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 .
This Office Action is in response to the request for continued examination correspondence filed 02/16/2026.
Claims 1-13, 15-17, 19-20, & 55-56.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 10/05/2025 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Continued Examination Under 37 CFR 1.114
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 02/16/2026 has been entered.
Response to Arguments
Applicant’s arguments, see Applicant Arguments/REMARKS, filed 02/16/2026, with respect to the rejection(s) of claims 1-13, 15-17, 19-20, & 55-56 under 35 USC 103 have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of claim amendments warranting further search and inquiry.
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 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.
Claims 1-4, 17, 19-20 & 55-56 are rejected under 35 U.S.C. 103 as being unpatentable over Martone et al (US 11,265,040) in view of Sofuoglu-1 et al (US10448261B2), in further view of Sofuoglu-2 et al (US20160277946A1).
Regarding claim 1, Martone teaches a system for spectrum sharing comprising:
at least one controller configured to:
receive first data pertaining to a first system spectrum (Fig 1. 100, 110, Figure 3 302, 304, 306, 112, col 3 line 39-42, col 2-3 lines 55-67 & lines 1-8 respectively - controller receive first data pertaining to a first system and send to and receive from a second system configured in same spectrum), the at least one controller being further configured to (Fig 1. 100, 110, Figure 3 302, 304, 306, 112, col 3 line 39-42, col 2-3 lines 55-67 & lines 1-8 respectively - controller receive first data pertaining to a first system and send to and receive from a second system configured in same spectrum); and
send to and receive second data from a second system, the first system and second system configured to use at least part of a same spectrum (Fig 1. 100, 110, Figure 3 302, 304, 306, 112, col 3 line 39-42, col 2-3 lines 55-67 & lines 1-8 respectively - controller receive first data pertaining to a first system and send to and receive from a second system configured in same spectrum), the at least one controller being further configured to (Fig 1. 100, 110, Figure 3 302, 304, 306, 112, col 3 line 39-42, col 2-3 lines 55-67 & lines 1-8 respectively - controller receive first data pertaining to a first system and send to and receive from a second system configured in same spectrum);
the at least one controller being further configured to:
based at least in part on the first data, determine that the first system is using at least part of the same spectrum (Fig 1. 100, 110, Figure 3 302, 304, 306, 112, col 3 lines 39-42 determine first system in same spectrum, spectrum sensing generating knowledge);
based at least in part on the second data, determine that the first system is likely to be interfered with by the second system (Fig 1, 100, 119, Figure 3 302, 304, 306, 112, col 3 lines 43-67 - determine likely to be interfered with by estimating power spectrum of the EME encompasses radar system and communication system);
and provide instructions instructing the second system to modify one or more operational parameters of the second system based on the interference mitigation technique to reduce interference with the first system (Fig 1, 100, 119, Figure 3 302, 304, 306, 112, Fig. 4, 406, col 3 lines 61-67, col 6 lines 44-59 - providing instruction/control/adjust operational parameters of UE - communication system - optimal sub-band information to reduce interference to radar system and communication base stations (CBS - communication system)).
But Martone fails to teach upon determining that the second system is likely to interfere with the first system, identify an interference mitigation technique to be applied to the second system to reduce interference by the second system.
However, Sofuoglu-1 teaches upon determining that the second system is likely to interfere with the first system, identify an interference mitigation technique to be applied to the second system to reduce interference by the second system (Firstly, col 5 lines 41-44, col 11 lines 6-19, Secondly, col 2 lines 58-60, col 3 lines 3-10; First, teaches determining that one cell/system is likely interfering with another by identifying a polluter cell based on detected signal measurements in a problem area and comparison to a pollution threshold, thereby indicating that the second system is likely to interfere with the first system; Secondly, teachings identifying an interference mitigation technique to be applied to the interfering second system by identifying a polluter cell and then applying a coverage shrink action, such as reducing power or changing antenna tilt to reduce interference caused by that polluter cell).
Martone teaches a spectrum sharing system in which a controller exchanges data with two systems that share at least part of the same spectrum, determines when spectrum usage by one system is likely to impact another, and issues control instructions to modify operational parameters (etc. power, frequency, timing) to mitigate interference. Furthermore, Sofuoglu-1 teaches determining that a second system is likely to interfere with a first system by identifying a polluter cell based on measurements, weak coverage events, and pollution threshold and further teaches identifying and applying an interference mitigation technique to that second system by updating polluter-cell parameters such as power decrease or antenna tilt adjustment to reduce interference.
A POSITA would have been motivated to combine Martone’s spectrum sharing architecture with Sofuoglu-1’s identifying polluter cell and implanting interference mitigation framework. Incorporating Martone’s controller-based spectrum coordination into the interference mitigation framework of Sofuoglu-1would predictably yield a system that not only detects potential interference but also determines spectrum usage conflicts and provides explicit operations parameter adjustments to the interfering system, thus arriving at the claimed invention. The combination would have been obvious because it represents a straightforward substitution of known interference coordination techniques from Martone into the existing interference mitigation process of Sofuoglu-1to improve system coexistence in shared spectrum environments.
