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 .
Response to Arguments
Applicant's arguments filed 07/02/2026 with respect to claim(s) 1, 8, and 15 have been considered but are moot in view of the new ground(s) of rejection under 103 based on Rogers et al. (US 2004/0203897 A1) and Hildebrandt et al. (US 2023/0012619 A1) and new reference Byrne et al. (US 2021/0409972 A1). Byrne teaches the amended limitation “wherein the predicting comprises determining, using image analysis of the visual data captured by the autonomous vehicle, the visual data a presence of a number of persons and devices at the at least one location, and wherein the at least one root cause is overcapacity of a network due to the presence of the number of persons and devices at the at least one location” in at least [0094]: The camera or optical sensor 460 a may be configured to capture still images or may be configured to capture video information (e.g., a collection of still images captured consecutively), [0095]: The camera or optical sensor 460 a may be configured to capture image information from a region 470 a surrounding the base station. The region 470 a may include a number of people, and images of these people may be captured by the camera or optical sensor 460 a. The image information may be analyzed by associated systems (e.g., a processing unit) to undergo an image recognition analysis to determine the number of people present on the collected image, [0097]: the camera or optical sensor 460 a may be communicatively coupled with calendar and/or event database information to better determine a human population density and potential network load during a certain time period, [0104]: the components of the system 500 may first determine how many people are detected by the population density sensor (e.g., by image analysis) and may then determine the effect that the detected people will have on a network load, [0123]: sensor and/or camera information of the location where the event is occurring may be captured. In some embodiments, image information may be analyzed and an estimated number of people attending the event may be determined, and [0124]: once the popularity of the event is determined, a cellular network load may be predicted and network enhancements (e.g., sending a message to users of the cellular network or load switching) may be performed.
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 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.
Claim(s) 1-6, 8-13, and 15-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Rogers et al. (US 2004/0203897 A1) in view of Hildebrandt et al. (US 2023/0012619 A1) and Byrne et al. (US 2021/0409972 A1).
Regarding claims 1, 8, and 15, Rogers discloses A computer-implemented method comprising, and A computer system for network optimization comprising: one or more processors, one or more computer-readable memories, one or more computer-readable tangible storage media, and program instructions stored on at least one of the one or more computer-readable tangible storage media for execution by at least one of the one or more processors via at least one of the one or more computer-readable memories, wherein the computer system is capable of performing a method comprising, and discloses A computer program product for network optimization comprising: one or more computer-readable storage media and program instructions collectively stored on the one or more computer-readable storage media, the program instructions executable by a processor to cause the processor to perform a method comprising (Fig. 2, [0026]-[0033]):
collecting, using the(Fig. 2, [0026], [0079]: monitoring device 1 measures and collects performance characteristics of a wireless communication network at a plurality of geographic points in the test area. [0086]: the monitoring device may be mounted on a remotely-controlled vehicle. [0088]: the vehicle travels in a geographic area in order to map the operating characteristics for the wireless network in the geographic area);
detecting, based on the network performance data, at least one location of the plurality of locations including below-threshold network performance ([0094]-[0095]: wireless network performance management system determines interference is present at location B when signal strength of a channel falls below a certain threshold at this position); and
predicting at least one root cause for the below-threshold network performance at the at least one location ([0094]-[0095]: wireless network performance management system uses propagation modeling data to identify the source of and location of interference signals for an identified geographic area, i.e., because the propagation modeling data identifies that a problem may exist, an error message may be displayed on the display device 3 identifying the possible reason the signal strength is below the threshold at location B),
Rogers does not disclose, but Hildebrant discloses constructing, from visual data captured by an autonomous vehicle, a map of a three-dimensional space and collecting, using the autonomous vehicle, network performance data ([0022]: the collection of geomagnetic data and/or other sensor data that is utilized in generating the 3D maps of the inside of the buildings can be performed by other devices and/or techniques, including autonomous devices, such as robots. As an example, an autonomous robot (e.g., utilizing Lidar or other ranging technology) can be utilized which is equipped with various sensors for capturing data, including geomagnetic data that allows for generating a magnetic footprint of the inside of the building).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify and program the monitoring device 1 that may be mounted on a remotely-controlled vehicle, as taught by Rogers, to be an autonomous device itself to collect geomagnetic data and/or other sensor data for generating 3D maps of the inside of buildings, as taught by Hildebrandt.
Doing so delivers precise or acceptable (e.g., within a particular threshold such as 1 foot—although other thresholds can be applied) indoor X, Y and Z-Axis location, and 3D location data can be overlayed onto a 3D map (e.g., one generated by Environmental Systems Research Institute (ESRI)) to show indoor location tracking in real-time, and privacy can be maintained by providing for opt-in for mapping data collection, anonymizing collected data, and so forth (Hildebrandt: [0021]).
