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 .
Response to Arguments
Applicant's arguments filed 01/20/26 have been fully considered but they are not persuasive.
Applicant argues that the claims have been further amended to claim a specific "ordered combination" of features that is "significantly more" than an abstract idea. Given the claims reflect a specific ordered combination of features for a practical application and improvement in computer technology, as explained above, the pending claims are patent eligible (Amendment, page 14).
The examiner disagrees, and points out that other than reciting “one or more generic circuits to use one or more neural networks”, nothing in the claim element precludes the steps from practically being performed in a human mind using a pen and a piece of paper. There are no additional elements that integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea, thus fail to integrate the abstract idea into a practical application. See MPEP 2106.05(g).
Applicant argues that Gupta in view of Penugonda does not teach to use one or more neural networks to: identify, for a given instance of the instances of natural language user feedback, one or more of the different application sessions corresponding to the given instance of natural language user feedback; select telemetry data specific to the identified one or more application sessions; generate, based at least in part upon the given instance of natural language user feedback, a classification of an issue impacting a performance of one or more applications corresponding to the identified one or more application sessions (Amendment, pages 12, 13).
The examiner disagrees, since Gupta discloses “This allows the memory anomaly detector to be “lightweight” because it is less computationally expensive to run a smaller artificial neural network. The fuzzy rule-based classifier applies fuzzy rules to the input vector and provides classification labels. The classification labels indicate a first probability/confidence that the input vector represents a pattern(s) that can be classified as a memory anomaly and a second probability/confidence that the input vector represents a pattern than can be classified as canonical memory behavior (i.e., not a memory anomaly). In addition to the fuzzy rule-based classifier providing output for application performance analysis, the input vector and labels used to train the artificial neural network (ANN). After being trained, the trained ANN is refined with supervised feedback (e.g., administrator or triage feedback) and presents its output of classification probabilities for application performance analysis… The lightweight memory anomaly detector 101 (“detector”) uses classifiers to detect memory anomalies and outputs detected memory anomalies to a detected anomaly interface 113. The detected anomaly interface 113 can be an application or application component for monitoring an application and analyzing anomalous behavior of an application” (paragraphs 17, 18).
And Penugonda et al. disclose “a chat bot that utilizes a natural language processing model can have a conversation with the user to extract user feedback about the events leading up to the defect. Defects can be identified based on error codes or by detecting a deviation from the baseline performance of applications. Additionally, the root cause of a defect can be determined automatically, and corrective actions can also be performed automatically, thus enabling a fully automatic, streamlined defect monitoring and remediation system to be provided… Defect analysis module 155 may analyze data collected about a defect, including video data, user feedback data, and/or application performance data, to identify how to reproduce the defect and accordingly, determine a fix for the defect. In some embodiments, defect analysis module 155 may employ conventional or other machine learning models to perform pattern analysis by comparing the video data, user feedback data, and/or application performance data to similar data that is associated with a knowledge base of other defects. The knowledge base may store instructions to reproduce known defects and solutions to the defects… the machine learning model may include a multidimensional classification model, such as a Bayesian model, neural network, and the like, that is trained to identify classes of defects based on the events leading up to the defects (e.g., the time-series history of events).” (paragraphs 11, 30 – 32).
Thus, the combination of Gupta and Penugonda et al. teaches all parts of the limitations.
Applicant argues that Gupta in view of Penugonda does not teach encoding the classification and the selected telemetry data into a feature vector; and inferring, based at least in part on the feature vector, an indication of one or more actions to be taken regarding the performance of the one or more applications (Amendment, page 13).
