The present application, filed on or after 16 March 2013, is being examined under the first inventor to file provisions of the AIA .
DETAILED ACTION
This office action is in response to Applicant’s submission filed on 15 May 2026. THIS ACTION IS NON-FINAL.
Status of Claims
Claims 1-7, 16-38 are pending.
Claims 8-15 are cancelled
Claim 1-7, 16-38 are rejected under 35 U.S.C. 101 for being directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Claims 1-7, 16-29 are rejected under 35 U.S.C. 103 as unpatentable.
There is no art rejection for claims 30-38.
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.
Judicial Exception
Claims 1-7, 16-38 of the claimed invention are directed to a judicial exception, an abstract idea, without significantly more.
(Independent Claims) With regards to claim 1 / 16 / 23,
Step 1: The claim recites a machine / article of manufacturing / process, which falls into one of the statutory categories.
Step 2A – Prong 1: the claim, in part, recites “…. iteratively processing the input vectors to compute a knowledge map, iteratively processing the input vectors to determine metadata associated with one or more knowledge elements, determining an anomaly value based on the knowledge map and the metadata, and raising an alert that an anomaly was detected if the anomaly value traverses a threshold…” (mental process and/or math concept), as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is, other than reciting generic computer elements, nothing in the claim element precludes the step from practically being performed in the mind. For example, but for the language about generic computer elements, “iteratively processing …”, “determining …”, “raising an alert …”, in the limitation citied above encompasses analyzing / processing observed data to build data prediction model for anomaly detection, which is based on observation, evaluation, judgement, and/or opinion, that could be performed by human using paper / pen / calculator. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Step 2A – Prong 2: This judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of: (a) “An anomaly detection system comprising: a memory; and one or more processors”, “… A non-transitory computer-readable medium storing computer-readable program code executable by one or more processors, the program code comprising instructions configured to…”, “computer …”, which is mere instructions to implement an abstract idea using generic computing device, or merely uses a computer as a tool to perform an abstract idea (see MPEP 2106.05(f)); (b) “…obtaining, in association with a learning process, a plurality of input vectors …”, which is extra-solution activity of pre-solution data gathering and/or post solution data output (see MPEP.2106.05(g)). Accordingly. the additional elements individually or in combination do not integrate the judicial exception into a practical application. The claim is directed to an abstract idea.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above, the claim recites the additional elements of: (a) “An anomaly detection system comprising: a memory; and one or more processors”, “… A non-transitory computer-readable medium storing computer-readable program code executable by one or more processors, the program code comprising instructions configured to…”, “computer …”, which merely uses a computer as a tool to perform an abstract idea (see MPEP 2106.05(f)); (b) “…obtaining, in association with a learning process, a plurality of input vectors …”, which is insignificant extra solution activity of pre-solution data gathering (see MPEP 2106.05(g)). The courts have found limitations directed to obtaining information electronically, recited at a high level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping," and "storing and retrieving information in memory"). Accordingly, the additional elements individually or in combination do not amount to significantly more than the judicial exception. The claim is not patent eligible.
(Dependent claims)
Claims 2-7, 30-32 / 17-22, 33-35 / 24-29, 36-38 are dependent on claim 1 / 16 / 23, and include all the limitations of claim 1 / 16 / 23. Therefore, claims 2-7, 30-32 / 17-22, 33-35 / 24-29, 36-38 recite the same abstract ideas.
With regards to claim 2 / 17 / 24, the claim recites limitation of “… detecting at least one of a discrete anomaly outlier or a drift anomaly outlier” (mental process and/or math concept), which is further steps of data processing using prediction models for anomaly detection, similar to the analysis before, this is a mental process. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Except citing generic computer elements to implement the abstract idea, there is no additional element showing integration into a practical application or adding something significantly more to the abstract idea. The claim is not patent eligible.
