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 .
Manner of Making Amendments under 37 C.F.R. § 1.121
Any subsequent submission must comply with the requirements of 37 C.F.R. § 1.121(c) and MPEP § 714(II)(C) regarding the manner of presenting amended claims. Specifically, amended text must be properly indicated.
Pursuant to MPEP § 714(II)(C), all changes to currently amended claims must be shown relative to the immediate prior version of the claims. Deleted matter ordinarily must be shown by strike-through; however, when deleting five (5) or fewer consecutive characters, deletion by double brackets may be used and, in certain circumstances, is required. In particular, where strike-through cannot be readily perceived—such as when deleting a single numeral, punctuation mark, or other short character string—double brackets must be used to clearly identify the deleted matter. For example, deletion of punctuation marks standing alone (e.g., commas, periods, semicolons, parentheses, or quotation marks) should be indicated by double brackets rather than strike-through.
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Failure to provide a compliant amendment may result in the amendment being treated as non-compliant and not entered.
Objection to the Drawings
The drawings are objected to as failing to comply with 37 CFR 1.84(p)(5) because Fig. 3 includes the following reference character not mentioned in the description:
“OTHER INTERNAL DATA SOURCES 314”
Corrected drawing sheets in compliance with 37 CFR 1.121(d), or amendment to the specification to add the reference character in the description in compliance with 37 CFR 1.121(b) are required in reply to the Office Action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
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.
Claims 1-3, 5-7, 17, 19, and 20 are rejected under 35 U.S.C. § 102(a)(1) and/or § 102(a)(2) as being anticipated by US 2020/0145852 (hereinafter, “AYALA”).
Regarding claim 1, AYALA discloses:
A method comprising: (process 700/812/926/1500)
obtaining, by a processing system including at least one processor, a set of training records in a wireless communication network, (¶ 0069: [A]t S706 the process 700 performs a data acquisition stage. Given the focus cell Cf the data acquisition stage retrieves the following information from the corresponding data source(s))
wherein each training record of the set of training records comprises:
downlink signal information (¶ 0071: A Downlink Power Metric (DLPwr(i)) signal for each cell Ci co-sited with Cf), uplink noise information associated with the downlink signal information (¶ 0070: An Uplink Interference Metric (UIM) signal for the focus cell Cf (e.g., the weighted average of the pmRadioRecInterferencePwr counter for LTE cells using Ericsson equipment). Potential data sources for UIM signals may include one or more short-term retention databases, an Operations Support System (OSS) directly, or both), and at least one label indicating whether passive intermodulation noise is present, (¶¶ 0127-0128: PIM Detection Assessment (PIMDA) stage 1216 computes a PIMDA score ρ according to key features obtained from the linear models generated by the Linear Model Analysis stage 1214. A large PIMDA score (i.e., for PIMDA scores having a range of 0 to 1, a PIMDA score ρ near 1) indicates a higher likelihood that the measured uplink interference was caused by PIM. [0128] The Decision Stage 1218 evaluates a PIMDA score ρ to determine whether the PIMDA score ρ is large enough to generate a PIM interference alert; ¶ 0141: Fault Management Data 1328 includes, for example, alarm data that indicates performance issues at one or more cell site)
wherein the wireless communication network utilizes a plurality of downlink carriers, wherein the set of training records is for different combinations of downlink carriers of the plurality of downlink carriers, and wherein the different combinations of downlink carriers comprise a subset of less than all possible combinations of the plurality of downlink carriers; (¶¶ 0076-0078: Sub-step 1a: For each intermodulation product I in IMn(Cf), compute the Aggregated Downlink Power AggDP signals (one for each contributing downlink frequency): [0077] Identify all different downlink frequencies available in the site, and the corresponding cells associated with each frequency. [0078] For each downlink frequency, add the DLPwr(k) signals of all cells associated with the downlink frequency to determine a kth Aggregated Downlink Power AggDPk signal)
training, by the processing system, a passive intermodulation noise detection model in accordance with the set of training records, (¶ 0082: In addition to the UIM signal, the set of WDP signals will be used to generate the set of linear models used by the process 700 to detect PIM-caused interference; ¶ 0127: PIM Detection Assessment (PIMDA) stage 1216 computes a PIMDA score ρ according to key features obtained from the linear models generated by the Linear Model Analysis stage 1214)
