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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 09/04/2024 has/have been considered by the examiner.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
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Claims 1-20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-20 of U.S. Patent No. 12165390. Although the claims at issue are not identical, they are not patentably distinct from each other because the claims 1-20 of instant application can be anticipated by the claims 1-20 of U.S. Patent No. 12165390.
Instant Application 18824483
U.S. Patent No. 12165390
1. A method comprising: receiving, at a pre-processor, raw data, wherein the pre-processor is configured to generate pre-processed data for input into a machine learning model; training the machine learning model based on the pre-processed data to generate output data; processing, at a post-processor, the output data to generate inference data; and adjusting, by a controller, configuration of one or a combination of the pre-processor and the post-processor based on the inference data.
2. The method of claim 1, further comprising: generating statistical data based on the training of the machine learning model and the inference data; and the adjusting further based on the statistical data.
1. A method comprising: receiving raw data at a pre-processor, the pre-processor being configured to generate pre-processed data; training a machine learning model based on the pre-processed data to generate output data; processing the output data at a post-processor to generate inference data; generating statistical data based on the training of the machine learning model and the inference data; and adjusting, by a controller, configuration of one or a combination of the pre-processor and the post-processor based on the inference data and the statistical data.
11. A system comprising: one or more processors; and a computer-readable medium comprising instructions stored therein, which when executed by the one or more processors, cause the one or more processors to: receive raw data at a pre-processor, the pre-processor being configured to generate pre-processed data for input into a machine learning model; train the machine learning model based on the pre-processed data to generate output data; process the output data at a post-processor to generate inference data; and adjust, by a controller, configuration of one or a combination of the pre-processor and the post-processor based on the inference data.
12. The system of claim 11, wherein the instructions, which when executed by the one or more processors, further cause the one or more processors to: generate statistical data based on the training of the machine learning model and the inference data; and adjust based on the statistical data.
10. A system comprising: one or more processors; and a computer-readable medium comprising instructions stored therein, which when executed by the one or more processors, cause the one or more processors to: receive raw data at a pre-processor, the pre-processor being configured to generate pre-processed data; train a machine learning model based on the pre-processed data to generate output data; process the output data at a post-processor to generate inference data; generate statistical data based on the training of the machine learning model and the inference data and adjust, by a controller, configuration of one or a combination of the pre-processor and the post-processor based on the inference data and the statistical data.
17. A non-transitory computer-readable storage medium comprising computer-readable instructions, which when executed by a computing system, cause the computing system to: receive raw data at a pre-processor, the pre-processor being configured to generate pre-processed data for input into a machine learning model; train the machine learning model based on the pre-processed data to generate output data; process the output data at a post-processor to generate inference data; and adjust, by a controller, configuration of one or a combination of the pre-processor and the post-processor based on the inference data.
18. The non-transitory computer-readable storage medium of claim 17, wherein the instructions, which when executed by the computing system, further cause the computing system to: generate statistical data based on the training of the machine learning model and the inference data; and adjust based on the statistical data.
15. A non-transitory computer-readable storage medium comprising computer-readable instructions, which when executed by a computing system, cause the computing system to: receive raw data at a pre-processor, the pre-processor being configured to generate pre-processed data; train a machine learning model based on the pre-processed data to generate output data; process the output data at a post-processor to generate inference data; generate statistical data based on the training of the machine learning model and the inference data and adjust, by a controller, configuration of one or a combination of the pre-processor and the post-processor based on the inference data and the statistical data.
Other rejected dependent claims of the instant application can be found basically word to word in the corresponding claims of U.S. Patent No. 12165390.
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 11-16 is/and rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claim(s) does/do not fall within at least one of the four categories of patent eligible subject matter because computer-readable medium may include transitory medium which do not fall within at least one of four categories of patent eligible subject matter.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1-20 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Parandehgheibi et al (US 10177998 B2), hereinafter Parandehgheibi.
