DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-30 are rejected under 35 U.S.C. 101 because the claimed invention as a whole, considering all claim elements both individually and in combination, is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
As summarized in MPEP § 2106, subject matter eligibility is determined based on a Two-Part Analysis for Judicial Exceptions. In Step 1, it must be determined whether the claimed invention is directed to a process, machine, manufacture or composition of matter. The instant application includes claims concerning one or more processors or system (i.e., a machine) in claims 1-12, 25-30, a method (i.e., a process) in claims 13-18 and a machine-readable medium (i.e. a manufacture) in claim 19-24.
In Prong 1 of Step 2A, it must be determined whether the claimed invention recites an Abstract Idea, Law of Nature or a Natural Phenomenon.
In particular exemplary presented claim 1 includes the following underlined claim elements:
1. One or more processors, comprising:
circuitry to:
receive one or more images of gameplay of a game;
detect cheating by one or more players of the game based, at least in part, on one or more neural networks to detect one or more anomalies within the one or more images of the gameplay of the game wherein the one or more visual anomalies comprise at least one of overlays, graphical artifacts, or inconsistencies between displayed elements and expected gameplay;
generate an indication of the cheating by the one or more players; and
modify operation of the game in response to the indication.
The claim elements underlined above, concern the court enumerated abstract ideas of Mental Processes including observation, evaluation, and judgement because the claims are directed to series of steps that evaluate image data to determine anomalies and indicate cheating as well as Certain Methods of Organizing Human Activity including managing personal behavior involving interactions between people including social activities and following rules or instructions because the claims set forth rules interactions involving one or more parties in the context of game cheating.
As the exemplary claim recites an Abstract Idea, Law of Nature or a Natural Phenomenon it is further considered under Prong 2 of Step 2A to determine if the claim recites additional elements that would integrate the judicial exception into a practical application. Wherein the practical applications are set forth by MPEP §2106.05(a-c,e) are broadly directed to: the improvement in technology, use of a particular machine and applying or using the judicial exception in a meaningful way beyond generally linking the use thereof to a technology environment. Limitations that explicitly do not support the integration of the judicial exception into a practical application are defined by MPEP 2106.05(f-h) and include merely using a computer to implement the abstract idea, insignificant extra solution activity, and generally linking the use of the judicial exception to a particular technology environment or field of use.
With respect to the above the claimed invention is not integrated into a practical application because it does not meet the criteria of MPEP §2106.05(a-c,e) and although it is performed on one or more processor(s) and circuitry it is not directed to a particular machine because the hardware elements are not linked to a specific device/machine and would reasonably include other devices such as generic computers, smart phones, game consoles, and the like. Accordingly, the claims limitations are not indicative of the integration of the identified judicial exception into a practical application, and the consideration of patent eligibility continues to step 2B.
Step 2B requires that if the claim encompasses a judicially recognized exception, it must be determined whether the claimed invention recites additional elements that amount to significantly more than the judicial exception. The additional element(s) or combination of elements in the claim(s) other than the abstract idea(s) per se including one or more processor(s) and circuitry amount(s) to no more than: (i) mere instructions to implement the idea on a computer, and/or (ii) recitation of generic computer structures that serves to perform generic computer functions that are well-understood, routine, and conventional activities previously known to the pertinent industry per the applicant’s description (Applicant’s specification Paragraphs [0097], [0106], [0259]). Viewed as a whole, these additional claim element(s) do not provide meaningful limitation(s) to transform the abstract idea into a patent eligible application of the abstract idea such that the claim(s) amounts to significantly more than the abstract idea itself.
Accordingly, as presented the claimed invention when considered as a whole amounts to the mere instructions to implement an abstract idea [i.e. software or equivalent process steps] on a generic computer [i.e. controller or processor] without causing the improvement of the generic computer or another technology field.