wherein the passive intermodulation noise detection model is trained to output indicators of whether instances of passive intermodulation noise are exhibited in input data samples; (¶ 0083: At S710, the process 700 constructs a set of piece-wise linear models (one linear model for each WDPI signal) for each of the data sets {WDPI, UIM}. The piece-wise linear models may be constructed using regression analysis; ¶ 0090: At S712, the process 700 performs PIM Detection Assessment (PIMDA) to detect the presence of PIM-caused interference. The process determines a PIMDA score ρ corresponding to a likelihood that the observed uplink interference is attributable to PIM. The process 700 may report the PIMDA score ρ to an operator of the focus cell Cf, who may then decide whether remedial action is appropriate to address the PIN-generated interference; ¶ 0073: [I]nformation described above is determined using samples collected on a periodic basis)
applying, by the processing system, a first input data sample to the passive intermodulation noise detection model to obtain a first output comprising a first indication that a first instance of passive intermodulation noise is exhibited in the first input data sample, (¶ 0074: At S708, the process 700 determines a set of Weighted DL Power (WDPi) signals from the individual downlink power signals of the inventoried nearby (e.g., co-sited) cells; ¶ 0090: At S712, the process 700 performs PIM Detection Assessment (PIMDA) to detect the presence of PIM-caused interference. The process determines a PIMDA score ρ corresponding to a likelihood that the observed uplink interference is attributable to PIM.; ¶ 0073: [I]nformation described above is determined using samples collected on a periodic basis)
wherein the first input data sample comprises first downlink signal information associated with a cell site of the wireless communication network and first uplink noise information associated with the first downlink signal information; and (¶ 0206: The PIM Interference Remote Detection system may have [ ] a long-term retention database where a set of intermodulation products is stored, and a short-term retention database in which uplink interference and downlink transmission power metrics are stored for each cell of a plurality of monitorable cells operating in a vicinity. The method then uses the information from these databases to compute a PIM Detection Assessment score that indicates the likelihood that uplink interference measurements can be attributed to PIM)
performing, by the processing system, at least one remedial action in the wireless communication network in response to the obtaining of the first indication that the first instance of passive intermodulation noise is exhibited in the first input data sample. (¶ 0090: [P]rocess 700 may report the PIMDA score ρ to an operator of the focus cell Cf, who may then decide whether remedial action is appropriate to address the PI[M]-generated interference; ¶ 0207: [R]emedy the physical cause of interference, such as replacing an oxidized connector or notifying a power company of a malfunctioning component)
Regarding claim 2, AYALA, as applied above, anticipates the method of claim 1. AYALA further discloses:
wherein the downlink signal information comprises a downlink signal information set that includes a plurality of downlink signal frequency information identifiers. (¶¶ 0076-0078: Sub-step 1a: For each intermodulation product I in IMn(Cf), compute the Aggregated Downlink Power AggDP signals (one for each contributing downlink frequency): [0077] Identify all different downlink frequencies available in the site, and the corresponding cells associated with each frequency. [0078] For each downlink frequency, add the DLPwr(k) signals of all cells associated with the downlink frequency to determine a kth Aggregated Downlink Power AggDPk signal)
Regarding claim 3, AYALA, as applied above, anticipates the method of claim 2. AYALA further discloses:
wherein the plurality of downlink signal frequency information identifiers comprise:
two or more channel frequencies; (¶ 0045: [B]andwidths of a first downlink (DL) signal DL1 having a first DL center frequency Df1 and a bandwidth of Δf, a second DL signal DL2 having a second DL center frequency Df2 and a bandwidth of Δf, a first uplink (UL) channel UL1 having a first UL center frequency Uf1 and a bandwidth of Δf, and second UL channel UL2 having a second UL center frequency Uf2 and a bandwidth of Δf Although FIG. 2 shows an example where all of the UL channels and DL signals have a same bandwidth of Δf, embodiments are not limited thereto)
two or more frequencies ranges that define two or more channels;
two or more sub-carrier range identifiers; or
two or more physical resource block identifiers.
