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 .
Priority
Acknowledgment is made of applicant’s claim for domestic priority based on provisional application 63/137361, filed on 01/14/2021.
Response to Amendment
The Amendment filed 12/29/2025 has been entered. Claims 1-14 are pending in the application, where claim 15 has been withdrawn. Applicant’s amendment overcomes the specification objections, drawing objections, and 101 rejections from the previously filed Office Action.
Response to Arguments
Applicant's arguments filed 12/29/2025 have been fully considered but they are not persuasive.
Regarding Applicant’s arguments for the USC § 103 rejection of claim 1, Applicant argues on pg. 13 of the Remarks,
“The weighted system used in Files never actually teaches determining which input has a larger effect on a detection performance than a corresponding adjustment to another parameter. Instead, it focuses on inputs that are more important in a particular use case, such as the email example given in the specification. The two are not necessarily the same. In the email example, Files examples that the antenna selection machine learning module 140 may receive data recommending the system use an antenna that does not rely on low latency or high bandwidth requirements. In this example, the learning module 140 has optimized antenna selection to avoid low latency or high bandwidth requirements. But File does not teach actually making a comparison between any two parameters to reach these conclusions.”
Examiner respectfully disagrees. Regarding the determination of which parameter has a larger effect, Examiner would like to reference pgs. 13-14, lines 31-4 of Applicant’s specification filed on 07/13/2023,
“Based on the root cause, the RF system configuration parameter which affects the detection performance the most, may be determined easily, e.g., based on rules and/or experience of the RF system. The RF system may be configured for determining the current sensing metric level for each of the one or more sensing metrics included in the detection performance or for a subset of them, e.g., a predetermined subset of sensing metrics of interest for performing RF-based sensing, such as a subset of sensing metrics which affect detection performance the most, e.g., as the sensing metrics have the highest weights, for example, based on rules and/or experience of the RF system.”
The specification of the claimed invention discloses multiple methods of determining which parameter has a larger effect, and one of those methods includes looking at weighted sensing metrics where the weights are based on rules and/or experience of the RF system. Regarding weighted parameters, the prior art of Files discloses on col. 38, lines 12-25,
“In such a way, the antenna selection machine learning module in an embodiment may adaptively learn how changes in these wireless signal parameters, location, orientation, configuration, system settings, indicators, and data usage metrics may affect an information handling system's optimal antenna use parameters. The weight matrices associated with the layers of the neural network model in such an embodiment may describe, mathematically, these correlations for an individual user. The neural network model (including designation of the node values in the input layer, and number of layers), along with the weight matrices associated with each layer in an embodiment may form a trained machine learning classifier to be used.”
Files discloses that parameter weights can be determined by the system’s experiences. As cited in the previous Office Action in the 103 rejection of claim 1, Files discloses comparing weighted values (see Files col. 15, lines 60-64, “Each assigned weight to any of the input values in the model created by the antenna selection machine learning module 140 describes a likelihood that any given input is more important relative to other inputs”; the assigned weights are compared to each other). Files col. 38, lines 12-25 (cited in previous paragraph above) discloses the mathematical value of the weighted parameters. Therefore, Files does discloses comparing parameters. Additionally, Files discloses in Fig. 6, step 635,
“Identify orientations, locations, and configurations of the information handling system and corresponding antenna performance parameters that most strongly correlate with optimized transceiving of data to and from a network during specific application usage and surrounding physical attributes”
It is reasonable to believe that a parameter that most strongly correlates with optimized transceiving of data would be the same as a parameter with the largest effect on the detection performance of the RF based sensing. According to broadest reasonable interpretation of the claims as written, the weighted parameter method of Files discloses the determination of a parameter with the largest effect on the detection performance method written in Applicant’s claim 1. The experience of the system in Files determines the weight of parameters, similar to what is cited above and disclosed on pgs. 13-14 of Applicant’s specification where effect is determined. Files compares weighted parameters to each other, and chooses the adjust the parameter with the most weight. Therefore, Files does disclose determining “a radio frequency system configuration parameter of the radio frequency system configuration parameters that has a larger effect on the detection performance of the radio frequency based sensing than a corresponding adjustment to another radio frequency system configuration parameter”.
