Office Action Predictor
Last updated: April 16, 2026
Application No. 18/827,558

DEPLOYING AN EDGE CLUSTER USING PODS

Non-Final OA §102
Filed
Sep 06, 2024
Examiner
HENDERSON, ESTHER BENOIT
Art Unit
2458
Tech Center
2400 — Computer Networks
Assignee
Vmware LLC
OA Round
1 (Non-Final)
79%
Grant Probability
Favorable
1-2
OA Rounds
3y 8m
To Grant
99%
With Interview

Examiner Intelligence

Grants 79% — above average
79%
Career Allow Rate
534 granted / 677 resolved
+20.9% vs TC avg
Strong +32% interview lift
Without
With
+32.2%
Interview Lift
resolved cases with interview
Typical timeline
3y 8m
Avg Prosecution
14 currently pending
Career history
691
Total Applications
across all art units

Statute-Specific Performance

§101
12.0%
-28.0% vs TC avg
§103
40.5%
+0.5% vs TC avg
§102
27.6%
-12.4% vs TC avg
§112
12.7%
-27.3% vs TC avg
Black line = Tech Center average estimate • Based on career data from 677 resolved cases

Office Action

§102
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 . DETAILED ACTION This action is in response to an application filed September 6, 2024. Claims 1-15 are pending in this application. 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) 1-15 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Mahler (Kubernetes on the edge: getting started with KubeEdge and Kubernetes for edge computing, August 2022). With respect to claim 1, Mahler discloses a method for deploying an edge device as a cluster of pods (pg. 5, “What is KubeEdge?”, deploy and manage containerized applications in cloud and on edge using same platform), the method comprising: receiving a set of criteria for deploying the edge device (pg. 4, “Kubernetes edge architectures”, options for deploying Kubernetes for edge computing factors on cost, complexity, resource-constraints, connectivity downtime, and etc.); using the set of criteria to deploy the edge device as a set of one or more pods executing on a set of one or more nodes (pg. 4, “Kubernetes edge architectures”, options for deploying Kubernetes for edge computing factors on cost, complexity, resource-constraints, connectivity downtime, and etc.); and implementing, on the set of pods, a set of one or more services to perform on data message flows, wherein at least two pods deployed for the edge device perform different service operations of different service types such that the different service types are able to be scaled independently (pg. 6, “Scalability”, KubeEdge successfully scales to 100,000 concurrent edge nodes and manages over 1,000,000 active pods on the edge nodes; pg. 7, “KubeEdge edge components”, EdgeHub is responsible for passing data from devices back to cloud and cloud data to devices). With respect to claim 2, Mahler discloses the method of claim 1, wherein a particular node implements a particular Layer 4 (L4) pod that performs L4 service operations for the data message flows; and/or wherein each pod is associated with a set of one or more networking interfaces (pgs. 2-3, “Cost savings”, sending lower granularity to cloud for long-term historical analysis). With respect to claim 3, Mahler discloses the method of claim 2, wherein the particular L4 pod is a first L4 pod of a pair of L4 pods deployed as a high availability (HA) pair for the edge device (pgs. 2-3). With respect to claim 4, Mahler discloses the method of claim 2, wherein the particular node also implements a set of one or more Layer 7 (L7) pods that performs L7 service operations for a particular set of the data message flows (pgs. 2-3). With respect to claim 5, Mahler discloses the method of claim 4, wherein: the particular node is a first node (pg. 4, “Kubernetes edge architectures”), the set of L7 pods is a first set of L7 pods (pgs. 2-3), the particular set of data message flows is a first set of data message flows (pg. 7, “KubeEdge edge components”), and a second node implements a second set of L7 pods that performs the L7 service operations for a second set of the data message flows (pgs. 2-3 and 7-8). With respect to claim 6, Mahler discloses the method of claim 5, wherein each L7 pod is associated with a different logical router represented by the edge device (pgs. 7-8). With respect to claim 7, Mahler discloses the method of claim 5, wherein each L7 pod performs a different L7 service for the edge device (pgs. 7-8). With respect to claim 8, Mahler discloses the method of claim 1, wherein receiving the set of criteria comprises receiving an Application Programming Interface (API) request specifying the set of criteria (pg. 7, “KubeEdge cloud components”); and/or