DETAILED ACTION
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 10/16/2025 has been entered.
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, 2, 5, 6, 11, 12, 13, 16, 17, 20, 21, 26, 28 – 32, and 34 - 37 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
As to claim 1,
Step 2A, Prong One
The claim recites in part:
encode said at least one second tensor in a signal using a Low Displacement Rank (LDR) based approximation of said at least one second tensor, said LDR based approximation of said at least one second tensor having a lower dimension than said first tensor
Under the broadest reasonable interpretation, these limitations are process steps that cover a mathematical relationship, mathematical formula, or algorithm, which is identified as an abstract idea. The recited encoding recites a data processing step involving organization and manipulation of data. Specifically a Low Displacement Rank (LDR) based approximation is an algorithmic technique used in numerical analysis, data compression, and scientific computing to efficiently represent large matrices or datasets.
Accordingly, at Step 2A, Prong One, the claim is directed to an abstract idea.
Step 2A, Prong Two
The judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of:
transmit and/or store the signal
which amounts to extra-solution activity of gathering data for use in the claimed process. As described in MPEP 2106.05(g), limitations that amount to merely adding insignificant extra-solution activity to a judicial exception do not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application.
The claim further recites:
reshape a first tensor of weights of a layer of a deep neural network into at least one second tensor
which is recited at a high-level of generality with no detail of the training process and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f))
The claim further recites a device and at least one processor are recited at a high-level of generality and amounts to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
In addition, the recitation of tensor, deep neural network, Low Displacement Rank (LDR), and dimension amounts to generally linking the use of the judicial exception to a particular environment of field of use (See MPEP 2106.05(h)). As such, the claim does not integrate the judicial exception into a practical application.
Accordingly, at Step 2A, Prong Two, the additional elements individually or in combination do no integrate the judicial exception into a practical application.
Step 2B
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the additional elements of:
transmit and/or store the signal
are recited at a high level of generality and amounts to extra-solution activity of receiving data i.e. pre-solution activity of gathering data for use in the claimed process. The courts have found limitations directed to obtaining information electronically, recited at a high level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping," and "storing and retrieving information in memory").
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception. The limitations:
reshape a first tensor of weights of a layer of a deep neural network into at least one second tensor
which is recited at a high-level of generality with no detail of the training process and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f))
The device and at least one processor which are recited at a high-level of generality and amounts to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
The recitation of tensor, deep neural network, Low Displacement Rank (LDR), and dimension amounts to generally linking the use of the judicial exception to a particular environment of field of use (See MPEP 2106.05(h)).
Accordingly, at Step 2B the additional elements individually or in combination do not amount to significantly more than the judicial exception.
Claim 2 has similar limitations as claim 1. Therefore, the claim is rejected for the same reasons as above.
As to claim 5,
Step 2A, Prong One
The claim does not recite an abstract idea or any other judicial exception and therefore passes Step 2A, Prong of the Alice/Mayo analysis.
Step 2A, Prong Two
The judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of:
obtain a plurality of 1-D vectors by vectorizing said first tensor and obtain said at least one second tensor by stacking said vectors as rows or columns of said at least one second tensor
which is recited at a high-level of generality with no detail of the training process and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f))
In addition, the recitation of 1-D vectors amounts to generally linking the use of the judicial exception to a particular environment of field of use (See MPEP 2106.05(h)). As such, the claim does not integrate the judicial exception into a practical application.
Accordingly, at Step 2A, Prong Two, the additional elements individually or in combination do no integrate the judicial exception into a practical application.
Step 2B
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the additional elements of:
obtain a plurality of 1-D vectors by vectorizing said first tensor and obtain said at least one second tensor by stacking said vectors as rows or columns of said at least one second tensor
which is recited at a high-level of generality with no detail of the training process and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f))
The recitation of 1-D vectors amounts to generally linking the use of the judicial exception to a particular environment of field of use (See MPEP 2106.05(h)).
Accordingly, at Step 2B the additional elements individually or in combination do not amount to significantly more than the judicial exception.
