DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
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.
This action is responsive to the Remark filed on 10/30/25.
Claim(s) 1-3, 6-12, 15-21, 24-27 is/are presented for examination.
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 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 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.
Claim(s) 1-2, 6-8, 10-11, 15-17, 19-20, 24-26 is/are rejected under 35 U.S.C. 103 as being unpatentable over Teslenko, U.S. Patent/Pub. No. 2023/0261958 A1 in view of Martin, U.S. Patent/Pub. No. 2017/0207997 A1, and Sang, US 2015/0312831 A1, and further in view of Bugenhagen, US 2010/0061266 A1.
As to claim 1, Teslenko teaches a method of wireless communication at a user equipment (UE), comprising:
monitoring application traffic of one or more applications installed on the UE (Teslenko, page 1, paragraph 7; page 6, paragraph 94; i.e., [0007] network traffic activities of a plurality of mobile devices is provided. Each data record includes a traffic type of a traffic activity of a mobile device; [0094] A second insight/monitoring type is called "Popular Application Categories". Here, PDU headers are analyzed to identify the applications generating the associated traffic activities,. Again, the actual application and the actual mobile device 108 using the application);
determining one or more observation features of the application traffic within an observation period (Teslenko, page 1, paragraph 7; page 6, paragraph 94; i.e., [0094] A second insight/monitoring type is called "Popular Application Categories". Here, PDU headers are analyzed to identify the applications generating the associated traffic activities, and to grouping of applications under one or more generic traffic activities as values. Over time, this insight can give an estimation of what type of applications are popular in a specific area and/or time);
predicting via a machine learning (ML) model, a traffic category of the observation period based on the one or more observation features (Teslenko, page 2, paragraph 18 & 23; page 6, paragraph 94; i.e., [0023] The type in this data structure may correspond to the monitoring type specified in the monitoring request. The value may be indicative of one or more of: a traffic type, a generic traffic type encompassing multiple traffic types on a lower hierarchy level, a prediction made by a machine learning algorithm based on the data records that fulfil the conditions, and a temporal validity of the prediction, and an average traffic duration; [0094] Here, PDU headers are analyzed to identify the applications generating the associated traffic activities, and to grouping of applications under one or more generic traffic activities as values);
and transmitting, by the UE to a network entity, the second application traffic in accordance with the optimization (Teslenko, figure 1).
But Teslenko failed to teach the claim limitation wherein monitoring, determining, predicting and applying application traffic by the UE; and based on one or more throughput bursts, the one or more throughput bursts comprising a period of time in which a throughput of the one or more applications is above a threshold; applying an optimization to second application traffic of the one or more applications at the UE based on the traffic category; wherein determining the one or more observation features of the application traffic within the observation period comprises identifying one or more throughput bursts within the observation period based on a plurality of observation window size and burst threshold pairs and determining the one or more observation features based on the one or more throughput bursts, wherein identifying the one or more throughput bursts within the observation period based on the plurality of observation window size and burst threshold pairs comprises determining that throughput of the application traffic is greater than a burst threshold within an observation window size, wherein the burst threshold and the observation window size are an observation window size-burst threshold pair of the plurality of observation window size and burst threshold pairs.
However, Martin teaches the limitation wherein applying an optimization to second application traffic of the one or more applications at the UE based on the traffic category (Martin, page 10, paragraph 101 & 104; i.e., [0101] specify that an application to optimize web traffic is to receive TCP packets on ports 80 and 443 and another application to optimize domain name system (DNS) names. The web cache communication protocol (WCCP) and the WANapp table support the specification of this type of rule; [0104] the selected application, the optimized communication traffic).
It would have been obvious to one of ordinary skill in the art before the effective date of the claimed invention to modify Teslenko to substitute routing domain from Martin for flow description from Teslenko to configure, monitor,
optimize according to data type and function, and analyze each node of a large network (Martin, page 2, paragraph 6).
