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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 1-11, 19, and 20 recite a method and claims 12-18 recite a device. 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 elements:
and generating… a cancer diagnostic prediction based on the latent variable – a medical researcher looks at the generated latent variables and generates a cancer diagnostic prediction.
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 method of generating a cancer diagnostic prediction via a neural network based model …. the method comprising: – 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).
implemented on one or more hardware processors – 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).
receiving, via a communication interface, a plurality of samples of orphan non-coding ribonucleic acid (oncRNA) count data – 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).
transforming, via an encoder, a sample of oncRNA count data into a latent variable in a latent space – 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).
via a decoder – 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 method of generating a cancer diagnostic prediction via a neural network based model …. the method comprising: – 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).
implemented on one or more hardware processors – 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).
receiving, via a communication interface, a plurality of samples of orphan non-coding ribonucleic acid (oncRNA) count data – 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.”
transforming, via an encoder, a sample of oncRNA count data into a latent variable in a latent space – 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).
via a decoder – 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 two, the judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of:
wherein the plurality of samples of oncRNA count data are received from one or more data sources – 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 plurality of samples of oncRNA count data are received from one or more data sources – 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 encoder comprises one or more of:
a first variational autoencoder (VAE) that encodes a first sample relating to ribonucleic acid (RNA) subtypes used for classification – 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 Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
and a second VAE that encodes a sample relating to endogenous highly expressed RNA biotypes used for library estimation – 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 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 encoder comprises one or more of:
a first variational autoencoder (VAE) that encodes a first sample relating to ribonucleic acid (RNA) subtypes used for classification – 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 Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
and a second VAE that encodes a sample relating to endogenous highly expressed RNA biotypes used for library estimation – 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 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 4:
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 cancer diagnostic prediction comprises any of:
a presence of cancer;
a tissue of origin; and
a cancer subtype – 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 cancer diagnostic prediction comprises any of:
a presence of cancer;
a tissue of origin; and
a cancer subtype – 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 5:
The claim recites the following additional mathematical concept elements:
and computing a first loss based on distance metrics between the training sample, the positive sample and the negative sample in the latent space – the first loss is a mathematical formula and computing the loss is a mathematical calculation (see, e.g., para. [0054] 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:
receiving a training sample of oncRNA count data during a training epoch – 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).
sampling a positive sample having a same label with the training sample of oncRNA count data – 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).
sampling a negative sample having a different label with the training sample of oncRNA count data – 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:
receiving a training sample of oncRNA count data during a training epoch – 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.”
sampling a positive sample having a same label with the training sample of oncRNA count data – 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.”
sampling a negative sample having a different label with the training sample of oncRNA count data – 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 6:
The claim recites the following additional mathematical concept elements:
computing a second loss based on a Kullback-Leibler divergence between a conditional distribution of an encoded latent variable of the training sample conditions on the training sample and a prior distribution of the encoded latent variable – the second loss/Kullback-Leibler divergence is a mathematical formula and computing it is a mathematical calculation (see, e.g., para. [0051] of the specification as filed).
Accordingly, at step 2A, prong one, the claim is directed to an abstract idea.
The claim does not include any additional elements, under step 2A prong two, or step 2B, except those listed above in prior claim(s). Accordingly, at step 2A, prong two, the claim as a whole does not integrate the judicial exception into a practical application. See MPEP § 2106.04(d). Furthermore, at step 2B, the claim 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:
The claim recites the following additional mathematical concept elements:
and computing a third loss based on the reconstructed distribution – the third loss is a mathematical formula and computing it is a mathematical calculation (see, e.g., para. [0053] 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:
generating, by the decoder, a reconstructed distribution of the training sample form the encoded latent variable – 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 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:
generating, by the decoder, a reconstructed distribution of the training sample form the encoded latent variable – 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 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:
The claim recites the following additional mathematical concept elements:
generating …a predicted classification of the training sample from the encoded latent variable – the researcher predicts a classification of the sample based upon the latent variable.
and computing a fourth loss as a cross-entropy between the predicted classification and an annotated label of the training sample – the fourth loss is a mathematical formula and computing it is a mathematical calculation (see, e.g., para. [0055] 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:
by a classification head of the decoder – 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 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:
by a classification head of the decoder – 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 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:
The claim recites the following additional mathematical concept elements:
based on a joint loss as a weighted sum of the first loss, the second loss, the third loss and the fourth loss – the weighted sum of loss functions is a mathematical formula (see, e.g., paras. [0056-57] 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:
training the encoder and the decoder – 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 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:
training the encoder and the decoder – 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 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:
The claim recites the following additional mathematical concept elements:
based at least in part on the first loss – the first loss is a mathematical formula (see, e.g., para. [0054] of the specification as filed).
based at least in part on the second loss or the fourth loss – the second and fourth loss functions are mathematical formulas (see, e.g., paras. [0051] and [0055] 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:
training the encoder… at a first training stage – 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 Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
and training the encoder and the decoder… at a second training stage after the first training stage – 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 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:
training the encoder… at a first training stage – 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 Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
and training the encoder and the decoder… at a second training stage after the first training stage – 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 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:
The claim recites the following additional mathematical concept elements:
and the cancer diagnostic prediction includes a prediction of a detection of a presence of lung cancer, and a prediction of a lung cancer subtype of adenocarcinoma and squamous cell carcinoma – the researcher predicts whether the sample includes lung cancer and the lung cancer subtype.
