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 .
DETAILED ACTION
This office action is in response to the application filed on 11/03/2015.
Claims 1-20 are currently pending.
Claims 1-20 are rejected.
Claims 1, 7 and 15 are independent claims.
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Claim Rejections - 35 USC § 103
5. 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.
6. 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 of this title, 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.
7. The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under pre-AIA 35 U.S.C. 103(a) 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.
8. Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over David James RYAN et al. (US 2020/0336228 A1), hereinafter RYAN, in view of Dmitry Dimov et al. (US 2025/0219744 A1), hereinafter Dimov.
For claim 1, RYAN teaches a method for predicting and mitigating tropospheric ducting events in a wireless telecommunications network, the method comprising:
receiving atmospheric data comprising atmospheric parameters from one or more data sources (RYAN, Fig. 4 and paragraphs 62-67.);
receiving current cell site configuration data of one or more cell sites of the wireless telecommunications network from one or more second data sources (RYAN, Fig. 4 and paragraphs 62-67.);
predicting one or more tropospheric ducting events (RYAN, Fig. 4 and paragraph 68.);
determining a likelihood of a tropospheric ducting event affecting a cell site among the one or more cell sites of the wireless telecommunications network in a geographic area (RYAN, Fig. 4 and paragraph 73.);
determining at least one mitigation action to perform at the cell site, wherein implementing the at least one mitigation action at the cell site effectuates reduction in an impact of the predicted one or more tropospheric ducting events at the cell site (RYAN, Fig. 4 and paragraph 102.).
Dimov further teaches a machine learning engine for data processing (Dimov, Fig .1 and abstract. See also Figs. 4-5 and paragraphs 48-61.).
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 method taught in RYAN with a machine learning engine for data processing taught in Dimov because neural networks can have the advantage of being particularly suitable for time series analyses and can be well-suited to operate on large, time-series vectors such as those included in the network usage data [Dimov: paragraph 37.].
For claim 2, RYAN and Dimov further teach the method of claim 1, further comprising: determining a predicted duration of the tropospheric ducting event (RYAN, Fig. 4 and paragraph 106.).
For claim 3, RYAN and Dimov further teach the method of claim 2, wherein determining the at least one mitigation action is based at least in part on the predicted duration of the tropospheric ducting event and the received current cell site configuration data for the cell site (RYAN, Fig. 4 and paragraph 102.).
For claim 4, RYAN and Dimov further teach the method of claim 3, wherein determining the at least one mitigation action reduces a number of antenna downtilt adjustments for a plurality of cell sites, and wherein reducing the number of antenna downtilt adjustments for the plurality of cell sites reduces greenhouse gas emissions by reducing a number of miles driven by a vehicle to perform the antenna downtilt adjustments (RYAN, Fig. 4 and paragraphs 102-103.).
For claim 5, RYAN and Dimov further teach the method of claim 3, wherein the least one mitigation action comprises: changing an uplink power control parameter, forcing handover to a healthy neighbor cell site, changing an antenna downtilt, increasing a time domain duplexing guard period, switching an uplink user plane, or preventing user equipment from remaining in idle mode on the cell site (RYAN, Fig. 4 and paragraphs 102-103. See also Figs. 6-7 and paragraph 41-42.).
For claim 6, RYAN and Dimov further teach the method of claim 1, wherein the atmospheric data comprises forecast data, wherein preprocessing the atmospheric data comprises: determining, for a plurality of cell site locations, a weather stability at each cell site location; determining, based on the weather stability, a forecast frequency for each cell site location; determining, based on the weather stability, a forecast duration for each cell site location; and trimming the atmospheric data based on the determining forecast frequency and the determined forecast duration for each cell site location (RYAN, Fig. 7 and paragraphs 81-110.).
For claim 7, RYAN teaches a method to predict tropospheric ducting events comprising:
receiving training data comprising historical atmospheric data and historical network performance data (RYAN, Fig. 4 and paragraphs 62-67.);
indicate whether or not a tropospheric ducting event occurred (RYAN, Fig. 4 and paragraph 68.); and
determining a likelihood of a tropospheric ducting event (RYAN, Fig. 4 and paragraph 73.).
Dimov further teaches a machine learning model for processing training data, wherein the preprocessing comprises at least one of: dropping a portion of the training data or modifying one or more attributes of the training data to conform to a standardized format; extracting features from the training data; generating labels for the training data; and training the machine learning model, wherein training is performed using supervised learning, wherein the supervised learning uses the extracted features as inputs and the generated labels as outputs (Dimov, Fig .1 and abstract. See also Figs. 4-5 and paragraphs 48-61.).
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 method taught in RYAN with a machine learning model for processing training data, wherein the preprocessing comprises at least one of: dropping a portion of the training data or modifying one or more attributes of the training data to conform to a standardized format; extracting features from the training data; generating labels for the training data; and training the machine learning model, wherein training is performed using supervised learning, wherein the supervised learning uses the extracted features as inputs and the generated labels as outputs taught in Dimov because neural networks can have the advantage of being particularly suitable for time series analyses and can be well-suited to operate on large, time-series vectors such as those included in the network usage data [Dimov: paragraph 37.].
For claim 8, RYAN and Dimov further teach the method of claim 7, wherein the training data further comprises historical cell site configuration data, wherein the machine learning model is additionally trained using at least one feature extracted from the historical cell site configuration data (RYAN, Fig. 4 and paragraph 102.).
