Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-10 are rejected under 35 U.S.C. 101 considering the following analysis:
Step 1 analysis for all claims:
Claims 1-4 and 7-10 are directed to a method (process), where claims 5 and 6 are directed to a system. Therefore, these claims fall within one of the statutory categories of invention (process, manufacture, composition of matter or machine). As such, the claim is eligible under 35 U.S.C. 101.
Claim 1:
Step 2A, Prong 1 analysis:
The claim(s) recite(s) in part:
• “calculating a predicted value of a label for each case included in the data Gd using parameters of a first neural network and information representing cases in which the labels are observed among the respective cases included in the data Gd;”. As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation encompasses analyzing the parameters of a neural network and information about cases that were identified with a particular label to predict whether the label has been correctly applied. This would be seen as a mental process because a person having ordinary skill of the art would be able to view the parameters of each case and generate a predicted value according to each label.
• “selecting one case from the respective cases included in the data Gd using parameters of a second neural network and information representing the cases in which the labels are observed among the respective cases included in the data Gd;”. As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation encompasses selecting a specific case within a dataset according to the parameters of a second neural network and information that represents the cases where labels are also considered. This would be seen as a mental process because a person having ordinary skill of the art would be able to select one of the cases included in the dataset after carefully determining the parameters of the second neural network and taking the information and/or labels into consideration.
Step 2A, Prong 2 analysis:
The judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of:
• “a computer” which is recited at a high level of generality and amount to adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. See MPEP 2106.05(f). As explained by the Supreme Court; in order to make a claim directed to a judicial exception patent-eligible, the additional element or combination of elements must do '"more than simply state the judicial exception] while adding the words 'apply it"'. Alice Corp. v. CLS Bank, 573 U.S. 208, 221, 110 USPQ2d 1976, 1982-83 (2014) (quoting Mayo Collaborative Servs. V. Prometheus Labs., Inc., 566 U.S. 66, 72, 101 USPQ2d 1961, 1965). Thus, limitations that amount to nothing more than an instruction to apply the abstract idea using a generic computer do not amount to significantly more than the exception itself and do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. See MPEP 2106.05(g).
• The additional elements of “receiving data Gd including cases and labels for the cases;” which is recited at a high level of generality and amounts to extra-solution activity of receiving data i.e. pre-solution activity of gathering data for use in the claimed process. The courts have found limitations directed to obtaining 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").
“training the parameters of the first neural network using a first error between the predicted value and a value of the label for each case included in the data Gd;” is/are recited at a high-level of generality and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f)). No detail is given as to how the training is performed or the task that it is trained to perform. Consequently, this limitation merely appears to be a generic training process performed on the general-purpose computer to apply the abstract idea and is not sufficient to integrate the abstract idea into a practical application or amount to significantly more (MPEP 2106.05(f)).
“training the parameters of the second neural network using the first error and a second error between a predicted value of a label for each case when the one case is additionally observed and a value of the label for the case.” is/are recited at a high-level of generality and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f)). No detail is given as to how the training is performed or the task that it is trained to perform. Consequently, this limitation merely appears to be a generic training process performed on the general-purpose computer to apply the abstract idea and is not sufficient to integrate the abstract idea into a practical application or amount to significantly more (MPEP 2106.05(f)).
Accordingly, at Step 2A, prong two, the additional elements individually or in combination do not integrate the judicial exception into a practical application.
Step 2B analysis:
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the additional elements of:
• “a computer” This limitation is recited at a high level of generality and amount to adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. See MPEP 2106.05(f). As explained by the Supreme Court; in order to make a claim directed to a judicia I exception patent-eligible, the additional element or combination of elements must do '"more than simply state the judicial exception] while adding the words 'apply it"'. Alice Corp. v. CLS Bank, 573 U.S. 208, 221, 110 USPQ2d 1976, 1982-83 (2014) (quoting Mayo Collaborative Servs. V. Prometheus Labs., Inc., 566 U.S. 66, 72, 101 USPQ2d 1961, 1965). Thus, limitations that amount to nothing more than an instruction to apply the abstract idea using a generic computer do not amount to significantly more than the exception itself and do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
• As discussed above, the additional elements of “receiving data Gd including cases and labels for the cases;” which is recited at a high level of generality and amounts to extra-solution activity of receiving data i.e. pre-solution activity of gathering data for use in the claimed process. The courts have found limitations directed to obtaining 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").
