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 .
Status of Claims
Claims 1, 12, and 19 were amended. Claims 1 – 20 are pending and examined herein.
Claims 1 – 20 are rejected under 35 U.S.C. 101.
Claims 1 – 20 are rejected under 35 U.S.C. 103.
Response to Amendment
The amendment filed February 10th, 2026 has been entered. Claims 1, 12, and 19 were amended. Claims 1 – 20 are pending and examined herein.
Response to Arguments
Applicant's arguments filed February 10th, 2026 regarding the 35 U.S.C. 101 rejection for being directed to an abstract idea without significantly more have been fully considered but they are not persuasive. Applicant’s arguments, on pages 7-10, argues that the claims do not recite an abstract idea and, even if an abstract idea is present, the claims integrate the abstract idea into a practical application because the claims involve artificial intelligence, machine learning, and pretraining. Examiner has reconsidered the rejection in view of Applicant’s arguments and the cited USPTO guidance. The rejection is not maintained on the basis that the machine learning classification process itself, standing alone, is practically performed in the human mind. However, the claims as a whole still recite collecting or detecting data source information and classifying data to determine an associated intent using computer implemented machine learning.
The amendment requiring that the machine learning classification process is “pre-trained based on a set of use case-based training data” does not change the eligibility analysis. The claims do not recite how the pretraining is performed. The claims also do not recite a particular model architecture, training algorithm, feature selection technique, or other specific mechanism that improves the operation of the machine learning model itself. Instead, the claims use a pretrained machine learning classification as a tool to perform the claimed data classification. The amended “two-tier framework including a direct detection tier and indirect detection tier” also does not establish eligibility. The claims recite the direct and indirect detection tiers at a high level, but do not require a particular filesystem monitoring, object store, streaming architectures, specific algorithms, or other specific technical implementation that improves computer functionality. The claims instead recite the result of detecting source or data information using direct and indirect tiers and then classifying the data.
Applicant’s reliance on Ex parte Desjardins is not persuasive. Desjardins involved claims that recited specific training mechanics directed to improving how a machine learning model itself operated. The present claims do not recite comparable training mechanics. Merely stating that a machine learning classification process is pre-trained based on use case-based training data does not claim a specific improvement to machine learning technology. Applicant also argues that the claims solve a computer technology problem because the specification discusses difficulty in selecting training datasets and addressing varied data structures. The argument is not persuasive because the asserted technological improvement must be reflected in the claims. The independent claims do not recite a specific meta learning framework, data structure representation, training set selection algorithm, feature selection process, model architecture, or other implementation detail that provides the alleged improvement.
Applicant’s argument regarding the “more likely than not” standard is also not persuasive. The rejection is not being maintained due to uncertainty. For the reasons above, the claims more likely than not remain directed to patent ineligible subject matter because they use generic computer components and pre-trained machine learning to perform data detection and classification, without claiming a specific technological improvement to the computer, the monitoring system, or the machine learning model. Accordingly, the rejection under 35 U.S.C. § 101 is maintained.
Applicant’s arguments with respect to claim(s) 1 – 20 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument. The present rejection has been revised to further rely on Jassal et al. (U.S. Pub. 2017/0300705 A1) for the amended limitation.
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 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
MPEP § 2109(III) sets out steps for evaluating whether a claim is drawn to patent-eligible subject matter. The analysis of claims 1-20, in accordance with these steps, follows.
Step 1 Analysis:
Step 1 is to determine whether the claim is directed to a statutory category (process, machine, manufacture, or composition of matter.
Claims 1 – 11 are directed to an apparatus, meaning that it is directed to the statutory category of machine. Claims 12 - 18 are directed to A computer program product comprising a non-transitory processor-readable storage medium, which is the statutory category of manufacture. Claims 19 - 20 are directed to a method, which can be the statutory category of process.
