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 .
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
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Claims 1-7, 9-20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-7, 9, 9-15, 15, 17-19, respectively, of U.S. Patent No. 12,184,473 (‘473). Although the claims at issue are not identical, they are not patentably distinct from each other because claims 1-7, 9-20 of the present application are essentially a broader version of that of claims1-7, 9, 9-15, 15, 17-19, respectively, in ‘473, as follows.
Present application
‘473
Claim 1:
A method for detecting and analyzing cable plant impairments, comprising:
removing a phase rotation from proactive network maintenance (PNM) management information base (MIB) data at a cable modem termination system (CMTS);
performing an inverse Fourier transform on the PNM MIB data to generate a transform result;
determining an impulse response value and a group delay value based on the generated transform result; and sending the determined impulse response value and the determined group delay value to a component in a service provider network.
Claim 1:
A method for detecting and analyzing cable plant impairments, comprising:
collecting, by a component with a data collector in a field deployed device, proactive network maintenance (PNM) management information base (MIB) data; parsing the collected PNM MIB data for all active subcarriers of an orthogonal frequency division multiple access (OFDMA) channel;
removing a phase rotation from the parsed PNM MIB data at a cable modem termination system (CMTS);
performing an inverse Fourier transform on the parsed PNM MIB data to generate a transform result; determining an impulse response value and a group delay value based on the generated transform result; and sending the determined impulse response value and the determined group delay value to a machine learning model in a streaming and analytics platform in a service provider network.
Note: “parsed PNM MIB data” is derived from PNM MIB and “a streaming and analytics platform” is a “component” in a service provider network.
Claim 2:
The method of claim 1, wherein:
determining the impulse response value and the group delay value based on the generated transform result comprises determining impulse response values and group delay values for each of a plurality of orthogonal frequency division multiple access (OFDMA) channels; and
the method further comprises determining whether an impairment is located inside a home network of a field deployed device based on the impulse response values and the group delay values.
Claim 2:
The method of claim 1, wherein:
collecting the PNM MIB data comprises collecting pre-equalizer coefficient values for all the active subcarriers in each of a plurality of OFDMA channels, and
determining the impulse response value and the group delay value based on the generated transform result comprises determining impulse response values and group delay values for each of the plurality of OFDMA channels; and
the method further comprises determining whether an impairment is located inside a home network of field deployed device based on the impulse response values and the group delay values.
Claim 3:
The method of claim 1, further comprising determining, based on the determined impulse response value and the determined group delay value, whether an impairment is located inside a home network of a field deployed device.
Claim 3:
The method of claim 1, further comprising determining, based on the determined impulse response value and the determined group delay value, whether an impairment is located inside a home network of the field deployed device.
Claim 4:
The method of claim 3, further comprising using a machine learning model to determine an origin or a characteristic of the impairment in response to determining, based on the determined impulse response value and the determined group delay value, that the impairment is located inside the home network of the field deployed device.
Claim 4:
The method of claim 3, further comprising using the machine learning model to determine an origin or a characteristic of the impairment in response to determining, based on the determined impulse response value and the determined group delay value, that the impairment is located inside the home network of the field deployed device.
Claim 5:
The method of claim 3, further comprising using a machine learning model to determine a distance between the impairment and a location of the field deployed device based on the determined impulse response value and the determined group delay value in response to determining that the impairment is not located inside of the home network of the field deployed device.
Claim 5:
The method of claim 3, further comprising using the machine learning model to determine a distance between the impairment and a location of field deployed device based on the determined impulse response value and the determined group delay value in response to determining that the impairment is not located inside of the home network of the field deployed device.
Claim 6:
The method of claim 5, wherein using the machine learning model to determine the distance between the impairment and the location of the field deployed device based on the determined impulse response value and the determined group delay value further comprises:
determining whether the impairment is due to corroded radio frequency (RF) splitters; determining whether the impairment is due to corroded coaxial connectors; determining whether the impairment is due to damaged coaxial cables; determining whether the impairment is due to damaged RF amplifiers; or determining whether the impairment is due to damaged coaxial taps.
Claim 6:
The method of claim 5, wherein using the machine learning model to determine the distance between the impairment and the location of field deployed device based on the determined impulse response value and the determined group delay value
in response to determining that the impairment is not located inside of the home network of the field deployed device further comprises:
determining whether the impairment is due to corroded radio frequency (RF) splitters; determining whether the impairment is due to corroded coaxial connectors; determining whether the impairment is due to damaged coaxial cables; determining whether the impairment is due to damaged RF amplifiers; or determining whether the impairment is due to damaged coaxial taps.
