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Claim objection regarding to claim 9 is withdrawn.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101
because the claimed invention is directed to an abstract idea without significantly
more.
When considering subject matter eligibility under 35 U.S.C. 101, it must be
determined whether the claim is directed to one of the four statutory categories of
invention, i.e., process, machine, manufacture, or composition of matter (Step 1). If the
claim does fall within one of the statutory categories, the second step in the analysis is
to determine whether the claim is directed to a judicial exception (Step 2A). The Step 2A
analysis is broken into two prongs. In the first prong (Step 2A, Prong 1), it is determined
whether or not the claims recite a judicial exception (e.g., mathematical concepts,
mental processes, certain methods of organizing human activity). If it is determined in
Step 2A, Prong 1 that the claims recite a judicial exception, the analysis proceeds to the
second prong (Step 2A, Prong 2), where it is determined whether or not the claims
integrate the judicial exception into a practical application. If it is determined at step 2A,
Prong 2 that the claims do not integrate the judicial exception into a practical
application, the analysis proceeds to determining whether the claim is a patent-eligible
application of the exception (Step 2B). If an abstract idea is present in the claim, any
element or combination of elements in the claim must be sufficient to ensure that the
claim integrates the judicial exception into a practical application, or else amounts to
significantly more than the abstract idea itself. Applicant is advised to consult the 2019
PEG for more details of the analysis.
Step 1
According to the first part of the analysis, in the instant case, claims 1-7, 8-13, 14-20 are directed to a method, method and apparatus of distributed learning. Thus, each of the claims falls within one of the four statutory categories (i.e. process, machine, manufacture, or composition of matter). Step 2A,
Step 2A, Prong 1
Following the determination of whether or not the claims fall within one of the four
categories (Step 1), it must be determined if the claims recite a judicial exception (e.g.
mathematical concepts, mental processes, certain methods of organizing human
activity) (Step 2A, Prong 1). In this case, the claims are determined to recite a judicial
exception as explained below.
Regarding Claims 1, 8 and 14 these claims recite
Claim 1, 14 recite processing first data using the first data model to obtain first intermediate data; determining an updated parameter of a first channel based on error information of second intermediate data, information about the first channel, and the first intermediate data, wherein the second intermediate data is a result of transmitting the first intermediate data to a second node through the first channel, and the first channel is a channel between the first node and the second node; updating the first channel based on the updated parameter of the first channel; and sending the first intermediate data to the second node through the updated first channel.
Claim 8 determining an updated parameter of a first channel based on error information of second intermediate data, information about the first channel, and the first intermediate data, wherein the second intermediate data is a result of transmitting the first intermediate data to a second node through the first channel, and the first channel is a channel between the first node and the second node; updating the first channel based on the updated parameter of the first channel; receiving the second intermediate data through the updated first channel; and processing the second intermediate data by using the second data model to obtain output data.
The claims recite a mental process. As set forth in MPEP 2106.04(a)(2)(III)(C), “Claims can recite a mental process even if they are claimed as being performed on a computer”. These are recited at a high level and they are disclosed as a human user performing these functions, simply using a computer as a tool-see spec, [0102]-[0116], Fig. 1, 2, etc. Thus, the claim recites abstract ideas.
Step 2A, Prong 2
Following the determination that the claims recite a judicial exception, it must be
determined if the claims recite additional elements that integrate the exception into a
practical application of the exception (Step 2A, Prong 2). In this case, after considering
all claim elements individually and as an ordered combination, it is determined that the
claims do not include additional elements that integrate the exception into a practical
application of the exception as explained below.
In Prong Two, a claim is evaluated as a whole to determine whether the recited judicial exception is integrated into a practical application of that exception. A claim is not “directed to” a judicial exception, and thus is patent eligible, if the claim as a whole integrates the recited judicial exception into a practical application of that exception. A claim that integrates a judicial exception into a practical application will apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the judicial exception. MPEP 2106.04(d). The claims recite an abstract idea and further the claims as a whole does not integrate the recited judicial exception into a practical application of the exception. A claim that integrates a judicial exception into a practical application will apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the judicial exception. MPEP 2106.04(d).