“training the parameters of the first neural network using a first error between the predicted value and a value of the label for each case included in the data Gd;” is/are recited at a high-level of generality and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f)). No detail is given as to how the training is performed or the task that it is trained to perform. Consequently, this limitation merely appears to be a generic training process performed on the general-purpose computer to apply the abstract idea and is not sufficient to integrate the abstract idea into a practical application or amount to significantly more (MPEP 2106.05(f)).
“training the parameters of the second neural network using the first error and a second error between a predicted value of a label for each case when the one case is additionally observed and a value of the label for the case.” is/are recited at a high-level of generality and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f)). No detail is given as to how the training is performed or the task that it is trained to perform. Consequently, this limitation merely appears to be a generic training process performed on the general-purpose computer to apply the abstract idea and is not sufficient to integrate the abstract idea into a practical application or amount to significantly more (MPEP 2106.05(f)).
Accordingly, at Step 2B the additional elements individually or in combination do not amount to significantly more than the judicial exception.
Claim 2:
Step 2A, Prong 2 analysis:
“training the parameters of the second neural network includes training the parameters of the second neural network such that a reduction rate of the second error with respect to the first error is maximized.” is/are recited at a high-level of generality and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f)). No detail is given as to how the training is performed or the task that it is trained to perform. Consequently, this limitation merely appears to be a generic training process performed on the general-purpose computer to apply the abstract idea and is not sufficient to integrate the abstract idea into a practical application or amount to significantly more (MPEP 2106.05(f)).
Accordingly, at Step 2A, prong two, the additional elements individually or in combination do not integrate the judicial exception into a practical application.
Step 2B analysis:
“training the parameters of the second neural network includes training the parameters of the second neural network such that a reduction rate of the second error with respect to the first error is maximized.” is/are recited at a high-level of generality and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f)). No detail is given as to how the training is performed or the task that it is trained to perform. Consequently, this limitation merely appears to be a generic training process performed on the general-purpose computer to apply the abstract idea and is not sufficient to integrate the abstract idea into a practical application or amount to significantly more (MPEP 2106.05(f)).
Accordingly, at Step 2B the additional elements individually or in combination do not amount to significantly more than the judicial exception.
Claim 3:
Step 2A, Prong 1 analysis:
The claim(s) recite(s) in part:
• “wherein the selecting includes calculating a score for selecting the one case and selecting the one case in accordance with a distribution based on the score.”. As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation encompasses calculating a value corresponding to a selected case. This would be seen as a mental process because a person having ordinary skill of the art would be able to select a case and calculating a score that corresponds with it.
Step 2A, Prong 2 analysis:
There are no additional elements that individually or in combination integrate the judicial element into practical application.
Accordingly, at Step 2A, prong two, the additional elements individually or in combination do not integrate the judicial exception into a practical application.
Step 2B analysis:
There are no additional elements that individually or in combination amount to significantly more than the judicial exception.
Accordingly, at Step 2B the additional elements individually or in combination do not amount to significantly more than the judicial exception.
Claim 4:
Step 2A, Prong 2 analysis:
The judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of:
• The additional elements of “wherein the data Gd is data represented in a graph format where cases are indicated as nodes, and” which is recited at a high level of generality and amounts to extra-solution activity of receiving data i.e. pre-solution activity of gathering data for use in the claimed process. The courts have found limitations directed to obtaining 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 2A, prong two, the additional elements individually or in combination do not integrate the judicial exception into a practical application.
Step 2B analysis:
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the additional elements of:
• As discussed above, the additional elements of “wherein the data Gd is data represented in a graph format where cases are indicated as nodes, and” which is recited at a high level of generality and amounts to extra-solution activity of receiving data i.e. pre-solution activity of gathering data for use in the claimed process. The courts have found limitations directed to obtaining 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 the additional elements individually or in combination do not amount to significantly more than the judicial exception.
Claim 5:
Step 2A, prong 1 analysis:
The limitations of claim 5 recite the same abstract ideas as claim 1, therefore it is analyzed under the same basis.
