DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Amendment
The amendment filed 16 October 2025 has been entered. Claims 1-20 remain
pending in the application. Applicant’s amendments to the claims have overcome
the 35 USC 112(a) and 35 USC 112(b) rejections previously set forth in the Non-Final Office Action mailed 19 May 2025.
Claim Objections
Claims 10 and 11 are objected to because of the following informalities:
there are two claims numbered “11”, for purposes of examination the claim followed by claim 9 will be interpreted as claim 10.
Appropriate correction is required.
Claim Construction
Regarding claim 14, the preamble is given patentable weight. Claim 20 contains the limitation “the one or more non-transitory, computer-readable storage media” and “the one or more computing devices” in the body, which are referring to the limitations as recited in the preamble of claim 14. A skilled person in the art reading the claims would consider the claim in view of the body and preamble, and identify them limited to the technological environment of the one or more non-transitory, computer-readable storage media and one or more computing devices performing the particular matrix multiplication operation. The body of the claim depends on the preamble for completeness, and gives life, meaning, and vitality to this claim. Therefore, the preamble of claim 14 should be afforded patentable weight.
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 a judicial exception (i.e., an abstract idea) without significantly more.
Regarding claim 1, under the Alice Framework Step 1 analysis, the claim falls within the four statutory categories of patentable subject matter: an apparatus.
Under the Alice Framework Step 2A Prong 1 analysis, the claim recites Mathematical Concepts and/or Mental Processes. The claim recites Mathematical Calculations, which is specifically identified as an exemplar in the Mathematical Concepts grouping of abstract ideas, and/or recites Evaluations, which is specifically identified as an exemplar in the Mental Processes grouping of abstract ideas:
“responsive to a request to perform a matrix multiply operation on a first matrix and a second matrix:
select a version of a plurality of versions of the second matrix to perform the matrix multiply operation based, at least in part, on a performance profile identified for the matrix multiply operation, wherein the plurality of versions of the second matrix comprise a transposed version of the second matrix and a non-transposed version of the second matrix to perform the matrix multiply operation; and
multiply the first matrix with the selected version of the second matrix to perform the matrix multiply operation.”
See specification ([0041], [0043]) describing responsive to a request. See specification ([0042-0043], [0045-0049]) describing selecting. See specification ([0014]) describing multiplying. For these reasons, the claim recites Mathematical Concepts and/or Mental Processes.
Under the Alice Framework Step 2A Prong 2 analysis, the claim recites the combination of the following additional elements: at least one processor, a memory comprising program instructions that when executed by the at least one processor cause the at least one processor to implement a machine learning system, and the machine learning system. At least one processor, a memory comprising program instructions that when executed by the at least one processor cause the at least one processor to implement a machine learning system, and the machine learning system are recited at a high level of generality, and are examples of generic computing elements, and/or merely generally linked to a particular technological environment (see MPEP 2106.05(h)(vi): Limiting the abstract idea of collecting information, analyzing it, and displaying certain results of the collection 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). Taken alone or in combination, they fail to integrate the judicial exception into a practical application.
Under the Alice Framework Step 2B Analysis, the additional elements recited above, taken alone or in combination, do not amount to significantly more than the judicial exception. As discussed in the Step 2A Prong 2 Analysis, the claim recites at least one processor, a memory comprising program instructions that when executed by the at least one processor cause the at least one processor to implement a machine learning system, and the machine learning system at a high level of generality, which merely result in “apply it” on a computer and/or generally link the abstract idea to a particular technological environment. Since the claim does not include additional elements that, alone or in combination, amount to significantly more than the judicial exception, claim 1 is ineligible.
Claims 2-3 and 5 merely further limits the mathematical concepts. Claims 2-3 and 5 do not recite any new additional elements.
Under the Alice Framework Step 2A Prong 1 analysis, claim 4 recites Mathematical Concepts and/or Mental Processes. The claim recites Mathematical Calculations, which is specifically identified as an exemplar in the Mathematical Concepts grouping of abstract ideas, and/or recites Evaluations, which is specifically identified as an exemplar in the Mental Processes grouping of abstract ideas:
“ the matrix multiply operation,
responsive to the selection of the version of the second matrix”
See specification ([0014]) describing the matrix multiplying operation. See specification ([0042], [0045-0046]) describing selecting. For these reasons, the claim recites Mathematical Concepts and/or Mental Processes.
