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 .
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 2/27/2026 has been entered.
Claims 1-11, 13-17 & 21-25 are pending and presented for examination.
Response to Amendment
Claims 1 & 14 have been amended.
Rejections to claims 1-11, 13-17 & 21-25 under 35 USC 103 in the previous record Final Rejection dated 11/28/2025 have been withdrawn based on amendments to claims 1 & 14. However, after further consideration, new grounds of rejections of these claims under 35 USC 103 based on new reference Shin et al. (US 9026169)(herein after “Shin”) have been introduced.
Response to Arguments
Applicant’s arguments, see “Remarks”, filed 2/27/2026, with respect to the rejections of claims 1-11, 13-17 & 21-25 under 35 USC 103 have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new grounds of rejections of these claims are made under 35 USC 103 in view of new reference Shin et al. (US 9026169)(herein after “Shin”).
Regarding claim 1, applicant argues that amendments to this claim traverse the rejection of this claim under 35 USC 103 made in the previous record Final Rejection dated 11/28/2025. Examiner agrees and withdraws rejection of claim 1 under 35 USC 103 made in the previous record. However, after further consideration, examiner introduces a new ground of rejection of claim 1 under 35 USC 103 based on new reference Shin. Applicant’s arguments with respect to claim 1 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Regarding claim 14, applicant submits that amendments to this claim similar to claim 1 traverse the rejection of this claim under 35 USC 103 made in the previous record Final Rejection dated 11/28/2025. Examiner agrees and withdraws rejection of claim 14 under 35 USC 103 made in the previous record. However, for the same reasons as discussed above for claim 1, examiner introduces a new ground of rejection of claim 14 under 35 USC 103 based on new reference Shin.
Regarding claims 2-11, 13, 15-17 & 21-25, applicant submits that amendments to claims 1 & 14 traverse the rejection of these claim under 35 USC 103 made in the previous record Final Rejection dated 11/28/2025 due to arguments made above for claims 1 & 14 and due to their dependency on claims 1 or 14. Examiner agrees and withdraws rejection of these claims under 35 USC 103 made in the previous record. However, for the same reasons as discussed above for claims 1 & 14, examiner introduces a new grounds of rejections of these claims under 35 USC 103 based on new reference Shin.
Claim Rejections - 35 USC § 103
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) A patent may not be obtained though the invention is not identically disclosed or described as set forth in section 102, if the differences between the subject matter sought to be patented and the prior art are such that the subject matter as a whole would have been obvious at the time the invention was made to a person having ordinary skill in the art to which said subject matter 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 at the time any inventions covered therein were made absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and invention dates of each claim that was not commonly owned at the time a later invention was made 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.
Claims 1-3, 5, 7-10, 13-16 & 21-23 rejected under 35 U.S.C. 103 as being unpatentable over Larish et al. (US 10039016)(herein after “Larish”) in view of Gaber et al. (A. Gaber, M. Zaki & A. Mohamed & M. Beshara, “Cellular Network Power Control Optimization Using Unsupervised Machine Learning”, ITCE 2019, Aswan, Egypt, February 2-4, 2019)(herein after “Gaber”) and further in view of El- Hoiydi et al. (US 9668070)(herein after “El-Hoiydi”) and Shin et al. (US 9026169)(herein after “Shin”).
Regarding Claim 1, Larish discloses a method being performed by a device comprising:
creating a model based on an identified at least one first cell cluster to map a nominal uplink power level to an estimated signal quality value for said at least one first cell cluster (Col 9, lines 19-35 disclose extracting a training model on a identified well-performing golden cell cluster that provides closed form expressions approximating the relationship between KPIs (e.g. an estimated signal quality value) and RRM parameters (e.g. nominal uplink power level).); and
adjusting a nominal uplink power level of the at least one cell of the identified at least one second cell cluster to attain a signal quality value in the at least one cell of the second cell cluster (Col 9, lines 36-67 & col 10, lines 1-7 disclose identifying a target cell (i.e. a second cell cluster) and applying the extracted training RRM model to the target cluster (e.g. adjusting a nominal power level of at least one cell of the target cluster by applying the nominal uplink power level derived from the extracted training RRM model) and checking the training RRM model for consistency (e.g. attaining a corresponding KPI or signal quality level of the target cluster to check against the estimated KPI from the training RRM model).).
Larish fails to discloses a method of controlling uplink power levels of radio cells in a wireless communications network comprising: grouping sets of neighboring cells into a plurality of cell clusters; identifying at least one first cell cluster comprising at least one cell being subjected to a first interference, the cells of the identified first cluster further having a signal quality exceeding a signal quality threshold value; and identifying at least one second cell cluster comprising at least one cell being subjected to a second interference, the at least one cell of the identified second cluster further having a signal quality below the signal quality threshold value.
However, Gaber teaches a method of controlling uplink power levels of radio cells in a wireless communications network (Section I, 4th & 5th paragraphs disclose a clustering technique (i.e. method) for settings for power control (e.g. for controlling uplink power levels) in a cellular network.) comprising:
grouping sets of neighboring cells into a plurality of cell clusters (Fig 3 & Section III disclose grouping of a plurality of cells into 16 cell clusters.);
identifying at least one first cell cluster comprising at least one cell being subjected to a first interference, the cells of the identified first cluster further having a signal quality exceeding a signal quality threshold value (Table 2 & Section IV disclose a table ranking the 16 cell clusters by Average UL Quality. Based on table 2, a first cell cluster can be identified as having signal quality exceeding a signal quality threshold value (e.g. Cluster ID 4 can be identified as having an Average UL Quality exceeding a 0.5 threshold value). Further, the Average UL Level of Cluster ID 4 is greater than Cluster IDs 13 & 15, even though Cluster ID 4 has a lower Average UL Quality than Cluster IDs 13 & 15, indicating that at least one cell in Cluster ID 4 is being subjected to a first interference.); and
identifying at least one second cell cluster comprising at least one cell being subjected to a second interference, the at least one cell of the identified second cluster further having a signal quality below the signal quality threshold value (Table 2 & Section IV disclose a table ranking the 16 cell clusters by Average UL Quality. Based on table 2, a second cell cluster can be identified as having signal quality below the signal quality threshold value (e.g. Cluster ID 2 can be identified as having an Average UL Quality below the 0.5 threshold value). Note that for the average UL Quality of Cluster ID 2 to be below 0.5, at least one cell in Cluster ID 2 must have an UL Quality below 0.5. Further, the Average UL Level of Cluster ID 2 is greater than Cluster IDs 10 & 12, even though Cluster ID 4 has a lower Average UL Quality than Cluster IDs 10 & 12, indicating that at least one cell in Cluster ID 2 is being subjected to a second interference.).