The examiner disagrees, since Gupta et al. disclose “the fuzzy rule-based classifier 107 generates a confidence/probability value for the first classification label “anomaly” (depicted as psi) and for the second classification label “no anomaly” (depicted as p.sub.c2). The fuzzy rule-based classifier 107 supplies the generated values to the detected anomaly interface 113. The fuzzy rule-based classifier 107 also supplies the generated values to the ANN 111 along with the input vector of extracted features for training… The trained ANN 211 revises itself based on this feedback, which allows the trained ANN 211 to further adapt to behavior of the application being monitored. Behavior of an application can vary based on deployment attributes (e.g., computational resources, governing policies, infrastructure, etc.)” (paragraphs 19 – 22).
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 – 30 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract without significantly more.
When considering subject matter eligibility under 35 USC 101, it must be determined whether the claim is directed to one of the four statutory categories of invention, i.e., process, machine, manufacture, or composition of matter.
Specifically, claims 1 - 30 are directed to a method/system. They hereby fall under one of the four statutory classes of invention.
If the claim does not fall within one of the statutory categories, it must then be determined whether the claim is directed to a judicial exception (i.e., law of nature, natural phenomenon, and abstract idea).
Claims 1 - 30 recite steps of observation, evaluation, and judgement that can be practically performed by a human, either mentally or with the use of pen and paper.
The limitation of “identify, for a given instance of the instances of natural language user feedback, one or more of the different application sessions corresponding to the given instance of natural language user feedback; select telemetry data specific to the identified one or more application sessions; generate, based at least in part upon the given instance of natural language user feedback, a classification of an issue impacting a performance of one or more applications corresponding to the identified one or more application sessions; encode the classification and the selected telemetry data into a feature vector” in claims 1 - 30, 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 “one or more circuits to use one or more neural networks”, nothing in the claim element precludes the steps from practically being performed in a human mind with the use of pen and paper.
The mere nominal recitation of one or more circuits to use one or more neural networks do not take the claim limitations out of the mental processes grouping.
If a claim limitation, under its broadest reasonable interpretation, covers mental processes but for the recitation of generic computer components, then it falls within the "Mental Processes" grouping of abstract ideas (concepts performed in the human mind including an observation, evaluation, judgement, and opinion). Accordingly, the claims recite an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the claims recite the additional elements “obtain instances of natural language user feedback from different client devices for different application sessions; infer, based at least in part on the feature vector, an indication of one or more actions to be taken regarding the performance of the one or more applications.”.
The limitation “obtain instances of natural language user feedback from different client devices for different application sessions”, amount to data-gathering steps which is considered to be insignificant extra-solution activity, (See MPEP 2106.05(g)).
The limitation “infer, based at least in part on the feature vector, an indication of one or more actions to be taken regarding the performance of the one or more applications.”, represents an extra-solution activity because it is a mere nominal or tangential addition to the claim, a mere generic transmission and presentation of collected and analyzed data. (See MPEP 2106.05 (g)).
The claimed “one or more circuits to use one or more neural networks” are recited at a high level of generality and are merely invoked as tool to perform existing issue classification .
Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea, thus fail to integrate the abstract idea into a practical application. See MPEP 2106.05(g).
The insignificant extra-solution activities identified above, which include the data-gathering (obtaining, generating, identifying, and inferring) steps, are recognized by the courts as well-understood, routine, and conventional activities when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity (See MPEP 2106.05(d)(II) (i) Receiving or transmitting data over a network, e.g., using the Internet to gather data, buySAPE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPO2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network); (v) Presenting (displaying) offers and gathering statistics, OIP Techs., 788 F.3d at 1362-63, 115 USPO2d at 1092- 93). The claims are not patent eligible.
Claims 1 – 30 do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using one or more circuits to use one or more neural networks to perform the identifying and generating steps amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept.
Even when considered in combination, these additional elements (one or more circuits to use one or more neural networks) represent mere instruction to apply an exception and insignificant extra-solution activity, which do not provide an inventive concept.
Claims 1 - 30 as a whole, do not amount to significantly more than the abstract idea itself. This is because the claims do not affect an improvement to the functioning of a computer itself; and the claims do not move beyond a general link of the use of an abstract idea to a particular technological environment.