Under the Alice Framework Step 2A Prong 2 analysis, the claim recites the combination of the following additional elements: the plurality of versions of the second matrix are stored before performance and access the stored versions of the second matrix to obtain the selected version of the matrix. The stored and access limitations are examples of insignificant extra-solution activities, mere data gathering (see MPEP 2106.05(g): Insignificant Extra-Solution Activity). Taken alone or in combination, they fail to integrate the judicial exception into a practical application.
Under the Alice Framework Step 2B Analysis, the additional elements recited above, taken alone or in combination, do not amount to significantly more than the judicial exception. As discussed in the Step 2A Prong 2 Analysis, the claim recites insignificant extra-solution activities which merely result in “apply it” on a computer and/or generally link the abstract idea to a particular technological environment. These activities are also well-understood, routine, or conventional (see MPEP 2106.05(d)(II)(iv): Storing and retrieving information in memory). Since the claim does not include additional elements that, alone or in combination, amount to significantly more than the judicial exception, claim 4 is ineligible.
Under the Alice Framework Step 2A Prong 1 analysis, claim 6 recites Mathematical Concepts and/or Mental Processes. The claim recites Mathematical Calculations, which is specifically identified as an exemplar in the Mathematical Concepts grouping of abstract ideas, and/or recites Evaluations, which is specifically identified as an exemplar in the Mental Processes grouping of abstract ideas:
“ the matrix multiply operation,
generating an inference and
wherein the second matrix is a weight matrix”
See specification ([0014]) describing the matrix multiplying operation. See specification ([0025-0026], [0030-0031]) describing generating an inference. See specification ([0041], [0045]) describing the second matrix as a weight matrix. For these reasons, the claim recites Mathematical Concepts and/or Mental Processes.
Under the Alice Framework Step 2A Prong 2 analysis, the claim recites the combination of the following additional elements: requesting and a machine learning model. The machine learning model is an example of generic computing elements, and/or merely generally linked to a particular technological environment (see MPEP 2106.05(h)(vi): Limiting the abstract idea of collecting information, analyzing it, and displaying certain results of the collection 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). The claim recites limitations which are examples of generic computing elements that result in “apply it” on a computer and/or generally link the abstract idea to a particular technological environment. The requesting limitation is an example of insignificant extra-solution activities, mere data gathering (see MPEP 2106.05(g): Insignificant Extra-Solution Activity). Taken alone or in combination, they fail to integrate the judicial exception into a practical application.
Under the Alice Framework Step 2B Analysis, the additional elements recited above, taken alone or in combination, do not amount to significantly more than the judicial exception. As discussed in the Step 2A Prong 2 Analysis, the claim recites generic computing components which merely result in “apply it” on a computer and/or generally link the abstract idea to a particular technological environment. The insignificant extra-solution activity described above is also well-understood, routine, or conventional (see MPEP 2106.05(d)(II)(ii): Performing repetitive calculations). Since the claim does not include additional elements that, alone or in combination, amount to significantly more than the judicial exception, claim 6 is ineligible.
Claims 7-12 are directed to a method that would be performed by the apparatus of claims 1-6, respectively. The claims 1-6 analysis equally applies.
Claims 14-19 are directed to a computer program product that when executed would perform the apparatus of claims 1-6, respectively. The claims 1-6 analysis equally applies.
Under the Alice Framework Step 2A Prong 1 analysis, claim 20 recites Mathematical Concepts and/or Mental Processes. The claim recites Mathematical Calculations, which is specifically identified as an exemplar in the Mathematical Concepts grouping of abstract ideas, and/or recites Evaluations, which is specifically identified as an exemplar in the Mental Processes grouping of abstract ideas:
“ detecting an event to reduce store matrices; and
responsive to detecting the event, selecting one or more of the plurality of versions of the second matrix to remove from storage.”
