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 Application filed on 7/20/2023. Claims 1-20 are pending in the case. Claims 1, 14, and 20 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.
When considering subject matter eligibility under 35 U.S.C. 101, it must be determined whether the claim is directed to one of the four statutory categories of invention, i.e., process, machine, manufacture, or composition of matter (Step 1). If the claim does fall within one of the statutory categories, the second step in the analysis is to determine whether the claim is directed to a judicial exception (Step 2A). The Step 2A analysis is broken into two prongs. In the first prong (Step 2A, Prong 1), it is determined whether or not the claims recite a judicial exception (e.g., mathematical concepts, mental processes, certain methods of organizing human activity). If it is determined in Step 2A, Prong 1 that the claims recite a judicial exception, the analysis proceeds to the second prong (Step 2A, Prong 2), where it is determined whether or not the claims integrate the judicial exception into a practical application. If itis determined at step 2A, Prong 2 that the claims do not integrate the judicial exception into a practical application, the analysis proceeds to determining whether the claim is a patent-eligible application of the exception (Step 2B). If an abstract idea is present in the claim, any element or combination of elements in the claim must be sufficient to ensure that the claim integrates the judicial exception into a practical application, or else amounts to significantly more than the abstract idea itself. Applicant is advised to consult the 2019 PEG for more details of the analysis.
Step 1 Analysis: Is the claim to a process, machine, manufacture or composition of matter? See MPEP § 2106.03.
Claims 1-13 are drawn to a method, claims 14-19 are drawn to a system and claim 20 is drawn to a computer program product embodies in a non-transitory computer readable medium, therefore each of these claim groups falls under one of four categories of statutory subject matter (machine/products/apparatus, process/method, manufactures and compositions of mater; Step 1). Nonetheless, the claims are directed to a judicially recognized exception of an abstract idea without significant more (Step 2A, see below). Independent claims 1, 14 and 20 are non-verbatim but similar in claim construction, hence share the same rationale that the claimed inventions are directed to non-statutory subject matter as follows:
As to claim 1:
Claim 1 recites “A method, comprising: training a first machine learning model using a synthetic training dataset; using the first machine learning model to predict a plurality of pseudo-labels corresponding to an unlabeled dataset associated with a specific group; selecting at least a portion of the unlabeled dataset and their corresponding pseudo-labels to form a pseudo-labeled dataset; and training a second machine learning model using the pseudo-labeled dataset and the synthetic training dataset as an improved version of the first machine learning model.”
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 “using … to predict a plurality of pseudo-labels corresponding to an unlabeled dataset associated with a specific group” and “selecting at least a portion of the unlabeled dataset and their corresponding pseudo-labels to form a pseudo-labeled dataset” are 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). Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of people thinking about what those data are and pick up some of those data to form another group of data, which is an observation or evaluation that is practically capable of being performed in the human mind with the assistance of pen and paper. 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, this limitation “a first machine learning model” and “a second machine learning model” are additional elements that amounts to adding the words “apply it” (or an equivalent) with the judicial exception and reciting only the idea of a solution or outcome, i.e., the claim fails to recite details of how a solution to a problem is accomplished because it is unclear how the “a first machine learning model” and “a second machine learning model” are used nor the specification makes it clear how these actions are performed. Thus, these additional elements are recited in a manner that represent no more than mere instructions to apply the judicial exceptions on a computer. See MPEP § 2106.05(f) and § 2106.04(d).
No, this limitation “training… using a synthetic training dataset”, “training… using the pseudo-labeled dataset and the synthetic training dataset as an improved version..” amounts to mere data gathering. It is necessary to acquire the data in order to use the recited judicial exception to perform “training”. Therefore, the additional limitation is insignificant extra-solution activity to the judicial exception, and as such is deemed insufficient to transform the judicial exception to a patentable invention. See MPEP §§ 2106.04(d), 2106.05(g).
Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea when considered as an ordered combination and as a whole.
Step 2B Analysis: Does the claim recite additional elements that amount to significantly more than the judicial exception? See MPEP § 2106.05.