The applicant’s specification is further noted as supporting the above rejection wherein neither the abstract idea nor the associated generic computer structure as claimed are disclosed as improving another technological field, improvements to the function of the computer itself, or meaningfully linking the use of an abstract idea to a particular technological environment (Applicant’s specification Paragraphs [0097], [0106], [0259]). In particular the applicant’s specification only contains computing elements which are conventional and generally widely known in the field of the invention described, and accordingly their exact nature or type is not necessary for an understanding and use of the invention by a person skilled in the art per the requirements of 37 CFR 1.71. Were these elements of the applicant’s invention to be presented in the future as non-conventional and non-generic involvement of a computing structure, such would stand at odds with the disclosure of the applicant's invention as found in their specification as originally filed.
“[I]f a patent’s recitation of a computer amounts to a mere instruction to ‘implemen[t]’ an abstract idea ‘on . . .a computer,’ . . . that addition cannot impart patent eligibility.” Alice, 134 S. Ct. at 2358 (quoting Mayo, 132S. Ct. at 1301). In this case, the claims recite a generic computer implementation of the covered abstract idea.
The remaining presented claims 2-30 incorporate substantially similar abstract concepts as noted with respect to the exemplary claim 1, while the additional elements recited by the additional claims including one or more of one or more processors, memory, circuitry, a machine-readable medium and a ticket as respectively presented in certain claims that when considered both individually and as a whole in the respective combinations of each of the additional claims are not sufficient to support patent eligibility under prong 2 of step 2A or step 2B because they each present substantially similar abstract concepts as noted with reflection to exemplary claim 1 above and accordingly for the same reasons set forth above with respect to the exemplary claim 1 are similarly directed to or otherwise include abstract ideas.
Therefore, the listed claim(s) are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
As the claimed invention incorporates the use of AI/Neural Networks. The 2024 Guidance Update on Patent Subject Matter Eligibility, Including Artificial Intelligence as published on July 17th, 2024 and Example 47 are of particular relevance in determining when the use of AI to detect anomalies would and would not be considered patent eligible. At present the claimed invention is most similar to ineligible claim 2 of Example 47.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-30 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by NAM (US 20200114265).
Claim 1: NAM teaches one or more processors (NAM Figure 1), comprising:
circuitry to:
receive one or more images of gameplay of a game (NAM Figures 2-3; Paragraphs [0008], [0047], [0074]);
detect cheating by one or more players of the game based, at least in part, on one or more neural networks to detect one or more anomalies within the one or more images of the gameplay of the game wherein the one or more visual anomalies comprise at least one of overlays, graphical artifacts, or inconsistencies between displayed elements and expected gameplay (NAM Paragraphs [0037], [0043], [0057], [0074], [0077], [0098], [0101], [0120], [0125]);
generate an indication of the cheating by the one or more players (NAM Figures 6 & 7; Paragraphs [0004], [0057], [0127]-[0128]); and
modify operation of the game in response to the indication (NAM Paragraphs [0058]-[0059]).
Claim 2: NAM teaches the one or more processors of claim 1, wherein the one or more neural networks include a reconstruction network for detecting the one or more visual anomalies at least in part by determining a reconstruction probability, for one or more segments of video data, using approved game input (-alternatively describing the identification of reconstruction error- NAM Paragraphs [0126]-[0128]).
Claim 3: NAM teaches the one or more processors of claim 2, wherein the one or more neural networks include a decision network for determining, based at least in part upon the reconstruction probability, whether cheating occurred during the one or more segments (-Alternatively describing the identification of abnormal patterns through the comparison of the same to defined threshold values- NAM Paragraphs [0076], [0126]-[0128]).
Claim 4: NAM teaches the one or more processors of claim 3, wherein the one or more neural networks include one or more labeling networks for labeling events and occurrences detected during the one or more segments, the events and occurrences providing contextual data for the one or more visual anomalies detected during the one or more segments by the reconstruction network, wherein the decision network is further to determine whether the cheating occurred based upon the contextual data (-Wherein the reconstruction error is necessarily based on contextual data describing the scene being reconstructed- NAM Paragraphs [0102], [0126]-[0128], [0132]).