However, Sofuoglu-1 fails to teach but Sofuoglu-2 teaches wherein identifying the interference mitigation technique includes evaluating a plurality of candidate interference mitigation techniques using one or more performance curves provided by a performance database ([0019]-[0020], [0028], [0032]-[0036], [0042], [0050], [0045]-[0048], [0057]-[0060], [0070]-[0072], teaches evaluating a plurality of candidate parameter-change solution sets where “solution set” is one or a combination of configuration parameters, and applying different solution sets over time to identify an optimum set based on KPI outcomes; further teaches use of database/OSS-backed framework storing configuration data, baseline data, and measured performance metrics including OSS and databases/datastores; further teaches performance relationships correlating to performance curves by analyzing multi-dimensional plots and KPI values as functions of operational parameters such as time-to-trigger and hysteresis, and by evaluating actual or predicted impacts of solution sets on KPI), the one or more performance curves correlating
(i) a predicted impact of the interference mitigation technique on the first system ([0042], [0068], corresponding first and second systems are the first and second wireless communication cells or source-cell and neighbor cells relations; teaches evaluation across multiple cells/cells relation including first and second wireless communication cells and source/cell/neighbor-cells)) and
(ii) a predicted impact of the interference mitigation technique on one or more network performance metrics of the second system for respective operational parameter settings ([0042], [0068], corresponding first and second systems are the first and second wireless communication cells or source-cell and neighbor cells relations; teaches evaluation across multiple cells/cells relation including first and second wireless communication cells and source/cell/neighbor-cells)).
Martone teaches a spectrum sharing system in which a controller exchanges data with two systems that share at least part of the same spectrum, determines when spectrum usage by one system is likely to impact another, and issues control instructions to modify operational parameters (etc. power, frequency, timing) to mitigate interference. Furthermore, Sofuoglu-1 teaches determining that a second system is likely to interfere with a first system by identifying a polluter cell based on measurements, weak coverage events, and pollution threshold and further teaches identifying and applying an interference mitigation technique to that second system by updating polluter-cell parameters such as power decrease or antenna tilt adjustment to reduce interference. Lastly, Sofuoglu-2 teaches evaluating multiple candidate configuration “solution sets” using stored OSS/database performance data and KPI-based relationships to determine an optimal set of parameter changes that balances impacts on networks performance metrics.
A POSITA would have been motivated to combine Martone’s spectrum sharing architecture with Sofuoglu-1’s identifying polluter cell and implanting interference mitigation framework. Incorporating Martone’s controller-based spectrum coordination into the interference mitigation framework of Sofuoglu-1would predictably yield a system that not only detects potential interference but also determines spectrum usage conflicts and provides explicit operations parameter adjustments to the interfering system, thus arriving at the claimed invention. The combination would have been obvious because it represents a straightforward substitution of known interference coordination techniques from Martone into the existing interference mitigation process of Sofuoglu-1to improve system coexistence in shared spectrum environments.
Regarding claim 2, Martone teaches the system wherein the first system is a radar and the second system is a 5G network (Fig 1, 100, 119, col 2-3 lines 62-67, 1-8 respectively: radar system and 5G network communication system).
Regarding claim 3, Martone teaches the system wherein the first data is reference signal data and the second data is network data (Fig 1. 100, 110, Figure 3 302, 304, 306, 112, col 3 line 39-42, col 2-3 lines 55-67 & lines 1-8 respectively - controller receive first data pertaining to a first system and send to and receive from a second system configured in same spectrum).
Regarding claim 4, Martone teaches the system further comprising :a sharing and coexistence system configured to detect the presence of that the first system is present and to provide the first data to the at least one controller (Abstract, Fig 1. 100, 110, Figure 3 302, 304, 306, 112, col 3 line 39-42, col 2-3 lines 55-67 & lines 1-8 respectively - controller receive first data pertaining to a first system and send to and receive from a second system configured in same spectrum; spectrum sharing system in which a controller exchanges data with two systems that share at least part of the same spectrum, determines when spectrum usage by one system is likely to impact another, and issues control instructions to modify operational parameters (etc. power, frequency, timing) to mitigate interference).
Regarding claim 17, Martone teaches the system wherein the first data is provided using an incumbent informing capability indicating that the first system is present (Fig 1. 100, 110, Figure 3 302, 304, 306, 112, col 3 line 39-42, col 2-3 lines 55-67 & lines 1-8 respectively - controller receive first data pertaining to a first system and send to and receive from a second system configured in same spectrum--sharing and coexistence system with electromagnetic environment).
Regarding claim 19, Martone teaches the system wherein the first system is radar, and the at least one controller is configured to determining that the radar is present includes:
the at least one controller being configured to calculate determine a probability that the radar is present based at least in part on data associated with the same spectrum data (Fig 1, 100, 119, Figure 3 302, 304, 306, 112, col 3 lines 43-67 - determine likely to be interfered with by estimating power spectrum of the EME encompasses radar system and communication system);
and the at least one controller being configured to determine that the probability that the radar is present is greater than a threshold value (Fig 1, 100, 119, Figure 3 302, 304, 306, 112, col 3 lines 43-67 - determine likely to be interfered with by estimating power spectrum of the EME encompasses radar system and communication system).
Regarding claim 20, Martone teaches the system wherein the first data includes one or more of location data indicative of a location of the first system and/or includes the channel data including information indicating at least one of which channels may cause interference and/or which channels are available for use (Figure 1 100, 108, P0 R10, R13 UE 104 , 110, col 2 62-67, col 3 1-38 - position of radar system and channel, interference - indication of utilized spectrum/channel).
Regarding claim 55, Martone teaches one or more non-transitory computer-readable media comprising computer-readable instructions (claim 16 non-transitory computer readable medium having software instructions executed on processors), which when executed by one or more processors of a system controller, cause the system controller to:
receive first data pertaining to a first system (Fig 1. 100, 110, Figure 3 302, 304, 306, 112, col 3 line 39-42, col 2-3 lines 55-67 & lines 1-8 respectively - controller receive first data pertaining to a first system and send to and receive from a second system configured in same spectrum);
send to and receive second data from a second system, the first system and second system configured to use at least part of a same spectrum (Fig 1. 100, 110, Figure 3 302, 304, 306, 112, col 3 line 39-42, col 2-3 lines 55-67 & lines 1-8 respectively - controller receive first data pertaining to a first system and send to and receive from a second system configured in same spectrum);
based at least in part on the first data, determine that the first system is using at least part of the same spectrum (Fig 1. 100, 110, Figure 3 302, 304, 306, 112, col 3 lines 39-42 determine first system in same spectrum, spectrum sensing generating knowledge);
based at least in part on the second data, determine that the first system is likely to be interfered with by the second system (Fig 1, 100, 119, Figure 3 302, 304, 306, 112, col 3 lines 43-67 - determine likely to be interfered with by estimating power spectrum of the EME encompasses radar system and communication system);
and provide instructions instructing the second system to modify one or more operational parameters of the second system based on the interference mitigation technique (Fig 1, 100, 119, Figure 3 302, 304, 306, 112, Fig. 4, 406, col 3 lines 61-67, col 6 lines 44-59 - providing instruction/control/adjust operational parameters of UE - communication system - optimal sub-band information to reduce interference to radar system and communication base stations (CBS - communication system)).
