DETAILED ACTION
This action is in response to the Applicant Response filed 12 August 2026 for application 18/554,517 filed 09 October 2023.
Claim(s) 1-2, 8, 14, 38-39 is/are currently amended.
Claim(s) 13, 15, 19-37, 41-55 is/are cancelled.
Claim(s) 1-12, 14, 16-18, 38-40 is/are pending.
Claim(s) 1-12, 14, 16-18, 38-40 is/are rejected.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Arguments
Applicant’s arguments regarding the 35 U.S.C. 101 rejection of the claims are based on the newly amended subject matter. All arguments are addressed in the 35 U.S.C. 101 rejection of the claims below.
Applicant’s arguments regarding the 35 U.S.C. 102 and/or 35 U.S.C. 103 rejections of the claims are based on the newly amended subject matter. All arguments are addressed in the 35 U.S.C. 102 and/or 35 U.S.C. 103 rejections of the claims below.
Claim Objections
Claim(s) 1-12, 38-40 is/are objected to because of the following informalities:
Claim 1, line 12, the first neural network decoder should read “a first neural network decoder”
Claim 7, lines 1-2, the wireless device should read “a wireless device”
Claim 7, line 2, the base station should read “a base station”
Claim 8, line 8, to enable the selection should read “to enable
Claim 38, line 15, the first neural network decoder should read “a first neural network decoder”
Claims 2-7, 9-12, 39-40 are objected to due to their dependence, either directly or indirectly, on claims 1, 7-8, 38
Appropriate correction is required.
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.
Claim(s) 1-12, 14, 16-18, 38-40 is/are rejected under 35 U.S.C. 101, because the claim(s) is/are directed to an abstract idea, and because the claim elements, whether considered individually or in combination, do not amount to significantly more than the abstract idea, see Alice Corporation Pty. Ltd. V. CLS Bank International et al., 573 US 208 (2014).
Regarding claim 1, the claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 1 is directed to a method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: The claim recites a(n) method of transfer learning for training neural networks in a wireless communication system.
The limitation of obtaining, by the training, a first set of weights for the second neural network encoder using a first data set, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process. The limitation is directed to observation, evaluation, judgment and opinion and is a process capable of being performed by a human mentally or using pen and paper.
The limitation of setting weights of a first neural network encoder of a first autoencoder in a target domain to the first set of weights, the first neural network encoder of the first autoencoder having a same structure as the second neural network encoder of the second autoencoder, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process. The limitation is directed to observation, evaluation, judgment and opinion and is a process capable of being performed by a human mentally or using pen and paper.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind, then it falls within the "Mental Processes" grouping. Accordingly, the claim recites an abstract idea.
Step 2A Prong Two Analysis: With respect to the abstract idea, the judicial exception is not integrated into a practical application.
The claim recites additional element(s) – wireless communication system. The additional element(s) is/are recited at a high-level of generality (i.e., as generic computer components performing generic computer functions of executing instructions on the computers) such that it amounts to no more than mere instructions to apply the exception using generic computer components (MPEP 2106.05(b)).
The claim recites additional element(s) – transfer learning, neural networks, second autoencoder, second neural network encoder, second neural network decoder, first neural network encoder, first autoencoder, first neural network decoder. The additional element(s) is/are recited at a high-level of generality such that it amounts to no more than indicating a field of use or technological environment in which to apply the judicial exception (MPEP 2106.05(h)).
The claim recites training a second autoencoder in a source domain, the second autoencoder comprising a second neural network encoder and a second neural network decoder; training the first neural network decoder of the first autoencoder using a second data set, during which the weights of the first neural network encoder are fixed which is simply generic training to perform the abstract idea of model generation and amounts to mere instructions to apply the exception (MPEP 2106.05(f)).
Accordingly, the additional element(s) do(es) not integrate the abstract idea into a practical application because the additional element(s) do(es) not impose any meaningful limits on practicing the abstract idea, and, therefore, the claim is directed to an abstract idea.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the additional element(s) of:
wireless communication system amount(s) to no more than mere instructions to apply the exception using generic computer components (MPEP 2106.05(b))
generic training to perform the abstract idea amount(s) to no more than mere instructions to apply the exception (MPEP 2106.05(f))
transfer learning, neural networks, second autoencoder, second neural network encoder, second neural network decoder, first neural network encoder, first autoencoder, first neural network decoder amount(s) to no more than indicating a field of use or technological environment in which to apply the judicial exception (MPEP 2106.05(h))
The additional element(s) do(es) not provide an inventive concept, and, therefore, the claim is not patent eligible.
Regarding claim 2, the claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 2 is directed to a method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: The claim recites a(n) method of transfer learning for training neural networks in a wireless communication system. The Step 2A Prong One Analysis for claim 1 is applicable here since claim 2 carries out the method of claim 1 but for the recitation of additional element(s) of wherein obtaining a first set of weights comprises training a second neural network encoder in a source domain using a first data set to determine the first set of weights.
Step 2A Prong Two Analysis: With respect to the abstract idea, the judicial exception is not integrated into a practical application.
The claim recites additional element(s) – second neural network encoder. The additional element(s) is/are recited at a high-level of generality such that it amounts to no more than indicating a field of use or technological environment in which to apply the judicial exception (MPEP 2106.05(h)).
The claim recites wherein obtaining a first set of weights comprises training a second neural network encoder in a source domain using a first data set to determine the first set of weights which is simply generic training to perform the abstract idea of model generation and amounts to mere instructions to apply the exception (MPEP 2106.05(f)).
Accordingly, the additional element(s) do(es) not integrate the abstract idea into a practical application because the additional element(s) do(es) not impose any meaningful limits on practicing the abstract idea, and, therefore, the claim is directed to an abstract idea.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the additional element(s) of:
generic training to perform the abstract idea amount(s) to no more than mere instructions to apply the exception (MPEP 2106.05(f))
second neural network encoder amount(s) to no more than indicating a field of use or technological environment in which to apply the judicial exception (MPEP 2106.05(h))
The additional element(s) do(es) not provide an inventive concept, and, therefore, the claim is not patent eligible.
Regarding claim 3, the claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 3 is directed to a method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: The claim recites a(n) method of transfer learning for training neural networks in a wireless communication system. The Step 2A Prong One Analysis for claim 1 is applicable here since claim 3 carries out the method of claim 1 but for the recitation of additional element(s) of wherein obtaining a first set of weights comprises receiving the first set of weights.
Step 2A Prong Two Analysis: With respect to the abstract idea, the judicial exception is not integrated into a practical application.
The claim recites wherein obtaining a first set of weights comprises receiving the first set of weights, which is simply receiving data recited at a high level of generality. This is nothing more than insignificant extra-solution activity (MPEP 2106.05(g)).