Claim 7:
A field deployed device, comprising:
a processor configured to:
remove a phase rotation from proactive network maintenance (PNM) management information base (MIB) data at a cable modem termination system (CMTS); perform an inverse Fourier transform on the PNM MIB data to generate a transform result; determine an impulse response value and a group delay value based on the generated transform result; and send the determined impulse response value and the determined group delay value to a component in a service provider network.
Claim 7:
A field deployed device, comprising:
a processor configured to:
collect proactive network maintenance (PNM) management information base (MIB) data; parse the collected PNM MIB data for all active subcarriers of an orthogonal frequency division multiple access (OFDMA) channel;
remove a phase rotation from the parsed PNM MIB data at a cable modem termination system (CMTS); perform an inverse Fourier transform on the parsed PNM MIB data to generate a transform result; determine an impulse response value and a group delay value based on the generated transform result; and send the determined impulse response value and the determined group delay value to a machine learning model in a streaming and analytics platform in a service provider network.
Note: “parsed PNM MIB data” is derived from PNM MIB and “a streaming and analytics platform” is a “component” in a service provider network.
Claim 9:
A computing system, comprising:
a processor configured to: receive collected and parsed proactive network maintenance (PNM) management information base (MIB) data from a field deployed device; receive impulse response values and group delay values for all active subcarriers in each of a plurality of OFDMA channels from a field deployed device; and determine whether an impairment is located inside a home network of the field deployed device based on the received impulse response values and the received group delay values.
Claim 9:
A computing system, comprising:
a processor configured to: receive collected and parsed proactive network maintenance (PNM) management information base (MIB) data from a field deployed device; receive impulse response values and group delay values for all active subcarriers in each of a plurality of OFDMA channels from the field deployed device; and use a machine learning model to determine whether an impairment is located inside a home network of the field deployed device based on the received impulse response values and the received group delay values.
Claim 10:
The computing system of claim 9, wherein the processor is further configured to use a machine learning model to determine whether the impairment is located inside the home network of the field deployed device based on the received impulse response values and the received group delay values.
Claim 9:
… and use a machine learning model to determine whether an impairment is located inside a home network of the field deployed device based on the received impulse response values and the received group delay values.
Claim 11:
The computing system of claim 9, wherein the processor is further configured to train a machine learning model to identify certain impairments in the network based on historical data received from a plurality of field deployed devices that share one or more characteristics with the field deployed device in response to determining that the impairment is not located inside the home network of the field deployed device.
Claim 10:
The computing system of claim 9, wherein the processor is further configured to train a machine learning model to identify certain impairments in the network based on historical data received from a plurality of field deployed devices that share one or more characteristics with the field deployed device in response to determining that the impairment is not located inside the home network of the field deployed device.
Claim 12:
The computing system of claim 9, wherein the processor is further configured to use a machine learning model to determine an origin or a characteristic of the impairment in response to determining that the impairment is located inside the home network of the field deployed device.
Claim 11:
The computing system of claim 9, wherein the processor is further configured to use the machine learning model to determine an origin or a characteristic of the impairment in response to determining that the impairment is located inside the home network of the field deployed device.
Claim 13:
The computing system of claim 9, wherein the processor is further configured to use a machine learning model to determine a distance between the impairment and a location of the field deployed device in response to determining that the impairment is not located inside of the home network of the field deployed device.
Claim 12:
The computing system of claim 9, wherein the processor is further configured to use the machine learning model to determine a distance between the impairment and a location of field deployed device in response to determining that the impairment is not located inside of the home network of the field deployed device.
Claim 14:
The computing system of claim 9, wherein the processor is further configured to: determine whether the impairment is due to corroded radio frequency (RF) splitters; determine whether the impairment is due to corroded coaxial connectors; determine whether the impairment is due to damaged coaxial cables; determine whether the impairment is due to damaged RF amplifiers; or determine whether the impairment is due to damaged coaxial taps.
Claim 13:
The computing system of claim 9, wherein the processor is further configured to: determine whether the impairment is due to corroded radio frequency (RF) splitters; determine whether the impairment is due to corroded coaxial connectors; determine whether the impairment is due to damaged coaxial cables; determine whether the impairment is due to damaged RF amplifiers; or determine whether the impairment is due to damaged coaxial taps.
Claim 15:
A system, comprising:
a field deployed device comprising a field deployed device processor configured to:
remove phase rotation from proactive network maintenance (PNM) management information base (MIB) data at a cable modem termination system (CMTS); perform an inverse Fourier transform on the PNM MIB data to generate a transform result; determine an impulse response value and a group delay value based on the generated transform result; and send the determined impulse response value and the determined group delay value to a component in a service provider network.
