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 .
Specification
The lengthy specification has not been checked to the extent necessary to determine the presence of all possible minor errors. Applicant’s cooperation is requested in correcting any errors of which applicant may become aware in the specification.
Drawings
The applicant’s submitted drawings appear to be acceptable for examination purposes. Applicant’s cooperation is requested in correcting any errors of which applicant may become aware in the drawings.
Priority
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Information Disclosure Statement
As required by M.P.E.P. 609(c), the applicant's submission of the Information Disclosure Statements, dated 29 September 2023 and 10 May 2024, are acknowledged by the examiner and the cited references have been considered in the examination of the claims now pending. As required by M.P.E.P 609 C(2), a copy of the PTOL-1449 forms, initialed and dated by the examiner, are attached to the instant office action.
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.
Claim(s) 1-20 is/are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claim(s) recite(s) mental processes and/or mathematical concepts. This judicial exception is not integrated into a practical application and does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception, as described below.
Step 1 for all claims:
Under the first part of the analysis, claims 1 recite a method, claims 2-19 recite a device, and claim 20 recites a manufacture. Accordingly, these claims fall within the four statutory categories of invention and the analysis proceeds to Step 2A, prongs 1 and 2, and Step 2B, as described below.
As per claim 1:
Under step 2A, prong 1, the claim recites an abstract idea including the following mental process and/or mathematical concept elements:
a method of dynamically selecting a number format for a set of network parameters of a neural network – a data scientist dynamically selects a number format (such as floating/fixed point, precision, bit-width, etc.) for parameters of a neural network. Additionally/alternatively – selecting the number format may be a mathematical calculation (see, e.g., para. [0110] of the specification as filed).
selecting a number format based on the collected one or more statistics – the data scientist dynamically selects a number format (such as floating/fixed point, precision, bit-width, etc.) for parameters of a neural network based upon the collected statistics. Additionally/alternatively – selecting the number format may be a mathematical calculation (see, e.g., para. [0110] of the specification as filed).
converting, … a second set of network parameters to the selected number format – converting a number to a different format is a mathematical calculation (see, e.g., paras. [107]-[108] of the specification as filed). Alternatively/additionally – the conversion can be performed by the data scientist, depending on the desired format.
If a claim, under the broadest reasonable interpretation covers a mathematical relationship between variables or numbers, a numerical formula or equation, or a mathematical calculation, it will be considered as falling within the “mathematical concepts” grouping of abstract ideas. If a claim, under the broadest reasonable interpretation covers concepts that can be performed in the human mind, or by a human using a pen and paper, including observation, evaluation, judgment, or opinion, it will be considered as falling within the “mental processes” grouping of abstract ideas. Additionally, performing mathematical calculations using a formula that could be practically performed in the human mind may be considered to fall within both the mathematical concepts grouping and the mental process grouping. See MPEP § 2106.04(a)(2).
Accordingly, at step 2A, prong one, the claim is directed to an abstract idea.
Under step 2A, prong two, the judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of:
the method comprising: collecting… one or more statistics on a first set of network parameters for a layer of the neural network while the neural network accelerator is performing a pass of the neural network – this is recited at a high level of generality and amounts to insignificant extra-solution activity as data gathering/storage that is limited to a particular type of data, generally linking the judicial exception to a particular technological environment or field of use. See MPEP § 2106.05(g) and (h), and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
using a statistics collection hardware unit of a neural network accelerator – this amounts to mere instructions to apply the exception using a generic computer component, recited at a high level of generality. See MPEP § 2106.05(f).
using a format conversion hardware unit of the neural network accelerator – this amounts to mere instructions to apply the exception using a generic computer component, recited at a high level of generality. See MPEP § 2106.05(f).
the second set of network parameters comprising (i) the first set of network parameters and/or another set of network parameters for the layer, or (ii) a set of network parameters for a subsequent pass of the neural network corresponding to the first set of network parameters – this amounts to generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
and processing, using one or more network processing hardware units of the neural network accelerator – this amounts to mere instructions to apply the exception using a generic computer component, recited at a high level of generality. See MPEP § 2106.05(f).
processing, …the converted second set of network parameters in accordance with the neural network to perform the pass of the neural network or to perform the subsequent pass of the neural network – this amounts to no more than a recitation of the words "apply it" (or an equivalent) including mere instructions to implement an abstract idea or other exception on a computer, and/or at most generally linking the use of the judicial exception to a particular technological environment or field of use. See MPEP § 2106.05(f) and (h).
Accordingly, at step 2A, prong two, these additional elements do not integrate the abstract idea into a practical application for the claim as a whole, because it does not impose any meaningful limits on practicing the abstract idea. See MPEP § 2106.04(d).
Under step 2B, the claims do not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the claim recites the additional elements of:
the method comprising: collecting… one or more statistics on a first set of network parameters for a layer of the neural network while the neural network accelerator is performing a pass of the neural network – this amounts to generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data). The courts have also found limitations directed to obtaining and storing 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.”
using a statistics collection hardware unit of a neural network accelerator – this amounts to mere instructions to apply the exception using a generic computer component, recited at a high level of generality. See MPEP § 2106.05(f).
using a format conversion hardware unit of the neural network accelerator – this amounts to mere instructions to apply the exception using a generic computer component, recited at a high level of generality. See MPEP § 2106.05(f).
the second set of network parameters comprising (i) the first set of network parameters and/or another set of network parameters for the layer, or (ii) a set of network parameters for a subsequent pass of the neural network corresponding to the first set of network parameters – this amounts to generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
and processing, using one or more network processing hardware units of the neural network accelerator – this amounts to mere instructions to apply the exception using a generic computer component, recited at a high level of generality. See MPEP § 2106.05(f).
processing, …the converted second set of network parameters in accordance with the neural network to perform the pass of the neural network or to perform the subsequent pass of the neural network – this amounts to no more than a recitation of the words "apply it" (or an equivalent) including mere instructions to implement an abstract idea or other exception on a computer, and/or at most generally linking the use of the judicial exception to a particular technological environment or field of use. See MPEP § 2106.05(f) and (h).
Accordingly, at step 2B, these additional elements, both individually and in combination, do not amount to significantly more than the judicial exception. See MPEP § 2106.05.
Therefore, the claim is not eligible subject matter under 35 U.S.C. 101.
As per claim 2:
Under step 2A, prong 1, the claim recites an abstract idea including the following mental process and/or mathematical concept elements:
configured to convert a second set of network parameters to a number format selected based on the collected one or more statistics – a data scientist dynamically selects a number format (such as floating/fixed point, precision, bit-width, etc.) for parameters of a neural network, based upon the collected statistics, and converts them. Additionally/alternatively – selecting the number format may be a mathematical calculation (see, e.g., para. [0110] of the specification as filed) and converting a number to a different format is a mathematical calculation (see, e.g., paras. [107]-[108] of the specification as filed).
If a claim, under the broadest reasonable interpretation covers a mathematical relationship between variables or numbers, a numerical formula or equation, or a mathematical calculation, it will be considered as falling within the “mathematical concepts” grouping of abstract ideas. If a claim, under the broadest reasonable interpretation covers concepts that can be performed in the human mind, or by a human using a pen and paper, including observation, evaluation, judgment, or opinion, it will be considered as falling within the “mental processes” grouping of abstract ideas. Additionally, performing mathematical calculations using a formula that could be practically performed in the human mind may be considered to fall within both the mathematical concepts grouping and the mental process grouping. See MPEP § 2106.04(a)(2).
