DETAILED ACTION This is in response to the application filed on September 7, 2023, where Claims 1 – 20 , of which Claims 1 and 15 are in independent form, are presented for examination. 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. Information Disclosure Statement The information disclosure statement (IDS) submitted on September 7, 2023 was filed before the mailing date of the current action. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner. 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. Claim s 1 – 20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. 1. Regarding Claim 1 , t he claim recite s the limitations of “ receiving … a first dataset comprising a first plurality of feature values, the first plurality of feature values corresponding to a plurality of features of an interaction , inputting the first datase t [into a function/analysis] … outputting … a second dataset, the second dataset comprising a second plurality of feature values corresponding to the plurality of features of the interaction , computing a feature deviation dataset using the first dataset and the second dataset , and determining a type of activity based on the feature deviation dataset . ” Under Step 2 A , Prong One , of the 2019 PEG, the claim limitations recited constitute a mental process (i.e., concepts performed in the human mind) and is considered an abstract idea. U nder Step 2A, Prong Two, t his judicial exception is not integrated into a practical application because of the remaining limitations are merely applying the abstract idea on a computer (the server computer), See MPEP 2106.05(f), or generally linking the use of the judicial exception to a particular technological environment or field of use (the auto-encoder module), See MPEP 2106.05(h). U nder Step 2A, Prong Two, t he claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because there are no other limitation that are directed to any improvements to the functioning of a computer or 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(a) and 20106.05(e). 2. Regarding Claim 15 , the claim provides additional element that merely apply the abstract idea on a computer (the server computer comprising a processor, memory with instructions ), See MPEP 2106.05(f) 3. Regarding Claims 2 – 14 and 16 – 20 , t he claim s do not include additional elements that are sufficient to amount to significantly more than the judicial exception because they elaborate on the mental process or the type of information used in the mental process (e.g. Claims 2, 4, 6, 8 – 14, 16, and 18 – 20), they merely describe well-understood, conventional activities previously known to the industry, specified at a high level of generality (e.g., Claims 5 and 17), or add insignificant extra-solution activity to the judicial exception (e.g., Claims 3 and 7). Therefore, the dependent claims are also rejected 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 – 20 are rejected under 35 U.S.C. 103 as being unpatentable over PGPub. 2016/0155136 (hereinafter “Zhang”), in view of PGPub. 2018/0365089 (hereinafter “Okanoharu”) . 4. Regarding Claims 1 and 15 , Zhang discloses a server computer [Para. 0055-56] comprising: a processor [Para. 0055-56]; a non-transitory computer readable medium comprising instructions executable by the processor to perform operations (Claim 1) [Para. 0055-56] including: receiving, by an auto-encoder module, a first dataset comprising a first plurality of feature values, the first plurality of feature values corresponding to a plurality of features of an interaction [Figs. 1-3; Para. 0030, 0034-39] ; inputting the first dataset into the auto-encoder module [ Figs. 1-3; Para. 0030, 0034-39 ] ; outputting, by the auto-encoder module, a second dataset, the second dataset comprising a second plurality of feature values corresponding to the plurality of features of the interaction [ Figs. 1-3; Para. 0030, 0034-39 ] ; computing, by the server computer, a feature deviation dataset using the first dataset and the second dataset; and determining, by the server computer, a type of activity based on the feature deviation dataset . Zhang, however, does not specifically disclose computing a feature deviation dataset using the first dataset and the second data ba se and determining a type of activity based on the feature deviation dataset . Okanoharu discloses a system and method of using an auto-encoder to determine if input samples are deviations based on learned models [Abstract; Figs. 1-3; Para. 0010, 0052]. Okanoharu further discloses of determining a feature deviation dataset using the first dataset and the second dataset and determining whether it is normal or abnormal activity based on the feature deviation dataset [ Abstract; Figs. 1-3; Para. 0010, 0052 ]. It would have been obvious to one skilled in the art before the effective filing date of the current invention to incorporate the teachings of Okanoharu with Zhang since both systems use auto-encoders to determine variances from input data. The motivation to do so is to improve accuracy and reduce false alarms [Para. 0020]. 5 . Regarding Claims 2 and 16 , Zhang, in view of Okanoharu, discloses the limitations of Claims 1 and 15. Okanoharu further discloses that determining the type of activity based on the feature deviation dataset comprises sorting the feature deviation dataset [Figs. 1-3; Para. 0010, 0052] . 