcome comprises a performance threshold;
calculate, by a machine learning model, an expected distribution of the base contest parameters across a plurality of outcomes associated with the predicted outcome;
determine a first modified performance threshold and a second modified performance threshold of the plurality of outcomes based on the expected distribution of the base contest parameters, wherein the first modified performance threshold is higher than the performance threshold and the second modified performance threshold is lower than the performance threshold and a plurality of selected modified outcome predictions associated with the fantasy sports players received from the plurality of client devices;
predict one or more adjusted contest parameters associated with each of the first modified performance threshold and the second modified performance threshold;
display, on a client device, a user interface comprising one or more selectable options, wherein the one or more selectable options comprise at least one first modifier and at least one second modifier, wherein the at least one first modifier comprises the first modified performance threshold and the one or more adjusted contest parameters and at least one second modifier comprises the second modified performance threshold and the one or more adjusted contest parameters;
or
and the indication from the client device bine based upon the first or second modifier,
or
…wherein the award is determined based on a payout ratio associated with the selection.
In a similar field of endeavor, Srinivasan discloses a game engine that utilizes both artificial intelligence and/or machine learning (Srinivasan 0037) that is trained to determine which side of a wager is being heavily wagered upon and adjust (i.e. modify) the price of future similar betting scenario (i.e. determine adjusted contest parameters) (Srinivasan 0236)
It would be obvious to one of ordinary skill in the art, at the time of filing, to modify Bravo in view of Srinivasan to utilize a known technique to improve similar devices in the same way to analyze which side was being bet upon and adjust game parameter to control the odds. This would ensure that the game provider is able to offer even odds despite the differences in skill level between bettors.
However, in a similar field of endeavor, Warren teaches the generation of wagering opportunities wherein the wagering opportunities that are presented for the user to select comprise a set of benchmark levels wherein each benchmark point level is associated with payout odds, wherein the benchmarks levels are below , at or above the projected point level (i.e. modifiers), wherein the projected scores may also be based upon at least in part upon the wagers placed by others (Warren 0029, 0031)
It would be obvious to one of ordinary skill in the art, at the time of filing, to modify Bravo in view of Warren in order to use a known technique of improving similar games in similar ways to by presenting options to a user that are a modification of the projected score below or above a projected benchmark. This would be beneficial as some players would be more apt to wager if they feel they are in control of their wager to some degree by having the ability to select a from multiple wagering outcomes that offer corresponding payouts based upon the likelihood of occurrence.
As per claim 2, Bravo disclose:
apply a scaling factor to the base contest parameters. (Bravo discloses a scaling factor or multiplier factor that is applied or modifies the athlete statistics (Bravo 0095 – 0098).
As per claim 7, Bravo discloses:
wherein the one or more adjusted contest parameters include a likelihood of winning and potential payout amounts. (Bravo discloses the use of booster that will increase (i.e. recalibrate) the players likelihood of winning based on players being able to be awarded higher points and the user being able to achieve higher potential points at the end of the game) (Bravo 0094)
As per claim 9, Bravo discloses:
wherein displaying the at least one first modifier and the at least one second modifier further comprises displaying a first award associated with the at least one first modifier and a second award associated with the at least one second modifier. (Combination of Bravo in view of Warren wherein Warren teaches each benchmark point level is associated with payout odds, wherein the benchmarks levels are below , at or above the projected point level (i.e. modifiers), wherein the projected scores may also be based upon at least in part upon the wagers placed by others (Warren 0029, 0031)
As per claim 10, Bravo discloses:
further configured to determine a performance variability associated with the participant, wherein the one or more adjusted contest parameters are determined based on the performance variability. (Bravo discloses the boosters affecting athletes based upon the position (performance variability) that the athlete plays) (Bravo 0077).
As per claim 12, Bravo discloses:
wherein the adjusted contest parameters are further based on one or more live game events associated with the one or more fantasy sports players. (Bravo discloses that some players with booster may be applied to players that become injured during the game and thus the adjusted game parameters affecting the players points would not be collected) (Bravo 0080).
As per claim 13, Bravo discloses:
further configured to determine, through collective filtering, one or more correlations associated with a plurality of selections, wherein the one or more adjusted contest parameters are further based on the one or more correlations. (Bravo discloses the users may adjust (filter) their lineups based upon players that are available from a current time onward (correlations) that will give them the best chances of winning) (Bravo 0084)
As per claim 15, Bravo discloses:
further configured to present, by a user interface on the client device, an adjusted contest parameter of the one or more adjusted contest parameters. (Bravo Fig 1)
Independent claim(s) 17 and 20 is/are obvious in view of Bravo, Srinivasan and Warren based on the same analysis set forth for claim(s) 1, which are similar in claim scope.
