DETAILED ACTION
In response to communication filed on 7/9/2026.
Claims 1-11 and 23-32 are pending.
Claims 1-11 and 23-32 are rejected.
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 .
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 7/9/2026 has been entered.
Response to Amendments
This communication is in response to Applicant’s reply filed under 3 CFR 1.111 on 7/9/2026. Claims 1 and 8 were amended, claims 12-22 were canceled, claims 23-32 were added and claims 1-11 and 23-32 remain pending.
Claim Rejections - 35 USC § 103
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
Claims 1,5,6,8,23-27, and 30-32 are rejected under 35 U.S.C. 103 as being unpatentable over Nair et al. (US Pub. 2022/038118)(N1 hereafter) in view of Anchala et al. (US Pub. 2024/0048256)(A1 hereafter).
Regarding claim 1, N1 teaches a wireless network optimization system [refer Fig. 1; 115][paragraph 0068] comprising:
a communication interface (i.e. antenna)[refer Fig. 1; 113] to receive data associated with a wireless network operational area while moving along a survey path through the wireless network operational area (a device can be on a vehicle and can move within an environment capturing transmitted signals to generate channel responses)[paragraph 0098], the data including one or more signal characteristic measurement values [paragraph 0115]; and
a controller (i.e. processor)[paragraph 0047] to: train a machine learning model [paragraph 0049], based at least partially on the data [paragraph 0088].
However, N1 fails to disclose building a signal propagation model for the wireless network operational area, the signal propagation model built using the trained machine learning model, the signal propagation model is configured to estimate signal propagation at locations within the wireless network operational area beyond the survey path along which the one or more signal characteristic measurement values were obtained.
A1, in the field of tuning a wireless network propagation model, for each location, a process can compare signal measurements from crowdsourced data with signal measurements predicted in the propagation model and can adjust the model to accurately represent network coverage in an area [paragraph 0035], when the predicted signal measurements of the propagation model at locations are outside of a predetermined range of the crowdsourced data measurements at those locations, the nominal propagation model is tuned with the collected crowdsourced data to more accurately simulate the network coverage provided to the coverage area and can predict signal strength at particular locations (i.e. beyond surveyed area)[paragraph 0036].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of N1 for training a channel decoder network to adjust variables over time to mirror the evolution of propagation effects [refer N1; paragraph 0086] to incorporate the collection of data and tuning of a wireless network propagation model for locations as taught by A1. One would be motivated to do so to improve quality of service [refer A1; paragraph 0036].
Regarding claim 5, N1 fails to disclose the controller further: estimates a location of one or more access points in the wireless network operational area, based at least partially on one or more Received Signal Strength Indicator (RSSI) measurements included in the data, and trains the machine learning model, based at least partially on the location of the one or more access points.
A1, in the field of tuning a wireless network propagation model, for each location, a process can compare signal measurements from crowdsourced data with signal measurements predicted in the propagation model and can adjust the model to accurately represent network coverage in an area [paragraph 0035], such that collecting data for network performance, a device can collect RF signal measurements, such as signal strength, from various locations [paragraph 0029].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of N1 for training a channel decoder network to adjust variables over time to mirror the evolution of propagation effects [refer N1; paragraph 0086] to incorporate the collection of data and tuning of a wireless network propagation model for locations as taught by A1. One would be motivated to do so to improve quality of service [refer A1; paragraph 0036].
Regarding claim 6, N1 teaches the controller further trains the machine learning model, based at least partially on a transmit power of one or more access points in the wireless network operational area (channel responses for signal approximation represent values of power received at different delays form one or more antennas)[paragraph 0063].
Regarding claim 8, N1 fails to disclose the signal propagation model covers a requirement area larger than the survey path along which the data is obtained.
