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 on 6/05/26 in which claims 1-4, 6-17, 19-31 are pending.
Response to Arguments
2. Applicant's arguments filed 6/05/26 have been fully considered but they are not persuasive. Applicant’s representative argues that Venkata fails to teach “using one or more aggregation techniques” However, Venkata clearly teaches collecting performance data from each network interface and metrics related to interfaces or devices to which the potentially bad cable is/was connected. (See paragraph [0013], performance data collected from each of the network interfaces, See paragraph [0014], collect metrics relating to interfaces or devices to which the potentially bad cable is/was connected).
Claim Rejections - 35 USC § 102
3. 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.
4. Claims 1, 5-6, 11, 15, 22-23, 26, 28 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by U.S. Publication No. 2025/0217245 to Venkata et al.
a. As per claim 1, Venkata et al teaches a computer-implemented method for detecting anomalies occurring in a copper network (See paragraph [0008 and 0010], detect potentially faulty or bad cables within a computer network), the method comprising: obtaining, by one or more processors (See paragraph [0091 and 0101]), historical data associated with a copper network, the historical data including data indicative of a multiplicity of historical copper network equipments (See paragraph [0008 and 0016], the techniques enable automatic detection of potential bad cable issues of wired network devices based on performance data collected from network interfaces of wired network devices), a multiplicity of historical operating behaviors of the copper network which have occurred (See paragraph [0016, 0038, 0080], pre-collected performance data may also be indicative of other network error condition), and a multiplicity of historical events corresponding to the copper network which have occurred ((See paragraph [0016, 0038-0039, 0080 and 0115], the features of the pre-collected performance data may be indicative of other network error conditions besides bad cable issues); extracting, by the one or more processors and from the historical data, a plurality of copper network equipment features, each copper network equipment feature indicative of a respective behavior of a respective copper network equipment, and at least one copper network equipment feature including at least one lagged feature (See paragraph [0012, 0038, 0077-0078 and 0080], NMS 130 may include an underlying analytics and network error identification engine and alerting system in accordance with various examples described herein. The underlying analytics engine may apply historical data and models to the inbound event streams to compute assertions, such as identified anomalies or predicted occurrences of events constituting network error conditions), the at least one lagged feature including at least one feature that occurs over time (See paragraph [0079, 0093 and 0158, 0166], the diagnostics include time domain reflector (TDR)-based diagnostics) ; transforming, by the one or more processors, and by using one or more aggregation techniques, (See paragraph [0013], performance data collected from each of the network interfaces, See paragraph [0014], collect metrics relating to interfaces or devices to which the potentially bad cable is/was connected) at least one extracted copper network equipment feature, thereby generating at least one additional copper network equipment feature (See paragraph [0079-0080 and 0141], bad cable detection engine 135 may be configured to process certain of the collected performance data 139 from a network device, such as AP 142A-1, with a selected machine learning model as input, and determine, as output from the selected machine learning model, whether features of the performance data 139 indicate a potential bad cable issue); training, by the one or more processors, a machine learning (ML) model on (i) the extracted plurality of copper network equipment features including the at least one lagged feature, and (ii) the at least one additional copper network equipment feature to discover one or more historical behaviors of the copper network, the one or more historical behaviors being indicative of the copper network operating within a target operating range (See paragraph [0080, 0114], any of the plurality of machine learning models, including the selected machine learning model, may include a supervised machine learning model that is trained using training data including pre-collected, labeled performance data received from network devices. The training data may include sets of performance data each labeled as being indicative of a bad cable issue or indicative of no bad cable issue. In this example, the machine learning model is trained using labeled sets of performance data because the features of the pre-collected performance data may also be indicative of other network error conditions); detecting, by the one or more processors based on current data of the copper network and by utilizing the trained ML model, one or more anomalies occurring in the copper network (See paragraph [0036, 0038 and 0084]); and initiating, by the one or more processors and responsive to the detecting, a mitigating action for the detected one or more anomalies occurring in the copper network (See paragraph [0081]).
