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 .
The Office Action is in response to claims filed on 6/9/2025 where claims 1-20 are pending and ready for examination.
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.
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.
Claims 1-4, 6, 8-14 and 18-20 are rejected under 35 USC 103 as being unpatentable over Gupta (US 2020/0358685) in view of Jain (US 20240007414) and in further view of Makineni (US 20100020819)
Regarding claim 1, Gupta discloses an electronic device comprising:
memory, comprising one or more storage media, storing instructions and storing information associated with a plurality of applications and association information between a plurality of artificial intelligence (AI) models (Gupta;
see e.g. [0033] “... memory 104 based on instructions from the example processor 106 and/or the example modem 108. For example, the memory controller 114 includes logic that reads an input (e.g., instructions) ...”
see e.g. [0019] “... In examples disclosed herein, ML/AI models are trained ...”); and
at least one processor communicatively coupled to the memory, wherein the instructions, when executed by the at least one processor individually or collectively, cause the electronic device to (Gupta;
see e.g. [0033] “... processor ... memory ...”
see e.g. Fig. 1)
identify at least one first application-associated information corresponding to a first application (Gupta; Gupta teaches ([0035]) that the device receives network packets corresponding to different workloads, expressly including an email application and a conference call, and identifies/separates the packets using the packet five-tuple comprising source address, source port, destination address, destination port, and protocol. Thus the five-tuple constitutes application-associated information because Gupta uses that information to identify which received packets correspond to the particular workload/application. Gupta further teaches ([0051]) that packet length, inter-arrival time, packet direction, QoS tags, header information, and an email specific protocol identifier are features that identify the corresponding workload. Therefore , Gupta identifies information associated with an corresponding to a particular application, rather than merely identifying generic network information),
identify a first AI model corresponding to the at least one first application- associated information from among the plurality of AI models by using the association information, (Gupta; Gupta ([0057]) teaches a model corresponding to video, audio, and streaming workload classes and also teaches a previously provided model corresponding to those workload classes. Thus, Gupta has a plurality of AI models and information defining the correspondence between a model and particular application/workload classes. The model publisher uses that correspondence and model status to determine which model is provided to the prediction controller. Functionally, the disclosed workload -class/model correspondence is the claimed association information, because it establishes which AI models corresponds to the identified application/workload information)), and
identify a flush time for generic receive offload (GRO) by inputting information related to a communication environment of the electronic device into the first AI model (Gupta; Gupta teaches ([0051]) communication environment information including packet inter-arrival time, packet direction, QoS, protocol, packet length, source and destination, Gupta ([0059]) then inputs the packets/features of the current network flow into the trained AI model to determine the current workload type, Gupta ([0060]) uses that Ai-model determination to generate a latency value determining whether the packets are buffered and for howl long, and expressly associates that time with dynamic interrupt coalescing. Gupta ([0063]) identifies that latency values as a buffer time, and paragraph [0067] uses that value to initiate a corresponding timer. Functionally, the latency/buffer time corresponds to the claimed flush time because it establishes the timer interval/expiration governing when buffered packets cease being held and are released for subsequent processing.)
Gupta does not expressly disclose:
each of the plurality of AI models being trained according to different compensation references for a training data set
perform a GRO operation for merging at least some of packets provided by a lower layer, based on the identified flush time for GRO
Jain discloses:
each of the plurality of AI models being trained according to different compensation references for a training data set (Jain; Jain teaches that each learning agent trains its neural network using samples form an experience replay buffer, with the reward function evaluating the training results according to performance criteria or specified targets. Paragraph ([503]) identifies different respective performance requirements for Agents A/, B, and C – latency/accuracy, model-size/accuracy, and sparsity/accuracy—which constitute the claimed different compensation references because those requirements provide the different targets against which the reward used during training is determined. Paragraph [0504] teaches three separately instantiated learning agents associated with the plurality of AI models)
Therefore it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Jain’s scheme. The motivation being the combined solution provides for incorporating a known technique.
Gupta in view of Jain does not expressly disclose:
perform a GRO operation for merging at least some of packets provided by a lower layer, based on the identified flush time for GRO
Makineni discloses:
perform a GRO operation for merging at least some of packets provided by a lower layer, based on the identified flush time for GRO (Makineni; Makineni teaches receive side coalescing that combines payloads of multiple packets of the same flow into a single coalesced packet ([0009]), wherein the packets are output from MC 204, which performs link layer operations, to coalescing circuitry 206 ([0019]), thereby providing packets form a lower layer . Makineni further teaches a configurable coalescing window whose expiration terminates the coalescing interval and causes completion of the coalesced packet for subsequent receive processing ([0016]), thereby corresponding to the claimed flush time)
Therefore it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Makineni’s scheme. The motivation being the combined solution provides for implanting a known technique. Moreover, it would have been obvious to use Gupta’s identified flush time as Makineni’s coalescing window expiration time to control when the lower-layer packets are merged and the resulting coalesced packet is completed for further receive processing.
