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 Amendment
Applicant's submission filed on 1/22/2026 has been entered. Claim(s) 1-20 is/are pending in the application.
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 (i.e., changing from AIA to pre-AIA ) 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) 1-4, 11-14, 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Das (U.S. Patent App Pub 20170171044) in view of Bearden (U.S. Patent App Pub 20030086425).
Regarding claim 1,
Das teaches a method for generating an application mix of simulated network traffic with a desired packet size statistic while maintaining traffic allocations in the application mix, the method comprising:configuring first and second simulated network traffic generator components to generate a defined application mix of network traffic, wherein the defined application mix includes a fixed network traffic allocation assigned to each of a plurality of simulated applications(See paragraphs 32-34, figures 2-3, Das teaches generating simulated traffic different types) and
generate simulated network traffic together using the throughput values (See paragraphs 34-36, figures 2-3, Das teaches throughput and packet size computed for the measurement phase and the defined application mix of network traffic; during a measurement phase, enabling the first simulated network traffic generator component to generate the defined application mix of network traffic (See paragraphs 34-36, figures 2-3, Das teaches throughput and packet size measurements)
and measurinq a throughput and a packet size statistic of the second simulated network traffic qenerator component; computing, using the measured throughputs, the measured packet size statistics, and a model, a throughput value for each of the first and second simulated network traffic generator components to achieve a desired value or distribution of values of the packet size statistic when the first and second simulated network traffic generator components are generating simulated network traffic together; (See paragraphs 34-36, figures 2-3, Das teaches throughput and packet size measurements)
Das does not explicitly teach but Bearden teaches configuring the first simulated network traffic generator component with a first set of open systems interconnect (OSI) layer 3, layer 4, and/or layer 7 parameters and confiqurinq the second network traffic generator component with a second set of OSI layer 3, layer 4, and/or layer 7 parameters different from the first set of OSI layer 3, layer 4, and/or layer 7 parameters; (See paragraphs 94-96, 215 fig 3-4, Bearden teaches generating and injecting network traffic into the network)
using the first set of OSI layer 3, layer 4 and/or layer 7 parameters and measuring a throughput and a packet size statistic of the first simulated network traffic generator component and enablinq the second simulated network traffic generator component to generate the defined application mix of network traffic using the second set of OSI layer 3, layer 4 and/or layer 7 parameters and (See paragraphs 94-96, fig 3-4, Bearden teaches packet statistics and others are generated)
during a test traffic generation phase, enabling the first and second simulated network traffic generator components (See paragraphs 94-96, 215, fig 3-4, Bearden teaches first and second traffic components)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to have known to combine the teachings of Bearden with Das because both deal with generating traffic and testing network. The advantage of incorporating the above limitation(s) of Bearden into Das is that Bearden provides a method to collect and integrate network element status and VOIP statistics in evaluating network performance, therefore making the overall system more robust and efficient. (See paragraphs [0005] - [0007], Bearden)
Regarding claim 2,
Das and Bearden teach the method of claim 1 wherein configuring the first and second simulated network traffic generator components to generate the application mix of simulated network traffic includes configuring the first and second simulated network traffic generator components with the same fixed network traffic allocation values for the same applications simulated by the first and second simulated network traffic generator components. (See paragraphs 34-35, figures 1-2, Das)
Regarding claim 3,
Das and Bearden teach the method of claim 1 wherein the first set of OSI layer 3, layer 4 and/or layer 7 parameters is expected to cause the first simulated network traffic generator component to generate simulated network traffic havinq a first value of the packet size statistic and the second set of OSI layer 3, layer 4 and/or layer 7 parameters is expected to cause the second simulated network traffic generator component to generate simulated network traffic havinq a second value of the packet size statistic different from the first value. (See paragraphs 27-28, figures 1-2, Das)
Regarding claim 4,
Das and Bearden teach the method of claim 3 wherein the packet size statistic comprises an average packet size and the first value of the packet size statistic comprises a first average packet size and the second value of the packet size statistic comprises a second average packet size that is less than the first average packet size. (See paragraphs 34-35, figures 2-3, Das)
Claims 11-14, list all the same elements of claims 1-4, but in system form rather than method form. Therefore, the supporting rationale of the rejection to claims 1-4 applies equally as well to claims 11-14. Furthermore with regards to the limitation of 11. A system for generating an application mix of simulated network traffic with a desired packet size statistic while maintaining traffic allocations in the application mix, the system comprising: (See paragraph 44-45, Das)
Claim 20 list all the same elements of claim 1, but in medium form rather than method form. Therefore, the supporting rationale of the rejection to claim 1 applies equally as well to claim 20. Furthermore with regards to the limitation of 20. A non-transitory computer readable medium having stored thereon executable instructions that when executed by a processor of a computer control the computer to perform steps comprising: (See paragraph 45, Das)
Claim(s) 6-8, 10, 16-18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Das (U.S. Patent App Pub 20170171044) in view of Bearden (U.S. Patent App Pub 20030086425) in view of Cohen (U.S. Patent 7139692).
Regarding claim 6,
Das and Bearden teach the method of claim 1.
