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 .
Response to Arguments
The terminal disclaimer filed November 20, 2025 is effective to obviate the double-patenting rejections. Accordingly, the rejections are withdrawn.
Applicant’s arguments with respect to the rejections under 35 USC 103 have been fully considered and are persuasive in light of the amendments. Accordingly, the rejections are withdrawn. However, upon further consideration, new grounds of rejection are made in view of Bliss (US Pat. No. 4,633,411).
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.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied 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.
Claims 1-9 are rejected under 35 U.S.C. 103 as being unpatentable over McFarland et al. (US 20120093240 A1) in view of Wolcott et al. (US 20140153624 A1) and Bliss (US Pat. No. 4,633,411).
As to claim 1, McFarland et al. teaches a system for noise profile generation comprising: a customer gateway communicatively coupled to one or more end devices over a communication medium; at least one noise information node, each of the at least one noise information node communicatively coupled to the customer gateway, wherein each of the at least one noise information node is programmed to extract an extracted noise information present on a communication path from the customer gateway to at least one of the one or more end devices (See ¶¶ [0012], [0021], Teaches that as shown in FIG. 1, a powerline communication (PLC) device 116 that uses the powerline network 102 for exchanging data, connecting to the Internet, etc. is connected to the powerline network 102 via the powerline socket 104. A powerline interference analyzer 112 connects to the powerline network 102 via the powerline socket 108. The powerline interference analyzer 112 comprises a power spectrum analyzer 126, an interference processing unit 122, a noise signature database 124, and a signal characteristics analyzer 128. The interference processing unit 122 is coupled with the power spectrum analyzer 126, the noise signature database 124, and the signal characteristics analyzer 128. In some implementations (as depicted in FIG. 1), the powerline interference analyzer 112 may be a standalone powerline device configured to analyze the noise characteristics of the powerline network 102 and to determine causes of noise on the powerline network 102. However, in other implementations, powerline interference analyzer 112 may be implemented as part of one or more PLC devices (e.g., the PLC device 116). At block 202, the powerline network noise characteristics are determined at a network analyzer of a powerline network) wherein the extracted noise information comprises at least one noise characteristic (See ¶ [0023]),;
a noise profile database storing one or more noise profiles, wherein each of the one or more noise profiles is respectively associated with at least one noise source (See ¶ [0017], Teaches that the noise signature database 124 can include predefined representations (in the time domain and/or the frequency domain) that uniquely represent each powerline or non-powerline device that can potentially be a noise source (e.g., to the PLC device 116) when connected to the powerline network 102.).
and a noise profile generator comprising: at least one processor; non-transitory computer readable media having encoded thereon computer software comprising a set of instructions executable by the at least one processor to: based on an identification, by the at least one noise information node, that the new end device has been connected to the communication path, identify, based on a corresponding first unique identifier associated with the at least one noise information node and at the noise profile database, existing noise information associated with the communication path (See ¶ [0023], Teaches that at block 204, one or more noise patterns that are representative of a noise source signature are determined based on analyzing the powerline network noise characteristics. For example, the power spectrum analyzer 126 can determine the one or more noise patterns based on analyzing the powerline network noise characteristics. Noise generated by various devices in the powerline network 102 (i.e., a noise signature of the devices) can have specific time-domain characteristics and frequency-domain characteristics that can enable identification of the powerline and non-powerline devices that generated the noise (i.e., the noise sources)).
However, it does not expressly teach wherein each of the at least one noise information node has a first unique identifier and is further programmed to identify a new end device connected to the communication path; associate a second unique identifier of the new end device with the extracted noise information, subtract the existing noise information from the extracted noise information resulting in an added noise information, and generate, based on the added noise information, an isolated noise profile associated with the new end device.
