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 . The present application is being examined under the pre-AIA first to invent provisions. 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.
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)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale or otherwise available to the public before the effective filing date of the claimed invention.
(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, 3 and 6 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by U.S. Pre-Grant Publication US-20190379592-A1 to Samadi (“Samadi”).
As to claim 1, Samadi disclose(s) a traffic prediction device for predicting a future traffic flow rate in a link accommodating a plurality of lines, the device comprising:
a data acquisition unit that acquires a requirement of the line and traffic data in the line; (Samadi; bandwidth and latency requirements; [0016]; historical data, live data; [0005])
a prediction data generation unit that generates a first feature amount corresponding to a traffic variation due to a change in a requirement of the line based on the acquired requirement, and generates a second feature amount based on traffic statistics representing a feature of the traffic variation for each time based on the acquired traffic data; (Samadi; predictor 208; fig. 2; generates predictions; first stage predict one or more metrics 562 based on historical data and real data; fig. 5)
and a prediction function unit that predicts a future traffic flow rate from the first feature amount and the second feature amount. (Samadi; predictor 208; fig. 2; generates predictions; second predications 564 include resource demand; fig. 5)
As to claim 3, Samadi disclose(s) a traffic prediction method for predicting a future traffic flow rate in a link accommodating a plurality of lines, the method comprising:
by a traffic prediction device performs, a step of acquiring a requirement of the line and traffic data in the line;
a step of generating a first feature amount corresponding to a traffic variation due to a change in a requirement of the line based on the acquired requirement;
a step of generating a second feature amount based on traffic statistics representing a feature of traffic variation for each time based on the acquired traffic data;
a step of predicting a future traffic flow rate from the first feature amount and the second feature amount.
See similar rejection to claim 1.
As to claim 6, Samadi disclose(s) a computer-readable non-transitory recording medium storing computer- executable program instructions that when executed by a processor cause a computer to execute a program generation method comprising:
acquiring a requirement of the line and traffic data in the line;
generating a first feature amount corresponding to a traffic variation due to a change in a requirement of the line based on the acquired requirement;
generating a second feature amount based on traffic statistics representing a feature of traffic variation for each time based on the acquired traffic data;
and predicting a future traffic flow rate from the first feature amount and the second feature amount.
See similar rejection to claim 1.
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.
Claim(s) 2, 5 and 7 is/are rejected under 35 U.S.C. 103 as being unpatentable over Samadi in view of U.S. Patent No. / U.S. Pre-Grant Publication US-20200036611-A1 to Lu et al. (“Lu”).
As to claim 2, Samadi-Lu disclose(s) the traffic prediction device according to claim 1, further comprising:
a data recording unit that sequentially records the traffic statistics, wherein the prediction data generation unit predicts a period of traffic variation based on a past traffic statistic recorded in the data recording unit, and generates the second feature amount based on a traffic statistic in a period corresponding to the predicted period.
Lu discloses a data recording unit that sequentially records the traffic statistics, wherein the prediction data generation unit predicts a period of traffic variation based on a past traffic statistic recorded in the data recording unit, and generates the second feature amount based on a traffic statistic in a period corresponding to the predicted period. (Lu collect traffic data sequence; [0106]
At the time of invention, it would have been obvious to a person of ordinary skill in the art to combine the network prediction of Samadi and the sequence collecting of Lu. One of ordinary skill in the art would have been motivated to combine the teachings as both are concerned with network flows. Using the collection of Lu would allow for the historical data to be stored in sequence.
Accordingly, the prior art references teach all of the claimed elements.
Furthermore, it would have been obvious to combine the teachings as all the claimed elements were known in the prior art and one skilled in the art could have combined the elements as claimed by known methods with no change in their respective functions, and the combination would have yielded nothing more than predictable results to one of ordinary skill in the art.
As to claim 5, Samadi-Lu disclose(s) the traffic prediction method for predicting a future traffic flow rate in a link accommodating a plurality of lines according to claim 3, the method further comprising:
predicting a period of traffic variation based on a past traffic statistic recorded in the data recording unit, and generating the second feature amount based on a traffic statistic in a period corresponding to the predicted period.
See similar rejection to claim 2.
As to claim 7, Samadi-Lu disclose(s) the computer-readable non-transitory recording medium according to claim 6 wherein the image processing method further comprises:
predicting a period of traffic variation based on a past traffic statistic recorded in the data recording unit, and generating the second feature amount based on a traffic statistic in a period corresponding to the predicted period.
See similar rejection to claim 2.
Conclusion
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/BRYAN Y LEE/Primary Examiner, Art Unit 2445