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.
Claims 1 – 30 are rejected under 35 U.S.C. 103 as being unpatentable over Gupta et al. (US PAP 2019/0391901) in view of Penugonda et al. (US PAP 2023/0147668).
As per claims 1, 7,13, 19, 25, Gupta et al. teach a processor, comprising:
one or more circuits to use one or more neural networks to generate, based at least in part upon the given instance of user feedback, a classification of an issue impacting a performance of one or more applications corresponding to the identified one or more application sessions; encode the classification and the selected telemetry data into a feature vector; infer, based at least in part on the feature vector, an indication of one or more actions to be taken regarding the performance of the one or more applications. (“A lightweight memory anomaly detector 101 is in communication with an application performance management (APM) metric repository 103. The lightweight memory anomaly detector 101 (“detector”) uses classifiers to detect memory anomalies and outputs detected memory anomalies to a detected anomaly interface 113… the fuzzy rule-based classifier 107 generates a confidence/probability value for the first classification label “anomaly” (depicted as psi) and for the second classification label “no anomaly” (depicted as p.sub.c2). The fuzzy rule-based classifier 107 supplies the generated values to the detected anomaly interface 113. The fuzzy rule-based classifier 107 also supplies the generated values to the ANN 111 along with the input vector of extracted features for training… The trained ANN 211 revises itself based on this feedback, which allows the trained ANN 211 to further adapt to behavior of the application being monitored. Behavior of an application can vary based on deployment attributes (e.g., computational resources, governing policies, infrastructure, etc.)…This feedback can be obtained from a user interface that allows a user to indicate whether behavior of an application component (e.g., a virtual machine) as represented by a vector of extracted feature values corresponds to anomalous behavior related to memory use/management” Abstract; paragraphs 18 - 22, 30).
Claim 25 further recites memory for storing network parameters for the one or more first neural networks (“the neural network has been trained with the specified training data size”; paragraph 61).
However, Gupta et al. do not specifically teach obtaining instances of natural language user feedback from different client devices for different application sessions; identify, for a given instance of the instances of natural language user feedback, one or more of the different application sessions corresponding to the given instance of natural language user feedback; select telemetry data specific to the identified one or more application sessions.
Penugonda et al. disclose “a chat bot that utilizes a natural language processing model can have a conversation with the user to extract user feedback about the events leading up to the defect. Defects can be identified based on error codes or by detecting a deviation from the baseline performance of applications. Additionally, the root cause of a defect can be determined automatically, and corrective actions can also be performed automatically, thus enabling a fully automatic, streamlined defect monitoring and remediation system to be provided… Defect analysis module 155 may analyze data collected about a defect, including video data, user feedback data, and/or application performance data, to identify how to reproduce the defect and accordingly, determine a fix for the defect. In some embodiments, defect analysis module 155 may employ conventional or other machine learning models to perform pattern analysis by comparing the video data, user feedback data, and/or application performance data to similar data that is associated with a knowledge base of other defects. The knowledge base may store instructions to reproduce known defects and solutions to the defects… any other affected client devices may be identified based on a similarity of those client devices' behavior and/or configuration (e.g., particular software and/or hardware elements) with the behavior and/or configuration of the client device that is associated with the defect. Thus, other at-risk devices can be notified so that the defect can be avoided by other end users. (paragraphs 11, 30 – 32, 56).
Therefore, it would have been obvious to one of ordinary skill in the art at the time the invention was made to use natural language feedback as taught by Penugonda et al. in Gupta et al., because that help provide the practical application of improving the field of root cause analysis by providing techniques for defect tracking and remediation that can be fully automated (paragraph 12).