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:
wherein the sample of oncRNA count data relates to a lung cancer sample – 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 sample of oncRNA count data relates to a lung cancer sample – 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 1, the claim recites an abstract idea including the following mental process elements:
and generating… a cancer diagnostic prediction based on the latent variable – the medical researcher looks at the generated latent variables and generates a cancer diagnostic prediction.
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 system of generating a cancer diagnostic prediction via a neural network based model, the system comprising: – 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 communication interface that receives a plurality of samples of orphan non-coding ribonucleic acid (oncRNA) count data – 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).
a memory storing the neural network based model and a plurality of processor-executable instructions – 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 one or more processors that execute the plurality of processor-executable instructions to perform operations comprising: – 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).
transforming, via an encoder, a sample of oncRNA count data into a latent variable in a latent space – 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).
via a decoder – 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 system of generating a cancer diagnostic prediction via a neural network based model, the system comprising: – 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 communication interface that receives a plurality of samples of orphan non-coding ribonucleic acid (oncRNA) count data – 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.”
a memory storing the neural network based model and a plurality of processor-executable instructions – 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 one or more processors that execute the plurality of processor-executable instructions to perform operations comprising: – 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).
transforming, via an encoder, a sample of oncRNA count data into a latent variable in a latent space – 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).
via a decoder – 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 13, see the rejection of claim 2 above.
As per claim 14, see the rejection of claim 3 above.
As per claim 15, see the rejection of claim 4 above.
As per claim 16, see the rejections of claims 5-8 above.
As per claim 17, see the rejection of claim 9 above.
As per claim 18, see the rejection of claim 11 above.
As per claim 19:
Under step 2A, prong 1, the claim recites an abstract idea including the following mental process elements:
and generating… a cancer diagnostic prediction including a first prediction of a presence of lung cancer and a second prediction on whether a subtype of the lung cancer sample is an adenocarcinoma or a squamous cell carcinoma, based on the latent variable – the medical researcher looks at the generated latent variables and generates a cancer diagnostic prediction including whether the sample includes lung cancer and the lung cancer subtype.
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 method of subtyping a lung cancer sample via a neural network based model – 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).
implemented on one or more hardware processors – 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 method comprising:
receiving, via a communication interface, input oncRNA count data relating to a lung cancer sample obtained from a subject – 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).
transforming, via an encoder, the input oncRNA count data into a latent variable in a latent space – 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).
via a decoder – 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 method of subtyping a lung cancer sample via a neural network based model – 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).
implemented on one or more hardware processors – 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 method comprising:
receiving, via a communication interface, input oncRNA count data relating to a lung cancer sample obtained from a subject – 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.”
transforming, via an encoder, the input oncRNA count data into a latent variable in a latent space – 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).
via a decoder – 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:
Under step 2A, prong 1, the claim recites an abstract idea including the following mental process elements:
a cancer diagnostic prediction based on the latent variable – the medical researcher looks at the generated latent variables and generates a cancer diagnostic prediction
generating a recommended treatment when the cancer diagnostic prediction indicates a presence cancer – the researcher generates a recommended treatment based upon their cancer diagnostic prediction.
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 method of cancer diagnostic and treatment prediction via a neural network based model – 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).
implemented on one or more hardware processors – 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 method comprising:
generating, by the neural network based model that transforms oncRNA count data into a latent variable – 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 method of cancer diagnostic and treatment prediction via a neural network based model – 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).
implemented on one or more hardware processors – 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 method comprising:
generating, by the neural network based model that transforms oncRNA count data into a latent variable – 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 § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 1-4, 11-15, and 18-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Doudna (US 11,584,930 – cited in an IDS) in view of Goodarzi (WO 2022/040106).
