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 .
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1-7 and 12-16 is/are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Svennebring et al. US Patent Pub. No.: 201900319868, hereinafter, ‘Svennebring’.
Consider Claim 15 and as Applied to the method of Claim 1, An apparatus (e.g., see hardware architecture in figures 1-5)for wireless communication, comprising at least one processor configured to cause the apparatus to implement a method comprising: generating, for a coverage area comprising wireless devices, a feature map that includes information about channel quality parameters at multiple geographical locations in the coverage area and channel quality estimates for the wireless devices, wherein the feature map is stored as a function of time(e.g., figure 7, 11 and 12 illustrates the “feature map”, Paragraph 0043 further teaches the LPPS 200 uses spatial and temporal (spatio-temporal) historical data and/or real-time data to predict link quality. The spatio-temporal historical data is data related to the performance experienced over multiple locations (e.g., space) and at multiple time instances (e.g., temporal), Paragraphs 0202-0203 teaches prediction layer mapping); predicting, based on a first snapshot of the feature map at a first time, a second snapshot of the feature map at a second time in future(e.g., see at least 0043 -the LPPS 200 uses spatial and temporal (spatio-temporal) historical data and/or real-time data to predict link quality. The spatio-temporal historical data is data related to the performance experienced over multiple locations (e.g., space) and at multiple time instances (e.g., temporal).); and controlling transmissions in the coverage area based on the predicted second snapshot of the feature map (e.g., see operation decisions -0011).
Consider Claim 2, Svennebring teaches wherein the coverage area is served by at least three base stations and wherein the multiple geographical locations are determined using a triangulation process based on signals received from or at the at least three base stations (e.g., see triangulation in 0203).
Consider Claims 3 and 16, Svennebring teaches wherein the first snapshot of the feature map is generated based on measurements in a first frequency band and the second snapshot of the feature map is in a second frequency band different from the first frequency band (e.g., this is met based on the description of 0211 and the illustration of figure 11).
Consider Claims 4 and 17, Svennebring teaches wherein the feature map is generated by associating, with each of the geographic locations, corresponding one or more channel quality parameters based on measurements performed on feedback reports from the wireless devices (e.g., this is met based on the context regarding the features and parameters outlined in paragraphs 0011 and 0027).
Consider Claim 5, Svennebring teaches the claimed invention further including: using the feature map for grouping wireless devices according to similarities in one or more channel quality estimates; and scheduling transmissions in the coverage area according to the grouping(e.g., this is met based on the context regarding the features and parameters outlined in paragraphs 0011 and 0027).
Consider Claim 6, Svennebring teaches wherein the feature map is generated using one or more of a capacity, an interference measurement, a line-of-sight (LOS) condition, a user density, a traffic measurement at the multiple geographic locations (e.g., this is met based on the context regarding the features and parameters outlined in paragraphs 0011 and 0027).
Consider Claim 7, Svennebring teaches wherein the feature map uses results from antenna calibration and user device localization (e.g., this is met based on the context regarding the features and parameters outlined in paragraphs 0011 and 0027 – see antenna adjustment).
Consider Claim 12, Svennebring teaches further including: using the feature map for scheduling transmission resource in a wireless network (e.g., this is met based on at least one of the scheduling suggestions in 0030, 0056 and 0071).
Consider Claim 13, Svennebring teaches using the feature map for deploying base stations for serving the coverage area(e.g., this is met based on at least 001 infrastructure deployment ).
Consider Claim 14, Svennebring teaches increasing transmission power in certain directions at certain times based on the feature map (e.g., see at least the suggestion in 0014 - recommended or needed transmission power, and/or a prediction of any other measurement or data type discussed herein.).
Claim Rejections - 35 USC § 103
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.
Claim(s) 8-11 and 18-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Svennebring et al. US Patent Pub. No.: 201900319868, hereinafter, ‘Svennebring’ in view of Vandikas et al. US Patent Pub. No.: 20210345138, ‘Vandikas’.
Consider Claims 8 and 18, Svennebring teaches the claimed invention except wherein the feature map is stored in a database that stores measurements at locations (x, y) in the coverage area using measurements made for multiple user devices at each of the locations (x, y) in the coverage area.