Claim 5: NAM teaches the one or more processors of claim 1, wherein the circuitry is further to log data for the detected cheating by the one or more players to a cheating log for use in future cheating determinations (-Describing the updating of the model based on identification and/or labeling of anomalies- NAM Paragraphs [0102], [0145]).
Claim 6: NAM teaches the one or more processors of claim 1, wherein the modifying the operation of the game comprises modifying an ability of the one or more players to play the game in response to detecting cheating by the one or more players (NAM Paragraphs [0058]-[0059]).
Claim 7: NAM teaches a system comprising:
one or more processors (NAM Figure 1), to:
receive one or more images of gameplay of a game (NAM Figures 2-3; Paragraphs [0008], [0047], [0074]);
detect cheating by one or more players of the game based, at least in part, on one or more neural networks to detect one or more visual anomalies within the one or more images of the gameplay of the game wherein the one or more visual anomalies comprise at least one of overlays, graphical artifacts, or inconsistencies between displayed elements and expected gameplay (NAM Paragraphs [0037], [0043], [0057], [0074], [0077], [0098], [0101], [0120], [0125]);
generate an indication of the cheating by the one or more players (NAM Figures 6 & 7; Paragraphs [0004], [0057], [0127]-[0128]);
modify operation of the game in response to the indication (NAM Paragraphs [0058]-[0059]).
Claim 8: NAM teaches the system of claim 7, wherein the one or more neural networks include a reconstruction network for detecting the one or more visual anomalies at least in part by determining a reconstruction probability, for one or more segments of video data, using approved game input (-alternatively describing the identification of reconstruction error- NAM Paragraphs [0126]-[0128]).
Claim 9: NAM teaches the system of claim 8, wherein the one or more neural networks include a decision network for determining, based at least in part upon the reconstruction probability, whether cheating occurred during the one or more segments (-Alternatively describing the identification of abnormal patterns through the comparison of the same to defined threshold values- NAM Paragraphs [0076], [0126]-[0128]).
Claim 10: NAM teaches the system of claim 9, wherein the one or more neural networks include one or more labeling networks for labeling events and occurrences detected during the one or more segments, the events and occurrences providing contextual data for the one or more visual anomalies detected during the one or more segments by the reconstruction network, wherein the decision network is further to determine whether the cheating occurred based upon the contextual data (-Wherein the reconstruction error is necessarily based on contextual data describing the scene being reconstructed- NAM Paragraphs [0102], [0126]-[0128], [0132]).
Claim 11: NAM teaches the system of claim 7, wherein the one or more processors are further to log data for the detected cheating by the one or more players to a cheating log for use in future cheating determinations (-Describing the updating of the model based on identification and/or labeling of anomalies- NAM Paragraphs [0102], [0145]).
Claim 12: NAM teaches the system of claim 7, wherein the modifying the operation of the game comprises modifying an ability of the one or more players to play the game in response to detecting cheating by the one or more players (NAM Paragraphs [0058]-[0059]).
Claim 13: NAM teaches a method comprising:
receiving one or more images of gameplay of a game (NAM Figures 2-3; Paragraphs [0008], [0047], [0074]);
detecting cheating by one or more players of the game based, at least in part, on one or more neural networks to detect one or more visual anomalies within the one or more images of the gameplay of the game wherein the one or more visual anomalies comprise at least one of overlays, graphical artifacts, or inconsistencies between displayed elements and expected gameplay (NAM Paragraphs [0037], [0043], [0057], [0074], [0077], [0098], [0101], [0120], [0125]);
generating an indication of the cheating by the one or more players (NAM Figures 6 & 7; Paragraphs [0004], [0057], [0127]-[0128]); and
modify operation of the game in response to the indication (NAM Paragraphs [0058]-[0059]).