Therefore, it would have been obvious to someone having ordinary skill in the art prior to the effective filing date of the claimed invention to have a method performed by a device that uses a model based on a golden/well performing cluster of cells to map RRM parameters, such as nominal uplink power level, to KPIs, such as a signal quality value, and adjusts RRM parameters in a target cluster of cells based on the model extracted from the golden cluster of cells to get estimated KPIs for the target cluster of cells, as disclosed by Larish, wherein the golden cluster of cells have UL Quality above an average UL Quality threshold and at least one cell in the golden cluster of cells experiences a first interference; and the target cluster of cells has at least one cell having UL Quality below the average UL Quality threshold and at least one cell in the target cluster of cells experiences a second interference, as taught by Gaber. The motivation to do so would be to have a method for a device to learn from a model mapping RRM parameter settings to KPIs for a golden cluster of cells experiencing interference but with KPIs above a threshold value, and use said learning to apply RRM settings to a target cluster of cells experiencing interference with KPIs below the threshold value, to achieve estimated, and ideally improved, KPIs in the target cluster of cells.
Larish fails to disclose determining whether the first interference and/or the second interference is a static-type interference; and based on determining that the first interference and/or the second interference is a static-type interference.
However, El-Hoiydi further teaches determining whether the first interference and/or the second interference is a static-type interference; based on determining that the first interference and/or the second interference is a static-type interference (Col 8, lines 63-67 & col 9, lines 1-14 disclose analyzing a metric vector to determine whether a static interference is present, and if so, then performing a blanking step to avoid interference (i.e. creating a model to avoid interference).).
Therefore, it would have been obvious to someone having ordinary skill in the art prior to the effective filing date of the claimed invention to have a method for a device to create a model based on an identified at least one first cell cluster to map a nominal uplink power level to an estimated signal quality value for said at least one first cell cluster, as disclosed by Larish, based on determining whether the first interference and/or the second interference is a static-type interference, as taught by El-Hoiydi. The motivation to do so would be to avoid degrading performance by only using a model that maps a nominal uplink power level to an estimated signal quality value when interference is static, and not using the model when dynamic interference would render the model inaccurate.
Larish fails to disclose but Shin further teaches wherein the adjusting is by inputting a desired signal quality value to the created model to compute the nominal uplink power level, and applying the computed nominal uplink power level to attain the desired signal quality value (Col 3, lines 6-13 discloses a combined open loop and closed loop scheme (i.e. model) for UL power control that controls a WTRU transmit power according to an Equation 1 of PSDTx = PSDopen + aDclosed + DMCS (i.e. a model for computing adjustments to a nominal uplink power level). Col 3, lines 65-67 & col 4, lines 1-15 disclose that the an open loop portion of the model is defined by PSDopen = PSDtarget +L (dBm) where PSDtarget is a WTRU-specific parameter that is adjusted according to a QoS such as a target block error rate (BLER) that would be input to the model. Col 5, lines 52-59 disclose that the WTRU adjusts the transmit PSD of a data channel by applying the computed PSDTx on the next UL TTI to attain the target BLER.).
Therefore, it would have been obvious to someone having ordinary skill in the art prior to the effective filing date of the claimed invention to have a method for a device to create a model based on an identified at least one first cell cluster to map a nominal uplink power level to an estimated signal quality value for said at least one first cell cluster; grouping sets of neighboring cells into a plurality of cell clusters; identifying at least one first cell cluster comprising at least one cell being subjected to a first interference, the cells of the identified first cluster further having a signal quality exceeding a signal quality threshold value; and identifying at least one second cell cluster comprising at least one cell being subjected to a second interference, the at least one cell of the identified second cluster further having a signal quality below the signal quality threshold value.; determining whether the first interference and/or the second interference is a static-type interference; and based on determining that the first interference and/or the second interference is a static-type interference; and adjusting a nominal uplink power level of the at least one cell of an identified at least one second cell cluster to attain a signal quality value in the at least one cell of the second cell cluster, as disclosed by Larish in view of Gaber and El-Hoiydi, wherein the adjusting is by inputting a desired signal quality value to the created model to compute the nominal uplink power level, and applying the computed nominal uplink power level to attain the desired signal quality value, as further taught by Shin. The motivation to do so would be to have a method where a UE, in a second cell within a second cluster of cells and experiencing static interference and signal quality below a signal quality threshold, creates a combined open and closed loop UL power control model for computing an UL Tx power based on a cell, within a first cluster of cells experiencing signal quality above the signal quality threshold, experiencing static interference and uses the model with input from the second cell regarding a desired target BLER, to apply an adjusted Tx power computed by the model in order to attain the target BLER, so an improved Quality of Experience (QoS) can be achieved in the second cell.
Regarding Claim 2, Larish in view of Gaber and El-Hoiydi and Shin disclose the method of claim 1.
Larish discloses the model being created using a machine learning process (Fig 3A & col 5, lines 39-43 disclose an ML engine 320 that may apply machine learning to identify and model target clusters in a RAN.).
Regarding claim 3, Larish in view of Gaber and El-Hoiydi and Shin disclose the method of claim 1.
Larish discloses the step of creating the model comprising: performing a regression analysis on the identified at least one first cell cluster to create the model mapping a nominal uplink power level to an estimated signal quality value for said at least one first cell cluster (Col 9, lines 19-35 disclose extracting a training model on a identified well-performing golden cell cluster that provides closed form expressions approximating the relationship between KPIs (e.g. an estimated signal quality value) and RRM parameters (e.g. nominal uplink power level). Fig 3A & col 5, lines 39-43 disclose that the model may be based on ML engine 320 that applies machine learning to identify and model target clusters in a RAN, and that the machine learning may include using neural networks and deep learning (e.g. regression analysis).).
