DETAILED ACTION
This Office Action is in response to Applicant’s amendments and arguments submitted on March 10, 2026 for Continuation Application # 18/249,191 filed on April 14, 2023 in which claims 1-30 are presented for examination.
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 .
Status of claims
Claims 1-30 are pending, of which claims 1, 3-14, 16-24, 26-27 and 29-30 are rejected under 35 U.S.C. 103 and also claims 1, 14, 24 and 27 are rejected under 35 U.S.C. 101
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, 14, 24 and 27 are rejected under 35 U.S.C. 101. because the claims are directed to an abstract idea; and because the claims as a whole, considering all claim elements both individually and in combination, do not amount to significantly more than the abstract idea, see Alice Corporation Pty. Ltd. v. CLS Bank International, et al, 573 U.S. (2014). In determining whether the claims are subject matter eligible, the Examiner applies the 2019 USPTO Patent Eligibility Guidelines. (2019 Revised Patent Subject Matter Eligibility Guidance, 84 Fed. Reg. 50, Jan. 7, 2019.)
Step 1: Is the claim to a process, machine, manufacture, or composition of matter? Yes—Claims 1-30 recite a method and apparatus respectively.
The analysis of claims 1, 14, 24 and 27 is as follows:
Step 2A, prong one: Does claims 1, 14, 24 and 27 recite an abstract idea, law of nature or natural phenomenon? Yes—the limitations of 1, 14, 24 and 27 “receiving, from a network entity, downlink signaling that indicates configuration information for reporting a plurality of compressed gradient vectors, each vector of the plurality of compressed gradient vectors associated with a different stage of a multi-stage compression procedure for a local stochastic gradient vector associated with a machine learning algorithm;
identifying each compressed gradient vector of the plurality of compressed gradient vectors based at least in part on the machine learning algorithm at the UE and the configuration information;
transmitting each compressed gradient vector of the plurality of compressed gradient vectors to the base station based at least in part on the configuration information”,
Claims 24 and 27 “transmitting, to a plurality of user equipment (UEs), downlink signaling that indicates configuration information for reporting a plurality of compressed gradient vectors, each vector of the plurality of compressed gradient vectors associated with a different stage of a multi-stage compression procedure for a local stochastic gradient vector associated with a machine learning algorithm;
initiating the machine learning algorithm at the plurality of UEs as part of a federated machine learning procedure; and
receiving, from each of the plurality of UEs, the plurality of compressed gradient vectors based at least in part on the machine learning algorithm at each UE and the configuration information” as drafted, are algorithmic steps based on various processes can be performed by an programming algorithmic of determining the training neural network and the stochastic gradient vector (acts of thinking, decision making). These limitations, therefore fall within the human mind processes group.
Step 2A, prong two: Does the claim recite additional elements that integrate the judicial exception into a practical application? No—the judicial exception is not integrated into a practical application as just stated as related to the technical field of computer science . Although the claim recites that the recited functionality includes “apparatus”, these computer components are recited at a high-level of generality such that it amounts to no more than a mere instructions to apply the exception using generic computer component. In addition, the claim recites 1, 14, 24 and 27 “receiving, from a network entity, downlink signaling that indicates configuration information for reporting a plurality of compressed gradient vectors, each vector of the plurality of compressed gradient vectors associated with a different stage of a multi-stage compression procedure for a local stochastic gradient vector associated with a machine learning algorithm;
identifying each compressed gradient vector of the plurality of compressed gradient vectors based at least in part on the machine learning algorithm at the UE and the configuration information;
transmitting each compressed gradient vector of the plurality of compressed gradient vectors to the base station based at least in part on the configuration information”,
Claims 24 and 27 “transmitting, to a plurality of user equipment (UEs), downlink signaling that indicates configuration information for reporting a plurality of compressed gradient vectors, each vector of the plurality of compressed gradient vectors associated with a different stage of a multi-stage compression procedure for a local stochastic gradient vector associated with a machine learning algorithm;
initiating the machine learning algorithm at the plurality of UEs as part of a federated machine learning procedure; and
receiving, from each of the plurality of UEs, the plurality of compressed gradient vectors based at least in part on the machine learning algorithm at each UE and the configuration information” are mere training neural networks by applying machine learning (i.e., applying algorithmic logic); the computers that perform those functions and the programmable logic are recited at a high level of generality that do not impose a meaningful limitation on the judicial exception and are insufficient to integrate the mental steps into a practical application. Although the claim recites the additional functionality “machine learning from input“, the training and normalization are also recited at a high level of generality and merely generally link to respective technological environments (e.g. training neural network) and therefore likewise amounts to no more than a mere instructions to apply the exception using generic computer components and is insufficient to integrate the steps into a practical application.