See specification ([0051]) describing detecting an event. See specification ([0052-0053]) describing response to detecting. For these reasons, the claim recites Mathematical Concepts and/or Mental Processes.
Under the Alice Framework Step 2A Prong 2 analysis, the claim recites the combination of the following additional elements: the plurality of versions of the second matrix are stored, and wherein the one or more non-transitory, computer-readable storage media store additional program instructions that when executed on or across the one or more computing devices. The one or more non-transitory, computer-readable storage media store additional program instructions that when executed on or across the one or more computing devices are recited at a high level of generality, and are examples of generic computing elements, and/or merely generally linked to a particular technological environment (see MPEP 2106.05(h)(vi): Limiting the abstract idea of collecting information, analyzing it, and displaying certain results of the collection 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). The stored limitation is an example of insignificant extra-solution activities, mere data gathering (see MPEP 2106.05(g): Insignificant Extra-Solution Activity). Taken alone or in combination, they fail to integrate the judicial exception into a practical application.
Under the Alice Framework Step 2B Analysis, the additional elements recited above, taken alone or in combination, do not amount to significantly more than the judicial exception. As discussed in the Step 2A Prong 2 Analysis, the claim recites insignificant extra-solution activities and generic computing components which merely result in “apply it” on a computer and/or generally link the abstract idea to a particular technological environment. The activities described as insignificant extra-solution are also well-understood, routine, or conventional (see MPEP 2106.05(d)(II)(iv): Storing and retrieving information in memory). Since the claim does not include additional elements that, alone or in combination, amount to significantly more than the judicial exception, claim 20 is ineligible.
Claim 13 recites similar limitations to claim 20. The claim 20 analysis similarly applies, and claim 13 is equally rejected.
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.
Claims 1-12, 14-19 are rejected under 35 U.S.C. 103 as being unpatentable over US 20210173895 A1 Han et al. (hereinafter “Han”) in view of G. Huang, G. Dai, Y. Wang and H. Yang, "GE-SpMM: General-Purpose Sparse Matrix-Matrix Multiplication on GPUs for Graph Neural Networks," SC20: International Conference for High Performance Computing, Networking, Storage and Analysis, Atlanta, GA, USA, 2020, pp. 1-12, doi: 10.1109/SC41405.2020.00076. (hereinafter “Huang”) in view of US 20200356837 A1 Hargil et al. (hereinafter “Hargil”).
Regarding claim 1, Han teaches a system, comprising:
at least one processor (Fig. 9, 910, [0117]);
a memory (Fig. 9, 920, [0118]), comprising program instructions that when executed by the at least one processor cause the at least one processor ([0118], [0133]) to implement a machine learning system (Fig. 9, 900, [0115]), the machine learning system configured to:
responsive to a request ([0007], [0133]) to perform a matrix multiply operation ([0108]) on a first matrix (Fig. 8, 810, 811, [0109]) and a second matrix (Fig. 8, 820, [0109-0110]):
select a version (Fig. 8, one of 831, 832, 833, [0111-0112]) of a plurality of versions of the second matrix (Fig. 8, 831, 832, 833, [0111-0112] columns) to perform the matrix multiply operation ([0112]) based, at least in part, on a performance profile identified for the matrix multiply operation ([0110]), wherein the plurality of versions of the second matrix comprise a transposed version of the second matrix and a non-transposed version of the second matrix ([0113]) to perform the matrix multiply operation ([0110], [0112]); and
multiply the first matrix with the selected version of the second matrix to perform the matrix multiply operation (Fig. 8, a11 matrix multiplied by 831, b11 matrix multiplied by 832, c11 matrix multiplied by 833, [0110-0113]).
Although Han teaches the matrix multiplication, they are silent with explicitly disclosing the system as a machine learning system and basing the matrix multiplication, at least in part, on a performance profile identified. Further, Han is silent with disclosing the logical AND of the second matrix comprising a transposed version of the second matrix and a non-transposed version of the second matrix.
Huang teaches machine learning (Pg. 1, Col. 1, Sec. I., Para. 1; Pg. 3, Col. 2, Sec. II-C, Para. 1; Pg. 6, Col. 2, Sec. IV-B, Para. 1) and basing, at least in part, on a performance profile identified (Pg. 7, Col. 2, V-B-1; Pg. 8, Col. 1, V-B-2).