For at least these reasons, Examiner is unpersuaded and maintains previous rejections corresponding to the USC § 103 rejection. The same analysis, rationale, and cited sections from claim 1 are applied to independent claim 12. Therefore, the Examiner asserts that Horne et al. (US 20210067412 A1) and Files et al. (US 11558090 B2) disclose each and every limitation of independent claims 1 and 12 based on the broadest reasonable interpretation of claims 1 and 12.
Examiner suggestions to overcome 103 rejections
Examiner would like to suggest that Applicant include an alternative method of determination of largest effect parameter to be included in independent claims 1 and 12 in order to overcome the current prior art 103 rejections. Many parts in the specification mention “root cause” and its correlation to “negatively affected detection performance” being two aspects that lead to determining a parameter effect. Examiner believes that an integration of these aspects and specifically how they come together to determine a parameter’s effect into the independent claims may be sufficient to overcome the current prior art.
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.
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 as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention 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 1-14 are rejected under 35 U.S.C. 103 as being unpatentable over Horne et al. (US 20210067412 A1) in view of Files et al. (US 11558090 B2).
Regarding claim 1, Horne discloses [Note: what Horne fails to disclose is strike-through]
A radio frequency system for optimizing detection performance for performing radio frequency based sensing (see pg. 1, paragraph 0001, system for improving performance of a wireless radio system) based on radio frequency system configuration parameters (see pg. 1, paragraph 0006, measured parameters help determine optimal parameters for the system), the radio frequency system comprising:
multiple nodes, a group of at least two nodes of the multiple nodes being configured to perform radio frequency based sensing in a sensing area (see Fig. 1, network element 70 with node elements 71-n and 72-m; pg. 2, paragraph 0025, “network 70 comprises a plurality of nodes”; Fig. 3A, nodes are covering sensing areas 74; pg. 2, paragraph 0026, the nodes can be radios), a first node of the at least two nodes being configured to transmit radio frequency signals, (see pg. 2, paragraph 0026, the 71 nodes may transmit signals to the 72 nodes) a second node of the at least two nodes being configured to receive the transmitted radio frequency signals (see pg. 2, paragraph 0026, the 72 nodes may receive communications from the 71 nodes), and the radio frequency based sensing being performed by analyzing disturbances to the radio frequency signals caused by interaction of the radio frequency signals with one or more objects or persons on transmission paths of the radio frequency signals between the at least two nodes (see pg. 5, paragraph 0046, “in some embodiments, measurement component 30 is configured to periodically determine whether there are design criteria changes (e.g., additional coverage and/or capacity needs), a change in environment (e.g., that would cause electromagnetic interference), a change in clutter, and/or a change in terrain.”; pg. 6, paragraph 0053, “Some embodiments of modeling component 32 may therefore simulate, via a model, radio wave signal power by determining an amount of path loss the signal undergoes between the transmitter and a receiver. The model may be used to calculate loss as a function of distance and frequency, and it may include the effects of obstructions, terrain effects, and/or other environmental factors.”; Fig. 1, measurement component 30 and modeling component 32 can be in the same processor element 20),
wherein the radio frequency system is configured (i) to determine a radio frequency system configuration parameter (see pg. 5, paragraph 0048, “measurement component 30 measures one or more RF parameters”) of (see pg. 5, paragraph 0050, modeling component 32 can determine which parameters are optimal for detection; pg. 2, paragraph 0023, the radio can self-configure with the improved operating parameters).