Claim 14: NAM teaches the method of claim 13, wherein the one or more neural networks include a reconstruction network for detecting the one or more visual anomalies at least in part by determining a reconstruction probability, for the one or more images of the gameplay, using approved game input (-alternatively describing the identification of reconstruction error- NAM Paragraphs [0126]-[0128]).
Claim 15: NAM teaches the method of claim 14, wherein the one or more neural networks include a decision network for determining, based at least in part upon the reconstruction probability, whether cheating occurred during the one or more images of gameplay (-Alternatively describing the identification of abnormal patterns through the comparison of the same to defined threshold values using visual comparisons- NAM Paragraphs [0057], [0076], [0098], [0125]-[0128]).
Claim 16: NAM teaches the method of claim 15, wherein the one or more neural networks include one or more labeling networks for labeling events and occurrences detected during the one or more segments, the events and occurrences providing contextual data for the one or more visual anomalies detected during the one or more segments by the reconstruction network, wherein the decision network is further to determine whether the cheating occurred based upon the contextual data (-Wherein the reconstruction error is necessarily based on contextual data describing the scene being reconstructed- NAM Paragraphs [0102], [0126]-[0128], [0132]).
Claim 17: NAM teaches the method of claim 13, further comprising logging data for the detected cheating by the one or more players to a cheating log for use in future cheating determinations (-Describing the updating of the model based on identification and/or labeling of anomalies- NAM Paragraphs [0102], [0145]).
Claim 18: NAM teaches the method of claim 13, further comprising modifying an ability of the one or more players to play the game in response to detecting cheating by the one or more players (NAM Paragraphs [0058]-[0059]).
Claim 19: NAM teaches a machine-readable medium having stored thereon a set of
instructions,
which if performed by one or more processors (NAM Figure 1), cause the one or more processors to at least:
receive one or more images of gameplay of a game (NAM Figures 2-3; Paragraphs [0008], [0047], [0074]);
detect cheating by one or more players of the game based, at least in part, on one or more neural networks to detect one or more visual anomalies within the one or more images of the gameplay of the game wherein the one or more visual anomalies comprise at least one of overlays, graphical artifacts, or inconsistencies between displayed elements and expected gameplay (NAM Paragraphs [0037], [0043], [0057], [0074], [0077], [0098], [0101], [0120], [0125]);
generate an indication of the cheating by the one or more players (NAM Figures 6 & 7; Paragraphs [0004], [0057], [0127]-[0128]); and
modify operation of the game in response to the indication (NAM Paragraphs [0058]-[0059]).
Claim 20: NAM teaches the machine-readable medium of claim 19, wherein the one or more neural networks include a reconstruction network for detecting the one or more visual anomalies at least in part by determining a reconstruction probability, for the one or more images of the gameplay, using approved game input (-alternatively describing the identification of reconstruction error- NAM Paragraphs [0126]-[0128]).
Claim 21: NAM teaches the machine-readable medium of claim 20, wherein the one or more neural networks include a decision network for determining, based at least in part upon the reconstruction probability, whether cheating occurred during the one or more segments (-Alternatively describing the identification of abnormal patterns through the comparison of the same to defined threshold values- NAM Paragraphs [0076], [0126]-[0128]).
Claim 22: NAM teaches the machine-readable medium of claim 21, wherein the one or more neural networks include one or more labeling networks for labeling events and occurrences detected during the one or more segments, the events and occurrences providing contextual data for the one or more visual anomalies detected during the one or more segments by the reconstruction network, wherein the decision network is further to determine whether the cheating occurred based upon the contextual data (-Wherein the reconstruction error is necessarily based on contextual data describing the scene being reconstructed- NAM Paragraphs [0102], [0126]-[0128], [0132]).
Claim 23: NAM teaches the machine-readable medium of claim 19, wherein the one or more processors are further to log data for the detected cheating by the one or more players to a cheating log for use in future cheating determinations (-Describing the updating of the model based on identification and/or labeling of anomalies- NAM Paragraphs [0102], [0145]).