Accordingly, at step 2A, prong one, the claim is directed to an abstract idea.
Under step 2A, prong two, the judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of:
a neural network accelerator, comprising: at least one network processing hardware unit – this amounts to mere instructions to apply the exception using a generic computer component, recited at a high level of generality. See MPEP § 2106.05(f).
configured to receive network parameters for layers of a neural network and perform one or more neural network operations on the received network parameters in accordance with the neural network – this amounts to no more than a recitation of the words "apply it" (or an equivalent) including mere instructions to implement an abstract idea or other exception on a computer, and/or at most generally linking the use of the judicial exception to a particular technological environment or field of use. See MPEP § 2106.05(f) and (h).
a statistics collection hardware unit – this amounts to mere instructions to apply the exception using a generic computer component, recited at a high level of generality. See MPEP § 2106.05(f).
configured to collect one or more statistics on a first set of network parameters for a layer while the neural network accelerator is performing a pass of the neural network – this is recited at a high level of generality and amounts to insignificant extra-solution activity as data gathering/storage that is limited to a particular type of data, generally linking the judicial exception to a particular technological environment or field of use. See MPEP § 2106.05(g) and (h), and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
and a format conversion hardware unit – this amounts to mere instructions to apply the exception using a generic computer component, recited at a high level of generality. See MPEP § 2106.05(f).
the second set of network parameters comprising (i) the first set of network parameters and/or another set of network parameters of the layer, or (ii) a set of network parameters for a subsequent pass of the neural network corresponding to the first set of network parameters – this amounts to generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
Accordingly, at step 2A, prong two, these additional elements do not integrate the abstract idea into a practical application for the claim as a whole, because it does not impose any meaningful limits on practicing the abstract idea. See MPEP § 2106.04(d).
Under step 2B, the claims do not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the claim recites the additional elements of:
a neural network accelerator, comprising: at least one network processing hardware unit – this amounts to mere instructions to apply the exception using a generic computer component, recited at a high level of generality. See MPEP § 2106.05(f).
configured to receive network parameters for layers of a neural network and perform one or more neural network operations on the received network parameters in accordance with the neural network – this amounts to no more than a recitation of the words "apply it" (or an equivalent) including mere instructions to implement an abstract idea or other exception on a computer, and/or at most generally linking the use of the judicial exception to a particular technological environment or field of use. See MPEP § 2106.05(f) and (h).
a statistics collection hardware unit – this amounts to mere instructions to apply the exception using a generic computer component, recited at a high level of generality. See MPEP § 2106.05(f).
configured to collect one or more statistics on a first set of network parameters for a layer while the neural network accelerator is performing a pass of the neural network – this amounts to generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data). The courts have also found limitations directed to obtaining and storing 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.”
and a format conversion hardware unit – this amounts to mere instructions to apply the exception using a generic computer component, recited at a high level of generality. See MPEP § 2106.05(f).
the second set of network parameters comprising (i) the first set of network parameters and/or another set of network parameters of the layer, or (ii) a set of network parameters for a subsequent pass of the neural network corresponding to the first set of network parameters – this amounts to generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
Accordingly, at step 2B, these additional elements, both individually and in combination, do not amount to significantly more than the judicial exception. See MPEP § 2106.05.
Therefore, the claim is not eligible subject matter under 35 U.S.C. 101.
As per claim 3:
Under step 2A, prong two, the judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of:
wherein the neural network accelerator is configured to provide the collected one or more statistics to an external unit coupled to the neural network accelerator – this is recited at a high level of generality and amounts to insignificant extra-solution activity as data gathering/storage that is limited to a particular type of data, generally linking the judicial exception to a particular technological environment or field of use. See MPEP § 2106.05(g) and (h), and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
the external unit configured to select the number format based on the collected one or more statistics in accordance with a format selection algorithm – this amounts to no more than a recitation of the words "apply it" (or an equivalent) including mere instructions to implement an abstract idea or other exception on a computer, and/or at most generally linking the use of the judicial exception to a particular technological environment or field of use. See MPEP § 2106.05(f) and (h).
Accordingly, at step 2A, prong two, these additional elements do not integrate the abstract idea into a practical application for the claim as a whole, because it does not impose any meaningful limits on practicing the abstract idea. See MPEP § 2106.04(d).
Under step 2B, the claims do not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the claim recites the additional elements of:
wherein the neural network accelerator is configured to provide the collected one or more statistics to an external unit coupled to the neural network accelerator – this amounts to generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data). The courts have also found limitations directed to obtaining and storing 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.”
the external unit configured to select the number format based on the collected one or more statistics in accordance with a format selection algorithm – this amounts to no more than a recitation of the words "apply it" (or an equivalent) including mere instructions to implement an abstract idea or other exception on a computer, and/or at most generally linking the use of the judicial exception to a particular technological environment or field of use. See MPEP § 2106.05(f) and (h).
Accordingly, at step 2B, these additional elements, both individually and in combination, do not amount to significantly more than the judicial exception. See MPEP § 2106.05.
Therefore, the claim is not eligible subject matter under 35 U.S.C. 101.
As per claim 4:
The claim recites the following additional mental process and/or mathematical concept elements:
select the number format based on the collected one or more statistics in accordance with a format selection algorithm – the data scientist selects the format based on a specified algorithm and the collected statistics. Alternatively/additionally – selecting the number format may be a mathematical calculation (see, e.g., para. [0110] of the specification as filed)
Accordingly, at step 2A, prong one, the claim is directed to an abstract idea.
Under step 2A, prong two, the judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of:
a format selection hardware unit that is configured to – this amounts to no more than a recitation of the words "apply it" (or an equivalent) including mere instructions to implement an abstract idea or other exception on a computer, and/or at most generally linking the use of the judicial exception to a particular technological environment or field of use. See MPEP § 2106.05(f) and (h).
Accordingly, at step 2A, prong two, these additional elements do not integrate the abstract idea into a practical application for the claim as a whole, because it does not impose any meaningful limits on practicing the abstract idea. See MPEP § 2106.04(d).
Under step 2B, the claims do not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the claim recites the additional elements of:
a format selection hardware unit that is configured to – this amounts to no more than a recitation of the words "apply it" (or an equivalent) including mere instructions to implement an abstract idea or other exception on a computer, and/or at most generally linking the use of the judicial exception to a particular technological environment or field of use. See MPEP § 2106.05(f) and (h).
Accordingly, at step 2B, these additional elements, both individually and in combination, do not amount to significantly more than the judicial exception. See MPEP § 2106.05.
Therefore, the claim is not eligible subject matter under 35 U.S.C. 101.
As per claim 5:
Under step 2A, prong two, the judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of:
wherein the first set of network parameters comprises all network parameters of a same type for the layer – this amounts to generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
and the second set of network parameters comprises a set of network parameters for a subsequent pass of the neural network that correspond to the first set of network parameters – this amounts to generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
Accordingly, at step 2A, prong two, these additional elements do not integrate the abstract idea into a practical application for the claim as a whole, because it does not impose any meaningful limits on practicing the abstract idea. See MPEP § 2106.04(d).