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Han’s matrix multiplication system with Huang’s machine learning and profiling techniques because they are in the claimed invention’s same field of endeavor of matrix multiplication [Abstract]. It would have been obvious to one of ordinary skill in the art to try Han’s techniques in a machine learning environment as Han’s techniques are already directed to a neural network system. It would have been obvious to try, with predictable results to apply the neural network in a machine learning environment, and would be beneficial as it would give Han’s neural network apparatus more applications, such as link prediction and node classification (Pg. 1, Col. 1, Sec. I, Para. 1). It would have been obvious to one of ordinary skill in the art to implement profiling techniques, as by implementing these features provides more support for designers to configure matrix multiplication devices. By providing designers the capability of analyzing performances of different matrices in a machine learning environment (Pg. 7, Col. 2, V-B-1; Pg. 8, Col. 1, V-B-2) they are better equipped to effectively improve efficiency of global data access, which leads to significant speedups and significant training time reduction (Pg. 11, Sec. V). It is through having more control over configurability via the types of data applied to different matrix multiplication systems and applications that they are able to achieve the benefits of this modification.
Further, Huang and Han in view of Huang are silent with disclosing the logical AND of the second matrix comprising a transposed version of the second matrix and a non-transposed version of the second matrix.
Hargil teaches the second matrix comprising a transposed version of the second matrix and a non-transposed version of the second matrix ([0038] 20’).
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Han in view of Huang’s matrix multiplication system with Hargil’s transposed version feature because they are in the claimed invention’s same field of endeavor of matrix multiplication [Abstract]. It would have been obvious to one of ordinary skill in the art to try Hargil’s transposed feature and apply it to Han’s weight matrix as Han’s techniques are already directed to utilizing different versions of the weight matrix for the matrix multiplication operation. It would have been obvious to try with predictable results, as applying the transpose to the weight matrix would be beneficial since it would improve the matrix multiplication operation efficiency ([0033], [0038]).
Regarding claim 2, in addition to the teachings addressed in the claim 1 analysis, the rejection of claim 1 is incorporated and Han teaches the system wherein the machine learning system is further configured to:
generate different respective test matrices with different respective shapes (Fig. 3A, 311, 312, 313, [0060-0062]; Fig. 8, 810, 811, [0109], [0113]);
compare performance of matrix multiplication ([0110], [0112]) between the different respective test matrices with the different versions of the second matrix (Fig. 8, 831, 832, 833, [0111-0112] columns); and
based on the comparison, generate the performance profile for the matrix multiply operation ([0110], [0112]).
Although Han teaches the matrix multiplication, they are silent with explicitly disclosing generating different respective test matrices, comparing performance between the different respective test matrices, and based on the comparison, generate the performance profile.
Huang teaches generating different respective test matrices (Fig. 8 and 9, matrix_id; Pg. 7, Col. 2, V-B-1, matrices in tests), comparing performance between the different respective test matrices (Fig. 8 and 9, speedup; Pg. 8, Col. 1, V-B-1), and based on the comparison, generate the performance profile (Table VI, Pg. 8, Col. 1, V-B-2, profiling three metrics: gld_transactions, gld_throughput, achieved_occupancy).
The motivation to combine provided with respect to claim 1 equally applies.
Regarding claim 3, in addition to the teachings addressed in the claim 1 analysis, the rejection of claim 1 is incorporated and Han teaches the system wherein the machine learning system is further configured to:
responsive to the selection of the version of the second matrix (see claim 1 mapping):
generate the selected version of the matrix (Fig. 8, one of 831, 832, 833, [0111-0112]) from another one of the plurality of versions of the second matrix (Fig. 8, 831, 832, 833, [0111-0112] columns).