But Martone fails to teach upon determining that the second system is likely to interfere with the first system, identify an interference mitigation technique to be applied to the second system to reduce interference by the second system .
However, Sofuoglu-1 teaches upon determining that the second system is likely to interfere with the first system, identify an interference mitigation technique to be applied to the second system to reduce interference by the second system (Firstly, col 5 lines 41-44, col 11 lines 6-19, Secondly, col 2 lines 58-60, col 3 lines 3-10; First, teaches determining that one cell/system is likely interfering with another by identifying a polluter cell based on detected signal measurements in a problem area and comparison to a pollution threshold, thereby indicating that the second system is likely to interfere with the first system; Secondly, teachings identifying an interference mitigation technique to be applied to the interfering second system by identifying a polluter cell and then applying a coverage shrink action, such as reducing power or changing antenna tilt to reduce interference caused by that polluter cell).
Martone teaches a spectrum sharing system in which a controller exchanges data with two systems that share at least part of the same spectrum, determines when spectrum usage by one system is likely to impact another, and issues control instructions to modify operational parameters (etc. power, frequency, timing) to mitigate interference. Furthermore, Sofuoglu-1 teaches determining that a second system is likely to interfere with a first system by identifying a polluter cell based on measurements, weak coverage events, and pollution threshold and further teaches identifying and applying an interference mitigation technique to that second system by updating polluter-cell parameters such as power decrease or antenna tilt adjustment to reduce interference.
A POSITA would have been motivated to combine Martone’s spectrum sharing architecture with Sofuoglu-1’s identifying polluter cell and implanting interference mitigation framework. Incorporating Martone’s controller-based spectrum coordination into the interference mitigation framework of Sofuoglu-1would predictably yield a system that not only detects potential interference but also determines spectrum usage conflicts and provides explicit operations parameter adjustments to the interfering system, thus arriving at the claimed invention. The combination would have been obvious because it represents a straightforward substitution of known interference coordination techniques from Martone into the existing interference mitigation process of Sofuoglu-1to improve system coexistence in shared spectrum environments.
However, Sofuoglu-1 fails to teach but Sofuoglu-2 teaches wherein identifying the interference mitigation technique includes evaluating a plurality of candidate interference mitigation techniques using one or more performance curves provided by a performance database ([0019]-[0020], [0028], [0032]-[0036], [0042], [0050], [0045]-[0048], [0057]-[0060], [0070]-[0072], teaches evaluating a plurality of candidate parameter-change solution sets where “solution set” is one or a combination of configuration parameters, and applying different solution sets over time to identify an optimum set based on KPI outcomes; further teaches use of database/OSS-backed framework storing configuration data, baseline data, and measured performance metrics including OSS and databases/datastores; further teaches performance relationships correlating to performance curves by analyzing multi-dimensional plots and KPI values as functions of operational parameters such as time-to-trigger and hysteresis, and by evaluating actual or predicted impacts of solution sets on KPI), the one or more performance curves correlating
(i) a predicted impact of the interference mitigation technique on the first system ([0042], [0068], corresponding first and second systems are the first and second wireless communication cells or source-cell and neighbor cells relations; teaches evaluation across multiple cells/cells relation including first and second wireless communication cells and source/cell/neighbor-cells)) and
(ii) a predicted impact of the interference mitigation technique on one or more network performance metrics of the second system for respective operational parameter settings ([0042], [0068], corresponding first and second systems are the first and second wireless communication cells or source-cell and neighbor cells relations; teaches evaluation across multiple cells/cells relation including first and second wireless communication cells and source/cell/neighbor-cells)).
Martone teaches a spectrum sharing system in which a controller exchanges data with two systems that share at least part of the same spectrum, determines when spectrum usage by one system is likely to impact another, and issues control instructions to modify operational parameters (etc. power, frequency, timing) to mitigate interference. Furthermore, Sofuoglu-1 teaches determining that a second system is likely to interfere with a first system by identifying a polluter cell based on measurements, weak coverage events, and pollution threshold and further teaches identifying and applying an interference mitigation technique to that second system by updating polluter-cell parameters such as power decrease or antenna tilt adjustment to reduce interference. Lastly, Sofuoglu-2 teaches evaluating multiple candidate configuration “solution sets” using stored OSS/database performance data and KPI-based relationships to determine an optimal set of parameter changes that balances impacts on networks performance metrics.
A POSITA would have been motivated to combine Martone’s spectrum sharing architecture with Sofuoglu-1’s identifying polluter cell and implanting interference mitigation framework. Incorporating Martone’s controller-based spectrum coordination into the interference mitigation framework of Sofuoglu-1would predictably yield a system that not only detects potential interference but also determines spectrum usage conflicts and provides explicit operations parameter adjustments to the interfering system, thus arriving at the claimed invention. The combination would have been obvious because it represents a straightforward substitution of known interference coordination techniques from Martone into the existing interference mitigation process of Sofuoglu-1to improve system coexistence in shared spectrum environments.