Regarding claim 5, AYALA, as applied above, anticipates the method of claim 1. AYALA further discloses:
wherein the uplink noise information comprises an uplink noise information set that includes:
one or more uplink frequency information identifiers; and (¶ 0200: [D]etermining first downlink and uplink frequencies for the first operator from the network configuration data, determining second downlink and uplink frequencies for the second operator from the network configuration data)
one or more uplink noise magnitudes associated with the one or more uplink frequency information identifiers. (¶ 0055: [T]he amount of interference power affecting first UL channel UL1 will be proportional to P1∙P2, while the amount of interference affecting the second UL channel UL2 will be proportional to P12)
Regarding claim 6, AYALA, as applied above, anticipates the method of claim 5. AYALA further discloses:
wherein the one or more uplink frequency information identifiers comprise:
one or more channel frequencies; (¶ 0045: [B]andwidths of a first downlink (DL) signal DL1 having a first DL center frequency Df1 and a bandwidth of Δf, a second DL signal DL2 having a second DL center frequency Df2 and a bandwidth of Δf, a first uplink (UL) channel UL1 having a first UL center frequency Uf1 and a bandwidth of Δf, and second UL channel UL2 having a second UL center frequency Uf2 and a bandwidth of Δf Although FIG. 2 shows an example where all of the UL channels and DL signals have a same bandwidth of Δf, embodiments are not limited thereto)
one or more frequencies ranges that define one or more channels;
one or more sub-carrier range identifiers; or
one or more physical resource block identifiers.
Regarding claim 7, AYALA, as applied above, anticipates the method of claim 1. AYALA further discloses:
wherein the first uplink noise information is associated with at least one of:
a physical uplink shared channel; or (¶ 0056: [I]f PIM-caused signals are the only source of interference, the UIM will vary over time in the same manner as the WDP does. An example UIM signal is a weighted average of the pmRadioRecInterferencePwr counter (corresponding to a measured uplink noise and interference power on a Physical UL Shared Channel (PUSCH)))
a physical uplink control channel.
Regarding claim 8, AYALA, as applied above, anticipates the method of claim 1. AYALA further discloses:
wherein the uplink noise information of each training record is associated with an uplink noise having a magnitude in excess of a pre-defined threshold.
Regarding claim 9, AYALA, as applied above, anticipates the method of claim 1. AYALA further discloses:
wherein the passive intermodulation noise detection model is trained to output:
the indicators when the instances of passive intermodulation noise are exhibited in the input data samples; and
for each of the input data samples for which a respective instance of the passive intermodulation noise is indicated to be exhibited, downlink signal frequency information identifiers associated with source frequencies of the respective instance of the passive intermodulation noise.
Regarding claim 10, AYALA, as applied above, anticipates the method of claim 9. AYALA further discloses:
wherein the first output further comprises first downlink signal frequency information identifiers associated with first source frequencies of the first instance of passive intermodulation noise exhibited in the first input data sample.
Regarding claim 11, AYALA, as applied above, anticipates the method of claim 10. AYALA further discloses:
wherein the at least one remedial action is performed further in response to the first downlink signal frequency information identifiers.
Regarding claim 12, AYALA, as applied above, anticipates the method of claim 11. AYALA further discloses:
wherein the at least one remedial action comprises reducing a transmission energy in the first source frequencies.
Regarding claim 14, AYALA, as applied above, anticipates the method of claim 1. AYALA further discloses:
further comprising:
generating synthetic training records based on at least a portion of the set of training records,
wherein the synthetic training records that are generated are for combinations of downlink carriers that are not present in existing training records in the set of training records; and
adding the synthetic training records to the set of training records.