In analogous art, Vandikas teaches in paragraph 0010, a method may further comprise the steps of: receiving feedback from at least one user equipment device, UE, relating to accuracy of the collectively applied machine learning models; and adjusting the weights based on the feedback. Paragraph 0011 follows with, the input properties may comprise keywords and/or key-value pairs as noted in Paragraph 0012 which states that the input properties may relate to at least one of: supported RATs, power source(s), geographical region, latitude and longitude, antenna height, tower height, battery installation date, number of diesel generators, fuel tank size, on-air-date, number of cells and spectrum coverage, location, battery capacity, sector azimuth(s), sector spectrum, area type, radio access channel success rate over time, throughput over time, and latency over time and the future operational condition may be any one of: power outage, sleeping cell, degradation of latency, and degradation of throughput, paragraph 0013.
Therefore, it would have been obvious to a PHOSITA before the effective filing date to include wherein the feature map is stored in a database that stores measurements at locations (x, y) in the coverage area using measurements made for multiple user devices at each of the locations (x, y) in the coverage area for the purpose of enabling prediction of a future operational condition for at least one site, each site comprising at least one radio network node of a radio access technology, RAT, of a cellular network.
Consider Claims 9 and 19, Svennebring teaches the claimed invention wherein the measurements for each wireless device are weighted relative to performance accuracy of the wireless device in comparison with other wireless devices in a wireless network.
In analogous art, Vandikas teaches in paragraph 0010, a method may further comprise the steps of: receiving feedback from at least one user equipment device, UE, relating to accuracy of the collectively applied machine learning models; and adjusting the weights based on the feedback. Paragraph 0011 follows with, the input properties may comprise keywords and/or key-value pairs as noted in Paragraph 0012 which states that the input properties may relate to at least one of: supported RATs, power source(s), geographical region, latitude and longitude, antenna height, tower height, battery installation date, number of diesel generators, fuel tank size, on-air-date, number of cells and spectrum coverage, location, battery capacity, sector azimuth(s), sector spectrum, area type, radio access channel success rate over time, throughput over time, and latency over time and the future operational condition may be any one of: power outage, sleeping cell, degradation of latency, and degradation of throughput, paragraph 0013.
Therefore, it would have been obvious to a PHOSITA before the effective filing date to include wherein the measurements for each wireless device are weighted relative to performance accuracy of the wireless device in comparison with other wireless devices in a wireless network. for the purpose of enabling prediction of a future operational condition for at least one site, each site comprising at least one radio network node of a radio access technology, RAT, of a cellular network.
Consider Claims 10 and 20, Svennebring teaches the claimed except wherein the measurements made for multiple user devices include localization and antenna calibration for the multiple user devices.
Svennebring teaches antenna calibration (e.g., this is met based on the context regarding the features and parameters outlined in paragraphs 0011 and 0027 – see antenna adjustment)
In analogous art, Vandikas teaches in paragraph 0010, a method may further comprise the steps of: receiving feedback from at least one user equipment device, UE, relating to accuracy of the collectively applied machine learning models; and adjusting the weights based on the feedback. Paragraph 0011 follows with, the input properties may comprise keywords and/or key-value pairs as noted in Paragraph 0012 which states that the input properties may relate to at least one of: supported RATs, power source(s), geographical region, latitude and longitude, antenna height, tower height, battery installation date, number of diesel generators, fuel tank size, on-air-date, number of cells and spectrum coverage, location, battery capacity, sector azimuth(s), sector spectrum, area type, radio access channel success rate over time, throughput over time, and latency over time and the future operational condition may be any one of: power outage, sleeping cell, degradation of latency, and degradation of throughput, paragraph 0013.
Therefore, it would have been obvious to a PHOSITA before the effective filing date to include wherein the measurements made for multiple user devices include localization and antenna calibration for the multiple user devices for the purpose of enabling prediction of a future operational condition for at least one site, each site comprising at least one radio network node of a radio access technology, RAT, of a cellular network.
Consider Claim 11, Svennebring teaches the claimed except wherein an accuracy of the localization and antenna calibration is improved using an iterative process in which a location estimate is first estimated and then used for antenna calibration and the location is estimated again using calibrated antenna.
