DETAILED ACTION
This Office Action is sent in response to Applicant’s Communication received 12/28/2023 for application number 18/399,182.
Claims 1-19 are pending.
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 1-8 and 10-18 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Independent claim 1 (representative of independent claims 10 and 11) recites:
A method for identifying a request of a service, comprising: generating vector embeddings for raw data of events using a machine learning model, wherein the machine learning model is trained to indicate semantic meaning of at least one event of the raw data of events; clustering, based on the vector embeddings, the at least one event of the raw data of events into a plurality of clusters, wherein a subset of the plurality of clusters includes relevant events in the raw data of events; and identifying a request as a sequence of events from the subset of the plurality of clusters.
(2A, prong 1) The underlined portions of the claim recite an abstract idea, specifically a mental process. The specification states that, “The vector embeddings describe raw data of events (i.e., random sequence of events) with semantic meaning including, for example, but not limited to, position, relation, and the like, between the events,” para. 0058. A human can look at a raw event long, mentally determine the semantic meaning of the event (like relation between events – see, e.g., Eng et al., US 2024/0250886 A1 at para. 0006), group the vector embeddings into a plurality of clusters, and identify a request from relevant events in a subset of the clusters.
(2A, prong 2) This judicial exception is not integrated into a practical application. The claims recite the additional element of [a] a machine learning model that is trained to generate vector embeddings and [b] generic computing hardware (for claim 11). Additional element [a] is a mere instruction to apply the exception: the claim merely recites the idea of an outcome (that a machine learning model generates vector embeddings from raw data) without how to accomplish the outcome (i.e. how the ML algorithm is trained or how it operates to generate vector embeddings). Additional element [b] is also a mere instruction to apply the exception because it adds generic computing hardware after-the-fact to the abstract idea. Even when the additional elements are considered in ordered combination with the recited abstract idea, the claim is not integrated into a practical application because the additional elements only add mere instructions to apply the exception to the mental process
(2B) The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception. Additional elements [a] and [b] are mere instructions to apply the exception, as explained above. Even when the additional elements re considered in ordered combination with the recited abstract idea, the claim as a whole does not amount to significantly more than the abstract idea itself because the additional elements only add mere instructions to apply the exception to the mental process.
With respect to dependent claims 2-3 and 12-13, these claims recite (2A, prong 1) the additional mental step of sorting relevant events based on rules and additional event information, including a timestamp. A human can mentally sort relevant events based on a rule and timestamp data.
With respect to dependent claims 4-5 and 14-15, these claims recite (2A, prong 2) the additional element of [b] receiving raw data of events and call stacks collected at predefined intervals. This additional element does not integrate the abstract idea into a practical application because it is insignificant extra-solution activity, because it is mere necessary data gathering for the abstract idea. (2B) Additional element [b] does not amount to significantly more than the abstract idea itself because it is well-understood, routine, and conventional, analogous to storing and retrieving information in memory, see MPEP 2106.05(d) citing Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015). Even when the additional element is considered in ordered combination with the recited abstract idea, the claim as a whole does not amount to significantly more than the abstract idea itself because the additional elements only add mere instructions to apply the exception and insignificant extra-solution activity that is well-understood, routine, and conventional to the mental process.
With respect to dependent claims 6 and 16, these claims recite (2A, prong 1) the additional mental step of clustering to two hierarchical levels, and the subset of clusters is in the first level. A human can mentally sort into hierarchical levels.
With respect to dependent claims 7 and 17, these claims (2A, prong 1) further limit clustering to k-means, BIRCH, or OPTICS clustering algorithms. These algorithms add mathematical calculations to recited abstract idea.
With respect to dependent claims 8 and 18, these claims recite (2A, prong 1) the additional mental step of determining a start and end point of the sequence of events and determine request performance. A human can mentally determine a start and end point of events and the performance of the request.