However, Sang teaches the limitation wherein monitoring, determining, predicting and applying application traffic by the UE (Sang, page 4, paragraph 64 & 66; page 5, paragraph 72; page 6, paragraph 79; page 7, paragraph 92; i.e., [0079] At step 701, the UE monitors the association status of each radio access link. At step 702, the UE monitors the application and/or data flaw's QoS requirements. At step 704, the UE obtains the QoS requirement value for the application/flow. At step 705, the UE dynamically estimates the throughput for each radio access network based on the association status and the QoS requirement value. At step 706, the UE compares the estimated throughputs for each radio access network; [0092] UE determines if the traffic is QoS traffic with association to access A, and the current performance is below a predefined threshold); and based on one or more throughput bursts, the one or more throughput bursts comprising a period of time in which a throughput of the one or more applications is above a threshold (Sang, page 4, paragraph 66; page 6, paragraph 79; ; page 7, paragraph 92; i.e., [0079] At step 702, the UE monitors the application and/or data flaw's QoS requirements. At step 705, the UE dynamically estimates the throughput for each radio access network based on the association status and the QoS requirement value; [0092] The dynamic real time measurements are further processed to determine the end-to-end throughput and traffic offloading decisions. At step 911, the UE determines if the traffic is QoS traffic with association to access A, and the current performance is below a predefined threshold).
It would have been obvious to one of ordinary skill in the art before the effective date of the claimed invention to modify Teslenko to substitute policy input from Sang for policy control from Teslenko to provide user better experience, e.g., to speed up throughput when downloading data through two radio interfaces simultaneously (Sang, page 1, paragraph 4).
However, Bugenhagen teaches the limitation wherein determining the one or more observation features of the application traffic within the observation period comprises identifying one or more throughput bursts within the observation period based on a plurality of observation window size and burst threshold pairs and determining the one or more observation features based on the one or more throughput bursts, wherein identifying the one or more throughput bursts within the observation period based on the plurality of observation window size and burst threshold pairs comprises determining that throughput of the application traffic is greater than a burst threshold within an observation window size, wherein the burst threshold and the observation window size are an observation window size-burst threshold pair of the plurality of observation window size and burst threshold pairs (Bugenhagen, page 7, paragraph 70-75; i.e., [0070] A determination of whether the user exceeds burst allocations may be determined by measuring CIR and bursting over a sliding window that follows the burst rate refresh window; [0073] A threshold crossing may occur when the specified CIR and burst rate as measured in bits are exceeded during the observance window. If there is not a threshold crossing during the observance window, the counter sets an observance window to the same time period as the CIR/CBS burst refresh rate; [0075] a time period such as 100/max rate, 10/max rate, 50/available rate, 25/the reciprocal of the rate, or some other factor of the maximum or available rate capacity may be utilized to evaluate the CIR and CBS rates over a mini-observance window. The CIR rate and CBS rate may be converted to the equivalent rate for the adjusted observance window. For example, instead of measuring traffic over one second, the observance window may be reduced to 100 nano seconds and threshold comparisons may occur for the CIR and CBS rate at those levels).
It would have been obvious to one of ordinary skill in the art before the effective date of the claimed invention to modify Teslenko to substitute NWDAF from Sang for policy function from Teslenko to determination may be made
whether there is a threshold crossing during the observance window (Bugenhagen, page 1, paragraph 5).