Regarding claim 17, AYALA, as applied above, anticipates the method of claim 1. AYALA further discloses:
wherein the passive intermodulation noise detection model is trained to output the indicators when the instances of passive intermodulation noise is exhibited in the input data samples is further based upon data of one or more external data sources. (¶ 0152: [S]pectrum analytics server 1340 represents a specific processing device that interfaces with one or more of the external data sources described above)
Regarding claim 19, AYALA discloses:
A non-transitory computer-readable medium storing instructions which, when executed by a processing system including at least one processor (¶ 0013: Embodiments of the present disclosure include a non-transitory computer-readable medium with computer-executable instructions stored thereon which, when executed by a processor, performs one or more of the steps described), cause the processing system to perform operations, the operations comprising:
obtaining a set of training records in a wireless communication network, (¶ 0069: [A]t S706 the process 700 performs a data acquisition stage. Given the focus cell Cf the data acquisition stage retrieves the following information from the corresponding data source(s))
wherein each training record of the set of training records comprises:
downlink signal information (¶ 0071: A Downlink Power Metric (DLPwr(i)) signal for each cell Ci co-sited with Cf), uplink noise information associated with the downlink signal information (¶ 0070: An Uplink Interference Metric (UIM) signal for the focus cell Cf (e.g., the weighted average of the pmRadioRecInterferencePwr counter for LTE cells using Ericsson equipment). Potential data sources for UIM signals may include one or more short-term retention databases, an Operations Support System (OSS) directly, or both), and at least one label indicating whether passive intermodulation noise is present, (¶¶ 0127-0128: PIM Detection Assessment (PIMDA) stage 1216 computes a PIMDA score ρ according to key features obtained from the linear models generated by the Linear Model Analysis stage 1214. A large PIMDA score (i.e., for PIMDA scores having a range of 0 to 1, a PIMDA score ρ near 1) indicates a higher likelihood that the measured uplink interference was caused by PIM. [0128] The Decision Stage 1218 evaluates a PIMDA score ρ to determine whether the PIMDA score ρ is large enough to generate a PIM interference alert; ¶ 0141: Fault Management Data 1328 includes, for example, alarm data that indicates performance issues at one or more cell site)
wherein the wireless communication network utilizes a plurality of downlink carriers, wherein the set of training records is for different combinations of downlink carriers of the plurality of downlink carriers, and wherein the different combinations of downlink carriers comprise a subset of less than all possible combinations of the plurality of downlink carriers; (¶¶ 0076-0078: Sub-step 1a: For each intermodulation product I in IMn(Cf), compute the Aggregated Downlink Power AggDP signals (one for each contributing downlink frequency): [0077] Identify all different downlink frequencies available in the site, and the corresponding cells associated with each frequency. [0078] For each downlink frequency, add the DLPwr(k) signals of all cells associated with the downlink frequency to determine a kth Aggregated Downlink Power AggDPk signal)
training a passive intermodulation noise detection model in accordance with the set of training records, (¶ 0082: In addition to the UIM signal, the set of WDP signals will be used to generate the set of linear models used by the process 700 to detect PIM-caused interference; ¶ 0127: PIM Detection Assessment (PIMDA) stage 1216 computes a PIMDA score ρ according to key features obtained from the linear models generated by the Linear Model Analysis stage 1214)
wherein the passive intermodulation noise detection model is trained to output indicators of whether instances of passive intermodulation noise is exhibited in input data samples; (¶ 0083: At S710, the process 700 constructs a set of piece-wise linear models (one linear model for each WDPI signal) for each of the data sets {WDPI, UIM}. The piece-wise linear models may be constructed using regression analysis; ¶ 0090: At S712, the process 700 performs PIM Detection Assessment (PIMDA) to detect the presence of PIM-caused interference. The process determines a PIMDA score ρ corresponding to a likelihood that the observed uplink interference is attributable to PIM. The process 700 may report the PIMDA score ρ to an operator of the focus cell Cf, who may then decide whether remedial action is appropriate to address the PIN-generated interference; ¶ 0073: [I]nformation described above is determined using samples collected on a periodic basis)