Under step 2B, the claims do not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the claim recites the additional elements of:
wherein the first set of network parameters comprises all network parameters of a same type for the layer – this amounts to generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
and the second set of network parameters comprises a set of network parameters for a subsequent pass of the neural network that correspond to the first set of network parameters – this amounts to generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
Accordingly, at step 2B, these additional elements, both individually and in combination, do not amount to significantly more than the judicial exception. See MPEP § 2106.05.
Therefore, the claim is not eligible subject matter under 35 U.S.C. 101.
As per claim 6:
Under step 2A, prong two, the judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of:
wherein the neural network accelerator is configured to perform a pass of the neural network in a plurality of hardware passes of the neural network accelerator – this amounts to no more than a recitation of the words "apply it" (or an equivalent) including mere instructions to implement an abstract idea or other exception on a computer, and/or at most generally linking the use of the judicial exception to a particular technological environment or field of use. See MPEP § 2106.05(f) and (h).
wherein for each hardware pass the neural network accelerator receives a set of input data corresponding to all or a portion of the input data to a layer of the neural network – this is recited at a high level of generality and amounts to insignificant extra-solution activity as data gathering/storage that is limited to a particular type of data, generally linking the judicial exception to a particular technological environment or field of use. See MPEP § 2106.05(g) and (h), and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
and processes that set of input data in accordance with at least the layer of the neural network for the pass of the neural network – this amounts to no more than a recitation of the words "apply it" (or an equivalent) including mere instructions to implement an abstract idea or other exception on a computer, and/or at most generally linking the use of the judicial exception to a particular technological environment or field of use. See MPEP § 2106.05(f) and (h).
Accordingly, at step 2A, prong two, these additional elements do not integrate the abstract idea into a practical application for the claim as a whole, because it does not impose any meaningful limits on practicing the abstract idea. See MPEP § 2106.04(d).
Under step 2B, the claims do not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the claim recites the additional elements of:
wherein the neural network accelerator is configured to perform a pass of the neural network in a plurality of hardware passes of the neural network accelerator – this amounts to no more than a recitation of the words "apply it" (or an equivalent) including mere instructions to implement an abstract idea or other exception on a computer, and/or at most generally linking the use of the judicial exception to a particular technological environment or field of use. See MPEP § 2106.05(f) and (h).
wherein for each hardware pass the neural network accelerator receives a set of input data corresponding to all or a portion of the input data to a layer of the neural network – this amounts to generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data). The courts have also found limitations directed to obtaining and storing 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.”
and processes that set of input data in accordance with at least the layer of the neural network for the pass of the neural network – this amounts to no more than a recitation of the words "apply it" (or an equivalent) including mere instructions to implement an abstract idea or other exception on a computer, and/or at most generally linking the use of the judicial exception to a particular technological environment or field of use. See MPEP § 2106.05(f) and (h).
Accordingly, at step 2B, these additional elements, both individually and in combination, do not amount to significantly more than the judicial exception. See MPEP § 2106.05.
Therefore, the claim is not eligible subject matter under 35 U.S.C. 101.
As per claim 7:
Under step 2A, prong two, the judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of:
wherein the first set of network parameters comprises all of the network parameters of a particular type for a layer that are in a hardware pass of the neural network accelerator for the pass of the neural network – this amounts to generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
Accordingly, at step 2A, prong two, these additional elements do not integrate the abstract idea into a practical application for the claim as a whole, because it does not impose any meaningful limits on practicing the abstract idea. See MPEP § 2106.04(d).
Under step 2B, the claims do not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the claim recites the additional elements of:
wherein the first set of network parameters comprises all of the network parameters of a particular type for a layer that are in a hardware pass of the neural network accelerator for the pass of the neural network – this amounts to generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
Accordingly, at step 2B, these additional elements, both individually and in combination, do not amount to significantly more than the judicial exception. See MPEP § 2106.05.
Therefore, the claim is not eligible subject matter under 35 U.S.C. 101.
As per claim 8:
Under step 2A, prong two, the judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of:
wherein the second set of network parameters comprises all of the network parameters of the particular type for the layer that are in another hardware pass of the neural network accelerator for the pass of the neural network and/or the first set of network parameters – this amounts to generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
Accordingly, at step 2A, prong two, these additional elements do not integrate the abstract idea into a practical application for the claim as a whole, because it does not impose any meaningful limits on practicing the abstract idea. See MPEP § 2106.04(d).
Under step 2B, the claims do not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the claim recites the additional elements of:
wherein the second set of network parameters comprises all of the network parameters of the particular type for the layer that are in another hardware pass of the neural network accelerator for the pass of the neural network and/or the first set of network parameters – this amounts to generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
Accordingly, at step 2B, these additional elements, both individually and in combination, do not amount to significantly more than the judicial exception. See MPEP § 2106.05.
Therefore, the claim is not eligible subject matter under 35 U.S.C. 101.
As per claim 9:
Under step 2A, prong two, the judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of:
wherein the second set of network parameters comprises the network parameters in a hardware pass of the neural network accelerator for a subsequent pass of the neural network that correspond to the first set of network parameters – this amounts to generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
Accordingly, at step 2A, prong two, these additional elements do not integrate the abstract idea into a practical application for the claim as a whole, because it does not impose any meaningful limits on practicing the abstract idea. See MPEP § 2106.04(d).
Under step 2B, the claims do not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the claim recites the additional elements of:
wherein the second set of network parameters comprises the network parameters in a hardware pass of the neural network accelerator for a subsequent pass of the neural network that correspond to the first set of network parameters – this amounts to generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
Accordingly, at step 2B, these additional elements, both individually and in combination, do not amount to significantly more than the judicial exception. See MPEP § 2106.05.
Therefore, the claim is not eligible subject matter under 35 U.S.C. 101.
As per claim 10:
Under step 2A, prong two, the judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of:
wherein the first set of network parameters comprises a subset of the network parameters of a particular type for the layer that are in a hardware pass of the neural network accelerator for the pass of the neural network – this amounts to generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
Accordingly, at step 2A, prong two, these additional elements do not integrate the abstract idea into a practical application for the claim as a whole, because it does not impose any meaningful limits on practicing the abstract idea. See MPEP § 2106.04(d).
Under step 2B, the claims do not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the claim recites the additional elements of:
wherein the first set of network parameters comprises a subset of the network parameters of a particular type for the layer that are in a hardware pass of the neural network accelerator for the pass of the neural network – this amounts to generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
Accordingly, at step 2B, these additional elements, both individually and in combination, do not amount to significantly more than the judicial exception. See MPEP § 2106.05.
Therefore, the claim is not eligible subject matter under 35 U.S.C. 101.
As per claim 11:
Under step 2A, prong two, the judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of:
wherein the second set of network parameters comprises another subset of the network parameters of the particular type for the layer that are in the hardware pass of the neural network accelerator for the pass of the neural network and/or the first set of network parameters – this amounts to generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
Accordingly, at step 2A, prong two, these additional elements do not integrate the abstract idea into a practical application for the claim as a whole, because it does not impose any meaningful limits on practicing the abstract idea. See MPEP § 2106.04(d).