As per claim 1, Doudna teaches a method of generating a cancer diagnostic prediction via a neural network based model [a method for identifying biomarkers and molecular features of normal and cancer cells. The method comprises the steps of: (a) single-cell RNAseq (scRNAseq) analysis of normal and cancer cells; (b) analyzing the single-cell RNAseq on a cloud platform; (c) classifying different types of cells; (d) developing computer algorithms; and (e) identifying biomarkers and molecular features of normal and cancer cells (col. 39, lines 8- 29; col. 48, lines 53-62; etc.), where systems and/or methods of the present disclosure may employ one or more machine learning algorithms, one or more artificial neural networks (col. 42, lines 40-43; etc.); where the cancer cell classification is a cancer diagnostic prediction] implemented on one or more hardware processors [Computer algorithms may be accessed and executed by a processor to perform the operations according to the instructions contained therein (col. 39, lines 51-53; etc.)], the method comprising:
receiving, via a communication interface, a plurality of samples of ribonucleic acid [RNA] count data [multiple samples of RNA-sequence profiles are received and used to train the model (col. 1, lines 46-63; col. 3, lines 8-14; etc.), including receiving remote data (col. 39, lines 47-67; etc.); and features may be extracted from generated alignments, including expression counts, expression summaries, etc. (col. 41, lines 59-61; etc.); where receiving remote data includes a communication interface];
transforming, via an encoder, a sample of [RNA] count data into a latent variable in a latent space [the algorithm trains a coupled set of encoder/decoder networks, which attempt to minimally and accurately represent the information content within RNA-seq profiles in a latent, low-dimensional space (col. 44, lines 17-20; col. 45, lines 3-11; etc.)]; and
generating, via a decoder a cancer diagnostic prediction based on the latent variable [generate a complete gene expression profile from any given point in the latent space by extrapolating via the tuned decoder network (col. 45, lines 34-36; col. 54, lines 1-2; etc.) for identifying biomarkers and molecular features of normal and cancer cells (col. 39, lines 8- 29; col. 48, lines 53-62; etc.) and, in some instances, methods of the present disclosure may involve analysis of cellular classification data. For example, following cellular classification, data pertaining to classified cells may obtained and further analyzed, which may include e.g., counts (e.g., the number of normal cells vs. abnormal cells; the number of detected cancer cells, the relative number of cells of particular cell types (including normal and cancerous cell types, etc.), averages, probabilities, sensitivity, specificity, likelihood analyses, and the like (col. 39, lines 30-40; etc.); where the cancer cell classification (and probabilities, etc.) is a cancer diagnostic prediction].
While Doudna teaches receiving RNA sequence data (see above), it has not been relied upon for teaching receiving a plurality of samples of orphan non-coding ribonucleic acid (oncRNA) count data and transforming the oncRNA.
Goodarzi teaches a method of generating a cancer diagnostic prediction via a neural network based model [detection of non-coding RNA molecules in a sample or diagnosis of a subject based upon detection or quantification of noncoding RNA molecules in a sample of the subject, specifically to identify and use of molecular biomarkers for cancer diagnosis (para. 0001, 0005, 0008, etc.) by a classification neural network (para. 0152, etc.)], the method comprising:
receiving a plurality of samples of orphan non-coding ribonucleic acid (oncRNA) count data and transforming the oncRNA [performing a systematic screen for orphan non-coding RNAs (oncRNAs) (paras. 0004, 0074, 0175-176, etc.) said method comprising detecting the presence, absence, and/or quantity of at least one non-coding RNA (paras. 0006, 0011-13; etc.) including total counts or normalized counts from a small non-coding RNA expression assay (para. 00151, etc.); for the RNA sequences being encoded by Doudna, above].
Doudna and Goodarzi are analogous art, as they are within the same field of endeavor, namely performing cancer cell classification and diagnostic prediction from RNA data.
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to use orphan non-coding RNA data for the inputs to the machine learning model for cancer diagnostic predictions, as taught by Goodarzi, as the RNA data inputs to the machine learning model for cancer diagnostic predictions in the system/method taught by Doudna.
Goodarzi provides motivation as [novel small non-coding RNAs that serve as biomarkers which are indicative of various types of cancer, tissue of origin, and status of the cancer, and which may be used to accurately diagnose cancer in a subject even at an early stage (para. 0005, etc.)].
As per claim 2, Doudna/Goodarzi teaches wherein the plurality of samples of oncRNA count data are received from one or more data sources [the oncRNA samples can be from multiple human samples (Goodarzi: paras. 0005-6, etc.) and/or from multiple RNA sequencing profiles (Doudna: col. 43, lines 37-43; etc.)].
As per claim 3, Doudna/Goodarzi teaches wherein the encoder comprises one or more of:
a first variational autoencoder (VAE) that encodes a first sample relating to ribonucleic acid (RNA) subtypes used for classification [a variable autoencoder network for latent, low-dimensional representation of RNA-seq profiles as part of a classifier to discriminate between wild-type and cancer cells (Doudna: col. 2, line 67 to col. 3, line 17; col. 9, lines 1-10; etc.) and employ the VAE for inference and/or analyses to identify the factors of the profiles that differentiate the cell types (Doudna: col. 42, lines 44-49; etc.); where the RNA-seq profiles relate to RNA subtypes used for the classification]; and
a second VAE that encodes a sample relating to endogenous highly expressed RNA biotypes used for library estimation [Gene-aligned, single-cell RNA profiles of pancreatic cancer cells are quantified based on their reversion to wild-type expression patterns following gene knockout, gene repression, or gene activation via the methods described above (CRISPR. CRISPRi, or CRISPRa, respectively). In principle, extent of reversion to wild-type expression can be quantified via several ways, including but not limited to: a) geometric methods; b) rank-order statistics; c) supervised machine learning methods; d) unsupervised clustering; or e) deep learning methods such as variational autoencoders (VAEs) and generative models (Doudna: col. 50, lines 47-57; etc.) where utilizing the CRISPR/i/a library is using the VAE for library estimation (see, e.g., Doudna: col. 49, lines 20-32; col. 52, lines 45-66; etc.) and where the oncRNA may be of endogenous origin (e.g., a human small non-coding RNA) (Goodarzi: para. 0074, etc.)].