First, the additional elements directed to generally linking the use of a judicial exception to a particular technological environment or field of use are deemed insufficient to transform the judicial exception to a patentable invention because the claimed limitations generally link the judicial exception to the technology environment, see MPEP 2106.05(h). However, they are included below for the sake of completeness.
Second, the additional elements mere application of the abstract idea or mere instructions to implement an abstract idea on a computer are deemed insufficient to transform the judicial exception to a patentable invention because the limitations generally apply the use of a generic computer and/or process with the judicial exception. See MPEP 2106.05(f). However, they are included below for the sake of completeness.
No, this limitation “a first machine learning model” and “a second machine learning model” are additional elements that amounts to adding the words “apply it” (or an equivalent) with the judicial exception and reciting only the idea of a solution or outcome, i.e., the claim fails to recite details of how a solution to a problem is accomplished because it is unclear how the “a first machine learning model” and “a second machine learning model” are used nor the specification makes it clear how these actions are performed. Thus, these additional elements are recited in a manner that represent no more than mere instructions to apply the judicial exceptions on a computer. See MPEP § 2106.05(f) and § 2106.04(d).
No, this limitation “training… using a synthetic training dataset”, “training… using the pseudo-labeled dataset and the synthetic training dataset as an improved version..” amounts to mere data gathering. It is necessary to acquire the data in order to use the recited judicial exception to perform “training”. Therefore, the additional limitation is insignificant extra-solution activity to the judicial exception, and as such is deemed insufficient to transform the judicial exception to a patentable invention. See MPEP §§ 2106.04(d), 2106.05(g).
Furthermore the additional element is directed to receiving or transmitting data over a network / performing repetitive calculations / electronic recordkeeping / storing and retrieving information in memory / electronically scanning or extracting data from a physical document, which the courts have recognized as well‐understood, routine, and conventional when they are claimed in a generic manner. See MPEP § 2106.05(d)(II).
Thus, considering the additional elements individually and in combination and the claims as a whole, the additional elements do not provide significantly more than the abstract idea. The claims are not eligible subject matter.
Therefore, in examining elements as recited by the limitations individually and as an ordered combination, as a whole the independent claim limitations do not recite what have the courts have identified as “significantly more”.
Furthermore, regarding dependent claims 2-13 which are dependent on claim 1, claims 15-19 which are dependent on claim 14, the claims are directed to a judicial exception without significantly more as highlighted below in the claim limitations by evaluating the claim limitations under Step 2A and 2B:
Dependent claim 2
Incorporates the rejection of independent claim
Step 2A Prong 1: does the claim recite an abstract idea, law of nature, or natural phenomenon? See MPEP § 2106.04(II)(A)(1). Yes, the limitation “further comprising: creating the synthetic training dataset, wherein the synthetic training dataset includes at least a text string or utterance and a corresponding synthetic label.” 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 2: the claim recite additional elements that integrate the judicial exception into a practical application? See MPEP § 2106.04(d) No.
Step 2B: the claim recite additional elements that amount to significantly more than the judicial exception? See MPEP § 2106.05. and Is the additional element recognized as well-understood, routine, and conventional? No.
Dependent claim 3
Incorporates the rejection of independent claim
Step 2A Prong 1: does the claim recite an abstract idea, law of nature, or natural phenomenon? See MPEP § 2106.04(II)(A)(1). Yes, the limitation “further comprising: determining whether the first machine learning model or the second machine learning model has converged based on a validation metric associated with a validation dataset.” 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 2: the claim recite additional elements that integrate the judicial exception into a practical application? See MPEP § 2106.04(d) No.
Step 2B: the claim recite additional elements that amount to significantly more than the judicial exception? See MPEP § 2106.05. and Is the additional element recognized as well-understood, routine, and conventional? No.
Dependent claim 4
Incorporates the rejection of independent claim
Step 2A Prong 1: does the claim recite an abstract idea, law of nature, or natural phenomenon? See MPEP § 2106.04(II)(A)(1). Yes, the limitation “further comprising: calibrating the second machine learning model using a validation dataset. ” 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 2: the claim recite additional elements that integrate the judicial exception into a practical application? See MPEP § 2106.04(d) No.
