DETAILED ACTION
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 .
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.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
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.
Claims 1-8, 10-18, 20-30 are rejected under 35 U.S.C. 103 as being unpatentable over Akdeniz et al(US 2023/0068386 A1) in view of Amend et al(US 2022/0400081 A1).
Regarding claims 1 and 23, Akdeniz ‘386 teaches, a network component([0082]-[0084] and Fig. 8, an edge computing node 850(network component)), comprising: a memory; at least one transceiver; and at least one processor communicatively coupled to the memory and the at least one transceiver([0082]-[0084] and Fig. 8, an edge computing node 850 includes processor 852, system memory 854 and transceiver 866), the at least one processor configured to: determine that a level of heterogeneity associated with a network layer of a hierarchical network layer arrangement with multiple network layers is above a threshold ([0106]-[0107], [0121], [0308], [0363] and Figs. 9, 18, the system manages environments with heterogeneous computing capabilities, communication rates and different number of training examples across the client devices. To track and manage this heterogeneity, the server explicitly monitors a client model/capability update timestamp and determine if a client has exceeded a specific threshold for an update Tlast, n > Tthreshold);
transmit, via the at least one transceiver, to at least one device associated with the network layer in response to the determination, a data reporting instruction associated with the network layer([0149], [0193], [0308], [0363] and Fig.18, when the server determines a client has exceeded the threshold(Tlast,n > Tthreshold), the server initiates and transmits a client capability update request command to the client. Furthermore the central server sends computed ML parameter and coding redundancies to the client computing nodes, serving as instructions on how to process and report their data); and
receive, via the at least one transceiver, data associated with the network layer from the at least one device in accordance with the data reporting instruction ([0308], [0363], [0441], [0442] and Figs. 18, 20, in response to the servers update request command/instruction, the clients respond to the server by transmitting their respective data, which includes their upload time(Tup) and computer rate(rc) parameters. Furthermore, the client computing nodes transmit their generated coded training data sets and computed model updates to the central server in accordance with the assigned compute parameters and instructions).
Akdeniz ‘386 does not explicitly tech, comparing a level of heterogeneity with a predetermined threshold.
Amend ‘081 teaches, comparing a level of heterogeneity with a predetermined threshold ([0036]. [0039], [0086], calculating a level heterogeneity and comparing the level of heterogeneity with a predetermined threshold).
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify the communication system of Akdeniz ‘386, by incorporating the teaching of Amend ‘081, since such modification would enable to switch off a network flow, which is more costly, if it is not needed in a situation where two technically different network flows cause different costs, as suggested by Amend ‘081([0009]).
Regarding claims 2 and 24, the combination of Akdeniz ‘386 and Amend ‘081 teaches all of the claim limitations, Akdeniz ‘386 further teaches, wherein the heterogeneity level determination is based upon a pre-configuration of the network layer([0308] and Fig. 8, the server maintains a client model/capability update timestamp and compares it against a predefined update threshold).
Regarding claims 3 and 25, the combination of Akdeniz ‘386 and Amend ‘081 teaches all of the claim limitations, Akdeniz ‘386 further teaches, wherein the heterogeneity level determination is based upon a set of heterogeneity metrics received from one or more devices associated with the network layer ([0287], [0308], [0440]-[0441] and Figs. 17-20, the central server(the network component) bases its heterogeneity evaluations and determination (e.g. triggering updates, client selection of load balancing) on specific metrics reported directly by the client commuting model).
Regarding claims 4, 14 and 29, the combination of Akdeniz ‘386 and Amend ‘081 teaches all of the claim limitations, Akdeniz ‘386 further teaches, wherein a computation rule for computing the set of heterogeneity metrics at the one or more devices is pre-defined([0289], [0290],[0367]-[0369] and Fig. 17, computation rules for calculating the heterogeneity metrics at the client devices are pre-defined), or wherein the computation rule for computing the set of heterogeneity metrics at the one or more devices is signaled to the one or more devices by the network component([0289], [0290],[0367]-[0369] and Fig. 17, The central MEC server signals necessary parameters to the clients to execute these computation rules, such as providing the target data distribution to the clients so they can compute the distance (heterogeneity) metric).