The Examiner notes that claims 9 and 19 are not rejected because they recite the additional element of, “training the machine learning model based on a reconstruction loss, wherein the reconstruction loss determined by comparing an input sequence of a training dataset to an output sequence; and fine-tuning the trained machine learning model using a labeled training dataset to indicate the relevant events.” This element is not a mental process, and, although based on mathematical concepts, the claim does not recite a mathematical concept.
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)(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.
Claim(s) 1-4, 8-14, 18-19 is/are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Eng et al. (US 2022/0214948 A1).
In reference to claim 1, Eng discloses a method (para. 0013) for identifying a request of a service, comprising: generating vector embeddings for raw data of events (raw log data, para. 0003-05) using a machine learning model (autoencoder outputs embedding for events, para. 0087-97, figs. 9-10), wherein the machine learning model is trained to indicate semantic meaning of at least one event of the raw data of events (autoencoder is trained to output embeddings including text element vectors, para. 0101-03, figs. 9-10); clustering, based on the vector embeddings, the at least one event of the raw data of events into a plurality of clusters (embeddings are clustered, para. para. 0087-97, figs. 9-10), wherein a subset of the plurality of clusters includes relevant events in the raw data of events; and identifying a request as a sequence of events from the subset of the plurality of cluster (once clustered, a series of session events belong to some of the clusters, and the clusters indicate a network status, the network status including a network service request like a registration request that is indicative of poor signal, 0103-09).
In reference to claim 2, Eng discloses the method of claim 1, further comprising: sorting the relevant events based on a plurality of rules that are defined by additional information on each of the relevant event (events include timestamp, para. 0067-68, and can be sorted by time, see fig. 12(b)).
In reference to claim 3, Eng discloses the method of claim 2, wherein the additional information includes at least one of: timestamp, thread identifier (ID), micro-thread identifier (ID), processor identifier (ID), event type, and file descriptor (timestamp, para. 0067-68, fig. 12(b)).
In reference to claim 4, Eng discloses the method of claim 1, further comprising: receiving raw data of events and call stacks that are collected during runtime of a workload, wherein raw data of events include additional information for each event in the raw data of events (logs include signaling events and requests, or calls, and additional information like timestamps, collected during the operation of network nodes, para. 0061-66).
In reference to claim 8, Eng discloses the method of claim 1, further comprising: determining a start point and an end point using the sequence of events of the identified request; and determining a request performance (see, e.g. fig. 12(b): a start and end time for the sequence of events are identified, and the sequence is determined to have poor performance due to poor signal, para. 0103-09).
In reference to claim 9, Eng discloses the method of claim 1, wherein training of the machine learning model further comprises: training the machine learning model based on a reconstruction loss, wherein the reconstruction loss determined by comparing an input sequence of a training dataset to an output sequence (reconstruction loss used to train auto-encoder, para. 0092-0101); and fine-tuning the trained machine learning model using a labeled training dataset to indicate the relevant events (during training, clusters are evaluated, i.e. labeled, by domain expert so that relevant events, i.e. events indicating a network problem, can be determined by the model, para. 0104-06).
In reference to claim 10, this claim is directed to a non-transitory computer-readable medium associated with the method claimed in claim 1 and is therefore rejected under a similar rationale.
In reference to claim 11, this claim is directed to a system associated with the method claimed in claim 1 and is therefore rejected under a similar rationale.
In reference to claim 11, this claim is directed to a system associated with the method claimed in claim 1 and is therefore rejected under a similar rationale
In reference to claim 12, this claim is directed to a system associated with the method claimed in claim 2 and is therefore rejected under a similar rationale.
In reference to claim 13, this claim is directed to a system associated with the method claimed in claim 3 and is therefore rejected under a similar rationale.
In reference to claim 14, this claim is directed to a system associated with the method claimed in claim 4 and is therefore rejected under a similar rationale.
In reference to claim 18, this claim is directed to a system associated with the method claimed in claim 1 and is therefore rejected under a similar rationale.