Regarding claim 5, Larish in view of Gaber and El-Hoiydi and Shin discloses the method of claim 1.
Larish fails to disclose wherein the cells of the identified first cluster has a signal quality exceeding a first signal quality threshold value while the at least one cell of the identified second cluster has a signal quality below a second signal quality threshold value.
However, Gaber teaches wherein the cells of the identified first cluster has a signal quality exceeding a first signal quality threshold value while the at least one cell of the identified second cluster has a signal quality below a second signal quality threshold value (Table 2 & Section IV disclose a table ranking 16 cell clusters by Average UL Quality. Based on table 2, a first cell cluster can be identified as having signal quality exceeding a first signal quality threshold value (e.g. Cluster ID 4 can be identified as having an Average UL Quality exceeding a 0.5 threshold value). Also based on table 2, a second cell cluster can be identified as having signal quality below a second signal quality threshold value (e.g. Cluster ID 2 can be identified as having an Average UL Quality below a 0.3 threshold value). Note that for the average UL Quality of Cluster ID 2 to be below 0.3, at least one cell in Cluster ID 2 must have an UL Quality below 0.3.).
Therefore, it would have been obvious to someone having ordinary skill in the art prior to the effective filing date of the claimed invention to have the method of claim 1, as disclosed by Larish in view of Gaber and El-Hoiydi and Shin, wherein the cells of the identified first cluster has a signal quality exceeding a first signal quality threshold value while the at least one cell of the identified second cluster has a signal quality below a second signal quality threshold value, as taught by Gaber. The motivation to do so would be to have a method for a device to learn from a model mapping RRM parameter settings to KPIs for a golden cluster of cells experiencing interference but with KPIs above a first threshold value, and use said learning to apply RRM settings to a target cluster of cells experiencing interference with KPIs below a second threshold value, to achieve estimated, and ideally improved, KPIs in the target cluster of cells.
Regarding claim 7, Larish in view of Gaber and El-Hoiydi and Shin disclose the method of claim 1.
Larish discloses wherein the first interference and/or the second interference further is restricted to a single radio channel (Fig 1A & col 2, lines 43-53 disclose cell towers 110-1 to 110-4 may operate using a UMTS carrier (i.e. single radio channel providing co-channel interference) or using LTE in a single 700 MHz frequency band.).
Regarding claim 8, Larish in view of Gaber and El-Hoiydi and Shin disclose the method of claim 1.
Larish discloses further comprising: updating the created model with a signal quality value of the identified at least one second cell cluster reflecting the adjusted nominal uplink power level of the at least one cell of the identified at least one second cell cluster (Col 7, lines 21-39 disclose a benchmarking module 450 that compares an RRM model against data from a target cluster to verify that the RRM model is providing a desired optimization. The benchmarking module monitors KPIs (e.g. signal quality) and links learning performance of a learner module 410 by providing benchmarking data from the target cluster to improve the learning model (e.g. adjusting nominal uplink power level of at least one of at least one cell in the target cluster) that was based on previously collected training data (i.e. from a golden cluster.).
Regarding claim 9, Larish in view of Gaber and El-Hoiydi and Shin disclose the method of claim 1.
Larish discloses further comprising: updating the created model with a signal quality value of one or more cells neighboring on the at least one cell of the identified at least one second cell cluster, the signal quality value reflecting the adjusted nominal uplink power level of the at least one cell of the identified at least one second cell cluster (Col 7, lines 21-39 disclose a benchmarking module 450 that compares an RRM model against data from a target cluster, which includes one or more cells neighboring at least one cell of the target cluster, to verify that the RRM model is providing a desired optimization. The benchmarking module monitors KPIs (e.g. signal quality) and links learning performance of a learner module 410 by providing benchmarking data from the target cluster to improve the learning model (e.g. adjusting nominal uplink power level of at least one of at least one cell in the target cluster) that was based on previously collected training data (i.e. from a golden cluster).).
Regarding claim 10, Larish in view of Gaber and El-Hoiydi and Shin disclose the method of claim 1.
Larish discloses the creating of a model further comprises: creating a model based on a plurality of cell clusters to map a nominal uplink power level to an estimated signal quality value for said plurality of cell clusters (Col 9, lines 19-35 disclose extracting a training model on a identified well-performing golden cell cluster that provides closed form expressions approximating the relationship between KPIs (e.g. an estimated signal quality value) and RRM parameters (e.g. nominal uplink power level). Col 7, lines 21-39 disclose a benchmarking module 450 that compares an RRM model against data from a target cluster, to verify that the RRM model is providing a desired optimization. The benchmarking module monitors KPIs (e.g. signal quality) and links learning performance of a learner module 410 by providing benchmarking data from the target cluster to improve the learning model (e.g. adjusting nominal uplink power level of at least one of at least one cell in the target cluster) that was based on previously collected training data (i.e. from a golden cluster). Thus, an updated RRM module is based on at least the golden cluster and a target cluster, and in general could be based on the golden cluster and a plurality of target clusters.).
Larish fails to disclose identifying of at least one first cell cluster comprising: identifying a plurality of cell clusters comprising at least one cell being subjected to the first interference, the cells of the identified plurality of cell clusters further having a signal quality exceeding a signal quality threshold value.
However, Gaber teaches identifying of at least one first cell cluster comprising: identifying a plurality of cell clusters comprising at least one cell being subjected to the first interference, the cells of the identified plurality of cell clusters further having a signal quality exceeding a signal quality threshold value (Table 2 & Section IV disclose a table ranking the 16 cell clusters by Average UL Quality. Based on table 2, a plurality of cell clusters can be identified as having signal quality exceeding a signal quality threshold value (e.g. Cluster IDs 4, 5, 11 can be identified as having an Average UL Quality exceeding a 0.3 threshold value).). Further, the Average UL Level of Clusters IDs 4, 5 & 11 are greater than Cluster ID 15, even though Cluster IDs 4, 5 & 11 have a lower Average UL Quality than Cluster ID 15, indicating that at least one cell in each of Cluster IDs 4, 5 and 11 is being subjected to a first interference.).