Step 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception? No— The recitation in the preamble is insufficient to transform a judicial exception to a patentable invention because the preamble elements are recited at a high level of generality that simply links to a field of use, see MPEP 2106.05(h). The claimed extra-solution of aggregate, mean, variance and grouping is acknowledged to be well-understood, routine, conventional activity (see, e.g., court recognized WURC examples in MPEP 2106.05(d)(II)(i). Similarly, the training, normalization process and determining are also recited at a high level of generality and merely generally link to respective technological environments. The claim thus recites computing components only at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using generic computer components. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept.
Taken alone, their additional elements do not amount to significantly more than the above- identified judicial exception (the abstract idea). Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation.
For the reasons above, claims 1, 14, 24 and 27 are rejected as being directed to non-patentable subject matter under §101.
Claim Interpretation - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
Use of the word “means” (or “step for”) in a claim with functional language creates a rebuttable presumption that the claim element is to be treated in accordance with 35 U.S.C. 112(f) (pre-AIA 35 U.S.C. 112, sixth paragraph). The presumption that 35 U.S.C. 112(f) (pre-AIA 35 U.S.C. 112, sixth paragraph) is invoked is rebutted when the function is recited with sufficient structure, material, or acts within the claim itself to entirely perform the recited function.
Absence of the word “means” (or “step for”) in a claim creates a rebuttable presumption that the claim element is not to be treated in accordance with 35 U.S.C. 112(f) (pre-AIA 35 U.S.C. 112, sixth paragraph). The presumption that 35 U.S.C. 112(f) (pre-AIA 35 U.S.C. 112, sixth paragraph) is not invoked is rebutted when the claim element recites function but fails to recite sufficiently definite structure, material or acts to perform that function.
Claim elements in this application that use the word “means” (or “step for”) are presumed to invoke 35 U.S.C. 112(f) except as otherwise indicated in an Office action. Similarly, claim elements that do not use the word “means” (or “step for”) are presumed not to invoke 35 U.S.C. 112(f) except as otherwise indicated in an Office action.
Claim limitations in claims 24-30 have been interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because they use a generic placeholder “means for” coupled with functional language without reciting sufficient structure to achieve the function. Furthermore, the generic placeholder is not preceded by a structural modifier. Specifically Examiner notes the following language interpreted in this manner:
Claims 24-30 limitation “means for”
Since the claim limitation(s) invokes 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, claim(s) 24-30 have been interpreted to cover the corresponding structure described in the specification that achieves the claimed function, and equivalents thereof.
Fig. 3, Fig. 4 and Fig. 5, also paragraph 0077-paragraph 0086
If applicant wishes to provide further explanation or dispute the examiner’s interpretation of the corresponding structure, applicant must identify the corresponding structure with reference to the specification by page and line number, and to the drawing, if any, by reference characters in response to this Office action.
If applicant does not intend to have the claim limitation(s) treated under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112 , sixth paragraph, applicant may amend the claim(s) so that it/they will clearly not invoke 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, or present a sufficient showing that the claim recites/recite sufficient structure, material, or acts for performing the claimed function to preclude application of 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
For more information, see MPEP § 2173 et seq. and Supplementary Examination Guidelines for Determining Compliance With 35 U.S.C. 112 and for Treatment of Related Issues in Patent Applications, 76 FR 7162, 7167 (Feb. 9, 2011).
.
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 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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1, 3-14, 16-24, 26-27 and 29-30 are rejected under 35 U.S.C. 103 as being unpatentable over QU X. et al., "Quantization and Knowledge Distillation for Efficient Federated Learning on Edge Devices", 2020 IEEE 22nd International Conference on High Performance Computing and Communications, IEEE 18th International Conference on Smart City, IEEE 6th International Conference on Data Science and Systems (HPCC/SMARTCITY/DSS), IEEE, 14 December 2020, pp.967-972, XP033906799, abstract, SectionsIl., IV. A and B,V.A,figures1,2 (hereinafter ‘Qu’) (IDS 9/25/2024) in view of Abelha Ferreira et al. US 2022/0138498 A1 (hereinafter ‘Abelha’) as applied, and further in view of Alistarh et al. US 2018/0075347 A1 (hereinafter ‘Alistarh’).
