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 .
1. This is in response to communication filed 5/20/25 in which claims 1-20 are pending.
Claim Rejections - 35 USC § 102
2. 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 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.
3. Claims 1-3, 6-9, 13-18 and 20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by U.S. Publication No. 2020/0186433 to Cui et al.
a. As per claim 1, Cui et al teaches a network equipment (NE) for wireless communication, comprising: at least one memory (See paragraph [0006 and 0139]); and at least one processor coupled with the at least one memory (See paragraph [0006 and 0139]) and configured to cause the NE to: receive policy information associated with a policy for a network function virtualization (NFV)-enabled wireless network (See paragraph [0022], system 200 is described in the context of …network function virtualization (NFV) because of the dynamic nature of the system's policy and the elastic nature of such environments); analyze the policy information using a machine learning model to generate results, wherein the machine learning model is trained to identify anomalies in one or more policies (See paragraph [0033], adaptive controller 200 may further include a machine learning tool 230. Machine learning tool 230 may instantiated as a microservice such as a virtual machine or virtual network function. Machine learning tool 230 may perform data analytics monitoring the implementation of policies by MSCo. For example, after actions are deployed, the machine learning tool 230 monitors network performance and provides feedback to MSCo regarding the effectiveness of the actions); and transmit the results of the machine learning model analysis, the results comprising an indication of whether the policy comprises an anomaly (See paragraph [0053]).
b. As per claim 2, Cui et al teaches the claimed invention as described above. Furthermore, Cui et al teaches wherein the policy information comprises the policy and a corresponding virtual network function descriptor (VNFD) (See paragraph [0023-0024]).
c. As per claim 3, Cui et al teaches the claimed invention as described above. Furthermore, Cui et al teaches wherein the at least one processor is configured to cause the NE to generate a data structure based on the policy information, the data structure provided to the machine learning model for the analysis (See paragraph [0041 and 0064]).
d. As per claim 6, Cui et al teaches the claimed invention as described above. Furthermore, Cui et al teaches wherein the indication of whether the policy comprises an anomaly comprises a score that is determined according to a score threshold (See paragraph [0032]).
e. As per claim 7, Cui et al teaches the claimed invention as described above. Furthermore, Cui et al teaches wherein the at least one processor is configured to cause the NE to generate a report comprising the results of the machine learning analysis and transmit the report (See paragraph [0004, 0123, 0130]).
f. As per claim 8, Cui et al teaches the claimed invention as described above. Furthermore, Cui et al teaches wherein the at least one processor is configured to cause the NE to generate an explanation of the results of the machine learning analysis using the machine learning model (See paragraph [0004], the machine learning tool generates a revised action based on implementation of the action on the plural microservices, and wherein the microservice coordinator reports the revised action to the policy tool).
g. As per claim 9, Cui et al teaches the claimed invention as described above. Furthermore, Cui et al teaches wherein the at least one processor is configured to cause the NE to generate one or more policy recommendations, using the machine learning model, based on the results of the machine learning analysis (See paragraph [0130], the machine learning tool is configured to provide a report on an efficacy of an action to the network coordinator, and wherein the network coordinator is configured to provide a feedback on the dynamic policy to the policy engine)
h. As per claim 13, Cui et al teaches the claimed invention as described above. Furthermore, Cui et al teaches wherein the NE is pre-authorized to access policies associated with the NFV-enabled wireless network (See paragraph [0040-0041 and 0055]) .
i. As per claim 14, Cui et al teaches the claimed invention as described above. Furthermore, Cui et al teaches wherein the NE comprises a virtual network function manager (VNFM) (See paragraph [0120]).
j. As per claim 15, Cui et al teaches a method of a network equipment (NE), comprising: receiving policy information associated with a policy for a network function virtualization (NFV)-enabled wireless network (See paragraph [0022]); analyzing the policy information using a machine learning model to generate results, wherein the machine learning model is trained to identify anomalies in one or more policies (See paragraph [0033]); and transmitting the results of the machine learning model analysis, the results comprising an indication of whether the policy comprises an anomaly (See paragraph [0053]).
k. As per claim 16, Cui et al teaches a network equipment (NE) for wireless communication, comprising: at least one memory (See paragraph [0039]); and at least one processor coupled with the at least one memory and configured to cause the NE to: transmit policy information associated with a policy for a network function virtualization (NFV)-enabled wireless network (See paragraph [0031 and 0033]); receive an indication of whether the policy comprises an anomaly (See paragraph [0033], (See paragraph [0033], adaptive controller 200 may further include a machine learning tool 230. Machine learning tool 230 may instantiated as a microservice such as a virtual machine or virtual network function. Machine learning tool 230 may perform data analytics monitoring the implementation of policies by MSCo. For example, after actions are deployed, the machine learning tool 230 monitors network performance and provides feedback to MSCo regarding the effectiveness of the actions); and deactivate the policy in response to the indication indicating that the policy comprises an anomaly (See paragraph [0033 and 0053]).
l. As per claim 17, Cui et al teaches the claimed invention as described above. Furthermore, Cui et al teaches wherein the at least one processor is configured to cause the NE to request instantiation of a virtual network function associated with the policy (See paragraph [0022, 0033 and 0035]).
m. As per claim 18, Cui et al teaches the claimed invention as described above. Furthermore, Cui et al teaches wherein the at least one processor is configured to cause the NE to request verification of the policy (See paragraph [0056]).
n. As per claim 20, Cui et al teaches a method of a network equipment (NE), comprising: transmitting policy information associated with a policy for a network function virtualization (NFV)-enabled wireless network (See paragraph [0022, 0041]); receiving an indication of whether the policy comprises an anomaly (See paragraph [0033]); and deactivating the policy in response to the indication indicating that the policy comprises an anomaly (See paragraph [0094]).