Therefore, it would have been obvious to someone having ordinary skill in the art prior to the effective filing date of the claimed invention to have the method of claim 1 wherein the creating of a model further comprises: creating a model based on a plurality of cell clusters to map a nominal uplink power level to an estimated signal quality value for said plurality of cell clusters, as disclosed by Larish in view of Gaber and El-Hoiydi and Shin, wherein the plurality of cell clusters is identified as at least one first cell cluster comprising: identifying a plurality of cell clusters comprising at least one cell being subjected to the first interference, the cells of the identified plurality of cell clusters further having a signal quality exceeding a signal quality threshold value, as taught by Gaber. The motivation to do so would be to have a method for creating a model mapping RRM parameter settings to KPIs based on a plurality of cluster of cells experiencing interference but with KPIs above a threshold value, and using the model to apply RRM settings to a target cluster of cells experiencing interference with KPIs below the threshold value, to achieve estimated, and ideally improved, KPIs in the target cluster of cells.
Regarding claim 13, Larish in view of Gaber and El- Hoiydi and Shin disclose the steps recited in claim 1.
Larish discloses a computer program product comprising a non-transitory computer readable medium, the computer readable medium storing a computer program comprising computer- executable instructions for causing a device to perform steps recited in claim 1, when the computer-executable instructions are executed on processing circuitry included in the device (Fig 2 & col 3, lines 52-67 disclose a device 200 comprising a processor 220 that can interpret and execute instructions that may be stored in dynamic on non-volatile storage in memory 230.).
Regarding claim 14, Larish discloses a device configured to control uplink power levels of radio cells in a wireless communications network (Fig 4 & Col 6, lines 19-35 disclose a device comprising a learner module 410 configured to receive training data and generate a model for controlling RRM parameters (e.g. uplink power levels of radio cells).), the device comprising processing circuitry and a memory, said memory containing instructions executable by said processing circuitry (Fig 2 & col 3, lines 52-67 disclose a device 200 comprising a processor 220 that can interpret and execute instructions that may be stored in dynamic on non-volatile storage in memory 230.), whereby the device is operative to:
create a model based on the identified at least one first cell cluster to map a nominal uplink power level to an estimated signal quality value for said at least one first cell cluster (Col 9, lines 19-35 disclose extracting a training model on a identified well-performing golden cell cluster that provides closed form expressions approximating the relationship between KPIs (e.g. an estimated signal quality value) and RRM parameters (e.g. nominal uplink power level).); and
adjust a nominal uplink power level of the at least one cell of the identified at least one second cell cluster to attain a signal quality value in the at least one cell of the second cell cluster (Col 9, lines 36-67 & col 10, lines 1-7 disclose identifying a target cell (i.e. a second cell cluster) and applying the extracted training RRM model to the target cluster (e.g. adjusting a nominal power level of at least one cell of the target cluster by applying the nominal uplink power level derived from the extracted training RRM model) and checking the training RRM model for consistency (e.g. attaining a corresponding KPI or signal quality of the target cluster to check against the estimated KPI from the training RRM model).).
Larish fails to disclose wherein the device is operative to group sets of neighboring cells into a plurality of cell clusters; identify at least one first cell cluster comprising at least one cell being subjected to a first interference, the cells of the identified first cluster further having a signal quality exceeding a signal quality threshold value; identify at least one second cell cluster comprising at least one cell being subjected to a second interference, the at least one cell of the identified second cluster further having a signal quality below the signal quality threshold value.
However, Gaber teaches wherein the device is operative to:
group sets of neighboring cells into a plurality of cell clusters (Fig 3 & Section III disclose grouping of a plurality of cells into 16 cell clusters.);
identify at least one first cell cluster comprising at least one cell being subjected to a first interference, the cells of the identified first cluster further having a signal quality exceeding a signal quality threshold value (Table 2 & Section IV disclose a table ranking the 16 cell clusters by Average UL Quality. Based on table 2, a first cell cluster can be identified as having signal quality exceeding a signal quality threshold value (e.g. Cluster ID 4 can be identified as having an Average UL Quality exceeding a 0.5 threshold value). Further, the Average UL Level of Cluster ID 4 is greater than Cluster IDs 13 & 15, even though Cluster ID 4 has a lower Average UL Quality than Cluster IDs 13 & 15, indicating that at least one cell in Cluster ID 4 is being subjected to a first interference.);
identify at least one second cell cluster comprising at least one cell being subjected to a second interference, the at least one cell of the identified second cluster further having a signal quality below the signal quality threshold value (Table 2 & Section IV disclose a table ranking the 16 cell clusters by Average UL Quality. Based on table 2, a second cell cluster can be identified as having signal quality below the signal quality threshold value (e.g. Cluster ID 2 can be identified as having an Average UL Quality below the 0.5 threshold value). Note that for the average UL Quality of Cluster ID 2 to be below 0.5, at least one cell in Cluster ID 2 must have an UL Quality below 0.5. Further, the Average UL Level of Cluster ID 2 is greater than Cluster IDs 10 & 12, even though Cluster ID 4 has a lower Average UL Quality than Cluster IDs 10 & 12, indicating that at least one cell in Cluster ID 2 is being subjected to a second interference.).
Therefore, it would have been obvious to someone having ordinary skill in the art prior to the effective filing date of the claimed invention to have a device that creates and uses a model based on a golden/well performing cluster of cells to map RRM parameters, such as nominal uplink power level, to KPIs, such as a signal quality value, and adjusts RRM parameters in a target cluster of cells based on the model extracted from the golden cluster of cells to get estimated KPIs for the target cluster of cells, as disclosed by Larish, wherein the golden cluster of cells have UL Quality above an average UL Quality threshold and at least one cell in the golden cluster of cells experiences a first interference; and the target cluster of cells has at least one cell having UL Quality below the average UL Quality threshold and at least one cell in the target cluster of cells experiences a second interference, as taught by Gaber. The motivation to do so would be to have a device that can learn from a model mapping RRM parameter settings to KPIs for a golden cluster of cells experiencing interference but with KPIs above a threshold value, and use said learning to apply RRM settings to a target cluster of cells experiencing interference with KPIs below the threshold value, to achieve estimated, and ideally improved, KPIs in the target cluster of cells.
