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 .
Priority
The present application has a provisional application No. 63/227,739 filed on July 30, 2021.
The present application has a preliminary amendment filed on Jan 25, 2024 with Claims 1-20 in pending.
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.
Regarding Claim 1
Step 1 – whether the claim falls within any statutory category. See MPEP 2016.03
Claim 1 is a machine claim thus it falls into one of the four categories of statutory subject matter.
Step 2A Prong 1 – whether the claim recites a judicial exception. See MPEP 2106.04, subsection II.
Regarding independent claim 1, following limitations recite a judicial exception:
“receive a plurality of signal vectors from the plurality of WDs, the plurality of signal vectors being based on a plurality of updated local models associated with the plurality of WDs;”
[Mathematical Calculations] – updating models involve mathematical computations such as gradient descents, backward-propagation, and more, which recites to an abstract idea.
“update a global model based at least on the plurality of signal vectors”
[Mathematical Calculations] – updating models involve mathematical computations such as gradient descents, backward-propagation, and more, which recites to an abstract idea.
Step 2A Prong 2 – whether the claim recites additional elements that integrate the exception into a practical application of the exception?
Regarding Claim 1, the claim recites additional elements of
“a communication interface configured to receive a plurality of signal vectors from the plurality of WDs, the plurality of signal vectors being based on a plurality of updated local models associated with the plurality of WDs”,
A communication interface is recited at a high level of generality and is merely adding words “apply it” to the judicial exception. (see MPEP 2106.05(f)).
Receiving signal vectors or data wirelessly is merely data gathering recited at a high level of generality, thus is insignificant extra-solution activity (See MPEP 2106.05(g)).
“processing circuitry in communication with the communication interface, the processing circuitry being configured to: cause at least one transmission of the updated global model to the plurality of WDs”
A processing circuitry is recited at a high level of generality and is merely adding words “apply it” to the judicial exception. (see MPEP 2106.05(f))
Transmitting data to other wireless devices is merely data gathering recited at a high level of generality, thus is insignificant extra-solution activity (See MPEP 2106.05(g)).
[Even when viewed in combination, the additional elements do no more than automate the mathematical calculations that a person could perform, using computer components as a tool, thus the claim as a whole does not integrate into a practical application.]
Step 2B – whether the claim as a whole amount to significantly more than the judicial exception? I.e. Are there any additional elements (features/limitations/step) recited in the claim beyond the abstract idea?
The claim does not provide an inventive concept (significantly more than the abstract idea). The claim is ineligible.
As explained above, the additional elements [1,2] are considered merely computer components that are just to store and execute code-based instructions which are considered a mere instruction to apply an exception and amount to storing and receiving information in memory, which is well-understood, routine, conventional activity (See MPEP 2106.05(d), subsection II). This limitation remains a mere instruction to apply an exception.
Also, the additional elements [1,2] are insignificant extra solution activities and at best the equivalent of a mere data gathering recited at a high level of generality and amount to receiving or transmitting data over a network, which is well-understood, routine, conventional activity (See MPEP 2106.05(d), subsection II). These limitations remain insignificant extra-solution activities even upon reconsideration. Even when considered in combination, the additional elements represent insignificant extra-solution activities, which cannot provide an inventive concept.
Regarding Claim 2
Step 1 – whether the claim falls within any statutory category. See MPEP 2016.03
Claim 2 is a dependent claim of 1, thus it falls within the same category of statutory subject matter.
Step 2A Prong 1 – whether the claim recites a judicial exception. See MPEP 2106.04, subsection II.
Regarding dependent claim 2, following limitations recite a judicial exception:
“initialize at least one of a first step-size parameter, a second step-size parameter, and a power regularization factor, the plurality of updated local models being based at least in part on the initialized at least one of the first step-size parameter, the second step-size parameter, and the power regularization factor”
[Mental Process] – initializing or setting up the parameters for further application which involves observations, evaluations, judgments, and opinions that is capable of being performed in the human mind with the assistance of paper and pen
[Mathematical Calculations] – updating models involve mathematical computations such as gradient descents, backward-propagation, and more, which recites to an abstract idea.
Step 2A Prong 2 – whether the claim recites additional elements that integrate the exception into a practical application of the exception?
Regarding Claim 2, the claim recites additional elements of
“the processing circuitry”
A processing circuitry is recited at a high level of generality and is merely adding words “apply it” to the judicial exception. (see MPEP 2106.05(f)).
“the communication interface is further configured to: transmit the initialized at least one of the first step-size parameter, the second step-size parameter, and the power regularization factor”
The communication interface is recited at a high level of generality and is merely adding words “apply it” to the judicial exception. (see MPEP 2106.05(f))
Transmitting information is merely data gathering recited at a high level of generality, thus is insignificant extra-solution activity (See MPEP 2106.05(g)).
[Even when viewed in combination, the additional elements do no more than automate the mental processes that a person could perform, using computer components as a tool, thus the claim as a whole does not integrate into a practical application.]
Step 2B – whether the claim as a whole amount to significantly more than the judicial exception? I.e. Are there any additional elements (features/limitations/step) recited in the claim beyond the abstract idea?
The claim does not provide an inventive concept (significantly more than the abstract idea). The claim is ineligible.
As explained above, the additional elements [1,2] are considered merely computer components that are just to store and execute code-based instructions which are considered a mere instruction to apply an exception and amount to storing and receiving information in memory, which is well-understood, routine, conventional activity (See MPEP 2106.05(d), subsection II). This limitation remains a mere instruction to apply an exception.
Also, the additional element [2] is an insignificant extra solution activity and at best the equivalent of a mere data gathering recited at a high level of generality and amount to receiving or transmitting data over a network, which is well-understood, routine, conventional activity (See MPEP 2106.05(d), subsection II). This limitation remains an insignificant extra-solution activity even upon reconsideration. Even when considered in combination, the additional element represents an insignificant extra-solution activity, which cannot provide an inventive concept.