As to claim 6,
Step 2A, Prong One
The claim recites in part:
further configured to encode in at least one single at least one information representative of:
a size of said first tensor
said at least one second tensor
a number of input channels of said layer
a number of output channels of said layer
a size of at least one filter of said layer
a bias vector of said layer
Under the broadest reasonable interpretation, these limitations are process steps that cover a mathematical relationship, mathematical formula, or algorithm, which is identified as an abstract idea. The recited encoding recites a data processing step involving organization and manipulation of data.
Accordingly, at Step 2A, Prong One, the claim is directed to an abstract idea.
Step 2A, Prong Two
Further the claim does not include additional elements that integrate this abstract idea into a practical application. “Encoding” is performed using generic computer components performing their typical functions and does not provide a meaningful technological improvement.
Accordingly, at Step 2A, Prong Two, the additional elements individually or in combination do no integrate the judicial exception into a practical application.
Step 2B
Nothing in the claim adds “significantly more” beyond generic computing.
Accordingly, at Step 2B the additional elements individually or in combination do not amount to significantly more than the judicial exception.
As to claim 11,
Step 2A, Prong One
The claim recites in part:
wherein said 1-D vectors have a size f1f2n1, and said at least one second tensor has a size n2 x f1f2n1 where:
n1 is a number of input channels of said layer
n2 is a number of output channels of said layer
f1 x f2 is the size of at least one filter of said layer
Under the broadest reasonable interpretation, these limitations are process steps that cover a mathematical relationship, mathematical formula, or algorithm, which is identified as an abstract idea.
Accordingly, at Step 2A, Prong One, the claim is directed to an abstract idea.
Step 2A, Prong Two
Further the claim does not include additional elements that integrate this abstract idea into a practical application. The equation performed using generic computer components performing their typical functions and does not provide a meaningful technological improvement.
Accordingly, at Step 2A, Prong Two, the additional elements individually or in combination do no integrate the judicial exception into a practical application.
Step 2B
Nothing in the claim adds “significantly more” beyond generic computing.
Accordingly, at Step 2B the additional elements individually or in combination do not amount to significantly more than the judicial exception.
As to claim 13,
Step 2A, Prong One
The claim recites in part:
further configured to encode in at least one signal an information representative of at least one factor rank of said LDR based approximation
Under the broadest reasonable interpretation, these limitations are process steps that cover a mathematical relationship, mathematical formula, or algorithm, which is identified as an abstract idea. The recited encoding recites a data processing step involving organization and manipulation of data.
Accordingly, at Step 2A, Prong One, the claim is directed to an abstract idea.
Step 2A, Prong Two
Further the claim does not include additional elements that integrate this abstract idea into a practical application. “Encoding” is performed using generic computer components performing their typical functions and does not provide a meaningful technological improvement.
Accordingly, at Step 2A, Prong Two, the additional elements individually or in combination do no integrate the judicial exception into a practical application.
Step 2B
Nothing in the claim adds “significantly more” beyond generic computing.
Accordingly, at Step 2B the additional elements individually or in combination do not amount to significantly more than the judicial exception.
As to claim 16,
Step 2A, Prong One
The claim recites in part:
decode at least one second tensor from the signal using a Low Displacement Rant (LDR) based approximation
Under the broadest reasonable interpretation, these limitations are process steps that cover a mathematical relationship, mathematical formula, or algorithm, which is identified as an abstract idea. The recited decoding recites a data processing step involving organization and manipulation of data. Specifically a Low Displacement Rank (LDR) based approximation is an algorithmic technique used in numerical analysis, data compression, and scientific computing to efficiently represent large matrices or datasets.
Accordingly, at Step 2A, Prong One, the claim is directed to an abstract idea.
Step 2A, Prong Two
The judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of:
receive and/or retrieve a signal
which amounts to extra-solution activity of gathering data for use in the claimed process. As described in MPEP 2106.05(g), limitations that amount to merely adding insignificant extra-solution activity to a judicial exception do not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application.