Regarding Claims 1, 8, 14 these claims
This limitation recites using one or more neural networks as a tool to perform an
abstract idea, which is not indicative of integration into a practical application. MPEP 2106.05(f).)
This limitation is understood to be generic computer equipment and mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.0S(f))
MPEP § 2106.05(f): Mere Instructions to Apply an Exception. Do the additional element(s) amount to merely the words “apply it” (or an equivalent) or are mere instructions to implement an abstract idea or other exception on a computer? (Yes)
Step 2B
Based on the determination in Step 2A of the analysis that the claims are
directed to a judicial exception, it must be determined if the claims contain any element
or combination of elements sufficient to ensure that the claim amounts to significantly
more than the judicial exception (Step 2B). In this case, after considering all claim
elements individually and as an ordered combination, it is determined that the claims do
not include additional elements that are sufficient to amount to significantly more than
the judicial exception for the same reasons given above in the Step 2A, Prong 2
analysis. Furthermore, each additional element identified above as being insignificant
extra-solution activity is also well-known, routine, conventional as described below.
Claims 1, 8 and 14: The claims do not include additional elements, alone or in combination, that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements amount to no more than generic computing components and field of use/technological environment which do not amount to significantly more than the abstract idea. The underlying concept merely receives information, analyzes it, and store the results of the analysis – this concept is not meaningfully different than concepts found by the courts to be abstract (see Electric Power Group, collecting information, analyzing it, and displaying certain results of the collection and analysis; see Cybersource, obtaining and comparing intangible data; see Digitech, organizing information through mathematical correlations; see Grams, diagnosing an abnormal condition by performing clinical tests and thinking about the results; see Cyberfone, using categories to organize store and transmit information; see Smartgene, comparing new and stored information and using rules to identify options). The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements when considered both individually and as a combination do not amount to significantly more than the abstract idea. For example, claim 1, 14 recite the additional elements of “processing..”, “determining…”, ”sending”… and claim 8 recite “determining…”, “receiving…” “processing…” These elements are recited at a high level of generality and are well-understood, routine, and conventional activities in the computer art. Generic computers performing generic computer functions, without an inventive concept, do not amount to significantly more than the abstract idea. Looking at the elements as a combination does not add anything more than the elements analyzed individually. Therefore, these claims do not amount to significantly more than the abstract idea itself.
Step 2A/2B Prong 2 Dependent Claims
Regarding to claim 2, 15
Claim 2, 15 merely recite other additional elements that define sending data to the second node which performing generic functions that when looking at the elements as a combination does not add anything more than the elements analyzed individually. Therefore, these claims also do not amount to significantly more than the abstract idea itself. These claims are not patent eligible.
Regarding to claim 3, 16
Claim 3, 16 merely recite other additional elements that define updating the channel based on the error information which performing generic functions that when looking at the elements as a combination does not add anything more than the elements analyzed individually. Therefore, these claims also do not amount to significantly more than the abstract idea itself. These claims are not patent eligible.
Regarding to claim 4, 17
Claim 4, 17 merely recite other additional elements that define receiving signal from the second node which performing generic functions that when looking at the elements as a combination does not add anything more than the elements analyzed individually. Therefore, these claims also do not amount to significantly more than the abstract idea itself. These claims are not patent eligible.
Regarding to claim 5, 18
Claim 5, 18 merely recite other additional elements that define sending signal to the second node which performing generic functions that when looking at the elements as a combination does not add anything more than the elements analyzed individually. Therefore, these claims also do not amount to significantly more than the abstract idea itself. These claims are not patent eligible.
Regarding to claim 6, 19
Claim 6, 19 merely recite other additional elements that define updating the first model based on error information which performing generic functions that when looking at the elements as a combination does not add anything more than the elements analyzed individually. Therefore, these claims also do not amount to significantly more than the abstract idea itself. These claims are not patent eligible.
Regarding to claim 7, 20
Claim 7, 20 merely recite other additional elements that define receiving second information from the second node and the second information has the error information which performing generic functions that when looking at the elements as a combination does not add anything more than the elements analyzed individually. Therefore, these claims also do not amount to significantly more than the abstract idea itself. These claims are not patent eligible.