Svennebring teaches antenna calibration (e.g., this is met based on the context regarding the features and parameters outlined in paragraphs 0011 and 0027 – see antenna adjustment)
In analogous art, Vandikas teaches in paragraph 0010, a method may further comprise the steps of: receiving feedback from at least one user equipment device, UE, relating to accuracy of the collectively applied machine learning models; and adjusting the weights based on the feedback. Paragraph 0011 follows with, the input properties may comprise keywords and/or key-value pairs as noted in Paragraph 0012 which states that the input properties may relate to at least one of: supported RATs, power source(s), geographical region, latitude and longitude, antenna height, tower height, battery installation date, number of diesel generators, fuel tank size, on-air-date, number of cells and spectrum coverage, location, battery capacity, sector azimuth(s), sector spectrum, area type, radio access channel success rate over time, throughput over time, and latency over time and the future operational condition may be any one of: power outage, sleeping cell, degradation of latency, and degradation of throughput, paragraph 0013.
Therefore, it would have been obvious to a PHOSITA before the effective filing date to include wherein an accuracy of the localization and antenna calibration is improved using an iterative process in which a location estimate is first estimated and then used for antenna calibration and the location is estimated again using calibrated antenna for the purpose of enabling prediction of a future operational condition for at least one site, each site comprising at least one radio network node of a radio access technology, RAT, of a cellular network.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US 20200033849 A1 teaches the base station 201 of the serving cell may send an aerial vehicle region map to the AV-UE 211 upon connection to indicate to the AV-UE 211 an area of the aerial vehicle region map in which interference control may be applied. In other words, the base station 211 may include the indication of an area of the map as well as one or more interference control features that the baseband processing circuitry 261 of the AV-UE 211, via an interface coupled with a physical layer of the AV-UE 211, may apply in response to entering the area of the map.
CN 109756911 B teaches a network quality prediction method, service adjusting method, related device and storage medium, wherein the network quality prediction method comprises: obtaining the historical network quality of the target geographic area to be predicted in the historical time period; the historical network quality is obtained based on the reported information submitted by the plurality of terminals; according to the historical network quality calling network quality prediction model for prediction processing, obtaining the prediction network quality of the target geographic area in the prediction time period, the starting time of the prediction time period is later than the end time of the history time period. The embodiment of the invention can better perform network quality prediction and improve the prediction precision of network quality. The prediction module is used for solving the prediction network quality and/or network quality heat map of the region to be predicted in the prediction time period according to the report information stored by the storage module; the control module is used for sending the information collection request to the terminal, sending or adjusting the report strategy, initiating the prediction request of network quality to the prediction module and so on. geographic location based on different geographic location.
US 20110306365 A1 teaches in 0021 -Terminal positions can be reported, for instance by including the terminal position into a report (e.g. a so-called fingerprint) and transmitting the report to a unit that is different from the terminal, for instance to a server.
US 20140087752 A1 teaches a Mobile device 106 may include a smartphone, a mobile phone, a personal digital assistant (PDA), a portable personal computer, a desktop computer, a multimedia player, an entertainment unit, a data communication device, a portable reading device, or any combination thereof equipped with Bluetooth module 206 and Wi-Fi module 236. Map server 106 may include and/or may be a database for storing and generating location information, signal map, and Wi-Fi fingerprint. For example, once the user of mobile device 106 is prompted for location information input, besides adding the location information to the signal map server 106, users may also choose to encode the location information and current Wi-Fi signal observations into a Bluetooth beacon. Subsequent user devices entering the same location may scan for Bluetooth beacon(s). If beacon(s) are found, the mobile device 106 may be localized instantly with high accuracy. If however, beacons are not found, the user may be prompted for location information to improve coverage or the mobile device 106 may resort to Wi-Fi-based localization using maps (e.g., from map server 108) built by previous users.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to CHARLES TERRELL SHEDRICK whose telephone number is (571)272-8621. The examiner can normally be reached 8A-5P.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Matthew D Anderson can be reached at 571 272 4177. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/CHARLES T SHEDRICK/Primary Examiner, Art Unit 2646
Prosecution Insights