Regarding claim 4, in addition to the teachings addressed in the claim 1 analysis, the rejection of claim 1 is incorporated and Han teaches the system wherein:
the plurality of versions of the second matrix are stored (Fig. 9, Weight Data, [0118]) before performance of the matrix multiply operation ([0110], [0112]), and wherein machine learning system (Fig. 9, 900, [0115]) is further configured to:
responsive to the selection of the version of the second matrix (see claim 1 mapping):
access the stored ([0109]) plurality of versions of the second matrix to obtain the selected version of the matrix (Fig. 8, 831, 832, 833, [0111-0112] columns).
Although Han teaches the matrix multiplication, they are silent with explicitly disclosing the system as a machine learning system and performance.
Huang teaches machine learning (Pg. 1, Col. 1, Sec. I., Para. 1; Pg. 3, Col. 2, Sec. II-C, Para. 1; Pg. 6, Col. 2, Sec. IV-B, Para. 1) and performance (Pg. 7, Col. 2, V-B-1; Pg. 8, Col. 1, V-B-2).
The motivation to combine provided with respect to claim 1 equally applies.
Regarding claim 5, in addition to the teachings addressed in the claim 1 analysis, the rejection of claim 1 is incorporated and Han teaches the system wherein:
the performance profile is an array of values that respectively specify the version of the plurality of versions of the second matrix (Fig. 8, 831, 832, 833, [0111-0112] columns) in different respective entries corresponding to different shapes of the first matrix (Fig. 3A, 311, 312, 313, [0060-0062]; Fig. 8, 810, 811, [0109], [0113]) and wherein to select the version of the plurality of versions of the second matrix (Fig. 8, 831, 832, 833, [0111-0112] columns) to perform the matrix multiply operation ([0110], [0112]), the machine learning system (Fig. 9, 900, [0115]) is configured to access one of the entries in the array of values identified according to a shape of the first matrix (Fig. 3A, 311, 312, 313, [0060-0062]; Fig. 8, 810, 811, [0109], [0113]).
Although Han teaches the matrix multiplication, they are silent with explicitly disclosing the performance profile is an array of values that respectively specify, in different respective entries, and machine learning is configured to access one of the entries in the array of values identified.
Huang teaches the performance profile is an array of values that respectively specify (Table VI, Pg. 8, Col. 1, V-B-2, profiling three metrics: gld_transactions, gld_throughput, achieved_occupancy), in different respective entries (Table VI contains five rows and four columns), and machine learning (Pg. 1, Col. 1, Sec. I., Para. 1; Pg. 3, Col. 2, Sec. II-C, Para. 1; Pg. 6, Col. 2, Sec. IV-B, Para. 1) is configured to access (Pg. 8, Col. 1, V-B-2, tested on one of the random graphs) one of the entries in the array of values identified (Table VI, Pg. 8, Col. 1, V-B-2, profiling three metrics: gld_transactions, gld_throughput, achieved_occupancy).
The motivation to combine provided with respect to claim 1 equally applies.
Regarding claim 6, in addition to the teachings addressed in the claim 1 analysis, the rejection of claim 1 is incorporated and Han teaches the system wherein:
the matrix multiply operation ([0110], [0112]) is requested as part of generating an inference of a machine learning model and wherein the second matrix is a weight matrix (Fig. 8, 831, 832, 833, [0111-0112] weight).
Although Han teaches the matrix multiplication, they are silent with explicitly disclosing the multiplication operation is requested as part of generating an inference of a machine learning model.
Huang teaches is requested as part of generating an inference (Pg. 2, Sec. I, Col. 1, Para. 1; Pg. 3, Col. 2, II-B, Para. 2, GNN inference) of a machine learning model (Pg. 1, Col. 1, Sec. I., Para. 1; Pg. 3, Col. 2, II-B, Para. 2; Pg. 3, Col. 2, Sec. II-C, Para. 1; Pg. 6, Col. 2, Sec. IV-B, Para. 1; Pg. 8, Col. 2, V-C, Para. 1-2, GNN models).
The motivation to combine provided with respect to claim 1 equally applies.
Claims 7-12 are directed to a method that would be performed by the apparatus of claims 1-6, respectively. The claims 1-6 analysis equally applies.
Claims 14-19 are directed to a computer program product that when executed would be perform the apparatus of claims 1-6, respectively. The claims 1-6 analysis equally applies.