As to claim 2, Teslenko-Martin-Sang-Bugenhagen teaches the method as recited in claim 1, wherein predicting the traffic category based on the one or more observation features, comprises:
determining traffic volume information for the application traffic during the observation period (Teslenko, page 9, paragraph 142; i.e., [0142] performed dependent on another condition, such as network traffic load (wherein the rerouting takes place when a predefined load threshold is exceeded));
determining that the traffic volume information meets a predetermined criteria (Teslenko, page 9, paragraph 142; i.e., [0142] performed dependent on another condition, such as network traffic load (wherein the rerouting takes place when a predefined load threshold is exceeded)); and
predicting the traffic category in response to the traffic volume information meeting the predetermined criteria (Teslenko, page 2, paragraph 18 & 23; page 6, paragraph 94; i.e., [0023] The value may be indicative of one or more of: a traffic type, a generic traffic type encompassing multiple traffic types on a lower hierarchy level, a prediction made by a machine learning algorithm based on the data records that fulfil the conditions, and a temporal validity of the prediction, and an average traffic duration; [0094] Again, the actual application and the actual mobile device 108 using the application. Over time, this insight can give an estimation of what type of applications are popular in a specific area and/or time).
As to claim 6, Teslenko-Martin-Sang-Bugenhagen teaches the method as recited in claim 1, wherein applying the optimization comprises:
transmitting, to a network entity, a network configuration request based on the optimization (Teslenko, page 2, paragraph 18 & 23; i.e., [0018] The traffic type included in a particular data record may be determined ( e.g., upon data record generation) by at least one of PDU header inspection and PDU payload inspection);
receiving, from the network entity, a configuration indication in response to the network configuration request (Teslenko, page 3, paragraph 37; i.e., [0037] The network apparatus is further configured to return, in response to the monitoring request, a monitoring report that is based on the calculated number of mobile devices).
But Teslenko-Sang-Bugenhagen failed to teach the claim limitation wherein transmitting the second application traffic in accordance with the optimization in response to the configuration indication.
However, Martin teaches the limitation wherein transmitting the second application traffic in accordance with the optimization in response to the configuration indication (Martin, page 10, paragraph 101 & 104; i.e., [0101] different algorithms that are in use simultaneously would be designed for different types of traffic. For example, a rule could specify that an application to optimize web traffic is to receive TCP packets on ports 80 and 443 and another application to optimize domain name system (DNS) names is to receive user datagram protocol (UDP) packets on port 53. The web cache communication protocol (WCCP) and the WANapp table support the specification of this type of rule; [0104] the selected application, the optimized communication traffic).
It would have been obvious to one of ordinary skill in the art before the effective date of the claimed invention to modify Teslenko-Sang-Bugenhagen to substitute routing domain from Martin for flow description from Teslenko-Sang-Bugenhagen to configure, monitor, optimize according to data type and function, and analyze each node of a large network (Martin, page 2, paragraph 6).
As to claim 7, Teslenko-Martin-Sang-Bugenhagen teaches the method as recited in claim 1. But Teslenko-Sang-Bugenhagen failed to teach the claim limitation wherein applying the optimization comprises at least one of: modifying one or more CDRX attributes; implementing a low latency mode; deactivating one or more antennas of the UE; reducing the internal processors or DSP clock speed; or implementing traffic prioritization.
However, Martin teaches the limitation wherein applying the optimization comprises at least one of: modifying one or more CDRX attributes; implementing a low latency mode (Martin, page 4, paragraph 40; i.e., [0040] Each path in each conduit in the APN is monitored for quality of communication by collecting quality metrics such as packet loss and latency); deactivating one or more antennas of the UE; reducing the internal processors or DSP clock speed; or implementing traffic prioritization.
It would have been obvious to one of ordinary skill in the art before the effective date of the claimed invention to modify Teslenko-Sang-Bugenhagen to substitute routing domain from Martin for flow description from Teslenko-Sang-Bugenhagen to configure, monitor, optimize according to data type and function, and analyze each node of a large network (Martin, page 2, paragraph 6).