Regarding Claim 3
Step 1 – whether the claim falls within any statutory category. See MPEP 2016.03
Claim 3 is a dependent claim of 1, thus it falls within the same category of statutory subject matter.
Step 2A Prong 1 – whether the claim recites a judicial exception. See MPEP 2106.04, subsection II.
Regarding dependent claim 3, following limitations recite a judicial exception:
“the global model is updated using model averaging based on at least one of a local gradient and a global gradient descent”
[Mathematical Calculations] – updating models using gradient descents involve mathematical computations which recites to an abstract idea.
Step 2A Prong 2 – whether the claim recites additional elements that integrate the exception into a practical application of the exception?
The claim 3 does not recite any additional elements other than abstract ideas, so it does not integrate into a practical application. Thus, this claim is directed to the abstract idea.
Regarding Claim 4
Step 1 – whether the claim falls within any statutory category. See MPEP 2016.03
Claim 4 is a dependent claim of 1, thus it falls within the same category of statutory subject matter.
Step 2A Prong 1 – whether the claim recites a judicial exception. See MPEP 2106.04, subsection II.
Regarding dependent claim 4, following limitations recite a judicial exception:
“each of the plurality of updated local models is based at least in part on respective local channel state information, CSI, and local data.”
[Mathematical Calculations] – updating models involve mathematical computations such as gradient descents, backward-propagation, and more. Also, updating the model while taking account of these extra information as numerical factors recites to an abstract idea.
Step 2A Prong 2 – whether the claim recites additional elements that integrate the exception into a practical application of the exception?
The claim 4 does not recite any additional elements other than abstract ideas, so it does not integrate into a practical application. Thus, this claim is directed to the abstract idea.
Regarding Claim 5
Step 1 – whether the claim falls within any statutory category. See MPEP 2016.03
Claim 5 is a dependent claim of 1, thus it falls within the same category of statutory subject matter.
Step 2A Prong 1 – whether the claim recites a judicial exception. See MPEP 2106.04, subsection II.
Regarding dependent claim 5, following limitations recite a judicial exception:
“the received plurality of signal vectors is based on at least one updated local virtual queue”
[Mental Process] – receiving signal vectors based on the virtual queue or according a timed schedule involves observations, evaluations, judgments, and opinions that is capable of being performed in the human mind with the assistance of paper and pen.
Step 2A Prong 2 – whether the claim recites additional elements that integrate the exception into a practical application of the exception?
The claim 5 does not recite any additional elements other than abstract ideas, so it does not integrate into a practical application. Thus, this claim is directed to the abstract idea.
Regarding Claim 6
Step 1 – whether the claim falls within any statutory category. See MPEP 2016.03
Claim 6 is a dependent claim of 1, thus it falls within the same category of statutory subject matter.
Step 2A Prong 1 – whether the claim recites a judicial exception. See MPEP 2106.04, subsection II.
Regarding dependent claim 6, following limitations recite a judicial exception:
“recover a version of the global model based on the received plurality of signal vectors”
[Mathematical Calculations] – recovering the model that has been modified with additive noise factors requires mathematical calculations which recites an abstract idea.
Step 2A Prong 2 – whether the claim recites additional elements that integrate the exception into a practical application of the exception?
Regarding Claim 6, the claim recites additional elements of
“the processing circuitry”
A processing circuitry is recited at a high level of generality and is merely adding words “apply it” to the judicial exception. (see MPEP 2106.05(f)).
[Even when viewed in combination, the additional elements do no more than automate the mental processes that a person could perform, using computer components as a tool, thus the claim as a whole does not integrate into a practical application.]
Step 2B – whether the claim as a whole amount to significantly more than the judicial exception? I.e. Are there any additional elements (features/limitations/step) recited in the claim beyond the abstract idea?
The claim does not provide an inventive concept (significantly more than the abstract idea). The claim is ineligible.
As explained above, the additional element [1] is considered merely computer components that are just to store and execute code-based instructions which are considered a mere instruction to apply an exception and amount to storing and receiving information in memory, which is well-understood, routine, conventional activity (See MPEP 2106.05(d), subsection II). This limitation remains a mere instruction to apply an exception. Even when considered in combination, the additional element represents merely computer components that are just to store and execute code-based instructions which are considered a mere instruction to apply an exception, which cannot provide an inventive concept.
Regarding Claim 7
Step 1 – whether the claim falls within any statutory category. See MPEP 2016.03
Claim 7 is a dependent claim of 6, thus it falls within the same category of statutory subject matter.
Step 2A Prong 1 – whether the claim recites a judicial exception. See MPEP 2106.04, subsection II.
Regarding dependent claim 7, following limitations recite a judicial exception:
“the recovered version of the global model is a noisy version of the global model based at least in part on a communication error.”
[Mathematical Calculations] – noisy version simply means it has added a numerical factor of noise value into the model and recovering from it requires mathematical calculations which recites an abstract idea.
Step 2A Prong 2 – whether the claim recites additional elements that integrate the exception into a practical application of the exception?
The claim 7 does not recite any additional elements other than abstract ideas, so it does not integrate into a practical application. Thus, this claim is directed to the abstract idea.
Regarding Claim 8
Step 1 – whether the claim falls within any statutory category. See MPEP 2016.03
Claim 8 is a dependent claim of 7, thus it falls within the same category of statutory subject matter.
Step 2A Prong 1 – whether the claim recites a judicial exception. See MPEP 2106.04, subsection II.
Regarding dependent claim 8, following limitations recite a judicial exception:
“the communication error is based at least in part on a noise value bounded by a predetermined threshold”
[Mental Process] – using the predetermined threshold to check whether the value falls within the range simply involves observations, evaluations, judgments, and opinions that is capable of being performed in the human mind with the assistance of paper and pen.