Claims 13 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Han in view of Huang in view of Hargil as applied to claims 1, 7, and 14 above, and further in view of US 20170169332 A1 Graves et al. (hereinafter “Graves”).
Regarding claim 20, in addition to the teachings addressed in the claim 14 analysis, the rejection of claim 14 is incorporated and Han teaches the one or more non-transitory, computer-readable storage media wherein:
the plurality of versions of the second matrix are stored (Fig. 9, Weight Data, [0118]), and wherein the one or more non-transitory, computer-readable storage media store additional program instructions that when executed on or across ([0007], [0133]) the one or more computing devices, cause the one or more computing devices ([0115], [0133]) to further implement:
detecting an event to reduce store matrices; and
responsive to detecting the event, selecting one or more of the plurality of versions of the second matrix (Fig. 8, 831, 832, 833, [0111-0112] columns) to remove from storage.
Although Han teaches the matrix multiplication, they are silent with explicitly disclosing detecting an event to reduce store matrices, and response to detecting the event, to remove from storage.
Similarly, Huang is silent with disclosing detecting an event to reduce store matrices, and response to detecting the event, to remove from storage.
Thus, Han in view of Huang are silent with disclosing detecting an event to reduce store matrices, and response to detecting the event, to remove from storage.
Han in view of Huang in view of Hargil are silent with disclosing detecting an event to reduce store matrices, and response to detecting the event, to remove from storage.
Graves teaches detecting an event to reduce store matrices (Fig. 2, 212, [0053-0054], as the event before 214), and response to detecting the event, to remove from storage (Fig. 2, 214, [0056]).
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Han in view of Huang in view of Hargil’s modified matrix multiplication system with Graves’ detection and removal techniques because they are in the claimed invention’s same field of endeavor of matrix multiplication [0107]. It would have been obvious to one of ordinary skill in the art to implement the detection and removal techniques, as by implementing these features provides more support for designers to configure matrix multiplication devices through the capability of removing unnecessary data. By removing data, it allows the architecture to make better use of the memory ([0008]). Thus, it would be beneficial to make this modification to allocate such memory resources in other areas of need.
Claim 13 recites similar limitations to claim 20. The claim 20 analysis similarly applies, and claim 13 is equally rejected.
Response to Arguments
Claim Objections. The objections have been withdrawn based on the amendment to the claims.
It appears, however, that the amendment to claim “10” (of which was originally numbered as ‘11’) maintains the issue of the claim numbering interfering with actual claim 11 as claim “10” (of which was never originally numbered as ‘10’) is amended to be numbered as claim “11” followed by “claim 11” as the next claim, and as interpreted by amendment conventions and notations of the double brackets and underlining.
35 USC 112(a). The rejections have been withdrawn based on the amendment to the claims.
35 USC 112(b). The rejections have been withdrawn based on the amendment to the claims.
35 USC 112(f). The interpretation is no longer invoked based on the amendment to the claims.
35 USC 101. Applicant argues the following in substance:
Applicant asserts that, the claims integrate the alleged exception(s) into a practical application. The claims are directed to a machine learning system that maintains multiple versions of a matrix and selects from among the versions, a best suited version based on a performance profile identified for a particular operation (the matrix multiply operation). Thus, the claims are directed to improving performance of a machine learning system, where a performance limitation is a direct result of implementing matrix multiplication using computer processor (see Remarks p. 12).
Examiner respectfully disagrees. The purported improvements are a direct result
of applying the abstract idea, the mathematical concepts and/or mental processes,
other than reciting “at least one processor”, “a memory comprising program instructions that when executed by the at least one processor cause the at least one processor to implement a machine learning system”, and “a machine learning system” in claim 1 there is nothing in the claim elements that precludes the steps from practically being performed in the human mind, and/or using pen and paper.
No additional elements and/or combination of additional elements are claimed
that result in the purported improvement beyond the mathematical concepts and/or
mental processes.
With respect to “improving performance of a machine learning system” it is the abstract idea, the manner of applying the mathematical relationships and/or mental processes to perform the matrix multiplication operation based on a performance profile identified that yields the purported improvements. See specification para. ([0014], [0041-0043]).