applying a first input data sample to the passive intermodulation noise detection model to obtain a first output comprising a first indication that a first instance of passive intermodulation noise is exhibited in the first input data sample, (¶ 0074: At S708, the process 700 determines a set of Weighted DL Power (WDPi) signals from the individual downlink power signals of the inventoried nearby (e.g., co-sited) cells; ¶ 0090: At S712, the process 700 performs PIM Detection Assessment (PIMDA) to detect the presence of PIM-caused interference. The process determines a PIMDA score ρ corresponding to a likelihood that the observed uplink interference is attributable to PIM.; ¶ 0073: [I]nformation described above is determined using samples collected on a periodic basis)
wherein the first input data sample comprises first downlink signal information associated with a cell site of the wireless communication network and first uplink noise information associated with the first downlink signal information; and (¶ 0206: The PIM Interference Remote Detection system may have [ ] a long-term retention database where a set of intermodulation products is stored, and a short-term retention database in which uplink interference and downlink transmission power metrics are stored for each cell of a plurality of monitorable cells operating in a vicinity. The method then uses the information from these databases to compute a PIM Detection Assessment score that indicates the likelihood that uplink interference measurements can be attributed to PIM)
performing at least one remedial action in the wireless communication network in response to the obtaining of the first indication that the first instance of passive intermodulation noise is exhibited in the first input data sample. (¶ 0090: [P]rocess 700 may report the PIMDA score ρ to an operator of the focus cell Cf, who may then decide whether remedial action is appropriate to address the PI[M]-generated interference; ¶ 0207: [R]emedy the physical cause of interference, such as replacing an oxidized connector or notifying a power company of a malfunctioning component)
Regarding claim 20, AYALA discloses:
An apparatus comprising: (system 1200)
a processing system including at least one processor; and (¶ 0121: [S]ystem 1200 may comprise hardware, software, or combinations thereof, wherein the hardware may include a processor, a memory)
a computer-readable medium storing instructions which, when executed by the processing system, cause the processing system to perform operations, the operations comprising: (¶ 0121: [S]ystem 1200 may comprise hardware, software, or combinations thereof, wherein the hardware may include a processor, a memory)
obtaining a set of training records in a wireless communication network, (¶ 0069: [A]t S706 the process 700 performs a data acquisition stage. Given the focus cell Cf the data acquisition stage retrieves the following information from the corresponding data source(s))
wherein each training record of the set of training records comprises:
downlink signal information (¶ 0071: A Downlink Power Metric (DLPwr(i)) signal for each cell Ci co-sited with Cf), uplink noise information associated with the downlink signal information (¶ 0070: An Uplink Interference Metric (UIM) signal for the focus cell Cf (e.g., the weighted average of the pmRadioRecInterferencePwr counter for LTE cells using Ericsson equipment). Potential data sources for UIM signals may include one or more short-term retention databases, an Operations Support System (OSS) directly, or both), and at least one label indicating whether passive intermodulation noise is present, (¶¶ 0127-0128: PIM Detection Assessment (PIMDA) stage 1216 computes a PIMDA score ρ according to key features obtained from the linear models generated by the Linear Model Analysis stage 1214. A large PIMDA score (i.e., for PIMDA scores having a range of 0 to 1, a PIMDA score ρ near 1) indicates a higher likelihood that the measured uplink interference was caused by PIM. [0128] The Decision Stage 1218 evaluates a PIMDA score ρ to determine whether the PIMDA score ρ is large enough to generate a PIM interference alert; ¶ 0141: Fault Management Data 1328 includes, for example, alarm data that indicates performance issues at one or more cell site)
wherein the wireless communication network utilizes a plurality of downlink carriers, wherein the set of training records is for different combinations of downlink carriers of the plurality of downlink carriers, and wherein the different combinations of downlink carriers comprise a subset of less than all possible combinations of the plurality of downlink carriers; (¶¶ 0076-0078: Sub-step 1a: For each intermodulation product I in IMn(Cf), compute the Aggregated Downlink Power AggDP signals (one for each contributing downlink frequency): [0077] Identify all different downlink frequencies available in the site, and the corresponding cells associated with each frequency. [0078] For each downlink frequency, add the DLPwr(k) signals of all cells associated with the downlink frequency to determine a kth Aggregated Downlink Power AggDPk signal)
training a passive intermodulation noise detection model in accordance with the set of training records, (¶ 0082: In addition to the UIM signal, the set of WDP signals will be used to generate the set of linear models used by the process 700 to detect PIM-caused interference; ¶ 0127: PIM Detection Assessment (PIMDA) stage 1216 computes a PIMDA score ρ according to key features obtained from the linear models generated by the Linear Model Analysis stage 1214)