Wolcott et al., from analogous art, teaches wherein each of the at least one noise information node has a first unique identifier and is further programmed to identify a new end device connected to the communication path (See ¶¶ [0022], [0020], Teaches that Analyzer 103 may communicate with hub 102 and/or other network elements over one or more network interfaces (i/f) 203. Interface 203 could be, e.g., a Gigabit Ethernet card, 802.11 wireless interface, etc. According to some embodiments, Analyzer 103 may process the retrieved data to characterize devices 101-1 through 101-n, to identify devices that share communication paths or portions of paths, to diagnose and locate network problems such as noise/interference ingress, to identify unauthorized and/or unprovisioned devices, and/or perform other operations described herein. A Gigabit Ethernet card or a 802.11 wireless interface both have a unique identifier known as a MAC address);
associate a second unique identifier of the new end device with the extracted noise information (See ¶ [0056], Teaches that in 860, the attenuation factor AFn for the access devices may be stored in a database 190 as illustrated in FIG. 9D. For convenience, FIG. 9D shows data in a simple table. The table of FIG. 9D is merely one example of how data can be arranged in accordance with various embodiments. The actual format of data and/or the tables or other data structures used to organize that data will vary among different embodiments. For each access device, a row entry is included that contains an index 191 uniquely identifying the entry, an access device identifier 192, and an attenuation factor AFn.),
subtract the existing noise information from the extracted noise information resulting in an added noise information (See ¶¶ [0040], Teaches that in other variations, a summed value (e.g., vector sum, sum of squared values, RMS, abs RMS, etc.) may be determined for the set of tap values themselves at the first and second sample times, respectively, and then a difference (e.g., subtraction, vector division, etc.) of the summed tap values may be used to compare noise received at different access devices),
and generate, based on the added noise information, an isolated noise profile associated with the new end device (See ¶¶ [0050], [0051] Teaches that In some embodiments, analyzer 103 may perform a Fast Fourier Transform (FFT) (e.g., a 100 point FFT), although other types of transforms can also be used. The transform may generate a frequency domain representation of the frequency response of the noise received at the access device. One or more values indicating the frequency response (or inverse frequency response) may be stored. The stored frequency data for each access device is illustrated as <f> in column 156 of FIG. 9B. Analyzer 103 may repeat step 820 periodically as new data is collected based on the iteratively collected data in step 810. Analyzer 103 may store every iteration of data in 154-1 through 154-P, 155, and/or 156, or may store only the most recently collected (e.g., the most recent 2, 3, 4, etc. iterations)).
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teaching of Wolcott et al. into McFarland et al. in order to locate and correct the cause of dynamic distortions (See Wolcott et al. ¶ [0003]).
The combination of McFarland and Wolcott does not explicitly show “wherein the extracted noise information comprises at least one noise characteristic, wherein the at least one noise characteristic includes noise spectral density.”
Bliss shows wherein extracted noise information comprises at least one noise characteristic, and wherein the at least one noise characteristic includes noise spectral density (see col. 3, lines 32-53).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to further modify the system of McFarland with the teachings of Bliss in order to achieve a more accurate understanding of the overall performance of the transmission link (see Bliss, col. 1, lines 28-41).
As to claim 2, the combination of McFarland, Wolcott, and Bliss teaches the system according to claim 1 above. McFarland et al. further teaches wherein the communication medium is one of a power line at a customer premises, twisted pair cable, coaxial cable, or optical fiber (See ¶ [0011], Teaches that a powerline interference analyzer can be implemented in the powerline network to detect the presence of powerline and non-powerline devices (“noise sources”) that can cause noise/interference on the powerline network.).
As to claim 3, the combination of McFarland, Wolcott, and Bliss teaches the system according to claim 1 above. McFarland et al. further teaches wherein the communication medium is a wireless connection for at least one of Wi-Fi, Bluetooth, infrared, near-field, or Z-Wave communications (See ¶ [0053], Teaches that the electronic device 400 also includes a bus 410 (e.g., PCI, ISA, PCI-Express, HyperTransport , InfiniBand , NuBus, AHB, AXI, etc.), and network interfaces 404 that include at least one wired network interface (e.g., a powerline communication interface) or a wireless network interface (e.g., a WLAN interface, a Bluetooth® interface, a WiMAX interface, a ZigBee® interface, a Wireless USB interface, etc.)).
As to claim 4, the combination of McFarland, Wolcott, and Bliss teaches the system according to claim 1 above. McFarland et al. further teaches wherein the at least one noise characteristic further includes a frequency spectrum of the noise signal, transient markers, time of day of occurrence, or occurrence patterns (See ¶ [0023], Teaches that the power spectrum analyzer 126 can analyze a frequency variation of the powerline network noise characteristics to detect time-invariant and frequency-invariant noise patterns).