Step 2A Prong One, Step 2A Prong Two, and Step 2B Analysis:
Step 2A Prong One asks if the claim recites a judicial exception (abstract idea, law of nature, or natural phenomenon). If the claim recites a judicial exception, analysis proceeds to Step 2A Prong Two, which asks if the claim recites additional elements that integrate the abstract idea into a practical application. If the claim does not integrate the judicial exception, analysis proceeds to Step 2B, which asks if the claim amounts to significantly more than the judicial exception. If the claim does not amount to significantly more than the judicial exception, the claim is not eligible subject matter under 35 U.S.C. 101.
Regarding claim 1, the following claim elements are abstract ideas:
detect a source application associated with data obtained from execution of at least one of a plurality of applications in an information processing system, (This is practical to perform in the human mind under its broadest reasonable interpretation aside from the recitation of generic computer components or by a human using a pen and paper.)
and classify the data to determine an intent associated with the data, (This is practical to perform in the human mind under its broadest reasonable interpretation aside from the recitation of generic computer components or by a human using a pen and paper.)
The following claim elements are additional elements which, taken alone or in combination with the other additional elements, do not integrate the judicial exception into a practical application nor amount to significantly more than the judicial exception:
at least one processing platform comprising at least one processor coupled to at least one memory, the at least one processing platform, when executing program code, is configured to (These are mere instructions to apply abstract idea on a generic computer. See MPEP § 2106.05(f). Therefore, this does not amount to significantly more than the judicial exception.)
wherein the plurality of applications comprise services associated with multiple different policies; (These are mere instructions to apply abstract idea on a generic computer. See MPEP § 2106.05(f). Therefore, this does not amount to significantly more than the judicial exception.)
wherein the detecting comprises a two-tier framework including a direct detection tier and an indirect detection tier; (These are mere instructions to apply abstract idea on a generic computer. See MPEP § 2106.05(f). Therefore, this does not amount to significantly more than the judicial exception.)
wherein classifying comprises utilizing a machine learning classification process. (These are mere instructions to apply abstract idea on a generic computer. See MPEP § 2106.05(f). Therefore, this does not amount to significantly more than the judicial exception.)
wherein the machine learning classification process is pre-trained based on a set of use case-based training data. (These are mere instructions to apply abstract idea on a generic computer. See MPEP § 2106.05(f). Therefore, this does not amount to significantly more than the judicial exception.)
Regarding claim 2, the rejection of claim 1 is incorporated herein. Further, claim 2 recites the following additional element:
wherein the direct detection tier further comprises a direct detection process comprising monitoring actions associated with sources of data in the information processing system. (This is mere data gathering, an insignificant extra solution activity, which is a well-understood, routine conventional activity. It does not integrate the judicial exception into a practical application. See MPEP § 2106.05(d). Therefore, this does not amount to significantly more than the judicial exception.)
Regarding claim 3, the rejection of claim 2 is incorporated herein. Further, claim 3 recites the following additional element:
wherein actions comprise one or more of a filesystem action, an object store action, and a streaming system action. (These are mere instructions to apply abstract idea on a generic computer. See MPEP § 2106.05(f). Therefore, this does not amount to significantly more than the judicial exception.)
Regarding claim 4, the rejection of claim 2 is incorporated herein. Further, claim 4 recites the following additional element:
wherein the indirect detection tier further comprises an indirect detection process comprising monitoring the sources of data for data changes over a given time period. (This is mere data gathering, an insignificant extra solution activity, which is a well-understood, routine conventional activity. It does not integrate the judicial exception into a practical application. See MPEP § 2106.05(d). Therefore, this does not amount to significantly more than the judicial exception.)
Regarding claim 5, the rejection of claim 1 is incorporated herein. Further, claim 5 recites the following abstract idea:
wherein classifying the data to determine the intent associated with the data further comprises utilizing a random forest classification process. (Classifying utilizing a random forest is merely reciting mathematical relationship, which is mathematical concept.)
Claim 5 does not recite any additional elements.