Step 2A Prong 2 – whether the claim recites additional elements that integrate the exception into a practical application of the exception?
The claim 8 does not recite any additional elements other than abstract ideas, so it does not integrate into a practical application. Thus, this claim is directed to the abstract idea.
Regarding Claim 9
Step 1 – whether the claim falls within any statutory category. See MPEP 2016.03
Claim 9 is a dependent claim of 1, thus it falls within the same category of statutory subject matter.
Step 2A Prong 1 – whether the claim recites a judicial exception. See MPEP 2106.04, subsection II.
Regarding dependent claim 9, following limitations recite a judicial exception:
“updating of the global model includes computing a weighted sum of the plurality of updated local models.”
[Mathematical Calculations] – computing weighted sum from the update local models involves mathematical computations such as addition of multiple weights from the models which recites to an abstract idea.
Step 2A Prong 2 – whether the claim recites additional elements that integrate the exception into a practical application of the exception?
The claim 9 does not recite any additional elements other than abstract ideas, so it does not integrate into a practical application. Thus, this claim is directed to the abstract idea.
Regarding Claim 10
Step 1 – whether the claim falls within any statutory category. See MPEP 2016.03
Claim 10 is a dependent claim of 1, thus it falls within the same category of statutory subject matter.
Step 2A Prong 1 – whether the claim recites a judicial exception. See MPEP 2106.04, subsection II.
Regarding dependent claim 10, following limitations recite a judicial exception:
“the updating of the global model is based on a federated learning”
[Mathematical Calculations] – updating models involve mathematical computations such as gradient descents, backward-propagation, and more, which recites to an abstract idea.
[Mental Process] – federated learning can be simply understood as a procedure of aggregating what multiple humans have found which involves observations, evaluations, judgments, and opinions that is capable of being performed in the human mind with the assistance of paper and pen.
Step 2A Prong 2 – whether the claim recites additional elements that integrate the exception into a practical application of the exception?
The claim 10 does not recite any additional elements other than abstract ideas, so it does not integrate into a practical application. Thus, this claim is directed to the abstract idea.
Regarding Claim 11
Step 1 – whether the claim falls within any statutory category. See MPEP 2016.03
Claim 11 is a method claim thus it falls into one of the four categories of statutory subject matter.
Step 2A Prong 1 – whether the claim recites a judicial exception. See MPEP 2106.04, subsection II.
Regarding independent claim 1, following limitations recite a judicial exception:
“receiving a plurality of signal vectors from the plurality of WDs, the plurality of signal vectors being based on a plurality of updated local models associated with the plurality of WDs;”
[Mathematical Calculations] – updating models involve mathematical computations such as gradient descents, backward-propagation, and more, which recites to an abstract idea.
“updating a global model based at least on the plurality of signal vectors”
[Mathematical Calculations] – updating models involve mathematical computations such as gradient descents, backward-propagation, and more, which recites to an abstract idea.
Step 2A Prong 2 – whether the claim recites additional elements that integrate the exception into a practical application of the exception?
Regarding Claim 11, the claim recites additional elements of
“receive a plurality of signal vectors from the plurality of WDs, the plurality of signal vectors being based on a plurality of updated local models associated with the plurality of WDs”
Receiving signal vectors or data wirelessly is merely data gathering recited at a high level of generality, thus is insignificant extra-solution activity (See MPEP 2106.05(g)).
“causing at least one transmission of the updated global model to the plurality of WDs”
Transmitting data to other wireless devices is merely data gathering recited at a high level of generality, thus is insignificant extra-solution activity (See MPEP 2106.05(g)).
[Even when viewed in combination, the additional elements do no more than automate the mathematical calculations that a person could perform, using computer components as a tool, thus the claim as a whole does not integrate into a practical application.]
Step 2B – whether the claim as a whole amount to significantly more than the judicial exception? I.e. Are there any additional elements (features/limitations/step) recited in the claim beyond the abstract idea?
The claim does not provide an inventive concept (significantly more than the abstract idea). The claim is ineligible.
As explained above, the additional element [1,2] are considered an insignificant extra solution activities and at best the equivalent of a mere data gathering recited at a high level of generality and amount to receiving or transmitting data over a network, which is well-understood, routine, conventional activity (See MPEP 2106.05(d), subsection II). These limitations remain insignificant extra-solution activities even upon reconsideration. Even when considered in combination, the additional elements represent insignificant extra-solution activities, which cannot provide an inventive concept.
Regarding Claim 12
Step 1 – whether the claim falls within any statutory category. See MPEP 2016.03
Claim 12 is a dependent claim of 11, thus it falls within the same category of statutory subject matter.
Step 2A Prong 1 – whether the claim recites a judicial exception. See MPEP 2106.04, subsection II.
Regarding dependent claim 12, following limitations recite a judicial exception:
“initializing at least one of a first step-size parameter, a second step-size parameter, and a power regularization factor, the plurality of updated local models being based at least in part on the initialized at least one of the first step-size parameter, the second step-size parameter, and the power regularization factor”
[Mental Process] – initializing or setting up the parameters for further application which involves observations, evaluations, judgments, and opinions that is capable of being performed in the human mind with the assistance of paper and pen
[Mathematical Calculations] – updating models involve mathematical computations such as gradient descents, backward-propagation, and more, which recites to an abstract idea.
Step 2A Prong 2 – whether the claim recites additional elements that integrate the exception into a practical application of the exception?
Regarding Claim 2, the claim recites additional elements of
“transmitting the initialized at least one of the first step-size parameter, the second step-size parameter, and the power regularization factor”
Transmitting information is merely data gathering recited at a high level of generality, thus is insignificant extra-solution activity (See MPEP 2106.05(g)).