wherein using the set of criteria to deploy the edge device comprises parsing the API request to extract the set of criteria (pg. 7, “KubeEdge cloud components”). With respect to claim 9, Mahler discloses the method of claim 1, wherein: the set of nodes is connected to a Single Route Input/Output Virtualization (SR-IOV) physical network interface card (PNIC) (pg. 8), the SR-IOV PNIC comprising (i) an embedded switch that executes a physical function (PF) that represents a port of the PNIC (pg. 8) and (ii) a set of one or more virtual functions (VFs) that represents a set of one or more virtual interfaces of the PNIC that connects to each pod (pg. 8), and each pod connects to a different VF through a particular networking interface of the pod (pg. 8). With respect to claim 10, Mahler discloses the method of claim 1, wherein the set of nodes is connected to an open virtual switch (OVS) (pgs. 7-8). With respect to claim 11, Mahler discloses the method of claim 10, wherein each pod connects to a different shared memory packet interface (memif) of the OVS to connect to the OVS (pgs. 7-8). With respect to claim 12, Mahler discloses the method of claim 1, wherein the edge cluster is deployed in a particular public cloud (pg. 7); and/or wherein the edge cluster is deployed in a particular private cloud (pg. 7). With respect to claim 13, Mahler discloses the method of claim 12, wherein the particular public cloud is managed by a particular public cloud provider (pg. 7); and/or wherein the edge cluster operates in a particular availability zone of the particular public cloud provider (pg. 7). With respect to claim 14, Mahler discloses the method of claim 1, wherein the set of nodes is a set of worker nodes managed by a master node (pg. 7); and/or wherein the edge device is deployed as the set of pods to perform edge services at a boundary between a logical network and an external network (pg. 7). With respect to claim 15, Mahler discloses a non-transitory machine readable medium storing a program for execution by at least one processing unit for deploying an edge device as a cluster of pods (pg. 5, “What is KubeEdge?”, deploy and manage containerized applications in cloud and on edge using same platform), the program comprising sets of instructions for: receiving a set of criteria for deploying the edge device (pg. 4, “Kubernetes edge architectures”, options for deploying Kubernetes for edge computing factors on cost, complexity, resource-constraints, connectivity downtime, and etc.); using the set of criteria to deploy the edge device as a set of one or more pods executing on a set of one or more nodes (pg. 4, “Kubernetes edge architectures”, options for deploying Kubernetes for edge computing factors on cost, complexity, resource-constraints, connectivity downtime, and etc.); and implementing, on the set of pods, a set of one or more services to perform on data message flows, wherein at least two pods deployed for the edge device perform different service operations of different service types such that the different service types are able to be scaled independently (pg. 6, “Scalability”, KubeEdge successfully scales to 100,000 concurrent edge nodes and manages over 1,000,000 active pods on the edge nodes; pg. 7, “KubeEdge edge components”, EdgeHub is responsible for passing data from devices back to cloud and cloud data to devices). Conclusion Any inquiry concerning this communication or earlier communications from the examiner should be directed to ESTHER B. HENDERSON whose telephone number is (571)270-3807. The examiner can normally be reached Monday-Friday 6a-2p ET. 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, Umar Cheema can be reached at 571-270-3037. 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. /ESTHER B. HENDERSON/Primary Examiner, Art Unit 2458 December 12, 2025
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Prosecution Timeline

Sep 06, 2024
Application Filed
Dec 12, 2025
Non-Final Rejection — §102
Mar 18, 2026
Applicant Interview (Telephonic)
Mar 19, 2026
Response Filed
Mar 19, 2026
Examiner Interview Summary

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Study what changed to get past this examiner. Based on 5 most recent grants.

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Prosecution Projections

1-2
Expected OA Rounds
79%
Grant Probability
99%
With Interview (+32.2%)
3y 8m
Median Time to Grant
Low
PTA Risk
Based on 677 resolved cases by this examiner. Grant probability derived from career allow rate.

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