Regarding claim 56, Martone teaches a method comprising:
receiving, at a system controller, first data pertaining to a first system (Fig 1. 100, 110, Figure 3 302, 304, 306, 112, col 3 line 39-42, col 2-3 lines 55-67 & lines 1-8 respectively - controller receive first data pertaining to a first system and send to and receive from a second system configured in same spectrum);
exchanging, by the system controller, second data from a second system, the first system and second system configured to use at least part of a same spectrum (Fig 1. 100, 110, Figure 3 302, 304, 306, 112, col 3 line 39-42, col 2-3 lines 55-67 & lines 1-8 respectively - controller receive first data pertaining to a first system and send to and receive from a second system configured in same spectrum);
based at least in part on the first data, determining, at the system controller, that the first system is using at least part of the same spectrum (Fig 1. 100, 110, Figure 3 302, 304, 306, 112, col 3 lines 39-42 determine first system in same spectrum, spectrum sensing generating knowledge);
based at least in part on the second data, determining, , at the system controller, that the first system is likely to be interfered with by the second system (Fig 1, 100, 119, Figure 3 302, 304, 306, 112, col 3 lines 43-67 - determine likely to be interfered with by estimating power spectrum of the EME encompasses radar system and communication system);
and providing, by the system controller, instructions instructing the second system to modify one or more operational parameters of the second system based on the interference mitigation technique (Fig 1, 100, 119, Figure 3 302, 304, 306, 112, Fig. 4, 406, col 3 lines 61-67, col 6 lines 44-59 - providing instruction/control/adjust operational parameters of UE - communication system - optimal sub-band information to reduce interference to radar system and communication base stations (CBS - communication system)).
But Martone fails to teach upon determining that the second system is likely to interfere with the first system, identifying an interference mitigation technique to be applied to the second system to reduce interference by the second system.
However, Sofuoglu-1 teaches upon determining that the second system is likely to interfere with the first system, identifying an interference mitigation technique to be applied to the second system to reduce interference by the second system (Firstly, col 5 lines 41-44, col 11 lines 6-19, Secondly, col 2 lines 58-60, col 3 lines 3-10; First, teaches determining that one cell/system is likely interfering with another by identifying a polluter cell based on detected signal measurements in a problem area and comparison to a pollution threshold, thereby indicating that the second system is likely to interfere with the first system; Secondly, teachings identifying an interference mitigation technique to be applied to the interfering second system by identifying a polluter cell and then applying a coverage shrink action, such as reducing power or changing antenna tilt to reduce interference caused by that polluter cell).
Martone teaches a spectrum sharing system in which a controller exchanges data with two systems that share at least part of the same spectrum, determines when spectrum usage by one system is likely to impact another, and issues control instructions to modify operational parameters (etc. power, frequency, timing) to mitigate interference. Furthermore, Sofuoglu-1 teaches determining that a second system is likely to interfere with a first system by identifying a polluter cell based on measurements, weak coverage events, and pollution threshold and further teaches identifying and applying an interference mitigation technique to that second system by updating polluter-cell parameters such as power decrease or antenna tilt adjustment to reduce interference.
A POSITA would have been motivated to combine Martone’s spectrum sharing architecture with Sofuoglu-1’s identifying polluter cell and implanting interference mitigation framework. Incorporating Martone’s controller-based spectrum coordination into the interference mitigation framework of Sofuoglu-1would predictably yield a system that not only detects potential interference but also determines spectrum usage conflicts and provides explicit operations parameter adjustments to the interfering system, thus arriving at the claimed invention. The combination would have been obvious because it represents a straightforward substitution of known interference coordination techniques from Martone into the existing interference mitigation process of Sofuoglu-1to improve system coexistence in shared spectrum environments.
However, Sofuoglu-1 fails to teach but Sofuoglu-2 teaches wherein identifying the interference mitigation technique includes evaluating a plurality of candidate interference mitigation techniques using one or more performance curves provided by a performance database ([0019]-[0020], [0028], [0032]-[0036], [0042], [0050], [0045]-[0048], [0057]-[0060], [0070]-[0072], teaches evaluating a plurality of candidate parameter-change solution sets where “solution set” is one or a combination of configuration parameters, and applying different solution sets over time to identify an optimum set based on KPI outcomes; further teaches use of database/OSS-backed framework storing configuration data, baseline data, and measured performance metrics including OSS and databases/datastores; further teaches performance relationships correlating to performance curves by analyzing multi-dimensional plots and KPI values as functions of operational parameters such as time-to-trigger and hysteresis, and by evaluating actual or predicted impacts of solution sets on KPI), the one or more performance curves correlating
(i) a predicted impact of the interference mitigation technique on the first system ([0042], [0068], corresponding first and second systems are the first and second wireless communication cells or source-cell and neighbor cells relations; teaches evaluation across multiple cells/cells relation including first and second wireless communication cells and source/cell/neighbor-cells)) and
(ii) a predicted impact of the interference mitigation technique on one or more network performance metrics of the second system for respective operational parameter settings ([0042], [0068], corresponding first and second systems are the first and second wireless communication cells or source-cell and neighbor cells relations; teaches evaluation across multiple cells/cells relation including first and second wireless communication cells and source/cell/neighbor-cells)).
Martone teaches a spectrum sharing system in which a controller exchanges data with two systems that share at least part of the same spectrum, determines when spectrum usage by one system is likely to impact another, and issues control instructions to modify operational parameters (etc. power, frequency, timing) to mitigate interference. Furthermore, Sofuoglu-1 teaches determining that a second system is likely to interfere with a first system by identifying a polluter cell based on measurements, weak coverage events, and pollution threshold and further teaches identifying and applying an interference mitigation technique to that second system by updating polluter-cell parameters such as power decrease or antenna tilt adjustment to reduce interference. Lastly, Sofuoglu-2 teaches evaluating multiple candidate configuration “solution sets” using stored OSS/database performance data and KPI-based relationships to determine an optimal set of parameter changes that balances impacts on networks performance metrics.