Step 2A, Prong 2 analysis:
The judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of:
• “a processor, the processor being configured to:” which is recited at a high level of generality and amount to adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. See MPEP 2106.05(f). As explained by the Supreme Court; in order to make a claim directed to a judicial exception patent-eligible, the additional element or combination of elements must do '"more than simply state the judicial exception] while adding the words 'apply it"'. Alice Corp. v. CLS Bank, 573 U.S. 208, 221, 110 USPQ2d 1976, 1982-83 (2014) (quoting Mayo Collaborative Servs. V. Prometheus Labs., Inc., 566 U.S. 66, 72, 101 USPQ2d 1961, 1965). Thus, limitations that amount to nothing more than an instruction to apply the abstract idea using a generic computer do not amount to significantly more than the exception itself and do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. See MPEP 2106.05(g).
As discussed above, the additional elements of “receive data Gd including cases and labels for the cases;” which is recited at a high level of generality and amounts to extra-solution activity of receiving data i.e. pre-solution activity of gathering data for use in the claimed process. The courts have found limitations directed to obtaining 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").
“training the parameters of the first neural network using a first error between the predicted value and a value of the label for each case included in the data Gd;” is/are recited at a high-level of generality and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f)). No detail is given as to how the training is performed or the task that it is trained to perform. Consequently, this limitation merely appears to be a generic training process performed on the general-purpose computer to apply the abstract idea and is not sufficient to integrate the abstract idea into a practical application or amount to significantly more (MPEP 2106.05(f)).
“training the parameters of the second neural network using the first error and a second error between a predicted value of a label for each case when the one case is additionally observed and a value of the label for the case.” is/are recited at a high-level of generality and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f)). No detail is given as to how the training is performed or the task that it is trained to perform. Consequently, this limitation merely appears to be a generic training process performed on the general-purpose computer to apply the abstract idea and is not sufficient to integrate the abstract idea into a practical application or amount to significantly more (MPEP 2106.05(f)).
Accordingly, at Step 2A, prong two, the additional elements individually or in combination do not integrate the judicial exception into a practical application.
Step 2B analysis:
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the additional elements of:
• “a processor” This limitation is recited at a high level of generality and amount to adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. See MPEP 2106.05(f). As explained by the Supreme Court; in order to make a claim directed to a judicia I exception patent-eligible, the additional element or combination of elements must do '"more than simply state the judicial exception] while adding the words 'apply it"'. Alice Corp. v. CLS Bank, 573 U.S. 208, 221, 110 USPQ2d 1976, 1982-83 (2014) (quoting Mayo Collaborative Servs. V. Prometheus Labs., Inc., 566 U.S. 66, 72, 101 USPQ2d 1961, 1965). Thus, limitations that amount to nothing more than an instruction to apply the abstract idea using a generic computer do not amount to significantly more than the exception itself and do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
As discussed above, the additional elements of “receive data Gd including cases and labels for the cases;” which is recited at a high level of generality and amounts to extra-solution activity of receiving data i.e. pre-solution activity of gathering data for use in the claimed process. The courts have found limitations directed to obtaining 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").
“training the parameters of the first neural network using a first error between the predicted value and a value of the label for each case included in the data Gd;” is/are recited at a high-level of generality and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f)). No detail is given as to how the training is performed or the task that it is trained to perform. Consequently, this limitation merely appears to be a generic training process performed on the general-purpose computer to apply the abstract idea and is not sufficient to integrate the abstract idea into a practical application or amount to significantly more (MPEP 2106.05(f)).
“training the parameters of the second neural network using the first error and a second error between a predicted value of a label for each case when the one case is additionally observed and a value of the label for the case.” is/are recited at a high-level of generality and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f)). No detail is given as to how the training is performed or the task that it is trained to perform. Consequently, this limitation merely appears to be a generic training process performed on the general-purpose computer to apply the abstract idea and is not sufficient to integrate the abstract idea into a practical application or amount to significantly more (MPEP 2106.05(f)).
Accordingly, at Step 2B the additional elements individually or in combination do not amount to significantly more than the judicial exception.