Accordingly, the additional element(s) do(es) not integrate the abstract idea into a practical application because the additional element(s) do(es) not impose any meaningful limits on practicing the abstract idea, and, therefore, the claim is directed to an abstract idea.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the additional element(s) of:
receiving data amount(s) to no more than insignificant extra-solution activity (MPEP 2106.05(g)), wherein the insignificant extra-solution activity is the well-understood routine and conventional activit(y/ies) of receiving or transmitting data over a network and/or storing and retrieving information in memory (MPEP 2016.05(d))
The additional element(s) do(es) not provide an inventive concept, and, therefore, the claim is not patent eligible.
Regarding claim 4, the claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 4 is directed to a method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: The claim recites a(n) method of transfer learning for training neural networks in a wireless communication system. The Step 2A Prong One Analysis for claim 1 is applicable here since claim 4 carries out the method of claim 1 but for the recitation of additional element(s) of wherein the first data set comprises simulated channel data of a wireless channel.
Step 2A Prong Two Analysis: With respect to the abstract idea, the judicial exception is not integrated into a practical application. In particular, the claim recites additional information regarding the data and the element(s) do(es) not apply the exception in a meaningful way (MPEP 2106.05(e)). Accordingly, the additional element(s) do(es) not integrate the abstract idea into a practical application because the additional element(s) do(es) not impose any meaningful limits on practicing the abstract idea, and, therefore, the claim is directed to an abstract idea.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the additional element(s) of additional information regarding the data do(es) not apply the exception in a meaningful way (MPEP 2106.05(e)). Not applying the exception in a meaningful way does not provide an inventive concept, and, therefore, the claim is not patent eligible.
Regarding claim 5, the claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 5 is directed to a method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: The claim recites a(n) method of transfer learning for training neural networks in a wireless communication system. The Step 2A Prong One Analysis for claim 1 is applicable here since claim 5 carries out the method of claim 1 but for the recitation of additional element(s) of wherein the second data set comprises measurements or uplink measurements.
Step 2A Prong Two Analysis: With respect to the abstract idea, the judicial exception is not integrated into a practical application. In particular, the claim recites additional information regarding the data and the element(s) do(es) not apply the exception in a meaningful way (MPEP 2106.05(e)). Accordingly, the additional element(s) do(es) not integrate the abstract idea into a practical application because the additional element(s) do(es) not impose any meaningful limits on practicing the abstract idea, and, therefore, the claim is directed to an abstract idea.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the additional element(s) of additional information regarding the data do(es) not apply the exception in a meaningful way (MPEP 2106.05(e)). Not applying the exception in a meaningful way does not provide an inventive concept, and, therefore, the claim is not patent eligible.
Regarding claim 6, the claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 6 is directed to a method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: The claim recites a(n) method of transfer learning for training neural networks in a wireless communication system. The Step 2A Prong One Analysis for claim 1 is applicable here since claim 6 carries out the method of claim 1 but for the recitation of additional element(s) of wherein the first neural network decoder is of a different structure to a second neural network decoder in the source domain.
Step 2A Prong Two Analysis: With respect to the abstract idea, the judicial exception is not integrated into a practical application.
The claim recites wherein the first neural network decoder is of a different structure to a second neural network decoder in the source domain which is simply additional information regarding the model, and the element(s) do(es) not apply the exception in a meaningful way (MPEP 2106.05(e)).
The claim recites additional element(s) – second neural network decoder. The additional element(s) is/are recited at a high-level of generality such that it amounts to no more than indicating a field of use or technological environment in which to apply the judicial exception (MPEP 2106.05(h)).
Accordingly, the additional element(s) do(es) not integrate the abstract idea into a practical application because the additional element(s) do(es) not impose any meaningful limits on practicing the abstract idea, and, therefore, the claim is directed to an abstract idea.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the additional element(s) of:
second neural network decoder amount(s) to no more than indicating a field of use or technological environment in which to apply the judicial exception (MPEP 2106.05(h))
additional information regarding the model do(es) not apply the exception in a meaningful way (MPEP 2106.05(e))
The additional element(s) do(es) not provide an inventive concept, and, therefore, the claim is not patent eligible.
Regarding claim 7, the claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 7 is directed to a method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: The claim recites a(n) method of transfer learning for training neural networks in a wireless communication system. The Step 2A Prong One Analysis for claim 1 is applicable here since claim 7 carries out the method of claim 1 but for the recitation of additional element(s) of wherein the wireless device signals to the base station a type of the first neural network encoder.
Step 2A Prong Two Analysis: With respect to the abstract idea, the judicial exception is not integrated into a practical application.
The claim recites wherein the wireless device signals to the base station a type of the first neural network encoder, which is simply transmitting data recited at a high level of generality. This is nothing more than insignificant extra-solution activity (MPEP 2106.05(g)).
Accordingly, the additional element(s) do(es) not integrate the abstract idea into a practical application because the additional element(s) do(es) not impose any meaningful limits on practicing the abstract idea, and, therefore, the claim is directed to an abstract idea.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the additional element(s) of:
transmitting data amount(s) to no more than insignificant extra-solution activity (MPEP 2106.05(g)), wherein the insignificant extra-solution activity is the well-understood routine and conventional activit(y/ies) of receiving or transmitting data over a network (MPEP 2016.05(d))
The additional element(s) do(es) not provide an inventive concept, and, therefore, the claim is not patent eligible.
Regarding claim 8, the claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 8 is directed to a method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: The claim recites a(n) method performed by a wireless device for providing compressed Channel State Information, CSI, to a base station.
The limitation of determining the compressed CSI …, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process. The limitation is directed to observation, evaluation, judgment and opinion and is a process capable of being performed by a human mentally or using pen and paper.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind, then it falls within the "Mental Processes" grouping. Accordingly, the claim recites an abstract idea.
Step 2A Prong Two Analysis: With respect to the abstract idea, the judicial exception is not integrated into a practical application.
The claim recites additional element(s) – wireless device, base station. The additional element(s) is/are recited at a high-level of generality (i.e., as generic computer components performing generic computer functions of executing instructions on the computers) such that it amounts to no more than mere instructions to apply the exception using generic computer components (MPEP 2106.05(b)).
The claim recites additional element(s) – neural network encoder, neural network decoder, transfer learning. The additional element(s) is/are recited at a high-level of generality such that it amounts to no more than indicating a field of use or technological environment in which to apply the judicial exception (MPEP 2106.05(h)).
The claim recites transmitting the compressed CSI to the base station; signalling an indication of a type of the neural network encoder to the base station to enable the selection of a neural network decoder corresponding to the indicated type of the neural network encoder, the neural network decoder trained using transfer learning while weights of a corresponding neural network encoder remained fixed, which is simply transmitting data recited at a high level of generality. This is nothing more than insignificant extra-solution activity (MPEP 2106.05(g)).