A POSITA would have been motivated to combine Martone’s spectrum sharing architecture with Sofuoglu-1’s identifying polluter cell and implanting interference mitigation framework. Incorporating Martone’s controller-based spectrum coordination into the interference mitigation framework of Sofuoglu-1 would predictably yield a system that not only detects potential interference but also determines spectrum usage conflicts and provides explicit operations parameter adjustments to the interfering system, thus arriving at the claimed invention. The combination would have been obvious because it represents a straightforward substitution of known interference coordination techniques from Martone into the existing interference mitigation process of Sofuoglu-1 to improve system coexistence in shared spectrum environments.
Claims 5-6, 8-10, 12-13, & 15 are rejected under 35 U.S.C. 103 as being unpatentable over Martone in view of Sofuoglu-1 in further view of Sofuoglu-2, in further view of Doshi et al. (US20220078626) (hereinafter "Doshi").
Regarding claim 5, Martone Sofuoglu-1 and Sofuoglu-2 fails to teach the system further comprising:
wherein the at least one controller comprises:
a cross layer sensing block component communicatively coupled to the second system and the sharing and coexistence system and configured to generate one or more decisions based on the interference mitigation technique;
a decision engine communicatively coupled to the cross layer sensing block component and the second system and configured to determine the one or more operational parameters using the one or more decisions;
and a performance database communicatively coupled to the decision engine and configured to provide one or more performance curves to the decision engine.
However, Doshi teaches the system further comprising:
wherein the at least one controller comprises:
a cross layer sensing block component communicatively coupled to the second system and the sharing and coexistence system and configured to generate one or more decisions based on the interference mitigation technique (Fig 4D 400, 432, 418, 429 Feature Extraction, Classification, Figure 5 550 [0049]-[0050] system on chip cross layer sensing block determine spectrum sharing parameters & deep learning convolutional network [0069]-[0072] normalization layer; decisions);
a decision engine communicatively coupled to the cross layer sensing block component and the second system and configured to determine the one or more operational parameters using the one or more decisions (Fig 4D 400, 432, 418, 429 Feature Extraction, Classification, Figure 5 550 [0049]-[0050] system on chip cross layer sensing block determine spectrum sharing parameters & deep learning convolutional network [0069]-[0072] normalization layer; decisions);
and a performance database communicatively coupled to the decision engine and configured to provide one or more performance curves to the decision engine (Fig 3 300, 302, Fig 8 800 [0047]-[0049]-SOC processors, neural networks, deep belief networks contained within SOC includes network information, determining spectrum sharing parameters based on sensing performed during sensing period -- performance curves fluctuation loss).
Martone teaches a spectrum sharing system in which a controller exchanges data with two systems that share at least part of the same spectrum, determines when spectrum usage by one system is likely to impact another, and issues control instructions to modify operational parameters (etc. power, frequency, timing) to mitigate interference. Furthermore, Sofuoglu-1 teaches determining that a second system is likely to interfere with a first system by identifying a polluter cell based on measurements, weak coverage events, and pollution threshold and further teaches identifying and applying an interference mitigation technique to that second system by updating polluter-cell parameters such as power decrease or antenna tilt adjustment to reduce interference. Moving on, Sofuoglu-2 teaches evaluating multiple candidate configuration “solution sets” using stored OSS/database performance data and KPI-based relationships to determine an optimal set of parameter changes that balances impacts on networks performance metrics. Doshi provides a spectrum sharing system containing a decision layer with feature extraction, a normalization layer, a system on chip cross layer sensing block that determines spectrum sharing parameters and deep learning convolutional network for enhanced decision making.
A POSITA would have been motivated to combine Martone’s spectrum sharing architecture with Sofuoglu-1’s identifying polluter cell and implanting interference mitigation framework. Incorporating Martone’s controller-based spectrum coordination into the interference mitigation framework of Sofuoglu-1 and further incorporating Doshi’s spectrum sharing framework containing decision layer with feature extraction would predictably yield a system that not only detects potential interference but also determines spectrum usage conflicts and provides explicit operations parameter adjustments to the interfering system, thus arriving at the claimed invention. The combination would have been obvious because it represents a straightforward substitution of known interference coordination techniques from Martone into the existing interference mitigation process of Sofuoglu-1to improve system coexistence in shared spectrum environments.
Regarding claim 6, Martone, Sofuoglu-1 and Sofuoglu-2 fails to teach wherein the cross layer sensing block further comprises a feature extraction and normalization block communicatively coupled to an environmental characterization and pattern classification block, the feature extraction and normalization block being configured to process data received from the cross layer sensing block, and the environmental characterization and pattern classification block being configured to generate the one or more decisions; and wherein the decision engine further comprises an edge compute decision engine configured to determine the operational parameters and a policy engine configured to provide policies to the edge compute decision engine.
However, Doshi teaches wherein the cross layer sensing component further comprises a feature extraction and normalization component communicatively coupled to an environmental characterization and pattern classification component, the feature extraction and normalization component being configured to process data received from the cross layer sensing component, and the environmental characterization and pattern classification component being configured to generate the one or more decisions (Fig 4D 400, 432, 418, 429 Feature Extraction, Classification, Figure 5 550 [0049]-[0050] system on chip cross layer sensing block determine spectrum sharing parameters & deep learning convolutional network [0069]-[0072] normalization layer; decisions);
and wherein the decision engine further comprises an edge compute decision engine configured to determine the one or more operational parameters and a policy engine configured to provide policies to the edge compute decision engine (Fig 4D 400, 432, 418, 429 Feature Extraction, Classification, Figure 5 550 [0049]-[0050] system on chip cross layer sensing block determine spectrum sharing parameters & deep learning convolutional network [0069]-[0072] normalization layer; decisions).