b. As per claim 15 Venkata et al teaches a system for detecting anomalies in a copper network (See paragraph [0008 and 0010], detect potentially faulty or bad cables within a computer network) , the system comprising: one or more processors (See paragraph [0101]);
and one or more memories storing computer-executable instructions that, when executed, cause the one or more processors (See paragraph [0101]) to: obtain historical data associated with a copper network, the historical data including data indicative of a multiplicity of historical copper network equipments (See paragraph [0008 and 0016], the techniques enable automatic detection of potential bad cable issues of wired network devices based on performance data collected from network interfaces of wired network devices), a multiplicity of historical operating behaviors of the copper network which have occurred (See paragraph [0016, 0038 and 0080], pre-collected performance data may also be indicative of other network error condition), and a multiplicity of historical events corresponding to the copper network which have occurred (See paragraph [0016, 0038-0039, 0080 and 0115], the features of the pre-collected performance data may be indicative of other network error conditions besides bad cable issues); extract, from the historical data, a plurality of copper network equipment features, each copper network equipment feature indicative of a respective behavior of a respective copper network equipment, and at least one copper network equipment feature including at least one lagged feature (See paragraph [0012, 0038, 0077-0078 and 0080], NMS 130 may include an underlying analytics and network error identification engine and alerting system in accordance with various examples described herein. The underlying analytics engine may apply historical data and models to the inbound event streams to compute assertions, such as identified anomalies or predicted occurrences of events constituting network error conditions), the at least one lagged feature including at least one feature that occurs over time (See paragraph [0079, 0093 and 0158, 0166], the diagnostics include time domain reflector (TDR)-based diagnostics); transform, by using one or more aggregation techniques (See paragraph [0013], performance data collected from each of the network interfaces, See paragraph [0014], collect metrics relating to interfaces or devices to which the potentially bad cable is/was connected), at least one extracted copper network equipment feature to generate at least one additional copper network equipment feature bad cable detection engine 135 may be configured to process certain of the collected performance data 139 from a network device, such as AP 142A-1, with a selected machine learning model as input, and determine, as output from the selected machine learning model, whether features of the performance data 139 indicate a potential bad cable issue);
train a machine learning (ML) model on (i) the extracted plurality of copper network equipment features including the at least one lagged feature, and (ii) the at least one additional copper network equipment feature to discover one or more historical features of the copper network that are indicative of the copper network operating within a target operating range (See paragraph [0080, 0114], any of the plurality of machine learning models, including the selected machine learning model, may include a supervised machine learning model that is trained using training data including pre-collected, labeled performance data received from network devices. The training data may include sets of performance data each labeled as being indicative of a bad cable issue or indicative of no bad cable issue. In this example, the machine learning model is trained using labeled sets of performance data because the features of the pre-collected performance data may also be indicative of other network error conditions); detect, based on current data of the copper network and by using the trained ML model, one or more anomalies occurring in the copper network (See paragraph [0036, 0038 and 0084]); and initiate a mitigating action for the detected one or more anomalies occurring in the copper network (See paragraph [0081]).
c. As per claim 5, Venkata et al teaches the claimed invention as described above. Furthermore, Venkata et al teaches wherein at least one of the detected one or more anomalies corresponds to a service operation that has been performed with respect to one or more copper network equipments included in the copper network (See paragraph [0010], to detect potentially faulty or bad cables within a computer network).
d. AS per claim 6, Venkata et al teaches the claimed invention as described above. Furthermore, Venkata et al teaches wherein the training of the ML model includes: validating, by the one or more processors, the ML model; and tuning, by the one or more processors, one or more hyperparameters corresponding to validating of the ML model (See paragraph [0138, 0155-0157]).
e. As per claims 11 and 23, Venkata et al teaches the claimed invention as described above. Furthermore, Venkata et al teaches further comprising augmenting, by the one or more processors (See paragraph [0101]), the historical data with the current data (See paragraph [0014]), and wherein: the extracting of the plurality of copper network equipment features from the historical data includes extracting the plurality of copper network equipment features from the augmented historical data (See paragraph [[0038 and 0080]); and the training of the machine learning model based on the historical data includes training the machine learning model based on the augmented historical data and the plurality of copper network features extracted from the augmented historical data (See paragraph [0038]).
f. As per claim 22, Venkata et al teaches the claimed invention as described above. Furthermore, Venkata et al teaches wherein at least one of the detected one or more anomalies corresponds to a service operation that has been performed with respect to one or more copper network equipments included in the copper network (See paragraph [0008 and 0038]).
g. As per claim 26, Venkata et al teaches the claimed invention as described above. Furthermore, Venkata et al teaches wherein training of the ML model includes an optimization of the ML model, and the optimization of the ML model includes a validation of the ML model and a tuning, based on the validation, of one or more hyperparameters associated with the training (See paragraph [0138]).
h. As per claim 28, Venkata et al teaches the claimed invention as described above. Furthermore, Venkata et al teaches wherein the training of the ML model utilizes an isolation forest algorithm (See paragraph [0115 and 0138], and the one or more hyperparameters include at least one of (i) a number of trees, (ii) a tree depth, or (iii) a sample size (See paragraph [0155]).