Claim 24: NAM teaches the machine-readable medium of claim 19, wherein the modifying the operation of the game comprises modifying an ability of the one or more players to play the game in response to detecting cheating by the one or more players (NAM Paragraphs [0058]-[0059]).
Claim 25: NAM teaches a cheating detection system, comprising:
one or more processors (NAM Figure 1), to:
receive one or more images of gameplay of a game (NAM Figures 2-3; Paragraphs [0008], [0047], [0074]);
detect cheating by one or more players of the game based, at least in part, on one or more neural networks to detect one or more anomalies within the one or more images of the gameplay of the game, wherein the one or more visual anomalies comprise at least one of overlays, graphical artifacts, or inconsistencies between displayed elements and expected gameplay (NAM Paragraphs [0037], [0043], [0057], [0074], [0077], [0098], [0101], [0120], [0125]);
generate an indication of the cheating by the one or more players (NAM Figures 6 & 7; Paragraphs [0004], [0057], [0127]-[0128]);
modify operation of the game in response to the indication (NAM Paragraphs [0058]-[0059]); and
memory for storing network parameters for the one or more neural networks (NAM Figure 1).
Claim 26: NAM teaches the cheating detection system of claim 25, wherein the one or more neural networks include a reconstruction network for detecting the one or more visual anomalies at least in part by determining a reconstruction probability, for the one or more images of the gameplay, using approved game input (-alternatively describing the identification of reconstruction error- NAM Paragraphs [0126]-[0128]).
Claim 27: NAM teaches the cheating detection system of claim 26, wherein the one or more neural networks include a decision network for determining, based at least in part upon the reconstruction probability, whether cheating occurred during the one or more segments (-Alternatively describing the identification of abnormal patterns through the comparison of the same to defined threshold values- NAM Paragraphs [0076], [0126]-[0128]).
Claim 28: NAM teaches the cheating detection system of claim 27, wherein the one or more neural networks include one or more labeling networks for labeling events and occurrences detected during the one or more segments, the events and occurrences providing contextual data for the one or more visual anomalies detected during the one or more segments by the reconstruction network, wherein the decision network is further to determine whether the cheating occurred based upon the contextual data (-Wherein the reconstruction error is necessarily based on contextual data describing the scene being reconstructed- NAM Paragraphs [0102], [0126]-[0128], [0132]).
Claim 29: NAM teaches the cheating detection system of claim 25, wherein the one or more processors are further to log data for the detected cheating by the one or more players to a cheating log for use in future cheating determinations (-Describing the updating of the model based on identification and/or labeling of anomalies- NAM Paragraphs [0102], [0145]).
Claim 30: NAM teaches the cheating detection system of claim 25, wherein the modifying the operation of the game comprises modifying an ability of the one or more players to play the game in response to detecting cheating by the one or more players (NAM Paragraphs [0058]-[0059]).
Response to Arguments
Applicant's arguments filed July 14th, 2026 have been fully considered but they are not persuasive.
Commencing on pages 8-9, section II.A, of the Applicant’s Reply the Applicant’s remarks propose that the claimed invention does not fall into the enumerated categories of Abstract idea as set forth in the 2019 Guidelines because it is not reasonable for a human, in their mind, to perform the claimed steps and modify operation of a game in response to an indication.