The claim recites ... the neural network encoder being configured with a first set of weights which is simply additional information regarding the model, and the element(s) do(es) not apply the exception in a meaningful way (MPEP 2106.05(e)).
Accordingly, the additional element(s) do(es) not integrate the abstract idea into a practical application because the additional element(s) do(es) not impose any meaningful limits on practicing the abstract idea, and, therefore, the claim is directed to an abstract idea.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the additional element(s) of:
wireless device, base station amount(s) to no more than mere instructions to apply the exception using generic computer components (MPEP 2106.05(b))
transmitting data amount(s) to no more than insignificant extra-solution activity (MPEP 2106.05(g)), wherein the insignificant extra-solution activity is the well-understood routine and conventional activit(y/ies) of receiving or transmitting data over a network (MPEP 2016.05(d))
neural network encoder, neural network decoder, transfer learning amount(s) to no more than indicating a field of use or technological environment in which to apply the judicial exception (MPEP 2106.05(h))
additional information regarding the model do(es) not apply the exception in a meaningful way (MPEP 2106.05(e))
The additional element(s) do(es) not provide an inventive concept, and, therefore, the claim is not patent eligible.
Regarding claim 9, the claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 9 is directed to a method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: The claim recites a(n) method performed by a wireless device for providing compressed Channel State Information, CSI, to a base station. The Step 2A Prong One Analysis for claim 8 is applicable here since claim 9 carries out the method of claim 8 but for the recitation of additional element(s) of receiving an update to one or more of the first set of weights from the base station.
Step 2A Prong Two Analysis: With respect to the abstract idea, the judicial exception is not integrated into a practical application.
The claim recites receiving an update to one or more of the first set of weights from the base station, which is simply transmitting data recited at a high level of generality. This is nothing more than insignificant extra-solution activity (MPEP 2106.05(g)).
Accordingly, the additional element(s) do(es) not integrate the abstract idea into a practical application because the additional element(s) do(es) not impose any meaningful limits on practicing the abstract idea, and, therefore, the claim is directed to an abstract idea.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the additional element(s) of:
transmitting data amount(s) to no more than insignificant extra-solution activity (MPEP 2106.05(g)), wherein the insignificant extra-solution activity is the well-understood routine and conventional activit(y/ies) of receiving or transmitting data over a network (MPEP 2016.05(d))
The additional element(s) do(es) not provide an inventive concept, and, therefore, the claim is not patent eligible.
Regarding claim 10, the claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 10 is directed to a method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: The claim recites a(n) method performed by a wireless device for providing compressed Channel State Information, CSI, to a base station. The Step 2A Prong One Analysis for claim 9 is applicable here since claim 10 carries out the method of claim 9 but for the recitation of additional element(s) of storing the first set of weights.
Step 2A Prong Two Analysis: With respect to the abstract idea, the judicial exception is not integrated into a practical application.
The claim recites storing the first set of weights, which is simply storing data recited at a high level of generality. This is nothing more than insignificant extra-solution activity (MPEP 2106.05(g)).
Accordingly, the additional element(s) do(es) not integrate the abstract idea into a practical application because the additional element(s) do(es) not impose any meaningful limits on practicing the abstract idea, and, therefore, the claim is directed to an abstract idea.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the additional element(s) of:
storing data amount(s) to no more than insignificant extra-solution activity (MPEP 2106.05(g)), wherein the insignificant extra-solution activity is the well-understood routine and conventional activit(y/ies) of storing and retrieving information in memory (MPEP 2016.05(d))
The additional element(s) do(es) not provide an inventive concept, and, therefore, the claim is not patent eligible.
Regarding claim 11, the claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 11 is directed to a method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: The claim recites a(n) method performed by a wireless device for providing compressed Channel State Information, CSI, to a base station. The Step 2A Prong One Analysis for claim 9 is applicable here since claim 11 carries out the method of claim 9 but for the recitation of additional element(s) of receiving a request from the base station to convert back to the first set of weights.
Step 2A Prong Two Analysis: With respect to the abstract idea, the judicial exception is not integrated into a practical application.
The claim recites receiving a request from the base station to convert back to the first set of weights, which is simply transmitting data recited at a high level of generality. This is nothing more than insignificant extra-solution activity (MPEP 2106.05(g)).
Accordingly, the additional element(s) do(es) not integrate the abstract idea into a practical application because the additional element(s) do(es) not impose any meaningful limits on practicing the abstract idea, and, therefore, the claim is directed to an abstract idea.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the additional element(s) of:
transmitting data amount(s) to no more than insignificant extra-solution activity (MPEP 2106.05(g)), wherein the insignificant extra-solution activity is the well-understood routine and conventional activit(y/ies) of receiving or transmitting data over a network (MPEP 2016.05(d))
The additional element(s) do(es) not provide an inventive concept, and, therefore, the claim is not patent eligible.
Regarding claim 12, the claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 12 is directed to a method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: The claim recites a(n) method performed by a wireless device for providing compressed Channel State Information, CSI, to a base station. The Step 2A Prong One Analysis for claim 9 is applicable here since claim 12 carries out the method of claim 9 but for the recitation of additional element(s) of wherein the update is received via Radio Resource Control signalling.
Step 2A Prong Two Analysis: With respect to the abstract idea, the judicial exception is not integrated into a practical application.
The claim recites additional element(s) – Radio Resource Control signalling. The additional element(s) is/are recited at a high-level of generality such that it amounts to no more than indicating a field of use or technological environment in which to apply the judicial exception (MPEP 2106.05(h)).
The claim recites wherein the update is received via Radio Resource Control signalling, which is simply transmitting data recited at a high level of generality. This is nothing more than insignificant extra-solution activity (MPEP 2106.05(g)).
Accordingly, the additional element(s) do(es) not integrate the abstract idea into a practical application because the additional element(s) do(es) not impose any meaningful limits on practicing the abstract idea, and, therefore, the claim is directed to an abstract idea.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the additional element(s) of:
transmitting data amount(s) to no more than insignificant extra-solution activity (MPEP 2106.05(g)), wherein the insignificant extra-solution activity is the well-understood routine and conventional activit(y/ies) of receiving or transmitting data over a network (MPEP 2016.05(d))
Radio Resource Control signalling amount(s) to no more than indicating a field of use or technological environment in which to apply the judicial exception (MPEP 2106.05(h))
The additional element(s) do(es) not provide an inventive concept, and, therefore, the claim is not patent eligible.
Regarding claim 14, the claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 14 is directed to a method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: The claim recites a(n) method performed by a base station for determining Channel State Information, CSI.
The limitation of selecting a neural network decoder corresponding to the indication of the type of neural network encoder, the selected neural network decoder trained using transfer learning while weights of a corresponding neural network encoder were fixed, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process. The limitation is directed to observation, evaluation, judgment and opinion and is a process capable of being performed by a human mentally or using pen and paper.