[Even when viewed in combination, the additional elements do no more than automate the mental processes that a person could perform, using computer components as a tool, thus the claim as a whole does not integrate into a practical application.]
Step 2B – whether the claim as a whole amount to significantly more than the judicial exception? I.e. Are there any additional elements (features/limitations/step) recited in the claim beyond the abstract idea?
The claim does not provide an inventive concept (significantly more than the abstract idea). The claim is ineligible.
As explained above, the additional elements [1] is considered an insignificant extra solution activity and at best the equivalent of a mere data gathering recited at a high level of generality and amount to receiving or transmitting data over a network, which is well-understood, routine, conventional activity (See MPEP 2106.05(d), subsection II). This limitation remains an insignificant extra-solution activity even upon reconsideration. Even when considered in combination, the additional element represents an insignificant extra-solution activity, which cannot provide an inventive concept.
Regarding Claim 13
Step 1 – whether the claim falls within any statutory category. See MPEP 2016.03
Claim 13 is a dependent claim of 11, thus it falls within the same category of statutory subject matter.
Step 2A Prong 1 – whether the claim recites a judicial exception. See MPEP 2106.04, subsection II.
Regarding dependent claim 13, following limitations recite a judicial exception:
“the global model is updated using model averaging based on at least one of a local gradient and a global gradient descent”
[Mathematical Calculations] – updating models using gradient descents involve mathematical computations which recites to an abstract idea.
Step 2A Prong 2 – whether the claim recites additional elements that integrate the exception into a practical application of the exception?
The claim 13 does not recite any additional elements other than abstract ideas, so it does not integrate into a practical application. Thus, this claim is directed to the abstract idea.
Regarding Claim 14
Step 1 – whether the claim falls within any statutory category. See MPEP 2016.03
Claim 14 is a dependent claim of 11, thus it falls within the same category of statutory subject matter.
Step 2A Prong 1 – whether the claim recites a judicial exception. See MPEP 2106.04, subsection II.
Regarding dependent claim 14, following limitations recite a judicial exception:
“each of the plurality of updated local models is based at least in part on respective local channel state information, CSI, and local data.”
[Mathematical Calculations] – updating models involve mathematical computations such as gradient descents, backward-propagation, and more. Also, updating the model while taking account of these extra information as numerical factors recites to an abstract idea.
Step 2A Prong 2 – whether the claim recites additional elements that integrate the exception into a practical application of the exception?
The claim 14 does not recite any additional elements other than abstract ideas, so it does not integrate into a practical application. Thus, this claim is directed to the abstract idea.
Regarding Claim 15
Step 1 – whether the claim falls within any statutory category. See MPEP 2016.03
Claim 15 is a dependent claim of 11, thus it falls within the same category of statutory subject matter.
Step 2A Prong 1 – whether the claim recites a judicial exception. See MPEP 2106.04, subsection II.
Regarding dependent claim 15, following limitations recite a judicial exception:
“the received plurality of signal vectors is based on at least one updated local virtual queue”
[Mental Process] – receiving signal vectors based on the virtual queue or according a timed schedule involves observations, evaluations, judgments, and opinions that is capable of being performed in the human mind with the assistance of paper and pen.
Step 2A Prong 2 – whether the claim recites additional elements that integrate the exception into a practical application of the exception?
The claim 15 does not recite any additional elements other than abstract ideas, so it does not integrate into a practical application. Thus, this claim is directed to the abstract idea.
Regarding Claim 16
Step 1 – whether the claim falls within any statutory category. See MPEP 2016.03
Claim 16 is a dependent claim of 11, thus it falls within the same category of statutory subject matter.
Step 2A Prong 1 – whether the claim recites a judicial exception. See MPEP 2106.04, subsection II.
Regarding dependent claim 16, following limitations recite a judicial exception:
“recovering a version of the global model based on the received plurality of signal vectors”
[Mathematical Calculations] – recovering the model that has been modified with additive noise factors requires mathematical calculations which recites an abstract idea.
Step 2A Prong 2 – whether the claim recites additional elements that integrate the exception into a practical application of the exception?
The claim 16 does not recite any additional elements other than abstract ideas, so it does not integrate into a practical application. Thus, this claim is directed to the abstract idea.
Regarding Claim 17
Step 1 – whether the claim falls within any statutory category. See MPEP 2016.03
Claim 17 is a dependent claim of 16, thus it falls within the same category of statutory subject matter.
Step 2A Prong 1 – whether the claim recites a judicial exception. See MPEP 2106.04, subsection II.
Regarding dependent claim 17, following limitations recite a judicial exception:
“the recovered version of the global model is a noisy version of the global model based at least in part on a communication error.”
[Mathematical Calculations] – noisy version simply means it has added a numerical factor of noise value into the model and recovering from it requires mathematical calculations which recites an abstract idea.
Step 2A Prong 2 – whether the claim recites additional elements that integrate the exception into a practical application of the exception?
The claim 17 does not recite any additional elements other than abstract ideas, so it does not integrate into a practical application. Thus, this claim is directed to the abstract idea.
Regarding Claim 18
Step 1 – whether the claim falls within any statutory category. See MPEP 2016.03
Claim 18 is a dependent claim of 17, thus it falls within the same category of statutory subject matter.
Step 2A Prong 1 – whether the claim recites a judicial exception. See MPEP 2106.04, subsection II.
Regarding dependent claim 18, following limitations recite a judicial exception:
“the communication error is based at least in part on a noise value bounded by a predetermined threshold”
[Mental Process] – using the predetermined threshold to check whether the value falls within the range simply involves observations, evaluations, judgments, and opinions that is capable of being performed in the human mind with the assistance of paper and pen.
Step 2A Prong 2 – whether the claim recites additional elements that integrate the exception into a practical application of the exception?
The claim 18 does not recite any additional elements other than abstract ideas, so it does not integrate into a practical application. Thus, this claim is directed to the abstract idea.