Under step 2B, the claims do not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the claim recites the additional elements of:
wherein the second set of network parameters comprises another subset of the network parameters of the particular type for the layer that are in the hardware pass of the neural network accelerator for the pass of the neural network and/or the first set of network parameters – this amounts to generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
Accordingly, at step 2B, these additional elements, both individually and in combination, do not amount to significantly more than the judicial exception. See MPEP § 2106.05.
Therefore, the claim is not eligible subject matter under 35 U.S.C. 101.
As per claim 12:
Under step 2A, prong two, the judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of:
wherein the second set of network parameters comprises the network parameters in a hardware pass of the neural network accelerator for a subsequent pass of the neural network that correspond to the first set of network parameters – this amounts to generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
Accordingly, at step 2A, prong two, these additional elements do not integrate the abstract idea into a practical application for the claim as a whole, because it does not impose any meaningful limits on practicing the abstract idea. See MPEP § 2106.04(d).
Under step 2B, the claims do not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the claim recites the additional elements of:
wherein the second set of network parameters comprises the network parameters in a hardware pass of the neural network accelerator for a subsequent pass of the neural network that correspond to the first set of network parameters – this amounts to generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
Accordingly, at step 2B, these additional elements, both individually and in combination, do not amount to significantly more than the judicial exception. See MPEP § 2106.05.
Therefore, the claim is not eligible subject matter under 35 U.S.C. 101.
As per claim 13:
The claim recites the following additional mental process and/or mathematical concept elements:
is configured to convert the first set of network parameters to a configurable number format– a data scientist dynamically selects a number format (such as floating/fixed point, precision, bit-width, etc.) for parameters of a neural network, based upon the collected statistics, and converts them. Additionally/alternatively – selecting the number format may be a mathematical calculation (see, e.g., para. [0110] of the specification as filed) and converting a number to a different format is a mathematical calculation (see, e.g., paras. [107]-[108] of the specification as filed).
Accordingly, at step 2A, prong one, the claim is directed to an abstract idea.
Under step 2A, prong two, the judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of:
the format conversion hardware unit … prior to the first set of network parameters being processed by one or more of the at least one network processing hardware unit – this amounts to no more than a recitation of the words "apply it" (or an equivalent) including mere instructions to implement an abstract idea or other exception on a computer, and/or at most generally linking the use of the judicial exception to a particular technological environment or field of use. See MPEP § 2106.05(f) and (h).
and the statistics collection hardware unit – this amounts to no more than a recitation of the words "apply it" (or an equivalent) including mere instructions to implement an abstract idea or other exception on a computer, and/or at most generally linking the use of the judicial exception to a particular technological environment or field of use. See MPEP § 2106.05(f) and (h).
is configured to collect the statistics on the first set of network parameters prior to the format conversion performed by the format conversion hardware unit – this is recited at a high level of generality and amounts to insignificant extra-solution activity as data gathering/storage that is limited to a particular type of data, generally linking the judicial exception to a particular technological environment or field of use. See MPEP § 2106.05(g) and (h), and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
Accordingly, at step 2A, prong two, these additional elements do not integrate the abstract idea into a practical application for the claim as a whole, because it does not impose any meaningful limits on practicing the abstract idea. See MPEP § 2106.04(d).
Under step 2B, the claims do not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the claim recites the additional elements of:
the format conversion hardware unit … prior to the first set of network parameters being processed by one or more of the at least one network processing hardware unit – this amounts to no more than a recitation of the words "apply it" (or an equivalent) including mere instructions to implement an abstract idea or other exception on a computer, and/or at most generally linking the use of the judicial exception to a particular technological environment or field of use. See MPEP § 2106.05(f) and (h).
and the statistics collection hardware unit – this amounts to no more than a recitation of the words "apply it" (or an equivalent) including mere instructions to implement an abstract idea or other exception on a computer, and/or at most generally linking the use of the judicial exception to a particular technological environment or field of use. See MPEP § 2106.05(f) and (h).
is configured to collect the statistics on the first set of network parameters prior to the format conversion performed by the format conversion hardware unit – this amounts to generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data). The courts have also found limitations directed to obtaining and storing 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.”
Accordingly, at step 2B, these additional elements, both individually and in combination, do not amount to significantly more than the judicial exception. See MPEP § 2106.05.
Therefore, the claim is not eligible subject matter under 35 U.S.C. 101.
As per claim 14:
The claim recites the following additional mental process and/or mathematical concept elements:
is configured to convert the first set of network parameters to a configurable number format– a data scientist dynamically selects a number format (such as floating/fixed point, precision, bit-width, etc.) for parameters of a neural network, based upon the collected statistics, and converts them. Additionally/alternatively – selecting the number format may be a mathematical calculation (see, e.g., para. [0110] of the specification as filed) and converting a number to a different format is a mathematical calculation (see, e.g., paras. [107]-[108] of the specification as filed).
Accordingly, at step 2A, prong one, the claim is directed to an abstract idea.
Under step 2A, prong two, the judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of:
the format conversion hardware unit … prior to the first set of network parameters being processed by one or more of the at least one network processing hardware unit – this amounts to no more than a recitation of the words "apply it" (or an equivalent) including mere instructions to implement an abstract idea or other exception on a computer, and/or at most generally linking the use of the judicial exception to a particular technological environment or field of use. See MPEP § 2106.05(f) and (h).
and the statistics collection hardware unit – this amounts to no more than a recitation of the words "apply it" (or an equivalent) including mere instructions to implement an abstract idea or other exception on a computer, and/or at most generally linking the use of the judicial exception to a particular technological environment or field of use. See MPEP § 2106.05(f) and (h).
is configured to collect the statistics on the first set of network parameters after the format conversion performed by the format conversion hardware unit – this is recited at a high level of generality and amounts to insignificant extra-solution activity as data gathering/storage that is limited to a particular type of data, generally linking the judicial exception to a particular technological environment or field of use. See MPEP § 2106.05(g) and (h), and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
the second set of network parameters comprises (i) another set of network parameters for the layer for the pass of the neural network, or (ii) a set of network parameters for a subsequent pass of the neural network corresponding to the first set of network parameters – this amounts to generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
Accordingly, at step 2A, prong two, these additional elements do not integrate the abstract idea into a practical application for the claim as a whole, because it does not impose any meaningful limits on practicing the abstract idea. See MPEP § 2106.04(d).
Under step 2B, the claims do not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the claim recites the additional elements of:
the format conversion hardware unit … prior to the first set of network parameters being processed by one or more of the at least one network processing hardware unit – this amounts to no more than a recitation of the words "apply it" (or an equivalent) including mere instructions to implement an abstract idea or other exception on a computer, and/or at most generally linking the use of the judicial exception to a particular technological environment or field of use. See MPEP § 2106.05(f) and (h).
and the statistics collection hardware unit – this amounts to no more than a recitation of the words "apply it" (or an equivalent) including mere instructions to implement an abstract idea or other exception on a computer, and/or at most generally linking the use of the judicial exception to a particular technological environment or field of use. See MPEP § 2106.05(f) and (h).
is configured to collect the statistics on the first set of network parameters after the format conversion performed by the format conversion hardware unit – this amounts to generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data). The courts have also found limitations directed to obtaining and storing 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.”
the second set of network parameters comprises (i) another set of network parameters for the layer for the pass of the neural network, or (ii) a set of network parameters for a subsequent pass of the neural network corresponding to the first set of network parameters – this amounts to generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
Accordingly, at step 2B, these additional elements, both individually and in combination, do not amount to significantly more than the judicial exception. See MPEP § 2106.05.