A1, in the field of tuning a wireless network propagation model, for each location, a process can compare signal measurements from crowdsourced data with signal measurements predicted in the propagation model and can adjust the model to accurately represent network coverage in an area [paragraph 0035], when the predicted signal measurements of the propagation model at locations are outside of a predetermined range of the crowdsourced data measurements at those locations, the nominal propagation model is tuned with the collected crowdsourced data to more accurately simulate the network coverage provided to the coverage area and can predict signal strength at particular locations (i.e. beyond surveyed area)[paragraph 0036].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of N1 for training a channel decoder network to adjust variables over time to mirror the evolution of propagation effects [refer N1; paragraph 0086] to incorporate the collection of data and tuning of a wireless network propagation model for locations as taught by A1. One would be motivated to do so to improve quality of service [refer A1; paragraph 0036].
Regarding claim 23, N1 teaches a wireless network optimization system [refer Fig. 1; 115][paragraph 0068] comprising:
a communication interface (i.e. antenna)[refer Fig. 1; 113] to receive data associated with a wireless network operational area while moving along a survey path through the wireless network operational area (a device can be on a vehicle and can move within an environment capturing transmitted signals to generate channel responses)[paragraph 0098], the data including one or more signal characteristic measurement values [paragraph 0115]; and
a controller (i.e. processor)[paragraph 0047] to: train a machine learning model [paragraph 0049], based at least partially on the data [paragraph 0088].
However, N1 fails to disclose building a signal propagation model for the wireless network operational area, the signal propagation model built using the trained machine learning model, and
use the signal propagation model to simulate an effect of adding a new access point within the wireless network operational area on signal propagation.
A1, in the field of tuning a wireless network propagation model, for each location, a process can compare signal measurements from crowdsourced data with signal measurements predicted in the propagation model and can adjust the model to accurately represent network coverage in an area [paragraph 0035], when the predicted signal measurements of the propagation model at locations are outside of a predetermined range of the crowdsourced data measurements at those locations, the nominal propagation model is tuned with the collected crowdsourced data to more accurately simulate the network coverage provided to the coverage area and can predict signal strength at particular locations, with a calibrated propagation model, users can identify locations to install equipment to improve quality of service [paragraph 0036].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of N1 for training a channel decoder network to adjust variables over time to mirror the evolution of propagation effects [refer N1; paragraph 0086] to incorporate the tuning of a wireless network propagation model for locations as taught by A1. One would be motivated to do so to improve quality of service [refer A1; paragraph 0036].
Regarding claim 24, N1 fails to disclose estimating a location of one or more access points in the wireless network operational area, based at least partially on one or more Received Signal Strength Indicator (RSSI) measurements included in the data, and trains the machine learning model, based at least partially on the location of the one or more access points.
A1, in the field of tuning a wireless network propagation model, for each location, a process can compare signal measurements from crowdsourced data with signal measurements predicted in the propagation model and can adjust the model to accurately represent network coverage in an area [paragraph 0035], such that collecting data for network performance, a device can collect RF signal measurements, such as signal strength, from various locations [paragraph 0029].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of N1 for training a channel decoder network to adjust variables over time to mirror the evolution of propagation effects [refer N1; paragraph 0086] to incorporate the collection of data and tuning of a wireless network propagation model for locations as taught by A1. One would be motivated to do so to improve quality of service [refer A1; paragraph 0036].
Regarding claim 25, N1 teaches the controller further trains the machine learning model, based at least partially on a transmit power of one or more access points in the wireless network operational area (channel responses for signal approximation represent values of power received at different delays form one or more antennas)[paragraph 0063].
Regarding claim 26, N1 fails to disclose the signal propagation model covers a requirement area larger than the survey path along which the data is obtained.
A1, in the field of tuning a wireless network propagation model, for each location, a process can compare signal measurements from crowdsourced data with signal measurements predicted in the propagation model and can adjust the model to accurately represent network coverage in an area [paragraph 0035], when the predicted signal measurements of the propagation model at locations are outside of a predetermined range of the crowdsourced data measurements at those locations, the nominal propagation model is tuned with the collected crowdsourced data to more accurately simulate the network coverage provided to the coverage area and can predict signal strength at particular locations (i.e. model covers a requirement area larger than the path from which data is obtained)[paragraph 0036].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of N1 for training a channel decoder network to adjust variables over time to mirror the evolution of propagation effects [refer N1; paragraph 0086] to incorporate the tuning of a wireless network propagation model for locations as taught by A1. One would be motivated to do so to provide a means of identifying locations to install equipment to improve quality of service [refer A1; paragraph 0036].