Regarding claim 6, the rejection of claim 1 is incorporated herein. Further, claim 6 recites the following additional element:
wherein the at least one processing platform is configured to implement a set of policy agent modules that respectively correspond to the multiple different policies, and a set of classifiers corresponding to the set of policy agent modules. (These are mere instructions to apply abstract idea on a generic computer. See MPEP § 2106.05(f). Therefore, this does not amount to significantly more than the judicial exception.)
Regarding claim 7, the rejection of claim 6 is incorporated herein. Further, claim 7 recites the following additional element:
wherein the set of classifiers is trained utilizing one of a bagging method or a boosting method. (These are mere instructions to apply abstract idea on a generic computer. See MPEP § 2106.05(f). Therefore, this does not amount to significantly more than the judicial exception.)
Regarding claim 8, the rejection of claim 6 is incorporated herein. Further, claim 8 recites the following additional element:
wherein the set of classifiers is dynamically modifiable to improve classification results based on one or more improvement criteria. (These are mere instructions to apply abstract idea on a generic computer. See MPEP § 2106.05(f). Therefore, this does not amount to significantly more than the judicial exception.)
Regarding claim 9, the rejection of claim 1 is incorporated herein. Further, claim 9 recites the following abstract idea:
wherein at least one of detecting and classifying utilizes information from one or more of a scheduling process and an orchestration process associated with the information processing system. (This is practical to perform in the human mind under its broadest reasonable interpretation aside from the recitation of generic computer components or by a human using a pen and paper.)
Claim 9 does not recite any additional elements.
Regarding claim 10, the rejection of claim 1 is incorporated herein. Further, claim 10 recites the following additional element:
wherein the information processing system comprises a distributed edge system. (These are mere instructions to apply abstract idea on a generic computer. See MPEP § 2106.05(f). Therefore, this does not amount to significantly more than the judicial exception.)
Regarding claim 11, the rejection of claim 10 is incorporated herein. Further, claim 11 recites the following additional element:
wherein the distributed edge system is part of a multicloud edge platform. (These are mere instructions to apply abstract idea on a generic computer. See MPEP § 2106.05(f). Therefore, this does not amount to significantly more than the judicial exception.)
Claims 12 – 18 recite substantially similar subject matter to claims 1 – 2 and 4 – 8 respectively and are rejected with the same rationale, mutatis mutandis.
Claims 19 – 20 recite substantially similar subject matter to claims 1 and 5 respectively and are rejected with the same rationale, mutatis mutandis.
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.
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, 2, 4, 5, 12 – 15, 19 – 20 are rejected under 35 U.S.C. 103 as being unpatentable over Smith et al. (U.S. Pub. 2018/0068219) in view of Bathwal et al. (U.S. Pub. US 2025/0217418 A1), further in view of Jassal et al. (U.S. Pub. 2017/0300705).
Regarding Claim 1, Smith teaches
detect a source application associated with data obtained from execution of at least one of a plurality of applications in an information processing system, wherein the plurality of applications comprise services associated with multiple different policies; ([0015] of Smith states “Some or all of the local security agents 106a-b may report the state of the local applications as well as the state of the network on their system to the policy management engine 112 (FIG. 2A, operation 202). For example, in FIG. 1, the local security agent 106a is on the same system as and monitors the source application 104a. The local security agent 106a may, therefore, obtain state information about the source application 104a and report some or all of that state information, and/or information derived therefrom, to the policy management engine 110.”)
Smith does not teach that
wherein the detecting comprises a two-tier framework including a direct detection tier and an indirect detection tier;
classify the data to determine an intent associated with the data, wherein classifying comprises utilizing a machine learning classification process.
wherein the machine learning classification process is pre-trained based on a set of use case-based training data.