Regarding to claim 9
Claim 9 merely recite other additional elements that define receiving data through the first channel which performing generic functions that when looking at the elements as a combination does not add anything more than the elements analyzed individually. Therefore, these claims also do not amount to significantly more than the abstract idea itself. These claims are not patent eligible.
Regarding to claim 10
Claim 19 merely recite other additional elements that define sending the error information of the data to the first node which performing generic functions that when looking at the elements as a combination does not add anything more than the elements analyzed individually. Therefore, these claims also do not amount to significantly more than the abstract idea itself. These claims are not patent eligible.
Regarding to claim 11
Claim 11 merely recite other additional elements that define receiving signal from the first node which performing generic functions that when looking at the elements as a combination does not add anything more than the elements analyzed individually. Therefore, these claims also do not amount to significantly more than the abstract idea itself. These claims are not patent eligible.
Regarding to claim 12
Claim 12 merely recite other additional elements that define sending signal to the first node which performing generic functions that when looking at the elements as a combination does not add anything more than the elements analyzed individually. Therefore, these claims also do not amount to significantly more than the abstract idea itself. These claims are not patent eligible.
Regarding to claim 13
Claim 13 merely recite other additional elements that define updating the second model based on error information which performing generic functions that when looking at the elements as a combination does not add anything more than the elements analyzed individually. Therefore, these claims also do not amount to significantly more than the abstract idea itself. These claims are not patent eligible.
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.
Claims 1-2, 6-8, 11, 13-15, 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over Sebastian Dörner et al. (Dörner)
“Deep Learning-Based Communication Over the Air” July, 2017, Arxiv.org, Cornell Universit Library, XP081279898, arXiv:1707.03384, https://doi.org/10.48550/arXiv.1707.03384;
DOI: https://doi.org/10.1109/JSTSP.2017.2784180 in view of Shin et al. (Shin) US 2014/0355513
In regard to claim 1, Dörner disclose A distributed learning method, (abstract)
applied to a first node, (Fig. 1, “Transmitter”, Section II) wherein the first node comprises a first data model, (Fig. 2 and 5, Section II. A. C. “Transmitter (TX)”) and the method comprises:
processing first data using the first data model to obtain first intermediate data; (Fig. 5: Section II. C “s.sub.t” “x.sub.t, the received samples passed into the RX block to obtain the data)
wherein the second intermediate data is a result of transmitting the first intermediate data to a second node through the first channel, (Fig. 5, Section II, this is the construction due to the present feature mapping) and the first channel is a channel between the first node and the second node; (Fig. 1, 10, Section I, II, III. transmitting a message from a source to a destination over a channel)
sending the first intermediate data to the second node (Fig. 1, 10, Section II, IV.C “transmitter and receiver”) through the updated first channel, (Fig. 5, Section I, Introduction, II. C, through a channel to transmit the data from the source to the destination)
But Dörner fail to explicitly disclose “determining an updated parameter of a first channel based on error information of second intermediate data, information about the first channel, and the first intermediate data, updating the first channel based on the updated parameter of the first channel;”
Shin disclose determining an updated parameter of a first channel based on error information of second intermediate data, information about the first channel, and the first intermediate data, ([0008]-[0029] [0044]-[0056] [0070]-[0074] [0096] determine an adjusted channel coefficient of the channel based on information received from the first node, channel information, and feedback information from the second node and noise information of the destination node. Note: please further define information about the channel, etc. and use functional language to help move forward the prosecution, call to discuss if necessary.)
updating the first channel based on the updated parameter of the first channel; ([0008]-[0029] [0044]-[0056] [0070]-[0074] [0096] the channel is updated with the adjusted channel coefficients)
It would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made to incorporate Shin‘s data transmission control into Dörner’s invention as they are related to the same field endeavor of data communication. The motivation to combine these arts, as proposed above, at least because Shin‘s data transmission control with adjustable channel parameters would help to provide more control for the data transmission between the nodes into Dörner’s system. Therefore it would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made that providing control for the data transmission between the noces would help to improve efficiency of data communication.