Files discloses
the radio frequency system configuration parameters that has a larger effect on the detection performance of the radio frequency based sensing than a corresponding adjustment to another radio frequency system configuration parameter (see Fig. 1, antenna selection machine learning module element 140; col. 15, lines 55-64, “the antenna selection machine learning module 140 may further add weighting values to the input (e.g., input from the sensor module 142 and antenna performance module 164) in order to provide a recommendation that is most appropriate for current-use scenarios of the information handling system 100. Each assigned weight to any of the input values in the model created by the antenna selection machine learning module 140 describes a likelihood that any given input is more important relative to other inputs”; col. 16, lines 45-47, the antenna performance module can measure performance parameters; col. 38, lines 12-25, “In such a way, the antenna selection machine learning module in an embodiment may adaptively learn how changes in these wireless signal parameters, location, orientation, configuration, system settings, indicators, and data usage metrics may affect an information handling system's optimal antenna use parameters. The weight matrices associated with the layers of the neural network model in such an embodiment may describe, mathematically, these correlations for an individual user. The neural network model (including designation of the node values in the input layer, and number of layers), along with the weight matrices associated with each layer in an embodiment may form a trained machine learning classifier to be used.”; Fig. 6, step 635, “Identify orientations, locations, and configurations of the information handling system and corresponding antenna performance parameters that most strongly correlate with optimized transceiving of data to and from a network during specific application usage and surrounding physical attributes”)
It would have been obvious to someone with ordinary skill in the art prior to the effective filing date of the claimed invention to incorporate the features as disclosed by Files into the invention of Horne. Both Horne and Files are considered analogous arts to the claimed invention as they both disclose methods and systems that detect and suggest performance improving changes to a radio system. Horne discloses the measurement of parameters, multiple nodes that can transmit and receive, radio frequency obstruction analysis, and implementation of optimized parameter changes to improve performance; however, Horne fails to disclose distinguishing which parameters have the largest effect on performance. This feature is disclosed by Files where different parameters of the antenna system can be measured, weighted, and compared for optimization. The combination of Horne and Files would be obvious with a reasonable expectation of success in order to improve efficiency and save time by determining which parameters will have the greatest improvement effect on system performance, reducing trial and error methods of figuring out which parameter can optimize a system.
Regarding claim 2, Horne further discloses
The radio frequency system according to claim 1, wherein the radio frequency system configuration parameters include one or more of:
a spatial arrangement of the at least two nodes of the group (see pg. 6, paragraph 0054, “modeling component 32 of deployed nodes may indicate, e.g., where a next AP radio 71 needs to be deployed (e.g., at a higher elevation point) or where any of the deployed radios need to be relocated.”),
a grouping of the multiple nodes into the group,
a number of nodes in the group,
capabilities of the nodes in the group, and
one or more sensing parameters for performing radio frequency based sensing.
Regarding claim 3, Horne further discloses
The radio frequency system according to claim 1, wherein the radio frequency system is configured for determining the radio frequency system configuration parameter which affects the detection performance of the radio frequency based sensing the most based on a current context (see pg. 7, paragraph 0061, “configuration component 34…may self-configure in response to a changing environment, including a change in interference, terrain, clutter, etc., by dynamically self-determining one or more of operating parameters, current-provided coverage, and a more optimal deployment location.”).
Regarding claim 4, Horne further discloses
The radio frequency system according to claim 3, wherein the current context includes one or more of:
a setting of one or more of the radio frequency system configuration parameters,
a sensing application,
a latency requirement,
an expected sensing event in the sensing area,
a privacy requirement,
a radio power consumption requirement,
a radio transmit power requirement (see pg. 4, paragraph 0044, “After the initial configuration, the radios may reconfigure themselves to improve the performance. The configuration may be based on such parameters as frequency, transmit power, modulation, etc”),
a radio beam shape requirement,
a radio receive beamforming requirement,
a current location of the radio frequency system,
a current location of at least one of the at least two nodes,
a current location of a tangible entity in the sensing area,
a current date,
a current operation mode of at least one of the at least two nodes,
environmental effects,
currently available bandwidth in the radio frequency system,
current capabilities of the at least one of the at least two nodes,
current properties of the sensing area,
a false event detection rate requirement, and
a missed event detection requirement.