Larish fails to disclose determining whether the first interference and/or the second interference is a static-type interference; and based on determining that the first interference and/or the second interference is a static-type interference.
However, El-Hoiydi further teaches determining whether the first interference and/or the second interference is a static-type interference; based on determining that the first interference and/or the second interference is a static-type interference (Col 8, lines 63-67 & col 9, lines 1-14 disclose analyzing a metric vector to determine whether a static interference is present, and if so, then performing a blanking step to avoid interference (i.e. creating a model to avoid interference).).
Therefore, it would have been obvious to someone having ordinary skill in the art prior to the effective filing date of the claimed invention to have a device create a model based on an identified at least one first cell cluster to map a nominal uplink power level to an estimated signal quality value for said at least one first cell cluster, as disclosed by Larish, based on determining whether the first interference and/or the second interference is a static-type interference, as taught by El-Hoiydi. The motivation to do so would be to avoid degrading performance by only using a model that maps a nominal uplink power level to an estimated signal quality value when interference is static, and not using the model when dynamic interference would render the model inaccurate.
Larish fails to disclose but Shin further teaches wherein the adjusting is by inputting a desired signal quality value to the created model to compute the nominal uplink power level, and applying the computed nominal uplink power level to attain the desired signal quality value (Col 3, lines 6-13 discloses a combined open loop and closed loop scheme (i.e. model) for UL power control that controls a WTRU transmit power according to an Equation 1 of PSDTx = PSDopen + aDclosed + DMCS (i.e. a model for computing adjustments to a nominal uplink power level). Col 3, lines 65-67 & col 4, lines 1-15 disclose that the an open loop portion of the model is defined by PSDopen = PSDtarget +L (dBm) where PSDtarget is a WTRU-specific parameter that is adjusted according to a QoS such as a target block error rate (BLER) that would be input to the model. Col 5, lines 52-59 disclose that the WTRU adjusts the transmit PSD of a data channel by applying the computed PSDTx on the next UL TTI to attain the target BLER.).
Therefore, it would have been obvious to someone having ordinary skill in the art prior to the effective filing date of the claimed invention to have a device create a model based on an identified at least one first cell cluster to map a nominal uplink power level to an estimated signal quality value for said at least one first cell cluster; grouping sets of neighboring cells into a plurality of cell clusters; identifying at least one first cell cluster comprising at least one cell being subjected to a first interference, the cells of the identified first cluster further having a signal quality exceeding a signal quality threshold value; and identifying at least one second cell cluster comprising at least one cell being subjected to a second interference, the at least one cell of the identified second cluster further having a signal quality below the signal quality threshold value.; determining whether the first interference and/or the second interference is a static-type interference; and based on determining that the first interference and/or the second interference is a static-type interference; and adjusting a nominal uplink power level of the at least one cell of an identified at least one second cell cluster to attain a signal quality value in the at least one cell of the second cell cluster, as disclosed by Larish in view of Gaber and El-Hoiydi, wherein the adjusting is by inputting a desired signal quality value to the created model to compute the nominal uplink power level, and applying the computed nominal uplink power level to attain the desired signal quality value, as further taught by Shin. The motivation to do so would be to have a UE, in a second cell within a second cluster of cells and experiencing static interference and signal quality below a signal quality threshold, create a combined open and closed loop UL power control model for computing an UL Tx power based on a cell, within a first cluster of cells experiencing signal quality above the signal quality threshold, experiencing static interference and use the model with input from the second cell regarding a desired target BLER, to apply an adjusted Tx power computed by the model in order to attain the target BLER, so an improved Quality of Experience (QoS) can be achieved in the second cell.
Regarding claim 15, Larish in view of Gaber and El-Hoiydi and Shin disclose the device of claim 14.
Larish discloses further being operative to create the model using machine learning (Fig 3A & col 5, lines 39-43 disclose an ML engine 320 that is part of a device that may apply machine learning to identify and model target clusters in a RAN.).
Regarding claim 16, Larish in view of Gaber and El-Hoiydi and Shin disclose the device of claim 15.
Larish discloses further being operative to, when creating the model: perform a regression analysis on the identified at least one first cell cluster to create the model mapping a nominal uplink power level to an estimated signal quality value for said at least one first cell cluster (Col 9, lines 19-35 disclose extracting a training model on a identified well-performing golden cell cluster that provides closed form expressions approximating the relationship between KPIs (e.g. an estimated signal quality value) and RRM parameters (e.g. nominal uplink power level). Fig 3A & col 5, lines 39-43 disclose that the model may be based on ML engine 320 as part of a device that applies machine learning to identify and model target clusters in a RAN, and that the machine learning may include using neural networks and deep learning (e.g. regression analysis).).
Regarding claim 21, Larish in view of Gaber and El-Hoiydi and Shin disclose the device of claim 14.
Larish discloses further being operative to: update the created model with a signal quality value of the identified at least one second cell cluster reflecting the adjusted nominal uplink power level of the at least one cell of the identified at least one second cell cluster (Col 7, lines 21-39 disclose a benchmarking module 450 that compares an RRM model against data from a target cluster to verify that the RRM model is providing a desired optimization. The benchmarking module monitors KPIs (e.g. signal quality) and links learning performance of a learner module 410 by providing benchmarking data from the target cluster to improve the learning model (e.g. adjusting nominal uplink power level of at least one of at least one cell in the target cluster) that was based on previously collected training data (i.e. from a golden cluster.).
Regarding claim 22, Larish in view of Gaber and El-Hoiydi and Shin disclose the device of claim 14.
Larish discloses further being operative to: update the created model with a signal quality value of one or more cells neighboring on the at least one cell of the identified at least one second cell cluster, the signal quality value reflecting the adjusted nominal uplink power level of the at least one cell of the identified at least one second cell cluster (Col 7, lines 21-39 disclose a benchmarking module 450 that compares an RRM model against data from a target cluster, which includes one or more cells neighboring at least one cell of the target cluster, to verify that the RRM model is providing a desired optimization. The benchmarking module monitors KPIs (e.g. signal quality) and links learning performance of a learner module 410 by providing benchmarking data from the target cluster to improve the learning model (e.g. adjusting nominal uplink power level of at least one of at least one cell in the target cluster) that was based on previously collected training data (i.e. from a golden cluster).).