The claim further recites:
reshape said at least one second tensor into a first tensor of weights of a deep neural network, said at least one second tensor having a lower dimension that said first tensor
which is recited at a high-level of generality with no detail of the training process and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f))
The claim further recites a device and at least one processor are recited at a high-level of generality and amounts to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
In addition, the recitation of tensor, deep neural network, Low Displacement Rank (LDR), and dimension amounts to generally linking the use of the judicial exception to a particular environment of field of use (See MPEP 2106.05(h)). As such, the claim does not integrate the judicial exception into a practical application.
Accordingly, at Step 2A, Prong Two, the additional elements individually or in combination do no integrate the judicial exception into a practical application.
Step 2B
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the additional elements of:
receive and/or retrieve a signal
are recited at a high level of generality and amounts to extra-solution activity of receiving data i.e. pre-solution activity of gathering data for use in the claimed process. The courts have found limitations directed to obtaining information electronically, recited at a high level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping," and "storing and retrieving information in memory").
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception. The limitations:
reshape said at least one second tensor into a first tensor of weights of a deep neural network, said at least one second tensor having a lower dimension that said first tensor
which is recited at a high-level of generality with no detail of the training process and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f))
The device and at least one processor which are recited at a high-level of generality and amounts to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
The recitation of tensor, deep neural network, Low Displacement Rank (LDR), and dimension amounts to generally linking the use of the judicial exception to a particular environment of field of use (See MPEP 2106.05(h)).
Accordingly, at Step 2B the additional elements individually or in combination do not amount to significantly more than the judicial exception.
Claim 17 has similar limitations as claim 16. Therefore, the claim is rejected for the same reasons as above.
Claim 20 has similar limitations as claim 5. Therefore, the claim is rejected for the same reasons as above.
Claim 21 has similar limitations as claim 6. Therefore, the claim is rejected for the same reasons as above.
Claim 26 has similar limitations as claim 11. Therefore, the claim is rejected for the same reasons as above.
Claim 28 has similar limitations as claim 13. Therefore, the claim is rejected for the same reasons as above.
As to claim 29, the limitations “wherein at least one of said at least one representative information is decoded at a layer level” amounts to generally linking the use of the judicial exception to a particular environment of field of use (See MPEP 2106.05(h)).
As to claim 30, the limitations “wherein at least one of said at least one representative information is decoded at a DNN level” amounts to generally linking the use of the judicial exception to a particular environment of field of use (See MPEP 2106.05(h)).
Claim 31 has similar limitations as claim 1. Therefore, the claim is rejected for the same reasons as above.
Claim 32 has similar limitations as claim 1. Therefore, the claim is rejected for the same reasons as above.
Claim 34 has similar limitations as claim 5. Therefore, the claim is rejected for the same reasons as above.
Claim 35 has similar limitations as claim 6. Therefore, the claim is rejected for the same reasons as above.
Claim 36 has similar limitations as claim 11. Therefore, the claim is rejected for the same reasons as above.
Claim 37 has similar limitations as claim 13. Therefore, the claim is rejected for the same reasons as above.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 1, 2, 6, 11, 12, 13, 16, 17, 20, 21, 26, 28 – 32, and 35 - 37 is/are rejected under 35 U.S.C. 103 as being unpatentable over Tate et al (US 2018/0089564) in view of Sainath et al US 10,515,307).
As to claim 1, Tate et al teaches a device (paragraph [0004]…an information processing apparatus) comprising at least one processor (paragraph [0081]…the computer may comprise one or more processors) configured to:
reshape a first tensor of weights of a layer (paragraph [0030]…the weight parameters of the individual layer) of deep neural network (paragraph [0077]…deep neural network) into at least one second tensor (paragraph [0028]… a four dimensional tensor having the size of 3×3×3×64 in the first layer is converted into a three dimensional tensor having a size of 3×3×192); and
encode said at least one second tensor in a signal using an approximation of said at least one second tensor (paragraph [0061]…the parameter encoding unit 102 updates the codebook coefficients in accordance with Expression 3 so that the weight parameter of the layer is approximated (step S307)), said approximation of said at least one second tensor having a lower dimension than said first tensor (Examiner’s Note: The second tensor is a three dimensional tensor and first tensor is a four dimensional tensor, three dimensions is less than four dimensions); and
transmit and/or store the signal (paragraph [0019]… The information processing apparatus further includes a codebook storage 103 which stores a codebook generated by the parameter encoding unit 102 and a codebook coefficient used for reconstruction of parameters).