Rogers does not disclose, but Byrne discloses wherein the predicting comprises determining, using image analysis of the visual data captured by the autonomous vehicle, the visual data a presence of a number of persons and devices at the at least one location, and wherein the at least one root cause is overcapacity of a network due to the presence of the number of persons and devices at the at least one location ([0094]: The camera or optical sensor 460 a may be configured to capture still images or may be configured to capture video information (e.g., a collection of still images captured consecutively). [0095]: The camera or optical sensor 460 a may be configured to capture image information from a region 470 a surrounding the base station. The region 470 a may include a number of people, and images of these people may be captured by the camera or optical sensor 460 a. The image information may be analyzed by associated systems (e.g., a processing unit) to undergo an image recognition analysis to determine the number of people present on the collected image. [0097]: the camera or optical sensor 460 a may be communicatively coupled with calendar and/or event database information to better determine a human population density and potential network load during a certain time period. [0104]: the components of the system 500 may first determine how many people are detected by the population density sensor (e.g., by image analysis) and may then determine the effect that the detected people will have on a network load. [0123]: sensor and/or camera information of the location where the event is occurring may be captured. In some embodiments, image information may be analyzed and an estimated number of people attending the event may be determined. [0124]: once the popularity of the event is determined, a cellular network load may be predicted and network enhancements (e.g., sending a message to users of the cellular network or load switching) may be performed).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify and program the monitoring device 1 that is an autonomous device to collect geomagnetic data and/or other sensor data for generating 3D maps of the inside of buildings, as taught by Rogers in view of Hildebrandt, to capture and analyze images / video information from a region, and determine the number of people on the images / video information and the potential network load based on the detected people, as taught by Byrne.
Doing so allows the system to predict the network load and take appropriate actions, such as rerouting signals, loaning / borrowing channels, enhancing network, (Byrne: [0004]-[0005], [0049], [0124]).
Regarding claim(s) 2, 9, 16, Rogers in view of Hildebrandt and Byrne discloses all features of claim(s) 1, 8, 15 as outlined above.
Rogers discloses
indicating, in the map of the three-dimensional space, the network performance data associated with the plurality of locations in the three-dimensional space ([0031]: display device 3 receives data from the monitoring device 1 and allows the viewing of the performance characteristics of the wireless communication network that is being measured. Fig. 6, [0036]: display device 3 may display different network performance information on the display screen 31, such as network performance information like dual-band radiofrequency scanner readings. [0079]-[0081]: display device 3 receives time-correlated network performance information and time-correlated monitoring device location in real time, and displays the combined information on a map of the test area. Fig. 4, [0002], [0036], [0074]: test area includes buildings, such as malls, airports, offices, apartments, hospitals, etc.).
Rogers does not disclose, but Hildebrant discloses the autonomous vehicle moving through the three-dimensional space ([0022]: the collection of geomagnetic data and/or other sensor data that is utilized in generating the 3D maps of the inside of the buildings can be performed by other devices and/or techniques, including autonomous devices, such as robots. As an example, an autonomous robot (e.g., utilizing Lidar or other ranging technology) can be utilized which is equipped with various sensors for capturing data, including geomagnetic data that allows for generating a magnetic footprint of the inside of the building).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify and program the monitoring device 1 that may be mounted on a remotely-controlled vehicle, as taught by Rogers, to be an autonomous device itself to collect geomagnetic data and/or other sensor data for generating 3D maps of the inside of buildings, as taught by Hildebrandt.
Doing so delivers precise or acceptable (e.g., within a particular threshold such as 1 foot—although other thresholds can be applied) indoor X, Y and Z-Axis location, and 3D location data can be overlayed onto a 3D map (e.g., one generated by Environmental Systems Research Institute (ESRI)) to show indoor location tracking in real-time, and privacy can be maintained by providing for opt-in for mapping data collection, anonymizing collected data, and so forth (Hildebrandt: [0021]).
Regarding claim(s) 3, 10, 17, Rogers in view of Hildebrandt and Byrne discloses all features of claim(s) 2, 9, 16 as outlined above.
Rogers discloses marking, in the map of the three-dimensional space, the at least one location of the plurality of locations including the below-threshold network performance ([0079]-[0081]: display device 3 receives time-correlated network performance information and time-correlated monitoring device location in real time, and displays the combined information on a map of the test area. [0094]-[0095]: display device 3 displays an error message identifying the possible reason the signal strength is below the threshold at location B); and
indicating, in the map of the three-dimensional space, the at least one root cause predicted for the below-threshold network performance at the at least one location ([0094]-[0095]: display device 3 displays an error message identifying the possible reason the signal strength is below the threshold at location B).
Regarding claim(s) 4, 11, 18, Rogers in view of Hildebrandt and Byrne discloses all features of claim(s) 3, 10, 17 as outlined above.