Step 2B: the claim recite additional elements that amount to significantly more than the judicial exception? See MPEP § 2106.05. and Is the additional element recognized as well-understood, routine, and conventional? No.
Dependent claim 5
Incorporates the rejection of independent claim
Step 2A Prong 1: does the claim recite an abstract idea, law of nature, or natural phenomenon? See MPEP § 2106.04(II)(A)(1). Yes, the limitation “further comprising: selecting the at least a portion of the unlabeled dataset and their corresponding pseudo- labels to form the pseudo-labeled dataset based on confidence scores” 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). This limitation is also the abstract idea of a mathematical relationship, as directed to “a mathematical relationship is a relationship between variables or numbers. A mathematical relationship may be expressed in words or using mathematical symbols”. See MPEP § 2106.04(a)(2)(I)(A).
Step 2A prong 2: the claim recite additional elements that integrate the judicial exception into a practical application? See MPEP § 2106.04(d) No.
Step 2B: the claim recite additional elements that amount to significantly more than the judicial exception? See MPEP § 2106.05. and Is the additional element recognized as well-understood, routine, and conventional? No.
Dependent claims 6 and 16
Incorporates the rejection of independent claim
Step 2A Prong 1: does the claim recite an abstract idea, law of nature, or natural phenomenon? See MPEP § 2106.04(II)(A)(1). Yes, the limitation “combining a loss associated with the subset of the pseudo-labeled dataset and a loss associated with the subset of the synthetic training dataset” 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 2: the claim recite additional elements that integrate the judicial exception into a practical application? See MPEP § 2106.04(d) No, this limitation “drawing a subset of the pseudo-labeled dataset; drawing a subset of the synthetic training dataset” amounts to mere data gathering. It is necessary to acquire the data in order to use the recited judicial exception to perform “drawing” data. Therefore, the additional limitation is insignificant extra-solution activity to the judicial exception, and as such is deemed insufficient to transform the judicial exception to a patentable invention. See MPEP §§ 2106.04(d), 2106.05(g).
Step 2B: the claim recite additional elements that amount to significantly more than the judicial exception? See MPEP § 2106.05. and Is the additional element recognized as well-understood, routine, and conventional? No, this limitation “drawing a subset of the pseudo-labeled dataset; drawing a subset of the synthetic training dataset” amounts to mere data gathering. It is necessary to acquire the data in order to use the recited judicial exception to perform “drawing” data. Therefore, the additional limitation is insignificant extra-solution activity to the judicial exception, and as such is deemed insufficient to transform the judicial exception to a patentable invention. See MPEP §§ 2106.04(d), 2106.05(g). Furthermore the additional element is directed to receiving or transmitting data over a network / performing repetitive calculations / electronic recordkeeping / storing and retrieving information in memory / electronically scanning or extracting data from a physical document, which the courts have recognized as well‐understood, routine, and conventional when they are claimed in a generic manner. See MPEP § 2106.05(d)(II).
Dependent claims 7 and 17
Incorporates the rejection of independent claim
Step 2A Prong 1: does the claim recite an abstract idea, law of nature, or natural phenomenon? See MPEP § 2106.04(II)(A)(1). Yes, the limitation “wherein the combined loss comprises a weighted sum of the loss associated with the subset of the pseudo-labeled dataset and the loss associated with the subset of the synthetic training dataset.” 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). This limitation is also the abstract idea of a mathematical relationship, as directed to “a mathematical relationship is a relationship between variables or numbers. A mathematical relationship may be expressed in words or using mathematical symbols”. See MPEP § 2106.04(a)(2)(I)(A).
Step 2A prong 2: the claim recite additional elements that integrate the judicial exception into a practical application? See MPEP § 2106.04(d) No.
Step 2B: the claim recite additional elements that amount to significantly more than the judicial exception? See MPEP § 2106.05. and Is the additional element recognized as well-understood, routine, and conventional? No.
