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 2/27/24 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 § 102
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 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)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1-3, 8-10, 13-15, 18 are rejected under 35 USC 102(a)(1) as being anticipated by Thakore (hereinafter “Tha”, US Patent 11831644 B1).
As per claims 1, 10, 15, Tha discloses A method, non-transitory machine-readable media, and apparatus comprising:
Generating a plurality of embeddings representing a plurality of device profiles, wherein each of the plurality of device profiles comprises at least one of data and metadata collected for a corresponding device in a network (column 11, lines 15-27, column 12, lines 60-67, column 53, lines 30-41, 48-55, 62-67, Device-association data is stored in a user registry or data store. The data store receives sensor data from the device such as data type of sensor data and/or other attributes of the sensor data);
Clustering the plurality of embeddings into one or more clusters (column 17, lines 25-30, column 49, lines 10-14, 29-31, column 53, lines 33-40);
identifying a subset of the plurality of device profiles as anomalous device profiles based on analyzing the one or more clusters (column 3, lines 6-10, 25-30, column 13, lines 60-67, column 14, lines 9-15, 60-67, The anomaly detector determines that an anomaly is present);
Prompting a language model to verify anomalousness of the subset of device profiles (column 3, lines 6-10, 25-30, column 13, lines 60-67, column 14, lines 9-15, 60-67);
Based on verifying anomalousness of one or more device profiles of the subset of device profiles, indicating that the one or more device profiles are anomalous (column 3, lines 50-53, 60-65, column 4, lines 1-8, column 5, lines 11-16).
As per claims 2, 13, Tha discloses The method of claim 1, wherein prompting the language model to verify anomalousness of the subset of device profiles comprises generating a set of prompts corresponding to the subset of device profiles for the language model and submitting each prompt of the set of prompts to the language model (column 5, lines 21-31, column 13, lines 64-67).
As per claims 3, 14, Tha discloses The method of claim 2 further comprising obtaining responses to submitting the set of prompts to the language model that indicate whether corresponding ones of the subset of device profiles are anomalous, wherein verifying anomalousness of the one or more device profiles comprises determining that one or more of the responses indicate that the corresponding one or more device profiles are anomalous (column 14, lines 1-20).
As per claim 8, Tha discloses The method of claim 1, wherein the language model was previously adapted to predict whether device profiles indicated in prompts are anomalous based on few shot prompting with sets of known anomalous device profiles and known non-anomalous device profiles (column 54, lines 56-67).
As per claim 9, Tha discloses The method of claim 1, wherein the language model comprises a pre-trained Transformer-based large language model (LLM) (column 48, lines 27-30).
As per claim 18, Tha discloses The apparatus of claim 15, wherein the instructions executable by the processor to cause the apparatus to prompt the language model to verify anomalousness of each device profile of the anomalous device profile candidates comprise instructions executable by the processor to cause the apparatus to generate a set of prompts corresponding to the anomalous device profile candidates for the language model and submit each prompt of the set of prompts to the language model, wherein the language model was previously adapted to predict whether device profiles indicated in prompts are anomalous (column 14, lines 1-20).
Allowable Subject Matter
Claims 4-7, 11-12, 16-17, 19-20 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to BARBARA BURGESS ANYAN whose telephone number is (571)272-3996. The examiner can normally be reached IFP M-F 8am-5pm.
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February 17, 2026
/BARBARA B Anyan/Primary Examiner, Art Unit 2457