As per claim 4, Doudna/Goodarzi teaches wherein the cancer diagnostic prediction comprises any of:
a presence of cancer;
a tissue of origin; and
a cancer subtype [generate a complete gene expression profile from any given point in the latent space by extrapolating via the tuned decoder network (Doudna: col. 45, lines 34-36; col. 54, lines 1-2; etc.) for identifying biomarkers and molecular features of normal and cancer cells (Doudna: col. 39, lines 8- 29; col. 48, lines 53-62; etc.), where the oncRNAs can now be used as biomarkers to determine the type of cancer, such as lung, breast, prostate, colorectal, pancreas, liver and ovarian, and the status thereof of a subject (Goodarzi: para. 0085, etc.); which includes a presence of cancer and/or cancer subtypes as well as the tissue of origin of the cancer cells].
As per claim 11, Doudna/Goodarzi teaches wherein the sample of oncRNA count data relates to a lung cancer sample, and the cancer diagnostic prediction includes a prediction of a detection of a presence of lung cancer, and a prediction of a lung cancer subtype of adenocarcinoma and squamous cell carcinoma [generate a complete gene expression profile from any given point in the latent space by extrapolating via the tuned decoder network (Doudna: col. 45, lines 34-36; col. 54, lines 1-2; etc.) for identifying biomarkers and molecular features of normal and cancer cells (Doudna: col. 39, lines 8- 29; col. 48, lines 53-62; etc.), where the oncRNAs can now be used as biomarkers to determine the type of cancer, such as lung, breast, prostate, colorectal, pancreas, liver and ovarian, and the status thereof of a subject (Goodarzi: para. 0085, etc.) for annotated oncRNAs found significantly in the following TCGA cancer types: Rectum adenocarcinoma (READ), Colon adenocarcinoma (COAD), Esophageal carcinoma (ESCA), Head and Neck squamous cell carcinoma (HNSC), Lymphoid Neoplasm Diffuse Large B-cell Lymphoma (DLBC), Liver hepatocellular carcinoma (LIHC), Lung adenocarcinoma (LU AD), Lung squamous cell carcinoma (LUSC), Ovarian serous cystadenocarcinoma (OV), Pancreatic adenocarcinoma (PAAD), Stomach adenocarcinoma (STAD), and Breast invasive carcinoma (BRCA) (Goodarzi: pgs. 214-215, Table X and description on pg. 215; etc.)].
As per claim 12, Doudna/Goodarzi teaches a system of generating a cancer diagnostic prediction via a neural network based model [a method for identifying biomarkers and molecular features of normal and cancer cells. The method comprises the steps of: (a) single-cell RNAseq (scRNAseq) analysis of normal and cancer cells; (b) analyzing the single-cell RNAseq on a cloud platform; (c) classifying different types of cells; (d) developing computer algorithms; and (e) identifying biomarkers and molecular features of normal and cancer cells (col. 39, lines 8- 29; col. 48, lines 53-62; etc.), where systems and/or methods of the present disclosure may employ one or more machine learning algorithms, one or more artificial neural networks (Doudna: col. 42, lines 40-43; etc.) and detection of non-coding RNA molecules in a sample or diagnosis of a subject based upon detection or quantification of noncoding RNA molecules in a sample of the subject, specifically to identify and use of molecular biomarkers for cancer diagnosis (Goodarzi: para. 0001, 0005, 0008, etc.) by a classification neural network (Goodarzi: para. 0152, etc.); where the cancer cell classification is a cancer diagnostic prediction], the system comprising:
a communication interface that receives a plurality of samples of orphan non-coding ribonucleic acid (oncRNA) count data [multiple samples of RNA-sequence profiles are received and used to train the model (Doudna: col. 1, lines 46-63; col. 3, lines 8-14; etc.), including receiving remote data (Doudna: col. 39, lines 47-67; etc.); and features may be extracted from generated alignments, including expression counts, expression summaries, etc. (Doudna: col. 41, lines 59-61; etc.); where receiving remote data includes a communication interface; and detection of non-coding RNA molecules in a sample or diagnosis of a subject based upon detection or quantification of noncoding RNA molecules in a sample of the subject, specifically to identify and use of molecular biomarkers for cancer diagnosis (Goodarzi: para. 0001, 0005, 0008, etc.) by a classification neural network (Goodarzi: para. 0152, etc.) including total counts or normalized counts from a small non-coding RNA expression assay (Goodarzi: para. 00151, etc.)];
a memory storing the neural network based model and a plurality of processor-executable instructions [Computer algorithms may be accessed and executed by a processor to perform the operations according to the instructions contained therein (Doudna: col. 39, lines 51-53; etc.)]; and
one or more processors that execute the plurality of processor-executable instructions [Computer algorithms may be accessed and executed by a processor to perform the operations according to the instructions contained therein (Doudna: col. 39, lines 51-53; etc.)] to perform operations comprising:
transforming, via an encoder, a sample of oncRNA count data into a latent variable in a latent space [the algorithm trains a coupled set of encoder/decoder networks, which attempt to minimally and accurately represent the information content within RNA-seq profiles in a latent, low-dimensional space (Doudna: col. 44, lines 17-20; col. 45, lines 3-11; etc.)]; and