Regarding claim 23, Larish in view of Gaber and El-Hoiydi and Shin disclose the device of claim 14.
Larish discloses further being operative to, when creating a model: create a model based on the plurality of cell clusters to map a nominal uplink power level to an estimated signal quality value for said plurality of cell clusters (Col 9, lines 19-35 disclose extracting a training model on a identified well-performing golden cell cluster that provides closed form expressions approximating the relationship between KPIs (e.g. an estimated signal quality value) and RRM parameters (e.g. nominal uplink power level). Col 7, lines 21-39 disclose a benchmarking module 450 that compares an RRM model against data from a target cluster, to verify that the RRM model is providing a desired optimization. The benchmarking module monitors KPIs (e.g. signal quality) and links learning performance of a learner module 410 by providing benchmarking data from the target cluster to improve the learning model (e.g. adjusting nominal uplink power level of at least one of at least one cell in the target cluster) that was based on previously collected training data (i.e. from a golden cluster). Thus, an updated RRM module is based on at least the golden cluster and a target cluster, and in general could be based on the golden cluster and a plurality of target clusters.).
Larish fails to disclose when identifying at least one first cell cluster: identify a plurality of cell clusters comprising at least one cell being subjected to the first interference, the cells of the identified plurality of cell clusters further having a signal quality exceeding a signal quality threshold value.
However, Gaber teaches when identifying at least one first cell cluster: identify a plurality of cell clusters comprising at least one cell being subjected to the first interference, the cells of the identified plurality of cell clusters further having a signal quality exceeding a signal quality threshold value (Table 2 & Section IV disclose a table ranking the 16 cell clusters by Average UL Quality. Based on table 2, a plurality of cell clusters can be identified as having signal quality exceeding a signal quality threshold value (e.g. Cluster IDs 4, 5, 11 can be identified as having an Average UL Quality exceeding a 0.3 threshold value).). Further, the Average UL Level of Clusters IDs 4, 5 & 11 are greater than Cluster ID 15, even though Cluster IDs 4, 5 & 11 have a lower Average UL Quality than Cluster ID 15, indicating that at least one cell in each of Cluster IDs 4, 5 and 11 is being subjected to a first interference.).
Therefore, it would have been obvious to someone having ordinary skill in the art prior to the effective filing date of the claimed invention to have the device of claim 14 further being operative to, when creating a model: create a model based on the plurality of cell clusters to map a nominal uplink power level to an estimated signal quality value for said plurality of cell clusters, as disclosed by Larish in view of Gaber and El-Hoiydi and Shin , wherein when identifying at least one first cell cluster: identify a plurality of cell clusters comprising at least one cell being subjected to a first interference, the cells of the identified plurality of cell clusters further having a signal quality exceeding a signal quality threshold value, as taught by Gaber. The motivation to do so would be to have a device capable of creating a model mapping RRM parameter settings to KPIs based on a plurality of cluster of cells experiencing interference but with KPIs above a threshold value, and using the model to apply RRM settings to a target cluster of cells experiencing interference with KPIs below the threshold value, to achieve estimated, and ideally improved, KPIs in the target cluster of cells.
Claim 4 rejected under pre-AIA 35 U.S.C. 103(a) as being unpatentable over Larish et al. (US 10039016)(herein after “Larish”) in view of Gaber et al. (A. Gaber, M. Zaki & A. Mohamed & M. Beshara, “Cellular Network Power Control Optimization Using Unsupervised Machine Learning”, ITCE 2019, Aswan, Egypt, February 2-4, 2019)(herein after “Gaber”) and El- Hoiydi et al. (US 9668070)(herein after “El-Hoiydi”) and Shin et al. (US 9026169)(herein after “Shin”), as applied to claim 3, and further in view of Vallejo-Mora et al. (A. B. Vallejo-Mora, M. Toril, S. Luna-Ramirez, M. Regueira & S. Pedraza, “Analytical Model for Estimating the Impact of Changing the Nominal Power Parameter in LTE”, Hindawi, Mobile Information Systems, Vol. 2018, Article ID 2458204, January 4, 2018)(herein after “Vallejo-Mora”).
Regarding claim 4, Larish in view of Gaber and El-Hoiydi and Shin disclose the method of claim 3.
Larish fails to disclose wherein at least one cell of the identified at least one first cell cluster has a nominal uplink power level differing from that of remaining cells in said at least one first cell cluster.
However, Vallejo-Mora further teaches wherein at least one cell of the identified at least one first cell cluster has a nominal uplink power level differing from that of remaining cells in said at least one first cell cluster (Section 2 discloses a nominal power parameter for UE transmit power setting (i.e. a nominal uplink power level setting) which can be adjusted on a cell-by-cell basis.).
Therefore, it would have been obvious to someone having ordinary skill in the art prior to the effective filing date of the claimed invention to have the method of claim 3, as disclosed by Larish in view of Gaber and El-Hoiydi and Shin, wherein at least one cell of the identified at least one first cell cluster has a nominal uplink power level differing from that of remaining cells in said at least one first cell cluster, as further taught by Vallejo-Mora. The motivation to do so would be to have a method performed by a device that uses a model based on a golden/well performing cluster of cells to map RRM parameters, such as nominal uplink power level, to KPIs, such as a signal quality value, and adjusts RRM parameters in a target cluster of cells that may have different RRM parameter settings (e.g. different nominal uplink power level settings) based on the model extracted from the golden cluster of cells to get estimated, and ideally improved, KPIs for the target cluster of cells.
Claim 6 rejected under pre-AIA 35 U.S.C. 103(a) as being unpatentable over Larish et al. (US 10039016)(herein after “Larish”) in view of Gaber et al. (A. Gaber, M. Zaki & A. Mohamed & M. Beshara, “Cellular Network Power Control Optimization Using Unsupervised Machine Learning”, ITCE 2019, Aswan, Egypt, February 2-4, 2019)(herein after “Gaber”) and El- Hoiydi et al. (US 9668070)(herein after “El-Hoiydi”) and Shin et al. (US 9026169)(herein after “Shin”), as applied to claim 1, and further in view of Shi et al. (US 2016/0157120)(herein after “Shi”).