Regarding Claim 19
Step 1 – whether the claim falls within any statutory category. See MPEP 2016.03
Claim 19 is a dependent claim of 11, thus it falls within the same category of statutory subject matter.
Step 2A Prong 1 – whether the claim recites a judicial exception. See MPEP 2106.04, subsection II.
Regarding dependent claim 19, following limitations recite a judicial exception:
“updating of the global model includes computing a weighted sum of the plurality of updated local models.”
[Mathematical Calculations] – computing weighted sum from the update local models involves mathematical computations such as addition of multiple weights from the models which recites to an abstract idea.
Step 2A Prong 2 – whether the claim recites additional elements that integrate the exception into a practical application of the exception?
The claim 19 does not recite any additional elements other than abstract ideas, so it does not integrate into a practical application. Thus, this claim is directed to the abstract idea.
Regarding Claim 20
Step 1 – whether the claim falls within any statutory category. See MPEP 2016.03
Claim 20 is a dependent claim of 11, thus it falls within the same category of statutory subject matter.
Step 2A Prong 1 – whether the claim recites a judicial exception. See MPEP 2106.04, subsection II.
Regarding dependent claim 20, following limitations recite a judicial exception:
“the updating of the global model is based on a federated learning”
[Mathematical Calculations] – updating models involve mathematical computations such as gradient descents, backward-propagation, and more, which recites to an abstract idea.
[Mental Process] – federated learning can be simply understood as a procedure of aggregating what multiple humans have found which involves observations, evaluations, judgments, and opinions that is capable of being performed in the human mind with the assistance of paper and pen.
Step 2A Prong 2 – whether the claim recites additional elements that integrate the exception into a practical application of the exception?
The claim 20 does not recite any additional elements other than abstract ideas, so it does not integrate into a practical application. Thus, this claim is directed to the abstract idea.
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1, 3, 4, 6, 7, 9, 10, 11, 13, 14, 16, 17, 19, 20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Amiri et al. (Amiri), Non-Patent Literature listed in IDS filed on 08/20/2025, “Federated Learning Over Wireless Fading Channels”, published on May 2020, 12Pages.
As to independent Claim 1,
Amiri teaches an edge node configured to communicate with a plurality of wireless devices, WDs, the edge node comprising (Amiri, Pg3546, Abstract, Lines1-4, "We study federated machine learning at the wireless network edge, where limited power wireless devices, each with its own dataset, build a joint model with the help of a remote parameter server (PS)" and Amiri, Pg3548, Section II System Model, Lines1-3, "We consider FL across wireless devices, each with its M own local dataset, which employ distributed stochastic gradient descent(DSGD) with the help of a remote PS", wherein the remote PS or the parameter server is the corresponding edge node or the edge server that acts like a main server where the computation and update to the model while it controls the federated learning to communicate with the wireless devices, which is functionally equivalent to the claimed invention):
a communication interface configured to:
receive a plurality of signal vectors from the plurality of WDs, the plurality of signal vectors being based on a plurality of updated local models associated with the plurality of WDs (Amiri, Pg3546, Right Column, Paragraph1, Lines12-18, "In distributed SGD (DSGD), at each iteration, device m computes a gradient vector based on the global parameter vector with respect to its local dataset, denoted by B_m, and sends the result to the PS, which updates the global parameter vector according to
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where M denotes the number of devices" and Pg3550, Right Column, Below Euqation21, "Here we analyze the received signal at the PS", wherein the PS, has the communication interface, receives the results or signals from M devices which these results are based on the equation1 which is used to update the local model's gradient vectors respect to its local dataset, which is functionally equivalent to the claimed invention);
processing circuitry in communication with the communication interface, the processing circuitry being configured to:
update a global model based at least on the plurality of signal vectors (Pg3548, Right Column, Paragraph1, Lines1-2, "The goal is to recover
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at the PS, which then updates the model parameter as in (1) after N time slots" and Pg3546, Right Column, Paragraph1, Lines19-21, "each device can carry out multiple local updates, and share the overall difference with respect to the global model with the PS [1]", wherein the PS, which has the processing circuitry, recovers the
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which is the total weighted sum of gradient vectors sent from M devices, then uses this information to update its model parameter or the global model, which is functionally equivalent to the claimed invention); and
cause at least one transmission of the updated global model to the plurality of WDs (Amiri, Pg3548, Right Column, Paragraph1, Lines7-10, "The updated model parameter is then multicast to the devices by the PS through an error-free shared link, so the devices receive a consistent parameter vector for their computations in the next iteration”, wherein the PS sends out the updated model using the shared link which can be used in the next iteration, thus equivalent to the claimed invention.)
As to dependent Claim 3,
Amiri teaches, as mentioned above, all the limitations of Claim 1 such that the edge node is configured to receive plurality of signal vectors of the locally updated models from the wireless devices to update its global model, then transmits the updated version back to the wireless devices.
Amiri further teaches the edge node of claim1, wherein
the global model is updated using model averaging based on at least one of a local gradient and a global gradient descent (Amiri, Pg3546, Right Column, Paragraph1, Lines12-18, "In distributed SGD (DSGD), at each iteration, device m computes a gradient vector based on the global parameter vector with respect to its local dataset, denoted by B_m, and sends the result to the PS, which updates the global parameter vector according to
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where M denotes the number of devices" and Amiri, Pg3548, Right Column, Paragraph1, Lines1-2, "The goal is to recover
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at the PS, which then updates the model parameter as in (1) after N time slots", wherein as mentioned in Claim1, each device will run its own DSGD to update the local gradient vector or the local model which will be sent to the PS to update the global model, which means it will run a gradient descent on the global model. Here 1/M from the function shows that 1/M indicates the weights are being averaged out, which is functionally equivalent to the claimed invention.)