wherein the passive intermodulation noise detection model is trained to output indicators of whether instances of passive intermodulation noise is exhibited in input data samples; (¶ 0083: At S710, the process 700 constructs a set of piece-wise linear models (one linear model for each WDPI signal) for each of the data sets {WDPI, UIM}. The piece-wise linear models may be constructed using regression analysis; ¶ 0090: At S712, the process 700 performs PIM Detection Assessment (PIMDA) to detect the presence of PIM-caused interference. The process determines a PIMDA score ρ corresponding to a likelihood that the observed uplink interference is attributable to PIM. The process 700 may report the PIMDA score ρ to an operator of the focus cell Cf, who may then decide whether remedial action is appropriate to address the PIN-generated interference; ¶ 0073: [I]nformation described above is determined using samples collected on a periodic basis)
applying a first input data sample to the passive intermodulation noise detection model to obtain a first output comprising a first indication that a first instance of passive intermodulation noise is exhibited in the first input data sample, (¶ 0074: At S708, the process 700 determines a set of Weighted DL Power (WDPi) signals from the individual downlink power signals of the inventoried nearby (e.g., co-sited) cells; ¶ 0090: At S712, the process 700 performs PIM Detection Assessment (PIMDA) to detect the presence of PIM-caused interference. The process determines a PIMDA score ρ corresponding to a likelihood that the observed uplink interference is attributable to PIM.; ¶ 0073: [I]nformation described above is determined using samples collected on a periodic basis)
wherein the first input data sample comprises first downlink signal information associated with a cell site of the wireless communication network and first uplink noise information associated with the first downlink signal information; and (¶ 0206: The PIM Interference Remote Detection system may have [ ] a long-term retention database where a set of intermodulation products is stored, and a short-term retention database in which uplink interference and downlink transmission power metrics are stored for each cell of a plurality of monitorable cells operating in a vicinity. The method then uses the information from these databases to compute a PIM Detection Assessment score that indicates the likelihood that uplink interference measurements can be attributed to PIM)
performing at least one remedial action in the wireless communication network in response to the obtaining of the first indication that the first instance of passive intermodulation noise is exhibited in the first input data sample. (¶ 0090: [P]rocess 700 may report the PIMDA score ρ to an operator of the focus cell Cf, who may then decide whether remedial action is appropriate to address the PI[M]-generated interference; ¶ 0207: [R]emedy the physical cause of interference, such as replacing an oxidized connector or notifying a power company of a malfunctioning component)
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 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.
Claim 4 is rejected under 35 U.S.C. § 103 as being unpatentable over AYALA in view of US 2025/0293718 (hereinafter, “TAYLOR”).
Regarding claim 4, AYALA, as applied above, anticipates the method of claim 2. AYALA does not explicitly disclose:
wherein the downlink signal information set further includes:
two or more downlink signal magnitudes associated with two or more of the downlink signal frequency information identifiers.
In the same field of endeavor, however, TAYLOR teaches:
wherein the downlink signal information set further includes:
two or more downlink signal magnitudes associated with two or more of the downlink signal frequency information identifiers. (¶ 0116: Each distinct PIM signal is characterized by its signal magnitude and its distance from the PIM test set)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify AYALA’s passive inter-modulation (PIM) noise/interference detection model to provide signal magnitudes, as taught by TAYLOR, to quantify PIM signal level based on the measured power of the intermodulation product, such that by incorporating PIM magnitude analysis, the method enhances source identification, enabling efficient mitigation strategies. See TAYLOR, at ¶ 0116.
Claim 13 is rejected under 35 U.S.C. § 103 as being unpatentable over AYALA in view of US 2019/0280763 (hereinafter, “SMYTH”).
Regarding claim 13, AYALA, as applied above, anticipates the method of claim 1. AYALA does not explicitly disclose:
wherein the first output further comprises at least a first uplink frequency information identifier associated with at least a first frequency comprising the first instance of passive intermodulation noise, and wherein the at least one remedial action comprises omitting a utilization of the at least the first frequency for uplink communications.