Claim 14:
A system for detecting and analyzing cable plant impairments, comprising:
a field deployed device comprising a field deployed device processor; and a computing system comprising a streaming and analytics processor, wherein the field deployed device processor is configured to: collect proactive network maintenance (PNM) management information base (MIB) data; parse the collected PNM MIB data for all active subcarriers of an orthogonal frequency division multiple access (OFDMA) channel; remove a phase rotation from the parsed PNM MIB data at a cable modem termination system (CMTS); perform an inverse Fourier transform on the parsed PNM MIB data to generate a transform result; determine an impulse response value and a group delay value based on the generated transform result; and send the determined impulse response value and the determined group delay value to a machine learning model of the streaming and analytics platform device.
Note: “parsed PNM MIB data” is derived from PNM MIB and “a streaming and analytics platform” is a “component” in a service provider network.
Claim 16:
The system of claim 15, further comprising a computing system comprising
a streaming and analytics processor configured to determine whether an impairment is located inside a home network of a field deployed device based on the determined impulse response value and the determined group delay value.
Claim 15:
The system of claim 14, wherein:
the field deployed device processor is configured to: parse the collected PNM MIB data for all the active subcarriers of the OFDMA channel by parsing the collected PNM MIB data for all the active subcarriers of a plurality of OFDMA channels; and determine the impulse response value and the group delay value based on the generated transform result by determining impulse response values and group delay values for each of the plurality of OFDMA channels; and the streaming and analytics processor is configured to determine whether an impairment is located inside a home network of field deployed device based on the impulse response values and the group delay values.
Claim 17:
The system of claim 16, wherein: the field deployed device processor is configured to:
parse the PNM MIB data for all active subcarriers of a plurality of orthogonal frequency division multiple access (OFDMA) channels; and
determine the impulse response value and the group delay value based on the generated transform result by determining impulse response values and group delay values for each of the plurality of OFDMA channels; and
the streaming and analytics processor is configured to determine whether the impairment is located inside the home network of the field deployed device based on the impulse response values and the group delay values.
Claim 15:
The system of claim 14, wherein:
the field deployed device processor is configured to:
parse the collected PNM MIB data for all the active subcarriers of the OFDMA channel by parsing the collected PNM MIB data for all the active subcarriers of a plurality of OFDMA channels; and
determine the impulse response value and the group delay value based on the generated transform result by determining impulse response values and group delay values for each of the plurality of OFDMA channels; and
the streaming and analytics processor is configured to determine whether an impairment is located inside a home network of field deployed device based on the impulse response values and the group delay values.
Claim 18:
The system of claim 16, wherein the streaming and analytics processor is configured to use a machine learning model to determine an origin or a characteristic of the impairment in response to determining, based on the determined impulse response value and the determined group delay value, that the impairment is located inside the home network of the field deployed device.
Claim 17:
The system of claim 16, wherein the streaming and analytics processor is further configured to use the machine learning model to determine an origin or a characteristic of the impairment in response to determining, based on the determined impulse response value and the determined group delay value, that the impairment is located inside the home network of the field deployed device.
Claim 19:
The system of claim 16, wherein the streaming and analytics processor is configured to: use a machine learning model to determine a distance between the impairment and a location of the field deployed device based on the determined impulse response value and the determined group delay value in response to determining that the impairment is not located inside of the home network of the field deployed device.
Claim 18:
The system of claim 16, wherein the streaming and analytics processor is further configured to use the machine learning model to determine a distance between the impairment and a location of field deployed device based on the determined impulse response value and the determined group delay value in response to determining that the impairment is not located inside of the home network of the field deployed device.
Claim 20:
The system of claim 16, wherein the streaming and analytics processor is further configured to: determine whether the impairment is due to corroded radio frequency (RF) splitters; determine whether the impairment is due to corroded coaxial connectors; determine whether the impairment is due to damaged coaxial cables; determine whether the impairment is due to damaged RF amplifiers; or determine whether the impairment is due to damaged coaxial taps.
Claim 19:
The system of claim 18, wherein the streaming and analytics processor is further configured to: determine whether the impairment is due to corroded radio frequency (RF) splitters; determine whether the impairment is due to corroded coaxial connectors; determine whether the impairment is due to damaged coaxial cables; determine whether the impairment is due to damaged RF amplifiers; or determine whether the impairment is due to damaged coaxial taps.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Yates et al. – US 2022/0182254
Bush et al. – US 2022/0109612
Milley et al. – US 11,178,003
Jin – US 10,348,554
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/DAC V HA/Primary Examiner, Art Unit 2633