generating, via a decoder, a cancer diagnostic prediction based on the latent variable [generate a complete gene expression profile from any given point in the latent space by extrapolating via the tuned decoder network (Doudna: col. 45, lines 34-36; col. 54, lines 1-2; etc.) for identifying biomarkers and molecular features of normal and cancer cells (Doudna: col. 39, lines 8- 29; col. 48, lines 53-62; etc.) and, in some instances, methods of the present disclosure may involve analysis of cellular classification data. For example, following cellular classification, data pertaining to classified cells may obtained and further analyzed, which may include e.g., counts (e.g., the number of normal cells vs. abnormal cells; the number of detected cancer cells, the relative number of cells of particular cell types (including normal and cancerous cell types, etc.), averages, probabilities, sensitivity, specificity, likelihood analyses, and the like (Doudna: col. 39, lines 30-40; etc.); where the cancer cell classification (and probabilities, etc.) is a cancer diagnostic prediction].
[Examiner’s Note: the reasoning and motivation for the combination are the same as that provided, above, in the rejection of claim 1.]
As per claim 13, see the rejection of claim 2 above.
As per claim 14, see the rejection of claim 3 above.
As per claim 15, see the rejection of claim 4 above.
As per claim 18, see the rejection of claim 11 above.
As per claim 19, Doudna/Goodarzi teaches a method of subtyping a lung cancer sample via a neural network based model [a method for identifying biomarkers and molecular features of normal and cancer cells. The method comprises the steps of: (a) single-cell RNAseq (scRNAseq) analysis of normal and cancer cells; (b) analyzing the single-cell RNAseq on a cloud platform; (c) classifying different types of cells; (d) developing computer algorithms; and (e) identifying biomarkers and molecular features of normal and cancer cells (col. 39, lines 8- 29; col. 48, lines 53-62; etc.), where systems and/or methods of the present disclosure may employ one or more machine learning algorithms, one or more artificial neural networks (Doudna: col. 42, lines 40-43; etc.) and detection of non-coding RNA molecules in a sample or diagnosis of a subject based upon detection or quantification of noncoding RNA molecules in a sample of the subject, specifically to identify and use of molecular biomarkers for cancer diagnosis (Goodarzi: para. 0001, 0005, 0008, etc.) by a classification neural network (Goodarzi: para. 0152, etc.) where the oncRNAs can be used as biomarkers to determine the type of cancer, such as lung, breast, prostate, colorectal, pancreas, liver and ovarian, and the status thereof of a subject (Goodarzi: para. 0085, etc.) as well as subtypes of the cancers (Goodarzi: pgs. 214-215, Table X and description on pg. 215; etc.); where the lung cancer cell classification is a cancer diagnostic prediction] implemented on one or more hardware processors [Computer algorithms may be accessed and executed by a processor to perform the operations according to the instructions contained therein (Doudna: col. 39, lines 51-53; etc.)], the method comprising:
receiving, via a communication interface, input oncRNA count data relating to a lung cancer sample obtained from a subject [multiple samples of RNA-sequence profiles are received and used to train the model (Doudna: col. 1, lines 46-63; col. 3, lines 8-14; etc.), including receiving remote data (Doudna: col. 39, lines 47-67; etc.); and features may be extracted from generated alignments, including expression counts, expression summaries, etc. (Doudna: col. 41, lines 59-61; etc.); where receiving remote data includes a communication interface; and detection of non-coding RNA molecules in a sample or diagnosis of a subject based upon detection or quantification of noncoding RNA molecules in a sample of the subject, specifically to identify and use of molecular biomarkers for cancer diagnosis (Goodarzi: para. 0001, 0005, 0008, etc.) including total counts or normalized counts from a small non-coding RNA expression assay (Goodarzi: para. 00151, etc.) where the oncRNAs can be used as biomarkers to determine the type of cancer, such as lung, breast, prostate, colorectal, pancreas, liver and ovarian, and the status thereof of a subject (Goodarzi: para. 0085, etc.) as well as subtypes of the cancers (Goodarzi: pgs. 214-215, Table X and description on pg. 215; etc.)];
transforming, via an encoder, the input oncRNA count data into a latent variable in a latent space [the algorithm trains a coupled set of encoder/decoder networks, which attempt to minimally and accurately represent the information content within RNA-seq profiles in a latent, low-dimensional space (Doudna: col. 44, lines 17-20; col. 45, lines 3-11; etc.)]; and
generating, via a decoder, a cancer diagnostic prediction including a first prediction of a presence of lung cancer [generate a complete gene expression profile from any given point in the latent space by extrapolating via the tuned decoder network (Doudna: col. 45, lines 34-36; col. 54, lines 1-2; etc.) for identifying biomarkers and molecular features of normal and cancer cells (Doudna: col. 39, lines 8- 29; col. 48, lines 53-62; etc.) and, in some instances, methods of the present disclosure may involve analysis of cellular classification data. For example, following cellular classification, data pertaining to classified cells may obtained and further analyzed, which may include e.g., counts (e.g., the number of normal cells vs. abnormal cells; the number of detected cancer cells, the relative number of cells of particular cell types (including normal and cancerous