-Regarding claim 1, Parandehgheibi discloses a method comprising (Abstract; FIGS. 1-7B; Col. 27, lines 47-61): receiving, at a pre-processor, raw data (FIG. 3, Pre-processing 304, Data Collection 302; FIG. 4, blocks 402-406; FIG. 6, blocks 602-604), wherein the pre-processor is configured to generate pre-processed data for input into a machine learning model (FIGS. 3-5B; FIG. 6, blocks 606-608; Col. 18, lines 29-34, “After pre-processing, the data pipeline 400 may proceed to a clustering stage 408. In the clustering stage 408, various machine learning techniques can be implemented to analyze feature vectors within a single domain or across different domains to determine the optimal clustering given a set of input nodes”; Col. 20, lines 5-10); training the machine learning model based on the pre-processed data to generate output data (FIG. 1, Analytics Engine 110, ADM 140; FIGS. 3-5B; Col. 8, line 59- Col. 9, line 5; Col. 20, lines 7-26, “Supervised or unsupervised learning techniques can be used depending on the availability of training data and other related information … an ADM module (or other suitable system) can receive configuration information … receive the configuration data in a proprietary … translate the information to training data observations for the particular machine learning approach(es) implemented by the ADM module … label nodes to create the training data”; Col. 21, lines 10-19); processing, at a post-processor, the output data to generate inference data (FIGS. 1, 3; FIG. 4, blocks 410-412; FIG. 6, blocks 608-610; Col. 22, lines 30-37, lines 48-65); and adjusting, by a controller, configuration of one or a combination of the pre-processor and the post-processor based on the inference data (FIGS. 1, 3-4; 7A-7B; FIG. 6, block 612; Col. 2, lines 30-47; Col. 4, line 62- Col. 5, line 7; Col. 25, lines 2-21, lines 22-30, “the similarity measure may be performed with respect to flows of other nodes to determine whether a source and/or destination host of the first flow belongs to a particular cluster of the network. If the first flow is found to be similar to flows of a particular cluster, then the policies applicable to that cluster may be applied to the first flow and subsequent flows similar to the first flow. The process 600 can conclude at step 612 by enforcing the policy with respect to the subsequent flows”).
-Regarding claim 11, Parandehgheibi discloses a system comprising (FIGS. 7A-7B): one or more processors; and a computer-readable medium comprising instructions stored therein, which when executed by the one or more processors, cause the one or more processors (Abstract; FIGS. 1-7B; Col. 27, lines 47-61): receive raw data at a pre-processor (FIG. 3, Pre-processing 304, Data Collection 302; FIG. 4, blocks 402-406; FIG. 6, blocks 602-604), the pre-processor being configured to generate pre-processed data for input into a machine learning model (FIGS. 3-5B; FIG. 6, blocks 606-608; Col. 18, lines 29-34, “After pre-processing, the data pipeline 400 may proceed to a clustering stage 408. In the clustering stage 408, various machine learning techniques can be implemented to analyze feature vectors within a single domain or across different domains to determine the optimal clustering given a set of input nodes”; Col. 20, lines 5-10); train the machine learning model based on the pre-processed data to generate output data (FIG. 1, Analytics Engine 110, ADM 140; FIGS. 3-5B; Col. 8, line 59- Col. 9, line 5; Col. 20, lines 7-26, “Supervised or unsupervised learning techniques can be used depending on the availability of training data and other related information … an ADM module (or other suitable system) can receive configuration information … receive the configuration data in a proprietary … translate the information to training data observations for the particular machine learning approach(es) implemented by the ADM module … label nodes to create the training data”; Col. 21, lines 10-19); process the output data at a post-processor to generate inference data (FIGS. 1, 3; FIG. 4, blocks 410-412; FIG. 6, blocks 608-610; Col. 22, lines 30-37, lines 48-65); and adjust, by a controller, configuration of one or a combination of the pre-processor and the post-processor based on the inference data (FIGS. 1, 3-4; 7A-7B; FIG. 6, block 612; Col. 2, lines 30-47; Col. 4, line 62- Col. 5, line 7; Col. 25, lines 2-21, lines 22-30, “the similarity measure may be performed with respect to flows of other nodes to determine whether a source and/or destination host of the first flow belongs to a particular cluster of the network. If the first flow is found to be similar to flows of a particular cluster, then the policies applicable to that cluster may be applied to the first flow and subsequent flows similar to the first flow. The process 600 can conclude at step 612 by enforcing the policy with respect to the subsequent flows”).