In regard to claim 2, Dörner and Shin disclose The distributed learning method according to claim 1,
Dörner disclose wherein the sending the first intermediate data to the second node through the updated first channel includes sending the first intermediate data to the second node through the updated first channel that includes a second channel and a third channel, (Fig. 5, Section II. A. C, the first channel is deemed to include a second channel which is controllable and the third channel is the non-controllable (channel) and the method further comprises:
updating the second channel based on the error information of the second intermediate data, information about the third channel, and the first intermediate data. (Section III.C(2) the FE and PE blocks are being updated during the learning process as part of the autoencoder modeling the communication system)
In regard to claim 6, Dörner and Shin disclose The distributed learning method according to claim 1,
Dörner wherein the method further comprises: updating the first data model based on error information of the first intermediate data to obtain a new first data model. (Abstract. Fig. 5, Section II, A. C, the resulting autoencoder can be trained by using stochastic gradient descent)
In regard to claim 7, Dörner and Shin disclose The distributed learning method according to claim 6,
Dörner disclose wherein the method further comprises: receiving second information sent by the second node, wherein the second information is used to obtain the error information of the first intermediate data; wherein the second information includes the error information of the first intermediate data; or the second information includes the error information of the second intermediate data and the information about the first channel, and the error information of the second intermediate data and the information about the first channel is used to determine the error information of the first intermediate data. (Section II. A and Fig. 5, the resulting autoencoder can be trained by using stochastic gradient descent. Since Tx is being deployed on the first node, and the receiver Rx is being deployed on the second node, the flow of information is eventually as the one of the claim by construction)
In regard to claim 8, Dörner disclose A distributed learning method, applied to a second node, (Fig. 1, “Receiver”) wherein the second node includes a second data model, (Fig. 2 and 5, “Receiver (RX)”) and the method comprises:
wherein the second intermediate data is a result of transmitting the first intermediate data to a second node through the first channel, (Fig. 5, Section II, this is the construction due to the present feature mapping) and the first channel is a channel between the first node and the second node; (Fig. 1, 10, Section I, II, III. transmitting a message from a source to a destination over a channel)
receiving the second intermediate data through the updated first channel, (Fig. 1, 10, Section IV.C “transmitter and receiver” and Fig. 5, through a channel) wherein the second intermediate data is a result of transmitting first intermediate data sent by a first node to the second node through the first channel, (Fig. 5, Section II, this is the construction due to the present feature mapping) and
processing the second intermediate data by using the second data model to obtain output data. (Fig. 2 and 5, “Receiver (RX)” process the data by the model to obtain data)
But Dörner fail to explicitly disclose “determining an updated parameter of a first channel based on error information of second intermediate data, information about the first channel, and the first intermediate data, updating the first channel based on the updated parameter of the first channel;”
Shin disclose determining an updated parameter of a first channel based on error information of second intermediate data, information about the first channel, and the first intermediate data, ([0008]-[0029] [0044]-[0056] [0070]-[0074] [0096] determine an adjusted channel coefficient of the channel based on information received from the first node, channel information, and feedback information from the second node and noise information of the destination node. Note: please further define first intermediate data, second intermediate data and information about the channel, etc. and use functional language to help move forward the prosecution, call to discuss if necessary.)
updating the first channel based on the updated parameter of the first channel; ([0008]-[0029] [0044]-[0056] [0070]-[0074] [0096] the channel is updated with the adjusted channel coefficients)
It would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made to incorporate Shin‘s data transmission control into Dörner’s invention as they are related to the same field endeavor of data communication. The motivation to combine these arts, as proposed above, at least because Shin‘s data transmission control with adjustable channel parameters would help to provide more control for the data transmission between the nodes into Dörner’s system. Therefore it would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made that providing control for the data transmission between the noces would help to improve efficiency of data communication.