However, Bathwal teaches that
classify the data to determine an intent associated with the data, wherein classifying comprises utilizing a machine learning classification process. ([0048] of Bathwal states “The example search interface component 126 further includes a search intent and classification 148 component, that parses the search query for specific terms, and/or that provides one or more classifications for the query, which may be utilized to determine which sources in the knowledge corpus 112 should be utilized, what the purpose of the search is, which type of information is most responsive to the search, etc. The search intent and classification 148 component may utilize any type of classifier and/or intent determiner known in the art. In certain embodiments, multiple intents and/or classifications of the search query may be determined and/or ranked and used to determine which answer or answers to construct.” [0056] of Bathwal states “Example operations that may be adjusted utilizing information from offset users includes, for example and without limitation, classifying and/or determining an intent of a search query.”)
wherein the machine learning classification process is pre-trained based on a set of use case-based training data. ([0009] of Bathwal states “FIG. 4 is a block diagram illustrating a fine-tuning system to fine-tune a pre-trained model that is further trained on a smaller, task-specific dataset, according to some example embodiments.” [0029] of Bathwal states ”The system further innovates model training, where it fine-tunes smaller, domain-specific models using, for example, labels generated from larger, more comprehensive models. This allows for efficient scaling and deployment of the system to handle real-time user queries with lower latency.” [0026] of Bathwal states “Examples of the training process involves leveraging large, pre-existing, and new models to generate high-quality training data, which can then be used to fine-tune smaller, more efficient models tailored to specific tasks, such as summarization tasks, citation tasks, web-interface building tasks, or the like.”)
Jassal teaches that
wherein the detecting comprises a two-tier framework including a direct detection tier and an indirect detection tier; ([0023] of Jassal states “Example methods of capturing/generating events are disclosed. In one example method the step of generating events includes scanning at least one of the data source types at different times. In another example method, the step of generating an event includes registering for callbacks from an application associated with at least one of the data source types. In yet another example method, the step of generating an event includes intercepting and filtering events from at least one of the data source types. Optionally, the steps of intercepting and filtering events from the at least one data source type includes installing an agent on-site with the local data storage system, the agent being configured to intercept and filter the events. The step of generating an event can include capturing metadata associated with the file system operation and/or the data object. In a particular example method, the step of capturing metadata includes capturing metadata identifying a particular user performing the file system operation on the data object. These example methods of capturing/generating events, as well as others, can be used individually or in any combination with one another, as the needs of a particular application might dictate.” These teachings correspond to the direct detection tier because Jassal captures data source operations in real time as they occur, which is the function of directly monitoring actions associated with source of data. [0017] of Jassal states ”In an example embodiment, the event collection interface is further configured to receive a metadata snapshot of the remote data storage system. The metadata snapshot is indicative of the remote file system, and the data governance service is further configured to generate a derivative data set based on the metadata snapshot. The event collection interface is configured to capture metadata associated with file system operation(s) and/or data object(s) associated with captured event(s). The data governance service is additionally configured to update the derivative data set based on the captured event(s) and to perform data analytics on the updated derivative data set.” These teachings correspond to the indirect detection tier because Jassal detects data source changes by examining the source at intervals and tracking divergence from a baseline snapshot over time, rather than capturing each operation as it occurs. Therefore, Jassal teaches or suggests a two-tier framework including a direct detection tier and an indirect detection tier within the same data governance system.)
It would have been obvious to one with ordinary skill in the art before the effective filing date of the invention to combine the teachings of Smith, Jassal, and Bathwal. Smith teaches detecting a source application by monitoring various applications within an information processing system and enforcing security policies based on the detected information of the system. Bathwal teaches using machine learning models to classify/determine intent associated with that data, which improves the accuracy of classification as it helps to sort data with similar intents. Jassal teaches known data governance techniques for monitoring data sources using multiple event generation approaches, including registering for callbacks, intercepting and filtering events using an installed agent, scanning data sources at different times, periodically polling for events, and receiving and processing metadata snapshots. One with the ordinary skill in the art would have been motivated to combine the teachings from Bathwal, Jassal with Smith to improve the robustness and accuracy of detecting and interpreting data from applications, enabling dynamic and intent aware information processing system. Jassal’s technique would have allowed more reliable approach on monitored source application by using both directly detected action-based events and indirectly derived change information from periodic scanning and snapshots. It would have been predictable combination to use known data-source monitoring and machine learning classification techniques in a known policy managed application monitoring system.
