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 .
DETAILED ACTION
This action is responsive to the following communication: Non-Provisional Application filed Jun. 27, 2024.
Claims 1-20 are pending in the case. Claims 1, 8 and 15 are independent claims.
Claim Rejections - 35 U.S.C. § 101
35 U.S.C. § 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to an abstract idea without significantly more.
As to claim 1:
Step 1 Analysis: Is the claim to a process, machine, manufacture or composition of matter? See MPEP § 2106.03.
Yes, the claim is to a machine.
Step 2A Prong One Analysis: Does the claim recite an abstract idea, law of nature, or natural phenomenon? See MPEP § 2106.04(II)(A)(1).
Yes, the limitation “predicting, based on a second machine learning model trained by using a set of a plurality of pieces of data and a prediction error of a first machine learning model for each of the plurality of pieces of data as training data, a prediction error of the first machine learning model for each of a plurality of pieces of unlabeled data” is the abstract idea of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion). See MPEP § 2106.04(a)(2)(III).
Yes, the limitation “generating labeled data by assigning a correct answer label to unlabeled data selected from the plurality of pieces of unlabeled data, based on the predicted prediction error; and retraining the first machine learning model by using the generated labeled data” is the abstract idea of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion). See MPEP § 2106.04(a)(2)(III).
Step 2A Prong Two Analysis: Does the claim recite additional elements that integrate the judicial exception into a practical application? See MPEP § 2106.04(d).
No, the limitation “predicting, based on a second machine learning model trained by using a set of a plurality of pieces of data and a prediction error of a first machine learning model for each of the plurality of pieces of data as training data, a prediction error of the first machine learning model for each of a plurality of pieces of unlabeled data” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP §§ 2106.04(d), 2106.05(f)(1), 2106.05(f)(2).
No, the limitation “generating labeled data by assigning a correct answer label to unlabeled data selected from the plurality of pieces of unlabeled data, based on the predicted prediction error; and retraining the first machine learning model by using the generated labeled data” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP §§ 2106.04(d), 2106.05(f)(1), 2106.05(f)(2).
Step 2B Analysis: Does the claim recite additional elements that amount to significantly more than the judicial exception? See MPEP § 2106.05.
No, the limitation “A non-transitory computer-readable recording medium storing an active learning program for causing a computer to execute a process” is an additional element that generally links the use of the judicial exception to a particular technological environment or field of use. See MPEP § 2106.05(h).
The additional elements, taken alone or in combination, fail to amount to significantly more than the judicial exception.
As to claim 8:
Step 1 Analysis: Is the claim to a process, machine, manufacture or composition of matter? See MPEP § 2106.03.
Yes, the claim is to a process.
Step 2A Prong One Analysis: Does the claim recite an abstract idea, law of nature, or natural phenomenon? See MPEP § 2106.04(II)(A)(1).
Yes, the limitation “predicting, based on a second machine learning model trained by using a set of a plurality of pieces of data and a prediction error of a first machine learning model for each of the plurality of pieces of data as training data, a prediction error of the first machine learning model for each of a plurality of pieces of unlabeled data” is the abstract idea of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion). See MPEP § 2106.04(a)(2)(III).
Yes, the limitation “generating labeled data by assigning a correct answer label to unlabeled data selected from the plurality of pieces of unlabeled data, based on the predicted prediction error; and retraining the first machine learning model by using the generated labeled data” is the abstract idea of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion). See MPEP § 2106.04(a)(2)(III).
Step 2A Prong Two Analysis: Does the claim recite additional elements that integrate the judicial exception into a practical application? See MPEP § 2106.04(d).
No, the limitation “predicting, based on a second machine learning model trained by using a set of a plurality of pieces of data and a prediction error of a first machine learning model for each of the plurality of pieces of data as training data, a prediction error of the first machine learning model for each of a plurality of pieces of unlabeled data” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP §§ 2106.04(d), 2106.05(f)(1), 2106.05(f)(2).
No, the limitation “generating labeled data by assigning a correct answer label to unlabeled data selected from the plurality of pieces of unlabeled data, based on the predicted prediction error; and retraining the first machine learning model by using the generated labeled data” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP §§ 2106.04(d), 2106.05(f)(1), 2106.05(f)(2).