Therefore, the claim is not eligible subject matter under 35 U.S.C. 101.
As per claim 15:
The claim recites the following additional mental process and/or mathematical concept elements:
configured to convert a fourth set of network parameters to a number format selected based on the one or more statistics collected by the other statistics collection hardware unit – a data scientist dynamically selects a number format (such as floating/fixed point, precision, bit-width, etc.) for parameters of a neural network, based upon the collected statistics, and converts them. Additionally/alternatively – selecting the number format may be a mathematical calculation (see, e.g., para. [0110] of the specification as filed) and converting a number to a different format is a mathematical calculation (see, e.g., paras. [107]-[108] of the specification as filed).
Accordingly, at step 2A, prong one, the claim is directed to an abstract idea.
Under step 2A, prong two, the judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of:
another statistics collection hardware unit – this amounts to no more than a recitation of the words "apply it" (or an equivalent) including mere instructions to implement an abstract idea or other exception on a computer, and/or at most generally linking the use of the judicial exception to a particular technological environment or field of use. See MPEP § 2106.05(f) and (h).
configured to collect one or more statistics on a third set of network parameters for another layer of the neural network while the neural network accelerator is performing the pass of the neural network – this is recited at a high level of generality and amounts to insignificant extra-solution activity as data gathering/storage that is limited to a particular type of data, generally linking the judicial exception to a particular technological environment or field of use. See MPEP § 2106.05(g) and (h), and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
and another format conversion hardware unit – this amounts to no more than a recitation of the words "apply it" (or an equivalent) including mere instructions to implement an abstract idea or other exception on a computer, and/or at most generally linking the use of the judicial exception to a particular technological environment or field of use. See MPEP § 2106.05(f) and (h).
the fourth set of network parameters comprising (i) the third set of network parameters and/or another set of network parameters for the other layer, or (ii) a set of network parameters for a subsequent pass of the neural network corresponding to the third set of network parameters – this amounts to generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
Accordingly, at step 2A, prong two, these additional elements do not integrate the abstract idea into a practical application for the claim as a whole, because it does not impose any meaningful limits on practicing the abstract idea. See MPEP § 2106.04(d).
Under step 2B, the claims do not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the claim recites the additional elements of:
another statistics collection hardware unit – this amounts to no more than a recitation of the words "apply it" (or an equivalent) including mere instructions to implement an abstract idea or other exception on a computer, and/or at most generally linking the use of the judicial exception to a particular technological environment or field of use. See MPEP § 2106.05(f) and (h).
configured to collect one or more statistics on a third set of network parameters for another layer of the neural network while the neural network accelerator is performing the pass of the neural network – this amounts to generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data). The courts have also found limitations directed to obtaining and storing 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.”
and another format conversion hardware unit – this amounts to no more than a recitation of the words "apply it" (or an equivalent) including mere instructions to implement an abstract idea or other exception on a computer, and/or at most generally linking the use of the judicial exception to a particular technological environment or field of use. See MPEP § 2106.05(f) and (h).
the fourth set of network parameters comprising (i) the third set of network parameters and/or another set of network parameters for the other layer, or (ii) a set of network parameters for a subsequent pass of the neural network corresponding to the third set of network parameters – this amounts to generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
Accordingly, at step 2B, these additional elements, both individually and in combination, do not amount to significantly more than the judicial exception. See MPEP § 2106.05.
Therefore, the claim is not eligible subject matter under 35 U.S.C. 101.
As per claim 16:
Under step 2A, prong two, the judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of:
wherein the pass of the neural network and the subsequent pass of the neural network are forward passes of the neural network; or
wherein the pass of the neural network and the subsequent pass of the network are backward passes of the neural network – this amounts to generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
Accordingly, at step 2A, prong two, these additional elements do not integrate the abstract idea into a practical application for the claim as a whole, because it does not impose any meaningful limits on practicing the abstract idea. See MPEP § 2106.04(d).
Under step 2B, the claims do not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the claim recites the additional elements of:
wherein the pass of the neural network and the subsequent pass of the neural network are forward passes of the neural network; or
wherein the pass of the neural network and the subsequent pass of the network are backward passes of the neural network – this amounts to generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
Accordingly, at step 2B, these additional elements, both individually and in combination, do not amount to significantly more than the judicial exception. See MPEP § 2106.05.
Therefore, the claim is not eligible subject matter under 35 U.S.C. 101.
As per claim 17:
See the rejection of claim 2, above, wherein under step 2A, prong two, the judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of:
A computer system comprising: the neural network accelerator as set forth in claim 2 – this amounts to no more than a recitation of the words "apply it" (or an equivalent) including mere instructions to implement an abstract idea or other exception on a computer, and/or at most generally linking the use of the judicial exception to a particular technological environment or field of use. See MPEP § 2106.05(f) and (h).
an external unit coupled to the neural network accelerator, the external unit configured to select the number format based on the collected one or more statistics in accordance with a format selection algorithm – this amounts to no more than a recitation of the words "apply it" (or an equivalent) including mere instructions to implement an abstract idea or other exception on a computer, and/or at most generally linking the use of the judicial exception to a particular technological environment or field of use. See MPEP § 2106.05(f) and (h).
Accordingly, at step 2A, prong two, these additional elements do not integrate the abstract idea into a practical application for the claim as a whole, because it does not impose any meaningful limits on practicing the abstract idea. See MPEP § 2106.04(d).
Under step 2B, the claims do not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the claim recites the additional elements of:
A computer system comprising: the neural network accelerator as set forth in claim 2 – this amounts to no more than a recitation of the words "apply it" (or an equivalent) including mere instructions to implement an abstract idea or other exception on a computer, and/or at most generally linking the use of the judicial exception to a particular technological environment or field of use. See MPEP § 2106.05(f) and (h).
an external unit coupled to the neural network accelerator, the external unit configured to select the number format based on the collected one or more statistics in accordance with a format selection algorithm – this amounts to no more than a recitation of the words "apply it" (or an equivalent) including mere instructions to implement an abstract idea or other exception on a computer, and/or at most generally linking the use of the judicial exception to a particular technological environment or field of use. See MPEP § 2106.05(f) and (h).
Accordingly, at step 2B, these additional elements, both individually and in combination, do not amount to significantly more than the judicial exception. See MPEP § 2106.05.
Therefore, the claim is not eligible subject matter under 35 U.S.C. 101.
As per claim 18:
Under step 2A, prong two, the judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of:
wherein the external unit is a central processing unit that controls the operation of the neural network accelerator – this amounts to no more than a recitation of the words "apply it" (or an equivalent) including mere instructions to implement an abstract idea or other exception on a computer, and/or at most generally linking the use of the judicial exception to a particular technological environment or field of use. See MPEP § 2106.05(f) and (h).
Accordingly, at step 2A, prong two, these additional elements do not integrate the abstract idea into a practical application for the claim as a whole, because it does not impose any meaningful limits on practicing the abstract idea. See MPEP § 2106.04(d).