Claim 6:
Prong 2A Step 1:
Claim 6 recites the same abstract ideas as claim 1, therefore it is analyzed under the same basis.
Step 2A, Prong 2 analysis:
The judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of:
• “a non-transitory computer-readable recording medium” which is recited at a high level of generality and amount to adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. See MPEP 2106.05(f). As explained by the Supreme Court; in order to make a claim directed to a judicial exception patent-eligible, the additional element or combination of elements must do '"more than simply state the judicial exception] while adding the words 'apply it"'. Alice Corp. v. CLS Bank, 573 U.S. 208, 221, 110 USPQ2d 1976, 1982-83 (2014) (quoting Mayo Collaborative Servs. V. Prometheus Labs., Inc., 566 U.S. 66, 72, 101 USPQ2d 1961, 1965). Thus, limitations that amount to nothing more than an instruction to apply the abstract idea using a generic computer do not amount to significantly more than the exception itself and do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. See MPEP 2106.05(g).
As discussed above, the additional elements of “receive data Ga including cases and labels for the cases;” which is recited at a high level of generality and amounts to extra-solution activity of receiving data i.e. pre-solution activity of gathering data for use in the claimed process. The courts have found limitations directed to obtaining 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").
• “train the parameters of the first neural network using a first error between the predicted value and a value of the label for each case included in the data Gd;” is/are recited at a high-level of generality and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f)). No detail is given as to how the training is performed or the task that it is trained to perform. Consequently, this limitation merely appears to be a generic training process performed on the general-purpose computer to apply the abstract idea and is not sufficient to integrate the abstract idea into a practical application or amount to significantly more (MPEP 2106.05(f)).
• “train the parameters of the second neural network using the first error and a second error between a predicted value of a label for each case obtained when the one case is additionally observed and a value of the label for the case.” is/are recited at a high-level of generality and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f)). No detail is given as to how the training is performed or the task that it is trained to perform. Consequently, this limitation merely appears to be a generic training process performed on the general-purpose computer to apply the abstract idea and is not sufficient to integrate the abstract idea into a practical application or amount to significantly more (MPEP 2106.05(f)).
Accordingly, at Step 2A, prong two, the additional elements individually or in combination do not integrate the judicial exception into a practical application.
Step 2B analysis:
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the additional elements of:
• “a non-transitory computer-readable recording medium” This limitation is recited at a high level of generality and amount to adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. See MPEP 2106.05(f). As explained by the Supreme Court; in order to make a claim directed to a judicia I exception patent-eligible, the additional element or combination of elements must do '"more than simply state the judicial exception] while adding the words 'apply it"'. Alice Corp. v. CLS Bank, 573 U.S. 208, 221, 110 USPQ2d 1976, 1982-83 (2014) (quoting Mayo Collaborative Servs. V. Prometheus Labs., Inc., 566 U.S. 66, 72, 101 USPQ2d 1961, 1965). Thus, limitations that amount to nothing more than an instruction to apply the abstract idea using a generic computer do not amount to significantly more than the exception itself and do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
As discussed above, the additional elements of “receive data Ga including cases and labels for the cases;” which is recited at a high level of generality and amounts to extra-solution activity of receiving data i.e. pre-solution activity of gathering data for use in the claimed process. The courts have found limitations directed to obtaining 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").
• “;train the parameters of the first neural network using a first error between the predicted value and a value of the label for each case included in the data Gd;” is/are recited at a high-level of generality and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f)). No detail is given as to how the training is performed or the task that it is trained to perform. Consequently, this limitation merely appears to be a generic training process performed on the general-purpose computer to apply the abstract idea and is not sufficient to integrate the abstract idea into a practical application or amount to significantly more (MPEP 2106.05(f)).
• “train the parameters of the second neural network using the first error and a second error between a predicted value of a label for each case obtained when the one case is additionally observed and a value of the label for the case.” is/are recited at a high-level of generality and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f)). No detail is given as to how the training is performed or the task that it is trained to perform. Consequently, this limitation merely appears to be a generic training process performed on the general-purpose computer to apply the abstract idea and is not sufficient to integrate the abstract idea into a practical application or amount to significantly more (MPEP 2106.05(f)).
Accordingly, at Step 2B the additional elements individually or in combination do not amount to significantly more than the judicial exception.