6 . Regarding Claim 3 , Zhang, in view of Okanoharu, discloses the limitations of Claim 1 . Zhang further discloses that the first dataset is received from an entity computer and wherein the interaction corresponds to an interaction performed in association with the entity computer [Para. 0017, 0037]. 7 . Regarding Claims 4 and 1 9 , Zhang, in view of Okanoharu, discloses the limitations of Claims 1 and 15. Zhang further discloses that the plurality of features of the interaction comprise one or more of interaction level features, account features, long term features, velocity features, or graph features [Para. 0037] . 8. Regarding Claims 5 and 1 7 , Zhang, in view of Okanoharu, discloses the limitations of Claims 1 and 15. Zhang further discloses that the auto-encoder module comprises an encoder comprising a plurality of neural network layers and a decoder comprising a plurality of neural network layers [Figs. 1 and 2; Para. 0030] . 9. Regarding Claim 6 , Zhang, in view of Okanoharu, discloses the limitations of Claim 1. Zhang further discloses that the type of activity is one of account take over fraud, email compromise fraud, authorized push interaction fraud, or pyramid scam fraud [Para. 0039] . 10. Regarding Claim 7 , Zhang, in view of Okanoharu, discloses the limitations of Claim 1. Zhang further discloses of transmitting, by the server computer to an entity computer, an indication of the interaction of the first dataset [Para. 0039, 0042; Claim 1 ] . 11. Regarding Claim 8 , Zhang, in view of Okanoharu, discloses the limitations of Claim 1. Zhang further discloses of determining, by the server computer, a loss of a loss function using the first dataset and the second dataset [Para. 0034] . 12. Regarding Claim 9 , Zhang, in view of Okanoharu, discloses the limitations of Claim 8 . Zhang further discloses of modifying, by the server computer, a first set of learnable parameters and a second set of learnable parameters to minimize the loss of the loss function [Para. 0039] . 13. Regarding Claim 10 , Zhang, in view of Okanoharu, discloses the limitations of Claim 1. Zhang further discloses that after inputting the first dataset into the auto-encoder module, determining, by the auto-encoder module, a hidden representation of the first dataset [Para. 0035-39; Claim 8] ; and generating, by the auto-encoder module, the second dataset by reconstructing the first dataset using the hidden representation of the first dataset [ Para. 0035-39; Claim 8 ] . 14. Regarding Claim 1 1 , Zhang, in view of Okanoharu, discloses the limitations of Claim 1. Okanoharu further discloses that the type of activity is associated w ith a feature network [Figs. 1-3; Para. 010, 0052] . 15. Regarding Claim 1 2 , Zhang, in view of Okanoharu, discloses the limitations of Claim 1. Zhang further discloses that the feature deviation dataset is determined by computing an absolute difference between the first dataset and the second dataset [Para. 0013] . 16. Regarding Claim 1 3 , Zhang, in view of Okanoharu, discloses the limitations of Claim 1. Okanoharu further discloses that the type of activity is associated with large deviations in a predetermined set of features [Para. 0121] . 17. Regarding Claim 1 4 , Zhang, in view of Okanoharu, discloses the limitations of Claim 1. Zhang further discloses that the auto-encoder module is trained using known l egitimate interactions [Para. 0035-39] . 18. Regarding Claim 1 8 , Zhang, in view of Okanoharu, discloses the limitations of Claim 1 5 . Zhang further discloses that the second dataset is determined using a sigmoid function [Para. 0035-39; Claim 8] . 19. Regarding Claim 20 , Zhang, in view of Okanoharu, discloses the limitations of Claim 15. Zhang further discloses that the auto-encoder m odule is associated with a loss function, and wherein the loss function is a mean squared error loss function [Para. 0034] . Conclusion The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. PGPub. 2021/0192548; PGPub. 202 1/037413 2 . Contacts Any inquiry concerning this communication or earlier communications from the examiner should be directed to Tae K. Kim, whose telephone number is (571) 270-1979. The examiner can normally be reached on Monday - Friday (10:00 AM - 6:30 PM EST). If attempts to reach the examiner by telephone are unsuccessful, the examiner's supervisor, Jorge Ortiz-Criado, can be reached on (571) 272-7624. The fax phone number for submitting all Official communications is (703) 872-9306. The fax phone number for submitting informal communications such as drafts, proposed amendments, etc., may be faxed directly to the examiner at (571) 270-2979. Information regarding the status of an application may be obtained from the Patent Application Information Retrieval (PAIR) system. Status information for published applications may be obtained from either Private PAIR or Public PAIR. Status information for unpublished applications is available through Private PAIR only. For more information about the PAIR system, see http://pair-direct.uspto.gov. Should you have questions on access to the Private PAIR system, contact the Electronic Business Center (EBC) at (866) 217-9197 (toll-free). /TAE K KIM/ Primary Examiner, Art Unit 2496