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 .
Amendment filed on 05/19/2026 has been entered. Claims 1-2, 4-8, 10-12, 14-18 and 20 are pending. Claims 1 and 11 are currently amended. Claims 3, 9, 13 and 19 have been cancelled without prejudice.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 07/09/2026 was filed after the mailing date of the Non-Final rejection on 04/03/2026. 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 § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
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 text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 8, 10, 18 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Liu et al. (Liu), US Patent Application Publication No. US 2023/0042327 A1, and further in view of Semichev et al. (Semichev), US Patent Application Publication No. US 2022/0084371 A1.
As independent claim 8, Liu discloses a method comprising:
self-supervised first training an encoder to encode a sequence of log messages into an encoded trace (paragraph [0046] and Figure 8: during self-supervised learning, one single encoder may be employed to generate embeddings of two views of one sequence);
connecting, after the first training, the encoder to a neural network (Figure 3 and Claim 1: performing contrastive learning to the neural network system to generate a trained neural network system, wherein the performing the contrastive learning includes performing first model augmentation to a first encoder of the neural network system to generate a first embedding of a sample);
second training the neural network (paragraphs [0025], [0027]: perform training using the neural network model for various tasks).
Liu, however, does not disclose detect whether a sequence of database command is anomalous, wherein the second training is not self-supervised.
In the same field of endeavor, Semichev discloses devices, systems, and methods for self-supervised and/or unsupervised training methods and implementation of a model for detecting anomalous automated teller machine (ATM) customer interactions for a respective ATM customer in real-time and for detecting anomalous behavior across a population (paragraph [0003]). Semichev further discloses the system may receive ATM logs comprising ATM customer data for a plurality of customers, wherein ATM logs may include data associated with a customer ATM session, including data indicative of every action taken by the customer during the interaction, for example, ATM logs may include data indicating that the ATM customer entered his or her PIN (database command) incorrectly on the first attempt or customer does not check balance before attempting to withdraw unusual for a given customer amounts of cash (paragraph [0025]). Semichev further discloses the system may employ a tokenization process in which an activities vocabulary is derived from the ATM logs during the tokenization process, which is referred as methos to split and encode raw sequences of ATM logs (text or binary) to a machine-readable form consumable by a machine learning model, or ATM session activities can be encoded as sequence (paragraph [0026]). Semichev further discloses the system (anomaly detection system) has undergone unsupervised training and may be used to determine anomalous ATM activity for a particular user and secure a customer session (paragraph [0029]).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the system of Liu to include detect whether a sequence of database command is anomalous, wherein the second training is not self-supervised, as taught by Semichev for the purpose of providing unsupervised detection of anomalous ATM customer interactions during a customer session (Semichev, paragraphs [0035], [0036]).
As to independent claim 10, Liu discloses a method comprising:
generating a neural network that accepts a complete sequence of data and a subsequence of data as input, wherein the neural network contains an encoder that can encode the complete sequence of data into an encoded trace (Figure 3 and Claim 1: performing contrastive learning to the neural network system to generate a trained neural network system, wherein the performing the contrastive learning includes performing first model augmentation to a first encoder of the neural network system to generate a first embedding of a sample; paragraph [0023]: neural network module may be used to translate structured text; paragraphs [0029]-[0031]: augmented sequence of data is inputted into encoder);
self-supervised training the neural network to detect whether the complete sequence of data contains the subsequence of data, wherein the training the neural network comprises training the encoder (paragraph [0046] and Figure 8: during self-supervised learning, one single encoder may be employed to generate embeddings of two views of one sequence);
deploying, after the training the encoder, the encoder without the neural network (paragraph [0047]: during the inference stage, only model encoder 306 is used).
As pointed out above that Liu teaches in paragraph [0023]: neural network module may be used to translate structured text; paragraphs [0029]-[0031]: augmented sequence of data (message) is inputted into encoder. However, Liu does not disclose sequence of data is sequence of database commands.
In the same field of endeavor, Semichev discloses devices, systems, and methods for self-supervised and/or unsupervised training methods and implementation of a model for detecting anomalous automated teller machine (ATM) customer interactions for a respective ATM customer in real-time and for detecting anomalous behavior across a population (paragraph [0003]). Semichev further discloses the system may receive ATM logs comprising ATM customer data for a plurality of customers, wherein ATM logs may include data associated with a customer ATM session, including data indicative of every action taken by the customer during the interaction, for example, ATM logs may include data indicating that the ATM customer entered his or her PIN (database commands) incorrectly on the first attempt or customer does not check balance before attempting to withdraw unusual for a given customer amounts of cash (paragraph [0025]). Semichev further discloses the system may employ a tokenization process in which an activities vocabulary is derived from the ATM logs during the tokenization process, which is referred as methos to split and encode raw sequences of ATM logs (text or binary) to a machine-readable form consumable by a machine learning model, or ATM session activities can be encoded as sequence (paragraph [0026]). Semichev further discloses the system (anomaly detection system) has undergone unsupervised training and may be sued to determine anomalous ATM activity for a particular user and secure a customer session (paragraph [0029]).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the system of Liu to include sequence of ATM logs (database commands) is inputted into a neural network, as taught by Semichev for the purpose of providing detection of anomalous ATM customer interactions during a customer session (Semichev, paragraphs [0035], [0036]).
Claim 18 is media claims that contain similar limitations of claim 8. Therefore, claims 18 is rejected under the same rationale.
Claim 20 is medium claim that contains similar limitations to claim 10. Therefore, claim 20 is rejected under the same rationale.
Allowable Subject Matter
Claims 1 and 11 are amended and overcome the rejection under 35 U.S.C. 112(a), which is hereby withdrawn. Therefore, claims 1-2, 4-7, 11-12 and 14-17 are allowed over the cited prior art.
Response to Amendment
In the Remarks, Applicant argued in substance that the prior art of record does not disclose detect whether a sequence of database command is anomalous, wherein the second training is not self-supervised.
In reply to this argument, Semichev discloses devices, systems, and methods for self-supervised and/or unsupervised training methods and implementation of a model for detecting anomalous automated teller machine (ATM) customer interactions for a respective ATM customer in real-time and for detecting anomalous behavior across a population (paragraph [0003]). Semichev further discloses the system may receive ATM logs comprising ATM customer data for a plurality of customers, wherein ATM logs may include data associated with a customer ATM session, including data indicative of every action taken by the customer during the interaction, for example, ATM logs may include data indicating that the ATM customer entered his or her PIN (database command) incorrectly on the first attempt or customer does not check balance before attempting to withdraw unusual for a given customer amounts of cash (paragraph [0025]). Semichev further discloses the system may employ a tokenization process in which an activities vocabulary is derived from the ATM logs during the tokenization process, which is referred as methos to split and encode raw sequences of ATM logs (text or binary) to a machine-readable form consumable by a machine learning model, or ATM session activities can be encoded as sequence (paragraph [0026]). Semichev further discloses the system (anomaly detection system) has undergone unsupervised training and may be used to determine anomalous ATM activity for a particular user and secure a customer session (paragraph [0029]). Thus, Semichev discloses “detect whether a sequence of database command is anomalous, wherein the second training is not self-supervised.”
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37CFR 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 extension fee 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 CHAU T NGUYEN whose telephone number is (571)272-4092. The examiner can normally be reached on Monday-Friday from 8am to 5pm.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Cesar Paula, can be reached at telephone number 5712724128. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/CHAU T NGUYEN/Primary Examiner, Art Unit 2145
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