Das and Bearden do not explicitly teach but Cohen teaches wherein the packet size statistic is variance in packet size values. (See col 9 lines 15-45, fig 12, Cohen teaches packet size variance)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to have known to combine the teachings of Cohen with Das and Bearden because both deal with simulating network traffic. The advantage of incorporating the above limitation(s) of Cohen into Das and Bearden is that Cohen teaches the method configures each tracer to optionally contain traffic-flow information related to other traffic-flows, thereby reducing the number of iterations required to converge on the steady-state values of throughput for each flow along the designated path, therefore making the overall system more robust and efficient. (See column 2, Cohen)
Regarding claim 7,
Das and Bearden teach the method of claim 1.
Das and Bearden do not explicitly teach but Cohen teaches wherein the packet size statistic is a desired distribution of packet size values. (See col 7 Ines 50-65, col 9 lines 15-45, fig 12, Cohen teaches distribution of packet sizes)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to have known to combine the teachings of Cohen with Das and Bearden because both deal with simulating network traffic. The advantage of incorporating the above limitation(s) of Cohen into Das and Bearden is that Cohen teaches the method configures each tracer to optionally contain traffic-flow information related to other traffic-flows, thereby reducing the number of iterations required to converge on the steady-state values of throughput for each flow along the designated path, therefore making the overall system more robust and efficient. (See column 2, Cohen)
Regarding claim 8,
Das and Bearden teach the method of claim 1.
Das and Bearden do not explicitly teach but Cohen teaches wherein the model relates the desired packet size statistic value or distribution of values to a linear combination of values or distributions of values of the packet size statistic for simulated network traffic generated by the simulated network traffic generator components. (See col 7 Ines 50-65, col 9 lines 15-45, fig 12, Cohen teaches packet distribution)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to have known to combine the teachings of Cohen with Das and Bearden because both deal with simulating network traffic. The advantage of incorporating the above limitation(s) of Cohen into Das and Bearden is that Cohen teaches the method configures each tracer to optionally contain traffic-flow information related to other traffic-flows, thereby reducing the number of iterations required to converge on the steady-state values of throughput for each flow along the designated path, therefore making the overall system more robust and efficient. (See column 2, Cohen)
Regarding claim 10,
Das and Bearden teach the method of claim 1.
Das and Bearden do not explicitly teach but Cohen teaches wherein the defined application mix of network traffic generated by the first and second simulated network traffic generator components together during the test traffic generation phase achieves a value or distribution of values of the packet size statistic that is equal to the desired value or distribution of values of the packet size statistic while maintaining the fixed network traffic allocations assigned to the simulated applications.. (See col 9 lines 15-67, col 7 line 25-65, fig 12, Cohen teaches packet size distribution)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to have known to combine the teachings of Cohen with Das and Bearden because both deal with simulating network traffic. The advantage of incorporating the above limitation(s) of Cohen into Das and Bearden is that Cohen teaches the method configures each tracer to optionally contain traffic-flow information related to other traffic-flows, thereby reducing the number of iterations required to converge on the steady-state values of throughput for each flow along the designated path, therefore making the overall system more robust and efficient. (See column 2, Cohen)
Claims 16-18, list all the same elements of claims 6-8, but in system form rather than method form. Therefore, the supporting rationale of the rejection to claims 6-8applies equally as well to claims 16-18. Furthermore with regards to the limitation of 11. A system for generating an application mix of simulated network traffic with a desired packet size statistic while maintaining traffic allocations in the application mix, the system comprising: (See paragraph 44-45, Das)
Allowable Subject Matter
Claims 5, 9, 15, 19 would be allowable if rewritten to overcome the rejection(s) under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), 2nd paragraph, set forth in this Office action and to include all of the limitations of the base claim and any intervening claims.
Response to Arguments
Applicant’s arguments with respect to claim(s) have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure and located in the PTO-892 form.
1.Kryskow, U.S. Patent App 20080005354, teaches a method is described for measuring system latency and latency jitter on a near real-time basis regarding packets of information moving from sources to destination. The method for measuring system latency and latency jitter includes time-stamping packets of information as they pass through a plurality of locations between the source and destination of these packets, with the time-stamping being performed by real-time clocks set to a universal standard. The time-stamped packets are analyzed to determine latency and latency jitter. The measurements of system latency and latency jitter can be performed based upon packet types so as to obtain accurate information concerning different types of business bandwidths associated with an observed network.
2. Shin, U.S. Patent App 20110310768, teaches a method for modeling network traffic includes: collecting traffic of data transmitted from a network; extracting a traffic density value based on any one of the data size, the packet size, and the IDT of the collected traffic and obtaining the probability density distribution on the raw domain; separating the data into the major dataset that is a group of data having the density value of the threshold value or more and the minor dataset that is a group of data having the data density value less than the threshold value; transforming the major dataset separated on the raw domain onto the major dataset domain formed to exclude a period corresponding to the data density value of a threshold value or less; and obtaining a major dataset analysis model by applying a graph fitting algorithm on the major dataset on the major dataset domain.
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 nonprovisional extension fee (37 CFR 1.17(a)) 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 mailing date of this final action.
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/NINOS DONABED/Primary Examiner, Art Unit 2444