As to claim 5, McFarland et al. teaches a noise profile generator, comprising: at least one processor; non-transitory computer readable media having encoded thereon computer software comprising a set of instructions executable by the at least one processor to: based on an identification, by at least one noise information node, of a new end device that has been connected to a communication path, identify, based on a corresponding first unique identifier associated with the at least one noise information node and at a noise profile database, existing noise information associated with the communication path, wherein the extracted noise information comprises at least one noise characteristic, and wherein the at least one noise characteristic includes a power spectrum (See ¶ [0023], Teaches that at block 204, one or more noise patterns that are representative of a noise source signature are determined based on analyzing the powerline network noise characteristics. For example, the power spectrum analyzer 126 can determine the one or more noise patterns based on analyzing the powerline network noise characteristics. Noise generated by various devices in the powerline network 102 (i.e., a noise signature of the devices) can have specific time-domain characteristics and frequency-domain characteristics that can enable identification of the powerline and non-powerline devices that generated the noise (i.e., the noise sources)).
However, it does not expressly teach associate a second unique identifier of the new end device with the extracted noise information, subtract the existing noise information from extracted noise information resulting in an added noise information, and generate, based on the added noise information, an isolated noise profile associated with the new end device.
Wolcott et al., from analogous art, teaches associate a second unique identifier of the new end device with the extracted noise information (See ¶ [0056], Teaches that in 860, the attenuation factor AFn for the access devices may be stored in a database 190 as illustrated in FIG. 9D. For convenience, FIG. 9D shows data in a simple table. The table of FIG. 9D is merely one example of how data can be arranged in accordance with various embodiments. The actual format of data and/or the tables or other data structures used to organize that data will vary among different embodiments. For each access device, a row entry is included that contains an index 191 uniquely identifying the entry, an access device identifier 192, and an attenuation factor AFn.),
subtract the existing noise information from extracted noise information resulting in an added noise information (See ¶¶ [0040], Teaches that in other variations, a summed value (e.g., vector sum, sum of squared values, RMS, abs RMS, etc.) may be determined for the set of tap values themselves at the first and second sample times, respectively, and then a difference (e.g., subtraction, vector division, etc.) of the summed tap values may be used to compare noise received at different access devices),
and generate, based on the added noise information, an isolated noise profile associated with the new end device (See ¶¶ [0050], [0051] Teaches that In some embodiments, analyzer 103 may perform a Fast Fourier Transform (FFT) (e.g., a 100 point FFT), although other types of transforms can also be used. The transform may generate a frequency domain representation of the frequency response of the noise received at the access device. One or more values indicating the frequency response (or inverse frequency response) may be stored. The stored frequency data for each access device is illustrated as <f> in column 156 of FIG. 9B. Analyzer 103 may repeat step 820 periodically as new data is collected based on the iteratively collected data in step 810. Analyzer 103 may store every iteration of data in 154-1 through 154-P, 155, and/or 156, or may store only the most recently collected (e.g., the most recent 2, 3, 4, etc. iterations)).
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teaching of Wolcott et al. into McFarland et al. in order to locate and correct the cause of dynamic distortions (See Wolcott et al. ¶ [0003]).
The combination does not explicitly show wherein the at least one noise characteristic includes power spectral density.
Bliss shows wherein extracted noise information includes spectral density (see col. 3, lines 32-53).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to further modify the system of McFarland with the teachings of Bliss in order to achieve a more accurate understanding of the overall performance of the transmission link (see Bliss, col. 1, lines 28-41).
As to claim 6, the combination of McFarland, Wolcott, and Bliss teaches the noise profile generator according to claim 5 above. McFarland et al. further teaches wherein the communication medium is one of a power line at a customer premises, twisted pair cable, coaxial cable, or optical fiber (See ¶ [0011], Teaches that a powerline interference analyzer can be implemented in the powerline network to detect the presence of powerline and non-powerline devices (“noise sources”) that can cause noise/interference on the powerline network.).
As to claim 7, the combination of McFarland, Wolcott, and Bliss teaches the noise profile generator according to claim 5 above. McFarland et al. further teaches wherein the communication medium is a wireless connection for at least one of Wi-Fi, Bluetooth, infrared, near-field, or Z-Wave communications (See ¶ [0053], Teaches that the electronic device 400 also includes a bus 410 (e.g., PCI, ISA, PCI-Express, HyperTransport , InfiniBand , NuBus, AHB, AXI, etc.), and network interfaces 404 that include at least one wired network interface (e.g., a powerline communication interface) or a wireless network interface (e.g., a WLAN interface, a Bluetooth® interface, a WiMAX interface, a ZigBee® interface, a Wireless USB interface, etc.)).