Rogers discloses determining at least one remedy for the below-threshold network performance at the at least one location based on the at least one root cause predicted for the below-threshold network performance ([0106]-[0107]: analysis device 5 receives the operation characteristics of the tested wireless communications network from the monitoring device 1, and determines the optimal location of servers, cell sites, and transmitters within a building and for each building tenant. For example, by utilizing the network performance information from the monitoring device 1, the analysis device 5 may be able to assist in identifying areas where the wireless network coverage is inadequate or non-existent. [0094]-[0095]: wireless network performance management system uses propagation modeling data to identify the source of and location of interference signals for an identified geographic area, i.e., because the propagation modeling data identifies that a problem may exist, an error message may be displayed on the display device 3 identifying the possible reason the signal strength is below the threshold at location B); and
indicating, in the map of the three-dimensional space, the at least one remedy for the below-threshold network performance at the at least one location ([0108]: analysis device 5 may be able to display the results of the test, if a display device 3 is not utilized. The analysis device 5 imports both the building bit map and collected performance data into its memory. The analysis device 5 also has the capability of importing and orienting the collected data onto the building map to be displayed on the analysis device monitor. [0080]-[0081]: the display device 3 may receive the combined monitoring device location information and information output (i.e., report) from the baseband decoder and controller 13 of the analysis device 5, and displays the information on a map of the test area. [0094]-[0095]: wireless network performance management system uses propagation modeling data to identify the source of and location of interference signals for an identified geographic area, i.e., because the propagation modeling data identifies that a problem may exist, an error message may be displayed on the display device 3 identifying the possible reason the signal strength is below the threshold at location B).
Regarding claim(s) 5, 12, 19, Rogers in view of Hildebrandt and Byrne discloses all features of claim(s) 2, 9, 16 as outlined above.
Rogers discloses wherein the at least one root cause for the below-threshold network performance at the at least one location is predicted based on the network performance data and the visual data associated with the at least one location ([0094]-[0095]: wireless network performance management system uses propagation modeling data to identify the source of and location of interference signals for an identified geographic area identifying by A longitude and A latitude, i.e., because the propagation modeling data identifies that a problem may exist, an error message may be displayed on the display device 3 identifying the possible reason the signal strength is below the threshold at location B).
Regarding claim(s) 6, 13, 20, Rogers in view of Hildebrandt and Byrne discloses all features of claim(s) 2, 9, 16 as outlined above.
Rogers discloses wherein the map of the three-dimensional space includes a two-dimensional map that is overlayed with the network performance data associated with the plurality of locations in the three-dimensional space ([0031]: display device 3 receives data from the monitoring device 1 and allows the viewing of the performance characteristics of the wireless communication network that is being measured. Fig. 6, [0036]: display device 3 may display different network performance information on the display screen 31, such as network performance information like dual-band radiofrequency scanner readings. [0079]-[0081]: display device 3 receives time-correlated network performance information and time-correlated monitoring device location in real time, and displays the combined information on a map of the test area. Fig. 4, [0002], [0036], [0074]: test area includes buildings, such as malls, airports, offices, apartments, hospitals, etc.).
Claim(s) 7 and 14 is/are rejected under 35 U.S.C. 103 as being unpatentable over Rogers et al. (US 2004/0203897 A1) in view of Hildebrandt et al. (US 2023/0012619 A1), Byrne et al. (US 2021/0409972 A1), and Nijim et al. (US 10,484,114 B1).
Regarding claim(s) 7, 14, Rogers in view of Hildebrandt and Byrne discloses all features of claim(s) 3, 10 as outlined above.
Rogers discloses wherein the at least one root cause is indicated in the map ([0094]-[0095]: wireless network performance management system uses propagation modeling data to identify the source of and location of interference signals for an identified geographic area, i.e., because the propagation modeling data identifies that a problem may exist, an error message may be displayed on the display device 3 identifying the possible reason the signal strength is below the threshold at location B).
Rogers does not disclose, but Nijim discloses wherein the at least one root cause is indicated in the map using a representative graphical icon (Fig. 3A, col. 14 ll. 16-27: signal data visual representation 120 includes an area of interest 106 indicating different WiFi signal strengths represented by wireless symbols 308 where red indicates range of poor signal strength).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to program the display device 3, as taught by Rogers, to use different wireless symbols 308 for indicating WiFi signal strengths where red indicates range of poor signal strength, as taught by Nijim.
Doing so enables generation and display of a visual representation that includes a representation of signal strength values based on assessed signal strengths needed to support particular services on particular user devices (Nijim: abstract).
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 THE HY NGUYEN whose telephone number is (571)270-3813. The examiner can normally be reached on Mo-Fr: 8am-4pm.
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/THE HY NGUYEN/Primary Examiner, Art Unit 2478
TheHy.Nguyen@USPTO.gov
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