Dependent claims 8 and 18
Incorporates the rejection of independent claim
Step 2A Prong 1: does the claim recite an abstract idea, law of nature, or natural phenomenon? See MPEP § 2106.04(II)(A)(1). Yes, the limitation “wherein the weighted sum comprises a scaling factor for scaling the loss associated with the subset of the pseudo-labeled dataset with respect to the loss associated with the subset of the synthetic training dataset” 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). This limitation is also the abstract idea of a mathematical relationship, as directed to “a mathematical relationship is a relationship between variables or numbers. A mathematical relationship may be expressed in words or using mathematical symbols”. See MPEP § 2106.04(a)(2)(I)(A).
Step 2A prong 2: the claim recite additional elements that integrate the judicial exception into a practical application? See MPEP § 2106.04(d) No.
Step 2B: the claim recite additional elements that amount to significantly more than the judicial exception? See MPEP § 2106.05. and Is the additional element recognized as well-understood, routine, and conventional? No.
Dependent claims 9 and 19
Incorporates the rejection of independent claim
Step 2A Prong 1: does the claim recite an abstract idea, law of nature, or natural phenomenon? See MPEP § 2106.04(II)(A)(1). Yes, the limitation “further comprising: increasing the scaling factor as the training of the second machine learning model using the pseudo-labeled dataset and the synthetic training dataset progresses.” 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). This limitation is also the abstract idea of a mathematical relationship, as directed to “a mathematical relationship is a relationship between variables or numbers. A mathematical relationship may be expressed in words or using mathematical symbols”. See MPEP § 2106.04(a)(2)(I)(A).
Step 2A prong 2: the claim recite additional elements that integrate the judicial exception into a practical application? See MPEP § 2106.04(d) No.
Step 2B: the claim recite additional elements that amount to significantly more than the judicial exception? See MPEP § 2106.05. and Is the additional element recognized as well-understood, routine, and conventional? No.
Dependent claim 10
Incorporates the rejection of independent claim
Step 2A Prong 1: does the claim recite an abstract idea, law of nature, or natural phenomenon? See MPEP § 2106.04(II)(A)(1). Yes, the limitation “wherein the scaling factor is increased until a measure of time has reached a predetermined maximum time.” 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). This limitation is also the abstract idea of a mathematical relationship, as directed to “a mathematical relationship is a relationship between variables or numbers. A mathematical relationship may be expressed in words or using mathematical symbols”. See MPEP § 2106.04(a)(2)(I)(A).
Step 2A prong 2: the claim recite additional elements that integrate the judicial exception into a practical application? See MPEP § 2106.04(d) No.
Step 2B: the claim recite additional elements that amount to significantly more than the judicial exception? See MPEP § 2106.05. and Is the additional element recognized as well-understood, routine, and conventional? No.
Dependent claim 11
Incorporates the rejection of independent claim
Step 2A Prong 1: does the claim recite an abstract idea, law of nature, or natural phenomenon? See MPEP § 2106.04(II)(A)(1). Yes, the limitation “wherein the scaling factor is increased until a number of iterations has reached a predetermined maximum number of iterations” 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). This limitation is also the abstract idea of a mathematical relationship, as directed to “a mathematical relationship is a relationship between variables or numbers. A mathematical relationship may be expressed in words or using mathematical symbols”. See MPEP § 2106.04(a)(2)(I)(A).
Step 2A prong 2: the claim recite additional elements that integrate the judicial exception into a practical application? See MPEP § 2106.04(d) No.
Step 2B: the claim recite additional elements that amount to significantly more than the judicial exception? See MPEP § 2106.05. and Is the additional element recognized as well-understood, routine, and conventional? No.
Dependent claim 12
Incorporates the rejection of independent claim
Step 2A Prong 1: does the claim recite an abstract idea, law of nature, or natural phenomenon? See MPEP § 2106.04(II)(A)(1). Yes, the limitation “wherein the scaling factor is increased until the scaling factor is set to a predetermined maximum scaling factor value. ” 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). This limitation is also the abstract idea of a mathematical relationship, as directed to “a mathematical relationship is a relationship between variables or numbers. A mathematical relationship may be expressed in words or using mathematical symbols”. See MPEP § 2106.04(a)(2)(I)(A).