Claim Rejections - 35 USC § 103
5. 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.
6. Claims 2-4, 9-10, 12-13, 16-21, 24-25 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Publication No. 2025/0217245 to Venkata et al. in view of U.S. Publication No. 2020/0112489 to Scherger et al
a. As per claims 2 and 16, Ventaka et al teaches the claimed invention as described above. However, Ventaka et fails to teach wherein: the copper network includes a plurality of copper network equipments, the plurality of copper network equipments includes a plurality of customer premises equipments (CPEs) and a plurality of digital subscriber line access multiplexers (DSLAMs), and at least one of the detected one or more anomalies includes a particular copper network equipment of the plurality of copper network equipments.
Scherger et al teaches wherein: the copper network includes a plurality of copper network equipments, the plurality of copper network equipments includes a plurality of customer premises equipments (CPEs) and a plurality of digital subscriber line access multiplexers (DSLAMs), and at least one of the detected one or more anomalies includes a particular copper network equipment of the plurality of copper network equipments (See paragraph [0042]).
It would have been obvious one with ordinary skill in the art to incorporate the teaching of Scherger et al in the claimed invention of Ventaka in order to provide a network equipment failure prediction among a vast selection of devices.
b. As per claim 3, Ventaka et al teaches the claimed invention as described above. However, Ventaka et fails to teach wherein the particular copper network equipment is a particular CPE or a particular DSLAM.
Scherger et al teaches wherein the particular copper network equipment is a particular CPE or a particular DSLAM (See paragraph [0042-0043]).
It would have been obvious one with ordinary skill in the art to incorporate the teaching of Scherger et al in the claimed invention of Ventaka in order to provide a network equipment failure prediction among a vast selection of devices.
c. As per claim 4, Ventaka et al teaches the claimed invention as described above. Furthermore, Ventaka et teaches wherein the initiating of the mitigating action includes at least one of: transmitting an instruction to the particular copper network equipment to change one or more settings of the particular copper network equipment; causing a diagnostic or test corresponding to the particular copper network equipment to be executed; or when the particular copper network equipment is a particular DSLAM, causing a communication signal to be re-routed through a DSLAM other than the particular DSLAM (See paragraph [0081], The indication may include identification information of the particular port of the particular network interface of the particular network device (e.g., switch 146A), e.g., to administrator 150 via UI device 152 of an entity that owns or has access to the particular network device. In some examples, NMS 130 may determine a recommended action based on the detected potential bad cable issue. The indication of the potential bad cable issue may include the recommended action, such as to test and/or replace the cable (e.g., cable 144A-1 connected to the particular port of switch 146A)
d. As per claim 9, Ventaka et al teaches the claimed invention as described above. However, Ventaka et al fails to explicitly teach wherein the detecting of the one or more anomalies occurring in the copper network includes detecting one or more features within the current data that are greater than a distance away from features corresponding to the discovered one or more historical behaviors.
Scherger et al teaches wherein the detecting of the one or more anomalies occurring in the copper network includes detecting one or more features within the current data that are greater than a distance away from features corresponding to the discovered one or more historical behaviors (See paragraph [0024, 0028 and 0044]).
It would have been obvious one with ordinary skill in the art to incorporate the teaching of Scherger et al in the claimed invention of Ventaka in order to provide a network equipment failure prediction among a vast selection of devices.
e. As per claim 10, Ventaka et al teaches the claimed invention as described above. However, Ventaka et al fails to explicitly teach further comprising tuning a sensitivity of anomaly detection by adjusting the distance.
Scherger et al teaches tuning a sensitivity of anomaly detection by adjusting the distance See paragraph [0024, 0028 and 0044]).
It would have been obvious one with ordinary skill in the art to incorporate the teaching of Scherger et al in the claimed invention of Ventaka in order to provide a network equipment failure prediction among a vast selection of devices.
f. As per claim 12, Ventaka et al teaches the claimed invention as described above. However, Ventaka et al fails to explicitly teach wherein: the extracted plurality of copper network equipment features include one or more of: link retrains, errored seconds, noise margins, or attenuation; and the additional copper network equipment features include one or more of: a mean, a median, a minimum, a maximum, a standard deviation, an aggregated feature, or a leave-one- out average of the at least one of the link retrains, the errored seconds, the noise margins, or the attenuation.
Scherger et al teaches wherein: the extracted plurality of copper network equipment features include one or more of: link retrains, errored seconds, noise margins, or attenuation (See paragraph [0034]); and the additional copper network equipment features include one or more of: a mean, a median, a minimum, a maximum, a standard deviation, an aggregated feature, or a leave-one- out average of the at least one of the link retrains, the errored seconds, the noise margins, or the attenuation (See paragraph [0013]).