Step 2B Analysis: Does the claim recite additional elements that amount to significantly more than the judicial exception? See MPEP § 2106.05.
No, the limitation “causing a computer to execute a process” is an additional element that generally links the use of the judicial exception to a particular technological environment or field of use. See MPEP § 2106.05(h).
The additional elements, taken alone or in combination, fail to amount to significantly more than the judicial exception.
As to claim 15:
Step 1 Analysis: Is the claim to a process, machine, manufacture or composition of matter? See MPEP § 2106.03.
Yes, the claim is to a machine.
Step 2A Prong One Analysis: Does the claim recite an abstract idea, law of nature, or natural phenomenon? See MPEP § 2106.04(II)(A)(1).
Yes, the limitation “predicting, based on a second machine learning model trained by using a set of a plurality of pieces of data and a prediction error of a first machine learning model for each of the plurality of pieces of data as training data, a prediction error of the first machine learning model for each of a plurality of pieces of unlabeled data” is the abstract idea of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion). See MPEP § 2106.04(a)(2)(III).
Yes, the limitation “generating labeled data by assigning a correct answer label to unlabeled data selected from the plurality of pieces of unlabeled data, based on the predicted prediction error; and retraining the first machine learning model by using the generated labeled data” is the abstract idea of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion). See MPEP § 2106.04(a)(2)(III).
Step 2A Prong Two Analysis: Does the claim recite additional elements that integrate the judicial exception into a practical application? See MPEP § 2106.04(d).
No, the limitation “predicting, based on a second machine learning model trained by using a set of a plurality of pieces of data and a prediction error of a first machine learning model for each of the plurality of pieces of data as training data, a prediction error of the first machine learning model for each of a plurality of pieces of unlabeled data” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP §§ 2106.04(d), 2106.05(f)(1), 2106.05(f)(2).
No, the limitation “generating labeled data by assigning a correct answer label to unlabeled data selected from the plurality of pieces of unlabeled data, based on the predicted prediction error; and retraining the first machine learning model by using the generated labeled data” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP §§ 2106.04(d), 2106.05(f)(1), 2106.05(f)(2).
Step 2B Analysis: Does the claim recite additional elements that amount to significantly more than the judicial exception? See MPEP § 2106.05.
No, the limitation “An active learning apparatus comprising: a memory; and a processor coupled to the memory and configured to” is an additional element that generally links the use of the judicial exception to a particular technological environment or field of use. See MPEP § 2106.05(h).
The additional elements, taken alone or in combination, fail to amount to significantly more than the judicial exception.
Claims 2-7, 9-14 and 16-20 are dependent claims and does not recite additional meaningful limits beyond the abstract idea, therefore are also rejected.
Claim Rejections - 35 USC § 102
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.
Claims 1-5, 8-12, 15-19 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Yoo et al. (hereinafter Yoo) “Learning Loss for Active Learning” 2019
With respect to independent claim 1, Yoo teaches a non-transitory computer-readable recording medium storing an active learning program for causing a computer to execute a process comprising:
predicting, based on a second machine learning model trained by using a set of a plurality of pieces of data and a prediction error of a first machine learning model for each of the plurality of pieces of data as training data, a prediction error of the first machine learning model for each of a plurality of pieces of unlabeled data (see e.g., Page 95, 96 Section 3.1-3.3 – “the active learning scenario with the proposed loss prediction module. In this scenario, we have a set of models composed of a target model Θtarget and a loss prediction module Θloss. The loss prediction module is attached to the target model as illus trated in Figure 1-(a). The target model conducts the target task as ˆy =Θtarget(x), while the loss prediction module predicts the loss ˆ l =Θloss(h). Here, h is a feature set of x extracted form several hidden layers of Θtarget.”);
generating labeled data by assigning a correct answer label to unlabeled data selected from the plurality of pieces of unlabeled data, based on the predicted prediction error (see e.g., Fig. 1 Section 3.1 – “After initial training, we evaluate all the data points in the unlabeled pool by the loss prediction module to obtain data-loss pairs {(x,ˆ l)|x ∈U0 N−K}. Then, human oracles annotate the data points of the K highest losses. The labeled dataset L0 K is updated with them and becomes L1 2K.”); and
retraining the first machine learning model by using the generated labeled data (see e.g., Section 3.1 – “Then, human oracles annotate the data points of the K highest losses. The labeled dataset L0 K is updated with them and becomes L1 2K. After that, we learn the model set over L1 2K to obtain {Θ1 target, Θ1 loss}. This cycle, illustrated in Fig ure 1-(b), repeats until we meet a satisfactory performance or until we have exhausted the budget for annotation”).