As per claim 1, Qu disclose, A method for wireless communication at a user equipment (UE) (Qu: Abstract disclose edge devices, which examiner equates to wireless user equipment and also Introduction disclose mobile devices),comprising:
receiving, from a configuration information for reporting a plurality of compressed gradient vectors (Qu: Introduction Page 967: disclose federated learning settings ‘configuration information’ is used to collect gradient from clients), each vector of the plurality of compressed gradient vectors (Qu: Introduction Page 967: disclose to collect gradient from clients ‘plurality’ and also examiner would discuss about compressed gradient vectors in secondary art below) associated with a different stage of a multi-stage compression (Qu: B. Model Compression Page 968: disclose compression pipeline using pruning. Weight sharing and Huffman coding ‘multi-stage’) procedure for a local stochastic gradient vector associated with a machine learning algorithm (Qu: B. Model Compression Page 968: disclose using procedure stochastic gradient descent for training neural networks);
identifying each compressed gradient vector of the plurality of compressed gradient vectors based at least in part on the machine learning algorithm at the UE and the configuration information (Qu: B. Adaptive Quantized Federated Average Algorithm Page 969: disclose federated learning, the centralized server is in charge of collecting gradients from clients, averaging collected gradients and distributing the average gradients);
transmitting each compressed gradient vector of the plurality of compressed gradient vectors to the base station based at least in part on the configuration information (Qu: B. Federated Knowledge Distillation (FKD) Page 969: disclose transfers ‘transmitting’ knowledge from the teacher model to student models).
It is noted, however, Qu did not specifically detail the aspects of
compressed gradient vectors as recited in claim 1.
On the other hand, Abelha achieved the aforementioned limitations by providing mechanisms of
compressed gradient vectors (Abelha: paragraph 0030: disclose sign compression reduces the amount of information being transmitted, eliminates the need for client node data to be transmitted out of the client node, and results in a similar or at least acceptable level of prediction accuracy as the actual gradient vector having been sent).
The motivation for doing so would have been for compression switching during model training (Abelha: paragraph 0002).
It is noted, however, neither Qu nor Abelha specifically detail the aspects of
network entity, downlink signaling that indicates configuration information for reporting a plurality of compressed gradient vectors as recited in claim 1.
On the other hand, Alistarh achieved the aforementioned limitations by providing mechanisms of
network entity, downlink signaling that indicates configuration information for reporting a plurality of compressed gradient vectors (Alistarh: paragraph 0030: disclose wireless network card ‘network entity’ enable encoded data to be sent between the peers ‘downlink signal’ to compress the stochastic gradients ‘compressed gradient vectors’).
The motivation for doing so would have been to train the neural network because of the huge amount of computational work (Alistarh: paragraph 0002).
Qu, Alistarh and Abelha are analogous art because they are from the “same field of endeavor” and both from the same “problem-solving area”. Namely, they are both from the field of “Training Neural Network Systems”.
It would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to combine the systems of Qu, Alistarh and Abelha because they are both directed to training neural network systems and both are from the same field of endeavor. The skilled person would therefore regard it as a normal option to include the restriction features of Abelha and Alistarh with the method described by Qu in order to solve the problem posed.
Therefore, it would have been obvious to combine Abelha and Alistarh with Qu to obtain the invention as specified in instant claim 1.
As per claim 3, most of the limitations of this claim have been noted in the rejection of claims 1 and 2 above. In addition, Qu disclose, the one or more partitioning parameters indicate a number of blocks of a stochastic gradient that are to be included in the local stochastic gradient vector, a vector length of each block of the number of blocks, or any combinations thereof; and the plurality of quantization codebooks include one or more available scalar quantization codebooks for quantizing the norm of the local stochastic gradient vector, one or more available uniform and even Grassmannian quantization codebooks for quantizing each block of the block gradient vector, one or more positive Grassmannian quantization codebooks for quantizing the hinge vector, or any combinations thereof (Qu: B. Model Compression Page 968: disclose using procedure stochastic gradient descent for training neural networks and examiner argues that the prior art teaches stochastic gradient and the remaining limitation is applicant’s preferred algorithmic logic).