For claim 9, RYAN and Dimov further teach the method of claim 7, wherein the historical atmospheric data comprises at least one of refractive index or a combination of humidity, air pressure, and atmospheric pressure (RYAN, Fig. 4 and paragraph 56.).
For claim 10, RYAN and Dimov further teach the method of claim 7, wherein historical tropospheric ducting events are determined based on a slope of remote interference power over time (RYAN, Fig. 4 and paragraphs 87, 90.).
For claim 11, RYAN and Dimov further teach the method of claim 7, wherein historical tropospheric ducting events are determined based on physical uplink shared channel (PUSCH) received interference power and uplink symbol interference plus noise delta (RYAN, Fig. 4 and paragraphs 35, 87, 90.).
For claim 12, RYAN and Dimov further teach the method of claim 7, wherein the machine learning model is additionally training using cell site configuration data (RYAN, Fig. 4 and paragraphs 63, 114.).
For claim 13, RYAN and Dimov further teach the method of claim 12, further comprising: determining one or more cell site configuration changes to mitigate a predicted tropospheric ducting event (RYAN, Fig. 4 and paragraph 102.).
For claim 14, RYAN and Dimov further teach the method of claim 7, wherein preprocessing the training data comprises:
determining, based on at least one of cell site density and weather stability, a size of a geographic area unit (RYAN, Fig. 7 and paragraphs 63, 66, 68.); and
averaging historical atmospheric data inside the geographic area unit (RYAN, Fig. 7 and paragraphs 63, 66, 68.).
For claim 15, RYAN teaches a system for predicting and mitigating tropospheric ducting events in a wireless telecommunications network (RYAN, Fig. 1), the system comprising:
at least one hardware processor (RYAN, Fig.1 and paragraph 15);
at least one non-transitory memory (RYAN, Fig. and paragraph 15) storing instructions executable by the at least one hardware processor;
a data collection module (RYAN, Fig. and paragraph 14) configured to:
receive atmospheric data comprising atmospheric parameters from one or more data sources (RYAN, Fig. 4 and paragraphs 62-67.);
receive current cell site configuration data of one or more cell sites of the wireless telecommunications network from one or more second data sources (RYAN, Fig. 4 and paragraphs 62-67.);
predict one or more tropospheric ducting events (RYAN, Fig. 4 and paragraph 68.);
determine a likelihood of a tropospheric ducting event affecting a cell site among the one or more cell sites of the wireless telecommunications network in a geographic area (RYAN, Fig. 4 and paragraph 73.);
determine at least one mitigation action to perform at the cell site, wherein implementing the at least one mitigation action at the cell site effectuates reduction in an impact of the predicted one or more tropospheric ducting events at the cell site (RYAN, Fig. 4 and paragraph 102.).
Dimov further teaches a machine learning engine for data processing (Dimov, Fig .1 and abstract. See also Figs. 4-5 and paragraphs 48-61.).
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 method taught in RYAN with a machine learning engine for data processing taught in Dimov because neural networks can have the advantage of being particularly suitable for time series analyses and can be well-suited to operate on large, time-series vectors such as those included in the network usage data [Dimov: paragraph 37.].
For claim 16, RYAN and Dimov further teach the system of claim 15, wherein the tropospheric ducting prediction model is further configured to predict a duration of the tropospheric ducting event (RYAN, Fig. 4 and paragraph 106.).
For claim 17, RYAN and Dimov further teach the system of claim 16, wherein determining the at least one mitigation action is based at least in part on the predicted duration of the tropospheric ducting event and the cell site configuration data (RYAN, Fig. 4 and paragraph 102.).
For claim 18, RYAN and Dimov further teach the system of claim 17, wherein the at least one mitigation action comprises adjusting an antenna downtilt, wherein determining the at least one mitigation action reduces a number of antenna downtilt adjustments for a plurality of cell sites, wherein reducing the number of antenna downtilt adjustments for the plurality of cell sites reduces greenhouse gas emissions by reducing a number of miles driven by a vehicle to perform the antenna downtilt adjustments (RYAN, Fig. 4 and paragraphs 102-103.).
For claim 19, RYAN and Dimov further teach the system of claim 17, wherein the least one mitigation action comprises changing an uplink power control parameter, forcing handover to a healthy neighbor cell site, changing an antenna tilt, increasing a time domain duplexing guard period, switching an uplink user plane, or preventing user equipment from remaining in idle mode on the cell site (RYAN, Fig. 4 and paragraphs 102-103. See also Figs. 6-7 and paragraph 41-42.).
For claim 20, RYAN and Dimov further teach the system of claim 15, wherein the atmospheric data comprises forecast data, wherein preprocessing the atmospheric data comprises: determining, for a plurality of cell site locations, a weather stability at each cell site location; determining, based on the weather stability, a forecast frequency for each cell site location; determining, based on the weather stability, a forecast duration for each cell site location; and trimming the atmospheric data based on the determining forecast frequency and the determined forecast duration for each cell site location (RYAN, Fig. 7 and paragraphs 81-110.).
Conclusion
9. Any inquiry concerning this communication or earlier communications from the examiner should be directed to WILL W LIN whose telephone number is (571)272-8749. The examiner can normally be reached M-F 8:00-5:00.
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/WILL W LIN/Primary Examiner, Art Unit 2412
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