Regarding claim 5, Horne further discloses
The radio frequency system according to claim l, wherein the detection performance includes one or more sensing metrics including one or more of:
a latency,
an accuracy,
a spatial resolution,
a false positive detection rate,
a false negative detection rate,
a confidence level for detecting a sensing event,
noise in a detection of a sensing event,
a generated data traffic (see Fig. 3D; pg. 9, paragraph 0084, “the communication links depicted in FIG. 3D may facilitate network traffic, such as command data, status data, and application/service data.”),
an energy consumption, and
a spatial confinement of a radio frequency signal used for performing radio frequency based sensing.
Regarding claim 6, Horne further discloses
The radio frequency system according to claim 5, wherein the radio frequency system is configured for determining the radio frequency system configuration parameter which affects the detection performance of the radio frequency based sensing the most based on the one or more sensing metrics included in the detection performance (see pg. 5, paragraph 0048, “measurement component 30 measures one or more RF parameters”; pg. 5, paragraph 0050, modeling component 32 can determine which parameters are optimal for detection; pg. 7, paragraph 0070, “the radios may measure, via one or more sensors, what the path loss is and how strong the signals are from the other radios, and they can use this information to tune the model”).
Regarding claim 7, Horne further discloses
The radio frequency system according to claim 5, wherein the radio frequency system is configured for
determining whether a respective current sensing metric level of the one or more sensing metrics included in the detection performance is above or below a respective threshold sensing metric level (see pg. 6, paragraph 0054, “For example, a deployed node may, upon being powered on, run propagation modeling and self-tests (e.g., a built-in self-test) to determine that it itself does not provide ideal coverage (e.g., a signal attenuation level breaches a lower-level threshold, resulting in suboptimal performance) to a particular transceiver radio requiring a connection”), and
determining the radio frequency system configuration parameter which affects the detection performance of the radio frequency based sensing the most based on which of the respective current sensing metric levels of the one or more sensing metrics included in the detection performance is above or below the respective threshold sensing metric level (see pg. 5, paragraph 0050, modeling component 32 can determine which parameters are optimal for detection; pg. 6, paragraph 0054, modeling component 32 may indicate a solution to improve performance based on detection of a node breaching a threshold).
Regarding claim 8, Horne further discloses
The radio frequency system according to claim l, wherein the radio frequency system is configured for
determining a current detection performance level of radio frequency based sensing (see pg. 5, paragraph 0049, “measurement component 30 is configured to automate bitrate testing to test throughput of system 10 by loading it with test data. RF measurements may then be taken, which are more representative of real world conditions than a statistical analysis. Some embodiments of system 10 may thus implement algorithms for performing self-testing of radio link performance, results of such testing improving the self-optimization.”), and
optimizing the detection performance based on whether the current detection performance level is above or below a threshold detection performance level (see pg. 6, paragraph 0054, the modeling component 32 can determine if a node’s “signal attenuation level breaches a lower-level threshold, resulting in suboptimal performance” and then indicate a solution to improve performance).
Regarding claim 9, Files discloses
The radio frequency system according to claim l, wherein the radio frequency system is configured for
determining a ranking for the one or more radio frequency system configuration parameters ranked according to how much they respectively affect the detection performance of radio frequency based sensing (see Fig. 6; col. 35, lines 44-48, Fig. 6 shows a method of developing a prioritized list of configuration recommendations to optimize transmission performance; col. 35, lines 23-26, the method includes recording performance parameter data to create the list; col. 15, lines 60-64, “Each assigned weight to any of the input values in the model created by the antenna selection machine learning module 140 describes a likelihood that any given input is more important relative to other inputs”; col. 16, lines 5-9, “any type of machine learning algorithm may be used along with any type of input weighting scheme in order to provide, to the information handling system 100, the prioritized list of antenna systems 132”), and
adjusting the radio frequency system configuration parameters based on the ranking in order to optimize the detection performance of radio frequency based sensing (see Fig. 7, step 725, the system can implement changes based on the prioritized recommendation list).