As per claim 4, most of the limitations of this claim have been noted in the rejection of claims 1 and 2 above. In addition, Qu disclose, receiving one or more configuration parameters in radio resource control signaling, in a medium access control (MAC) control element, in downlink control information, in one or more higher layer or application layer communications, or any combinations thereof (Qu: B. Model Compression Page 968: disclose using procedure stochastic gradient descent for training neural networks and examiner argues that the prior art teaches stochastic gradient and the remaining limitation is applicant’s preferred algorithmic logic).
As per claim 5, most of the limitations of this claim have been noted in the rejection of claims 1 and 2 above. In addition, Qu disclose, selecting, based at least in part on the configuration information and the local stochastic gradient vector, a first partitioning parameter of the one or more partitioning parameters, one or more quantization codebooks of the plurality of quantization codebooks, and a first bit allocation scheme parameter of the one or more bit allocation scheme parameters; and
transmitting, to the base station, an indication of the selected first partitioning parameter, the one or more quantization codebooks, and the first bit allocation scheme parameter using a set of bits that is configured by the configuration information (Qu: B. Model Compression Page 968: disclose using procedure stochastic gradient descent for training neural networks and examiner argues that the prior art teaches stochastic gradient and the remaining limitation is applicant’s preferred algorithmic logic).
As per claim 6, most of the limitations of this claim have been noted in the rejection of claims 1, 2 and 5 above. In addition, Qu disclose, wherein the set of bits is explicitly indicated by the base station or identified based at least in part on an uplink resource for reporting the plurality of compressed gradient vectors, and wherein an order of bits within a payload that provides the plurality of compressed gradient vectors is indicated in the configuration information or is predefined at the UE (Qu: B. Model Compression Page 968: disclose using procedure stochastic gradient descent for training neural networks and examiner argues that the prior art teaches stochastic gradient and the remaining limitation is applicant’s preferred algorithmic logic).
As per claim 7, most of the limitations of this claim have been noted in the rejection of claim 1 above. In addition, Qu disclose, wherein the machine learning algorithm provides a plurality of rounds of compressed gradient vector reporting, and wherein the configuration information is provided separately for each of the plurality or rounds, or the configuration information is applied to each of the plurality of rounds (Qu: B. Model Compression Page 968: disclose using procedure stochastic gradient descent for training neural networks and examiner argues that the prior art teaches stochastic gradient and the remaining limitation is applicant’s preferred algorithmic logic).
As per claim 8, most of the limitations of this claim have been noted in the rejection of claim 1 above. In addition, Qu disclose, the plurality of compressed gradient vectors is determined based at least in part on a partitioning parameter for the local stochastic gradient vector, one or more quantization codebooks associated with each of the plurality of compressed gradient vectors, and a payload format for reporting the plurality of compressed gradient vectors, and wherein the partitioning parameter, the one or more quantization codebooks, and the payload format are provided with the configuration information, are selected from a set of available parameters, codebooks, and payload formats that are provided by the base station, or are determined entirely or partly by the UE (Qu: B. Model Compression Page 968 and V. Evaluations page 970: disclose using procedure stochastic gradient descent for training neural networks and examiner argues that the prior art teaches stochastic gradient and the remaining limitation is applicant’s preferred algorithmic logic).
As per claim 9, most of the limitations of this claim have been noted in the rejection of claim 1 above. In addition, Qu disclose, wherein the configuration information further indicates a format for a block-quantized gradient report that includes a plurality of parts for reporting the plurality of compressed gradient vectors, and a quantity of bits in each of the plurality of parts (Qu: V. Evaluations Page 971: disclose quantized federated learning and also disclose three quantization levels: 4bits, 8bits and 16bits).
As per claim 10, most of the limitations of this claim have been noted in the rejection of claims 1 and 9 above. In addition, Qu disclose, wherein at least one of the plurality of parts has a different quantity of bits than one or more other of the plurality of parts, and wherein the quantity of bits of each of the plurality of parts is provided by the configuration information or is predefined at the UE (Qu: Abstract disclose edge devices, which examiner equates to wireless user equipment and also Introduction disclose mobile devices. Examiner argues that the mobile devices and edge devices have plurality of parts).