As to dependent Claim 4,
Amiri teaches, as mentioned above, all the limitations of Claim 1 such that the edge node is configured to receive plurality of signal vectors of the locally updated models from the wireless devices to update its global model, then transmits the updated version back to the wireless devices.
Amiri further teaches the edge node of claim 1, wherein
each of the plurality of updated local models is based at least in part on respective local channel state information, CSI, and local data (Amiri, Pg3548, Left Column, Paragraph1, Lines28-33, "The channel input vector of device m at the n-th time slot of iteration t, n in [N], is a function of the channel gain vector hnm(t), current parameter vector theta_t, the local dataset B_m, and the current gradient estimate at device m, g_m(theta_t), m in [M]. We assume that, at each time slot, the CSI is known by the devices and the PS" and Pg3548, Left Column, Paragraph1, Lines9-11, "the stochastic gradient computed by device with respect to m local data samples", wherein the input vector, the corresponding signal vector, is the aggregation of the channel gain vector, which is the corresponding CSI, current parameter vector, current gradient estimate which was computed using the local dataset, and the local dataset such that these information is used to create this vector to be sent out to the PS, which is functionally equivalent to the claimed invention of sending out the signal vector based on this CSI and the locally updated models.).
As to dependent Claim 6,
Amiri teaches, as mentioned above, all the limitations of Claim 1 such that the edge node is configured to receive plurality of signal vectors of the locally updated models from the wireless devices to update its global model, then transmits the updated version back to the wireless devices.
Amiri further teaches The edge node of claim 1, wherein the processing circuitry is further configured to:
recover a version of the global model based on the received plurality of signal vectors (Amiri, Pg3548, Right Column, Paragraph1, Lines1-2, "The goal is to recover
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at the PS, which then updates the model parameter as in (1) after N time slots" and Pg3548, Section II System Model, Lines18-21, "The channel output y^n(t) in C^s received by the PS at the n-th time slot of the t-th iteration, n in [N], is given by
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", wherein the as mentioned above in Claims 1 and 3,
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is the averaged out global model or the sum of all devices' gradients that has removed or recovered from the noise vectors, z, which is based on the all signal vectors of M devices, which is functionally equivalent to the claimed invention.)
As to dependent Claim 7,
Amiri teaches, as mentioned above, all the limitations of Claim 6 such that the edge node is configured to recover a global model that has been produced from the plurality of signal vectors of wireless devices.
Amiri further teaches the edge node of claim 6, wherein
the recovered version of the global model is a noisy version of the global model based at least in part on a communication error (Amiri, Pg3548, Left Column, Paragraph1, Lines26-27, "z^n(t) in C^s is complex Gaussian noise vector with the i-th entry", Pg3548, Section II System Model, Lines18-21, "The channel output y^n(t) in C^s received by the PS at the n-th time slot of the t-th iteration, n in [N], is given by
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" and, Pg3548, Right Column, Paragraph1, Lines1-2, "The goal is to recover
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at the PS, which then updates the model parameter as in (1) after N time slots", wherein as mentioned in Claim6, the z^n(t) is the corresponding communication error or the noise vector is that is used in the equation 2 which is the summation of all the signal vectors from M devices with possible noises, which is the corresponding noisy version of the global model, whereas the
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is the recovered version, thus it is functionally equivalent to the claimed invention.)
As to dependent Claim 9,
Amiri teaches, as mentioned above, all the limitations of Claim 1 such that the edge node is configured to receive plurality of signal vectors of the locally updated models from the wireless devices to update its global model, then transmits the updated version back to the wireless devices.
Amiri further teaches the edge node of claim1, wherein
updating of the global model includes computing a weighted sum of the plurality of updated local models (Amiri, Pg3546, Right Column, Paragraph1, Lines12-18, "In distributed SGD (DSGD), at each iteration, device m computes a gradient vector based on the global parameter vector with respect to its local dataset, denoted by B_m, and sends the result to the PS, which updates the global parameter vector according to
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where M denotes the number of devices”, Pg3548, Section II System Model, Lines18-21, "The channel output y^n(t) in C^s recevied by the PS at the n-th time slot of the t-th iteration, n in [N], is given by
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" and Pg3548, Right Column, Paragraph1, Lines1-2, "The goal is to recover
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at the PS, which then updates the model parameter as in (1) after N time slots.").
As to dependent Claim 10,
Amiri teaches, as mentioned above, all the limitations of Claim 1 such that the edge node is configured to receive plurality of signal vectors of the locally updated models from the wireless devices to update its global model, then transmits the updated version back to the wireless devices.
Amiri further teaches the edge node of claim1, wherein
the updating of the global model is based on a federated learning (Amiri, Pg3546, Abstract, Lines1-8, "We study federated machine learning at the wireless network edge, where limited power wireless devices, each with its own dataset, build a joint model with the help of a remote parameter server (PS). We consider a bandwidth-limited fading multiple access channel (MAC) from the wireless devices to the PS, and propose various techniques to implement distributed stochastic gradient descent (DSGD) over this shared noisy wireless channel" and Pg3548, Right Column, Paragraph1, Lines1-2, "The goal is to recover
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at the PS, which then updates the model parameter as in (1) after N time slots", wherein the federated learning which relies on the DSGD to update the global model is functionally equivalent to the claimed invention.)
As to independent Claim 11,
it is a method claim that contains similar limitations of Claim 1 and thus rejected under the same rationale.
As to dependent Claim 13,
it is a method claim that contains similar limitations of Claim 3 and thus rejected under the same rationale.
As to dependent Claim 14,
it is a method claim that contains similar limitations of Claim 4 and thus rejected under the same rationale.
As to dependent Claim 16,
it is a method claim that contains similar limitations of Claim 6 and thus rejected under the same rationale.
As to dependent Claim 17,
it is a method claim that contains similar limitations of Claim 7 and thus rejected under the same rationale.