It would have been obvious one with ordinary skill in the art to incorporate the teaching of Scherger et al in the claimed invention of Ventaka in order to provide a network equipment failure prediction among a vast selection of devices.
g. As per claim 13, Ventaka et al teaches the claimed invention as described above. However, Ventaka et al fails to explicitly teach further comprising at least one of: ranking respective likelihoods of the detected one or more anomalies being actual anomalies, and wherein the initiating of the mitigating action is based on the ranking; or transmitting, by the one or more processors, an indication of the detected one or more anomalies to at least one of: a user interface, an application, or a computing device.
Scherger et al teaches ranking respective likelihoods of the detected one or more anomalies being actual anomalies, and wherein the initiating of the mitigating action is based on the ranking (See paragraph [0026 and 0032]); or transmitting, by the one or more processors, an indication of the detected one or more anomalies to at least one of: a user interface, an application, or a computing device (See paragraph [0024], to perform an action (e.g. alarm)).
It would have been obvious one with ordinary skill in the art to incorporate the teaching of Scherger et al in the claimed invention of Ventaka in order to provide a network equipment failure prediction among a vast selection of devices.
h. As per claim 17, Ventaka et al teaches the claimed invention as described above. Furthermore, Ventaka et al teaches wherein at least one of: the particular copper network equipment is a particular CPE, and the mitigating action is for the particular CPE (See paragraph [0010 and 0081]); or the particular copper network equipment is a particular DSLAM, and the mitigating action is for the particular DSLAM.
j. As per claim 19, Ventaka et al teaches the claimed invention as described above. However, Ventaka et fails to teach wherein the particular copper network equipment is a particular DSLAM, and the mitigating action includes a re-routing of a communication signal through a DSLAM other than the particular DSLAM.
Scherer et al teaches wherein the particular copper network equipment is a particular DSLAM, and the mitigating action includes a re-routing of a communication signal through a DSLAM other than the particular DSLAM (See paragraph [0042 and 0045]).
It would have been obvious one with ordinary skill in the art to incorporate the teaching of Scherger et al in the claimed invention of Ventaka in order to provide a network equipment failure prediction among a vast selection of devices.
k. As per claim 20, Ventaka et al teaches the claimed invention as described above. Furthermore, Ventaka et al teaches wherein the mitigating action is a change to one or more settings of the particular copper network equipment (See paragraph [0095 and 0107]) .
l. As per claim 21, Ventaka et al teaches the claimed invention as described above. Furthermore, Ventaka et al teaches wherein the mitigating action is a diagnostic of the particular copper network equipment (See paragraph [0015-0016]).
m. As per claim 24, Ventaka et al teaches the claimed invention as described above. However, Ventaka et fails to teach wherein the extracted plurality of copper network equipment features include one or more of: link retrains, errored seconds, noise margins, or attenuation.
Scherger et al teaches wherein the extracted plurality of copper network equipment features include one or more of: link retrains, errored seconds, noise margins, or attenuation (See paragraph [0034], the learning management system 105 may be configured to update and/or adjust priorities assigned to each of the identified network elements based on a current state of the repairs and/or replacements for the respective network elements).
It would have been obvious one with ordinary skill in the art to incorporate the teaching of Scherger et al in the claimed invention of Ventaka in order to provide a network equipment failure prediction among a vast selection of devices.
n. As per claim 25, Ventaka et al teaches the claimed invention as described above. Furthermore, Ventaka et fails to teach wherein the additional copper network equipment features include one or more of: a mean, a median, a minimum, a maximum, a standard deviation, an aggregated feature, or a leave-one-out average (See paragraph [0013]).
o. As per claims 30 and 31, Ventaka et al teaches the claimed invention as described above. Furthermore, Ventaka et al teaches wherein the one or more aggregation techniques include determining an aggregate feature of a copper network equipment based on extracted features of one or more sub-components of the copper network equipment or one or more other copper network equipments connected to the copper network equipment (See paragraph [0013], performance data collected from each of the network interfaces, See paragraph [0014], collect metrics relating to interfaces or devices to which the potentially bad cable is/was connected).
Allowable Subject Matter
7. Claims 7-8, 14, 27 and 29 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Conclusion
8. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
U.S. Publication No. 2020/0019935 to Jan et al teaches cognitive Prioritization Model for Hardware Device Prediction Maintenance Delivery.
9. 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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