Regarding claim 2, the rejection of claim 1 is incorporated herein. Furthermore, the combination of Smith, Jassal, and Bathwal teaches
wherein detecting further comprises a direct detection process comprising monitoring actions associated with sources of data in the information processing system. ([0023] of Jassal states “In another example method, the step of generating an event includes registering for callbacks from an application associated with at least one of the data source types. In yet another example method, the step of generating an event includes intercepting and filtering events from at least one of the data source types. Optionally, the steps of intercepting and filtering events from the at least one data source type includes installing an agent on-site with the local data storage system, the agent being configured to intercept and filter the events. The step of generating an event can include capturing metadata associated with the file system operation and/or the data object. “ [0027] of Jassal states “In an example system, the event collection service includes a plurality of data monitors. Each of the plurality of data monitors is associated with one of a plurality of different data source types. Each data monitor is also operative to detect file system operations executed on an associated data source of the associated type, to generate events indicative of the file system operations, and push the events to the data governance system.”)
Regarding claim 4, the rejection of claim 2 is incorporated herein. Furthermore, the combination of Smith, Jassal, and Bathwal teaches
wherein detecting further comprises an indirect detection process comprising monitoring the sources of data for data changes over a given time period. ([0009] of Jassal states “Another particular example method additionally includes receiving a metadata snapshot from the remote data storage system. The metadata snapshot is indicative of the remote file system, and the example method also includes generating a derivative data set indicative of the remote file system based on the metadata snapshot. The step of capturing an event associated with the remote file system includes capturing metadata associated with one or both of at least one file system operation and a data object of the file system. “ [0016] of Jassal states “The event collection interface is configured to receive the events from the remote data storage system via the event collection service. Optionally, the event collection interface can periodically poll the event collection service for the events.” [0017] of Jassal states “In an example embodiment, the event collection interface is further configured to receive a metadata snapshot of the remote data storage system. The metadata snapshot is indicative of the remote file system, and the data governance service is further configured to generate a derivative data set based on the metadata snapshot. The event collection interface is configured to capture metadata associated with file system operation(s) and/or data object(s) associated with captured event(s).” [0023] of Jassal states “Example methods of capturing/generating events are disclosed. In one example method the step of generating events includes scanning at least one of the data source types at different times.” )
Regarding claim 5, the rejection of claim 1 is incorporated herein. Furthermore, the combination of Smith, Jassal, and Bathwal teaches
wherein classifying the data to determine the intent associated with the data further comprises utilizing a random forest classification process. ([0206] of Bathwal states “Examples of specific machine learning algorithms that may be deployed, according to some examples, include… Random Forest is another type of supervised learning algorithm used for classification, regression, and other tasks. Random Forest builds a collection of decision trees and combines their outputs to make predictions.”)
Claims 12 – 15 recite substantially similar subject matter as claims 1, 2, 4, 5 respectively, and are rejected with the same rationale, mutatis mutandis.
Claims 19 – 20 recite substantially similar subject matter to claims 1 and 5 respectively and are rejected with the same rationale, mutatis mutandis.
Claims 3, 9 – 11 are rejected under 35 U.S.C. 103 as being unpatentable over Smith et al. (U.S. Pub. 2018/0068219) in view of Bathwal et al. (U.S. Pub. US 2025/0217418 A1), Jassal et al. (U.S. Pub. 2017/0300705), further in view of Cowan et al. (U.S. Pub. US 12192274 B1).
Regarding claim 3, the rejection of claim 2 is incorporated herein. The combination of Smith, Jassal, and Bathwal does not explicitly teach
wherein actions comprise one or more of a filesystem action, an object store action, and a streaming system action
However, Cowan teaches
wherein actions comprise one or more of a filesystem action, an object store action, and a streaming system action (Column 34 Lines 18 – 26 of Cowan states “The content delivery network (CDN) enables the mobile edge platform nodes to collect data from the IoT sensor network and from other edge devices for real time or near real time analysis. The mobile edge platform nodes are then operable to deliver responsive content to the edge devices based on the IoT sensor network. The content includes but is not limited to status updates, alerts, schedules, advertisements, suggestions, questions, social network interactions, videos, images, and/or streaming.”)
