DETAILED ACTION
This action is responsive to the following communications: Original Application filed on September 30, 2022. All references to this application refer to the U.S. Patent Application Publication No. 2024/0112071 A1.
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claims 1-20 are pending in this case. Claims 1, 10, and 16 are the independent claims. Claims 1-20 are rejected.
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 1-20 rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
With regard to claim 1,
Step 2A, Prong 1
This part of the eligibility analysis evaluates whether the claim recites a judicial exception. As explained in MPEP 2106.04, subsection II, a claim “recites” a judicial exception when the judicial exception is “set forth” or “described” in the claim.
Claim 1 recites:
A computer program product, the computer program product being tangibly embodied on a non-transitory computer-readable storage medium and comprising instructions that, when executed by at least one computing device, are configured to cause the at least one computing device to:
receive metric values of monitored objects;
access a plurality of hash signatures that include a hash signature for each object of the monitored objects, each hash signature including a bin value of a first plurality of bin values producible by a first hashing algorithm and a bin value of a second plurality of bin values producible by a second hashing algorithm;
score aggregated metric values of each subset of the objects having a corresponding bin value of the first plurality of bin values and aggregated metric values of each subset of the objects having a corresponding bin value of the second plurality of bin values against at least one trained machine learning model to obtain a first plurality of scores and a second plurality of scores;
identify, from the first plurality of scores, a first subset of the plurality of hash signatures and corresponding subset of the objects having at least one anomalous score;
identify, from the second plurality of scores, a second subset of the plurality of hash signatures and corresponding subset of the objects having at least one anomalous score; and
identify at least one object included in both the first subset and the second subset as an anomalous object.
The broadest reasonable interpretation of the bolded limitations above are directed to a mental process able to be performed in the human mind or by a human using pen and paper. A human can examine data, score the examined data (e.g., perform some math or computation), identify particular data (make observations about the data), and find matches from multiple sets of data (observation). A human can perform these actions mentally or with pen and paper.
Step 2A, Prong 1 (Yes).
Step 2A, Prong 2
This part of the eligibility analysis evaluates whether the claim as a whole integrates the recited judicial exception into a practical application of the exception or whether the claim is “directed to” the judicial exception. This evaluation is performed by (1) identifying whether there are any additional elements recited in the claim beyond the judicial exception, and (2) evaluating those additional elements individually and in combination to determine whether the claim as a whole integrates the exception into a practical application. See MPEP 2106.04(d).
The additional elements in this claim are “computer program product, the computer program product being tangibly embodied on a non-transitory computer-readable storage medium and comprising instructions that, when executed by at least one computing device, are configured to cause the at least one computing device to….” This element is recited at a high level of generality and thus is a generic computer component performing computer functions. Thus these are mere instructions to apply the exception using a generic computer component. See MPEP 2106.05(f).
Even when viewed in combination the additional element does not integrate the recited judicial exception into a practical application.
Step 2A, Prong 2 (Yes).
Step 2B
This part of the eligibility analysis evaluates whether the claim as a whole amounts to significantly more than the recited exception i.e., whether any additional element, or combination of additional elements, adds an inventive concept to the claim. See MPEP 2106.05.
As explained with respect to Step 2A, the only additional element is “computer program product, the computer program product being tangibly embodied on a non-transitory computer-readable storage medium and comprising instructions that, when executed by at least one computing device, are configured to cause the at least one computing device to….” which at best is mere instructions to apply the abstract ideas and cannot provide an inventive concept, even when considered in combination. See MPEP 2106.05(f).
Step 2B (Yes). Claim 1 is ineligible.
With respect to independent claims 10 and 16,
These claims are similar in scope to Claim 1 and are rejected under a similar rationale. The processors and memory recited in these claims are also generic computing components.
Claims 10 and 16 are ineligible.
Dependent Claims:
Claims 2-9: These claims only recite further abstract ideas (mental processes) and thus are ineligible.
To expedite a complete examination of the instant application, the claims rejected above under 35 U.S.C. 101, as relating to judicial exceptions without significantly more, are further rejected as set forth below in anticipation of amendments to these claims to place them within the four statutory categories of invention.
Examiner’s Note
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.
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.
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 1-7 and 9-20 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent Application No. 2019/0102553 A1, filed by Herwadkar et al., on September 26, 2018, and published on April 4, 2019 (hereinafter Herwadkar).
With respect to independent claim 1, Herwadkar discloses a computer program product, the computer program product being tangibly embodied on a non-transitory computer-readable storage medium and comprising instructions that, when executed by at least one computing device, are configured to cause the at least one computing device to:
Receive metric values of monitored objects; Herwadkar discloses receiving metric values (e.g., sensor data) from monitored objects (see Herwadkar, paragraphs 0065-0108 [describing the first half of the process of Fig. 2, in which data is received from devices, compared to known or expected values, determines if anomalies are present (or unknown/unexpected data is returned, and process the data].