As per claims 2, 8, 14, 20, 26, Gupta et al. in view of Penugonda et al. further disclose the natural language user feedback includes user-provided text relating to the performance of the one or more applications in at least one user session (“This feedback can be obtained from a user interface that allows a user to indicate whether behavior of an application component (e.g., a virtual machine) as represented by a vector of extracted feature values corresponds to anomalous behavior related to memory use/management.” Gupta et al. paragraph 30; see also Penugonda et al., paragraphs 11, 12, 30 - 32 “a chat bot that utilizes a natural language processing model can have a conversation with the user to extract user feedback about the events leading up to the defect. Defects can be identified based on error codes or by detecting a deviation from the baseline performance of applications.”).
As per claims 3, 9, 15, 21, 27, Gupta et al. in view of Penugonda et al. further disclose the one or more circuits are further to extract textual features from the one or more communications including the natural language user feedback using a natural language processing (NLP)-based neural network (“the fuzzy rule-based classifier providing output for application performance analysis, the input vector and labels used to train the artificial neural network (ANN). After being trained, the trained ANN is refined with supervised feedback (e.g., administrator or triage feedback) and presents its output of classification probabilities for application performance analysis.”; paragraph 17; see also Penugonda et al., paragraphs 11, 12, 30 – 32; “Defects can be identified based on error codes or by detecting a deviation from the baseline performance of applications. Additionally, the root cause of a defect can be determined automatically, and corrective actions can also be performed automatically, thus enabling a fully automatic, streamlined defect monitoring and remediation system to be provided”).
As per claims 4, 10, 16, 22, 28, Gupta et al. in view of Penugonda et al. further disclose a textual feature of the textual features extracted from the one or more communications using the natural language processing (NLP)-based neural network indicates the at least one user session, wherein the one or more circuits are further to obtain telemetry data (“Probes or agents of an APM application will collect values of application metrics and store the collected metric values into the repository 103.”) for the at least one user session and application data for the one or more applications, and encode the telemetry and application data with the extracted textual features into one or more feature vectors (“The anomaly feature extractor 107 assembles the extracted features into an input vector represented as v(m.sub.1, m.sub.2, m.sub.3, m.sub.4, m.sub.n)”; paragraphs 19, 21; see also Penugonda et al., paragraphs 11, 12, 30 - 32 “a chat bot that utilizes a natural language processing model can have a conversation with the user to extract user feedback about the events leading up to the defect. Defects can be identified based on error codes or by detecting a deviation from the baseline performance of applications.”).
As per claims 5, 11, 17, 23, 29, Gupta et al. in view of Penugonda et al. further disclose the one or more circuits are further to use the one or more neural networks to infer, based at least in part upon the one or more feature vectors, the classification of the issue impacting the performance of the one or more applications, and obtain additional information relating to the performance based at least in part upon the inferred classification (“The classification labels indicate a first probability/confidence that the input vector represents a pattern(s) that can be classified as a memory anomaly and a second probability/confidence that the input vector represents a pattern than can be classified as canonical memory behavior (i.e., not a memory anomaly).”; Gupta et al. paragraphs 17 – 25).
As per claims 6, 12, 18, 24, 30, Gupta et al. in view of Penugonda et al. further disclose generating the indication of the one or more actions to be taken, the one or more circuits are further to use the one or more neural networks to generate an actionability decision for the issue, and generate the textual description of the one or more actions to provide explainability for the actionability decision (“If it does, then the time slice is filtered out and not forwarded to the anomaly detector 511. For those time slices that fall outside of the canonical probability range, the probability based filter 509 communicates or passes the time slice of metric values to the memory anomaly detector 511 for anomaly analysis that will generate an event to be consumed by the detected anomaly interface 513.”; Gupta; paragraph 44, see also paragraphs 17 - 25).
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 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 nonprovisional extension fee (37 CFR 1.17(a)) 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 LEONARD SAINT-CYR whose telephone number is (571)272-4247. The examiner can normally be reached Monday- Friday.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Richemond Dorvil can be reached on (571)272-7602. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/LEONARD SAINT-CYR/ Primary Examiner, Art Unit 2658