The limitation of decompressing the compressed CSI ... to determine a reconstructed CSI, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process. The limitation is directed to observation, evaluation, judgment and opinion and is a process capable of being performed by a human mentally or using pen and paper.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind, then it falls within the "Mental Processes" grouping. Accordingly, the claim recites an abstract idea.
Step 2A Prong Two Analysis: With respect to the abstract idea, the judicial exception is not integrated into a practical application.
The claim recites additional element(s) – base station, wireless device. The additional element(s) is/are recited at a high-level of generality (i.e., as generic computer components performing generic computer functions of executing instructions on the computers) such that it amounts to no more than mere instructions to apply the exception using generic computer components (MPEP 2106.05(b)).
The claim recites additional element(s) – neural network encoder, neural network decoder, transfer learning. The additional element(s) is/are recited at a high-level of generality such that it amounts to no more than indicating a field of use or technological environment in which to apply the judicial exception (MPEP 2106.05(h)).
The claim recites receiving, from a wireless device, CSI compressed by a neural network encoder, the neural network encoder configured with a first set of weights; receiving, from the wireless device, an indication of a type of neural network encoder used by a wireless device, which is simply transmitting data recited at a high level of generality. This is nothing more than insignificant extra-solution activity (MPEP 2106.05(g)).
Accordingly, the additional element(s) do(es) not integrate the abstract idea into a practical application because the additional element(s) do(es) not impose any meaningful limits on practicing the abstract idea, and, therefore, the claim is directed to an abstract idea.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the additional element(s) of:
base station, wireless device amount(s) to no more than mere instructions to apply the exception using generic computer components (MPEP 2106.05(b))
transmitting data amount(s) to no more than insignificant extra-solution activity (MPEP 2106.05(g)), wherein the insignificant extra-solution activity is the well-understood routine and conventional activit(y/ies) of receiving or transmitting data over a network (MPEP 2016.05(d))
neural network encoder, neural network decoder, transfer learning amount(s) to no more than indicating a field of use or technological environment in which to apply the judicial exception (MPEP 2106.05(h))
The additional element(s) do(es) not provide an inventive concept, and, therefore, the claim is not patent eligible.
Regarding claim 16, the claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 16 is directed to a method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: The claim recites a(n) method performed by a base station for determining Channel State Information, CSI. The Step 2A Prong One Analysis for claim 14 is applicable here since claim 16 carries out the method of claim 14 but for the recitation of additional element(s) of transmitting an update to one or more of the first set of weights to the wireless device.
Step 2A Prong Two Analysis: With respect to the abstract idea, the judicial exception is not integrated into a practical application.
The claim recites transmitting an update to one or more of the first set of weights to the wireless device, which is simply transmitting data recited at a high level of generality. This is nothing more than insignificant extra-solution activity (MPEP 2106.05(g)).
Accordingly, the additional element(s) do(es) not integrate the abstract idea into a practical application because the additional element(s) do(es) not impose any meaningful limits on practicing the abstract idea, and, therefore, the claim is directed to an abstract idea.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the additional element(s) of:
transmitting data amount(s) to no more than insignificant extra-solution activity (MPEP 2106.05(g)), wherein the insignificant extra-solution activity is the well-understood routine and conventional activit(y/ies) of receiving or transmitting data over a network (MPEP 2016.05(d))
The additional element(s) do(es) not provide an inventive concept, and, therefore, the claim is not patent eligible.
Regarding claim 17, the claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 17 is directed to a method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: The claim recites a(n) method performed by a base station for determining Channel State Information, CSI. The Step 2A Prong One Analysis for claim 16 is applicable here since claim 17 carries out the method of claim 16 but for the recitation of additional element(s) of transmitting a request to the wireless device to convert back to the first set of weights.
Step 2A Prong Two Analysis: With respect to the abstract idea, the judicial exception is not integrated into a practical application.
The claim recites transmitting a request to the wireless device to convert back to the first set of weights, which is simply transmitting data recited at a high level of generality. This is nothing more than insignificant extra-solution activity (MPEP 2106.05(g)).
Accordingly, the additional element(s) do(es) not integrate the abstract idea into a practical application because the additional element(s) do(es) not impose any meaningful limits on practicing the abstract idea, and, therefore, the claim is directed to an abstract idea.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the additional element(s) of:
transmitting data amount(s) to no more than insignificant extra-solution activity (MPEP 2106.05(g)), wherein the insignificant extra-solution activity is the well-understood routine and conventional activit(y/ies) of receiving or transmitting data over a network (MPEP 2016.05(d))
The additional element(s) do(es) not provide an inventive concept, and, therefore, the claim is not patent eligible.
Regarding claim 18, the claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 18 is directed to a method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: The claim recites a(n) method performed by a wireless device for providing compressed Channel State Information, CSI, to a base station. The Step 2A Prong One Analysis for claim 16 is applicable here since claim 18 carries out the method of claim 16 but for the recitation of additional element(s) of wherein the update is transmitted via Radio Resource Control signalling.
Step 2A Prong Two Analysis: With respect to the abstract idea, the judicial exception is not integrated into a practical application.
The claim recites additional element(s) – Radio Resource Control signalling. The additional element(s) is/are recited at a high-level of generality such that it amounts to no more than indicating a field of use or technological environment in which to apply the judicial exception (MPEP 2106.05(h)).
The claim recites wherein the update is transmitted via Radio Resource Control signalling, which is simply transmitting data recited at a high level of generality. This is nothing more than insignificant extra-solution activity (MPEP 2106.05(g)).
Accordingly, the additional element(s) do(es) not integrate the abstract idea into a practical application because the additional element(s) do(es) not impose any meaningful limits on practicing the abstract idea, and, therefore, the claim is directed to an abstract idea.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the additional element(s) of:
transmitting data amount(s) to no more than insignificant extra-solution activity (MPEP 2106.05(g)), wherein the insignificant extra-solution activity is the well-understood routine and conventional activit(y/ies) of receiving or transmitting data over a network (MPEP 2016.05(d))
Radio Resource Control signalling amount(s) to no more than indicating a field of use or technological environment in which to apply the judicial exception (MPEP 2106.05(h))
The additional element(s) do(es) not provide an inventive concept, and, therefore, the claim is not patent eligible.
Regarding claim 38, the claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 28 is directed to a(n) network node, which is directed to a machine, one of the statutory categories.
Step 2A Prong One Analysis: The claim recites a(n) network node for training neural networks using transfer learning for a wireless communication system.
The limitation of obtain, by the training, a first set of weights for the second neural network encoder using a first data set, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process. The limitation is directed to observation, evaluation, judgment and opinion and is a process capable of being performed by a human mentally or using pen and paper.