See MPEP 2106.05(a)(I). “Examples that the courts have indicated may not be
sufficient to show an improvement in computer-functionality”.
See MPEP 2106.05(a)(II). “It is important to keep in mind that an improvement in
the abstract idea itself (e.g. a recited fundamental economic concept) is not an
improvement in technology.”
See also MPEP 2106.05. An inventive concept "cannot be furnished by the
unpatentable law of nature (or natural phenomenon or abstract idea) itself."
To the extent Applicant is arguing mere recitation of “machine learning” integrates the abstract idea into a practical application, this does not add a meaningful limitation as it is merely a nominal or a generic component of the claim, and is nothing more than an attempt to generally link the abstract idea to a particular technological environment. See MPEP 2106.05(b)(III).
Applicant asserts that, the claims are rooted in computer technology in order to overcome a problem specifically arising in the realm of computer processing, analogous to claims of DDR Holdings, LLC v. Hotels.com L.P (Fed. Cir., December 5, 2014), found patent-eligible. Guidance also notes that "because revised Step 2A does not evaluate whether an additional element is well-understood, routine, conventional activity, examiners are reminded that a claim that includes conventional elements may still integrate an exception into a practical application, thereby satisfying the subject matter eligibility requirement of Section 101." Applicant therefore respectfully submits that the present claims represent significantly more than a judicial except and are patent eligible (see Remarks p. 12).
Examiner respectfully disagrees. The cited DDR Holdings case is not analogous as those claims “were directed to systems and methods of generating a composite webpage that combines certain visual elements of a host website with the content of a third-party merchant,” which are not similar or equivalent to the instant application. See MPEP 2106.05(d).
The additional elements recited in claims 1, 7, and 14 do not integrate the abstract idea into a practical application because they have been recited at a high level of generality and an example of generic computing elements that merely result in “apply it” on a computer (or equivalent), and/or merely generally linked to a particular technological environment, and/or are examples of insignificant extra-solution activity, mere data gathering.
See MPEP 2106.04(d)(I). “The courts have also identified limitations that did not integrate a judicial exception into a practical application”.
35 USC 103. Applicant’s arguments, see Remarks p. 9 – bottom with respect to a transposed version, filed 10/16/2025, with respect to the rejection(s) of claim(s) 1, 7, and 14 under 35 USC 103 have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of Hargil, as necessitated by the amendment.
To the extent Applicant is arguing the previous features and prior art applied, Applicant argues the following in substance:
Applicant asserts that, the cited references do not disclose the select feature nor the multiple versions of the second matrix. Han clearly states weight matrices 831, 832 and 833 are different divisions of initial weight matrix 820 and therefore are not different versions of a same matrix, both because they are different one another and because none of them are versions of matrix 820 (see Remarks p. 9). Therefore, Han fails to disclose the select feature of the claim nor the multiple versions of the second matrix (see Remarks p. 10 - top).
Examiner respectfully disagrees. The claim language “version” recited in the independent claims is given is ‘plain meaning’ under the broadest reasonable interpretation, unless such meaning is inconsistent with the specification. See MPEP 2111.01(I).
In Fig. 8, Han discloses divisions (831 to 833) of the weight matrix (820), of which the elements in 831 to 833 are unchanged to those of the weight matrix 820. In essence, divisions 831 to 833 are versions of the weight matrix 820 by virtue of the divisions being from the same source and containing the same elements, but not being equivalent (or the exact same) due to the exclusion of certain elements by its divisions. The definition of version, given its plain meaning, is defined as “a particular form of something that is slightly different from other forms of the same thing”. See Anonymous, Definition of version from the Cambridge Advanced Learner's Dictionary & Thesaurus © Cambridge University Press, Online Accessed 01/01/2026 (hereinafter “Cambridge”) p. 1 definition.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MARKUS A VILLANUEVA whose telephone number is (703)756-1603. The examiner can normally be reached M - F 8:30 am - 5:30 pm.
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/MARKUS ANTHONY VILLANUEVA/Examiner, Art Unit 2151 /EMILY E LAROCQUE/Primary Examiner, Art Unit 2182
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