DETAILED ACTION
In response to communication filed on 2/19/2026.
Claims 1-22 are pending.
Claims 1-22 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 .
Response to Amendments
This communication is in response to Applicant’s reply filed under 3 CFR 1.111 on 2/19/2026. Claims 1 and 12 were amended and claims 1-22 remain pending.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 2/19/2026 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
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 1,5,6,12,16 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Ramiro et al. (US Pub. 2023/0224055)(R1 hereafter) in view of Delson et al. (US Pub. 2024/0007402)(D1 hereafter).
Regarding claims 1 and 12, R1 teaches wireless network optimization system [refer Fig. 3A][refer Fig. 8; 800][paragraph 0089] comprising:
a communication interface [refer Fig. 8; 810][paragraph 0089] to receive data associated with a wireless network operational area (i.e. signal strength maps are classified to identify indoor and outdoor measurements)[paragraph 0040](input data for the ML model includes reference signal strength maps)[paragraph 0053], the data including one or more signal characteristic measurement values (i.e. signal strength)[paragraph 0053]; and
a controller [refer Fig. 8; 850] to: train a machine learning model [refer Fig. 3A; 350], based at least partially on the data (i.e. Input Preprocessor)[refer Fig. 3A; 340][paragraph 0053], and
build a signal propagation model (i.e. target signal strength map) for the wireless network operational area [paragraph 0053], the signal propagation model built using the trained machine learning model [paragraph 0053].
However, R1 fails to disclose that the signal propagation model is configured to simulate predicted effects of changing at least one network configuration.
D1 discloses machine learning algorithms in which machine learning models can simulate changes to the network by altering the number of nodes and connections to the network, the simulated data can also include different data flows and changing network hardware and configuration settings [paragraph 0035].
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 R1 for efficient planning of a network communication network using signal strength maps and machine learning [refer R1; paragraph 0092] to incorporate a machine learning model to simulate changes to the network that includes data paths, network hardware and configurations as taught by D1. One would be motivated to do so to provide a means of improving network performance [refer D1; paragraph 0039].
Regarding claims 5 and 16, R1 teaches the controller further: estimates a location [paragraph 0030] of one or more access points (i.e. access nodes in network) in the wireless network operational area [paragraph 0066], based at least partially on one or more Received Signal Strength Indicator (RSSI) measurements (i.e. signal strengths) included in the data [paragraph 0064], and trains the machine learning model, based at least partially on the location of the one or more access points [paragraph 0068].
Regarding claims 6 and 17, R1 teaches the controller further trains the machine learning model, based at least partially on a transmit power (i.e. RSRP measurements) of one or more access points (i.e. per cell in LTE) in the wireless network operational area [paragraph 0054].
Claims 2-4,8,13-15 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over R1 in view of D1, as applied to claims 1 and 12, in further view of Valenza et al. (US Pub. 2023/0027175)(V1 hereafter).
Regarding claims 2 and 13, R1 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 R1 for efficient planning of a network communication network using signal strength maps and machine learning [refer R1; paragraph 0092] 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 claims 3 and 14, R1 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 R1 for efficient planning of a network communication network using signal strength maps and machine learning [refer R1; paragraph 0092] 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 claims 4 and 15, R1 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 R1 for efficient planning of a network communication network using signal strength maps and machine learning [refer R1; paragraph 0092] 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 claims 8 and 19, R1 fails to disclose the signal propagation model covers a requirement area larger than a survey path along which the data is obtained.
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 provides calculations that include projecting a plurality of ray-paths in a variety of directions in the 3D space, showing whether ray-paths interface with one or more objects defined in a building plan (i.e. covers a requirement area larger than a survey path)[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 R1 for efficient planning of a network communication network using signal strength maps and machine learning [refer R1; paragraph 0092] 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 7,11,18 and 22 are rejected under 35 U.S.C. 103 as being unpatentable over R1 in view of D1, as applied to claims 1 and 12, in further view of Ergen et al. (US Pub. 2021/0297866)(E1 hereafter).
Regarding claims 7 and 18, R1 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 R1 for efficient planning of a network communication network using signal strength maps and machine learning [refer R1; paragraph 0092] 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 claims 11 and 22, R1 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 R1 for efficient planning of a network communication network using signal strength maps and machine learning [refer R1; paragraph 0092] 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].
Claims 10 and 21 are rejected under 35 U.S.C. 103 as being unpatentable over R1 in view of D1, as applied to claims 1 and 12, in further view of Keaton et al. (US Pub. 2024/0155365)(K1 hereafter).
Regarding claims 10 and 21, R1 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 R1 for efficient planning of a network communication network using signal strength maps and machine learning [refer R1; paragraph 0092] 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].
Claims 9 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over R1 in view of D1 in further view of V1, as applied to claims 8 and 19, in further view of E1.
Regarding claims 9 and 20, R1 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 R1 for efficient planning of a network communication network using signal strength maps and machine learning [refer R1; paragraph 0092] 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 6-7, filed 2/19/2026, with respect to the rejection of claims 1,5,6,12,17 and 17 under 35 U.S.C. 102(a)(1) have been fully considered and are persuasive in view of new amendments to the claims. Therefore, the rejection has been withdrawn. However, upon further consideration, a new grounds of rejection is made in view of the teachings of Delson et al. (US Pub. 2024/0007402)(D1 hereafter), as noted in the above rejection under 35 U.S.C. 103, to address the new claim limitations.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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 extension fee 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 date of this final action.
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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