Under step 2B, the claims do not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the claim recites the additional elements of:
wherein the external unit is a central processing unit that controls the operation of the neural network accelerator – this amounts to no more than a recitation of the words "apply it" (or an equivalent) including mere instructions to implement an abstract idea or other exception on a computer, and/or at most generally linking the use of the judicial exception to a particular technological environment or field of use. See MPEP § 2106.05(f) and (h).
Accordingly, at step 2B, these additional elements, both individually and in combination, do not amount to significantly more than the judicial exception. See MPEP § 2106.05.
Therefore, the claim is not eligible subject matter under 35 U.S.C. 101.
As per claim 19:
Under step 2A, prong two, the judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of:
wherein the external unit is distinct from a central processing unit that controls the operation of the neural network accelerator – this amounts to no more than a recitation of the words "apply it" (or an equivalent) including mere instructions to implement an abstract idea or other exception on a computer, and/or at most generally linking the use of the judicial exception to a particular technological environment or field of use. See MPEP § 2106.05(f) and (h).
Accordingly, at step 2A, prong two, these additional elements do not integrate the abstract idea into a practical application for the claim as a whole, because it does not impose any meaningful limits on practicing the abstract idea. See MPEP § 2106.04(d).
Under step 2B, the claims do not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the claim recites the additional elements of:
wherein the external unit is distinct from a central processing unit that controls the operation of the neural network accelerator – this amounts to no more than a recitation of the words "apply it" (or an equivalent) including mere instructions to implement an abstract idea or other exception on a computer, and/or at most generally linking the use of the judicial exception to a particular technological environment or field of use. See MPEP § 2106.05(f) and (h).
Accordingly, at step 2B, these additional elements, both individually and in combination, do not amount to significantly more than the judicial exception. See MPEP § 2106.05.
Therefore, the claim is not eligible subject matter under 35 U.S.C. 101.
As per claim 20:
See the rejection of claim 2, above, wherein under step 2A, prong two, the judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of:
A non-transitory computer readable storage medium having stored thereon a computer readable description of the neural network accelerator as set forth in claim 2 – this amounts to no more than a recitation of the words "apply it" (or an equivalent) including mere instructions to implement an abstract idea or other exception on a computer, and/or at most generally linking the use of the judicial exception to a particular technological environment or field of use. See MPEP § 2106.05(f) and (h).
that, when processed in an integrated circuit manufacturing system, causes the integrated circuit manufacturing system to manufacture an integrated circuit embodying the neural network accelerator – this amounts to no more than a recitation of the words "apply it" (or an equivalent) including mere instructions to implement an abstract idea or other exception on a computer, and/or at most generally linking the use of the judicial exception to a particular technological environment or field of use. See MPEP § 2106.05(f) and (h).
Accordingly, at step 2A, prong two, these additional elements do not integrate the abstract idea into a practical application for the claim as a whole, because it does not impose any meaningful limits on practicing the abstract idea. See MPEP § 2106.04(d).
Under step 2B, the claims do not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the claim recites the additional elements of:
A non-transitory computer readable storage medium having stored thereon a computer readable description of the neural network accelerator as set forth in claim 2 – this amounts to no more than a recitation of the words "apply it" (or an equivalent) including mere instructions to implement an abstract idea or other exception on a computer, and/or at most generally linking the use of the judicial exception to a particular technological environment or field of use. See MPEP § 2106.05(f) and (h).
that, when processed in an integrated circuit manufacturing system, causes the integrated circuit manufacturing system to manufacture an integrated circuit embodying the neural network accelerator – this amounts to no more than a recitation of the words "apply it" (or an equivalent) including mere instructions to implement an abstract idea or other exception on a computer, and/or at most generally linking the use of the judicial exception to a particular technological environment or field of use. See MPEP § 2106.05(f) and (h).
Accordingly, at step 2B, these additional elements, both individually and in combination, do not amount to significantly more than the judicial exception. See MPEP § 2106.05.
Therefore, the claim is not eligible subject matter under 35 U.S.C. 101.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1-20 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Rouhani (US 2020/0210840 – cited in an IDS)
As per claim 1, Rouhani teaches a method of dynamically selecting a number format for a set of network parameters of a neural network [a performance metric(s) for a neural network undergoing training may be determined and an adjustment can be made to a precision parameter for the neural network can be made based upon the determined metric(s) (paras. 0002-3; fig. 11; etc.) which includes determining precision formats for weights of layers of a neural network (paras. 0002-3, 0108, 0140-141, 0165, etc.), where the precision of the weights is the number format of a set of network parameters], the method comprising:
collecting, using a statistics collection hardware unit of a neural network accelerator, one or more statistics on a first set of network parameters for a layer of the neural network while the neural network accelerator is performing a pass of the neural network [the performance metrics can be collected at different points during iterations of the training process using dedicated circuitry (paras. 0091, 0099, 0108, 0160, etc.)];
selecting a number format based on the collected one or more statistics [a performance metric(s) for a neural network undergoing training may be determined and an adjustment can be made to a precision parameter for the neural network can be made based upon the determined metric(s) (paras. 0002-3; fig. 11; etc.) which includes determining precision formats for weights of layers of a neural network (paras. 0002-3, 0108, 0140-141, 0165, etc.), where the precision of the weights is the number format of a set of network parameters];
converting, using a format conversion hardware unit of the neural network accelerator, a second set of network parameters to the selected number format [outputs can be determined for a first layer of the network, a performance metric determined, and the precision parameter adjusted for a subsequent layer and/or subsequent iterations, based upon the metric (para. 0165, see also: paras. 0002-3, 0108, 0140-141, etc.) using dedicated or configurable circuits (paras. 0003, 0041-47, etc.)], the second set of network parameters comprising (i) the first set of network parameters and/or another set of network parameters for the layer, or (ii) a set of network parameters for a subsequent pass of the neural network corresponding to the first set of network parameters [outputs can be determined for a first layer of the network, a performance metric determined, and the precision parameter adjusted for a subsequent layer and/or subsequent iterations, based upon the metric (para. 0165, see also: paras. 0002-3, 0108, 0140-141, etc.)]; and
processing, using one or more network processing hardware units of the neural network accelerator, the converted second set of network parameters in accordance with the neural network to perform the pass of the neural network or to perform the subsequent pass of the neural network [outputs can be determined for a first layer of the network, a performance metric determined, and the precision parameter adjusted for a subsequent layer and/or subsequent iterations, based upon the metric (para. 0165, see also: paras. 0002-3, 0108, 0140-141, etc.), which iterations are performed on one or more NN processing units or accelerators (paras. 0026, 0042-46; fig. 1; etc.), which includes subsequent passes of the neural network].
As per claim 2, Rouhani teaches a neural network accelerator [a neural network accelerator system (fig. 1, etc.)], comprising:
at least one network processing hardware unit configured to receive network parameters for layers of a neural network and perform one or more neural network operations on the received network parameters in accordance with the neural network [iterations of training are performed on one or more NN processing units or accelerators using received inputs, weights, precision parameters, etc. (paras. 0026, 0041-46; figs. 1, 7; etc.)];
a statistics collection hardware unit configured to collect one or more statistics on a first set of network parameters for a layer while the neural network accelerator is performing a pass of the neural network [the performance metrics can be collected at different points during iterations of the training process using dedicated circuitry (paras. 0091, 0099, 0108, 0160, etc.)]; and
a format conversion hardware unit configured to convert a second set of network parameters to a number format selected based on the collected one or more statistics [outputs can be determined for a first layer of the network, a performance metric determined, and the precision parameter adjusted for a subsequent layer and/or subsequent iterations, based upon the metric (para. 0165, see also: paras. 0002-3, 0108, 0140-141, etc.) using dedicated or configurable circuits (paras. 0003, 0041-47, etc.)], the second set of network parameters comprising (i) the first set of network parameters and/or another set of network parameters of the layer, or (ii) a set of network parameters for a subsequent pass of the neural network corresponding to the first set of network parameters [outputs can be determined for a first layer of the network, a performance metric determined, and the precision parameter adjusted for a subsequent layer and/or subsequent iterations, based upon the metric (para. 0165, see also: paras. 0002-3, 0108, 0140-141, etc.)].
