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 .
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 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.
This office action is in response to the communication filed on 5/19/2024.
Claims 1-19 are pending.
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.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1-5, 7-12, 14-15, 19 is/are rejected under AIA 35 U.S.C. 102(a)(1) as being anticipated by Tapia et al. (US 2020/0304364, “Tapia”).
As to claim 1, Tapia discloses a computer-implemented method for processing a plurality of data inputs comprising information about a network, the method comprising:
training a plurality of machine learning models on the plurality of data inputs, wherein each machine learning model of the plurality of machine learning models is trained to generate a representation of at least one data input of the plurality of data inputs (fig. 3, [0086]-[0089], training a model with plurality of machine learning (ML) algorithms (each is read as a model, such as Bayesian, decision tree, SVM…) to generate results (representations of inputs) from training data inputs);
and generating a representation of each data input of the plurality of data inputs using at least one trained machine learning model of the plurality of trained machine learning models, wherein the plurality of trained machine learning models and the generated representation of each data input are for use in resolving an issue in the network ([0091], applying live inputs of a wireless network to the trained model to generate a root cause for an issue affecting subscribers of the wireless network).
As to claim 9, Tapia discloses a computer-implemented method for identifying one or more data inputs for use in resolving an issue in a network, the method comprising:
in response to receiving information about the issue in the network ([0091], applying live inputs of a wireless network about an issue, fig. 13, #1302, 1304, receiving network performance data for user devices and network components):
generating a representation of the information about the issue using at least one trained machine learning model of a plurality of trained machine learning models (fig. 3, [0086]-[0089], training a model with plurality of machine learning (ML) algorithms (each is read as a model) to generate results (representations of inputs) from training data inputs); identifying one or more data inputs of a plurality of data inputs that are relevant to the issue based on the generated representation of the information about the issue, wherein the one or more identified data inputs are for use in resolving the issue (fig. 13, #1304, identify a specific set of network components and device components of a user device that supported an instance of network usage by the user device, #1306, pinpoint one or more network components that negatively impacted to the QoS experienced during an instance, #1308, provide data on the one or more components that negatively impacted the quality of service for presentation).
As to claim 19, Tapia discloses a system comprising one or both of:
a first entity comprising processing circuitry (fig. 2) configured to:
train a plurality of machine learning models on a plurality of data inputs comprising information about a network, wherein each machine learning model of the plurality of machine learning models is trained to generate a representation of at least one data input of the plurality of data inputs (fig. 3, [0086]-[0089], training a model with plurality of machine learning (ML) algorithms (each is read as a model) to generate results (representations of inputs) from training data inputs); and
generate a representation of each data input of the plurality of data inputs using at least one trained machine learning model of the plurality of trained machine learning models, wherein the plurality of trained machine learning models and the generated representation of each data input are for use in resolving an issue in the network ([0091], applying live inputs of a wireless network to the trained model to generate a root cause for an issue affecting subscribers of the wireless network); and
a second entity comprising processing circuitry configured to, in response to receiving information about the issue in the network ([0091], applying live inputs of a wireless network about an issue, fig. 13, #1302, 1304, receiving network performance data for user devices and network components), generate a representation of the information about the issue using at least one trained machine learning model of the plurality of trained machine learning models (fig. 3, [0086]-[0089], training a model with plurality of machine learning (ML) algorithms (each is read as a model) to generate results (representations of inputs) from training data inputs); and
identify one or more data inputs of the plurality of data inputs that are relevant to the issue based on the generated representation of the information about the issue, wherein the one or more identified data inputs are for use in resolving the issue (fig. 13, #1304, identify a specific set of network components and device components of a user device that supported an instance of network usage by the user device, #1306, pinpoint one or more network components that negatively impacted to the QoS experienced during an instance, #1308, provide data on the one or more components that negatively impacted the quality of service for presentation).
As to claim 2, Tapia discloses the plurality of data inputs comprise one or more data inputs in an unstructured format ([0039], unstructured data input); and the generated representation of each data input of the plurality of data inputs represents each data input of the plurality of data inputs in a structured format ([0066], structured data such as user KPIs, network KPIs, alerts, component health indicators…).
As to claim 3, Tapia discloses the generated representation of each data input captures one or more semantic relationships in the data input ([0063]).
As to claim 4, Tapia discloses analysing the plurality of data inputs to generate information about a plurality of nodes of the network, wherein the generated information about the plurality of nodes is for use in resolving the issue ([0061], nodes such as cells involving the issue are identified, fig. 13, specific set of network components and device components of a user device, analyze the components’ performance to pinpoint ones that negatively affect the QoS).
As to claim 5, Tapia discloses receiving feedback from a user following the use of the generated information about the plurality of nodes in resolving the issue; and adapting, based on the received feedback, a subsequent analysis of the plurality of data inputs to generate updated information about the plurality of nodes of the network ([0056], user feedback to update data points for retraining the ML algorithms, [0061], nodes such as cells involving the issue are identified, fig. 13, specific set of network components and device components of a user device, analyze the components’ performance to pinpoint ones that negatively affect the QoS).
