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 .
Claims 1-20 are pending.
Information Disclosure Statement
No IDSs have been received by the Office.
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.
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.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over US Patent No. 11,420,642 to Tautschnig et al. (hereinafter Tautschnig) in view of US PG Pub. No. 2020/0097357 to Shwartz et al. (hereinafter Shwartz) in view of US PG Pub. No. 2022/0400131 to Shao et al. (hereinafter Shao).
As to claim 1, Tautschnig teaches:
a. A processing system including a processor (processor) (Tautschnig, 3:58-4:17).
b. A memory that stores executable instructions (memory) (Tautschnig, 24:1-9).
c. Obtaining first configuration information associated with a communications network, wherein the first configuration information is obtained responsive to a first provisioning request (provisioning request) (Tautschnig, 3:58-4:17).
Tautschnig automatically scans the provisioned components using machine learning (Tautschnig, 4:3-17 and claim 7) but does not offer any details of the machine learning. However, in an analogous art, Shwartz teaches:
d. Applying the first configuration information to an AI process that utilizes an AI model that had been trained using first training data, wherein the AI process determines at least one first potential adverse effect that would be caused by implementing the first configuration information, and wherein the AI process further determines a first risk level associated with the at least one first potential adverse effect (automation engine customizes scripts with machine learning processes and determines risk of executing those customized scripts to find adverse effects) (Shwartz, [0086-0087 and 0116]).
Therefore, one of ordinary skill in the art before the effective filing date of the instant application would have been motivated to implement the automatic processing of configuration information of Tautschnig with the detailed use of machine learning techniques of Shwartz in order to “provide improved, artificially intelligent script-customization functionality” as suggested by Shwartz (Shwartz, [0009]).
Tautschnig as modified further teaches:
e. Receiving from the AI process the first risk level associated with the at least one first potential adverse effect (risk is used to determine whether to run the customized script) (Shwartz, [0118]).
Tautschnig as modified does not explicitly recite outputting the risk level. However, in an analogous art, Shao teaches:
f. Outputting the first risk level (user interface displays network risk) (Shao, [0045] and fig. 2).
Therefore, one of ordinary skill in the art before the effective filing date of the instant application would have been motivated to implement the automatic processing of configuration information of Tautschnig as modified with the graphical display of the risk determination of Shao in order to present the user with the outcomes and their corresponding risk as suggested by Shao (Shao, [0019]).
As to claim 2, Tautschnig as modified teaches the first risk level is output graphically (Shao, fig. 2).
As to claim 3, Tautschnig as modified teaches the first risk level is output as one of a plurality of possible risk levels (degrees of risk are assigned) (Shwartz, [0111]).
As to claim 4, Tautschnig as modified teaches:
a. Receiving from the AI process an indication of the at least one first potential adverse effect (risk score and associated adverse effect (e.g., hardware failures, amount of network resources…) (Shao, [0046]).
b. Outputting, along with the first risk level, the indication of the at least one first potential adverse effect (risk score and associated adverse effect (e.g., hardware failures, amount of network resources…) (Shao, [0046]).
As to claim 5, Tautschnig as modified teaches the at least one first potential adverse effect results from a total bandwidth allocation being greater than 100%, an invalid Internet Protocol (IP) address, or any combination thereof (bandwidth resources) (Shao, [0046]).
As to claim 6, Tautschnig as modified teaches the communications network comprises a wired communications network, a wireless communications network, or any combination thereof (Tautschnig, 7:11-24).
As to claim 7, Tautschnig as modified teaches the wired communications network comprises a fiber optic network (Shwartz, [0059]).
As to claim 8, Tautschnig as modified teaches an eNodeB, a gNodeB, a fourth-generation (4G) cellular communications base station, a fifth-generation (5G) cellular communications base station, a subsequent generation cellular communications base station, or any combination thereof (Shao, [0101]).
As to claims 9 and 18, Tautschnig as modified teaches the first configuration information is obtained from the first provisioning request (provisioning request) (Tautschnig, 3:58-4:17).
As to claim 10, Tautschnig as modified teaches:
a. Facilitating a re-training of the AI model using the first configuration information and the at least one first potential adverse effect as second training data, resulting in an updated AI model (model is updated using historical logs and inferred success rates and results (adverse effects) of previous attempts) (Shwartz, [0087]).
b. Obtaining second configuration information associated with the communications network, wherein the second configuration information is obtained responsive to a second provisioning request (provisioning request) (Tautschnig, 3:58-4:17).
c. Applying the second configuration information to the AI process that utilizes the updated AI model, wherein the AI process determines at least one second potential adverse effect that would be caused by implementing the second configuration information, and wherein the AI process further determines a second risk level associated with the at least one second potential adverse effect (automation engine customizes scripts with machine learning processes and determines risk of executing those customized scripts to find adverse effects) (Shwartz, [0086-0087 and 0116]).
d. Receiving from the AI process the second risk level associated with the at least one second potential adverse effect (risk is used to determine whether to run the customized script) (Shwartz, [0118]).
e. Outputting the second risk level (user interface displays network risk) (Shao, [0045] and fig. 2).