Regarding claim 6, Larish in view of Gaber and El-Hoiydi and Shin discloses the method of claim 1.
Larish fails to disclose wherein the at least one cell of the identified second cluster has a lower signal quality than remaining cells in the identified second cluster.
However, Shi further teaches wherein the at least one cell of the identified second cluster has a lower signal quality than remaining cells in the identified second cluster ([0013] discloses a UE reporting signal quality of a neighbor cell being higher than the signal quality of a primary cell.).
Therefore, it would have been obvious to someone having ordinary skill in the art prior to the effective filing date of the claimed invention to have the method of claim 1, as disclosed by Larish in view of Gaber and El-Hoiydi and Shin, wherein the at least one cell of the identified second cluster has a lower signal quality than remaining cells in the identified second cluster, as further taught by Shi. The motivation to do so would be to have a method performed by a device that uses a model based on a golden/well performing cluster of cells to map RRM parameters, such as nominal uplink power level, to KPIs, such as a signal quality value, and adjusts RRM parameters in a target cluster of cells that may have a particular cell experiencing poor signal quality, based on the model extracted from the golden cluster of cells to get estimated, and ideally improved, KPIs for the target cluster of cells.
Claim 11 rejected under pre-AIA 35 U.S.C. 103(a) as being unpatentable over Larish et al. (US 10039016)(herein after “Larish”) in view of Gaber et al. (A. Gaber, M. Zaki & A. Mohamed & M. Beshara, “Cellular Network Power Control Optimization Using Unsupervised Machine Learning”, ITCE 2019, Aswan, Egypt, February 2-4, 2019)(herein after “Gaber”) and El- Hoiydi et al. (US 9668070)(herein after “El-Hoiydi”) and Shin et al. (US 9026169)(herein after “Shin”), as applied to claim 1, and further in view of Sakamoto et al. (JPH04297137)(herein after “Sakamoto”).
Regarding claim 11, Larish in view of Gaber and El-Hoiydi and Shin disclose the method of claim 1.
Larish fails to disclose the first interference and/or the second interference not varying more than a maximum allowable value from a nominal interference level over a time period.
However, Sakamoto further teaches the first interference and/or the second interference not varying more than a maximum allowable value from a nominal interference level over a time period ([0010] discloses receiving an interference amount over a certain period of time and determining if the amount of interference is less than a predetermined value).
Therefore, it would have been obvious to someone having ordinary skill in the art prior to the effective filing date of the claimed invention to have the method of claim 1, as disclosed by Larish in view of Gaber and El-Hoiydi and Shin, wherein the first interference and/or the second interference is not varying more than a maximum allowable value from a nominal interference level over a time period, as further taught by Sakamoto. The motivation to do so would be to have a method for a model mapping RRM parameter settings to KPIs for a golden cluster of cells experiencing an amount of interference not varying to be more than a maximum predetermined value, but with KPIs above a threshold value, and use said learning to apply RRM settings to a target cluster of cells experiencing interference not varying to be more than a maximum predetermined value with KPIs below the threshold value, to achieve estimated, and ideally improved, KPIs in the target cluster of cells.
Claim 17 rejected under pre-AIA 35 U.S.C. 103(a) as being unpatentable over Larish et al. (US 10039016)(herein after “Larish”) in view of Gaber et al. (A. Gaber, M. Zaki & A. Mohamed & M. Beshara, “Cellular Network Power Control Optimization Using Unsupervised Machine Learning”, ITCE 2019, Aswan, Egypt, February 2-4, 2019)(herein after “Gaber”) and El- Hoiydi et al. (US 9668070)(herein after “El-Hoiydi”) and Shin et al. (US 9026169)(herein after “Shin”), as applied to claim 16, and further in view of Vallejo-Mora et al. (A. B. Vallejo-Mora, M. Toril, S. Luna-Ramirez, M. Regueira & S. Pedraza, “Analytical Model for Estimating the Impact of Changing the Nominal Power Parameter in LTE”, Hindawi, Mobile Information Systems, Vol. 2018, Article ID 2458204, January 4, 2018)(herein after “Vallejo-Mora”).
Regarding claim 17, Larish in view of Gaber and El-Hoiydi and Shin disclose the device of claim 16.
Larish fails to disclose wherein at least one cell of the identified at least one first cell cluster has a nominal uplink power level differing from that of remaining cells in said at least one first cell cluster.
However, Vallejo-Mora further teaches wherein at least one cell of the identified at least one first cell cluster has a nominal uplink power level differing from that of remaining cells in said at least one first cell cluster (Section 2 discloses a nominal power parameter for UE transmit power setting (i.e. a nominal uplink power level setting) which can be adjusted on a cell-by-cell basis.).
Therefore, it would have been obvious to someone having ordinary skill in the art prior to the effective filing date of the claimed invention to have the device of claim 16, as disclosed by Larish in view of Gaber and El-Hoiydi and Shin, wherein at least one cell of the identified at least one first cell cluster has a nominal uplink power level differing from that of remaining cells in said at least one first cell cluster, as further taught by Vallejo-Mora. The motivation to do so would be to have a device that uses a model based on a golden/well performing cluster of cells to map RRM parameters, such as nominal uplink power level, to KPIs, such as a signal quality value, and adjusts RRM parameters in a target cluster of cells that may have different RRM parameter settings (e.g. different nominal uplink power level settings) based on the model extracted from the golden cluster of cells to get estimated, and ideally improved, KPIs for the target cluster of cells.
Claim 24 rejected under pre-AIA 35 U.S.C. 103(a) as being unpatentable over Larish et al. (US 10039016)(herein after “Larish”) in view of Gaber et al. (A. Gaber, M. Zaki & A. Mohamed & M. Beshara, “Cellular Network Power Control Optimization Using Unsupervised Machine Learning”, ITCE 2019, Aswan, Egypt, February 2-4, 2019)(herein after “Gaber”) and El- Hoiydi et al. (US 9668070)(herein after “El-Hoiydi”) and Shin et al. (US 9026169)(herein after “Shin”), as applied to claim 14, and further in view of Sakamoto et al. (JPH04297137)(herein after “Sakamoto”).