With regards to claim 3 / 18 / 25, the claim recites limitation of “… identifying an anomaly event based on a statistical measure of a number of the input vectors within a designated timeframe corresponding to the anomaly value traversing the threshold” (mental process and/or math concept), which is further steps of data processing using prediction models for anomaly detection, similar to the analysis before, this is a mental process. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Except citing generic computer elements to implement the abstract idea, there is no additional element showing integration into a practical application or adding something significantly more to the abstract idea. The claim is not patent eligible.
With regards to claim 4 / 19 / 26, the claim recites limitation of “… a number of input vectors determined to be abnormal, or a speed by which a statistical measure of the number of abnormal vectors changes within a designated timeframe” (mental process and/or math concept), which is further steps of data processing using prediction models for anomaly detection, similar to the analysis before, this is a mental process. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Except citing generic computer elements to implement the abstract idea, there is no additional element showing integration into a practical application or adding something significantly more to the abstract idea. The claim is not patent eligible.
With regards to claim 5 / 20 / 27, The claim recites additional element of “wherein the learning process includes one or more of: a continuous learning process or a periodic learning process”, which is merely using computer as a tool to perform an abstract idea (see MPEP 2106.05(f)). Accordingly, at Step 2A, prong two, the additional elements individually or in combination do not integrate the judicial exception into a practical application. The claim is directed to an abstract idea.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above, the additional element of “wherein the learning process includes one or more of:a continuous learning process or a periodic learning process”, which is merely using computer as a tool to perform an abstract idea (see MPEP 2106.05(f). Accordingly, at Step 2B the additional elements individually or in combination do not amount to significantly more than the judicial exception.
The claim is not patent eligible.
With regards to claim 6 / 21 / 28, the claim recites limitation of “… wherein the threshold is dynamically adjustable” (mental process and/or math concept), which is further steps of data processing using prediction models for anomaly detection, similar to the analysis before, this is a mental process. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Except citing generic computer elements to implement the abstract idea, there is no additional element showing integration into a practical application or adding something significantly more to the abstract idea. The claim is not patent eligible.
With regards to claim 7 / 22 / 29, the claim recites limitation of “… wherein the knowledge map includes one or more of: a quality of the one or more knowledge elements, a model miss rate, a number of the one or more knowledge elements, a model hit count, one or more weights associated with the one or more knowledge elements, or an age of the one or more knowledge elements” (mental process and/or math concept), which is further steps of data processing using prediction models for anomaly detection, similar to the analysis before, this is a mental process. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Except citing generic computer elements to implement the abstract idea, there is no additional element showing integration into a practical application or adding something significantly more to the abstract idea. The claim is not patent eligible.
With regards to claim 30 / 33 / 36, the claim recites limitation of “… wherein the threshold is dynamically adjustable based on an intersection between a cumulative distribution function and a user-defined cumulative distribution function threshold” (mental process and/or math concept), which is further steps of data processing using prediction models for anomaly detection, similar to the analysis before, this is a mental process. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Except citing generic computer elements to implement the abstract idea, there is no additional element showing integration into a practical application or adding something significantly more to the abstract idea. The claim is not patent eligible.
With regards to claim 31 / 34 / 37, the claim recites limitation of “… wherein the cumulative distribution function includes a histogram representing a count of anomaly input vectors over a designated time” (mental process and/or math concept), as drafted, is a process that, under its broadest reasonable interpretation, covers mathematical concepts but for the recitation of generic computer components. That is, other than reciting generic computing elements, content of distribution function, based on their broadest reasonable interpretation, describe mathematical relationships and algorithms. Mathematical relationship and algorithms have been found by the courts to be abstract ideas, e.g., see MPEP 2106.04(a)(2) A. Mathematical Relationships, iv. organizing information and manipulating information through mathematical correlations, Digitech Image Techs., LLC v. Electronics for Imaging, Inc., 758 F.3d 1344, 1350, 111 USPQ2d 1717, 1721 (Fed. Cir. 2014). The patentee in Digitech claimed methods of generating first and second data by taking existing information, manipulating the data using mathematical functions, and organizing this information into a new form. The court explained that such claims were directed to an abstract idea because they described a process of organizing information through mathematical correlations, like Flook's method of calculating using a mathematical formula. 758 F.3d at 1350, 111 USPQ2d at 1721. If a claim limitation, under its broadest reasonable interpretation, covers mathematical relationships, then it falls within the “Mathematical Concepts” grouping of abstract ideas. Except citing generic computer elements to implement the abstract idea, there is no additional element showing integration into a practical application or adding something significantly more to the abstract idea. The claim is not patent eligible.