As per claim 11, most of the limitations of this claim have been noted in the rejection of claims 1 and 9 above. In addition, Qu disclose, wherein the block-quantized gradient report includes a first part that indicates a quantized value of a norm of the local stochastic gradient vector that is based on a first bit allocation for an associated scalar quantizer, a second part that indicates quantized values of a plurality of normalized block gradients of the local stochastic gradient vector that are based on a second bit allocation for a uniform and even Grassmannian quantizer, and a third part that indicates quantized values of a hinge vector associated with the plurality of normalized block gradients that are based on a third bit allocation for a positive Grassmannian quantizer (Qu: B. Model Compression Page 968 and V. Evaluations page 970: disclose using procedure stochastic gradient descent for training neural networks and examiner argues that the prior art teaches stochastic gradient and the remaining limitation is applicant’s preferred algorithmic logic. Examiner argues that the limitation citing positive Grassmannian quantizer is just applying the quantizer).
As per claim 12, most of the limitations of this claim have been noted in the rejection of claim 1 above. In addition, Qu disclose, receiving, from the base station, an indication of one or more uplink resources for transmission of each of the plurality of compressed gradient vectors, wherein the indication is provided in one or more of uplink control information, a medium access control (MAC) control element, in radio resource control signaling, in one or more upper layer messages, or any combinations thereof, and wherein the plurality of compressed gradient vectors are transmitted using one or more different uplink resources provided in one or more different uplink grants (Qu: Abstract disclose edge devices, which examiner equates to wireless user equipment and also Introduction disclose mobile devices. Examiner argues that the mobile devices and edge devices have plurality of parts and Qu: B. Federated Knowledge Distillation (FKD) Page 969: disclose transfers ‘transmitting’ knowledge from the teacher model to student models).
As per claim 13, most of the limitations of this claim have been noted in the rejection of claims 1 and 12 above. In addition, Qu disclose, the one or more different uplink grants include a plurality of configured uplink grants for uplink shared channel transmissions, and wherein different compressed gradient vectors of the plurality of compressed gradient vectors are transmitted using different configured uplink grants of the plurality of configured uplink grants, and wherein the plurality of configured uplink grants are each associated with different compressed gradient vectors based at least in part on an indication provided in the configuration information, a predefined association at the UE, or based on a determination made at the UE and reported to the base station (Qu: Abstract disclose edge devices, which examiner equates to wireless user equipment and also Introduction disclose mobile devices. Examiner argues that the mobile devices and edge devices have plurality of parts and Qu: B. Federated Knowledge Distillation (FKD) Page 969: disclose transfers ‘transmitting’ knowledge from the teacher model to student models).
As per claim 14, Qu disclose, A method for wireless communication at a base station (Qu: Abstract disclose edge devices, which examiner equates to base station and also Introduction disclose mobile devices), comprising:
transmitting, to a plurality of user equipment (UEs) (Qu: B. Federated Knowledge Distillation (FKD) Page 969: disclose transfers ‘transmitting’ knowledge from the teacher model to student models), configuration information for reporting a plurality of compressed gradient vectors (Qu: Introduction Page 967: disclose federated learning settings ‘configuration information’ is used to collect gradient from clients), each vector of the plurality of compressed gradient vectors associated with a different stage of a multi-stage compression (Qu: B. Model Compression Page 968: disclose compression pipeline using pruning. Weight sharing and Huffman coding ‘multi-stage’) procedure for a local stochastic gradient vector associated with a machine learning algorithm (Qu: B. Model Compression Page 968: disclose using procedure stochastic gradient descent for training neural networks);
initiating the machine learning algorithm at the plurality of UEs as part of a federated machine learning procedure (Qu: B. Adaptive Quantized Federated Average Algorithm Page 969: disclose federated learning, the centralized server is in charge of collecting gradients from clients, averaging collected gradients and distributing the average gradients); and
receiving, from each of the plurality of UEs (Qu: Introduction Page 967: disclose learning settings, a centralized server ‘UE’s’), the plurality of compressed gradient vectors based at least in part on the machine learning algorithm at each UE and the configuration information (Qu: Introduction Page 967: disclose to collect gradient from clients ‘plurality’ and also examiner would discuss about compressed gradient vectors in secondary art below).