cell types, etc.), averages, probabilities, sensitivity, specificity, likelihood analyses, and the like (Doudna: col. 39, lines 30-40; etc.); where the cancer cell classification (and probabilities, etc.) is a cancer diagnostic prediction] and a second prediction on whether a subtype of the lung cancer sample is an adenocarcinoma or a squamous cell carcinoma, based on the latent variable [detection of non-coding RNA molecules in a sample or diagnosis of a subject based upon detection or quantification of noncoding RNA molecules in a sample of the subject, specifically to identify and use of molecular biomarkers for cancer diagnosis (Goodarzi: para. 0001, 0005, 0008, etc.) where the oncRNAs can be used as biomarkers to determine the type of cancer, such as lung, breast, prostate, colorectal, pancreas, liver and ovarian, and the status thereof of a subject (Goodarzi: para. 0085, etc.) as well as subtypes of the cancers (Goodarzi: pgs. 214-215, Table X and description on pg. 215; etc.) from any given point in the latent space by extrapolating via the tuned decoder network (Doudna: col. 45, lines 34-36; col. 54, lines 1-2; etc.) for identifying biomarkers and molecular features of normal and cancer cells (Doudna: col. 39, lines 8- 29; col. 48, lines 53-62; etc.)].
[Examiner’s Note: the reasoning and motivation for the combination are the same as that provided, above, in the rejection of claim 1.]
As per claim 20, Doudna/Goodarzi teaches a method of cancer diagnostic and treatment prediction via a neural network based model [a method for identifying biomarkers and molecular features of normal and cancer cells. The method comprises the steps of: (a) single-cell RNAseq (scRNAseq) analysis of normal and cancer cells; (b) analyzing the single-cell RNAseq on a cloud platform; (c) classifying different types of cells; (d) developing computer algorithms; and (e) identifying biomarkers and molecular features of normal and cancer cells (col. 39, lines 8- 29; col. 48, lines 53-62; etc.), where systems and/or methods of the present disclosure may employ one or more machine learning algorithms, one or more artificial neural networks (Doudna: col. 42, lines 40-43; etc.) and detection of non-coding RNA molecules in a sample or diagnosis of a subject based upon detection or quantification of noncoding RNA molecules in a sample of the subject, specifically to identify and use of molecular biomarkers for cancer diagnosis (Goodarzi: para. 0001, 0005, 0008, etc.) by a classification neural network (Goodarzi: para. 0152, etc.)] implemented on one or more hardware processors [Computer algorithms may be accessed and executed by a processor to perform the operations according to the instructions contained therein (Doudna: col. 39, lines 51-53; etc.)], the method comprising:
generating, by the neural network based model that transforms oncRNA count data into a latent variable, a cancer diagnostic prediction based on the latent variable [the algorithm trains a coupled set of encoder/decoder networks, which attempt to minimally and accurately represent the information content within RNA-seq profiles in a latent, low-dimensional space (Doudna: col. 44, lines 17-20; col. 45, lines 3-11; etc.) and generates a complete gene expression profile from any given point in the latent space by extrapolating via the tuned decoder network (Doudna: col. 45, lines 34-36; col. 54, lines 1-2; etc.) for identifying biomarkers and molecular features of normal and cancer cells (Doudna: col. 39, lines 8- 29; col. 48, lines 53-62; etc.) and, in some instances, methods of the present disclosure may involve analysis of cellular classification data. For example, following cellular classification, data pertaining to classified cells may obtained and further analyzed, which may include e.g., counts (e.g., the number of normal cells vs. abnormal cells; the number of detected cancer cells, the relative number of cells of particular cell types (including normal and cancerous cell types, etc.), averages, probabilities, sensitivity, specificity, likelihood analyses, and the like (Doudna: col. 39, lines 30-40; etc.) and detection of non-coding RNA molecules in a sample or diagnosis of a subject based upon detection or quantification of noncoding RNA molecules in a sample of the subject, specifically to identify and use of molecular biomarkers for cancer diagnosis (Goodarzi: para. 0001, 0005, 0008, etc.) by a classification neural network (Goodarzi: para. 0152, etc.) including total counts or normalized counts from a small non-coding RNA expression assay (Goodarzi: para. 00151, etc.); where the cancer cell classification (and probabilities, etc.) is a cancer diagnostic prediction]; and
generating a recommended treatment when the cancer diagnostic prediction indicates a presence cancer [in some embodiments, the disclosure relates to a method of treating cancer in a subject, comprising determining the level of at least one small non-coding RNA biomarker in a sample from the subject, wherein a difference in the level of at least one small non-coding RNA biomarker versus that in a normal subject as determined relative to a suitable control is indicative of cancer in the subject, and administering a therapeutically effective amount of a cancer therapeutic to the subject (Goodarzi: para. 00159, etc.) and where the one or more molecular feature and disease phenotype indicates a reduction in the cancerous state of the cancer cell, identifying the target gene as a candidate cancer treatment target or combinatorial targets (Doudna: col. 47, lines 46-49; etc.)].