Sainath et al discloses the claimed invention except for the approximation being a Low Displacement Rank based approximation.
However, Tate et al teaches an approximation being a Low Displacement Rank based approximation (Saimath et al teaches in column 5, lines 5 – 20… In some embodiments, Low displacement rank corresponds to highly structured matrices such as circulant and Toeplitz matrices and their inverses. High displacement rank matrices can be used to model increasingly unstructured matrices. in some examples, the displacement rank can be used to control the computational complexity, storage requirements, and modeling capacity of for a compression scheme. In some examples, the displacement rank can be tuned based on application requirements).
Therefore, it would have been obvious for one having ordinary skill in the art, at the time the invention was made for Tate et al’s approximation to be a Low Displacement Rank based approximation, as in Sainath et al, for the purpose of controlling the computational complexity, storage requirements, and modeling capacity of for a compression scheme.
Claim 2 has similar limitations as claim 1. Therefore, the claim is rejected for the same reasons as above.
As to claim 6, Tate et al teaches the device wherein said at least one processor (paragraph [0081]…the computer may comprise one or more processors) further configured to encode in at least one single at least one information representative (paragraph [0061]…the parameter encoding unit 102 updates the codebook coefficients in accordance with Expression 3 so that the weight parameter of the layer is approximated (step S307)) of:
a size of said first tensor or said at least one second tensor (paragraph [0028]… a four dimensional tensor having the size of 3×3×3×64 in the first layer is converted into a three dimensional tensor having a size of 3×3×192);
a number of input channels of said layer (paragraph [0025]…the number of feature channels of input data);
a number of output channels of said layer (paragraph [0025]… the number of feature channels of output data which is output as a result of the convolution),
a size of at least one filter of said layer or
a bias vector of said layer
As to claim 11, Tate et al in view of Sainath et al discloses the claimed invention except for wherein said 1-D vectors have a size f1f2n1, and said at least one second tensor has a size n2 x f1f2n1 where:
n1 is a number of input channels of said layer
n2 is a number of output channels of said layer
f1 x f2 is the size of at least one filter of said layer
It would have been obvious to one having ordinary skill in the art at the time the invention was made to wherein said 1-D vectors have a size f1f2n1, and said at least one second tensor has a size n2 x f1f2n1, since it has been held that discovering an optimum value of a result effective variable involves only routine skill in the art. In re Boesch, 617 F.2d 272, 205 USPQ 215 (CCPA 1980).
As to claim 13, Saimath et al teaches said at least one processor being further configured to encode in at least one signal an information representative of at least one factor rank of said LDR based approximation (Saimath et al teaches in column 5, lines 5 – 20… In some embodiments, Low displacement rank corresponds to highly structured matrices such as circulant and Toeplitz matrices and their inverses. High displacement rank matrices can be used to model increasingly unstructured matrices. in some examples, the displacement rank can be used to control the computational complexity, storage requirements, and modeling capacity of for a compression scheme. In some examples, the displacement rank can be tuned based on application requirements).
It would have been obvious for said at least one processor being further configured to encode in at least one signal an information representative of at least one factor rank of said LDR based approximation for the same reasons as above.
Claim 16 has similar limitations as claim 1. Therefore, the claim is rejected for the same reasons as above (Examiner’s Note: it is well known in the art that the encoded data is reconstructed and then decoded as taught in Aliper er al (US 2020/0090049)).
Claim 17 has similar limitations as claim 1. Therefore, the claim is rejected for the same reasons as above (Examiner’s Note: it is well known in the art that the encoded data is reconstructed and then decoded as taught in Aliper er al (US 2020/0090049)).
Claim 20 has similar limitations as claim 5. Therefore, the claim is rejected for the same reasons as above.
Claim 21 has similar limitations as claim 6. Therefore, the claim is rejected for the same reasons as above.
Claim 26 has similar limitations as claim 11. Therefore, the claim is rejected for the same reasons as above.
Claim 28 has similar limitations as claim 13. Therefore, the claim is rejected for the same reasons as above.
