DETAILED ACTION
Remarks
This Office Action is in response to the application 18/335715 filed on 15 June 2023.
Claims 1-20 have been examined.
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 .
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
As to claims 1, 9, and 15, these claims recite “a first interleaved data tensor.” The claims do not specify nor place any limits upon the claimed data tensor other than describing it as “having an unrealized set of dimensions.” In computer science and machine learning, a “tensor” is defined as a multi-dimensional array of data1. The broadest reasonable interpretation (BRI) of this limitation encompasses a simple data tensor having just two dimensions. These claims recite the following:
“determining a transformation operation to apply to the first interleaved data tensor”; and
“determining a realized set of dimensions for output of the transformation operation based on the unrealized set of dimensions and the transformation operation.”
These “determining” limitations amount to no more than a series of evaluations/judgements; i.e. evaluating/judging various possible transformation operations to determine a particular transformation operation to apply, and evaluating/judging a set of dimensions to determine a realized set of dimensions, as claimed. Given that the BRI of the claims encompasses a simple case, as set forth above, a human could, with the aid of pencil and paper, mentally perform the judgements/evaluations necessary to achieve the claimed “determining” limitations. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind (and/or with a pencil and paper) but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Alternatively, these “determining” limitations may be deemed abstract ideas under the “Mathematical Concepts” grouping since they amount to no more than a series of mathematical operations, as is apparent from the dependent claims.
These claims also recite generating a second interleaved data tensor by applying the transformation operation in the manner described in the claims. Given that the BRI of the claims encompasses a simple case, as set forth above, a human could, with the aid of pencil and paper, mentally generate a second interleaved data tensor in the manner claimed. A human could write down on a piece of paper the first interleaved data tensor and copy its input elements to output elements in the second interleaved data tensor, as claimed. Hence, this limitation is also an abstract idea under the “Mental Processes” grouping. Alternatively, this limitation may be deemed an abstract idea under the “Mathematical Concepts” grouping since it amounts to no more than a series of mathematical operations, as is apparent from the dependent claims. Accordingly, the claims recite an abstract idea.
This judicial exception is not integrated into a practical application. Other than the abstract idea, the claims recite the following:
a) “receiving a first interleaved data tensor having an unrealized set of dimensions” (claim 1, and similar limitations of claims 9 and 15);
b) “one or more computer processors” (claim 9);
c) “a memory containing a program” for execution by the one or more computer processors (claim 9);
d) “A computer program product comprising a computer-readable storage medium having computer-readable program code embodied therewith” (claim 15).
Limitation (a) amounts to no more than mere data gathering, which has been deemed by the courts to be insignificant extra-solution activity. See MPEP 2106.05(g). Limitations (b) through (d) are recited at a high level of generality, i.e. as generic computer components performing generic computing functions. Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Looking at the additional elements as a whole adds nothing beyond the additional elements considered individually—they still represent insignificant extra-solution activity and/or generic computer implementation. Hence, the claim as a whole, looking at the additional elements individually and in combination, does not integrate the abstract idea into a practical application. The claim is directed to an abstract idea.
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception. Limitation (a) amounts to no more than mere data gathering, which has been deemed by the courts to be insignificant extra-solution activity. See MPEP 2106.05(g). In addition, the courts have deemed receiving data to be well-understood, routine, and conventional activity, as in the following cases: Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network); Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015) (storing and retrieving information in memory). See MPEP 2106.05(d)(II). As discussed above with respect to integration of the abstract idea into a practical application, additional elements (b) through (d) amount to no more than mere field of use limitations and instructions to apply the exception using generic computer components. Mere instructions to apply an exception using conventional computer components and functions cannot provide an inventive concept. Looking at the additional elements as a whole adds nothing beyond the additional elements considered individually—they still represent insignificant extra-solution activity; well-understood, routine, and conventional subject matter; and/or generic computer implementation. Hence, the claim as a whole, looking at the additional elements individually and in combination, does not amount to significantly more than the abstract idea. These claims are not patent eligible.
As to dependent claims 2-4, 10-12, and 16-18, these claims provide more details of the claimed “determining the realized set of dimensions,” describing as a series of mathematical calculations. Hence, these claims are an abstract idea under the “Mathematical Concepts” grouping. Alternatively, these claims may be deemed an abstract idea under the “Mental Processes” grouping, since a human could, with the aid of pencil and paper, mentally perform these calculations for the simple case encompassed by the BRI of the claims.
As to dependent claims 5-6, 13-14, and 19-20, these claims provide more details of the claimed “generating the second interleaved data tensor,” describing as a series of mathematical calculations. Hence, these claims are an abstract idea under the “Mathematical Concepts” grouping. Alternatively, these claims may be deemed an abstract idea under the “Mental Processes” grouping, since a human could, with the aid of pencil and paper, mentally perform these calculations for the simple case encompassed by the BRI of the claims.
As to dependent claim 7, this claim recites certain details of the realized set of dimensions. However, given that the BRI of the claims encompasses a simple case, as set forth above, nothing in this claim goes beyond what a human could mentally perform with the aid of pencil and paper. Hence, this claim is also directed to an abstract idea under the “Mental Processes” grouping.
As to dependent claim 8, this claim recites certain of copying input elements to output elements. However, given that the BRI of the claims encompasses a simple case, as set forth above, nothing in this claim goes beyond what a human could mentally perform with the aid of pencil and paper. Hence, this claim is also directed to an abstract idea under the “Mental Processes” grouping.
Additional Art Considered
The prior art made of record and not relied upon is considered pertinent to the Applicants’ disclosure.
The following patents and papers are cited to further show the state of the art at the time of Applicants’ invention with respect to machine learning transformation operations for interleaved data.
a. del Mundo et al.; “Optimizing Binary Convolutional Neural Networks”; U.S. PGPub. No. 20200410318 A1.
Teaches transforming a first interleaved data tensor by applying a transformation operation that involves copying input regions into output regions (see para. 0033-0035 and claims 3 and 9).
b. Edwards et al.; “APPLICATION PROGRAMMING INTERFACE TO GENERATE A TENSOR ACCORDING TO A TENSOR MAP”; U.S. PGPub. No. 20240161224 A1.
Teaches transforming an interleaved tensor data structure by applying a transformation operation that involves copying input locations to destination locations (see para. 0079, 0082-0083, 0153, and Tables 31-32).
c. Mills; Christopher L.; “MULTI-DIMENSIONAL TENSOR SUPPORT EXTENSION IN NEURAL NETWORK PROCESSOR”; U.S. PGPub. No. 20220156575 A1.
Teaches converting/transforming an interleaved tensor data structure by applying a conversion/transformation operation that involves copying inputs to outputs (see para. 0083, 0097, and 0103).
Contact Information
Any inquiry concerning this communication or earlier communications from the examiner should be directed to UMAR MIAN whose telephone number is (571)270-3970. The examiner can normally be reached Monday to Friday, 10 am to 6:30 pm.
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/Umar Mian/
Primary Examiner, Art Unit 2163
1 Adhikari, Nabin. “Tensors in machine learning.” Published 31 Dec. 2022. Accessed 24 Jul. 2026 from https://medium.com/@nabinadhikari190/tensors-in-machine-learning-2c4f6c336244
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