Martone teaches a spectrum sharing system in which a controller exchanges data with two systems that share at least part of the same spectrum, determines when spectrum usage by one system is likely to impact another, and issues control instructions to modify operational parameters (etc. power, frequency, timing) to mitigate interference. Furthermore, Sofuoglu-1 teaches determining that a second system is likely to interfere with a first system by identifying a polluter cell based on measurements, weak coverage events, and pollution threshold and further teaches identifying and applying an interference mitigation technique to that second system by updating polluter-cell parameters such as power decrease or antenna tilt adjustment to reduce interference. Moving on, Sofuoglu-2 teaches evaluating multiple candidate configuration “solution sets” using stored OSS/database performance data and KPI-based relationships to determine an optimal set of parameter changes that balances impacts on networks performance metrics. Doshi provides a spectrum sharing system containing a decision layer with feature extraction, a normalization layer, a system on chip cross layer sensing block that determines spectrum sharing parameters and deep learning convolutional network for enhanced decision making.
A POSITA would have been motivated to combine Martone’s spectrum sharing architecture with Sofuoglu-1’s identifying polluter cell and implanting interference mitigation framework. Incorporating Martone’s controller-based spectrum coordination into the interference mitigation framework of Sofuoglu-1 and further incorporating Doshi’s spectrum sharing framework containing decision layer with feature extraction would predictably yield a system that not only detects potential interference but also determines spectrum usage conflicts and provides explicit operations parameter adjustments to the interfering system, thus arriving at the claimed invention. The combination would have been obvious because it represents a straightforward substitution of known interference coordination techniques from Martone into the existing interference mitigation process of Sofuoglu-1to improve system coexistence in shared spectrum environments.
Regarding claim 8, Martone, Sofuoglu-1 and Sofuoglu-2 fail to teach the system wherein the cross layer sensing component block is further configured to process the first data and the second data.
However, Doshi teaches the system wherein the cross layer sensing component block is further configured to process the first data and the second data (Fig 4D 400, 432, 418, 429 Feature Extraction, Classification, Figure 5 550 [0049]-[0050] system on chip cross layer sensing block determine spectrum sharing parameters & deep learning convolutional network [0069]-[0072] normalization layer; decisions, processing first system data and second system data).
Martone teaches a spectrum sharing system in which a controller exchanges data with two systems that share at least part of the same spectrum, determines when spectrum usage by one system is likely to impact another, and issues control instructions to modify operational parameters (etc. power, frequency, timing) to mitigate interference. Furthermore, Sofuoglu-1 teaches determining that a second system is likely to interfere with a first system by identifying a polluter cell based on measurements, weak coverage events, and pollution threshold and further teaches identifying and applying an interference mitigation technique to that second system by updating polluter-cell parameters such as power decrease or antenna tilt adjustment to reduce interference. Moving on, Sofuoglu-2 teaches evaluating multiple candidate configuration “solution sets” using stored OSS/database performance data and KPI-based relationships to determine an optimal set of parameter changes that balances impacts on networks performance metrics. Doshi provides a spectrum sharing system containing a decision layer with feature extraction, a normalization layer, a system on chip cross layer sensing block that determines spectrum sharing parameters and deep learning convolutional network for enhanced decision making.
A POSITA would have been motivated to combine Martone’s spectrum sharing architecture with Sofuoglu-1’s identifying polluter cell and implanting interference mitigation framework. Incorporating Martone’s controller-based spectrum coordination into the interference mitigation framework of Sofuoglu-1 and further incorporating Doshi’s spectrum sharing framework containing decision layer with feature extraction would predictably yield a system that not only detects potential interference but also determines spectrum usage conflicts and provides explicit operations parameter adjustments to the interfering system, thus arriving at the claimed invention. The combination would have been obvious because it represents a straightforward substitution of known interference coordination techniques from Martone into the existing interference mitigation process of Sofuoglu-1to improve system coexistence in shared spectrum environments.
Regarding claim 9, Martone, Sofuoglu-1 and Sofuoglu-2 fail to teach the system wherein the cross layer sensing component block is further configured to generate the one or more decisions responsive to processing the second data.
However, Doshi teaches the system wherein the cross layer sensing component block is further configured to generate the one or more decisions responsive to processing the second data (Fig 4D 400, 432, 418, 429 Feature Extraction, Classification, Figure 5 550 [0049]-[0050] system on chip cross layer sensing block determine spectrum sharing parameters & deep learning convolutional network [0069]-[0072] normalization layer data; decisions, generate decisions to process second system data).
Martone teaches a spectrum sharing system in which a controller exchanges data with two systems that share at least part of the same spectrum, determines when spectrum usage by one system is likely to impact another, and issues control instructions to modify operational parameters (etc. power, frequency, timing) to mitigate interference. Furthermore, Sofuoglu-1 teaches determining that a second system is likely to interfere with a first system by identifying a polluter cell based on measurements, weak coverage events, and pollution threshold and further teaches identifying and applying an interference mitigation technique to that second system by updating polluter-cell parameters such as power decrease or antenna tilt adjustment to reduce interference. Moving on, Sofuoglu-2 teaches evaluating multiple candidate configuration “solution sets” using stored OSS/database performance data and KPI-based relationships to determine an optimal set of parameter changes that balances impacts on networks performance metrics. Doshi provides a spectrum sharing system containing a decision layer with feature extraction, a normalization layer, a system on chip cross layer sensing block that determines spectrum sharing parameters and deep learning convolutional network for enhanced decision making.
A POSITA would have been motivated to combine Martone’s spectrum sharing architecture with Sofuoglu-1’s identifying polluter cell and implanting interference mitigation framework. Incorporating Martone’s controller-based spectrum coordination into the interference mitigation framework of Sofuoglu-1 and further incorporating Doshi’s spectrum sharing framework containing decision layer with feature extraction would predictably yield a system that not only detects potential interference but also determines spectrum usage conflicts and provides explicit operations parameter adjustments to the interfering system, thus arriving at the claimed invention. The combination would have been obvious because it represents a straightforward substitution of known interference coordination techniques from Martone into the existing interference mitigation process of Sofuoglu-1to improve system coexistence in shared spectrum environments.