In reference to claim 19, this claim is directed to a system associated with the method claimed in claim 1 and is therefore rejected under a similar rationale.
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 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.
Claim(s) 5, 7, 15, and 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Eng et al. (US 2022/0214948 A1) as applied to claims 1 and 11 above, and in further view of Verma et al. (US 2021/0357282 A1).
In reference to claim 5, Eng does not explicitly teach the method of claim 1, wherein the raw data of events are collected at predefined intervals.
Verma teaches the method of claim 1, wherein the raw data of events are collected at predefined intervals (logs are aggregated during particular time interval for anomaly analysis, para. 0029).
It would have been obvious to one of ordinary skill in art, having the teachings of Eng and Verma before the earliest effective filing date, to modify the log data of Eng to include the interval collection of Verma.
One of ordinary skill in the art would have been motivated to modify the log data of Eng to include the interval collection of Verma because Eng does not disclose when or how it collects log data, and the periodic collection of Verma would provide a method for collecting the log data for analysis.
In reference to claim 7, Eng does not explicitly teach the method of claim 1, wherein clustering is performed using at least one of: K- means clustering, Balanced Iterative Reducing and Clustering using Hierarchies (BIRCH), and Ordering Points To Identify the Clustering Structure (OPTICS) clustering (Eng teaches k-nearest-neighbors).
Verma teaches the method of claim 1, wherein clustering is performed using at least one of: K- means clustering, Balanced Iterative Reducing and Clustering using Hierarchies (BIRCH), and Ordering Points To Identify the Clustering Structure (OPTICS) clustering (k-means, para. 0057).
It would have been obvious to one of ordinary skill in art, having the teachings of Eng and Verma before the earliest effective filing date, to modify the clustering of Eng to include the k-means of Verma because it is the simple substitution for one known element for another with predictable results. K-nearest-neighbor and k-means are both known clustering algorithms, and a person having ordinary skill in the art could substitute one clustering algorithm for another to produce the predictable result of Eng clustering via k-means clustering.
In reference to claim 15, this claim is directed to a system associated with the method claimed in claim 5 and is therefore rejected under a similar rationale
In reference to claim 17, this claim is directed to a system associated with the method claimed in claim 7 and is therefore rejected under a similar rationale.
Claim(s) 6 and 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Eng et al. (US 2022/0214948 A1) as applied to claims 1 and 11 above, and in further view of Lee et al., Sub-clusters of Normal Data for Anomaly Detection (see NPL [U] in Notice of References Cited).
In reference to claim 6, Eng does not explicitly teach the method of claim 1, wherein the plurality of clusters includes a first hierarchical level of clusters and at least one second hierarchical level of sub-clusters, wherein the subset of the plurality of clusters is a cluster of the first hierarchical level of clusters.
Lee teaches the method of claim 1, wherein the plurality of clusters includes a first hierarchical level of clusters and at least one second hierarchical level of sub-clusters, wherein the subset of the plurality of clusters is a cluster of the first hierarchical level of clusters (see pages 4323-24: data can have clusters and subclusters for anomaly determination).
It would have been obvious to one of ordinary skill in art, having the teachings of Eng and Lee before the earliest effective filing date, to modify the clustering of Eng to include the sub-clusters of Lee.
One of ordinary skill in the art would have been motivated to modify the clustering of Eng to include the sub-clusters of Lee because it can help better find anomalous data (Lee, 3. Experimental Evaluation, pages 2324-29).
In reference to claim 16, this claim is directed to a system associated with the method claimed in claim 6 and is therefore rejected under a similar rationale
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Andrew T. Chiusano whose telephone number is (571)272-5231. The examiner can normally be reached M-F, 10am-6pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Tamara Kyle can be reached at 571-272-4241. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/ANDREW T CHIUSANO/Primary Examiner, Art Unit 2144
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