Regarding claim 2, Gupta in view of Jain and in further view of Makineni disclose The electronic device of claim 1, wherein the instructions, when executed by the at least one processor individually or collectively, further cause the electronic device to, as at least a portion of the identifying of the at least one first application- associated information corresponding to the first application: identify the at least one first application-associated information corresponding to the first application, based on execution of the first application, download of the first application, installation of the first application, a network connection request from the first application, and/or establishment of a protocol data unit (PDU) session corresponding to the first application (The combined solution provides for “based on the execution of the first application” see e.g. Gupta [0035], [0037]).
Therefore it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Jain’s scheme. The motivation being the combined solution provides for incorporating a known technique.
Therefore it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Makineni’s scheme. The motivation being the combined solution provides for implanting a known technique. Moreover, it would have been obvious to use Gupta’s identified flush time as Makineni’s coalescing window expiration time to control when the lower-layer packets are merged and the resulting coalesced packet is completed for further receive processing.
Regarding claim 3, Gupta in view of Jain and in further view of Makineni disclose The electronic device of claim 1, wherein the first application-associated information comprises at least some of an application identifier (app ID), a deep neural network (DNN), a resource type, a 5QI value, a service priority, a delay, a traffic descriptor and/or route descriptor included in a u(The combined solution per Jain; see e.g. [0662] “... The processor(s) ID11_F102 may execute instructions ID11_F120 (e.g., a specialized kernel inside a Math Kernel Library for Deep Learning Networks/MKL-DNN ...”)ser equipment (UE) route selection policy (URSP) rule, a fully qualified domain name (FQDN), a destination Internet protocol (IP) address, or group information.
Therefore it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Jain’s scheme. The motivation being the combined solution provides for incorporating a known technique.
Therefore it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Makineni’s scheme. The motivation being the combined solution provides for implanting a known technique. Moreover, it would have been obvious to use Gupta’s identified flush time as Makineni’s coalescing window expiration time to control when the lower-layer packets are merged and the resulting coalesced packet is completed for further receive processing.
Regarding claim 4, Gupta in view of Jain and in further view of Makineni disclose The electronic device of claim 1, wherein the instructions, when executed by the at least one processor individually or collectively, further cause the electronic device to, as at least a portion of the identifying of the flush time by inputting the information related to the communication environment of the electronic device into the first AI model: identify the flush time by inputting, as the information related to the communication environment of the electronic device, at least one first parameter configured as an input value of the first AI model into the first AI model (The combined solution per Gupta; see e.g. [0051], [0059], [0060], [0063], [0067])
Therefore it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Jain’s scheme. The motivation being the combined solution provides for incorporating a known technique.
Therefore it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Makineni’s scheme. The motivation being the combined solution provides for implanting a known technique. Moreover, it would have been obvious to use Gupta’s identified flush time as Makineni’s coalescing window expiration time to control when the lower-layer packets are merged and the resulting coalesced packet is completed for further receive processing.
Regarding claim 6, Gupta in view of Jain and in further view of Makineni disclose The electronic device of claim 1,
wherein the information related to the communication environment comprises at least one of information related to a channel environment, information related to a delay, or information related to a network environment (The combined solution per Gupta has information related to a network environment; see e.g. [0035], [0051], [0059]),
wherein the information related to the channel environment comprises at least one of a reference signal received power (RSRP), a reference signal received quality (RSRQ), a block error rate (BLER), a buffer status, a channel quality index (CQI), a downlink throughput (DL TP), a uplink throughput (UL TP), a received signal strength indicator (RSSI), or a signal to interference-plus-noise ratio (SINR)(The combined solution as Gupta has information about a buffers status; see e.g. [0060], [0063], [0067]),
wherein the information related to the delay comprises at least one of a destination IP address, a round trip time (RTT), or an FQDN (The combined solution per Gupta’s tuple (see e.g. [0035]), and
wherein the information related to the network environment comprises at least one of an activated radio access technology (RAT), a modulation and coding scheme (MCS), a mobile satellite services (MSS), packet data convergence protocol (PDCP) T- ordering, whether carrier aggregation (CA) is activated, a number of activated carrier components (CCs), a number of data streams, whether dual connectivity (DC) is activated, an operating band, a multiple-input and multiple-output (MIMO) layer, or a bandwidth (The combined solution as Gupta teaches a plurality of wireless platforms which necessarily require and operating band; see e.g. [0109]).
Therefore it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Jain’s scheme. The motivation being the combined solution provides for incorporating a known technique.