Regarding claim 27, N1 teaches a wireless network optimization system [refer Fig. 1; 115][paragraph 0068] comprising:
a communication interface (i.e. antenna)[refer Fig. 1; 113] to receive data associated with a wireless network operational area while moving along a survey path through the wireless network operational area (a device can be on a vehicle and can move within an environment capturing transmitted signals to generate channel responses)[paragraph 0098], the data including one or more signal characteristic measurement values [paragraph 0115]; and
a controller (i.e. processor)[paragraph 0047] to: train a machine learning model [paragraph 0049], based at least partially on the data [paragraph 0088].
However, N1 fails to disclose building a signal propagation model for the wireless network operational area, the signal propagation model built using the trained machine learning model, and
use the signal propagation model to simulate an effect of changing a location of an access point associated with the wireless network operational area on signal propagation.
A1, in the field of tuning a wireless network propagation model, for each location, a process can compare signal measurements from crowdsourced data with signal measurements predicted in the propagation model and can adjust the model to accurately represent network coverage in an area [paragraph 0035], when the predicted signal measurements of the propagation model at locations are outside of a predetermined range of the crowdsourced data measurements at those locations, the nominal propagation model is tuned with the collected crowdsourced data to more accurately simulate the network coverage provided to the coverage area that can change in characteristics and can predict signal strength at particular locations, with a calibrated propagation model, users can identify locations to install equipment to improve quality of service [paragraph 0036].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of N1 for training a channel decoder network to adjust variables over time to mirror the evolution of propagation effects [refer N1; paragraph 0086] to incorporate the tuning of a wireless network propagation model for locations as taught by A1. One would be motivated to do so to improve quality of service [refer A1; paragraph 0036].
Regarding claim 30, N1 fails to disclose estimating a location of one or more access points in the wireless network operational area, based at least partially on one or more Received Signal Strength Indicator (RSSI) measurements included in the data, and trains the machine learning model, based at least partially on the location of the one or more access points.
A1, in the field of tuning a wireless network propagation model, for each location, a process can compare signal measurements from crowdsourced data with signal measurements predicted in the propagation model and can adjust the model to accurately represent network coverage in an area [paragraph 0035], such that collecting data for network performance, a device can collect RF signal measurements, such as signal strength, from various locations [paragraph 0029].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of N1 for training a channel decoder network to adjust variables over time to mirror the evolution of propagation effects [refer N1; paragraph 0086] to incorporate the collection of data and tuning of a wireless network propagation model for locations as taught by A1. One would be motivated to do so to improve quality of service [refer A1; paragraph 0036].
Regarding claim 31, N1 teaches the controller further trains the machine learning model, based at least partially on a transmit power of one or more access points in the wireless network operational area (channel responses for signal approximation represent values of power received at different delays form one or more antennas)[paragraph 0063].
Regarding claim 32, N1 fails to disclose the signal propagation model covers a requirement area larger than the survey path along which the data is obtained.
A1, in the field of tuning a wireless network propagation model, for each location, a process can compare signal measurements from crowdsourced data with signal measurements predicted in the propagation model and can adjust the model to accurately represent network coverage in an area [paragraph 0035], when the predicted signal measurements of the propagation model at locations are outside of a predetermined range of the crowdsourced data measurements at those locations, the nominal propagation model is tuned with the collected crowdsourced data to more accurately simulate the network coverage provided to the coverage area that can change in characteristics and can predict signal strength at particular locations, with a calibrated propagation model, users can identify locations to install equipment to improve quality of service [paragraph 0036].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of N1 for training a channel decoder network to adjust variables over time to mirror the evolution of propagation effects [refer N1; paragraph 0086] to incorporate the tuning of a wireless network propagation model for locations as taught by A1. One would be motivated to do so to improve quality of service [refer A1; paragraph 0036].