Access a plurality of hash signatures that include a hash signature for each object of the monitored objects…; Herwadkar discloses accessing a database of hash sigs for each monitored object including vector representations of the of data (see Herwadkar, paragraphs 0065-0108, described supra).
Herwadkar fails to expressly disclose each hash signature including a bin value of a first plurality of bin values producible by a first hashing algorithm and a bin value of a second plurality of bin values producible by a second hashing algorithm.
However, using multiple hash algorithms to generate hash signatures based on data from each component hash algorithms is known to those of ordinary skill in the art (see Herwadkar, paragraph 0054 [use of various locally-sensitive hashing algorithms to generate and match queries to known/anomalous objects]).
Accordingly, it would have been obvious to one of ordinary skill in the art, having the teachings of Herwadkar before him before the effective filing date of the claimed invention, to modify the product of Herwadkar to incorporate hash sigs including bin values from multiple hash algorithms. One would have been motivated to make such a modification because this is a known manner of generating has signatures to one of ordinary skill in the art.
Herwadkar further teaches:
Score aggregated metric values of each subset of the objects having a corresponding bin value of the first plurality of bin values and aggregated metric values of each subset of the objects having a corresponding bin value of the second plurality of bin values against at least one trained machine learning model to obtain a first plurality of scores and a second plurality of scores; Herwadkar further teaches aggregating and scoring the received data in order to determine similarities/differences from expected data for determination of anomalies (see Herwadkar, paragraphs 0111-0115 [describing the second half of the process in Fig. 2, for comparing the data to expected/known data for determination of anomalous data]).
Identify, from the first plurality of scores, a first subset of the plurality of hash signatures and corresponding subset of the objects having at least one anomalous score; Herwadkar further teaches identifying from the first scores a first subset of scores/objects having anomalies (see Herwadkar, paragraphs 0111-0115, described supra).
Identify, from the second plurality of scores, a second subset of the plurality of hash signatures and corresponding subset of the objects having at least one anomalous score; Herwadkar further teaches identifying from the second scores a second subset of scores/objects having anomalies (see Herwadkar, paragraphs 0111-0115, described supra).
Identify at least one object included in both the first subset and the second subset as an anomalous object; Herwadkar further teaches identifying a monitored object which appears in both sets as an anomaly (see Herwadkar, paragraphs 0111-0115, described supra).
With respect to dependent claim 2, Herwadkar teaches the product of claim 1, as described above.
Herwadkar further teaches the product wherein the hash signature includes a bin value of a third plurality of bin values producible by a third hashing algorithm.
Herwadkar further teaches using multiple hash algorithms to generate hash sigs for each object (see Herwadkar, paragraphs 0054 and 0108, described supra, claim 1).
With respect to dependent claim 3, Herwadkar teaches the product of claim 2, as described above.
Herwadkar further teaches the product wherein the instructions, when executed, are further configured to cause the at least one computing device to: score aggregated metric values of each subset of the objects having a corresponding bin value of the third plurality of bin values against the at least one trained machine learning model to obtain a third plurality of scores.
Herwadkar further teaches aggregating data to generate scores to generate data for comparisons (see Herwadkar, paragraphs 0111-0115, described supra, claim 1).
With respect to dependent claim 4, Herwadkar teaches the product of claim 3, as described above.
Herwadkar further teaches the product wherein the instructions, when executed, are further configured to cause the at least one computing device to:
Identify, from the third plurality of scores, a third subset of the plurality of hash signatures and corresponding subset of the objects having at least one anomalous score; Herwadkar further teaches identifying from the third scores a third subset of scores/objects having anomalies (see Herwadkar, paragraphs 0111-0115, described supra, claim 1).
Identify the at least one object as being included in the first subset, the second subset, and the third subset; Herwadkar further teaches identifying a monitored object which appears in all sets as an anomaly (see Herwadkar, paragraphs 0111-0115, described supra, claim 1).
With respect to dependent claim 5, Herwadkar teaches the product of claim 1, as described above.
Herwadkar further teaches the product wherein the instructions, when executed, are further configured to cause the at least one computing device to: hash an object identifier of each object of the monitored objects using the first hashing algorithm and the second hashing algorithm to obtain the plurality of hash signatures.
Herwadkar further teaches using the object ID as part of the hash (see Herwadkar, paragraphs 0065-0108, described supra, claim 1).
With respect to dependent claim 6, Herwadkar teaches the product of claim 1, as described above.