Claim 7:
Claim 7 recites similarly to claim 3, therefore it is rejected under the same basis.
Claims 8, 9 and 10:
Claims 8, 9, and 10 recite the same as claim 4, therefore they are rejected under the same basis.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1-3 and 5-7 are rejected under 35 U.S.C. 102 in view of Pang et, al (2018). (Meta-Learning Transferable Active Learning Policies by Deep Reinforcement Learning, referred to as Pang hereinafter.)
Regarding claim 1:
receiving data Gd including cases and labels for the cases (Pang)[Section 2: Active Learning (AL)]” Active Learning (AL) A dataset D = {(xi,yi)}n i=1 contains n instances xi ∈ RD and labels yi ∈ {1,2,...,C}, most or all of which are unknown in advance. In AL, the data is split between a labelled L and unlabeled U = D \ L set where |L| ≪ |U| and a classifier f has been trained on L so far. In each iteration, a pool-based active learner selects an instance/point from unlabeled pool U to query its label (L,U,f) → i, where i ∈ {1,...,|U|}. Then the selected point i is removed from U and added to L along with its label, and the classifier f is retrained based on the updated L.” Pang defines the input as a dataset containing instances (cases) and labels, with a partition into labeled and unlabeled subsets. This directly corresponds to ‘receiving data Gd including cases and labels for the cases’ where some labels are observed and some are not.
a prediction procedure for calculating a predicted value of a label for each case included in the data Gd using parameters of a first neural network and information representing cases in which the labels are observed among the respective cases included in the data Gd; (Pang)[Section 2: Active Learning (AL)]” a classifier f has been trained on L so far. In each iteration, a pool-based active learner selects an instance/point from unlabeled pool U to query its label (L,U,f) → i, where i ∈ {1,...,|U|}. Then the selected point i is removed from U and added to L along with its label, and the classifier f is retrained based on the updated L… Let the world state st = {Lt,Ut,ft} contain a featurization of the dataset and the base classifier.”[]” As base learner we use linear SVM with class balancing. All results are averages over 100 trials of training and testing dataset splits.” Pang teaches of a base classifier (first model) which is trained on the set of currently labeled cases (L) and uses its learned parameters to generate predictions for all instances. The classifier state f_t encodes information about which labels are observed. This maps to the prediction model, taught by the claim.
selecting one case from the respective cases included in the data Gd using parameters of a second neural network and information representing the cases in which the labels are observed among the respective cases included in the data Gd; (Pang)[Abstract]” We model an active learning algorithm as a deep neural network that inputs the base learner state and the unlabeled point set and predicts the best point to annotate next.”[Section 3]” The policy π inputs the N currently unlabeled instances Zu ∈ RN× d and outputs an N-way SoftMax for selecting the instance to query.” Pang teaches of a policy network (a second deep neural network) that takes as an input, the current state that encodes which labels are being observed, and the unlabeled instances. It then outputs a selection over the unlabeled pool, selecting one case to query.
training the parameters of the first neural network using a first error between the predicted value and a value of the label for each case included in the data Gd; [(Pang)[Section 2: Active Learning (AL)]”the selected point i is removed from U and added to L along with its label, and the classifier f is retrained based on the updated L... After a query, the state is updated to st+1 as the point is moved from U to L and the classifier f updates accordingly. The reward is the quantity we wish to maximize, e.g., Acct, the accuracy at query t.” Pang teaches of a base learner that uses classification error/loss to update its parameters on the labeled set. After each selection, the base classifier (first neural network) is retrained on the updated labeled set using the error between its predictions and the true label values, as taught by the claim.
training the parameters of the second neural network using the first error and a second error between a predicted value of a label for each case when the one case is additionally observed and a value of the label for the case. (Pang)[ 3.2. Reinforcement Learning Training and Objective Functions]” We define the reward the improvement in test split accuracy: rt = Acct − Acct−1. We then optimize the return of an active learning session J(θ) = E[P∞ t=1 γt−1rt(s,πθ(·,s))].” The reward r_t = Acc_t = Acc_(t-1) measures the change in prediction performance i.e., the difference between the error before and after a new case is labeled. The policy network (second neural network) is trained via reinforcing to maximize the cumulative award, being functionally similar to the limitation ‘using the first error and a second error between a predicted value of a label for each case when the one case is additionally observed’, thus rendering the claim obvious.