As to dependent Claim 19,
it is a method claim that contains similar limitations of Claim 9 and thus rejected under the same rationale.
As to dependent Claim 20,
it is a method claim that contains similar limitations of Claim 10 and thus rejected under the same rationale.
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.
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 2 and 12 are rejected under 35 U.S.C. 103 as being unpatentable over Amiri mentioned in Claim 1 in view of Guo et al. (Guo), Non-Patent Literature listed in IDS filed on 08/20/2025, “Analog Gradient Aggregation for Federated Learning Over Wireless Networks: Customized Design and Convergence Analysis”, published on Jan 2021, 14Pages and in further view of Amirnavaei et al. (Amirnavaei), Non-Patent Literature listed in IDS filed on 08/20/2025, “Online Power Control Optimization for Wireless Transmission With Energy Harvesting and Storage”, published on July 2016, 14Pages.
As to dependent Claim 2,
Amiri teaches, as mentioned above, all the limitations of Claim 1 such that the edge node is configured to receive plurality of signal vectors of the locally updated models from the wireless devices to update its global model, then transmits the updated version back to the wireless devices. Also, Amiri discloses the uplink and downlink in the federated learning architecture.
Amiri, however, does not teach the edge node of Claim 1, wherein: the processing circuitry is further configured to:
initialize at least one of a first step-size parameter, a second step-size parameter, and a power regularization factor, the plurality of updated local models being based at least in part on the initialized at least one of the first step-size parameter, the second step-size parameter, and the power regularization factor; and
the communication interface is further configured to:
transmit the initialized at least one of the first step-size parameter, the second step-size parameter, and the power regularization factor.
In the same field of endeavor, Guo teaches about initializing and transmitting these parameters (Guo,Pg199, Left Column, Lines1-6, "in every communication round, the server first broadcasts w to synchronize all the models in the clients over an error-free shared link. Then, the local gradients with respect to the current model are computed by the clients based on their own data sets. Finally, all the local gradients are aggregated over the wireless channel to update w at the server", wherein every round of the training, the parameter w is synchronized or initialized and broadcasted using the error-free shared link.)
Guo further teaches about one of the first or second step-size parameter as an adaptive learning rate that varies every communication round (Guo, Pg198, Right Column, Lines4-7, "we propose a novel learning rate design for the SGD algorithm, which is adaptive to the quality of the gradient estimation in every communication round", wherein the parameter w can broadcasted to synchronization, this adaptive learning rate can be downlinked as well per round.)
And Guo further teaches that this rate is used in the gradient descent algorithm which is used to update the local models (Guo, Pg197, Abstract, Lines11-15, "the parameters in the transceiver are optimized with the consideration of the nonstationarity in the local gradients based on a simple feedback variable. Moreover, a novel learning rate design is proposed for the stochastic gradient descent algorithm, which is adaptive to the quality of the gradient estimation.)
Amiri and Guo are analogous to the claimed invention as they are from the same field of endeavor of optimizing the federated learning. Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date, to combine the over-the-air(OTA) federated learning framework of Amiri with the adaptive learning rate used in optimizing the federated learning. The motivation is as recited by Guo (Pg197, Abstract, Lines1-8, “This article investigates the analog gradient aggregation (AGA) solution to overcome the communication bottleneck for wireless federated learning applications by exploiting the idea of analog over-the-air transmission. Despite the various advantages, this special transmission solution also brings new challenges to both transceiver design and learning algorithm design due to the nonstationary local gradients and the time varying wireless channels in different communication rounds”) such that using this adaptive learning rate to control the ratio of the data transmitted to avoid the bottleneck situation that happens every round and optimize the machine learning process.
As mentioned above, Amiri and Guo do not teach this third parameter, or the power regularization factor. In the same field of endeavor, Amirnavaei teaches this limitation (Amirnavaei, Pg4892, Left Column, Paragraph2, Lines10-15, "The drift-plus-cost metric is defined by
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which is a weighted sum of the per-slot Lyapunov drift
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and the cost function (i,e., negative of the rate) conditioned on X(t) with V > 0 being the weight", wherein to stabilize a queue while optimizing the time-averaged objective function, it uses V as the weight or the regularization factor) (Amirnavaei, Pg4982, Left Column, Paragraph4, Lines8-11, "We have the following equivalent per-slot optimization problem
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subject to (2)", wherein P3 shows how the factor V actually regulate the online power control) (Guo, Pg4892, Right Column, Paragraph2, Lines2-7, “To ensure the solution p*(t) of P3 is feasible to P1, we need to guarantee SOB Eb(t) satisfies the battery capacity constraint (1). Recall that two parameters A and V are introduced in developing the online power solution P*(t) for P3. We will design the values of A and V to ensure the feasibility”, wherein this factor V is used to strictly control the battery usage in the battery capacity constraint.)
Amiri, Guo and Amirnavaei are analogous to the claimed invention as they are from the same field of endeavor of wireless communication network optimization under time-varying fading channels, specifically aiming to optimize transceiver and power allocation parameters dynamically without requiring prior statistical knowledge of the network states. Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date, to combine the over-the-air(OTA) federated learning framework of Amiri with the adaptive learning rate used in optimizing the federated learning with the Lyapunov-based online power control optimization of Amirnavaei. The motivation is as recited by Amirnavaei (Amirnavaei, Pg4888, Abstract, Lines3-6, “we design an online power control strategy aiming at maximizing the long-term time averaged transmission rate under battery operational constraints for energy harvesting” and Lines17-20, “It not only provides strategic energy conservation through the battery energy control, but also reveals an opportunistic transmission style based on fading condition, both of which improve the long term time-averaged transmission rate”) such that integrating the Lyapunov-based online power control or the factor V with the adaptive learning rate which then send out this information every communication round to strictly control the learning rate according to the battery status of individual devices.