It would have been obvious to someone with ordinary skill in the art prior to the effective filing date of the claimed invention to incorporate the features as disclosed by Files into the invention of Horne. Horne fails to disclose parameter ranking. This feature is disclosed by Files where different parameters of the antenna system are weighted and then placed in a prioritized list of configuration recommendations. The combination of Horne and Files would be obvious with a reasonable expectation of success in order to optimize efficiency by letting the user know which parameter changes will have the greatest effect on system performance, reducing trial and error methods of figuring out how to optimize a system.
Regarding claim 10, Horne further discloses
The radio frequency system according to claim l, wherein the radio frequency system is configured for one or both of:
providing the radio frequency system configuration parameter which has a larger effect on the detection performance of the radio frequency based sensing than a corresponding adjustment to another radio frequency system configuration parameter (see pg. 5, paragraph 0050, modeling component 32 can determine which parameters are optimal for detection; pg. 6, paragraph 0053, “Some embodiments of modeling component 32 may therefore simulate, via a model, radio wave signal power by determining an amount of path loss the signal undergoes between the transmitter and a receiver. The model may be used to calculate loss as a function of distance and frequency, and it may include the effects of obstructions, terrain effects, and/or other environmental factors.”), and
providing how the radio frequency system configuration parameter which has a larger effect on the detection performance of the radio frequency based sensing than a corresponding adjustment to another radio frequency system configuration parameter is to be adjusted for optimizing the detection performance.
Regarding claim 11, Horne further discloses
The radio frequency system according to claim 10, wherein the radio frequency system comprises a communication interface for one or both of:
providing the radio frequency system configuration parameter which has a larger effect on the detection performance of the radio frequency based sensing than a corresponding adjustment to another radio frequency system configuration parameter to a user (see Fig. 3E; pg. 6, paragraph 0057, parameter suggestions can be conveyed via user interface device 18 to a technician and “a user may remotely control and receive status from computing devices of system 10”), and
providing to the user, how the radio frequency system configuration parameter which has a larger effect on the detection performance of the radio frequency based sensing than a corresponding adjustment to another radio frequency system configuration parameter is to be adjusted for optimizing the detection performance.
Regarding claim 12, Horne further discloses [Note: what Horne fails to disclose is strike-through]
A method for optimizing detection performance for performing radio frequency based sensing (see pg. 1, paragraph 0001, method for improving performance of a wireless radio system) based on radio frequency system configuration parameters (see pg. 1, paragraph 0006, measured parameters help determine optimal parameters for the system), comprising:
performing radio frequency based sensing in a sensing area by a group of at least two nodes of a radio frequency system having multiple nodes (see Fig. 1, network element 70 with node elements 71-n and 72-m; pg. 2, paragraph 0025, “ network 70 comprises a plurality of nodes”; Fig. 3A, nodes are covering sensing areas 74; pg. 2, paragraph 0026, the nodes can be radios), a first node of the at least two nodes being configured to transmit radio frequency signals, (see pg. 2, paragraph 0026, the 71 nodes may transmit signals to the 72 nodes) a second node of the at least two nodes being configured to receive the transmitted radio frequency signals (see pg. 2, paragraph 0026, the 72 nodes may receive communications from the 71 nodes), and the radio frequency based sensing being performed by analyzing disturbances to the radio frequency signals caused by interaction of the radio frequency signals with one or more objects or persons on transmission paths of the radio frequency signals between the at least two nodes (see pg. 5, paragraph 0046, “in some embodiments, measurement component 30 is configured to periodically determine whether there are design criteria changes (e.g., additional coverage and/or capacity needs), a change in environment (e.g., that would cause electromagnetic interference), a change in clutter, and/or a change in terrain.”; pg. 6, paragraph 0053, “Some embodiments of modeling component 32 may therefore simulate, via a model, radio wave signal power by determining an amount of path loss the signal undergoes between the transmitter and a receiver. The model may be used to calculate loss as a function of distance and frequency, and it may include the effects of obstructions, terrain effects, and/or other environmental factors.”; Fig. 1, measurement component 30 and modeling component 32 can be in the same processor element 20),
adjusting the radio frequency system configuration parameter that has a larger effect on the detection performance of the radio frequency based sensing than a corresponding adjustment to another radio frequency system configuration parameter in order to optimize the detection performance of radio frequency based sensing (see pg. 2, paragraph 0023, the system can adjust via self-configuring with the improved operating parameters).