Regarding claim 2:
The learning method according to claim 1, wherein the training the parameters of the second neural network includes training the parameters of the second neural network such that a reduction rate of the second error with respect to the first error is maximized. (Pang)[ 3.2. Reinforcement Learning Training and Objective Functions]” We define the reward the improvement in test split accuracy: rt = Acct − Acct−1. We then optimize the return of an active learning session J(θ) = E[P∞ t=1 γt−1rt(s,πθ(·,s))]… , we train both networks where end-to-end, maximizing θ = {θp,θem} in: F =Jθ(Φ)−λ1Aθdm (Zu)+λ2H(πθ(a|Zu))” J being the cumulative accuracy improvement. The reward ‘r_t’ measures the change in prediction performance i.e., the difference between the error before and after a new case is labeled. The policy is trained to maximize the cumulative reward J, which is the sum of error reductions across the entire labeling sequence. Maximizing J directly maximized the rate at which prediction error is reduced. This teaches the same functionality of the limitation “training the parameters of the second neural network such that a reduction rate of the second error with respect to the first error is maximized”, thus rendering the claim obvious.
Regarding claim 3:
selecting includes calculating a score for selecting the one case and selecting the one case in accordance with a distribution based on the score. (Pang)[Section 3]“ It selects actions via the SoftMax π(ai|st) ∝ expΦθp(WTezi), where zi ∈ Rd is the ith unlabeled instance...[Section 3.2] The policy π inputs the N currently unlabeled instances Zu ∈ RN× d and outputs an N-way SoftMax for selecting the instance to query.” The policy network computes a score Phi(W_e^Tz_i) for each unlabeled instance, then applies the SoftMax to convert these scores into a probability distribution. The instance to query is selected according to this distribution. This teaches the same functionality as ‘calculating a score for selecting the one case and selecting the one case in accordance with a distribution based on the score’, thus rendering the claim obvious.
Regarding claim 5:
Claim 5 recites similar to claim 1, therefore it is rejected under the same basis.
Regarding claim 6:
Claim 6 recites similar to claim 1, therefore it is rejected under the same basis.
Regarding claim 7:
Claim 7 recites similar to claim 3, therefore it is rejected under the same basis.
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.
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.
Regarding claim 4:
Claim 4 is rejected under 35 U.S.C. 103 over Pang, and in further view of Chen et. Al, (CN110458957A, referred to as Chen hereinafter)
wherein the data Gd is data represented in a graph format where cases are indicated as nodes, and
(Chen)[Description] “the graph structure can be well used for representing the Mesh structure, and better results can be obtained through a graph-based convolutional neural network.” [Description]”the mesh model may be an ellipsoid model, that is, an original ellipsoid is a mesh model including 156 vertexes, each vertex is represented by three-dimensional coordinates (x, y, z), the vertexes are connected through a connecting edge, and a size of the mesh model is defined” Chen teaches of a 3D mesh model as graph-structured data where each vertex (case) is a node with the coordinates and features, connected by edges, thus rendering the claim obvious.
the first neural network and the second neural network are graph convolutional neural networks.
(Chen)[S103]”the first GCN comprises plurality of convolutional layer. Its input is the original graph structure SUPPORT, graph structure is represented by the form of the adjacency matrix” [S106]“using the second GCN GCN convolution module” Chen teaches of using a first and second neural networks that are both graph convolutional networks, as taught by the claim.
It would be obvious to a person having ordinary skill of the art to combine the calculation of a predicted value as taught by Pang, with the use of first and second graph convolutional networks, as taught by Chen. A person would be motivated to do so to perform good feature extraction on the data with the data structure as the graph (Chen)[Background].
Regarding claims 8, 9 and 10:
Claims 8, 9, 10 recite the same as claim 4, therefore they are rejected under the same basis.
Conclusion
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/AYAAN AYAZ SHEIKH/Examiner, Art Unit 2128
/OMAR F FERNANDEZ RIVAS/Supervisory Patent Examiner, Art Unit 2128
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