Claims 2-4,28 and 29 are rejected under 35 U.S.C. 103 as being unpatentable over N1 in view of A1, as applied to claim 1, in further view of Valenza et al. (US Pub. 2023/0027175)(V1 hereafter).
Regarding claim 2, N1 fails to disclose the controller further generates one or more network performance visualizations, based on the signal propagation model.
V1, in the field of designing and planning a network [paragraph 0023], discloses generating a 3D visualization of Wi-Fi signal propagation based upon an RF propagation model [paragraph 0026].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of N1 in view of A1 to incorporate a visualization of a building plan for network planning as taught by V1. One would be motivated to do so to provide an optimized means of designing a network and where to place or how to configure access points [refer V1; paragraph 0003].
Regarding claim 3, N1 fails to disclose the controller further renders, based on the signal propagation model, a heatmap of the wireless network operational area, the heatmap based on values of one or more performance indicators determined by the signal propagation model.
V1, in the field of designing and planning a network [paragraph 0023], discloses generating a 3D visualization of Wi-Fi signal propagation based upon an RF propagation model [paragraph 0026], the 3D visualization system including visualization of signal propagation in the form of a heat map [paragraph 0033].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of N1 in view of A1 to incorporate a visualization of a building plan for network planning as taught by V1. One would be motivated to do so to provide an optimized means of designing a network and where to place or how to configure access points [refer V1; paragraph 0003].
Regarding claim 4, N1 fails to disclose the controller further generates one or more recommendations to optimize one or more performance indicators of the wireless network.
V1, in the field of designing and planning a network [paragraph 0023], discloses generating a 3D visualization of Wi-Fi signal propagation based upon an RF propagation model [paragraph 0026], the 3D visualization allowing for proposals (i.e. recommendations) for improved coverage or capacity [paragraph 0119].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of N1 in view of A1 to incorporate a visualization of a building plan for network planning as taught by V1. One would be motivated to do so to provide an optimized means of designing a network and where to place or how to configure access points [refer V1; paragraph 0003].
Regarding claim 28, N1 in view of A1 fails to disclose that changing the location of the access point comprises moving the access point from a first location within the wireless network operational area to a second location within the wireless network operational area.
V1, in the field of designing and planning a network [paragraph 0023], discloses generating a 3D visualization of Wi-Fi signal propagation based upon an RF propagation model [paragraph 0026], the optimization service can visualize dynamic changes as conditions change and propose different layouts of APs for a given space [paragraph 0038].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of N1 in view of A1 to incorporate a visualization of a building plan for network planning as taught by V1. One would be motivated to do so to provide an optimized means of designing a network and where to place or how to configure access points [refer V1; paragraph 0003].
Regarding claim 29, N1 in view of A1 fails to disclose changing the location of the access point comprises removing the access point from the wireless network operational area.
V1, in the field of designing and planning a network [paragraph 0023], discloses generating a 3D visualization of Wi-Fi signal propagation based upon an RF propagation model [paragraph 0026], the optimization service can visualize dynamic changes as conditions change and propose different layouts of APs for a given space [paragraph 0038].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of N1 in view of A1 to incorporate a visualization of a building plan for network planning as taught by V1. One would be motivated to do so to provide an optimized means of designing a network and where to place or how to configure access points [refer V1; paragraph 0003].
Claims 7 and 11 are rejected under 35 U.S.C. 103 as being unpatentable over N1 in view of A1, as applied to claim 1, in further view of Ergen et al. (US Pub. 2021/0297866)(E1 hereafter).
Regarding claim 7, N1 fails to disclose the controller further trains the machine learning model, based at least partially on a distance of a measurement device from one or more access points in the wireless network operational area when the data is obtained.