It would have been obvious to one with ordinary skill in the art before the effective filing date of the invention to combine the teachings of Smith, Bathwal, Jassal, and Cowan. Smith teaches detecting a source application by monitoring various applications within an information processing system and enforcing security policies based on the detected information of the system. Jassal teaches known data governance techniques for monitoring data sources using multiple event generation approaches, including registering for callbacks, intercepting and filtering events using an installed agent, scanning data sources at different times, periodically polling for events, and receiving and processing metadata snapshots. Bathwal teaches using machine learning models to classify/determine intent associated with that data, which improves the accuracy of classification as it helps to sort data with similar intents. Cowan teaches managing and coordinating actions across multiple applications and services associated with different policies in a distributed edge environment. One with the ordinary skill in the art would have been motivated to combine the teachings from Cowan with the combination of Bathwal, Jassal, and Smith to extend Smith and Bathwal’s system to a distributed edge computing environment by Cowan, allowing classification of data by intent across multiple applications and different policies. The combination would have predictably yielded a system capable of performing intent based data classification and source identification across distributed processing platforms.
Regarding claim 9, the rejection of claim 1 is incorporated herein. The combination of Smith, Bathwal, Jassal, and Cowan teaches
wherein at least one of detecting and classifying utilizes information from one or more of a scheduling process and an orchestration process associated with the information processing system. (Column 32 63 – 67 of Cowan states “As a non-limiting example, the first mobile edge platform node is operable to request a schedule, a status update, expected data (e.g., an expected event, an expected condition), historical data, and/or context data. The first mobile edge platform node is then operable to integrate the requested data with the IoT sensor data for more accurate analysis and to detect anomalies in the IoT sensor data.”)
Regarding claim 10, the rejection of claim 1 is incorporated herein. The combination of Smith, Bathwal, Jassal, and Cowan teaches
wherein the information processing system comprises a distributed edge system. (Column 21 Lines 62 – Column 22 Lines 4 of Cowan states “Advantageously, the distributed edge computing platform of the present invention provides increased responsiveness, improved agility, simplified operations, and increased data integrity. The distributed edge computing platform enables proximal data processing and computation in order to reduce application latencies and increase responsiveness. Building decentralized applications with the distributed edge computing platform of the present invention enables near real-time data fusion and algorithms to work seamlessly across a plurality of connected devices.”)
Regarding claim 11, the rejection of claim 10 is incorporated herein. The combination of Smith, Bathwal, Jassal, and Cowan teaches
wherein the distributed edge system is part of a multicloud edge platform. (Fig. 1 of Cowan
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and Column 11 Lines 40 – 44 of Cowan states “The edge computing platform is Application Programming Interface (API) compatible with cloud infrastructures including, but not limited to, GOOGLE CLOUD PLATFORM, AMAZON WEB SERVICES (AWS), and MICROSOFT AZURE.”)
Claims 6 – 8, 16 – 18 are rejected under 35 U.S.C. 103 as being unpatentable over Smith et al. (U.S. Pub. 2018/0068219) in view of Bathwal et al. (U.S. Pub. US 2025/0217418 A1), Jassal et al. (U.S. Pub. 2017/0300705), further in view of Biswas et al. (U.S. Pub. US 2013/0044631 A1).
Regarding claim 6, the rejection of claim 1 is incorporated herein. The combination of Smith, Jassal, and Bathwal does not explicitly teach
wherein the at least one processing platform is configured to implement a set of policy agent modules that respectively correspond to the multiple different policies, and a set of classifiers corresponding to the set of policy agent modules.