Step 2A prong 2: the claim recite additional elements that integrate the judicial exception into a practical application? See MPEP § 2106.04(d) No.
Step 2B: the claim recite additional elements that amount to significantly more than the judicial exception? See MPEP § 2106.05. and Is the additional element recognized as well-understood, routine, and conventional? No.
Dependent claim 13
Incorporates the rejection of independent claim
Step 2A Prong 1: does the claim recite an abstract idea, law of nature, or natural phenomenon? See MPEP § 2106.04(II)(A)(1). Incorporates the abstract idea of independent claim.
Step 2A prong 2: the claim recite additional elements that integrate the judicial exception into a practical application? See MPEP § 2106.04(d) No
Step 2B: the claim recite additional elements that amount to significantly more than the judicial exception? See MPEP § 2106.05. and Is the additional element recognized as well-understood, routine, and conventional? No.
Dependent claim 15
Incorporates the rejection of independent claim
Step 2A Prong 1: does the claim recite an abstract idea, law of nature, or natural phenomenon? See MPEP § 2106.04(II)(A)(1). Incorporates the abstract idea of independent claim.
Step 2A prong 2: the claim recite additional elements that integrate the judicial exception into a practical application? See MPEP § 2106.04(d) No.
Step 2B: the claim recite additional elements that amount to significantly more than the judicial exception? See MPEP § 2106.05. and Is the additional element recognized as well-understood, routine, and conventional? No.
The dependent claims as analyzed above, do not recite limitations that integrated the judicial exception into a practical application. In addition, the claim limitations do not include additional elements that are sufficient to amount to significantly more than the judicial exception (Step 2B). Therefore, the claims do not recite any limitations, when considered individually or as a whole, that recite what the courts have identified as “significantly more”, see MPEP 2106.05; and therefore, as a whole the claims are not patent eligible.
As shown above, the dependent claims do not provide any additional elements that when considered individually or as an ordered combination, amount to significantly more than the abstract idea identified. Therefore, as a whole the dependent claims do not recite what the courts have identified as “significantly more” than the recited judicial exception.
Therefore, claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception and does not recite, when claim elements are examined individually and as a whole, elements that the courts have identified as “significantly more” than the recited judicial exception.
Examiner’s Note:
Without any specific claim of how the “machine learning model” having specific structure and functionality with data, under BRI, “machine learning model” is a software module that processes data.
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 1-7, 13-17 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over
Li et al (WO 2022265755 A1) in view of “Pseudo-label: The simple and efficient semi-supervised learning method for deep neural networks”, Lee, 2013.
Referring to claims 1, 14 and 20, Li discloses a method, comprising:
training a first machine learning model using a synthetic training dataset; (the [0033] of the specification defines “synthetic training dataset as “the synthetic training dataset is created to be used across different enterprises. Synthetic training data is information that is artificially generated rather than produced by real-world events. In some embodiments, the synthetic training dataset may be created by an algorithm or a computer simulation. In some embodiments, the synthetic training dataset may be created by a human agent, such as a linguist who is experienced in the language used in the IT field. An agent or a computer program may create a synthetic taxonomy (intent-utterance map) as a synthetic training dataset, which is an approximation that tries to capture the real distribution of the text or utterances that users may use to report IT incidents and initiate IT requests”. Hence, Synthetic training dataset is not produced by real world events. Page 5 of Li, “when the unlabeled data set is filtered through the predefined dictionary, a sentence in the unlabeled data set that includes a word that does not appear in the predefined dictionary may be masked, e.g., the sentence is deleted or replaced with a predetermined character set, etc. In this way, words that do not appear in the predefined dictionary are not used in subsequent pretraining steps and training steps, etc., so as to prevent information related to these words from being leaked. The words that do not appear in the predefined dictionary may be terms that have not been widely used, custom words, etc”)
using the first machine learning model to predict a plurality of pseudo-labels corresponding to an unlabeled dataset associated with a specific group; (page 3 of Li, “the embodiments of the present disclosure propose to combine corpus obtained through a plurality of approaches or from a plurality of sources into an unlabeled data set for training a target model. The corpus obtained through different approaches or from different sources usually includes samples involving different domains. Herein, a domain of a sample may broadly refer to a field, a region and a type, etc. related to the sample. The corpora of different domains usually have different ways of expression and language features, and therefore have different data distribution and characteristics, etc. Training a target model with an unlabeled data set including a plurality of corpora involving a plurality of domains may guarantee the fairness of the trained target model.”)