As to claim 8, the combination of McFarland, Wolcott, and Bliss teaches the noise profile generator according to claim 5 above. McFarland et al. further teaches wherein the at least one noise characteristic further includes a frequency spectrum of the noise signal, transient markers, time of day of occurrence, or occurrence patterns (See ¶ [0023], Teaches that the power spectrum analyzer 126 can analyze a frequency variation of the powerline network noise characteristics to detect time-invariant and frequency-invariant noise patterns).
As to claim 9, McFarland et al. teaches a method of noise profile generation comprising: identifying, by at least one noise information node, a new end device that has been connected to a communication path; and based on an identification, by the at least one noise information node, of the new end device that has been connected to the communication path, identifying, based on a corresponding first unique identifier associated with the at least one noise information node and at a noise profile database, existing noise information associated with the communication path, wherein the extracted noise information comprises at least one noise characteristic (See ¶ [0023], Teaches that at block 204, one or more noise patterns that are representative of a noise source signature are determined based on analyzing the powerline network noise characteristics. For example, the power spectrum analyzer 126 can determine the one or more noise patterns based on analyzing the powerline network noise characteristics. Noise generated by various devices in the powerline network 102 (i.e., a noise signature of the devices) can have specific time-domain characteristics and frequency-domain characteristics that can enable identification of the powerline and non-powerline devices that generated the noise (i.e., the noise sources)).
However, it does not expressly teach associating a second unique identifier of the new end device with the extracted noise information, subtracting the existing noise information from extracted noise information resulting in an added noise information, and generating, based on the added noise information, an isolated noise profile associated with the new end device.
Wolcott et al., from analogous art, teaches associating a second unique identifier of the new end device with the extracted noise information (See ¶ [0056], Teaches that in 860, the attenuation factor AFn for the access devices may be stored in a database 190 as illustrated in FIG. 9D. For convenience, FIG. 9D shows data in a simple table. The table of FIG. 9D is merely one example of how data can be arranged in accordance with various embodiments. The actual format of data and/or the tables or other data structures used to organize that data will vary among different embodiments. For each access device, a row entry is included that contains an index 191 uniquely identifying the entry, an access device identifier 192, and an attenuation factor AFn.),
subtracting the existing noise information from extracted noise information resulting in an added noise information (See ¶¶ [0040], Teaches that in other variations, a summed value (e.g., vector sum, sum of squared values, RMS, abs RMS, etc.) may be determined for the set of tap values themselves at the first and second sample times, respectively, and then a difference (e.g., subtraction, vector division, etc.) of the summed tap values may be used to compare noise received at different access devices),
and generating, based on the added noise information, an isolated noise profile associated with the new end device (See ¶¶ [0050], [0051] Teaches that In some embodiments, analyzer 103 may perform a Fast Fourier Transform (FFT) (e.g., a 100 point FFT), although other types of transforms can also be used. The transform may generate a frequency domain representation of the frequency response of the noise received at the access device. One or more values indicating the frequency response (or inverse frequency response) may be stored. The stored frequency data for each access device is illustrated as <f> in column 156 of FIG. 9B. Analyzer 103 may repeat step 820 periodically as new data is collected based on the iteratively collected data in step 810. Analyzer 103 may store every iteration of data in 154-1 through 154-P, 155, and/or 156, or may store only the most recently collected (e.g., the most recent 2, 3, 4, etc. iterations)).
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teaching of Wolcott et al. into McFarland et al. in order to locate and correct the cause of dynamic distortions (See Wolcott et al. ¶ [0003]).
The combination of McFarland and Wolcott does not explicitly show wherein the at least one noise characteristic includes noise spectral density.
Bliss shows wherein extracted noise information comprises at least one noise characteristic, and wherein the at least one noise characteristic includes noise spectral density (see col. 3, lines 32-53).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to further modify the system of McFarland with the teachings of Bliss in order to achieve a more accurate understanding of the overall performance of the transmission link (see Bliss, col. 1, lines 28-41).