However, Biswas teaches
wherein the at least one processing platform is configured to implement a set of policy agent modules that respectively correspond to the multiple different policies, and a set of classifiers corresponding to the set of policy agent modules. ([0055] of Biswas states “At block 502, the overlay agent 302 of the overlay system 300 can be implemented in the access switch 16 or related network edge device. As described above, the overlay agent 302 can include a management interface 306, a policy agent 308, an address handler 310, and a classifier 312A.” [0079] of Biswas states “The address handler 310 of the source overlay agent 904 can receive the request via the classifier 312, and query the policy agent 308 for the IP address of the destination end station 206, for example, the virtual network (OVNX) of the source end station 202. The policy agent 308 of the source overlay agent 904 can first access its policy cache (not shown), which can store mapping information related to the destination end station 206 to which the source end station 202 wishes to communicate.” The policy agent manages policies for the source and destination end station. They respectively correspond to the multiple different policies. Classifier receive the request and feed result to the policy agent to determine which policies apply.)
It would have been obvious to one with ordinary skill in the art before the effective filing date of the invention to combine the teachings of Smith, Bathwal, Jassal and Biswas. Smith teaches detecting a source application by monitoring various applications within an information processing system and enforcing security policies based on the detected information of the system. Jassal teaches known data governance techniques for monitoring data sources using multiple event generation approaches, including registering for callbacks, intercepting and filtering events using an installed agent, scanning data sources at different times, periodically polling for events, and receiving and processing metadata snapshots. Bathwal teaches using machine learning models to classify/determine intent associated with that data, which improves the accuracy of classification as it helps to sort data with similar intents. Biswas teaches a network overlay system that includes policy agents and classifiers, where policy agent manage rules and classifiers work with policy modules to manage data dynamically. One with the ordinary skill in the art would have been motivated to combine the teachings from Biswas with the combination of Smith, Bathwal, and Jassal to implement the policy-based classification functionality using separate agent modules corresponding to different policies. The combination would have been predictable use of known modular policy agent and classifier techniques in a known policy managed application system.
Regarding claim 7, the rejection of claim 6 is incorporated herein. The combination of Smith, Bathwal, Jassal, and Biswas teaches
wherein the set of classifiers is trained utilizing one of a bagging method or a boosting method. ([0206] of Bathwal states “Examples of specific machine learning algorithms that may be deployed, according to some examples, include… Random Forest is another type of supervised learning algorithm used for classification, regression, and other tasks. Random Forest builds a collection of decision trees and combines their outputs to make predictions.” [0209] of Bathwal states “Although several specific examples of machine learning algorithms are discussed herein, the principles discussed herein can be applied to other machine learning algorithms as well. Deep learning algorithms such as convolutional neural networks, recurrent neural networks, and transformers, as well as more traditional machine learning algorithms like decision trees, random forests, and gradient boosting may be used in various machine learning applications.” Random forest is a bagging technique, but with an extra layer of randomness as known concept in the art. )
Regarding claim 8, the rejection of claim 6 is incorporated herein. The combination of Smith, Bathwal, Jassal, and Biswas teaches
wherein the set of classifiers is dynamically modifiable to improve classification results based on one or more improvement criteria. ([0056] of Bathwal states “The example user information processing component 110 further includes an offset user management 152 component, for example allowing the controller 102 to utilize information from offset users that have similarity to the user (e.g., based on user characteristics, user information, and/or user context), and may adjust any operations herein utilizing the offset user information. Example operations that may be adjusted utilizing information from offset users includes, for example and without limitation, classifying and/or determining an intent of a search query, tracking user patterns and/or trajectories (e.g., determining which context elements may be indicating a particular pattern, and/or following a search trajectory which may be utilized to help the user more quickly find the information they need), and/or inferring user information and/or preferences, for example where explicit versions of this information are not available.”)
Claims 16 – 18 recite substantially similar subject matter as claims 6 – 8 respectively, and are rejected with the same rationale, mutatis mutandis.
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 BYUNGKWON HAN whose telephone number is (571)272-5294. The examiner can normally be reached M-F: 9:00AM-6PM PST.
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, Li B Zhen can be reached at (571)272-3768. 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.
/BYUNGKWON HAN/ Examiner, Art Unit 2121
/Li B. Zhen/ Supervisory Patent Examiner, Art Unit 2121
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