As to claim 29, Tate et al teaches the device wherein at least one of said at least one representative information is decoded at layer level (paragraph [0019]…the information processing apparatus further includes a codebook storage 103 which stores a codebook generated by the parameter encoding unit 102 and a codebook coefficient used for reconstruction of parameters. The information processing apparatus further includes a parameter reconstruction unit 104 which receives the codebook and the codebook coefficient and which performs approximate reconstruction on the weight parameters of the neural network and a neural network calculator 105 which receives the weight parameters and which performs calculation processes of the neural network) (Examiner’s Note: it is well known in the art that the encoded data is reconstructed and then decoded as taught in Aliper er al (US 2020/0090049)).
As to claim 30, Tate et al teaches the device wherein at least one of said at least one representative information is decoded (paragraph [0019]…the information processing apparatus further includes a codebook storage 103 which stores a codebook generated by the parameter encoding unit 102 and a codebook coefficient used for reconstruction of parameters. The information processing apparatus further includes a parameter reconstruction unit 104 which receives the codebook and the codebook coefficient and which performs approximate reconstruction on the weight parameters of the neural network and a neural network calculator 105 which receives the weight parameters and which performs calculation processes of the neural network) at DNN level (paragraph [0077]…deep neural network) (Examiner’s Note: it is well known in the art that the encoded data is reconstructed and then decoded as taught in Aliper er al (US 2020/0090049))
Claim 31 has similar limitations as claim 1. Therefore, the claim is rejected for the same reasons as above.
Claim 32 has similar limitations as claim 1. Therefore, the claim is rejected for the same reasons as above.
Claim 35 has similar limitations as claim 6. Therefore, the claim is rejected for the same reasons as above.
Claim 36 has similar limitations as claim 11. Therefore, the claim is rejected for the same reasons as above.
Claim 37 has similar limitations as claim 13. Therefore, the claim is rejected for the same reasons as above.
Claim(s) 5 and 34 is/are rejected under 35 U.S.C. 103 as being unpatentable over Tate et al (US 2018/0089564) in view of Sainath et al US 10,515,307) and in further view of Wu et al (US 2019/0287685).
As to claim 4, Tate et al teaches at least one processor (paragraph [0081]…the computer may comprise one or more processors).
Tate et al and Sainath et al both fails to explicitly show/teach wherein said at least one processor being further configured to obtain a plurality of 1-D vectors by vectorizing said first tensor and obtain said at least one second tensor by stacking said vectors as rows or columns of said at least one second tensor.
However, Wu et al teaches at least one processor being further configured to obtain a plurality of 1-D vectors by vectorizing said first tensor and obtain said at least one second tensor by stacking said vectors as rows or columns of said at least one second tensor (paragraph [0044]… the example tensor generator 212 receives the tokenized HPI 211. The example tensor generator 212 receives the tokenized HPI 211 and outputs a tensor 213. In some examples, the tensor generator 212 converts each token of the tokenized HPI 211 into a vector. In some examples, the vector is a binary sparse vector in which one dimension (e.g., one index) has a value of “1” and each of the other dimensions are “0.” In some examples, each dimension of the vector represents a different possible token. For example, if the tokenized HPI 211 can be composed from any number of 50,000 different tokens, each vector has 50,000 different dimensions. In this example, if the tokenized HPI 211 is one hundred tokens in length, the tensor generator 212 vectorizes each of the one hundred tokens into a vector. In some examples, the example tensor 213 includes each of these vectors concatenated (e.g., “stacked”, appended, etc.) together. In some examples, to save memory, the tensor generator 212 vectorizes each token into a scalar value representing the would-be index of sparse value of the associated vector. In this example, the tensor 213 is a vector of these scalar values).
Therefore, it would have been obvious for one having ordinary skill in the art, at the time the invention was made for Wu et al’s at least one processor being further configured to obtain a plurality of 1-D vectors by vectorizing said first tensor and obtain said at least one second tensor by stacking said vectors as rows or columns of said at least one second tensor, as in Wu et al, for the purpose of efficiently classify
Claim 34 has similar limitations as claim 5. Therefore, the claim is rejected for the same reasons as above.