Claim 10 is rejected under 35 U.S.C. 103 as being unpatentable over McFarland et al. (US 20120093240 A1), Wolcott et al. (US 20140153624 A1), and Bliss (US Patent No. 4,633,411), and further in view of Hwang et al. (US 20150124891 A1).
As to claim 10, the combination of McFarland, Wolcott, and Bliss teaches the method according to claim 9 above. However, it does not expressly teach further comprising: extracting, via the at least one noise information node, noise information present on the communication path from the customer gateway to at least one of one or more end devices, the communication path utilizing a communication medium; transmitting, via the at least one noise information node, the extracted noise information to a noise information repository; retrieving, via the noise information repository, the extracted noise information associated with the communication path; determining, via a noise profile generator, whether at least one noise characteristic of the extracted noise information matches with one or more noise profiles at the noise profile database, wherein each of the one or more noise profiles comprises at least one noise characteristic derived from noise information, wherein each of the one or more noise profiles is respectively associated with at least one noise source; generating, via the noise profile generator, in response to determining that at least one noise characteristic of the extracted noise information does not match with the one or more noise profiles, a new noise profile based on the extracted noise information; associating, at the noise profile database, at least one of the one or more end devices with the new noise profile; and identifying, based on either the new noise profile or a matching noise profile of the one or more noise profiles, the at least one noise source on the communication path.
Hwang et al., from analogous art, teaches further comprising: extracting, via the at least one noise information node, noise information present on the communication path from the customer gateway to at least one of one or more end devices, the communication path utilizing a communication medium (See ¶ [0042], Teaches that the apparatus includes: an impulse noise detector (noise information node) to detect impulse noise affecting communications on the DSL line);
transmitting, via the at least one noise information node, the extracted noise information to a noise information repository (See ¶ [0105], Teaches that the apparatus further includes a collector to collect new samples of impulse noises. Such samples may be input into the noise database);
retrieving, via the noise information repository, the extracted noise information associated with the communication path (See ¶ [0102], Teaches that previously received and stored impulse noise events from the noise databases are provided to the clustering blocks);
determining, via a noise profile generator, whether at least one noise characteristic of the extracted noise information matches with one or more noise profiles at the noise profile database, wherein each of the one or more noise profiles comprises at least one noise characteristic derived from noise information, wherein each of the one or more noise profiles is respectively associated with at least one noise source (See ¶ [0102], Teaches that previously received and stored impulse noise events from the noise databases are provided to the clustering blocks which cluster the plurality of impulse noise events into groups resulting in clusters, respectively, representative of the impulse noise events provided by the noise databases);
generating, via the noise profile generator, in response to determining that at least one noise characteristic of the extracted noise information does not match with the one or more noise profiles, a new noise profile based on the extracted noise information (See ¶¶ [0105]-[0106], Teaches that the classifier of the apparatus determines that an unknown type of impulse noise event has been detected and the collector captures and sends a waveform of the unknown type of impulse noise event to the clustering engine via the control interface);
associating, at the noise profile database, at least one of the one or more end devices with the new noise profile (See ¶ [0105], Teaches that the clustering engine updates the plurality of noise mitigation strategies and classification stored in the database based on the new samples of impulse noises uploaded to the clustering engine);
and identifying, based on either the new noise profile or a matching noise profile of the one or more noise profiles, the at least one noise source on the communication path (See ¶ [0078], Teaches that classifying the detected impulse noise includes: (a) applying distinct classification filters to one of a plurality of reference channels, in which each of the distinct classification filters correspond to a different class; (b) grading effectiveness (e.g., via a validator) of each of the distinct classification filters based on a decrease of energy output from each of the plurality of reference channels; and (c) ranking the distinct classification filters based on the grading to establish a classification for the detected impulse noise).
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teaching of Hwang et al. into the combination of McFarland, Wolcott, and Bliss in order to impulse noises and attempt to mitigate them based on an applied mitigation strategy taken from the available impulse noise mitigation strategies (See Hwang et al. ¶ [0021]).
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Christopher D. Biagini whose telephone number is (571)272-9743. The examiner can normally be reached weekdays from 9 AM - 5 PM.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Oscar Louie can be reached at (571) 270-1684. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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Christopher D. Biagini
Primary Examiner
Art Unit 2445
/Christopher Biagini/Primary Examiner, Art Unit 2445