Herwadkar further teaches the product wherein the instructions, when executed, are further configured to cause the at least one computing device to:
Aggregate, for each object, training metric values within a corresponding bin value of the first plurality of bin values, to obtain first aggregated training metric values; Herwadkar further teaches aggregating training data within corresponding bins to obtain first training metrics (see Herwadkar, paragraphs 0126-0145 [describing the use of ML models and training for use with anomaly detection]).
Aggregate, for each object, training metric values within a corresponding bin value of the second plurality of bin values, to obtain second aggregated metric values; Herwadkar further teaches aggregating training data within corresponding bins to obtain second training metrics (see Herwadkar, paragraphs 0126-0145, described supra).
Train the at least one trained machine learning model using the first aggregated training metric values and the second aggregated training metric values; Herwadkar further teaches training at least one ML model for anomaly detection using the sets of training metrics (see Herwadkar, paragraphs 0126-0145, described supra).
With respect to dependent claim 7, Herwadkar teaches the product of claim 1, as described above.
Herwadkar further teaches the product wherein each hash signature of the plurality of hash signatures is unique.
Herwadkar further teaches the hash sigs are unique (see Herwadkar, paragraphs 0065-0108, described supra, claim 1).
With respect to dependent claim 9, Herwadkar teaches the product of claim 1, as described above.
Herwadkar further teaches the product wherein the instructions, when executed, are further configured to cause the at least one computing device to:
Identify the at least one object as including at least two objects included in both the first subset and the second subset; Herwadkar further teaches .
Identify the anomalous object from the at least two objects; Herwadkar further teaches.
Independent claim 10, and its respective dependent claims 11-15, recite a computer-implemented method performed by the computer program product of independent claim 1, and its respective dependent claims 2-6. Accordingly, independent claim 10, and its respective dependent claims 11-15, are rejected under the same rationales used to reject independent claim 1, and its respective dependent claims 2-6, which are incorporated herein.
Independent claim 16, and its respective dependent claims 17-20, recite a system comprising: at least one memory including instructions; and at least one processor that is operably coupled to the at least one memory and that is arranged and configured to execute instructions that, when executed, cause the at least one processor to perform the method performed by the computer program product of independent claim 1, and its respective dependent claims 2-6. Accordingly, independent claim 16, and its respective dependent claims 17-20, are rejected under the same rationales used to reject independent claim 1, and its respective dependent claims 2-6, which are incorporated herein.
Claim 8 is rejected under 35 U.S.C. 103 as being unpatentable over Herwadkar, in view of U.S. Patent No. 10,200,262, issued to Leverich et al., on February 5, 2019, and filed on July 8, 2016 (hereinafter Leverich).
With respect to dependent claim 8, Herwadkar teaches the product of claim 1, as described above.
Herwadkar fails to further teach the product wherein the metric values include Key Performance Indicators (KPIs) and the objects include system assets of an Information Technology (IT) landscape.
However, Leverich teaches tracking KPIs for devices and services to determine how effectively a device or service performs (see Leverich, col. 14, lines 15-56 [describing KPIs, what they are used for, and how they are tracked and used]).
Accordingly, it would have been obvious to one of ordinary skill in the art, having the teachings of Herwadkar and Leverich before him before the effective filing date of the claimed invention, to modify the product of Herwadkar to incorporate tracking KPIs as taught by Leverich. One would have been motivated to make such a combination because KPIs are known to one of ordinary skill in the art as ideal means to measure performance of devices, as taught by Leverich (see Leverich, col. 14, lines 15-56, described supra).
Conclusion
It is noted that any citation to specific pages, columns, figures, or lines in the prior art references any interpretation of the references should not be considered to be limiting in any way. A reference is relevant for all it contains and may be relied upon for all that it would have reasonably suggested to one having ordinary skill in the art. In re Heck, 699 F.2d 1331-33, 216 USPQ 1038-39 (Fed. Cir. 1983) (quoting In re Lemelson, 397 F.2d 1006, 1009, 158 USPQ 275, 277 (CCPA 1968)).
Any inquiry concerning this communication or earlier communications from the Examiner should be directed to ERIC J. BYCER whose telephone number is (571) 270-3741. The Examiner can normally be reached Monday - Thursday 9am-6pm, and alternate Fridays 9am-5pm.
Examiner interviews are available via a variety of formats. See MPEP § 713.01. To schedule an interview, Applicant is encouraged to use the USPTO Automated Interview Request (AIR) Form at https://www.uspto.gov/InterviewPractice.
If attempts to reach the Examiner by telephone are unsuccessful, the Examiner’s supervisor, MATT ELL can be reached on (571) 270-3264. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of an application may be obtained from Patent Center. Status information for published applications may be obtained from Patent Center. Status information for unpublished applications is available through Patent Center to authorized users only. Should you have questions about access to the USPTO patent electronic filing system, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free).
/ERIC J. BYCER/
Primary Examiner
Art Unit 2141
Prosecution Insights