selecting at least a portion of the unlabeled dataset and their corresponding pseudo-labels to form a pseudo-labeled dataset; (page 7 of Li, “ the filtered unlabeled data set may be labeled through the target model, to obtain a pseudo-labeled data set. The filtered unlabeled data set may include a plurality of unlabeled samples, e g., a plurality of invisible samples. The target model may label each sample of the plurality of samples to obtain a pseudo-labeled corresponding to the sample, and obtain a pseudo-labeled sample through adding the obtained pseudo-labeled to the sample. A plurality of pseudo-labeled samples corresponding to the plurality of unlabeled samples may be combined into the pseudo-labeled data set.”) and
Li does not specifically disclose “training a second machine learning model using the pseudo-labeled dataset and the synthetic training dataset as an improved version of the first machine learning model.”
However, Lee discloses training a second machine learning model using the pseudo-labeled dataset and the synthetic training dataset as an improved version of the first machine learning model (page 4 of Lee, Figure 1 shows t-SNE (Van der Maaten et al., 2008) 2 D embedding results of the network output of MNIST test data (not included in unlabeled data). The neural network was trained with 600 labeled data and with or without 60000 unlabeled data and Pseudo-Labels. Though the train error is zero in the two cases, the network outputs of test data is more condensed near 1-of K code by training with unlabeled data and Pseudo Labels)
Li and Lee are analogous art because both references concern training raw data with pseudo labeling techniques. Accordingly, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to modify Li’s labeling unlabeled data with training combining unlabeled data with pseudo labels taught by Lee. The motivation for doing so would have been to improve a training for raw input data with semi-supervised learning.
Referring to claim 2, Li in view of Lee disclose the method of claim 1, further comprising: creating the synthetic training dataset, wherein the synthetic training dataset includes at least a text string or utterance and a corresponding synthetic label. (page 6 of Li, “the ground-truth-labeled data set may be data-augmented through employing a word replacement method. For example, for a specific sample in the ground-truth-labeled data set, some tokens in a text of the sample may be randomly selected and the selected tokens may be replaced. For example, a predetermined proportion of tokens may be selected from tokens of the text of the sample. In the selected tokens, 80% of the tokens are replaced with special tokens, such as "[PAD].sup.1', 10% of the tokens remain unchanged, and 10% of the tokens are replaces with tokens randomly selected from a vocabulary. Training the target model with such samples having replaced tokens may improve the robustness of the target model.”)