It is noted, however, Qu did not specifically detail the aspects of
compressed gradient vectors as recited in claim 14.
On the other hand, Abelha achieved the aforementioned limitations by providing mechanisms of
compressed gradient vectors (Abelha: paragraph 0030: disclose sign compression reduces the amount of information being transmitted, eliminates the need for client node data to be transmitted out of the client node, and results in a similar or at least acceptable level of prediction accuracy as the actual gradient vector having been sent).
It is noted, however, neither Qu nor Abelha specifically detail the aspects of
network entity, downlink signaling that indicates configuration information for reporting a plurality of compressed gradient vectors as recited in claim 14.
On the other hand, Alistarh achieved the aforementioned limitations by providing mechanisms of
network entity, downlink signaling that indicates configuration information for reporting a plurality of compressed gradient vectors (Alistarh: paragraph 0030: disclose wireless network card ‘network entity’ enable encoded data to be sent between the peers ‘downlink signal’ to compress the stochastic gradients ‘compressed gradient vectors’).
As per claim 16, most of the limitations of this claim have been noted in the rejection of claims 14 and 15 above. In addition, Qu disclose, the one or more partitioning parameters indicate a number of blocks of a stochastic gradient that are to be included in the local stochastic gradient vector, a vector length of each block of the number of blocks, or any combinations thereof; and the plurality of quantization codebooks include one or more available scalar quantization codebooks for quantizing the norm of the local stochastic gradient vector, one or more available uniform and even Grassmannian quantization codebooks for quantizing each block of the block gradient vector, one or more positive Grassmannian quantization codebooks for quantizing the hinge vector, or any combinations thereof (Qu: B. Model Compression Page 968: disclose using procedure stochastic gradient descent for training neural networks and examiner argues that the prior art teaches stochastic gradient and the remaining limitation is applicant’s preferred algorithmic logic).
As per claim 17, most of the limitations of this claim have been noted in the rejection of claims 14 and 15 above. In addition, Qu disclose, configuring the plurality of UEs to select, based at least in part on the configuration information and the associated local stochastic gradient vector, a first partitioning parameter of the one or more partitioning parameters, one or more quantization codebooks of the plurality of quantization codebooks, and a first bit allocation scheme parameter of the one or more bit allocation scheme parameters: configuring the plurality of UEs to report the selected parameter, codebooks and allocation scheme parameter, using a set of bits; and receiving, from the plurality of UEs, the set of bits that provide associated indications of the selected first partitioning parameter, the one or more quantization codebooks, and the first bit allocation scheme parameter (Qu: B. Model Compression Page 968: disclose using procedure stochastic gradient descent for training neural networks and examiner argues that the prior art teaches stochastic gradient and the remaining limitation is applicant’s preferred algorithmic logic).
As per claim 18, most of the limitations of this claim have been noted in the rejection of claim 14 above. In addition, Qu disclose, the plurality of compressed gradient vectors are determined based at least in part on a partitioning parameter for the local stochastic gradient vector, one or more quantization codebooks associated with each of the plurality of compressed gradient vectors, and a payload format for reporting the plurality of compressed gradient vectors, and wherein the partitioning parameter, the one or more quantization codebooks, and the payload format are provided with the configuration information, are selected from a set of available parameters, codebooks, and payload formats that are provided by the base station, or are determined entirely or partly by the UE (Qu: B. Model Compression Page 968: disclose using procedure stochastic gradient descent for training neural networks and examiner argues that the prior art teaches stochastic gradient and the remaining limitation is applicant’s preferred algorithmic logic).
As per claim 19, most of the limitations of this claim have been noted in the rejection of claim 14 above. In addition, Qu disclose, wherein the configuration information further indicates a format for a block-quantized gradient report that includes a plurality of parts for reporting the plurality of compressed gradient vectors, and a quantity of bits in each of the plurality of parts (Qu: V. Evaluations Page 971: disclose quantized federated learning and also disclose three quantization levels: 4bits, 8bits and 16bits).