E1, in the field of enhancing signal qualities for a wireless network using RF measurements [refer E1; Abstract], discloses that RF measurements from a plurality of devices are used to calculate a number of clusters/rooms [paragraph 0051], the calculation model then uses the number of clusters/rooms to determine an average distance of individual measurements from client devices [paragraph 0052].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of N1 in view of A1 to incorporate the determination of distances for measurements in network planning as taught by E1. One would be motivated to do so to provide a utilization of interference measurements based on RF measurements in order to enhance signal qualities for a network [refer E1; Abstract].
Regarding claim 11, N1 fails to disclose the controller estimates a quality of the data, based at least partially on a number and a quality of survey measurements taken when performing a survey of the wireless network operational area.
E1, in the field of enhancing signal qualities for a wireless network using RF measurements [refer E1; Abstract], discloses that a weighting of network elements can be implemented in calculations in order to maximize connection quality [paragraph 0070], the quality of signals and interference changes can be used to provide indications on impact on changes [paragraph 0072].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of N1 in view of A1 to incorporate calculations using measurements and interference in order to provide better connection quality analysis as taught by E1. One would be motivated to do so to provide a utilization of interference measurements based on RF measurements in order to enhance signal qualities for a network [refer E1; Abstract].
Claim 10 is rejected under 35 U.S.C. 103 as being unpatentable over N1 in view of A1, as applied to claim 1, in further view of Keaton et al. (US Pub. 2024/0155365)(K1 hereafter).
Regarding claim 10, N1 fails to disclose the controller determines, based at least partially on one or more Received Signal Strength Indicator (RSSI) measurements included in the data, a recommended arrangement of a plurality of floors of the wireless network operational area relative to each other, the wireless network optimization system further comprising a display to display the recommended arrangement of the plurality of floors to a user for approval.
K1 discloses improving of a wireless network by receiving knowledge of signal strength at various locations within a premises in order to be used to recommend or determine preferred locations of wireless devices for a network management application, some instances of information that can be use is information about the premises, such as materials, floor plans, etc. (i.e. arrangement of a plurality of floors) to be provided for a recommendation engine [paragraph 0017], the network management application can provide information for display via a user interface (i.e. display to display recommended arrangement)[paragraph 0027].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of N1 in view of A1 to incorporate the reception of signal strength and network information of an area for recommended placement of wireless devices as taught by K1. One would be motivated to do so to provide a means of improving coverage of a wireless network [refer K1; Abstract].
Claim 9 is rejected under 35 U.S.C. 103 as being unpatentable over N1 in view of A1 in further view of V1, as applied to claim 8, in further view of E1.
Regarding claim 9, N1 fails to disclose the controller determines boundaries of the requirement area, based at least partially on a convex hull of the data.
E1, in the field of solving optimization problems for a wireless network [refer Abstract], discloses that for measurements, clusters/rooms for are used for a mathematical function, such as a convex hull, to map the cluster to determine positioning [paragraph 0064].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of N1 in view of A1 in view of V1 for an RF propagation model visualization for network planning [refer V1; Abstract] to incorporate the use of a mathematical function, such as a convex hull, to determine positioning as taught by E1. One would be motivated to do so to provide a means of effectively computing signal qualities [refer E1; paragraph 0017].
Response to Arguments
Applicant’s arguments, see pages 7-8, filed 7/9/2026, with respect to the rejection of claims 1-12 under 35 U.S.C. 103 have been fully considered and are persuasive in view of the amendments. Therefore, the rejection has been withdrawn. However, upon further consideration, a new grounds of rejection is made in view of the teachings of Nair et al. (US Pub. 2022/038118)(N1 hereafter) and Anchala et al. (US Pub. 2024/0048256)(A1 hereafter), as noted in the above rejection to address the new claim language introduced in the amendments.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to RYAN C KAVLESKI whose telephone number is (571)270-3619. The examiner can normally be reached M-F 6:30am-3pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Charles C Jiang can be reached on 571-270-7191. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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Ryan Kavleski
/R.C.K/Examiner, Art Unit 2412
/CHARLES C JIANG/Supervisory Patent Examiner, Art Unit 2412
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