In regard to claim 11, Dörner and Shin disclose The distributed learning method according to claim 8,
Dörner disclose wherein the method further comprises: receiving a second signal from the first node through the first channel, wherein the second signal is a signal obtained after a first signal is transmitted to the second node through the first channel, (Fig. 5, x transmitting at time t+1) and the first signal is used to determine the information about the first channel; and obtaining the information about the first channel based on the second signal. (Section III. C(2) “the performance for continuous transmissions can be improved by letting the receiver estimate the phase offset explicitly over multiple subsequent messages” )
In regard to claim 13, Dörner and Shin disclose The distributed learning method according to claim 8,
Dörner disclose wherein the method further comprises: updating the second data model based on the output data to obtain a new second data model. (abstract, Fig. 1, 5 and 10. Section II, A, C, The resulting autoencoder can be trained end-to-end using SGD, “learning of transmitter and receiver implementations of deep NNs)
In regard to claims 14-15, 19-20, claims14-15, 19-20 are apparatus claims corresponding to the method claims 1-2, 6-7 above and, therefore, are rejected for the same reasons set forth in the rejections of claims 1-2, 6-7.
Claims 3-5, 9-10,12, 16-18 are rejected under 35 U.S.C. 103 as being unpatentable over Sebastian Dörner et al. (Dörner) “Deep Learning-Based Communication Over the Air” July, 2017, Arxiv.org, Cornell University Library, XP081279898, arXiv:1707.03384, https://doi.org/10.48550/arXiv.1707.03384;
DOI: https://doi.org/10.1109/JSTSP.2017.2784180 and Shin et al. (Shin) US 2014/0355513 as applied to claim 1, further in view of Prakash et al. (Prakash) US 2019/0138934
In regard to claim 3, Dörner and Shin disclose The distributed learning method according to claim 2,
Dörner disclose wherein the updating the second channel based on the error information of the second intermediate data, information about the third channel, and the first intermediate data includes: (Section II (A) III.C(2) the FE and PE blocks are being updated during the learning process as part of the autoencoder modeling the communication system, The error information are the gradient. This gradients flow through the backward propagation algorithms including the second intermediate data, Fig. 5, channel and “x.sub.t”)
wherein the first information (Section II. A the gradient on the second channel)
is determined based on the error information of the second intermediate data (Fig. 5, the gradient on PE block depends on the gradient of the second intermediate data (input of RX) and the information about the third channel; (Fig. 5, “Channel”, it is evident how the gradient on PE during backpropagation depends on the “Channel” during forward propagation) and updating the second channel based on the first information and the first intermediate data. (Section III. C(2,3) PE are updated during the training phase)
But Dörner and Shin fail to explicitly disclose “receiving first information sent by the second node,”
Prakash disclose receiving first information sent by the second node, ([0043]-[0049] receive information sent by the second node (from MEC (201a) to UE (101a) for example)
It would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made to incorporate Prakash‘s distributing learning method into Shin and Dörner’s invention as they are related to the same field endeavor of model training and learning. The motivation to combine these arts, as proposed above, at least because Prakash‘s distributing learning method with communicating information from the nodes would help to provide more information between the nodes into Shin and Dörner’s system. Therefore it would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made that providing more information between the nodes would help to improve efficiency of ML model training.
In regard to claim 4, Dörner and Shin disclose The distributed learning method according to claim 2,
Dörner disclose wherein the updating the second channel based on the error information of the second intermediate data, information about the third channel, and the first intermediate data includes: (Section II (A) III.C(2) the FE and PE blocks are being updated during the learning process as part of the autoencoder modeling the communication system, The error information are the gradient. This gradients flow through the backward propagation algorithms including the second intermediate data, Fig. 5, channel and “x.sub.t”)
and updating the second channel based on the first information and the first intermediate data. (Section III. C(2,3) PE are updated during the training phase)
But Dörner and Shin fail to explicitly disclose “receiving a third signal from the second node through the first channel, wherein the third signal is a signal obtained after a fourth signal is transmitted to the first node through the first channel, and the fourth signal includes a signal generated by mapping the error information of the second intermediate data to an air interface resource; obtaining first information based on the third signal, wherein the first information is determined based on the error information of the second intermediate data and the information about the third channel;”
Prakash disclose receiving a third signal from the second node through the first channel, wherein the third signal is a signal obtained after a fourth signal is transmitted to the first node through the first channel, and the fourth signal includes a signal generated by mapping the error information of the second intermediate data to an air interface resource; obtaining first information based on the third signal, wherein the first information is determined based on the error information of the second intermediate data and the information about the third channel; (Fig. 1, [0022] [0030]-[0049][0065]-[0073] receive information from MEC (201a) to UE (101a) for example, the computed full gradients is sent back to the edge computer nodes based on the partial gradients computed by the edge computer nodes, such as achievable data rate per channel usage, etc. and the obtain first information based on the received information and the first information is based on the gradient and various operational contexts or constraints of the various channels. Note: please further define the first, second, third, etc. information, signal, and channel using structure, time, function, etc. to help move forward the prosecution.)