Dependent claim(s) 18 is/are obvious in view of Bravo, Srinivasan and Warren based on the same analysis set forth for claim(s) 2, which are similar in claim scope.
Claim(s) 6 is/are rejected under 35 U.S.C. 103 as being unpatentable over Bravo (US 2022/0387888) in view of Srinivasan (2021/0134124) in view of Warren (US 2021/0065516) in view of Tallarico et al (US 2021/0038998).
As per claim 6, Bravo fails to disclose:
further configured to determine, based on a Bayesian model, one or more selection patterns of a plurality of participants, wherein the one or more adjusted contest parameters are determined based on the one or more selection patterns.
However, in a similar field of endeavor, Tallarico discloses the use of player data such as previous choices that is used to feed into a machine learning algorithm based upon a Bayesian model to determine which parameters to adjust in order to balance out the difficulty of the game (Tallarico 0029, 0114, 0140).
It would be obvious to one of ordinary skill in the art, at the time of filing, to modify Bravo with machine learning to adjust parameters of the game as taught by Tallarico in order to use a known technique to improve similar devices in the same way. This would enable the system to efficiently adjust the difficulty of the game in response to players have more or less experience.
Claim(s) 11 is/are rejected under 35 U.S.C. 103 as being unpatentable over Bravo (US 2022/0387888) in view of Srinivasan (2021/0134124) in view of Warren (US 2021/0065516) in view of Jacob (US 2008/0266250)
As per claim 11, Bravo fails to disclose:
wherein the adjusted contest parameters are further based on a historical accuracy of one or more previously adjusted contest parameters.
However in a similar field of endeavor, Jacob discloses a game system adjusting game parameters, namely the difficulty of the game based upon historical accuracy of the players previous choices. (Jacob 0046)
It would be obvious to one of ordinary skill in the art, at the time of filing, to modify Bravo to adjust parameters of the game based on a player’s historical accuracy of choices as taught by Jacob, to use a known technique of improving similar games in similar ways. This would ensure that as players get more and more experiences the game does not become too easy for them to win.
Claim(s) 14 is/are rejected under 35 U.S.C. 103 as being unpatentable over Bravo et al (US 2022/0387888) in view of Srinivasan (2021/0134124) in view of Warren (US 2021/0065516) in view of Joung (US 2024/0194031)
As per claim 14, Bravo specifically fails to disclose;
further configured to determine, using an elasticity-based algorithm, the award based on a plurality of selections made in response to changes in contest parameters.
However, Joung teaches the use of an award that is based upon a mathematical algorithm with respect how much a player pays to play (i.e. elasticity algorithm) (Joung Fig 1G, 0066).
It would be obvious to one of ordinary skill in the art, at the time of filing, to modify Bravo to use an algorithm that rewards a player based upon how much they paid (elasticity algorithm) as taught by Joung, using a known technique of improving similar devices in similar ways. This would ensure that players are rewarded commensurate with the amount they risk.
Claim(s) 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Bravo et al (US 2022/0387888) in view of Srinivasan (2021/0134124) in view of Warren (US 2021/0065516) in view of Morrison et al (US 2016/0071355)
As per claim 16, Bravo fails to disclose:
further configured to determine, through an aggregation model, a collective wisdom of a plurality of participants, wherein the one or more adjusted contest parameters are further based on the collective wisdom of the plurality of the participants.
However, in a similar field of endeavor, Morrison teaches a system of monitoring matchups and performing an adjustment to the matchup based on the majority of players betting on one particular side (collective wisdom) thus creating an unbalanced matchup (Morrison 0129, 0183).
It would be obvious to one of ordinary skill in the art, at the time of filing, to modify Bravo to adjust game parameters in response to the collective wisdom of many players as taught by Morrison using a known technique of improving similar devices in similar ways. This would ensure that game matchups are fair for all players and participants by the system being able to change the matchup offered when to many participants are choosing the same side.
Claim(s) 21 is/are rejected under 35 U.S.C. 103 as being unpatentable over Bravo et al (US 2022/0387888) in view of Srinivasan (2021/0134124) in view of Warren (US 2021/0065516) in view of Huke et al (US
As per claim 21, Bravo fails to disclose:
continuously aggregate, from the plurality of client devices over a network, the plurality of selected outcome predictions and the plurality of selected modified outcome predictions as received in real-time; and determine, using the machine learning model, a distribution imbalance indicating a disproportionately high volume of selections for a specific outcome of the aggregated selected outcome predictions, wherein the first modified performance threshold and the second modified performance threshold are determined based on the distribution imbalance.
