Notice of Pre-AIA or AIA Status
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Art Rejection
2. 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.
3. 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.
4. Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Boyapati, U.S. pat. No. 11,134,402.
Per claim 1, Boyapati discloses a system comprising at least one hardware processor, and a non-transitory computer-readable storage medium having stored thereon program instructions, the program instructions executable by the at least one hardware processor to:
a) receive, at a network interface, telemetry data from a plurality of uniquely-identified end-devices in multiple communication networks (see col 2, ln 17-42), wherein the telemetry data is captured with respect to each of said end-devices over one or more measuring periods of a predefined duration (see col 2, ln 43-67);
b) process said telemetry data to calculate features, e.g., usage signatures, indicating usage patterns associated with each of said end-devices (see col 3, ln 6-20); and
c) at a training stage, train a machine learning model on a training dataset comprising:
i) the features indicating usage patterns associated with each of said end-devices (see col 4, ln 36-60); and
ii) information indicating one or more attributes associated with each of said end-devices (see col 8, ln 59-62);
d) to obtain a trained machine learning model configured to predict said one or more attributes with respect to an unknown target end-device, by applying said trained machine learning model to telemetry data obtained from said unknown target end-device (see col 4, ln 16-28).
Boyapati does not explicitly teach using a classifier to predict the attributes with respect to the target end-device. Boyapati however teaches using a trained machine learning model to classify usage associated with the target device (see col 5, ln 60-67).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to recognize using Boyapati’s trained machine learning model as a classifier to predict the attributes with respect to the target end-device.
Boyapati also does not teach labeling the attributes of the end devices. Such use a labeling to train a model is known in the art as disclosed by Flynn (see Flynn, par 0064-0066).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to utilize such labeling to train model in Boyapati because it would have enabled the model to learn pattern and make predictions from the labels.
Per claim 2, Boyapati teaches that attributes comprises type, make and model, or the like of the device (see col 8, ln 59-62).
Per claim 3, Boyapati teaches that usage comprises various usage times (see col 2, ln 33-42), wherein the usage time is measured separately with respect to each of the service categories (see col 3, ln 21-35).
It would have been obvious to one skilled in the art to utilize any usage time in practicing Boyapati.
Per claim 4, Boyapati teaches that set of services comprises media streaming, web browsing, etc., (see col 2, ln 1-6).
It would have been obvious to one skilled in the art to practicing invention with any known type of services including file downloading mail, etc.
Per claim 5, Boyapati teaches that wireless link metrics comprise network parameters including QoS metrics, e.g., throughput, latency, beam width, antenna power, etc., (see col 5, ln 24-44 and col 9, ln 49-59).
It would have been obvious to one skilled in the art to utilize any network QoS metrics including WiFi bandwidth, bitrate, etc., to train Boyapati model.
Per claim 6, Flynn teaches that training dataset further comprises features indicating one or more event categories including failures and errors occurs during the measuring periods (see Flynn, par 0031).
It would have been obvious to one skilled in the art to further modify Boyapati with Flynn teaching because it would have enabled training the model to predict problematic conditions which can then be used to manage the resource more effectively.
Per claim 7, Boyapati teaches that predefine duration could be of any time interval (see col 2, ln 43-49).
Claims 8-20 are similar in scope as that of claims 1-7.
Conclusion
5. Any inquiry concerning this communication or earlier communications from the examiner should be directed to Viet Vu whose telephone number is 571-272-3977. The examiner can normally be reached on Monday through Thursday from 8:00am to 6:00pm. The Group general information number is 571-272-2400. The Group fax number is 571-273-8300.
If attempts to reach the examiner by telephone are unsuccessful, the examiner's supervisor, Emmanuel Moise, can be reached at 571-272-3865.
Information regarding the status of an application may be obtained from the Patent Application Information Retrieval (PAIR) system. Status information for published applications may be obtained from either Private PAIR or Public PAIR. Status information for unpublished applications is available through Private PAIR only. For more information about the PAIR system, see http://pair-direct.uspto.gov. Should you have questions on access to the Private PAIR system, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free).
/Viet D Vu/
Primary Examiner, Art Unit 2455
7/16/26
Prosecution Insights