The limitation of set weights of a first neural network encoder of a first autoencoder in a target domain to the first set of weights, the first neural network encoder of the first autoencoder having a same structure as the second neural network encoder of the second autoencoder, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process. The limitation is directed to observation, evaluation, judgment and opinion and is a process capable of being performed by a human mentally or using pen and paper.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind, then it falls within the "Mental Processes" grouping. Accordingly, the claim recites an abstract idea.
Step 2A Prong Two Analysis: With respect to the abstract idea, the judicial exception is not integrated into a practical application.
The claim recites additional element(s) – network node, wireless communication system, processor, memory, instructions. The additional element(s) is/are recited at a high-level of generality (i.e., as generic computer components performing generic computer functions of executing instructions on the computers) such that it amounts to no more than mere instructions to apply the exception using generic computer components (MPEP 2106.05(b)).
The claim recites additional element(s) – neural networks, transfer learning, second autoencoder, second neural network encoder, second neural network decoder, first neural network encoder, first autoencoder. first neural network decoder. The additional element(s) is/are recited at a high-level of generality such that it amounts to no more than indicating a field of use or technological environment in which to apply the judicial exception (MPEP 2106.05(h)).
The claim recites train a second autoencoder in a source domain, the second autoencoder comprising a second neural network encoder and a second neural network decoder; first neural network decoder; train the first neural network decoder of the first autoencoder using a second data set, during which the weights of the first neural network encoder are fixed which is simply generic training to perform the abstract idea of model generation and amounts to mere instructions to apply the exception (MPEP 2106.05(f)).
Accordingly, the additional element(s) do(es) not integrate the abstract idea into a practical application because the additional element(s) do(es) not impose any meaningful limits on practicing the abstract idea, and, therefore, the claim is directed to an abstract idea.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the additional element(s) of:
network node, wireless communication system, processor, memory, instructions amount(s) to no more than mere instructions to apply the exception using generic computer components (MPEP 2106.05(b))
generic training to perform the abstract idea amount(s) to no more than mere instructions to apply the exception (MPEP 2106.05(f))
neural networks, transfer learning, second autoencoder, second neural network encoder, second neural network decoder, first neural network encoder, first autoencoder. first neural network decoder amount(s) to no more than indicating a field of use or technological environment in which to apply the judicial exception (MPEP 2106.05(h))
The additional element(s) do(es) not provide an inventive concept, and, therefore, the claim is not patent eligible.
Regarding claim 39, the claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 39 is directed to a(n) network node, which is directed to a machine, one of the statutory categories.
Step 2A Prong One Analysis: The claim recites a(n) network node for training neural networks using transfer learning for a wireless communication system. The Step 2A Prong One Analysis for claim 38 is applicable here since claim 39 carries out the network node of claim 38 but for the recitation of additional element(s) of wherein the network node is operative to obtain the first set of weights by training a second neural network encoder in a source domain using a first data set.
Step 2A Prong Two Analysis: With respect to the abstract idea, the judicial exception is not integrated into a practical application.
The claim recites additional element(s) – second neural network encoder. The additional element(s) is/are recited at a high-level of generality such that it amounts to no more than indicating a field of use or technological environment in which to apply the judicial exception (MPEP 2106.05(h)).
The claim recites wherein the network node is operative to obtain the first set of weights by training a second neural network encoder in a source domain using a first data set which is simply generic training to perform the abstract idea of model generation and amounts to mere instructions to apply the exception (MPEP 2106.05(f)).
Accordingly, the additional element(s) do(es) not integrate the abstract idea into a practical application because the additional element(s) do(es) not impose any meaningful limits on practicing the abstract idea, and, therefore, the claim is directed to an abstract idea.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the additional element(s) of:
generic training to perform the abstract idea amount(s) to no more than mere instructions to apply the exception (MPEP 2106.05(f))
second neural network encoder amount(s) to no more than indicating a field of use or technological environment in which to apply the judicial exception (MPEP 2106.05(h))
The additional element(s) do(es) not provide an inventive concept, and, therefore, the claim is not patent eligible.
Regarding claim 40, the claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 40 is directed to a(n) network node, which is directed to a machine, one of the statutory categories.
Step 2A Prong One Analysis: The claim recites a(n) network node for training neural networks using transfer learning for a wireless communication system. The Step 2A Prong One Analysis for claim 38 is applicable here since claim 40 carries out the network node of claim 38 but for the recitation of additional element(s) of wherein the network node is operative to obtain the first set of weights by receiving the first set of weights.
Step 2A Prong Two Analysis: With respect to the abstract idea, the judicial exception is not integrated into a practical application.
The claim recites wherein the network node is operative to obtain the first set of weights by receiving the first set of weights, which is simply receiving data recited at a high level of generality. This is nothing more than insignificant extra-solution activity (MPEP 2106.05(g)).
Accordingly, the additional element(s) do(es) not integrate the abstract idea into a practical application because the additional element(s) do(es) not impose any meaningful limits on practicing the abstract idea, and, therefore, the claim is directed to an abstract idea.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the additional element(s) of:
receiving data amount(s) to no more than insignificant extra-solution activity (MPEP 2106.05(g)), wherein the insignificant extra-solution activity is the well-understood routine and conventional activit(y/ies) of receiving or transmitting data over a network and/or storing and retrieving information in memory (MPEP 2016.05(d))
The additional element(s) do(es) not provide an inventive concept, and, therefore, the claim is not patent eligible.
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 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.
Claim(s) 1-4, 6, 38-40 is/are rejected under 35 U.S.C. 103 as being unpatentable over Thathachar et al. (US 2020/0090035 A1 – Encoder-Decoder Memory Augmented Neural Network Architectures, hereinafter referred to as “Thathachar”) in view of Zeng et al. (Deep Transfer Learning for 5G Massive MIMO Downlink CSI Feedback, hereinafter referred to as “Zeng).
Regarding claim 1 (Currently Amended), Thathachar teaches a method of transfer learning for training neural networks (Thathachar, [0095]-[0097] – teaches training autoencoders using transfer learning for a plurality of tasks [domains] including at least a first task [target] and a second task [source]) …, the method comprising:
training a second autoencoder in a source domain (Thathachar, [0094]-[0097] – teaches training a first task [source] autoencoder), the second autoencoder comprising a second neural network encoder and a second neural network decoder (Thathachar, [0097] – teaches an encoder-decoder autoencoder for each task [domain]);
obtaining, by the training, a first set of weights for the second neural network encoder using a first data set (Thathachar, [0094]-[0097] – teaches training a first task [source] autoencoder);
setting weights of a first neural network encoder of a first autoencoder in a target domain to the first set of weights (Thathachar, [0097] – teaches secondary tasks [target domain] receiving weights from the first task [source domain] encoder after training and instantiating and freezing the weights for the second task [target domain] encoder to train the second task [target domain] decoder), the first neural network encoder of the first autoencoder having a same structure as the second neural network encoder of the second autoencoder (Thathachar, [0095] – teaches all encoders have identical structure); and
training the first neural network decoder of the first autoencoder using a second data set, during which the weights of the first neural network encoder are fixed (Thathachar, [0095]-[0097] – teaches training, using data associated with the second task [second data set], the second task [target domain/first neural network] decoder using frozen encoder weights acquired from the first task [source domain] encoder which was trained using data associated with the first task [first data set]).