As to claim 7, Tapia discloses receiving feedback from a user following the use of the generated representation of each data input in resolving the issue ([0056]); and
adapting, based on the received feedback, a weight assigned to the at least one trained machine learning model used to generate the representation of each data input, wherein the weight assigned to the at least one trained machine learning model defines a priority with which the at least one machine learning model is used relative to other machine learning models in a subsequent generation of the representation of each data input ([0056], changing weights such as removing anomalous data that caused the ML model to generate ineffective solutions from the training corpus to improve future solutions; [0092], user feedback to refine ML algorithm selection. The modifications to the algorithm selection rules may change a range of training error measurement values that correspond to a type machine of learning algorithm, cause specific ranges of training error measurement values to match to different types of machine learning algorithms, and/or so forth).
As to claim 8, Tapia discloses in response to an update to the plurality of data inputs, retraining the plurality of machine learning models on the updated plurality of data inputs ([0056]).
As to claim 10, Tapia discloses the received information about the issue comprises information in an unstructured format ([0039]); and the generated representation of the information about the issue represents the information in a structured format ([0066]).
As to claim 11, Tapia discloses the generated representation of the information about the issue captures one or more semantic relationships in the information about the issue ([0063]).
As to claim 12, Tapia discloses acquiring information about one or more nodes of a plurality of nodes of the network that are mentioned in the information about the issue, wherein the acquired information about the one or more nodes is for use in resolving the issue ([0061], nodes such as cells involving the issue are identified, fig. 13, specific set of network components and device components of a user device, analyze the components’ performance to pinpoint ones that negatively affect the QoS).
As to claim 14, Tapia discloses receiving feedback from a user following the use of the one or more identified data inputs in resolving the issue; and adapting, based on the received feedback, a weight assigned to the at least one trained machine learning model used to generate the representation of the information about the issue, wherein the weight assigned to the at least one trained machine learning model defines a priority with which the at least one machine learning model is used relative to other machine learning models in a subsequent generation of the representation of information about the issue ([0056], changing weights such as removing anomalous data that caused the ML model to generate ineffective solutions from the training corpus to improve future solutions; [0092], user feedback to refine ML algorithm selection. The modifications to the algorithm selection rules may change a range of training error measurement values that correspond to a type machine of learning algorithm, cause specific ranges of training error measurement values to match to different types of machine learning algorithms, and/or so forth).
As to claim 15, Tapia discloses identifying one or more data inputs of a plurality of data inputs that are relevant to the issue based on the generated representation of the information about the issue comprises: comparing the generated representation of the information about the issue to a representation of each data input of the plurality of data inputs generated using at least one trained machine learning model of the plurality of trained machine learning models; and identifying one or more data inputs of the plurality of data inputs that are relevant to the issue based on the comparison ([0056], changing weights such as removing anomalous data that caused the ML model to generate ineffective solutions from the training corpus to improve future solutions; [0092], user feedback to refine ML algorithm selection. The modifications to the algorithm selection rules may change a range of training error measurement values that correspond to a type machine of learning algorithm, cause specific ranges of training error measurement values to match to different types of machine learning algorithms, and/or so forth).
Claim Rejections - 35 USC § 103
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.
Claim(s) 6, 13 is/are rejected under AIA 35 U.S.C. 103 as being unpatentable over Tapia in view of Bird et al. (US 2018/0048661, “Bird”).
As to claim 6, Tapia does not disclose the information about the plurality of nodes of the network is generated in the form of a knowledge graph, wherein each node of the knowledge graph represents a node of the plurality of nodes of the network and each edge of the knowledge graph represents a relationship between two nodes of the plurality of nodes of the network.
Bird discloses the information about the plurality of nodes of the network is generated in the form of a knowledge graph, wherein each node of the knowledge graph represents a node of the plurality of nodes of the network and each edge of the knowledge graph represents a relationship between two nodes of the plurality of nodes of the network ([0047], [0049]).
It would have been obvious to one skilled in the art before the effective filing date of the claimed invention to apply knowledge graph of Bird to Tapia’s system in order to distill large data down to a short list of priority offenses using contextual data and knowledge graph (Bird, [0002]).
As to claim 13, the claim recites substantially the same subject matter in claim 6 and is thus rejected for the same rationale.
Allowable Subject Matter and Reasons for Allowance
Claims 16-18 would be allowable if rewritten to include all of the limitations of the base claim and any intervening claims.
The following is an examiner's statement of reasons for allowance:
By interpreting the claims in light of the Specification, the Examiner finds the claimed invention to be patentably distinct from the prior art of records. Specifically, the prior art of records, individually or in combination, fail to explicitly teach, suggest or render obvious the claimed invention as recited, including “identifying one or more data inputs of the plurality of data inputs that are relevant to the issue based on the comparison comprises: determining, based on the comparison, at least one similarity metric between the generated representation of the information about the issue and the generated representation of each data input; and identifying one or more data inputs of the plurality of data inputs that are relevant to the issue based on the at least one determined similarity metric”.
Any comments considered necessary by applicant must be submitted no later than the payment of the issue fee and, to avoid processing delays, should preferably accompany the issue fee. Such submissions should be clearly labeled "Comments on Statement of Reasons for Allowance."
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure is included in form PTO 892.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to HIEU T HOANG whose telephone number is (571) 270-1253. The examiner can normally be reached Mon-Fri 9 AM -5 PM.
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/HIEU T HOANG/Primary Examiner, Art Unit 2449