[Examiner’s Note: the reasoning and motivation for the combination are the same as that provided, above, in the rejection of claim 1.]
Claim(s) 5-10, 16, and 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Doudna (US 11,584,930 – cited in an IDS), in view of Goodarzi (WO 2022/040106), and further in view of Akino (US 2023/0419075).
As per claim 5, Doudna/Goodarzi teaches further comprising:
receiving a training sample of oncRNA count data during a training epoch [orphan non-coding RNAs (oncRNAs) (Goodarzi: paras. 0004, 0074, 0175-176, etc.) data that are generated using samples such as “known samples” can then be used to “train” a classification model. A “known sample” is a sample that has been pre-classified, e.g., classified as being derived from a normal subject or from a subject having a particular type of cancer. The data that are derived from the spectra and are used to form the classification model can be referred to as a “training data set.” (Goodarzi: para. 00150, etc.) including total counts or normalized counts from a small non-coding RNA expression assay (Goodarzi: para. 00151, etc.)];
sampling a positive sample having a same label with the training sample of oncRNA count data;
sampling a negative sample having a different label with the training sample of oncRNA count data [data that are generated using samples such as “known samples” can then be used to “train” a classification model. A “known sample” is a sample that has been pre-classified, e.g., classified as being derived from a normal subject or from a subject having a particular type of cancer. The data that are derived from the spectra and are used to form the classification model can be referred to as a “training data set.” (Goodarzi: para. 00150, etc.) which can include positive and negative (control) samples of cancer/normal subjects’ biomarkers (Goodarzi: paras. 0088, 00113-114, etc.) including total counts or normalized counts from a small non-coding RNA expression assay (Goodarzi: para. 00151, etc.); which are positive and negative samples having a same/different labels, respectively, with the oncRNA count data].
While Doudna/Goodarzi teaches utilizing distance metrics (see, e.g., Doudna: col. 50, line 60 to col. 51, line 15; etc.), it has not been relied upon for teaching computing a first loss based on distance metrics between the training sample, the positive sample and the negative sample in the latent space.
Akino teaches computing a first loss based on distance metrics between the training sample, the positive sample and the negative sample in the latent space [the VAE (paras. 0009-10, etc.) may be trained utilizing an objective/loss function that combines various functions, including negative sample, distance between samples, triplets, etc. (paras. 0054-55, 0120, etc.); where the triplet and distance between samples including negative samples is a first loss based on a distance metric between the training sample, positive sample, and negative sample of Doudna/Goodarzi, above].
Doudna/Goodarzi and Akino are analogous art, as they are within the same field of endeavor, namely optimizing a model including a variational autoencoder (VAE).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to utilize a combined objective function that includes a loss function of the distance between triplet samples (as a first loss in the combined objective function), including negative samples, for training a VAE, as taught by Akino, as an objective function to optimize the VAE during training in the system/method taught by Doudna/Goodarzi.
Akino provides motivation as [combining optimization methods is necessary to determine the optimal hyperparameters for the model(s) and can be selected based upon the type of dataset to which the model(s) is being applied (paras. 0055-56, etc.)].
As per claim 6, Doudna/Goodarzi/Akino teaches further comprising:
computing a second loss based on a Kullback-Leibler divergence between a conditional distribution of an encoded latent variable of the training sample conditions on the training sample and a prior distribution of the encoded latent variable [the VAE (Doudna: col. 44, lines 17-20; col. 45, lines 3-11; Akino: paras. 0009-10, etc.) may be trained utilizing an objective/loss function that combines various functions, including Kullback-Leibler divergence (KLD), etc. (Akino: paras. 0054-55, 0120, etc.) using an emphasized Kullback-Leibler divergence (KLD) to regularize the latent distribution (Akino: paras. 0004, 0008-9, etc.); where the KLD is the second loss and the latent distribution is the conditional distribution of the encoded latent variable of the training sample conditions and the prior distribution is the distribution being compared].