It would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made to incorporate Prakash‘s distributing learning method into Shin and Dörner’s invention as they are related to the same field endeavor of model training and learning. The motivation to combine these arts, as proposed above, at least because Prakash‘s distributing learning method with communicating information from the nodes would help to provide more constraint or operational context information between the nodes into Shin and Dörner’s system. Therefore it would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made that providing constraint or operational context information between the nodes would help to improve efficiency of ML model training.
In regard to claim 5, Dörner and Shin disclose The distributed learning method according to claim 1,
Dörner disclose wherein the method further comprises: sending a fifth signal to the second node through the first channel, wherein the fifth signal includes a signal generated by mapping third intermediate data to an air interface resource, and the third intermediate data is for updating the first channel. (Fig. 1, [0022] [0030]-[0049] [0065]-[0073] [0101]-[0106] sending information from UE (101a) to MEC (201a) for example, the information include achievable data rate per channel usage, etc. and the information is used for updating the channel. Note: please further define the first, second, third, etc. information, signal, and channel using structure, time, function, etc. to help move forward the prosecution.)
It would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made to incorporate Prakash‘s distributing learning method into Shin and Dörner’s invention as they are related to the same field endeavor of model training and learning. The motivation to combine these arts, as proposed above, at least because Prakash‘s distributing learning method with communicating information from the nodes would help to provide more constraint or operational context information between the nodes into Shin and Dörner’s system. Therefore it would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made that providing constraint or operational context information between the nodes would help to improve efficiency of ML model training.
In regard to claim 9, Dörner and Shin disclose The method according to claim 9,
But Dörner and Shin fail to explicitly disclose “wherein the receiving the second intermediate data through the updated first channel includes receiving the second intermediate data through the updated first channel that includes a second channel and a third channel, and the method further comprises: sending the error information of the second intermediate data and information about the third channel to the first node.”
Prakash disclose wherein the receiving the second intermediate data through the updated first channel includes receiving the second intermediate data through the updated first channel that includes a second channel and a third channel, and the method further comprises: sending the error information of the second intermediate data and information about the third channel to the first node. (Fig. 1, [0022] [0030]-[0049] [0065]-[0073] [0101]-[0106] receive information from UE (101a) to MEC (201a) for example, through the channel, the channel could be multiple channel paths and sending the gradient information and other information about the various channels to the UE node. Note: please further define the first, second, third, etc. information, signal, and channel using structure, time, function, etc. to help move forward the prosecution.)
It would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made to incorporate Prakash‘s distributing learning method into Shin and Dörner’s invention as they are related to the same field endeavor of model training and learning. The motivation to combine these arts, as proposed above, at least because Prakash‘s distributing learning method with communicating information from the nodes would help to provide more constraint or operational context information between the nodes into Shin and Dörner’s system. Therefore it would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made that providing constraint or operational context information between the nodes would help to improve efficiency of ML model training.
In regard to claim 10, Dörner and Shin disclose The distributed learning method according to claim 9,
But Dörner and Shin fail to explicitly disclose “wherein the sending the error information of the second intermediate data and information about the third channel to the first node includes: sending first information to the first node, wherein the first information is determined based on the error information of the second intermediate data and the information about the third channel; or sending a fourth signal to the first node through the first channel, wherein the fourth signal includes a signal generated by mapping the error information of the second intermediate data to an air interface resource.”