However, in a similar field of endeavor, Huke teaches a system of utilizing machine learning to determine how heavily a particular side of a wagering opportunity is being wagered upon based upon the distribution of wagers that are made by users (Huke 0076-0079, 0084-0085)
It would be obvious to one of ordinary skill in the art, at the time of filing, to modify Bravo in view of Huke to use a known technique to improve similar systems in the same way by AI or machine learning to determine if a wagering opportunity is imbalances or not by determining if too many users are betting on a particular side. This would help the game establishment to remain profitable as they may be able to offer incentives for player to bet on the other side of the wager.
Response to Arguments
Applicant’s arguments with respect to claim(s) 1,2,6,7,9-18, 20 and 21 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument. Please see above rejection addressing the newly amended claim limitations in view of Warren
Regarding the rejection of the claims under 35 U.S.C. 101, the Applicant argues:
Instead, the claims recite a system that uses a machine learning model to calculate an expected distribution of base contest parameters across multiple outcomes, then determines modified performance thresholds (one higher and one lower than the base performance threshold) based on that expected distribution and selections received from multiple client devices. The system receives a participant's selection of fantasy sports players and predicted outcome along with an indication of a modifier, then transmits an award based on the modifier, the adjusted contest parameters, and the actual outcome. This requires the system to aggregate data from multiple client devices, process it through a machine learning model, dynamically adjust parameters based on the aggregated selections, and coordinate the transmission of awards across a distributed network. Thus, claim 1 does not recite rules for conducting a wagering game as defined by the MPEP.
The Examiner respectfully disagrees and notes that the claims are clearly directed towards rules for presenting and operating a fantasy sports game that is presented to players or users to enable the user to make predictions and receive award payouts. The game clearly requires players or users to make selections of predictions and in response to the predictions that the players make, the users or players are awarded a payout determined by a payout ratio. This concept falls under the management of user behavior and fundamental economic principles.
Applicant goes on to state:
“…claim 1 recites additional elements that integrate the judicial exception into a practical application. Instead, claim 1 presents an improvement to computing technology used to implement a fantasy sports contest because claim 1 recites a solution the technical problem of maintaining balanced contest parameters when multiple users simultaneously make selections that could concentrate on specific outcomes. The system uses a machine learning model to calculate an expected distribution of parameters across outcomes, then automatically determines modified performance thresholds (both higher and lower than the base threshold) based on aggregated selections received from multiple client devices. The recited solution includes a dynamic adjustment mechanism that prevents system imbalance by recalibrating parameters based on real-time network data received from distributed client devices.
A claim does not recite an abstract idea when "the claim covers a particular solution to a problem or a particular way to achieve a desired outcome, as opposed to merely claiming the idea of a solution or outcome. ... The specification does not need to explicitly set forth the improvement, but it must describe the invention such that the improvement would be apparent to one of ordinary skill in the art."4 Claim 1 presents a particular solution (e.g., adjusting modifiers based on selected predictions from a plurality of client devices) instead of merely claiming the idea of conducting a wagering game. Thus, claim 1 presents an improvement to computing technology used to implement a fantasy sports contests and integrates the alleged abstract idea into practical application.
The Examiner respectfully disagrees and notes that the Applicant alleges that “claim 1 presents an improvement to computing technology used to implement a fantasy sports contests and integrates the alleged abstract idea into practical application.”, but the Applicant fails to specifically point out or identify what the actual improvement to the computing technology actually is. The mere recalibration of parameters based upon real-time network data (i.e. multiple user predictions) to thereby determine when a prediction opportunity is imbalanced is a process that a human can clearly accomplish utilizing manuals means such as pen and paper and the powers of observation. The Examiner notes the following regarding limitations that are indicative of integration into a practical application:
Improvements to the functioning of a computer, or to any other technology or technical field - see MPEP 2106.05(a)
Applying or using a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition – see Vanda Memo
Applying the judicial exception with, or by use of, a particular machine - see MPEP 2106.05(b)
Effecting a transformation or reduction of a particular article to a different state or thing - see MPEP 2106.05(c)
Applying or using the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception - see MPEP 2106.05(e) and Vanda Memo
The Examiner is unpersuaded by the Applicant’s arguments and maintains the rejection.
Conclusion
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/RAW/Examiner, Art Unit 3715 6/23/2026
/KANG HU/Supervisory Patent Examiner, Art Unit 3715
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