With respect to dependent claim 2, Yoo teaches unlabeled data with which the predicted prediction error is equal to or more than a predetermined value or a predetermined number of pieces of unlabeled data in descending order of the predicted prediction errors is selected, from among the plurality of pieces of unlabeled data (see e.g., Section 3.1 – “The subscript 0 means it is the initial stage. This process reduces the size of the unlabeled pool as … After initial training, we evaluate all the data points in the unlabeled pool by the loss prediction module to obtain data-loss pairs {(x,ˆ l)|x ∈U0 N−K}. Then, human oracles annotate the data points of the K highest losses.”).
With respect to dependent claim 3, Yoo teaches the active learning program for causing the computer to execute the process further comprising:
calculating each of the prediction errors from each of prediction results obtained by inputting each of a plurality of pieces of the labeled data to the first machine learning model and each of correct answer labels of the plurality of pieces of labeled data (see e.g., Section 3.3 – “Given a training data point x, we obtain a target pre diction through the target model as ˆy =Θtarget(x), and also a predicted loss through the loss prediction module as ˆ l =Θloss(h). With the target annotation y of x, the target loss can be computed as l = Ltarget(ˆy,y) to learn the tar get model. Since this loss l is a ground-truth target of h for the loss prediction module, we can also compute the loss for the loss prediction module as Lloss(ˆ l,l). Then, the final loss function to jointly learn both of the target model and the loss prediction module is defined as”), and generating a plurality of pieces of the training data from each of the calculated prediction errors and each of the plurality of pieces of labeled data (see e.g., Section 3.3 – “the target loss is regarded as a ground-truth loss for the loss prediction module, and used to compute the loss-prediction loss.”); and training the second machine learning model by using the training data (see e.g., Section 3.3 – “the final loss function to jointly learn both of the target model and the loss prediction module …”).
With respect to dependent claim 4, Yoo teaches the predicting of the prediction error and the generating of the labeled data are repeated until accuracy of the retrained first machine learning model satisfies a predetermined criterion (see e.g., Section 3.1 – “This cycle, illustrated in Figure 1-(b), repeats until we meet a satisfactory performance or until we have exhausted the budget for annotation.”).
With respect to dependent claim 5, Yoo teaches the second machine learning model is retrained by using the training data generated based on the labeled data generated from the selected unlabeled data (see e.g., Section 3.1 and 3.3 – “the final loss function to jointly learn both of the target model and the loss prediction module …”).
Claim 8 is rejected for the similar reasons discussed above with respect to claim 1.
Claim 9 is rejected for the similar reasons discussed above with respect to claim 2.
Claim 10 is rejected for the similar reasons discussed above with respect to claim 3.
Claim 11 is rejected for the similar reasons discussed above with respect to claim 4.
Claim 12 is rejected for the similar reasons discussed above with respect to claim 5.
Claim 15 is rejected for the similar reasons discussed above with respect to claim 1.
Claim 16 is rejected for the similar reasons discussed above with respect to claim 2.
Claim 17 is rejected for the similar reasons discussed above with respect to claim 3.
Claim 18 is rejected for the similar reasons discussed above with respect to claim 4.
Claim 19 is rejected for the similar reasons discussed above with respect to claim 5.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 6, 7,13,14 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Yoo in view of Smith et al. (hereinafter Smith) “The ANI-1ccx and ANI-1x data sets, coupled-cluster and density functional theory properties for molecules” 2020.