Regarding claims 5 and 15, the combination of Akdeniz ‘386 and Amend ‘081 teaches all of the claim limitations, Akdeniz ‘386 further teaches, wherein the computation rule is implemented via a neural network (NN)([0354], [0376], [0384] and Fig. 19, implementing the computation rule for the heterogeneity metrics via a neural network(NN) for example training loss metric to determine client heterogeneity).
Regarding claims 6, 16 and 26, the combination of Akdeniz ‘386 and Amend ‘081 teaches all of the claim limitations, Akdeniz ‘386 further teaches, wherein the hierarchical network layer arrangement comprises an over the air (OTA) layer ([0036], [0043], [0051] and Figs. 1-2, endpoint layer containing access network endpoint devices (e.g. mobile decides, drones and IoT) that communicate wirelessly with the network over the air)and one or more backhaul, midhaul or fronthaul component layers([0002], [0039], [0053], network layers that rely on backhaul networks to connect edge aggregation nodes to core data centers, notice the claim limitations are written in alternative form thus examiner is required to show only one of the alternative claim limitations).
Regarding claims 7 and 17, the combination of Akdeniz ‘386 and Amend ‘081 teaches all of the claim limitations, Akdeniz ‘386 further teaches, wherein the one or more backhaul, midhaul or fronthaul component layers comprise a remote unit (RU) layer, a distributed unit (DU) layer, a centralized unit (CU) layer, a core network (CN) layer, or any combination thereof([0036], [0043], [0044], [0106] and Figs.2, 9, the hierarchical network layer arrangement comprises a core network (CN) layer, notice the claim limitations are written in alternative form thus examiner is required to show only one of the alternative claim limitations).
Regarding claims 8 and 18, the combination of Akdeniz ‘386 and Amend ‘081 teaches all of the claim limitations, Akdeniz ‘386 further teaches, wherein the at least one device corresponds to at least one user equipment (UE), and wherein the network layer corresponds to an over the air (OTA) layer([0036], [0043], [0051] and Figs. 1-2, endpoint layer containing access network endpoint devices (e.g. mobile decides, drones and IoT) that communicate wirelessly with the network over the air).
Regarding claims 10, 20, 27 and 30, the combination of Akdeniz ‘386 and Amend ‘081 teaches all of the claim limitations, Akdeniz ‘386 further teaches, wherein the data from the at least one device comprises gradients between parameters or measurements at two or more devices associated with the network layer([0127], [0130]-[0134] and Fig. 13, each local computing node locally computes gradients based on it local training data and communicates those gradients update to the central server).
Regarding claims 11 and 21, the combination of Akdeniz ‘386 and Amend ‘081 teaches all of the claim limitations, Akdeniz ‘386 further teaches, wherein the network component is associated with a backhaul, midhaul or fronthaul component layer of the hierarchical network layer arrangement([0053], [0114] and Fig. 3, 10, an edge computing architecture distributed across multiple network layers where the network components (e.g. edge aggregation nodes or core data center servers) are associated with and utilize backhaul networks, notice the claim limitations are written in alternative form thus examiner is required to show only one of the alternative claim limitations).
Regarding claim 12, the combination of Akdeniz ‘386 and Amend ‘081 teaches all of the claim limitations, Akdeniz ‘386 further teaches, wherein the at least one device comprises one or more user equipment’s (UEs) associated with an over-the-air (OTA) layer of the hierarchical network layer arrangement, or wherein the at least one device comprises one or more remote units (RUs) associated with an RU layer of the hierarchical network layer arrangement, or wherein the at least one device comprises one or more distributed units (DUs) associated with a DU layer of the hierarchical network layer arrangement, or wherein the at least one device comprises one or more centralized units (CUs) associated with a CU layer of the hierarchical network layer arrangement([0036], [0043], [0051] and Figs. 1-2, endpoint layer containing access network endpoint devices (e.g. mobile decides, drones and IoT) that communicate wirelessly with the network over the air, notice the claim limitations are written in alternative form thus examiner is required to show only one of the alternative claim limitations).