Files discloses
determining a radio frequency system configuration parameter of the radio frequency system configuration parameters that has a larger effect on the detection performance of the radio frequency based sensing than a corresponding adjustment to another radio frequency system configuration parameter, (see Fig. 1, antenna selection machine learning module element 140; col. 15, lines 55-64, “the antenna selection machine learning module 140 may further add weighting values to the input (e.g., input from the sensor module 142 and antenna performance module 164) in order to provide a recommendation that is most appropriate for current-use scenarios of the information handling system 100. Each assigned weight to any of the input values in the model created by the antenna selection machine learning module 140 describes a likelihood that any given input is more important relative to other inputs”; col. 16, lines 45-47, the antenna performance module can measure performance parameters; col. 38, lines 12-25, “In such a way, the antenna selection machine learning module in an embodiment may adaptively learn how changes in these wireless signal parameters, location, orientation, configuration, system settings, indicators, and data usage metrics may affect an information handling system's optimal antenna use parameters. The weight matrices associated with the layers of the neural network model in such an embodiment may describe, mathematically, these correlations for an individual user. The neural network model (including designation of the node values in the input layer, and number of layers), along with the weight matrices associated with each layer in an embodiment may form a trained machine learning classifier to be used.”; Fig. 6, step 635, “Identify orientations, locations, and configurations of the information handling system and corresponding antenna performance parameters that most strongly correlate with optimized transceiving of data to and from a network during specific application usage and surrounding physical attributes”)
It would have been obvious to someone with ordinary skill in the art prior to the effective filing date of the claimed invention to incorporate the features as disclosed by Files into the invention of Horne. Both Horne and Files are considered analogous arts to the claimed invention as they both disclose methods and systems that detect and suggest performance improving changes to a radio system. Horne discloses the measurement of parameters, multi-node sensing with transmission and reception, radio frequency obstruction analysis, and adjustment of parameters changes to improve system performance; however, Horne fails to disclose distinguishing which parameters have the largest effect on performance. This feature is disclosed by Files where different parameters of the antenna system can be measured, weighted, and compared for optimization. The combination of Horne and Files would be obvious with a reasonable expectation of success in order to improve efficiency and save time by determining which parameters will have the greatest improvement effect on system performance, reducing trial and error methods of figuring out which parameter can optimize a system.