Prakash disclose wherein the sending the error information of the second intermediate data and information about the third channel to the first node includes: (Fig. 1, [0022] [0030]-[0049] [0065]-[0073] [0101]-[0106] sending the gradient information and other information about the various channels to the UE node)
sending first information to the first node, (Fig. 1, [0022] [0030]-[0049] [0065]-[0073] [0101]-[0106] sending information to the UE node) wherein the first information is determined based on the error information of the second intermediate data and the information about the third channel; or sending a fourth signal to the first node through the first channel, wherein the fourth signal includes a signal generated by mapping the error information of the second intermediate data to an air interface resource. (Fig. 1, [0022] [0030]-[0049] [0065]-[0073] [0101]-[0106] sending the gradient information and other information about the various channels to the UE node)
It would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made to incorporate Prakash‘s distributing learning method into Shin and Dörner’s invention as they are related to the same field endeavor of model training and learning. The motivation to combine these arts, as proposed above, at least because Prakash‘s distributing learning method with communicating information from the nodes would help to provide more constraint or operational context information between the nodes into Shin and Dörner’s system. Therefore it would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made that providing constraint or operational context information between the nodes would help to improve efficiency of ML model training.
In regard to claim 12, Dörner and Shin disclose The distributed learning method according to claim 8,
Dörner disclose wherein the second information is used to obtain error information of the first intermediate data, wherein the second information includes the error information of the first intermediate data; or the second information includes the error information of the second intermediate data and the information about the first channel, and the error information of the second intermediate data and the information about the first channel is used to determine the error information of the first intermediate data. (Section II. A and Fig. 5, the resulting autoencoder can be trained by using stochastic gradient descent. Since Tx is being deployed on the first node, and the receiver Rx is being deployed on the second node, the information is flowed between the Tx and Rx and gradient descent are calculated, the FE and PE blocks are being updated during the learning process as part of the autoencoder modeling the communication system, The error information are the gradient. This gradients flow through the backward propagation algorithms including the second intermediate data, Fig. 5, channel and “x.sub.t” etc.)
But Dörner and Shin fail to explicitly disclose “wherein the method further comprises: sending second information to the first node,”
Prakash disclose wherein the method further comprises: sending second information to the first node, ([0043]-[0049] sending information by the second node to the first node (from MEC (201a) to UE (101a) for example)
It would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made to incorporate Prakash‘s distributing learning method into Shin and Dörner’s invention as they are related to the same field endeavor of model training and learning. The motivation to combine these arts, as proposed above, at least because Prakash‘s distributing learning method with communicating information from the nodes would help to provide more information between the nodes into Shin and Dörner’s system. Therefore it would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made that providing more information between the nodes would help to improve efficiency of ML model training.
In regard to claims 16-18, claims16-18 are apparatus claims corresponding to the method claims 3-5 above and, therefore, are rejected for the same reasons set forth in the rejections of claims 3-5.
Response to Arguments
Applicant’s arguments with respect to claims 1-20 filed on 7/20/2026 have been considered but are moot because the arguments do not apply to the current rejection.
With respect to 35 USC § 101 rejection with regarding to claims 1-20, please see the detailed rejection above.
Conclusion
The prior art made of record and not relied upon is considered pertinent to Applicant's disclosure.
U.S. Patent Documents PATENT DATE INVENTOR(S) TITLE
US 20180359050 A1 2018-12-13 Lauridsen et al.
ADAPTIVE NETWORK CODING IN WIRELESS COMMUNICATIONS
Lauridsen et al. disclose A first network node (eNB) is configured to receive (404), from a second network node (UE), channel performance indicator values regarding a serving cell, and estimate (404) a number of network-coded packets based on the received channel performance indicator values, such that the estimated number of network-coded packets defines a number of network-coded packets required by the second network node for successful detection of payload data. The second network node is configured to generate (402) the value of a channel performance indicator regarding the serving cell, and cause (403) transmission of the generated value of the channel performance indicator to the first network node, wherein the generated value of the channel performance indicator directly or indirectly indicates the number of network-coded packets required by the second network node for successful reception of payload data that is an input to network coding… see abstract.
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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XUYANG XIA
Primary Examiner
Art Unit 2143
/XUYANG XIA/Primary Examiner, Art Unit 2143
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