While Thathachar teaches transfer learning of autoencoders, Thathachar does not explicitly teach transfer learning of autoencoder in a wireless communication system.
Zeng teaches a method of transfer learning for training neural networks in a wireless communication system (Zeng, sections II-III – teaches transfer learning of autoencoders wherein the encoder of the autoencoders is used in user equipment [wireless device] and the decoder is used in a base station; see also Zeng Figs. 1, 3; Zeng, Algorithm 1) …
It would have been obvious to one of ordinary skill in the art before the filing date of the claimed invention to modify Thathachar with the teachings of Zeng in order to reduce training costs by using a relatively small number of samples to fine-tune a pre-trained model in the field of transfer learning of autoencoders (Zeng, Abstract – “Acquisition of downlink channel state information (CSI) is an important procedure performed at the base station (BS) for high quality wireless communication in frequency division duplexing (FDD) communication system. Generally, the downlink CSI is fed back to the BS through the user equipment (UE). Compared with traditional methods, neural network (NN) can effectively compress the downlink CSI, thus greatly reducing the feedback overhead. However, the generalization of the NN is poor, hence it is necessary to train a NN from scratch whenever there is a change in the wireless channel environment. Nevertheless, training a NN this way requires huge data and time cost in 5G massive MIMO systems. In this paper, deep transfer learning (DTL) is proposed to solve the problem of high training cost of the downlink CSI feedback NN. In a new wireless environment, our proposed technique utilises relatively small number of samples to fine-tune a pre-trained model, in order to obtain a new model with low training cost. The performance of this model is shown to be comparable with that of the NN trained with large samples. Experiment results demonstrate the effectiveness and superiority of the proposed method.”).
Regarding claim 2 (Currently Amended), Thathachar in view of Zeng teaches all of the limitations of the method of claim 1 as noted above. Thathachar further teaches wherein obtaining a first set of weights comprises training a second neural network encoder in a source domain using a first data set to determine the first set of weights (Thathachar, [0095]-[0097] – teaches training a first autoencoder [second NN] using first task [domain] data [first data] and sending the weights of the trained first encoder to a second [target] encoder).
It would have been obvious to one of ordinary skill in the art before the filing data of the claimed invention to combine the teachings of Thathachar and Zeng for the same reasons as disclosed in claim 1 above.
Regarding claim 3 (Previously Presented), Thathachar in view of Zeng teaches all of the limitations of the method of claim 1 as noted above. Thathachar further teaches wherein obtaining a first set of weights comprises receiving the first set of weights (Thathachar, [0097] – teaches source encoder weights are instantiated and frozen for target encoders).
It would have been obvious to one of ordinary skill in the art before the filing data of the claimed invention to combine the teachings of Thathachar and Zeng for the same reasons as disclosed in claim 1 above.
Regarding claim 4 (Previously Presented), Thathachar in view of Zeng teaches all of the limitations of the method of claim 1 as noted above. Zeng further teaches wherein the first data set comprises simulated channel data of a wireless channel (Zeng, section IV – teaches simulated channel data).
It would have been obvious to one of ordinary skill in the art before the filing date of the claimed invention to combine the teachings of Thathachar and Zeng in order to identity data to reduce training costs by using a relatively small number of samples to fine-tune a pre-trained model (Zeng, Abstract).
Regarding claim 6 (Previously Presented), Thathachar in view of Zeng teaches all of the limitations of the method of claim 1 as noted above. Thathachar further teaches wherein the first neural network decoder is of a different structure to a second neural network decoder in the source domain (Thathachar, [0097] – teaches separately train target decoders for different tasks).
It would have been obvious to one of ordinary skill in the art before the filing data of the claimed invention to combine the teachings of Thathachar and Zeng for the same reasons as disclosed in claim 1 above.
Regarding claim 38 (Currently Amended), it is the network node embodiment of claim 1 with similar limitations to claim 1 and is rejected using the same reasoning found in claim 1.
Zeng further teaches a network node for training neural networks using transfer learning for a wireless communication system (Zeng, sections II-III – teaches transfer learning of autoencoders wherein the encoder of the autoencoders is used in user equipment [wireless device] and the decoder is used in a base station; see also Zeng Figs. 1, 3; Zeng, Algorithm 1), the network node comprising:
a processor and a memory, the memory containing instructions executable by said processor, the network node being configured to (Zeng, section II – teaches base station in 5G Massive MIMO system) …
It would have been obvious to one of ordinary skill in the art before the filing data of the claimed invention to combine the teachings of Thathachar and Zeng for the same reasons as disclosed in claim 1 above.
Regarding claim 39 (Currently Amended), the rejection of claim 38 is incorporated herein. Further, the limitations in this claim are taught by Thathachar in view of Zeng for the reasons set forth in the rejection of claim 2.
Regarding claim 40 (Previously Presented), the rejection of claim 38 is incorporated herein. Further, the limitations in this claim are taught by Thathachar in view of Zeng for the reasons set forth in the rejection of claim 3.
Claim(s) 5, 7 is/are rejected under 35 U.S.C. 103 as being unpatentable over Thathachar in view of Zeng and further in view of Lee et al. (US 2022/0368570 A1 – Model Transfer Within Wireless Networks for Channel Estimation, hereinafter referred to as “Lee”).
Regarding claim 5 (Previously Presented), Thathachar in view of Zeng teaches all of the limitations of the method of claim 1 as noted above. However, Thathachar in view of Zeng does not explicitly teach wherein the second data set comprises measurements or uplink measurements.
Lee teaches wherein the second data set comprises measurements or uplink measurements (Lee, [0047] – teaches channel estimation data).
It would have been obvious to one of ordinary skill in the art before the filing date of the claimed invention to modify Thathachar in view of Zeng with the teachings of Lee in order to reduce resources of computing, time, and energy to perform model training in the field of transfer learning of autoencoders (Lee, [0043] – “Therefore, according to an example embodiment, a UE may take advantage of transfer learning, wherein the UE may obtain information relating to a pre-trained neural network model, e.g., which may have been trained by another UE or by a gNB, and then may be used or re-used) by the UE (e.g., as a starting point) to perform channel estimation, rather than training a full NN model from scratch (or starting from a random set of weights). Transfer learning has been proposed to reuse a pretrained NN model for another ML task. The conventional ML (or neural network model) starts a learning process (performs NN model training) from scratch (e.g., which may be a set of random weights, or an initial set of weights), thus, requiring large resources of computing, time, and energy to perform NN model training. In transfer learning, reusing a pre-trained NN model can significantly reduce the training time and computing resource usage.”).