As to claim 8, Teslenko-Martin-Sang-Bugenhagen teaches the method as recited in claim 1, wherein the ML model is a first ML model, the one or more observation features are a second plurality of a plurality of observation window size and burst threshold pairs, and further comprising down selecting, using a second ML during a training phase of the first ML model, from a first plurality of observation window size and burst threshold pairs to the second plurality of a plurality of observation window size and burst threshold pairs (Teslenko, page 7, paragraph 116; i.e., [0116] The table of FIG. 7 exemplarily illustrates that all User Datagram Protocol (UDP)/ Real-Time Transport Protocol (RTP) traffic for a specific mobile device 108 (, i.e., traffic received from and sent by that device 108) is filtered and assigned a high priority data tunnel ("EPS bearer" in 3GPP terminology) with guaranteed 100 Kbps uplink/downlink. In addition to SDFs, it is also possible to supply application filters to PCC rules).
Claim(s) 10-11, 15-17 & 19-20, 24-26 is/are directed to system and non-transitory computer readable medium claims and they do not teach or further define over the limitations recited in claim(s) 1-2, 6-8. Therefore, claim(s) 10-11, 15-17 & 19-20, 24-26 is/are also rejected for similar reasons set forth in claim(s) 1-2, 6-8.
Claim(s) 3, 12 & 21 is/are rejected under 35 U.S.C. 103 as being unpatentable over Teslenko, U.S. Patent/Pub. No. 2023/0261958 A1 in view of Martin, U.S. Patent/Pub. No. 2017/0207997 A1, and Sang, US 2015/0312831 A1, and Bugenhagen, US 2010/0061266 A1, and further in view of Makino, U.S. Pub. No. 2009/0190546 A1.
As to claim 3, Teslenko-Martin-Sang-Bugenhagen teaches the method as recited in claim 1, wherein the observation period is a first observation period, the one or more observation features are one or more first observation features, the traffic category is a first traffic category, and further comprising:
determining traffic volume information for the application traffic during a second observation period (Teslenko, page 9, paragraph 142; i.e., [0142] performed dependent on another condition, such as network traffic load (wherein the rerouting takes place when a predefined load threshold is exceeded));
determining that the traffic volume information fails to meet a predetermined criteria (Teslenko, page 9, paragraph 142; i.e., [0142] performed dependent on another condition, such as network traffic load (wherein the rerouting takes place when a predefined load threshold is exceeded)).
But Teslenko-Martin-Sang-Bugenhagen failed to teach the claim limitation wherein skipping prediction of a second traffic category in response to the traffic volume information failing to meet the predetermined criteria.
However, Makino teaches the limitation wherein skipping prediction of a second traffic category in response to the traffic volume information failing to meet the predetermined criteria (Makino, page 6, paragraph 80; i.e., [0080] the communication speed is not used as the determination criteria, the calculation of the estimated communication speed may be omitted).
It would have been obvious to one of ordinary skill in the art before the effective date of the claimed invention to modify Teslenko-Martin-Sang-Bugenhagen to substitute channel quality from Makino for service quality from Teslenko-Martin-Sang-Bugenhagen to satisfy desired communication quality with respect to the modulation method and encoding ratio specified by the base station (Makino, page 1, paragraph 13).
Claim(s) 21 & 21 is/are directed to system and non-transitory computer readable medium claims and they do not teach or further define over the limitations recited in claim(s) 3. Therefore, claim(s) 12 & 21 is/are also rejected for similar reasons set forth in claim(s) 3.
Claim(s) 9, 18, 27 is/are rejected under 35 U.S.C. 103 as being unpatentable over Teslenko, U.S. Patent/Pub. No. 2023/0261958 A1 in view of Martin, U.S. Patent/Pub. No. 2017/0207997 A1, and Sang, US 2015/0312831 A1, and Bugenhagen, US 2010/0061266 A1, and further in view of Ihlar, U.S. Pub. No. 2023/0208735 A1.
As to claim 9, Teslenko-Martin-Sang-Bugenhagen teaches the method as recited in claim 1. But Teslenko-Martin-Sang-Bugenhagen failed to teach the claim limitation wherein the one or more observation features include throughput bursts per minute, throughput burst occupancy, throughput burst volume percentage, throughput burst volume standard deviation, throughput burst gap standard deviation, or downlink volume ratio.