As per claim 20, most of the limitations of this claim have been noted in the rejection of claims 14 and 19 above. In addition, Qu disclose, wherein at least one of the plurality of parts has a different quantity of bits than one or more other of the plurality of parts, and wherein the quantity of bits of each of the plurality of parts is provided by the configuration information or is predefined at the UE (Qu: Abstract disclose edge devices, which examiner equates to wireless user equipment and also Introduction disclose mobile devices. Examiner argues that the mobile devices and edge devices have plurality of parts).
As per claim 21, most of the limitations of this claim have been noted in the rejection of claims 14 and 19 above. In addition, Qu disclose, wherein the block-quantized gradient report includes a first part that indicates a quantized value of a norm of the local stochastic gradient vector that is based on a first bit allocation for an associated scalar quantizer, a second part that indicates quantized values of a plurality of normalized block gradients of the local stochastic gradient vector that are based on a second bit allocation for a uniform and even Grassmannian quantizer, and a third part that indicates quantized values of a hinge vector associated with the plurality of normalized block gradients that are based on a third bit allocation for a positive Grassmannian quantizer (Qu: B. Model Compression Page 968 and V. Evaluations page 970: disclose using procedure stochastic gradient descent for training neural networks and examiner argues that the prior art teaches stochastic gradient and the remaining limitation is applicant’s preferred algorithmic logic. Examiner argues that the limitation citing positive Grassmannian quantizer is just applying the quantizer).
As per claim 22, most of the limitations of this claim have been noted in the rejection of claim 14 above. In addition, Qu disclose, transmitting, to each of the plurality of UEs, an indication of one or more uplink resources for transmission of each of the plurality of compressed gradient vectors, wherein the indication is provided in one or more of uplink control information, a medium access control (MAC) control element, in radio resource control signaling, in one or more upper layer messages, or any combinations thereof, and wherein the plurality of compressed gradient vectors are transmitted using one or more different uplink resources provided in one or more different uplink grants (Qu: Abstract disclose edge devices, which examiner equates to wireless user equipment and also Introduction disclose mobile devices. Examiner argues that the mobile devices and edge devices have plurality of parts and Qu: B. Federated Knowledge Distillation (FKD) Page 969: disclose transfers ‘transmitting’ knowledge from the teacher model to student models).
As per claim 23, most of the limitations of this claim have been noted in the rejection of claims 14 and 22 above. In addition, Qu disclose, the one or more different uplink grants include a plurality of configured uplink grants for uplink shared channel transmissions, and wherein different compressed gradient vectors of the plurality of compressed gradient vectors are transmitted using different configured uplink grants of the plurality of configured uplink grants, and wherein the plurality of configured uplink grants are each associated with different compressed gradient vectors based at least in part on an indication provided in the configuration information, a predefined association at the UE, or based on a determination made at the UE and reported to the base station (Qu: Abstract disclose edge devices, which examiner equates to wireless user equipment and also Introduction disclose mobile devices. Examiner argues that the mobile devices and edge devices have plurality of parts and Qu: B. Federated Knowledge Distillation (FKD) Page 969: disclose transfers ‘transmitting’ knowledge from the teacher model to student models).
As per claim 24, Qu disclose, An apparatus (Qu: Fig. 1: disclose a local computation phone) for wireless communication at a user equipment (UE),comprising: remaining limitations in this claim 24 are similar to the limitations in claim 1. Therefore, examiner rejects these remaining limitations under the same rationale as limitations rejected under claim 1.
As per claim 26, limitations of this claim are similar to claim 3. Therefore, examiner rejects claim 26 limitations under the same rationale as claim 3.
As per claim 27, Qu disclose, An apparatus for wireless communication (Qu: Fig. 1: disclose a local computation phone) at a base station, comprising: remaining limitations in this claim 27 are similar to the limitations in claim 14. Therefore, examiner rejects these remaining limitations under the same rationale as limitations rejected under claim 14.
As per claim 29, limitations of this claim are similar to claim 16. Therefore, examiner rejects claim 29 limitations under the same rationale as claim 16.
As per claim 30, limitations of this claim are similar to claim 17. Therefore, examiner rejects claim 30 limitations under the same rationale as claim 17.
Response to Arguments
Applicant's arguments filed on March 10, 2026 regarding 35 U.S.C. 101 have been fully considered but they are not persuasive.