Regarding claim 7 (Previously Presented), Thathachar in view of Zeng teaches all of the limitations of the method of claim 1 as noted above. However, Thathachar in view of Zeng does not explicitly teach wherein the wireless device signals to the base station a type of the first neural network encoder.
Lee teaches wherein the wireless device signals to the base station a type of the first neural network encoder (Lee, [0070] – teaches the user device [wireless device] transmitting an indication to a network node [base station] of the availability of a pretrained model).
It would have been obvious to one of ordinary skill in the art before the filing date of the claimed invention to modify Thathachar in view of Zeng with the teachings of Lee in order to reduce resources of computing, time, and energy to perform model training in the field of transfer learning of autoencoders (Lee, [0043] – “Therefore, according to an example embodiment, a UE may take advantage of transfer learning, wherein the UE may obtain information relating to a pre-trained neural network model, e.g., which may have been trained by another UE or by a gNB, and then may be used or re-used) by the UE (e.g., as a starting point) to perform channel estimation, rather than training a full NN model from scratch (or starting from a random set of weights). Transfer learning has been proposed to reuse a pretrained NN model for another ML task. The conventional ML (or neural network model) starts a learning process (performs NN model training) from scratch (e.g., which may be a set of random weights, or an initial set of weights), thus, requiring large resources of computing, time, and energy to perform NN model training. In transfer learning, reusing a pre-trained NN model can significantly reduce the training time and computing resource usage.”).
Claim(s) 8-12, 14-18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Zeng et al. (Deep Transfer Learning for 5G Massive MIMO Downlink CSI Feedback, hereinafter referred to as “Zeng) in view of Lee et al. (US 2022/0368570 A1 – Model Transfer Within Wireless Networks for Channel Estimation, hereinafter referred to as “Lee”) and further in view of Thathachar et al. (US 2020/0090035 A1 – Encoder-Decoder Memory Augmented Neural Network Architectures, hereinafter referred to as “Thathachar”).
Regarding claim 8 (Currently Amended) Zeng teaches a method performed by a wireless device for providing compressed Channel State Information, CSI, to a base station (Zeng, section II – teaches transferring CSI from a wireless device to a base station; see also Zeng, Fig. 1), the method comprising:
determining the compressed CSI using a neural network encoder (Zeng, section II – teaches the wireless device has an encoder model to compress the CSI; see also Zeng, section III; Zeng, Fig. 2), the neural network encoder being configured with a first set of weights (Zeng, sections II-III – teaches training model weights for various domains, including a first model with a first set of weights);
transmitting the compressed CSI to the base station (Zeng, section II – teaches transferring CSI from a wireless device to a base station; see also Zeng, Fig. 1).
While Zeng teaches an autoencoder comprising an encoder on a wireless device and a decoder on a base station, Zeng does not explicitly teach signalling an indication of a type of the neural network encoder to the base station to enable the selection of a neural network decoder corresponding to the indicated type of the neural network encoder, the neural network decoder trained using transfer learning while weights of a corresponding neural network encoder remained fixed.
Lee teaches signalling an indication of a type of the neural network encoder to the base station to enable the selection of a neural network decoder corresponding to the indicated type of the neural network encoder (Lee, [0070] – teaches the user device [wireless device] transmitting an indication to a network node [base station] of the availability of a pretrained model) …
It would have been obvious to one of ordinary skill in the art before the filing date of the claimed invention to modify Zeng with the teachings of Lee in order to reduce resources of computing, time, and energy to perform model training in the field of wireless device and base station communication regarding models (Lee, [0043] – “Therefore, according to an example embodiment, a UE may take advantage of transfer learning, wherein the UE may obtain information relating to a pre-trained neural network model, e.g., which may have been trained by another UE or by a gNB, and then may be used or re-used) by the UE (e.g., as a starting point) to perform channel estimation, rather than training a full NN model from scratch (or starting from a random set of weights). Transfer learning has been proposed to reuse a pretrained NN model for another ML task. The conventional ML (or neural network model) starts a learning process (performs NN model training) from scratch (e.g., which may be a set of random weights, or an initial set of weights), thus, requiring large resources of computing, time, and energy to perform NN model training. In transfer learning, reusing a pre-trained NN model can significantly reduce the training time and computing resource usage.”).
While Zeng in view of Lee teaches transfer learning of autoencoders comprising an encoder on a wireless device and a decoder on a base station, Zeng in view of Lee does not explicitly teach the neural network decoder trained using transfer learning while weights of a corresponding neural network encoder remained fixed.
Thathachar teaches the neural network decoder trained using transfer learning while weights of a corresponding neural network encoder remained fixed (Thathachar, [0095]-[0097] – teaches training, using data associated with the second task [second data set], the second task [target domain/first neural network] decoder using frozen encoder weights acquired from the first task [source domain] encoder which was trained using data associated with the first task [first data set]).
It would have been obvious to one of ordinary skill in the art before the filing date of the claimed invention to modify Zeng in view of Lee with the teachings of Thathachar in order to enable transfer learning and solve complex working memory tasks in the field of transfer learning of autoencoders in wireless systems (Thathachar, [0027] – “The present disclosure provides a MANN architecture using a Neural Turing Machine ( NTM ). This memory-augmented neural network architecture enables transfer learning and solves complex working memory tasks. In various embodiments, Neural Turing Machines are combined with an Encoder - Decoder approach . This model is general purpose and capable of solving multiple problems.”).
Regarding claim 9 (Previously Presented), Zeng in view of Lee and further in view of Thathachar teaches all of the limitations of the method of claim 8 as noted above. Lee further teaches receiving an update to one or more of the first set of weights from the base station (Lee, [0086] – teaches the wireless device receiving an updated model from the base station).
It would have been obvious to one of ordinary skill in the art before the filing date of the claimed invention to combine the teachings of Zeng, Lee and Thathachar in order to update model parameters to reduce resources of computing, time, and energy to perform model training (Lee, [0043]).
Regarding claim 10 (Previously Presented), Zeng in view of Lee and further in view of Thathachar teaches all of the limitations of the method of claim 9 as noted above. Zeng further teaches storing the first set of weights (Zeng, section II – teaches storing the encoder on the wireless device [storing the encoder means storing the weights]).
It would have been obvious to one of ordinary skill in the art before the filing data of the claimed invention to combine the teachings of Zeng, Lee and Thathachar for the same reasons as disclosed in claim 9 above.