The limitations of claim 1 are not limited to a single instance of provisioning request of the combined references.
As to claim 11, Tautschnig as modified teaches:
a. The operations further comprise receiving from the AI process an indication of the at least one potential adverse effect (risk score and associated adverse effect (e.g., hardware failures, amount of network resources…) (Shao, [0046]).
b. The operations further comprise outputting, along with the second risk level, the indication of the at least one second potential adverse effect (risk score and associated adverse effect (e.g., hardware failures, amount of network resources…) (Shao, [0046]).
c. The second configuration information is obtained from the second provisioning request (provisioning request) (Tautschnig, 3:58-4:17).
As to claim 12, Tautschnig as modified teaches:
a. Obtaining historic provisioning information associated with the communications network, wherein the historic provisioning information is indicative of a plurality of previous network provisioning actions (training involves the use of historic actions) (Shwartz, [0087]).
b. Facilitating use of the historic provisioning information as the first training data (training involves the use of historic actions) (Shwartz, [0087]).
As to claim 13, Tautschnig as modified teaches each of the previous network provisioning actions comprises a respective: setting of a voice traffic bandwidth percentage, setting of a video traffic bandwidth percentage, setting of a default traffic bandwidth percentage, setting of an interface name, setting of an IP address, setting of a subnet mask, setting of a routing protocol, setting of a VLAN ID, or any combination thereof (bandwidth resources) (Shao, [0046]).
As to claim 14, Tautschnig as modified teaches the AI process comprises a generative AI process (Shao, [0015]).
As to claim 15, Tautschnig as modified teaches the AI process comprises a machine learning (ML) process (Shwartz, Abstract).
As to claims 16 and 17, Tautschnig teaches:
a. A processor to facilitate performance of operations (processor) (Tautschnig, 3:58-4:17).
Tautschnig does not expressly mention past provisioning information. However, in an analogous art, Shwartz teaches:
b. Obtaining historic provisioning information associated with a network, (historical data of the network is used in training) (Shwartz, [0087]).
Therefore, one of ordinary skill in the art before the effective filing date of the instant application would have been motivated to implement the automatic processing of configuration information of Tautschnig with the use of historic provisioning data of Shwartz in order to train AI models as suggested by Shwartz (Shwartz, [0087]).
Tautschnig as modified further teaches wherein the network comprises a wired communications network, a wireless communications network, or any combination thereof, (Tautschnig, 7:11-24) and wherein the historic provisioning information is indicative of a plurality of previous network provisioning actions (historic provisioning data) (Shwartz, [0087]).
c. Utilizing the historic provisioning information as training data for an artificial intelligence (AI) model (machine learning training with historical data) (Shwartz, [0027 and 0087]).
d. Obtaining current provisioning information associated with the network, wherein the current provisioning information comprises a first prospective configuration element and a second configuration element (provisioning information (configuration element) is customized when unexpected conditions occur with template scripts) (Shwartz, [0086-0087]).
e. Applying the current provisioning information to an AI process that utilizes the AI model, wherein the AI process determines at least one potential interaction between a first expected result of the first prospective configuration element and a second expected result of the second prospective configuration element, and wherein the AI process further determines a risk level associated with the at least one potential interaction (provisioning information is customized when unexpected conditions occur and risk analysis is conducted) (Shwartz, [0086-0087 and 0118]).
As to claims 17 and 20, Tautschnig as modified teaches:
a. The first expected result of the first prospective configuration element is a first bandwidth allocation (network bandwidth resources is a configuration element) (Shao, [0046]).
b. The second expected result of the second prospective configuration element is a first bandwidth allocation (network bandwidth resources is a configuration element) (Shao, [0046]).
c. The at least one potential interaction comprises a total of the first bandwidth allocation plus the second bandwidth allocation being greater than 100% (Network bandwidth resources are a configuration element. It is obvious that if two elements required bandwidth is more than the total allocation that is an error) (Shao, [0046]).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to WILLIAM S POWERS whose telephone number is (571)272-8573. The examiner can normally be reached M-F 7:30-17:30.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Jorge L Ortiz-Criado can be reached at (571) 272-7624. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/WILLIAM S POWERS/Primary Examiner, Art Unit 2496
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