Claim Rejections - 35 USC § 103
4. 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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
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.
5. Claims 4-5 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Publication No. 2020/0186433 to Cui et al in view of U.S. Publication No. 2017/0006083 to McDonnell.
a. As per claim 4, Cui et al teaches the claimed invention as described above. Furthermore, Cui et al fails to teach wherein the data structure comprises a key-value pair based on the policy information, wherein a key of the key-value pair comprises an identifier for a virtual network function and a value of the key-value pair comprises data from the policy and the VNFD.
McDonnell teaches wherein the data structure comprises a key-value pair based on the policy information, wherein a key of the key-value pair comprises an identifier for a virtual network function and a value of the key-value pair comprises data from the policy and the VNFD (See paragraph [0040]).
It would have been obvious to one with ordinary skill in the art to incorporate the teaching of key-value pair of McDonnell in the claimed invention of Cui et al in order to uniquely identify the elements of the data structure.
b. As per claim 5, Cui et al in view of McDonnell teaches the claimed invention as described above. However, Cui et al fails to explicitly teach wherein the data from the policy and the VNFD comprises parameters and primitives associated with the policy and the VNFD.
McDonnell teaches wherein the data from the policy and the VNFD comprises parameters and primitives associated with the policy and the VNFD (See paragraph [0042-0043]).
It would have been obvious to one with ordinary skill in the art to incorporate the teaching of key-value pair of McDonnell in the claimed invention of Cui et al in order to uniquely identify the elements of the data structure.
c. As per claim 19, Cui et al in view of McDonnell teaches the claimed invention as described above. However, Furthermore, Cui et al teaches wherein the policy information comprises the policy and a corresponding virtual network function descriptor (VNFD).
McDonnell teaches wherein the policy information comprises the policy and a corresponding virtual network function descriptor (VNFD) (See paragraph [0022, 0042-0043]).
It would have been obvious to one with ordinary skill in the art to incorporate the teaching of key-value pair of McDonnell in the claimed invention of Cui et al in order to uniquely identify the elements of the data structure.
6. Claims 10-12 are rejected under 35 U.S.C. 0103 as being unpatentable over U.S. Publication No. 2020/0186433 to Cui et al in view of U.S. Publication No. 2024/0211368 to Kommula et al.
a. As per claim 10, Cui et al teaches the claimed invention as described above. However, Cui et al fails to explicitly teach wherein the at least one processor is configured to cause the NE to train the machine learning model using historical policy information associated with NFV-enabled wireless networks.
Kommula et al teaches wherein the at least one processor is configured to cause the NE to train the machine learning model using historical policy information associated with NFV-enabled wireless networks (See paragraph [0170]).
It would have been obvious to one with ordinary skill in the art to incorporate the teaching of Kommula et al in the claimed invention of Cui et al in order to analyze and evaluate rules based on historical pattern.
b. As per claim 11, Cui et l teaches the claimed invention as described above. However, Cui et al teaches fails to explicitly teach wherein the historical policy information comprises policy information associated with historical virtual network function (VNF) management operations.
Kommula et al teaches wherein the historical policy information comprises policy information associated with historical virtual network function (VNF) management operations (See paragraph [0170]).
It would have been obvious to one with ordinary skill in the art to incorporate the teaching of Kommula et al in the claimed invention of Cui et al in order to analyze and evaluate rules based on historical pattern.
c. As per claim 12, Cui et l teaches the claimed invention as described above. Furthermore, Cui et al teaches fails to explicitly teach wherein the historical VNF management operations comprise operations associated with VNF creation, reading, updating, deletion, scaling-up, scaling to level, instantiation, termination, or a combination thereof (See paragraph [0035, 0118]).
Conclusion
7. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
U.S. Publication No. 2021/0168035 to Cui et al teaches Intelligent Policy Control Engine for 5G or other Next Generation Network.
U.S. Patent No. 10,805171 to Anwer et al teaches Understanding Network Entity Relationships.
U.S. Publication No. 2019/0280918 to Hermoni et al teaches System, Method and Computer Program for Automatically Generating Training Data for Analyzing a New Configuration of a Communication Network.
8. Any inquiry concerning this communication or earlier communications from the examiner should be directed to DJENANE BAYARD whose telephone number is (571)272-3878. The examiner can normally be reached 9-5.
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, John Follansbee can be reached at (571)272-3964. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/DJENANE M BAYARD/Primary Examiner, Art Unit 2444
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