With regards to claim 32 / 35 / 38, the claim recites limitation of “… wherein the histogram is refreshed periodically” (mental process and/or math concept), which is further steps of data processing using prediction models for anomaly detection, similar to the analysis before, this is a mental process. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Except citing generic computer elements to implement the abstract idea, there is no additional element showing integration into a practical application or adding something significantly more to the abstract idea. The claim is not patent eligible.
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 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.
Claims 1-7, 16-29 are rejected under 35 U.S.C. 103 as being unpatentable over Thomas et al, WO-PGPUB NO.2015034759A1 [hereafter Thomas] in view of Phadke et al, US-PATENT NO.10904276B2 [hereafter Phadke].
With regards to claim 1, Thomas teaches
“An … system comprising: a memory; and one or more processors configured to cause (Thomas, FIG.35-40,
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obtaining, in association with a learning process, a plurality of input vectors (Thomas, FIG.9, 17-18,
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614
511
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iteratively processing the input vectors to compute a knowledge map, iteratively processing the input vectors to determine metadata associated with one or more knowledge elements, determining an … value based on the knowledge map and the metadata (Thomas, FIG.18-19, 29-30, Claim 58-59, [00155-00177] shows iterative knowledge map processing, [00172] ‘The system maintains a count for each knowledge element. The count represents the number of data vectors, including the input vector previously matched or currently matched to that knowledge element ..’ shows a meta data example and its value,
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) …”.
Thomas does not explicitly detail “anomaly detection”, “anomaly…”, “raising an alert that an anomaly was detected if the anomaly value traverses a threshold”.
However Phadke teaches “anomaly detection”, “anomaly…”, “raising an alert that an anomaly was detected if the anomaly value traverses a threshold” (Phadke, FIG.1-2,
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C2L64-C3L16 ‘Anomalies in general may be understood as instances of data that lies outside of a normal or expected range or threshold … generates an alert that indicated an anomaly …’)”.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, and having the teachings of Thomas and Phadke before him or her, to modify the pattern recognition and detection system and method of Thomas to include anomaly as shown in Phadke.
The motivation for doing so would have been for anomaly detection (Phadke, Abstract).
With regards to claim 2, Thomas in view of Phadke teaches
“The system of claim 1”.
Thomas does not explicitly detail “the one or more processors further configured to cause: detecting at least one of a discrete anomaly outlier or a drift anomaly outlier”.
However Phadke teaches “the one or more processors further configured to cause: detecting at least one of a discrete anomaly outlier or a drift anomaly outlier (Phadke, FIG.1-2, C6L7-11 ‘…the machine learning unit 106 may be configured to generate and use a C4.5 decision tree to detect reported anomalies as outliers in categorical data …’)”.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, and having the teachings of Thomas and Phadke before him or her, to modify the pattern recognition and detection system and method of Thomas to include detecting outliers as shown in Phadke.
The motivation for doing so would have been for anomaly detection (Phadke, Abstract).
With regards to claim 3, Thomas in view of Phadke teaches
“The system of claim 1”.
Thomas does not explicitly detail “the one or more processors further configured to cause: identifying an anomaly event based on a statistical measure of a number of the input vectors within a designated timeframe corresponding to the anomaly value traversing the threshold”.