In the same field of endeavor, however, SMYTH teaches:
wherein the first output further comprises at least a first uplink frequency information identifier associated with at least a first frequency comprising the first instance of passive intermodulation noise, and wherein the at least one remedial action comprises omitting a utilization of the at least the first frequency for uplink communications. (¶ 0512: [R]emedial action may include controlling the source of interference by reducing its transmitter power, changing its frequency of operation, and/or shutting down its transmission)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify AYALA’s passive inter-modulation (PIM) noise/interference detection model to provide frequency-aware remedial action, as taught by SMYTH, such that the new frequency of operation may be pre-determined and pre-planned for mission-critical systems to reduce the time to change-over. See SMYTH, at ¶ 0512.
Claims 15 and 16 are rejected under 35 U.S.C. § 103 as being unpatentable over AYALA in view of US 2025/0088208 (hereinafter, “ZHU”).
Regarding claim 15, AYALA, as applied above, anticipates the method of claim 1. AYALA further discloses:
wherein the passive intermodulation noise detection model comprises a machine learning model.
In the same field of endeavor, however, ZHU teaches:
wherein the passive intermodulation noise detection model comprises a machine learning model. (¶ 0019: FIG. 5 is a schematic illustration of the component view of the artificial intelligence/machine learning (AI/ML) powered PIM detection system (APPDS))
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify AYALA’s passive inter-modulation (PIM) noise/interference detection model to provide machine learning, as taught by ZHU, to transition from the traditional static PIM detection model based on static algorithms toward a dynamic neural network based trained model, so as to define a framework that can facilitate the transition in commercial products, such as fifth generation (5G)/Beyond 5G radio base station. See ZHU, at ¶ 0030.
Regarding claim 16, AYALA, as applied above, anticipates the method of claim 16. AYALA does not explicitly disclose:
wherein the machine learning model comprises:
a supervised machine learning model;
an unsupervised machine learning model;
a deep learning model;
a reinforcement learning model;
a recurrent neural network; or
a generative machine learning model.
In the same field of endeavor, however, ZHU teaches:
wherein the machine learning model comprises:
a supervised machine learning model; (¶ 0060: AMTU 306 is an important component in OAMTM 214. It might be neural network based supervised/unsupervised machine learning or traditional machine learning model)
an unsupervised machine learning model; (¶ 0060: AMTU 306 is an important component in OAMTM 214. It might be neural network based supervised/unsupervised machine learning or traditional machine learning model)
a deep learning model; (¶ 0029: PIM detection can be realized using an intelligent model built based on deep neural network)
Claim 18 is rejected under 35 U.S.C. § 103 as being unpatentable over AYALA in view of US 2024/0073113 (hereinafter, “ARORA”).
Regarding claim 18, AYALA, as applied above, anticipates the method of claim 1. AYALA does not explicitly disclose:
wherein the at least one remedial action is selected via a generative model.
In the same field of endeavor, however, Arora teaches:
wherein the at least one remedial action is selected via a generative model. (¶ 0046: [S]ystem 110 performs two processes that facilitate this monitoring: a first process 170 that collects and analyzes network data to generate a machine learning model 111, and a second process 180 that uses the generated model 111 to classify terminals 130a-130c and automatically select and initiate actions to correct or mitigate network problems)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify AYALA’s passive inter-modulation (PIM) noise/interference detection model to provide remedial action selection, as taught by ARORA, so as to provide an automated and scalable method, with the added advantage of distilling of learning from multiple networks. See ARORA, at ¶ 0050.
Allowable Subject Matter
Claims 8-12 and 14 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
Any inquiry concerning this communication or earlier communications from the Examiner should be directed to Garth D Richmond whose telephone number is (703)756-4559. The Examiner can normally be reached M-F 8 a.m. - 5 p.m. ET.
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/GARTH D RICHMOND/Examiner, Art Unit 2644
/KATHY W WANG-HURST/Supervisory Patent Examiner, Art Unit 2644
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