As per claim 3, Rouhani teaches wherein the neural network accelerator is configured to provide the collected one or more statistics to an external unit coupled to the neural network accelerator, the external unit configured to select the number format based on the collected one or more statistics in accordance with a format selection algorithm [outputs can be determined for a first layer of the network, a performance metric determined, and the precision parameter adjusted for a subsequent layer and/or subsequent iterations, based upon the metric (para. 0165, see also: paras. 0002-3, 0108, 0140-141, etc.) where the metrics and adjustments can be passed/collected/performed remotely across multiple computer systems, or by a cloud server system, etc. (paras. 0063, 0155-159, etc.)].
As per claim 4, Rouhani teaches a format selection hardware unit that is configured to select the number format based on the collected one or more statistics in accordance with a format selection algorithm [outputs can be determined for a first layer of the network, a performance metric determined, and the precision parameter adjusted for a subsequent layer and/or subsequent iterations, based upon the metric (para. 0165, see also: paras. 0002-3, 0108, 0140-141, etc.) using dedicated or configurable circuits (paras. 0003, 0041-47, etc.) which can be based upon multiple different performance metrics or selection methods (paras. 0112, 0123-127, 0138, etc.)].
As per claim 5, Rouhani teaches wherein the first set of network parameters comprises all network parameters of a same type for the layer, and the second set of network parameters comprises a set of network parameters for a subsequent pass of the neural network that correspond to the first set of network parameters [outputs can be determined for a first layer of the network, a performance metric determined, and the precision parameter adjusted for a subsequent layer and/or subsequent iterations, based upon the metric (para. 0165, see also: paras. 0002-3, 0108, 0140-141, etc.); where the parameters of the layer are a type, and where this can also be applied to selected nodes or filters (paras. 0056-58, 0063, 0088-90, etc.) or to account for different types of data (para. 0094, etc.)].
As per claim 6, Rouhani teaches wherein the neural network accelerator is configured to perform a pass of the neural network in a plurality of hardware passes of the neural network accelerator, wherein for each hardware pass the neural network accelerator receives a set of input data corresponding to all or a portion of the input data to a layer of the neural network and processes that set of input data in accordance with at least the layer of the neural network for the pass of the neural network [outputs can be determined for a first layer of the network, a performance metric determined, and the precision parameter adjusted for a subsequent layer and/or subsequent iterations, based upon the metric (para. 0165, see also: paras. 0002-3, 0108, 0140-141, etc.), which iterations are performed on one or more NN processing units or accelerators (paras. 0026, 0042-46; fig. 1; etc.); where each iteration is a pass].
As per claim 7, Rouhani teaches wherein the first set of network parameters comprises all of the network parameters of a particular type for a layer that are in a hardware pass of the neural network accelerator for the pass of the neural network [outputs can be determined for a first layer of the network, a performance metric determined, and the precision parameter adjusted for a subsequent layer and/or subsequent iterations, based upon the metric (para. 0165, see also: paras. 0002-3, 0108, 0140-141, etc.); where the parameters of the layer are a type, and where this can also be applied to selected nodes or filters (paras. 0056-58, 0063, 0088-90, etc.) or to account for different types of data (para. 0094, etc.)].
As per claim 8, Rouhani teaches wherein the second set of network parameters comprises all of the network parameters of the particular type for the layer that are in another hardware pass of the neural network accelerator for the pass of the neural network and/or the first set of network parameters [outputs can be determined for a first layer of the network, a performance metric determined, and the precision parameter adjusted for a subsequent layer and/or subsequent iterations, based upon the metric (para. 0165, see also: paras. 0002-3, 0108, 0140-141, etc.); where the parameters of the layer are a type, and where this can also be applied to selected nodes or filters (paras. 0056-58, 0063, 0088-90, etc.) or to account for different types of data (para. 0094, etc.)].
As per claim 9, Rouhani teaches wherein the second set of network parameters comprises the network parameters in a hardware pass of the neural network accelerator for a subsequent pass of the neural network that correspond to the first set of network parameters [outputs can be determined for a first layer of the network, a performance metric determined, and the precision parameter adjusted for a subsequent layer and/or subsequent iterations, based upon the metric (para. 0165, see also: paras. 0002-3, 0108, 0140-141, etc.), which iterations are performed on one or more NN processing units or accelerators (paras. 0026, 0042-46; fig. 1; etc.); where each iteration is a pass].
As per claim 10, Rouhani teaches wherein the first set of network parameters comprises a subset of the network parameters of a particular type for the layer that are in a hardware pass of the neural network accelerator for the pass of the neural network [outputs can be determined for a first layer of the network, a performance metric determined, and the precision parameter adjusted for a subsequent layer and/or subsequent iterations, based upon the metric (para. 0165, see also: paras. 0002-3, 0108, 0140-141, etc.); where the parameters of the layer are a type, and where this can also be applied to selected nodes or filters (paras. 0056-58, 0063, 0088-90, etc.) or to account for different types of data (para. 0094, etc.)].
As per claim 11, Rouhani teaches wherein the second set of network parameters comprises another subset of the network parameters of the particular type for the layer that are in the hardware pass of the neural network accelerator for the pass of the neural network and/or the first set of network parameters [outputs can be determined for a first layer of the network, a performance metric determined, and the precision parameter adjusted for a subsequent layer and/or subsequent iterations, based upon the metric (para. 0165, see also: paras. 0002-3, 0108, 0140-141, etc.); where the parameters of the layer are a type, and where this can also be applied to selected nodes or filters (paras. 0056-58, 0063, 0088-90, etc.) or to account for different types of data (para. 0094, etc.); where selecting another node/filter of the layer, or the same type of data, would apply the precision parameter to a second set of network parameters comprising another subset of the network parameters of the particular type for the layer].
As per claim 12, Rouhani teaches wherein the second set of network parameters comprises the network parameters in a hardware pass of the neural network accelerator for a subsequent pass of the neural network that correspond to the first set of network parameters [outputs can be determined for a first layer of the network, a performance metric determined, and the precision parameter adjusted for a subsequent layer and/or subsequent iterations, based upon the metric (para. 0165, see also: paras. 0002-3, 0108, 0140-141, etc.), which iterations are performed on one or more NN processing units or accelerators (paras. 0026, 0042-46; fig. 1; etc.); where each iteration is a pass].