Regarding claim 10, Martone, Sofuoglu-1 and Sofuoglu-2 fails to teach wherein the one or more decisions include a probability that the first system is present.
However, Doshi teaches wherein the one or more decisions include a probability that the first system is present (Figure 5 550, 566 - [0071]-[0072] output decisions are probabilities within the deep learning model, probability detection).
Martone teaches a spectrum sharing system in which a controller exchanges data with two systems that share at least part of the same spectrum, determines when spectrum usage by one system is likely to impact another, and issues control instructions to modify operational parameters (etc. power, frequency, timing) to mitigate interference. Furthermore, Sofuoglu-1 teaches determining that a second system is likely to interfere with a first system by identifying a polluter cell based on measurements, weak coverage events, and pollution threshold and further teaches identifying and applying an interference mitigation technique to that second system by updating polluter-cell parameters such as power decrease or antenna tilt adjustment to reduce interference. Moving on, Sofuoglu-2 teaches evaluating multiple candidate configuration “solution sets” using stored OSS/database performance data and KPI-based relationships to determine an optimal set of parameter changes that balances impacts on networks performance metrics. Doshi provides a spectrum sharing system containing a decision layer with feature extraction, a normalization layer, a system on chip cross layer sensing block that determines spectrum sharing parameters and deep learning convolutional network for enhanced decision making.
A POSITA would have been motivated to combine Martone’s spectrum sharing architecture with Sofuoglu-1’s identifying polluter cell and implanting interference mitigation framework. Incorporating Martone’s controller-based spectrum coordination into the interference mitigation framework of Sofuoglu-1 and further incorporating Doshi’s spectrum sharing framework containing decision layer with feature extraction would predictably yield a system that not only detects potential interference but also determines spectrum usage conflicts and provides explicit operations parameter adjustments to the interfering system, thus arriving at the claimed invention. The combination would have been obvious because it represents a straightforward substitution of known interference coordination techniques from Martone into the existing interference mitigation process of Sofuoglu-1to improve system coexistence in shared spectrum environments.
Regarding claim 12, Martone discloses the system wherein the one or more decisions are determined based on at least one of a pulse repetition interval, a pulse width, and a modulation on a pulse of a signal originating from the first system (col 4 lines 50-67 & col 5 lines 1-8 decision vector determined based on pulses per coherent processing interval, pulse width).
Regarding claim 13, Martone discloses the system wherein the one or more decisions include an estimate of at least one base station or user device which is likely to cause interference with the first system (col 5 lines 9-37 estimation of base station or user device likely to cause interference with other radar system).
Regarding claim 15, Martone discloses the system wherein the decision engine is further configured to determine the one or more operational parameters based on one or more policies (Fig 1, 100, 119, Figure 3 302, 304, 306, 112, col 3 lines 43-67 - determine likely to be interfered with by estimating power spectrum of the EME encompasses radar system and communication system—col 5 lines 9-37 estimation of base station or user device likely to cause interference with other radar system).
Claims 7 & 16 are rejected under 35 U.S.C. 103 as being unpatentable over Martone et al. (US 11,265,040) (hereinafter "Martone") in view of Sofuoglu-1, in further view of Sofuoglu-2, in further view of Doshi et al. (US20220078626) (hereinafter "Doshi") as applied to claim 6 and 15, and in further view of Kim, Byung‐Kwan et al. “Drone Detection with Chirp‐Pulse Radar Based on Target Fluctuation Models.” ETRI journal 40.2 (2018): 188–196. Web. (hereinafter "Kim").
Regarding claim 7, Martone, Sofuoglu-1, Sofuoglu-2, and Doshi fail to teach wherein the policy engine further comprises a dynamic games module.
However, Kim teaches wherein the policy engine further comprises a dynamic games module (pg. 2 Section II Chirp pulse Doppler Radar Prototype -- pulse radar system to detect drones (movable targets) based on target fluctuation model, more specifically the Swerling target model -- dynamic games module carried out using Swerling models).
Martone teaches a spectrum sharing system in which a controller exchanges data with two systems that share at least part of the same spectrum, determines when spectrum usage by one system is likely to impact another, and issues control instructions to modify operational parameters (etc. power, frequency, timing) to mitigate interference. Furthermore, Sofuoglu-1 teaches determining that a second system is likely to interfere with a first system by identifying a polluter cell based on measurements, weak coverage events, and pollution threshold and further teaches identifying and applying an interference mitigation technique to that second system by updating polluter-cell parameters such as power decrease or antenna tilt adjustment to reduce interference. Moving on, Sofuoglu-2 teaches evaluating multiple candidate configuration “solution sets” using stored OSS/database performance data and KPI-based relationships to determine an optimal set of parameter changes that balances impacts on networks performance metrics. Doshi provides a spectrum sharing system containing a decision layer with feature extraction, a normalization layer, a system on chip cross layer sensing block that determines spectrum sharing parameters and deep learning convolutional network for enhanced decision making. Lastly, Kim provides a method for target detection via pulse radar based on target fluctuation models.
A POSITA would have been motivated to combine Martone’s spectrum sharing architecture with Sofuoglu-1’s identifying polluter cell and implanting interference mitigation framework. Incorporating Martone’s controller-based spectrum coordination into the interference mitigation framework of Sofuoglu-1 and further incorporating Doshi’s spectrum sharing framework containing decision layer with feature extraction would predictably yield a system that not only detects potential interference but also determines spectrum usage conflicts and provides explicit operations parameter adjustments to the interfering system, thus arriving at the claimed invention. The combination would have been obvious because it represents a straightforward substitution of known interference coordination techniques from Martone into the existing interference mitigation process of Sofuoglu-1to improve system coexistence in shared spectrum environments.