Regarding claim 24, Larish in view of Gaber and El-Hoiydi and Shin disclose the device of claim 14.
Larish fails to disclose the first interference and/or the second interference not varying more than a maximum allowable value from a nominal interference level over a time period.
However, Sakamoto further teaches the first interference and/or the second interference not varying more than a maximum allowable value from a nominal interference level over a time period ([0010] discloses receiving and interference amount over a certain period of time and determining if the amount of interference is less than a predetermined value).
Therefore, it would have been obvious to someone having ordinary skill in the art prior to the effective filing date of the claimed invention to have the device of claim 14, as disclosed by Larish in view of Gaber and El-Hoiydi and Shin, wherein the first interference and/or the second interference not varying more than a maximum allowable value from a nominal interference level over a time period, as further taught by Sakamoto. The motivation to do so would be to have a device capable of creating a model mapping RRM parameter settings to KPIs for a golden cluster of cells experiencing an amount of interference not varying to be more than a maximum predetermined value, but with KPIs above a threshold value, and use said learning to apply RRM settings to a target cluster of cells experiencing interference not varying to be more than a maximum predetermined value with KPIs below the threshold value, to achieve estimated, and ideally improved, KPIs in the target cluster of cells.
Claim 25 rejected under 35 U.S.C. 103 as being unpatentable over Larish et al. (US 10039016)(herein after “Larish”) in view of Gaber et al. (A. Gaber, M. Zaki & A. Mohamed & M. Beshara, “Cellular Network Power Control Optimization Using Unsupervised Machine Learning”, ITCE 2019, Aswan, Egypt, February 2-4, 2019)(herein after “Gaber”) and El- Hoiydi et al. (US 9668070)(herein after “El-Hoiydi”) and Shin et al. (US 9026169)(herein after “Shin”), as applied to claim 1, and further in view of Perkal et al. (US 20190207821)(herein after “Perkal”) and Therianos et al. (US 20150299802)(herein after “Therianos”) and Shachar et al. (US 20210342847)(herein after “Shachar”).
Regarding claim 25, Larish in view of Gaber and El-Hoiydi and Shin disclose the method of claim 1.
Larish fails to disclose wherein determining whether the first interference and/or the second interference is a static-type interference comprises: applying a plurality of rule-based thresholds or a statistical analysis on normalized interference values.
However, E-Hoiydi further teaches wherein determining whether the first interference and/or the second interference is a static-type interference comprises: applying a plurality of rule-based thresholds or a statistical analysis on normalized interference values (Col 8, lines 62-67 & col 9, lines 1-2 disclose that the determining of whether a first and/or second interference is a static interference comprises comparison between a maximum value derived from channel traffic values of a metric vector and an average value (i.e. normalized value) derived from the channel traffic values of a metric vector (i.e. a statistical analysis on normalized interference values).).
Therefore, it would have been obvious to someone having ordinary skill in the art prior to the effective filing date of the claimed invention to have the method of claim 1, as disclosed by Larish in view of Gaber and El-Hoiydi and Shin, wherein determining whether the first interference and/or the second interference is a static-type interference comprises: applying a plurality of rule-based thresholds or a statistical analysis on normalized interference values, as further taught by El-Hoiydi. The motivation to do so would be to have a method for determining whether an interference is a static interference based on a statistical analysis comparing a maximum value of the interference to an average value of the interference in order to have a simple, low complexity and fast processing method for determining if an interference is a static interference.
Larish fails to disclose applying a random forest-based classifier.
However, Perkal further teaches applying a random forest-based classifier ([0025] discloses applying a random forest-based machine learning classifier.).
Therefore, it would have been obvious to someone having ordinary skill in the art prior to the effective filing date of the claimed invention to have the method of claim 1, as disclosed by Larish in view of Gaber and El-Hoiydi and Shin, and applying a random forest-based classifier, as further taught by Perkal. The motivation to do so would be to have a more accurate method for determining if an interference is a static interference by using a random forest-based classifier.
Larish fails to disclose applying an L1-norm logistic regression model; and applying an L2-norm logistic regression model.
However, Therianos further teaches applying an L1-norm logistic regression model; applying an L2-norm logistic regression model ([0107] discloses applying a logistical regression model with L2-norm or L1-norm penalty.).
Therefore, it would have been obvious to someone having ordinary skill in the art prior to the effective filing date of the claimed invention to have the method of claim 1, as disclosed by Larish in view of Gaber and El-Hoiydi and Shin, and applying an L1-norm logistic regression model and/or an L2-norm logistic regression model, as further taught by Therianos. The motivation to do so would be to have a more accurate method for determining if an interference is a static interference by using an L1-norm logistic regression model and/or an L2-norm logistic regression model.
Larish fails to disclose applying Local Interpretable Model-Agnostic Explanations (LIME) and SHapley Adaptive exPlanations (SHAP) analysis.
However, Shachar further teaches applying Local Interpretable Model-Agnostic Explanations (LIME) and SHapley Adaptive exPlanations (SHAP) analysis ([0017] discloses use of SHapley Additive exPlanations (SHAP) and/or Local Interpretable Model-agnostic Explanations (LIME) analysis.).
Therefore, it would have been obvious to someone having ordinary skill in the art prior to the effective filing date of the claimed invention to have the method of claim 1, as disclosed by Larish in view of Gaber and El-Hoiydi and Shin, and applying Local Interpretable Model-Agnostic Explanations (LIME) and SHapley Adaptive exPlanations (SHAP) analysis, as further taught by Shachar. The motivation to do so would be to have a more accurate method for determining if an interference is a static interference by using Model-Agnostic Explanations (LIME) and SHapley Adaptive exPlanations (SHAP) analysis.
Conclusion
The following prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Wang et al. (US 2006/0262840) discloses a Method and Apparatus for Transmit Power Control.
Okumura et al. (US 20030003942) discloses a Transmit Power Control Method and Transmit Power Control System Suitable to Mobile Communications.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JAMES P SEYMOUR whose telephone number is (571)272-7654. The examiner can normally be reached M-F 8-5 EST.
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/JAMES P SEYMOUR/Examiner, Art Unit 2419
/JACKIE ZUNIGA ABAD/Primary Examiner, Art Unit 2469
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