However Phadke teaches “the one or more processors further configured to cause: identifying an anomaly event based on a statistical measure of a number of the input vectors within a designated timeframe corresponding to the anomaly value traversing the threshold (Phadke, FIG.1-2, C7L4-13 ‘…Intrinsic features may include, for example the duration of the anomaly, the size or magnitude of the anomaly, information regarding other anomalies that occurred in the same duration, etc. …’)”.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, and having the teachings of Thomas and Phadke before him or her, to modify the pattern recognition and detection system and method of Thomas to include detecting outliers as shown in Phadke.
The motivation for doing so would have been for anomaly detection (Phadke, Abstract).
With regards to claim 4, Thomas in view of Phadke teaches
“The system of claim 1”.
Thomas does not explicitly detail “the one or more processors further configured to cause: applying the threshold to one or more of: a number of input vectors determined to be abnormal, or a speed by which a statistical measure of the number of abnormal vectors changes within a designated timeframe”.
However Phadke teaches “the one or more processors further configured to cause: applying the threshold to one or more of: a number of input vectors determined to be abnormal, or a speed by which a statistical measure of the number of abnormal vectors changes within a designated timeframe (Phadke, FIG.1-2, C4L23-20 ‘…detecting deviations of a data item within source data 110 using a conventional statistical algorithm. …’, C3L30-50 ‘an anomaly detection system that monitors network traffic may generate alerts based on sharp spike in the traffic volume at fixed time during the day …)”.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, and having the teachings of Thomas and Phadke before him or her, to modify the pattern recognition and detection system and method of Thomas to include more detailed anomaly detection criteria as shown in Phadke.
The motivation for doing so would have been for anomaly detection (Phadke, Abstract).
With regards to claim 5, Thomas in view of Phadke teaches
“The system of claim 1, wherein the learning process includes one or more of: a continuous learning process or a periodic learning process (Thomas, FIG.18-19, 29-30, Claim 65-69, [00173-00177] shows dynamic learning which is a continuous learning process.)”.
With regards to claim 6, Thomas in view of Phadke teaches
“The system of claim 1”.
Thomas does not explicitly detail “wherein the threshold is dynamically adjustable”.
However Phadke teaches “wherein the threshold is dynamically adjustable (Phadke, FIG.1-2, C8L46-58 ‘…feedback may be iteratively and periodically received to compute an accuracy threshold …’)”.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, and having the teachings of Thomas and Phadke before him or her, to modify the pattern recognition and detection system and method of Thomas to include adjustable threshold as shown in Phadke.
The motivation for doing so would have been for anomaly detection (Phadke, Abstract).
With regards to claim 7, Thomas in view of Phadke teaches
“The system of claim 1, wherein the knowledge map includes one or more of: a quality of the one or more knowledge elements, a model miss rate, a number of the one or more knowledge elements, a model hit count, one or more weights associated with the one or more knowledge elements, or an age of the one or more knowledge elements (Thomas, FIG.29-30, Claim 58-59, [00172] ‘The system maintains a count for each knowledge element. The count represents the number of data vectors, including the input vector previously matched or currently matched to that knowledge element ..’)”.
Claims 16-29 are substantially similar to claims 1-7. The arguments as given above for claims 1-7 are applied, mutatis mutandis, to claims 16-29, therefore the rejection of claims 1-7 are applied accordingly.
Additional Relevant Art
The prior art made of record is considered pertinent to applicant’s disclosure and is recorded on Form PTO-892. Applicant is required under 37 C.F.R. § 1.111 (c) to consider these references fully when responding to this action, with particular attention paid to:
Adams et al., US-PATENT NO.7966274B2 [hereafter Adams] shows pattern recognition with knowledge map.
Nesen et al., “Knowledge graphs for semantic-aware anomaly detection in video”, 2020 IEEE third international conference on artificial intelligence and knowledge engineering (AIKE), 2020 [hereafter Nesen] shows using knowledge map for anomaly detection.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to TSU-CHANG LEE whose telephone number is 571-272-3567. The fax number is 571-273-3567.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Omar Fernandez Rivas, can be reached 571-272-2589.
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/TSU-CHANG LEE/
Primary Examiner, Art Unit 2128
Prosecution Insights