Regarding claim 11 (Previously Presented), Zeng in view of Lee and further in view of Thathachar teaches all of the limitations of the method of claim 9 as noted above. Lee further teaches receiving a request from the base station to convert back to the first set of weights (Lee, [0085] - teaches that the base station has an old/stale model [set of weights] and can still send the old model to the wireless device [reverting to previous model weights]).
It would have been obvious to one of ordinary skill in the art before the filing date of the claimed invention to combine the teachings of Zeng, Lee and Thathachar in order to revert to previous weights to reduce resources of computing, time, and energy to perform model training (Lee, [0043]).
Regarding claim 12 (Previously Presented), Zeng in view of Lee and further in view of Thathachar teaches all of the limitations of the method of claim 9 as noted above. Lee further teaches wherein the update is received via Radio Resource Control signalling (Lee, [0090] – teaches transferring model data using radio resource control).
It would have been obvious to one of ordinary skill in the art before the filing date of the claimed invention to combine the teachings of Zeng, Lee and Thathachar in order to transmit signals to reduce resources of computing, time, and energy to perform model training (Lee, [0043]).
Regarding claim 14 (Currently Amended), Zeng teaches a method performed by a base station for determining Channel State Information, CSI (Zeng, section II – teaches transferring CSI from a wireless device to a base station; see also Zeng, Fig. 1), the method comprising:
receiving, from a wireless device, CSI compressed by a neural network encoder (Zeng, section II – teaches the base station has a decoder model to decompress the CSI from the wireless device; see also Zeng, section III; Zeng, Fig. 2), the neural network encoder configured with a first set of weights (Zeng, sections II-III – teaches training model weights for various domains, including a first model with a first set of weights);
selecting a neural network decoder corresponding to the indication of the type of neural network encoder (Zeng, sections II-IV – teaches a plurality of autoencoders each associated with a given channel data and selected based on the channel data) …; and
decompressing the compressed CSI using the selected neural network decoder to determine a reconstructed CSI (Zeng, section II – teaches using a neural network decoder to decompress CSI received from a wireless device).
While Zeng teaches an autoencoder comprising an encoder on a wireless device and a decoder on a base station, Zeng does not explicitly teach receiving, from the wireless device, an indication of a type of neural network encoder used by a wireless device; … the selected neural network decoder trained using transfer learning while weights of a corresponding neural network encoder were fixed.
Lee teaches receiving, from the wireless device, an indication of a type of neural network encoder used by a wireless device (Lee, [0070] – teaches the user device [wireless device] transmitting an indication to a network node [base station] of the availability of a pretrained model).
It would have been obvious to one of ordinary skill in the art before the filing date of the claimed invention to modify Zeng with the teachings of Lee in order to reduce resources of computing, time, and energy to perform model training in the field of wireless device and base station communication regarding models (Lee, [0043] – “Therefore, according to an example embodiment, a UE may take advantage of transfer learning, wherein the UE may obtain information relating to a pre-trained neural network model, e.g., which may have been trained by another UE or by a gNB, and then may be used or re-used) by the UE (e.g., as a starting point) to perform channel estimation, rather than training a full NN model from scratch (or starting from a random set of weights). Transfer learning has been proposed to reuse a pretrained NN model for another ML task. The conventional ML (or neural network model) starts a learning process (performs NN model training) from scratch (e.g., which may be a set of random weights, or an initial set of weights), thus, requiring large resources of computing, time, and energy to perform NN model training. In transfer learning, reusing a pre-trained NN model can significantly reduce the training time and computing resource usage.”).
While Zeng in view of Lee teaches transfer learning of autoencoders comprising an encoder on a wireless device and a decoder on a base station, Zeng in view of Lee does not explicitly teach the selected neural network decoder trained using transfer learning while weights of a corresponding neural network encoder were fixed.
Thathachar teaches the selected neural network decoder trained using transfer learning while weights of a corresponding neural network encoder were fixed (Thathachar, [0095]-[0097] – teaches training, using data associated with the second task [second data set], the second task [target domain/first neural network] decoder using frozen encoder weights acquired from the first task [source domain] encoder which was trained using data associated with the first task [first data set]).
It would have been obvious to one of ordinary skill in the art before the filing date of the claimed invention to modify Zeng in view of Lee with the teachings of Thathachar in order to enable transfer learning and solve complex working memory tasks in the field of transfer learning of autoencoders in wireless systems (Thathachar, [0027] – “The present disclosure provides a MANN architecture using a Neural Turing Machine ( NTM ). This memory-augmented neural network architecture enables transfer learning and solves complex working memory tasks. In various embodiments, Neural Turing Machines are combined with an Encoder - Decoder approach . This model is general purpose and capable of solving multiple problems.”).
Regarding claim 16 (Previously Presented), Zeng in view of Lee and further in view of Thathachar teaches all of the limitations of the method of claim 14 as noted above. Lee further teaches transmitting an update to one or more of the first set of weights to the wireless device (Lee, [0086] – teaches the wireless device receiving an updated model from the base station).
It would have been obvious to one of ordinary skill in the art before the filing date of the claimed invention to combine the teachings of Zeng, Lee and Thathachar in order to update model parameters to reduce resources of computing, time, and energy to perform model training (Lee, [0043]).
Regarding claim 17 (Previously Presented), Zeng in view of Lee and further in view of Thathachar teaches all of the limitations of the method of claim 16 as noted above. Lee further teaches transmitting a request to the wireless device to convert back to the first set of weights (Lee, [0085] - teaches that the base station has an old/stale model [set of weights] and can still send the old model to the wireless device [reverting to previous model weights]).
It would have been obvious to one of ordinary skill in the art before the filing date of the claimed invention to combine the teachings of Zeng, Lee and Thathachar in order to revert to previous weights to reduce resources of computing, time, and energy to perform model training (Lee, [0043]).
Regarding claim 18 (Previously Presented), Zeng in view of Lee and further in view of Thathachar teaches all of the limitations of the method of claim 16 as noted above. Lee further teaches wherein the update is transmitted via Radio Resource Control signalling (Lee, [0090] – teaches transferring model data using radio resource control).
It would have been obvious to one of ordinary skill in the art before the filing date of the claimed invention to combine the teachings of Zeng, Lee and Thathachar in order to transmit signals to reduce resources of computing, time, and energy to perform model training (Lee, [0043]).
Conclusion
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.
Any inquiry concerning this communication or earlier communication from the examiner should be directed to MARSHALL WERNER whose telephone number is (469) 295-9143. The examiner can normally be reached on Monday – Thursday 7:30 AM – 4:30 PM ET.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Kamran Afshar, can be reached at (571) 272-7796. The fax number for the organization where this application or proceeding is assigned is (571) 273-8300.
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/MARSHALL L WERNER/ Primary Examiner, Art Unit 2125