With respect to dependent claim 6, Yoo does not expressly show the labeled data is data in which information on energy of a molecule is assigned to structure data of the molecule as the correct answer label, and the first machine learning model is a machine learning model that outputs the energy of the molecule as a prediction result in a case where the structure data of the molecule is input. However, Smith teaches similar features (see e.g. Abstract and Summary – “The ANI-1x data set contains multiple QM
properties from 5 M density functional theory calculations, while the ANI-1ccx data set contains 500 k data points obtained with an accurate CCSD(T)/CBS extrapolation. Approximately 14 million CPU core-hours were expended to generate this data. Multiple QM calculated properties for the chemical elements C, H, N, and O are provided: energies, atomic forces, multipole moments, atomic charges, etc.” QM-calculated property labels are disclosed. See e.g., Page 2 - “The data for training such highly flexible ML models must contain the necessary information for predicting a complete potential energy surface for a class of molecules. … The ANI-1x data set contains DFT calculations for approximately five million diverse molecular conformations.” ML models are trained on molecular conformation/DFT data to predict energy surfaces. “Active learning is where an ML model is used to determine what new
data should be included in later generations to improve predictive ability.” See e.g., Page 3 - “Every 5 MD time steps ρ is computed for the molecular structure x at that time step. If the measure ρ is larger than a predetermined value, x is selected and added to a set ¯X of high ρ molecules. The MD simulation is terminated once x is selected, because we assume the current potential no longer describes … If the measure ρ is larger than a predetermined value, x is selected and added to a set ¯X of high ρ molecules.”). Both Yoo and Smith are directed to active machine learning. Accordingly, it would have been obvious to the skilled artisan before the effective filing date of the claimed invention having Yoo and Smith in front of them to modify the system of Yoo to include the above feature. The motivation to combine Yoo and Smith comes from Smith. Smith discloses the motivation to apply active machine learning to specific molecular-energy context (see e.g. page 1-3).
With respect to dependent claim 7, the modified Yoo teaches in the generating of the labeled data, the energy of the molecule is calculated from the structure data of the molecule which is the selected unlabeled data, by using density functional theory (see e.g., Page 3 – “When the uncertainty metric hints that a given molecular structure is poorly described (i.e., a large ρ value), new DFT data is generated and added to the training data set … DFT data is generated for all molecules in ¯X and added to the training data set.”).
Claim 13 is rejected for the similar reasons discussed above with respect to claim 6.
Claim 14 is rejected for the similar reasons discussed above with respect to claim 7.
Claim 20 is rejected for the similar reasons discussed above with respect to claim 6.
It is noted that any citation to specific pages, columns, lines, or figures in the prior art references and any interpretation of the references should not be considered to be limiting in any way. “The use of patents as references is not limited to what the patentees describe as their own inventions or to the problems with which they are concerned. They are part of the literature of the art, relevant for all they contain.” In re Heck, 699 F.2d 1331, 1332-33, 216 USPQ 1038, 1039 (Fed. Cir. 1983) (quoting In re Lemelson, 397 F.2d 1006, 1009, 158 USPQ 275, 277 (CCPA 1968)). Further, a reference may be relied upon for all that it would have reasonably suggested to one having ordinary skill the art, including nonpreferred embodiments. Merck & Co. v. Biocraft Laboratories, 874 F.2d 804, 10 USPQ2d 1843 (Fed. Cir.), cert. denied, 493 U.S. 975 (1989). See also Upsher-Smith Labs. v. Pamlab, LLC, 412 F.3d 1319, 1323, 75 USPQ2d 1213, 1215 (Fed. Cir. 2005); Celeritas Technologies Ltd. v. Rockwell International Corp., 150 F.3d 1354, 1361, 47 USPQ2d 1516, 1522-23 (Fed. Cir. 1998).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to PEIYONG WENG whose telephone number is (571)270-1660. The examiner can normally be reached on Mon.-Fri. 8 am to 5 pm.
If attempts to reach the examiner by telephone are unsuccessful, the examiner's supervisor, Matthew Ell, can be reached on (571) 270-3264. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/PEI YONG WENG/Primary Examiner, Art Unit 2141
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