-Regarding claim 17, Parandehgheibi discloses a non-transitory computer-readable storage medium comprising computer-readable instructions, which when executed by a computing system (FIGS. 7A-7B), cause the computing system to (Abstract; FIGS. 1-7B; Col. 27, lines 47-61): receive raw data at a pre-processor (FIG. 3, Pre-processing 304, Data Collection 302; FIG. 4, blocks 402-406; FIG. 6, blocks 602-604), the pre-processor being configured to generate pre-processed data for input into a machine learning model (FIGS. 3-5B; FIG. 6, blocks 606-608; Col. 18, lines 29-34, “After pre-processing, the data pipeline 400 may proceed to a clustering stage 408. In the clustering stage 408, various machine learning techniques can be implemented to analyze feature vectors within a single domain or across different domains to determine the optimal clustering given a set of input nodes”; Col. 20, lines 5-10); train the machine learning model based on the pre-processed data to generate output data (FIG. 1, Analytics Engine 110, ADM 140; FIGS. 3-5B; Col. 8, line 59- Col. 9, line 5; Col. 20, lines 7-26, “Supervised or unsupervised learning techniques can be used depending on the availability of training data and other related information … an ADM module (or other suitable system) can receive configuration information … receive the configuration data in a proprietary … translate the information to training data observations for the particular machine learning approach(es) implemented by the ADM module … label nodes to create the training data”; Col. 21, lines 10-19); process the output data at a post-processor to generate inference data (FIGS. 1, 3; FIG. 4, blocks 410-412; FIG. 6, blocks 608-610; Col. 22, lines 30-37, lines 48-65); and adjust, by a controller, configuration of one or a combination of the pre-processor and the post-processor based on the inference data (FIGS. 1, 3-4; 7A-7B; FIG. 6, block 612; Col. 2, lines 30-47; Col. 4, line 62- Col. 5, line 7; Col. 25, lines 2-21, lines 22-30, “the similarity measure may be performed with respect to flows of other nodes to determine whether a source and/or destination host of the first flow belongs to a particular cluster of the network. If the first flow is found to be similar to flows of a particular cluster, then the policies applicable to that cluster may be applied to the first flow and subsequent flows similar to the first flow. The process 600 can conclude at step 612 by enforcing the policy with respect to the subsequent flows”).
-Regarding claims 2, 12, and 18, Parandehgheibi discloses the method of claim 1, the system of claim 11, and non-transitory computer-readable storage medium of claim 17. Parandehgheibi further discloses generating statistical data based on the training of the machine learning model and the inference data; and the adjusting further based on the statistical data (FIG. 1, policy attributes 138; FIG. 4, blocks 410-412; FIGS. 3, 5A-5B; 7A-7B; FIG. 6, blocks 608- 612; Col. 10, lines 44-59, “policy statistics”; Col. 19, lines 41-60, “Clustering is a process that groups a set of objects into the same group … k-means algorithm in which a number of n nodes are partitioned into k clusters … The algorithm proceeds by alternating steps, assignment and update. During assignment, each node is assigned to a cluster whose mean yields the least within-cluster sum of squares (WCSS) (i.e., the nearest mean). During update, the new means is calculated to be the centroids of the nodes in the new clusters … clustering include hierarchical clustering … density-based clustering … EM or DBSCAN… decision trees or neural networks …”, lines 61-64, “statistical model”; Col. 22, lines 30-35, lines 48-65, “… statistics on clusters … create application profiles, perform ADM re-runs, and/or export policies for cluster edge …”; Col. 25, lines 2-21, lines 22-30).
-Regarding claims 3 and 13, Parandehgheibi discloses the method of claim 2 and the system of claim 12. Parandehgheibi further discloses wherein the statistical data includes at least one of a memory usage of the inference data, a workload of the training of the machine learning model, and a resource usage of a processing unit (FIGS. 1, 3-4, 6; Col. 3, lines 20-44, "analyzing a narrow data set for flows is application dependency mapping (ADM) ...determining the interrelationships between and among workloads ..."; Col. 4, line 62 - Col. 5, line 7, "clustering or identifying endpoint groups (i.e., endpoints performing similar workloads ..."; Col. 2, lines 30-47; Col. 23, lines 45-54, "CPU usage, memory usage, network usage"; Col. 25, lines 2-21, lines 22-30).