As per claim 13, Rouhani teaches wherein:
the format conversion hardware unit is configured to convert the first set of network parameters to a configurable number format prior to the first set of network parameters being processed by one or more of the at least one network processing hardware unit [outputs can be determined for a first layer of the network, a performance metric determined, and the precision parameter adjusted for a subsequent layer and/or subsequent iterations, based upon the metric (para. 0165, see also: paras. 0002-3, 0108, 0140-141, etc.), which iterations are performed on one or more NN processing units or accelerators (paras. 0026, 0042-46; fig. 1; etc.); where converting for a subsequent iteration is converting prior to being processed by the hardware unit]; and
the statistics collection hardware unit is configured to collect the statistics on the first set of network parameters prior to the format conversion performed by the format conversion hardware unit [outputs can be determined for a first layer of the network, a performance metric determined, and the precision parameter adjusted for a subsequent layer and/or subsequent iterations, based upon the metric (para. 0165, see also: paras. 0002-3, 0108, 0140-141, etc.), which iterations are performed on one or more NN processing units or accelerators (paras. 0026, 0042-46; fig. 1; etc.); where collecting the metric after each or multiple iterations is prior to the next format conversion].
As per claim 14, Rouhani teaches wherein:
the format conversion hardware unit is configured to convert the first set of network parameters to a configurable number format prior to the first set of network parameters being processed by one or more of the at least one network processing hardware unit [outputs can be determined for a first layer of the network, a performance metric determined, and the precision parameter adjusted for a subsequent layer and/or subsequent iterations, based upon the metric (para. 0165, see also: paras. 0002-3, 0108, 0140-141, etc.), which iterations are performed on one or more NN processing units or accelerators (paras. 0026, 0042-46; fig. 1; etc.); where converting for a subsequent iteration is converting prior to being processed by the hardware unit];
the statistics collection hardware unit is configured to collect the statistics on the first set of network parameters after the format conversion performed by the format conversion hardware unit [outputs can be determined for a first layer of the network, a performance metric determined, and the precision parameter adjusted for a subsequent layer and/or subsequent iterations, based upon the metric (para. 0165, see also: paras. 0002-3, 0108, 0140-141, etc.), which iterations are performed on one or more NN processing units or accelerators (paras. 0026, 0042-46; fig. 1; etc.); where collecting the metric after each or multiple iterations is collecting prior to- and after conversions (happening at each iteration)]; and
the second set of network parameters comprises (i) another set of network parameters for the layer for the pass of the neural network, or (ii) a set of network parameters for a subsequent pass of the neural network corresponding to the first set of network parameters [outputs can be determined for a first layer of the network, a performance metric determined, and the precision parameter adjusted for a subsequent layer and/or subsequent iterations, based upon the metric (para. 0165, see also: paras. 0002-3, 0108, 0140-141, etc.)].
As per claim 15, Rouhani teaches further comprising:
another statistics collection hardware unit configured to collect one or more statistics on a third set of network parameters for another layer of the neural network while the neural network accelerator is performing the pass of the neural network [outputs can be determined for a first layer of the network, a performance metric determined, and the precision parameter adjusted for a subsequent layer and/or subsequent iterations, based upon the metric (para. 0165, see also: paras. 0002-3, 0108, 0140-141, etc.) where the metrics can be passed/collected remotely across multiple computer systems, or by a cloud server system, etc. (paras. 0063, 0155-159, etc.)]; and
another format conversion hardware unit configured to convert a fourth set of network parameters to a number format selected based on the one or more statistics collected by the other statistics collection hardware unit [outputs can be determined for a first layer of the network, a performance metric determined, and the precision parameter adjusted for a subsequent layer and/or subsequent iterations, based upon the metric (para. 0165, see also: paras. 0002-3, 0108, 0140-141, etc.), which iterations are performed on one or more NN processing units or accelerators (paras. 0026, 0042-46; fig. 1; etc.)], the fourth set of network parameters comprising (i) the third set of network parameters and/or another set of network parameters for the other layer, or (ii) a set of network parameters for a subsequent pass of the neural network corresponding to the third set of network parameters [outputs can be determined for a first layer of the network, a performance metric determined, and the precision parameter adjusted for a subsequent layer and/or subsequent iterations, based upon the metric (para. 0165, see also: paras. 0002-3, 0108, 0140-141, etc.), which is repeated for multiple layers and multiple iterations].
As per claim 16, Rouhani teaches wherein the pass of the neural network and the subsequent pass of the neural network are forward passes of the neural network; or
wherein the pass of the neural network and the subsequent pass of the network are backward passes of the neural network [the operations can be performed during forward-propagation and/or back-propagation modes of the neural network (para. 0042, etc.)].
As per claim 17, Rouhani teaches a computer system comprising:
the neural network accelerator as set forth in claim 2 [see above]; and
an external unit coupled to the neural network accelerator, the external unit configured to select the number format based on the collected one or more statistics in accordance with a format selection algorithm [outputs can be determined for a first layer of the network, a performance metric determined, and the precision parameter adjusted for a subsequent layer and/or subsequent iterations, based upon the metric (para. 0165, see also: paras. 0002-3, 0108, 0140-141, etc.) where the metrics and adjustments can be passed/collected/performed remotely across multiple computer systems, or by a cloud server system, etc. (paras. 0063, 0155-159, etc.)].
As per claim 18, Rouhani teaches wherein the external unit is a central processing unit that controls the operation of the neural network accelerator [the metrics and adjustments can be passed/collected/performed remotely across multiple computer systems, or by a cloud server system, etc. (paras. 0063, 0155-159, etc.), which can include a CPU controlling the NN accelerator (fig. 1, etc.), and where the remote CPUs making adjustments are also controlling operations of the NN accelerator].
As per claim 19, Rouhani teaches wherein the external unit is distinct from a central processing unit that controls the operation of the neural network accelerator [the metrics and adjustments can be passed/collected/performed remotely across multiple computer systems, or by a cloud server system, etc. (paras. 0063, 0155-159, etc.), which can include a CPU controlling the NN accelerator (fig. 1, etc.), and where the remote CPUs making adjustments are also controlling operations of the NN accelerator].
As per claim 20, Rouhani teaches a non-transitory computer readable storage medium having stored thereon a computer readable description of the neural network accelerator as set forth in claim 2 that, when processed in an integrated circuit manufacturing system, causes the integrated circuit manufacturing system to manufacture an integrated circuit embodying the neural network accelerator [the neural network operations can be stored as computer instructions (paras. 0023-25, etc.) and performed by appropriately programming a configurable processor or FPGA to implement the nodes of the network (para. 0067, etc.); which is manufacturing an integrated circuit embodying the neural network accelerator].
Conclusion
The following is a summary of the treatment and status of all claims in the application as recommended by M.P.E.P. 707.07(i): claims 1-20 are rejected.
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Silfa et al. (Boosting LSTM Performance Through Dynamic Precision Selection, Nov 2019, pgs. 1-12) – discloses a system/method of dynamically selecting precision/format for numerical representations for an LSTM based upon LSTM cell states.
Judd et al. (Stripes: Bit-Serial Deep Neural Network Computing, Dec 2016, pgs. 1-12) – discloses a system/method utilizing configurable per-layer and per-bit precision control for a DNN accelerator.
Hubara et al. (Improving Post Training Neural Quantization: Layer-wise Calibration and Integer Programming, Dec 2020, pgs. 1-15) – discloses a system/method of controlling per-layer quantization/precision on calibration data, post-training.
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/GEORGE GIROUX/Primary Examiner, Art Unit 2128
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