Response to Arguments
Applicant's arguments filed 10/16/2025 have been fully considered but they are not persuasive.
Claim Rejections - 35 USC § 101
The 101 Rejection still has not been overcome. The claims are abstract and the steps in the claims can be completed with a mental process and/or generic computer components. Additionally, the steps in the claims do not describe an improvement of technology in any way.
The applicant argues:
It is agreed that the claims relate to statutory subject matter at Step 1, however, the Office Action maintains that the Application recites an abstract idea under revised step 2A, prong 1. Applicant respectfully submits however that even if this were assumed, for the sake of argument only (and Applicant does not concede this), the claims clearly recite additional elements that integrate the alleged abstract idea into a practical application under step 2A, prong 2.
The additional elements of “reshape a first tensor of weights of a layer of deep neural network into at least one second tensor” do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. MPEP 2106.5(h)(vi): Limiting the abstract idea of collecting information analyzing it, and displaying certain results of the collection and analysis to data related to the electric power grid, because limiting application of the abstract idea to power-grid monitoring is simply an attempt to limit the use of the abstract idea to a particular technological environment, Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350, 1354, 119 USPQ2d 1739, 1742 (Fed. Cir. 22016).
The applicant argues:
It is unambiguous from these passages that the claimed invention relates to an improvement in the functioning of a computer — e.g., compression of a deep neural network using LDR based approximation of weight tensors. Further, it is also clear that the claim includes the components or steps of the invention that provide the improvement described in the specification. For example, the claim recites the following, which clearly provides the improvement. Here, the improvement discussed in the section of the specification quoted above is clearly and unambiguously reflected in the claim language.
The examiner disagrees. The claims broadly recite applying low-dimensional representation (LDR)-based approximations to neural network weight tensors without specifying how much approximations are technically implemented in a manner that improves computer functionality itself. Rather, the claims are directed to an abstract mathematical concept – namely, data compression and approximation techniques – applied to a generic computing environment.
The claims fail to recite any specific hardware modifications, unconventional processing techniques, or non-generic computer components that effect that alleged improvement. Instead, the claims merely instruct a generic computer to perform mathematical operations on data, which amounts to an abstract idea implemented using conventional computer technology.
Accordingly, the claims do not integrate the alleged abstract idea into a practical application, nor to the recite an inventive concept that transforms the nature of the claim into patent-eligible subject matter. The recitation of neural networks and tensor approximations, without more, does not render the claims directed to a technological improvement.
The applicant argues:
Applicant respectfully submits that even if it were assumed, for the sake of argument only (and Applicant does not concede this) that “...reshape a first tensor...” and “...encode said at least one second tensor...” were abstract, it is clear that claim 1 both includes unambiguously non-abstract elements (e.g., processor configured to reshape... encode... and transmit and/or store...”) and that these elements, in combination with the allegedly abstract elements, clearly amount to significantly more than the alleged judicial exception. It is clear that the claim elements in combination relates to an improvement in the functioning of a computer — e.g., compression of an artificial neural network using LDR based approximation of weight tensors.
The examiner disagrees. The applicant’s argument is not persusive because the recited processor elements merely describe generic computer components performing routine functions. The inclusion of a “processor configured to reshape, encode, and transmit and/or store” data does not add an inventive concept, as these functions are conventional and do not reflect a specific technological improvement.
The alleged improvement relates to mathematical processing of data (tensor approximation and compression) rather than an improvement to the computer itself. When viewed as a whole, the claim merely applies an abstract idea using generic computer components and does not amount to significantly more than the judicial exception.
Claim Rejections - 35 USC § 103
Applicant’s arguments with respect to claim(s) 1, 2, 5, 6, 11, 12, 13, 16, 17, 20, 21, 26, 28 – 32, and 34 - 37 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.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to BRANDON S COLE whose telephone number is (571)270-5075. The examiner can normally be reached Mon - Fri 7:30pm - 5pm EST (Alternate Friday's Off).
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Omar Fernandez can be reached on 571-272-2589. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/BRANDON S COLE/ Primary Examiner, Art Unit 2128
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