-Regarding claim 4, Parandehgheibi discloses the method of claim 3. Parandehgheibi further discloses wherein the statistical data is generated periodically (Col. 8, lines 30-43, "... periodically replace detailed network traffic data with consolidated summaries ...").
-Regarding claims 5, 14, and 19, Parandehgheibi discloses the method of claim 1, the system of claim 11, and non-transitory computer-readable storage medium of claim 17. Parandehgheibi further discloses to adjust the configuration of one or a combination of the pre-processor and the post-processor based on at least one of metadata associated with the raw data, metadata associated with the output data, metadata associated with the inference data, user input, characteristics of the raw data, characteristics of the output data, and characteristics of the inference data (FIG. 1, 3-4; 7A-7B; FIG. 6, block 612; Col. 2, lines 30-47; Col. 4, line 62- Col. 5, line 7; Col. 6, lines 15-30, "the network traffic data can include metadata relating to a packet, a collection of packets, a flow, a bidirectional flow, a group of flows"; Col. 9, lines 12-25, "HDFS™"; Col. 17, lines 13-18, "During a policy analysis stage 314, one or more policies can be determined for handling the suspect network traffic"; Col. 25, lines 2-21, lines 22-30) .
-Regarding claims 6 and 15, Parandehgheibi discloses the method of claim 1 and the system of claim 11. Parandehgheibi further discloses wherein the adjustment of the configuration is performed based on one or more configuration rules or a heuristic algorithm (FIG. 6; Col. 25, lines 3-30).
-Regarding claim 7, Parandehgheibi discloses the method of claim 1. Parandehgheibi further discloses wherein the machine learning model is a computer vision model (FIGS. 1-4; Col. 5, lines 17-36, “the sensors 104 can be implemented as virtual partition images (e.g., virtual machine (VM) images or container images), and the configuration manager 102 can distribute the images to host machines …”; Col. 9, lines 12-25, "HDFS™").
-Regarding claim 8, Parandehgheibi discloses the method of claim 1. Parandehgheibi further discloses wherein the raw data is image data, and wherein the adjustment of the configuration of one or a combination of the pre-processor and the post-processor includes reducing a size of the image data (FIG. 1, 3-4; 7A-7B; FIG. 6, block 612; Col. 2, lines 30-47; Col. 25, lines 2-30, “a policy may be applied to drop subsequent flows similar to the first flow”).
-Regarding claims 9, 16, and 20, Parandehgheibi discloses the method of claim 1, the system of claim 11, and non-transitory computer-readable storage medium of claim 17. Parandehgheibi further discloses adjusting the configuration of one or a combination of the pre-processor and the post-processor associated with a first end device based on the inference data associated with a second end device (FIG. 2, servers 208a-208d, spine switches 210a-210d, leaf switches 212a-212d, sensors 220; FIGS. 1, 3-4; 7A-7B; FIG. 6, block 612; Col. 2, lines 30-47; Col. 4, line 62- Col. 5, line 7, “An integral task of ADM is clustering or identifying endpoint groups … determine whether the endpoints form a cluster or endpoint group, and how the cluster or endpoint group may relate to other endpoints or clusters or endpoint groups …”; Col. 15, lines 4-34, "network environment 200 ... network traffic monitoring system ... analytics engine ..."; Col. 25, lines 2-21).
-Regarding claim 10, Parandehgheibi discloses the method of claim 1. Parandehgheibi further discloses wherein the pre-processed data is generated based on at least one of characteristics of the raw data, a mechanism used for generating the raw data, user requirements, contextual information associated with the raw data, the output data, and the inference data (FIGS. 1, 3-4, 6).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Wadhwa et al (US 20220006842 A1), hereinafter Wadhwa teaches a method for determining effectiveness of network segmentation policies, and teaches training a machine learning model based on the pre-processed data , processing output data using a post-processor to generate inference data, and adjusting configuration of pre-processor and/or the post-processor based on the inference data. (Wadhwa: FIGS. 3-5).
Venkataraman et al (US 12511555 B2), hereinafter Venkataraman teaches a method for de-biasing camping segmentation using a pre-processing, a post-processing, and a machine learning model (Venkataraman: FIGS. 6, 8)
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/XIAO LIU/Primary Examiner, Art Unit 2664
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