Regarding claims 13 and 28, Akdeniz ‘386 teaches, a device([0082]-[0084] and Fig. 8, an edge computing node 850(which acts as a client device)), comprising: a memory; at least one transceiver; and at least one processor communicatively coupled to the memory and the at least one transceiver([0082]-[0084] and Fig. 8, an edge computing node 850 includes processor 852, system memory 854 and transceiver 866), the at least one processor configured to:
compute a set of heterogeneity metrics associated with a network layer of a hierarchical network layer arrangement with multiple network layers([0106], [0107], [0288], [0289] and Figs. 9, 17, an edge computing system distributed across a hierarchical arrangement including multiple layers. These layers including endpoint layer 910, edge device layer 920, 930), wherein the network layer includes the device and one or more other devices([0107], [0121] and Figs.9, 12, the endpoint layer 910 includes multiple client computing nodes 902), wherein the set of level of heterogeneity metrics is indicative of a level of heterogeneity associated with the network layer being above a threshold([0308], [0443], [0444] and Figs. 18, 21, the client device evaluates thresholds based on the heterogeneity metrics of the network, specifically the client computing node receives a selected coding redundancy value from the central server which represents the heterogeneity and load balancing limitations for the network and determines whether this value is greater than its own maximum coding redundancy threshold. Furthermore, the system also monitors update timestamps and compare it with a predetermined threshold);
transmit, via the at least one transceiver, an indication of the set of heterogeneity metrics to a network component ([0287], [0308], [0354] and Figs. 17-19, the client computing nodes transmit the computed heterogeneity metrics to the central MEC server (the network component). Specifically, the client respond to the server with their communication rates estimates of their compute rates and their training loss values); receive, via the at least one transceiver, a data reporting instruction associated with the network layer ([0193], [0308], [0441] and Fig. 18, the client computing nodes receive data reporting instructions from the central server); collect information associated with the device and the one or more other devices ([0072], [0282] and Fig. 6, the client device maintains and collects local training examples and data features to be used in the learning process, furthermore the edge nodes located in vehicles utilizing V2V communication to act as network edge nodes for other cars, performing caching, reporting and data aggregation from the other devices); and
report data associated with the collected information to the network component in accordance with the data reporting instruction ([0149], [0150], [0194] and Figs. 13-1, the client device report data to the central server according to the received instructions. The clients privately encode their local training data and transmit the respective coded training data sets, coded label sets and computed model updates to the central server).
Akdeniz ‘386 does not explicitly teach, comparing a level of heterogeneity with a predetermined threshold.
Amend ‘081 teaches, comparing a level of heterogeneity with a predetermined threshold ([0036]. [0039], [0086], calculating a level heterogeneity and comparing the level of heterogeneity with a predetermined threshold).
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify the communication system of Akdeniz ‘386, by incorporating the teaching of Amend ‘081, since such modification would enable to switch off a network flow, which is more costly, if it is not needed in a situation where two technically different network flows cause different costs, as suggested by Amend ‘081([0009]).
Claims 9 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Akdeniz ‘386 and Amend ‘081 as applied to claims above, and further in view of Zhang et al(US 2023/0094992 A1).
Regarding claims 9 and 19, the combination of Akdeniz ‘386 and Amend ‘081 teaches all of the claim limitations except, wherein the data from the at least one device is associated with gradients between radio frequency (RF) fingerprints at two or more UEs.
Zhang ‘992 teaches, wherein the data from the at least one device is associated with gradients between radio frequency (RF) fingerprints at two or more UEs ([0051]-[0058] and Fig .3.determinig differences relating to wireless signal fingerprints).
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify the communication system of Akdeniz ‘386, by incorporating the teaching of Zhang ‘992, since such modification would provide a Wi-Fi fingerprints collected from indoor locations, which can be used for an indoor positioning of a terminal, as suggested by Zhang ‘992([0003]).
Internet Communications
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Any inquiry concerning this communication or earlier communications from the examiner should be directed to AWET A HAILE whose telephone number is (571)270-3114. The examiner can normally be reached Monday through Friday 8:30 AM - 4:30 PM EST.
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/AWET HAILE/Primary Examiner, Art Unit 2474
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