Therefore it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Makineni’s scheme. The motivation being the combined solution provides for implanting a known technique. Moreover, it would have been obvious to use Gupta’s identified flush time as Makineni’s coalescing window expiration time to control when the lower-layer packets are merged and the resulting coalesced packet is completed for further receive processing.
Regarding claim 8, Gupta in view of Jain and in further view of Makineni disclose The electronic device of claim 1, wherein the instructions, when executed by the at least one processor individually or collectively, further cause the electronic device to, as at least a port of the performing of the GRO operation, based on the identified flush time for GRO:
identify a flush time for comparison by inputting the information related to the communication environment of the electronic device into a classification type AI model for comparison (The combined solution provides for identifying a flush time for comparison using a classification type AI model, wherein Gupta inputs current packet/network environment features into a trained workload classifications model ([0051], [0059]), and the resulting classification is used to determine the corresponding latency/buffer timer, i.e., the flush time ([0060], [0063], [0067]), thereby providing the claimed flush time generated using the classification type AI model) ; and
perform the GRO operation, based on the flush time for comparison and the identified flush time for GRO satisfying a similarity association condition (The combined solution provides for performing the GRO operation based on the comparison flush time and identified GRO flush time satisfying a similarity condition, wherein Makineni performs the packet coalescing operation according to the selected timing window ([0016]). It would have been obvious to compare the independently generated timing values and condition use of the timing value on their agreement/similarity thereby validating the timing determination before the coalescing operation).
Therefore it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Jain’s scheme. The motivation being the combined solution provides for incorporating a known technique.
Therefore it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Makineni’s scheme. The motivation being the combined solution provides for implanting a known technique. Moreover, it would have been obvious to use Gupta’s identified flush time as Makineni’s coalescing window expiration time to control when the lower-layer packets are merged and the resulting coalesced packet is completed for further receive processing.
Regarding claim 9, Gupta in view of Jain and in further view of Makineni disclose The electronic device of claim 1, wherein the instructions, when executed by the at least one processor individually or collectively, further cause the electronic device to: perform reinforcement learning on at least some of the plurality of AI models, based on an input value into at least some of the plurality of AI models, an output value from at least some of the plurality of AI models, and a value related to a throughput (TP) associated with at least some of the plurality of AI models (The combined solution per Jain provides for performing reinforcement learning on at some of the the plurality of AI models based on an input value into the AI models and throughput; see e.g.. [0497]).
Therefore it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Jain’s scheme. The motivation being the combined solution provides for incorporating a known technique.
Therefore it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Makineni’s scheme. The motivation being the combined solution provides for implanting a known technique. Moreover, it would have been obvious to use Gupta’s identified flush time as Makineni’s coalescing window expiration time to control when the lower-layer packets are merged and the resulting coalesced packet is completed for further receive processing.
Regarding claim 10, Gupta in view of Jain and in further view of Makineni disclose The electronic device of claim 9,
wherein the first AI model among the plurality of AI models is reinforcement- learned using, as a compensation reference, a value identified based on a first processing scheme as the value related to the TP (The combined solution provides the first AI model being reinforcement – learned using as a compensation reference, a throughput related value identified according to a first processing scheme, wherein Jain trains the learning agent’s neural network using a reward function that evaluates performance information including throughput according to specified performance criteria/targets ([0497)]), and
wherein a second AI model different from the first AI model among the plurality of AI models is reinforcement-learned using, as a compensation reference, a value identified based on a second processing scheme different from the first processing scheme as the value related to the TP (The combined solution further provides a second AI model different from the first AI model being reinforcement -learned using a throughput related compensation value identified according to a different processing scheme, wherein Jain teaches separately training learning agents ([0504]) having different respective performance requirements targets ([0503]), with the reward based training on ([0497]) processing throughput related performance information according to those respective targets).
Therefore it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Jain’s scheme. The motivation being the combined solution provides for incorporating a known technique.
Therefore it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Makineni’s scheme. The motivation being the combined solution provides for implanting a known technique. Moreover, it would have been obvious to use Gupta’s identified flush time as Makineni’s coalescing window expiration time to control when the lower-layer packets are merged and the resulting coalesced packet is completed for further receive processing.
Regarding claim 11, clam 11 comprises the same and/or similar subject matter as claim 1 and is considered an obvious variation; therefore it is rejected under the same rationale.
Regarding claim 12, claim 12 comprises the same and/or similar subject matter as claim 2 and is considered an obvious variation; therefore it is rejected under the same rationale.
Regarding claim 13, claim 13 comprises the same and/or similar subject matter as claim 3 and is considered an obvious variation; therefore it is rejected under the same rationale.
Regarding claim 14, claim 14 comprises the same and/or similar subject matter as claim 4 and is considered an obvious variation; therefore it is rejected under the same rationale.