Regarding claim 16, Martone, Sofuoglu-1, Sofuoglu-2 and Doshi fail to teach the system wherein the performance database is further configured to provide one or more the performance curves based on Swerling models to the decision engine, and the decision engine is configured to use the one or more performance curves to determine the one or more operational parameters.
However, Kim teaches the system wherein the performance database is further configured to provide one or more the performance curves based on Swerling models to the decision engine, and the decision engine is configured to use the one or more performance curves to determine the one or more operational parameters (pg. 2 Section II Chirp pulse Doppler Radar Prototype -- pulse radar system to detect drones (movable targets) based on target fluctuation model, more specifically the Swerling target model -- dynamic games module carried out using Swerling models).
Martone teaches a spectrum sharing system in which a controller exchanges data with two systems that share at least part of the same spectrum, determines when spectrum usage by one system is likely to impact another, and issues control instructions to modify operational parameters (etc. power, frequency, timing) to mitigate interference. Furthermore, Sofuoglu-1 teaches determining that a second system is likely to interfere with a first system by identifying a polluter cell based on measurements, weak coverage events, and pollution threshold and further teaches identifying and applying an interference mitigation technique to that second system by updating polluter-cell parameters such as power decrease or antenna tilt adjustment to reduce interference. Moving on, Sofuoglu-2 teaches evaluating multiple candidate configuration “solution sets” using stored OSS/database performance data and KPI-based relationships to determine an optimal set of parameter changes that balances impacts on networks performance metrics. Doshi provides a spectrum sharing system containing a decision layer with feature extraction, a normalization layer, a system on chip cross layer sensing block that determines spectrum sharing parameters and deep learning convolutional network for enhanced decision making. Lastly, Kim provides a method for target detection via pulse radar based on target fluctuation models.
A POSITA would have been motivated to combine Martone’s spectrum sharing architecture with Sofuoglu-1’s identifying polluter cell and implanting interference mitigation framework. Incorporating Martone’s controller-based spectrum coordination into the interference mitigation framework of Sofuoglu-1 and further incorporating Doshi’s spectrum sharing framework containing decision layer with feature extraction would predictably yield a system that not only detects potential interference but also determines spectrum usage conflicts and provides explicit operations parameter adjustments to the interfering system, thus arriving at the claimed invention. The combination would have been obvious because it represents a straightforward substitution of known interference coordination techniques from Martone into the existing interference mitigation process of Sofuoglu-1to improve system coexistence in shared spectrum environments.
Claims 11 is rejected under 35 U.S.C. 103 as being unpatentable over Martone et al. (US 11,265,040) (hereinafter "Martone") in view of Sofuoglu-1, in further view Sofuoglu-2, in further view of Doshi et al. (US20220078626) (hereinafter "Doshi") as applied to claim 8, and in further view of Kim, in further view of Selvi, Ersin et al. “On the Use of Markov Decision Processes in Cognitive Radar: An Application to Target Tracking.” 2018 IEEE Radar Conference (RadarConf18). IEEE, 2018. 0537–0542. Web. (hereinafter "Selvi").
Regarding claim 11, Martone, Sofuoglu-1, Sofuoglu-2, Doshi, and Kim fail to disclose wherein the one or more decisions are determined using a partially observable Markov decision process.
However, Selvi discloses wherein the one or more decisions are determined using a partially observable Markov decision process (pg. 1-2 Proposes System Model: The model considered in our current development uses the Markov Decision Process and reinforcement learning to solve an optimization problem which mitigates mutual interference between the radar and communication systems).
Martone teaches a spectrum sharing system in which a controller exchanges data with two systems that share at least part of the same spectrum, determines when spectrum usage by one system is likely to impact another, and issues control instructions to modify operational parameters (etc. power, frequency, timing) to mitigate interference. Furthermore, Sofuoglu-1 teaches determining that a second system is likely to interfere with a first system by identifying a polluter cell based on measurements, weak coverage events, and pollution threshold and further teaches identifying and applying an interference mitigation technique to that second system by updating polluter-cell parameters such as power decrease or antenna tilt adjustment to reduce interference. Moving on, Sofuoglu-2 teaches evaluating multiple candidate configuration “solution sets” using stored OSS/database performance data and KPI-based relationships to determine an optimal set of parameter changes that balances impacts on networks performance metrics. Doshi provides a spectrum sharing system containing a decision layer with feature extraction, a normalization layer, a system on chip cross layer sensing block that determines spectrum sharing parameters and deep learning convolutional network for enhanced decision making. Kim provides a method for target detection via pulse radar based on target fluctuation models. Lastly, Selvi a method and system model for use of Markov decision processes and reinforced learning in cognitive radar to solve an optimization problem which mitigates mutual interference between the radar and communication systems.
A POSITA would have been motivated to combine Martone’s spectrum sharing architecture with Sofuoglu-1’s identifying polluter cell and implanting interference mitigation framework. Incorporating Martone’s controller-based spectrum coordination into the interference mitigation framework of Sofuoglu-1 and further incorporating Doshi’s spectrum sharing framework containing decision layer with feature extraction would predictably yield a system that not only detects potential interference but also determines spectrum usage conflicts and provides explicit operations parameter adjustments to the interfering system, thus arriving at the claimed invention. The combination would have been obvious because it represents a straightforward substitution of known interference coordination techniques from Martone into the existing interference mitigation process of Sofuoglu-1to improve system coexistence in shared spectrum environments.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Yang et al (US20130040648A1) discloses identifying locations for small cells.
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/MICHAEL WILLIAM ABBATINE JR./Examiner, Art Unit 2419
/JENEE HOLLAND/Primary Examiner, Art Unit 2469