However, Ihlar teaches the limitation wherein the one or more observation features include throughput bursts per minute, throughput burst occupancy, throughput burst volume percentage, throughput burst volume standard deviation, throughput burst gap standard deviation, or downlink volume ratio (Ihlar, page 8, paragraph 76; i.e., [0076] based on length of the transmission period and/or silence period of an overlapping burst interval. The predefined quiescent period, e.g. 411b; 411c, may be predefined as a fraction of the suitable burst interval, e.g. 410b; 410c, and/or as a fraction of the subsequent burst interval, e.g. 410c. the suitable burst interval or a percentage of its silence period, and/or the begin period may be predefined as another or the same percentage, e.g. 25%, of the subsequent burst interval or a percentage of its transmission period).
It would have been obvious to one of ordinary skill in the art before the effective date of the claimed invention to modify Teslenko-Martin-Sang-Bugenhagen to substitute communication path from Ihlar for service flow from Teslenko-Martin-Sang-Bugenhagen to figure out about current conditions in the network and thereby for example get indication if there is congestion or other problems, or potential such problems ahead (Ihlar, page 2, paragraph 17).
Claim(s) 18 & 27 is/are directed to system and non-transitory computer readable medium claims and they do not teach or further define over the limitations recited in claim(s) 9. Therefore, claim(s) 18 & 27 is/are also rejected for similar reasons set forth in claim(s) 9.
Response to Arguments
Applicant’s arguments with respect to claim(s) 1-3, 6-12, 15-21, 24-27 has/have been considered but are moot in view of the new ground(s) of rejection. Applicant’s arguments include the failure of previously applied art to expressly disclose “determining the one or more observation features of the application traffic within the observation period comprises identifying one or more throughput bursts within the observation period based on a plurality of observation window size and burst threshold pairs and determining the one or more observation features based on the one or more throughput bursts, wherein identifying the one or more throughput bursts within the observation period based on the plurality of observation window size and burst threshold pairs comprises determining that throughput of the application traffic is greater than a burst threshold within an observation window size, wherein the burst threshold and the observation window size are an observation window size-burst threshold pair of the plurality of observation window size and burst threshold pairs” (see Applicant’s response, 3/17/26, page 12-14). It is evident from the detailed mappings found in the above rejection(s) that Bugenhagen disclosed this functionality (see Bugenhagen, page 7, paragraph 70-75). Further, it is clear from the numerous teachings (previously and currently cited) that the provision for “determining the one or more observation features of the application traffic within the observation period comprises identifying one or more throughput bursts within the observation period based on a plurality of observation window size and burst threshold pairs and determining the one or more observation features based on the one or more throughput bursts, wherein identifying the one or more throughput bursts within the observation period based on the plurality of observation window size and burst threshold pairs comprises determining that throughput of the application traffic is greater than a burst threshold within an observation window size, wherein the burst threshold and the observation window size are an observation window size-burst threshold pair of the plurality of observation window size and burst threshold pairs” was widely implemented in the networking art. Thus, Applicant’s arguments drawn toward distinction of the claimed invention and the prior art teachings on this point are not considered persuasive.
Listing of Relevant Arts
Bader, U.S. Patent/Pub. No. US 20230261996 A1 discloses machine learning and estimated of the QoE level and the type of data traffic.
Wang, U.S. Patent/Pub. No. US 20220222688 A1 discloses machine learning and predictive categories of the text string.
Contact Information
The present application is being examined under the pre-AIA first to invent provisions.
THUONG NGUYEN whose telephone number is (571)272-3864. The examiner can normally be reached on Monday-Friday 9:00-6:00.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Noel Beharry can be reached on 571-270-5630. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/THUONG NGUYEN/Primary Examiner, Art Unit 2416
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