As per claim 7, Doudna/Goodarzi/Akino teaches further comprising:
generating, by the decoder, a reconstructed distribution of the training sample from the encoded latent variable [generate a complete gene expression profile from any given point in the latent space by extrapolating via the tuned decoder network (Doudna: col. 45, lines 34-36; col. 54, lines 1-2; etc.) for identifying biomarkers and molecular features of normal and cancer cells (Doudna: col. 39, lines 8- 29; col. 48, lines 53-62; etc.), where the decoder produces a reconstructed distribution from the training samples from the encoded latent variables (Akino: paras 0101, 0109, etc.)]; and
computing a third loss based on the reconstructed distribution [during training of the VAE (Doudna: col. 44, lines 17-20; col. 45, lines 3-11; Akino: paras. 0009-10, etc.) the decoder minimizes error in reconstructing the profiles distribution from the latent encoding (Doudna: col. 42, lines 50-52; col. 44, lines 19-22; etc.) and can use an objective/loss function that combines various functions, including a reconstruction loss (third loss), etc. (Akino: paras. 0054-55, 0058, 0079, etc.)].
As per claim 8, Doudna/Goodarzi/Akino teaches further comprising:
generating, by a classification head of the decoder, a predicted classification of the training sample from the encoded latent variable [generate a complete gene expression profile from any given point in the latent space by extrapolating via the tuned decoder network (Doudna: col. 45, lines 34-36; col. 54, lines 1-2; etc.) for identifying biomarkers and molecular features of normal and cancer cells (Doudna: col. 39, lines 8- 29; col. 48, lines 53-62; etc.) and, in some instances, methods of the present disclosure may involve analysis of cellular classification data. For example, following cellular classification, data pertaining to classified cells may obtained and further analyzed, which may include e.g., counts (e.g., the number of normal cells vs. abnormal cells; the number of detected cancer cells, the relative number of cells of particular cell types (including normal and cancerous cell types, etc.), averages, probabilities, sensitivity, specificity, likelihood analyses, and the like (Doudna: col. 39, lines 30-40; etc.)]; and
computing a fourth loss as a cross-entropy between the predicted classification and an annotated label of the training sample [the VAE (Doudna: col. 44, lines 17-20; col. 45, lines 3-11; Akino: paras. 0009-10, etc.) may be trained utilizing an objective/loss function that combines various functions, cross-entropy (a fourth loss) as a differentiable loss, etc. (Akino: paras. 0054-55, 0108, 0120, etc.); where the difference is between the predicted classification and the annotated label (see, e.g., Akino: para. 0108; Doudna: col. 42, lines 50-52; col. 44, lines 19-22; etc.)].
As per claim 9, Doudna/Goodarzi/Akino teaches further comprising:
training the encoder and the decoder based on a joint loss as a weighted sum of the first loss, the second loss, the third loss and the fourth loss [the VAE (encoder/decoder) (Doudna: col. 44, lines 17-20; col. 45, lines 3-11; Akino: paras. 0009-10, etc.) may be trained utilizing an objective/loss function that includes a weighted combination of various functions (Akino: paras. 0054-55, 0120, etc.), which includes the first, second, third, and fourth loss functions described above].
As per claim 10, Doudna/Goodarzi/Akino teaches further comprising:
training the encoder based at least in part on the first loss at a first training stage; and
training the encoder and the decoder based at least in part on the second loss or the fourth loss at a second training stage after the first training stage [the encoder may be trained (Doudna: col. 44, lines 17-20; col. 45, lines 3-11; Akino: paras. 0009-10, etc.) using the distance measure between samples (Akino: paras. 0012-14, 0054-55, 0120, etc.) followed by adversarial training with the decoder (Akino: paras. 0006, 0012, etc.); where the combined loss includes the second and/or fourth loss]. [Examiner’s Note: regarding the selection or combination of the various loss functions taught above, it has also been held that rearranging parts of an invention (i.e., using the second loss, or the fourth loss, or both, or the other loss functions, etc.) involves only routine skill in the art. In re Japikse, 86 USPQ 70.]
As per claim 16, see the rejections of claims 5-8 above.
As per claim 17, see the rejection of claim 9 above.
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.
Frey (US 2021/0383890 – cited in an IDS) – discloses a system for classifying genetic variants including multiple loss functions and a human manually making diagnostic predictions.
Teng et al. (Orphan noncoding RNAs: novel regulators and cancer biomarkers, 2019, pgs. 1-5) – discloses making cancer/diagnostic predictions from orphan noncoding RNA samples.
Dosovitskiy et al. (You Only Train Once: Loss-Conditional Training of Deep Networks, Dec 2019, pgs. 1-17) – discloses using weighted sums of loss functions for training a model.
The examiner requests, in response to this Office action, that support be shown for language added to any original claims on amendment and any new claims. That is, indicate support for newly added claim language by specifically pointing to page(s) and line number(s) in the specification and/or drawing figure(s). This will assist the examiner in prosecuting the application.
When responding to this office action, Applicant is advised to clearly point out the patentable novelty which he or she thinks the claims present, in view of the state of the art disclosed by the references cited or the objections made. He or she must also show how the amendments avoid such references or objections. See 37 CFR 1.111(c).
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/GEORGE GIROUX/Primary Examiner, Art Unit 2128