Claims 1, 14, 24 and 27 are directed to the abstract idea of receiving, from a network entity, downlink signaling that indicates configuration information for reporting a plurality of compressed gradient vectors, each vector of the plurality of compressed gradient vectors associated with a different stage of a multi-stage compression procedure for a local stochastic gradient vector associated with a machine learning algorithm;
identifying each compressed gradient vector of the plurality of compressed gradient vectors based at least in part on the machine learning algorithm at the UE and the configuration information; and
transmitting each compressed gradient vector of the plurality of compressed gradient vectors to the network entity based at least in part on the configuration information. The claim(s) do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional computer elements such as different stage of a multi-stage compression procedure for a local stochastic gradient vector associated with a machine learning algorithm, which are recited at a high level of generality, provide conventional computer functions that do not add meaningful limits to practicing the abstract idea.
Claims 1, 14, 24 and 27 are directed to an abstract recited in the form of a generalized invention which can be performed in a human mind with a pencil and paper. The particular claimed elements which constitute the abstract idea include
receiving, from a network entity, downlink signaling that indicates configuration information for reporting a plurality of compressed gradient vectors, each vector of the plurality of compressed gradient vectors associated with a different stage of a multi-stage compression procedure for a local stochastic gradient vector associated with a machine learning algorithm;
identifying each compressed gradient vector of the plurality of compressed gradient vectors based at least in part on the machine learning algorithm at the UE and the configuration information; and
transmitting each compressed gradient vector of the plurality of compressed gradient vectors to the network entity based at least in part on the configuration information.
A different stage of a multi-stage compression procedure for a local stochastic gradient vector associated with a machine learning algorithm, based on the broadest reasonable interpretation in view of the specification.
Mathematical relationships and algorithms have been found by the courts (e.g. Benson, Flook, Diehr, Grams) to be abstract ideas. For example, in Benson, a mathematical procedure for converting one form of numerical representation to another was found to be an exception, as was an algorithm for calculating parameters indicating an abnormal condition in Grams. The concept described in claims 1, 14, 24 and 27 does not meaningfully differ from those found by the courts to constitute mathematical algorithms.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements when considered both individually and as an ordered combination do not amount to significantly more than the abstract idea. Additional elements recited in the claim include the limitations: a computer-readable medium storing computer-executable instructions that when executed by a computer cause the computer to perform the method; identifying each compressed gradient vector of the plurality of compressed gradient vectors based at least in part on the machine learning algorithm at the UE and the configuration information, can also be interpreted as algorithmic logic. These limitations are directed to realizing the mathematical algorithm in a computer system. Executing the using a model to transmitting compressed gradient vector is little more than a broad recitation of generic use of a computer (i.e., executing). Providing the machine learning algorithm is at most insignificant post solution activity of identifying and transmitting each compressed gradient vector. The preamble's recitation of a "computer-readable medium" and a "computer" are recited at a high level of generality and are recited as performing generic computer functions routinely used in computer applications. Generic computer components recited as performing generic computer functions that are well-understood, routine and conventional activities amount to no more than implementing the abstract idea with a computerized system. Further, the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because merely providing a result and executing the algorithm by a computer is akin to adding the words “apply it” with a computer in conjunction with the abstract idea. Such limitations are not enough to add significantly more to the method of business rules of grouping into incidents, which represent mathematical relationships and algorithms.
Considering all the limitations in combination, the claimed additional computer elements do not show any inventive concept in applying the mathematical operations, such as improving the performance of a computer or any other technology. The steps describe nothing more than a computer’s basic function of identifying and transmitting compressed gradient vector in part of the machine learning algorithm, and do not meaningfully limit the performance of the calculation. Therefore, the claim does not amount to significantly more than the abstract idea itself.
Applicant’s arguments with respect to claims 1, 14, 24 and 27 regarding 35 U.S.C. 103 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Allowable Subject Matter
Claims 2, 15, 25 and 28 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US Pub. US 2021/0089899 A1 disclose “FEDERATED LEARNING SYSTEM AND METHOD FOR DETECTING FINANCIAL CRIME BEHAVIOR ACROSS PARTICIPATING ENTITIES”
US Pub. US 2018/0075347 A1 disclose “EFFICIENT TRAINING OF NEURAL NETWORKS”
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/PAVAN MAMILLAPALLI/
Primary Examiner, Art Unit 2159
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