Regarding claim 18, claim 18 comprises the same and/or similar subject matter as claim 8 and is considered an obvious variation; therefore it is rejected under the same rationale.
Regarding claim 19, clam 19 comprises the same and/or similar subject matter as claim 1 and is considered an obvious variation; therefore it is rejected under the same rationale.
Regarding claim 20, claim 20 comprises the same and/or similar subject matter as claim 2 and is considered an obvious variation; therefore it is rejected under the same rationale.
Claims 7 and 17 are rejected under 35 USC 103 as being unpatentable over Gupta in view of Jain and in further view of Makineni and in further view of Wang (US 20220198661)
Regarding claim 7, Gupta in view of Jain and in further view of Makineni disclose The electronic device of claim 1, Gupta does not expressly disclose wherein the instructions, when executed by the at least one processor individually or collectively, further cause the electronic device to, as at least a portion of the performing of the GRO operation, based on the identified flush time for GRO:
perform the GRO operation, based on a probability corresponding to the identified flush time for GRO being higher than or equal to a threshold probability.
Wang discloses:
a threshold probability: (Wang;
see e.g. [0019] “... prediction probability thresholds of the AI classification model ...”)
Therefore it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Wang’s probability threshold. The motivation being the combined solution provides for implanting a known technique.
Gupta in view of Jain and in further view of Makineni and in further view of Wang disclose:
perform the GRO operation, based on a probability corresponding to the identified flush time for GRO being higher than or equal to a threshold probability (The combined solution per Gupta’s GRO operation in tandem with Wang’s prediction probability threshold)
Therefore it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Jain’s scheme. The motivation being the combined solution provides for incorporating a known technique.
Therefore it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Makineni’s scheme. The motivation being the combined solution provides for implanting a known technique. Moreover, it would have been obvious to use Gupta’s identified flush time as Makineni’s coalescing window expiration time to control when the lower-layer packets are merged and the resulting coalesced packet is completed for further receive processing.
Regarding claim 17, claim 17 comprises the same and/or similar subject matter as claim 7 and is considered an obvious variation; therefore it is rejected under the same rationale.
Claim 5 is rejected under 35 USC 103 as being unpatentable over Gupta in view of Jain and in further view of Makineni and in further view of Hillard(US 20180189674)
Regarding claim 5, Gupta in view of Jain and in further view of Makineni disclose The electronic device of claim 1, Gupta does not expressly disclose wherein the instructions, when executed by the at least one processor individually or collectively, further cause the electronic device to:
identify at least one second application-associated information corresponding to a second application different from the first application,
based on no AI model corresponding to the at least one second application- associated information existing, by using the association information, and
identify the flush time for GRO by inputting the information related to the communication environment of the electronic device into a default AI model or identify a default value as the flush time.
HIllard discloses:
default AI model (Hillard; Hillard teaches a fallback machine learning model maintained as a predetermined alternative to another machine learning model, thereby corresponding to the claimed default AI model;
see e.g. [0003] “... processing the first real-time processing request by a fallback machine learning model to generate a fallback ...”)
Therefore it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Hillard’s fallback model. The motivation being the combined solution provides for implementing a known technique.
Gupta in view of Jain and in further view of Makineni and in further view of Hillard disclose:
identify at least one second application-associated information corresponding to a second application different from the first application, based on no AI model corresponding to the at least one second application- associated information existing, by using the association information(The combined solution per Gupta simultaneously executing an email application and a different conference call application and identifying their respective packet flows ([0035] within the context of no AI model corresponding to the second application-associated information existing), and
identify the flush time for GRO by inputting the information related to the communication environment of the electronic device into a default AI model or identify a default value as the flush time (The combined solution as Gupta provides the flush time determination ([0060], [0063], [0067]) and Hillard provides the fallback machine learning model corresponding to the claimed default AI model).
Therefore it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Jain’s scheme. The motivation being the combined solution provides for incorporating a known technique.
Therefore it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Makineni’s scheme. The motivation being the combined solution provides for implanting a known technique. Moreover, it would have been obvious to use Gupta’s identified flush time as Makineni’s coalescing window expiration time to control when the lower-layer packets are merged and the resulting coalesced packet is completed for further receive processing.
Regarding claim 15, claim 15 comprises the same and/or similar subject matter as claim 5 and is considered an obvious variation; therefore it is rejected under the same rationale.
Any inquiry concerning this communication or earlier communications from the Examiner should be directed to TODD L. BARKER whose telephone number is (571) 270 0257. The Examiner can normally be reached on Monday through Friday, 7:30am to 5:00pm.
If attempts to reach the Examiner by telephone are unsuccessful, the Examiner's supervisor Vivek Srivastava can be reached on (571) 272 7304.
/TODD L BARKER/Primary Examiner, Art Unit 2449
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