Referring to claim 3, Li in view of Lee disclose the method of claim 1, further comprising: determining whether the first machine learning model or the second machine learning model has converged based on a validation metric associated with a validation dataset. (page 5 of Lee, We used validation set for determining some hyper-parameters. The remaining data were used for unlabeled data. Because we could not get the same split of dataset, 10 experiments on random split were done using the identical network and parameters)
Referring to claim 4, Li in view of Lee disclose the method of claim 1, further comprising: calibrating the second machine learning model using a validation dataset. (page 5 of Lee, We used validation set for determining some hyper-parameters. The remaining data were used for unlabeled data. Because we could not get the same split of dataset, 10 experiments on random split were done using the identical network and parameters)
Referring to claims 5 and 15, Li in view of Lee disclose the method of claim 1, further comprising: selecting the at least a portion of the unlabeled dataset and their corresponding pseudo- labels to form the pseudo-labeled dataset based on confidence scores. (page 5 of Lee, In the case of 100 labeled data, the results heavily de ended on data split so that 30 experiments were done. 95% confidence interval is about ±1∼1.5% for 100 labeled data, about ±0.1∼0.15% for 600 labeled data, less than ±0.1% for 1000 and 3000 labeled data)
Referring to claims 6 and 16, Li in view of Lee disclose the method of claim 1, further comprising: drawing a subset of the pseudo-labeled dataset; drawing a subset of the synthetic training dataset; and combining a loss associated with the subset of the pseudo-labeled dataset and a loss associated with the subset of the synthetic training dataset. (page 2 of Lee, cross entropy as a loss function L(yi,fi)=−yilogfi−(1−yi)log(1−fi))
Referring to claims 7 and 17, Li in view of Lee disclose the method of claim 6, wherein the combined loss comprises a weighted sum of the loss associated with the subset of the pseudo-labeled dataset and the loss associated with the subset of the synthetic training dataset. (page 2 of Lee, cross entropy as a loss function L(yi,fi)=−yilogfi−(1−yi)log(1−fi))
Referring to claim 13, Li in view of Lee disclose the method of claim 1, wherein the second machine learning model is used for automated classification of service requests for the specific group. (page 4 of Li, “Taking a classification task as an example, a task prediction value of a sample may indicate a probability that a text included in the sample is classified into a corresponding class. The higher the task prediction value, the higher the probability that the text is in this class. A task prediction value of each sample in a pseudo-labeled data set may be predicted through a target model. A pseudo- labeled data subset may be formed through selecting, from the pseudo-labeled data set, samples having task prediction values higher than a predetermined threshold. Retraining the target model with such pseudo-labeled data subset may guarantee the accuracy of the retrained target model. A domain prediction value of a sample may indicate a probability that a text included in the sample is classified into a corresponding domain)
Allowable Subject Matter
Claims 8-12 and 18-19 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims. 101 rejections still remain.
The prior art made of record and not relied upon is considered pertinent to Applicant's disclosure:
Sundaram et al (US 20190114544 A1): constructing a convolutional neural network-based classifier for variant classification. In particular, it relates to training a convolutional neural network-based classifier on training data using a backpropagation-based gradient update technique that progressively match outputs of the convolutional network network-based classifier with corresponding ground truth labels. The convolutional neural network-based classifier comprises groups of residual blocks, each group of residual blocks is parameterized by a number of convolution filters in the residual blocks, a convolution window size of the residual blocks, and an atrous convolution rate of the residual blocks, the size of convolution window varies between groups of residual blocks, the atrous convolution rate varies between groups of residual blocks. The training data includes benign training examples and pathogenic training examples of translated sequence pairs generated from benign variants and pathogenic variants.
Applicant is required under 37 C.F.R. § 1.111(c) to consider these references fully when responding to this action.
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. A reference is relevant for all it contains and may be relied upon for all that it would have reasonably suggested to one having ordinary skill in the art. In re Heck, 699 F.2d 1331, 1332-33, 216 U.S.P.Q. 1038, 1039 (Fed. Cir. 1983) (quoting In re Lemelson, 397 F.2d 1006, 1009, 158 U.S.P.Q. 275, 277 (C.C.P.A. 1968)).
In the interests of compact prosecution, Applicant is invited to contact the examiner via electronic media pursuant to USPTO policy outlined MPEP § 502.03. All electronic communication must be authorized in writing. Applicant may wish to file an Internet Communications Authorization Form PTO/SB/439. Applicant may wish to request an interview using the Interview Practice website: http://;www.uspto.gov/patent/laws-and-regulations/interview-practice.
Applicant is reminded Internet e-mail may not be used for communication for matters under 35 U.S.C. § 132 or which otherwise require a signature. A reply to an Office action may NOT be communicated by Applicant to the USPTO via Internet e- mail. If such a reply is submitted by Applicant via Internet e-mail, a paper copy will be placed in the appropriate patent application file with an indication that the reply is NOT ENTERED. See MPEP § 502.03(II).
Conclusion
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/HAIMEI JIANG/Primary Examiner, Art Unit 2142
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