Regarding claim 13, Horne further discloses
The method according to claim 12 comprising one or more of the steps:
determining a current context for performing radio frequency based sensing by the group in the sensing area,
determining the radio frequency system configuration parameter which has a larger effect on the detection performance of the radio frequency based sensing than a corresponding adjustment to another radio frequency system configuration parameter based on the current context,
determining the radio frequency system configuration parameter which has a larger effect on the detection performance of the radio frequency based sensing than a corresponding adjustment to another radio frequency system configuration parameter based on one or more sensing metrics included in the detection performance,
determining whether a respective current sensing metric level of the one or more sensing metrics included in the detection performance is above or below a respective threshold sensing metric level,
determining the radio frequency system configuration parameter which has a larger effect on the detection performance of the radio frequency based sensing than a corresponding adjustment to another radio frequency system configuration parameter based on which of the respective current sensing metric levels of the one or more sensing metrics included in the detection performance is above or below the respective threshold sensing metric level,
determining a current detection performance level of radio frequency based sensing,
optimizing the detection performance based on whether the current detection performance level is above or below a threshold detection performance level,
determining a ranking for the one or more radio frequency system configuration parameters ranked according to how much they respectively affect the detection performance of radio frequency based sensing,
adjusting the radio frequency system configuration parameters based on the ranking in order to optimize the detection performance of radio frequency based sensing,
providing the radio frequency system configuration parameter which has a larger effect on the detection performance of the radio frequency based sensing than a corresponding adjustment to another radio frequency system configuration parameter,
providing how the radio frequency system configuration parameter which has a larger effect on the detection performance of the radio frequency based sensing than a corresponding adjustment to another radio frequency system configuration parameter is to be adjusted for optimizing the detection performance,
providing the radio frequency system configuration parameter which has a larger effect on the detection performance of the radio frequency based sensing than a corresponding adjustment to another radio frequency system configuration parameter to a user (see Fig. 3E; pg. 6, paragraph 0057, parameter suggestions can be conveyed via user interface device 18 to a technician and “a user may remotely control and receive status from computing devices of system 10”), and
providing to the user, how the radio frequency system configuration parameter which has a larger effect on the detection performance of the radio frequency based sensing than a corresponding adjustment to another radio frequency system configuration parameter is to be adjusted for optimizing the detection performance.
Regarding claim 14, Horne further discloses
A non-transitory computer readable medium comprising program code to carry out the method according to claim 12 when executed on a processor (see pg. 12, claim 19, “An apparatus, comprising: …a non-transitory memory including instructions stored thereon for configuring a network…and one or more processors operably coupled to the non-transitory memory, the one or more processors being configured to execute the instructions of:”; pg. 9, paragraph 0087, the method “may be performed with a computer system comprising one or more computer processors and/or other components. The processors are configured by machine readable instructions to execute computer program components”).
Additional Relevant Art
The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure and may be found on the accompanying PTO-892 Notice of References Cited:
Turk et al. (US 20220382615 A1); A system, method and non-transitory computer readable media for effectuating ML-based fault analysis in a network (102A, 102B) comprising a plurality of nodes (104-N, 120-M). An example method (200A) comprises determining (202) that at least one of a topological configuration of the network (102 A, 102B) and one or more key performance indicator (KPI) requirements associated with the network (102A, 102B) have changed; and responsive to the determining, selecting (204) a machine language (ML) engine optimally adapted to facilitate root cause determination of any faults detected in the network (102 A, 102B) after the topological configuration or any KPI requirements of the network (102A, 102B) have changed.
Wootton et al. (US 20170366938 A1), which relates to comparing wireless signal properties; Systems and methods for detecting the presence of a body in a network without fiducial elements, using signal absorption, and signal forward and reflected backscatter of radio frequency (RF) waves caused by the presence of a biological mass in a communications network.
Graefe et al. (US 20190222652 A1); Systems, methods, and computer-readable media are provided for wireless sensor networks (WSNs), including sensor deployment mechanisms for road surveillance. Disclosed embodiments are applied to design roadside infrastructure with optimal perception for a given geographic area. The deployment mechanisms account for the presence of static and dynamic obstacles, as well as symmetry aspects of the underlying environment. The deployment mechanisms minimize the number of required sensors to reduce costs and conserve compute and network resources, and extended infrastructure the sensing capabilities of sensor networks. Other embodiments are disclosed and/or claimed; see paragraph 0089, example of node arrangement comparison.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/ISABELLA A EDRADA/Examiner, Art Unit 3648
/William Kelleher/Supervisory Patent Examiner, Art Unit 3648
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