Responsive to the preceding, it is not immediately clear why the ability of a human to detect cheating in a game (i.e.. Marked cards) and react thereto by declaring game play invalid would be beyond the capability of the human mind. Insomuch as the Applicant’s argument maybe understood to propose that the detection of cheating in a game and modification of electronic game play would be separately eligible based on the use of a computing environment, the grouping of Mental processes includes mental processes performed on a computer (MPEP 2106.4(a)(2) Sub III.c) and the courts have repeated recognized that the use of computer to perform analogous operations involving the collection and analysis of data as falling under the grouping of mental process in at least both Berkheimer v. HP, Inc., 881 F.3d 1360, 125 USPQ2d 1649 (Fed. Cir. 2018), and Electric Power Group v. Alstom, S.A., 830 F.3d 1350, 1353-54, 119 USPQ2d 1739, 1741-42 (Fed. Cir. 2016). The latter additionally noted, Limiting the abstract idea of collecting information, analyzing it, and displaying certain results of the collection and analysis to data related to the electric power grid, did not support eligibility because limiting application of the abstract idea to power-grid monitoring is simply an attempt to limit the use of the abstract idea to a particular technological environment, Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350, 1354, 119 USPQ2d 1739, 1742 (Fed. Cir. 2016). Accordingly, the claimed invention reasonably falls under the grouping of mental process, consistent with the grouping as defined by the MPEP §2106.04(a)(2) and the considerations of analogous inventions by the Federal Circuit.
Continuing on pages 9-11, section II.B, of the Applicant’s Reply, the Applicant’s remarks propose, that the claimed invention integrates any recited abstract idea into a patent eligible practical application of video game management that analogous to considerations addressed in Enfish LLC v. Microsoft Corp. (Fed. Cir. 2016) provides the technical improvement of detecting cheating during game play and modifying game play responsive thereto.
Responsive to the Applicant’s remarks of this section, it is noted that Enfish, LLC v. Microsoft Corp. explicitly presents on page 12 of the decision “In this case, however, the plain focus of the claims is on an improvement to computer functionality itself, not on economic or other tasks for which a computer is used in its ordinary capacity…Rather, they are directed to a specific improvement to the way computers operate, embodied in the self-referential table” (emphasis added). Accordingly, the enhanced functionality that is referenced in Enfish is fairly understood to describe the functionality of the computer itself and a specific improvement to the way computers operate but would specifically not support other tasks for which a computer is used in its ordinary capacity. In the instant application the plain focus of the claim is on an algorithm and rules for determining a game cheating for which a computer is utilized in its ordinary capacity to enact and accordingly the claimed invention does not meet the eligibility criteria as discussed in Enfish, LLC v. Microsoft Corp. as presented. Additionally, as the proposed improvement is reflective of prior art features of NAM (US 20200114265) as applied under section 102 above, it is not immediately that the Applicant’s proposed improvement would depart from well-understood, routine, conventional activity as required by MPEP 2106.05(a).
Further continuing on pages 11-13, sections III.A-III.B, of the Applicant’s Reply the Applicant’s remarks propose that the claimed invention of claims 1, 7, 13, 19 and 25 is allowable over the applied prior art of NAM because the claimed invention utilizes the visual detection of anomalies which are proposed as not being present in NAM.
Responsive to the preceding it is respectfully noted that NAM utilizes image base data to detect visual anomalies and inconsistences (NAM Paragraphs [0037], [0043], [0057], [0074], [0077], [0098], [0101], [0120], [0125]) and as such this feature would not distinguish from the applied prior art of NAM as proposed.
Yet further continuing on page 13, section IV of the Applicant’s Reply the Applicant’s remarks present that the remaining dependent claims are allowable over the applied prior art of NAM based the incorporation of features from the previously argued independent claims.
Responsive to the preceding and reflective of the discussion of the independent claims presented herein above, the independent claims do not incorporate features that would separate them from the applied prior art of NAM and accordingly respectfully would not provide a basis to separate the additionally presented dependent claims based on claim dependency as proposed.
In view of the preceding the rejection of claims is respectfully maintained as presented herein above.
Conclusion
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 ROBERT E MOSSER whose telephone number is (571)272-4451. The examiner can normally be reached M-F 6:45-3:45.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Dmitry Suhol can be reached at 571-272-4430. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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ROBERT E. MOSSER
Primary Examiner
Art Unit 3715
/ROBERT E MOSSER/Primary Examiner, Art Unit 3715
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