As to dependent Claim 12,
it is a method claim that contains similar limitations of Claim 2 and thus rejected under the same rationale.
Claim 5 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Amiri mentioned in Claim 1 in view of Amirnavaei et al. (Amirnavaei), Non-Patent Literature listed in IDS filed on 08/20/2025, “Online Power Control Optimization for Wireless Transmission With Energy Harvesting and Storage”, published on July 2016, 14Pages
As to dependent Claim 5,
Amiri teaches, as mentioned above, all the limitations of Claim 1 such that the edge node is configured to receive plurality of signal vectors of the locally updated models from the wireless devices to update its global model, then transmits the updated version back to the wireless devices.
Amiri, however, does not teach the edge node of claim 1, wherein
the received plurality of signal vectors is based on at least one updated local virtual queue. In the same field of endeavor, Amirnavaei teaches this limitation (Amirnavaei, Pg4890, Section II, System Model, Lines12-14, "Let Eb(t) denote the energy state of batter (SOB) at the beginning of time slot t", Pg4890, Right Column, Paragraph1, Lines 2-4, ""Let Pmax denote the maximum transmit power that can be drawn from the battery, which should satisfy
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", Pg4891, Left Column, Section III. Power Control Design for Rate Maximization, Lines3-6, "Our objective is to design a power control algorithm for {P(t)} to maximize the long-term time-averaged expected rate, while satisfying the battery operational constraints" and Amirnavaei, Pg4891, Right Column, Subsection B. Online Power control via Lyapunov Optimization, Lines1-7, "We now develop an online power control algorithm to solve P2. Based on Lyapunov optimization [18], we introduce a virtual queue X(t) for the SOB Eb(t) as
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where A is a time-independent constant. It can be shown [18] that keeping stability of the queue X(t) is equivalent to satisfying constraint (6)", wherein the virtual queue introduced by Amirnavaei is to control the transmit power P based on the SOB that is used in the queue to maximize the transmit rate, thereby combining this algorithm into the Amiri's transmitting signal vector architectures would be functionally equivalent to the claimed invention.)
Amiri and Amirnavaei are analogous to the claimed invention as they are from the same field of endeavor of wireless communication network optimization. Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date, to combine the over-the-air(OTA) federated learning framework of Amiri with the Lyapunov-based online power control optimization of Amirnavaei. The motivation is as recited by Amirnavaei (Amirnavaei, Pg4888, Abstract, Lines3-6, “we design an online power control strategy aiming at maximizing the long-term time averaged transmission rate under battery operational constraints for energy harvesting” and Lines17-20, “It not only provides strategic energy conservation through the battery energy control, but also reveals an opportunistic transmission style based on fading condition, both of which improve the long term
time-averaged transmission rate”) such that integrating the Lyapunov-based online power control, the system gains the ability to manage long-term energy constraints and battery status in dynamic fading environments, thereby ensuring the stability and sustainability of the federated learning process over wireless edge devices.
As to dependent Claim 15,
it is a method claim that contains similar limitations of Claim 5 and thus rejected under the same rationale.
Claim 8 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Amiri mentioned in Claim 7 in view of Xia et al. (Xia), Non-Patent Literature, “Fast Convergence Algorithm for Analog Federated Learning”, published on Oct 2020, 6Pages
As to dependent Claim 8,
Amiri teaches, as mentioned above, all the limitations of Claim 7 such that the edge node is configured to recover a noisy version of the global model which has a communication error as a part.
Amiri, however, does not teach the edge node of claim 7, wherein
the communication error is based at least in part on a noise value bounded by a predetermined threshold. In the same field of endeavor, Xia teaches this limitation (Xia, Pg1, Right Column, Paragraph2, Lines12-15, "Furthermore, we establish an error bound in term of the expected loss of the objective function to reveal the impact of channel fading and noise over convergence behavior" and Pg6, Left Column, Equation19, "Since the equivalent noise w~t is independent of the models and zero-mean, the term
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is zero. In addition, the noise also satisfies
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where the last inequality comes from that the definition of alpha_t and Assumption2", wherein Xia discloses the boundary for errors related to the noise over convergence behavior. Also, the inequality in Equation19 indicates that the noise should be within the boundary such that if the noise vector, z, from Amiri of Claim 7 is combined with this predetermined boundary of Xia, it will be functionally equivalent to the claimed invention.)
Amiri and Xia are analogous to the claimed invention as they are from the same field of endeavor of wireless distributed machine learning at the network edge, specifically focusing on optimizing federated learning frameworks over bandwidth-limited wireless fading multiple access channels ulilizing analog over-the-air computation. Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date, to combine the multi-device analog gradient transmission framework of Amiri with the threshold-based device selection and mathematical noise-bounding verification mechanism of Xia. The motivation is as recited by Xia (Xia, Pg1, Abstract, Lines4-8, “To realize efficient analog federated learning over wireless channels, we propose an AirComp-based FedSplit algorithm, where a threshold-based device selection scheme is adopted to achieve reliable local model uploading”) such that to mitigate the communication bottleneck and guarantee stable convergence in wireless analog federated learning, which channel fading and additive noise distort the superimposed signals received at the edge server that severely degrades training performance and can lead to divergence.
As to dependent Claim 18,
it is a method claim that contains similar limitations of Claim 8 and thus rejected under the same rationale.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Lorenzo et al. NPL - “DYNAMIC RESOURCE OPTIMIZATION FOR ADAPTIVE FEDERATED LEARNING AT THE WIRELESS NETWORK EDGE”, published on 2021-06-06, IEEE, 5Pages
Balevi et al. U.S. Patent NO. 2023/0297875-A1
Park et al. Korean Patent NO. 102542901-B1
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/DONG YOON JUNG/Examiner, Art Unit 2145
/CESAR B PAULA/Supervisory Patent Examiner, Art Unit 2145
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