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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 1/31/2024. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
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 1-20 rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter without significantly more.
According to the USPTO guidelines, a claim is directed to non-statutory subject matter if:
Step 1: The claim does not fall within one of the four statutory categories of invention (process, machine, manufacture, or composition of matter), or,
Step 2: The claim recites a judicial exception, e.g. an abstract idea, without reciting additional elements that amount to significantly more than the judicial Page 4 exception, as determined using the following analysis:
Step 2A, Prong 1: Does the claim recite an abstract idea, law of nature, or natural phenomenon?
Step 2A, Prong 2: Does the claim recite additional elements that integrate the judicial exception into a practical application?
Step 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception?
MPEP 2106.04(a)(2)(I) states: "The mathematical concepts grouping is defined as mathematical relationships, mathematical formulas or equations, and mathematical calculations.”
MPEP 2106.04(a)(2)(III) states: "Accordingly, the "mental processes" abstract idea grouping is defined as concepts performed in the human mind, and examples of mental processes include observations, evaluations, judgements, and opinions.
Further, the MPEP states: "The courts do not distinguish between mental processes that are performed entirely in the human mind and mental processes that require a human to use a physical aid (e.g. pen and paper or a slide run) to perform the claim limitation.
Using the two-step inquiry, it is clear that Claims 1-20 are each directed to non-statutory subject matter as shown below:
Please note the following:
The following groups of claims are expressed in different statutory categories:
Claims 1-10 are drawn to a method and claims 11-20 are drawn to an apparatus, 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 matter; 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 and 10 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:
Step 1 Analysis: Is the claim to a process, machine, manufacture or composition of matter? See MPEP § 2106.03.
Yes, this claim is to a process.
Claim 1 recites “A method for mitigating forgetting in a machine learning model, the method comprising: augmenting an input batch of one or more real data items with one or more synthetic data items; and generating, by a first machine learning model, a prediction for each data item in the augmented batch, the first machine learning model trained using a dataset of past synthetic data items.”
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).
augmenting an input batch of one or more real data items with one or more synthetic data items (mental process of judgement – augmenting can be interpreted as selecting so, to select an input batch consisting of one generated data and one “real” observation data);
generating… a prediction for each data item in the augmented batch (mental process of judgement – creating a prediction based on selected ideas);
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 “a machine learning model” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer. See MPEP §§ 2106.04(d), 2106.05(f)(1).
No, the limitation “the first machine learning model trained using a dataset of past synthetic data items” is an additional element that amounts to adding insignificant extra-solution activity to the judicial exception. See MPEP §§ 2106.04(d), 2106.05(g).
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 machine learning model” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer. See MPEP § 2106.05(f)(1). There is no significance or structure present in the claim to make the model an improvement, hence the instructions to implement the abstract idea on a computer are redundant.
No, the limitation “the first machine learning model trained using a dataset of past synthetic data items” is an additional element that amounts to adding insignificant extra-solution activity to the judicial exception. See MPEP § 2106.05(g). There is no significance to this limitation as machine learning models must be trained with a dataset, hence adding dataset of past synthetic data items does not explain how the model is being trained and is insignificant extra-solution activity.
As to claim 2:
Claim 2 recites “The method of claim 1, comprising generating, by a second machine learning model, one or more of the synthetic data items based on the input batch, the second machine learning model trained using a dataset of past real data items.”
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).
generating… one or more of the synthetic data items based on the input batch (creating a prediction based on selected ideas (mental process of judgement – creating a prediction based on selected ideas);
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 “a second machine learning model” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer. See MPEP §§ 2106.04(d), 2106.05(f)(1).
No, the limitation “trained using a dataset of past real data items” is an additional element that amounts to adding insignificant extra-solution activity to the judicial exception. See MPEP §§ 2106.04(d), 2106.05(g).
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 second machine learning model” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer. See MPEP § 2106.05(f)(1). There is no significance or structure present in the claim to make the model an improvement, hence the instructions to implement the abstract idea on a computer are redundant.
No, the limitation “trained using a dataset of past real data items” is an additional element that amounts to adding insignificant extra-solution activity to the judicial exception. See MPEP § 2106.05(g). There is no significance to this limitation as machine learning models must be trained with a dataset, hence adding dataset of past real data items does not justify how they are being trained and is insignificant.
As to claim 3:
Claim 3 recites “The method of claim 1, wherein the augmenting comprises, for each synthetic data item: if the synthetic data item corresponds to a plurality of statistical properties, adding the synthetic data item to the augmented batch, wherein the statistical properties describe the input batch.”
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).
augmenting…for each synthetic data item… (mental process of judgement – augmenting can be interpreted as selecting so, to select an input batch consisting of one generated data and one “real” observation data);
if the synthetic data item corresponds to a plurality of statistical properties, adding the synthetic data item to the augmented batch (mental process of judgement – a condition of when a data item has certain properties, statistical property present);
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 “wherein the statistical properties describe the input batch” is an additional element that amounts to adding insignificant extra-solution activity to the judicial exception. See MPEP § 2106.05(g).
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 “wherein the statistical properties describe the input batch” is an additional element that amounts to adding insignificant extra-solution activity to the judicial exception. See MPEP § 2106.05(g). The limitation does not add any significance as statistical property can mean any value and does not discern the specific use, hence this claim does not recite any element that is significantly more than the judicial exception.
As to claim 4:
Claim 4 recites “The method of claim 2, comprising, for each synthetic data item: mapping a correspondence between the synthetic data item and one or more of the real data items in the input batch; and adjusting the augmenting of the input batch based on the mapping and based on at least one of: the dataset of past synthetic data items, and the dataset of past real data items.”
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).
mapping a correspondence between the synthetic data item and one or more of the real data items in the input batch (mental process, performed mentally or with use of a pen and paper – mapping a correspondence is interpreted as drawing a relation);
adjusting the augmenting of the input batch based on the mapping and based on at least one of: the dataset of past synthetic data items, and the dataset of past real data items. (mental process of judgement – augmenting can be interpreted as selecting so, to adjust the selected input batch based on previous knowledge);
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).
Yes, the analysis of the parent claim is incorporated.
Step 2B Analysis: Does the claim recite additional elements that amount to significantly more than the judicial exception? See MPEP § 2106.05.
Yes, the analysis of the parent claim is incorporated.
As to claim 5:
Claim 5 recites “The method of claim 2, comprising: learning a distribution of an updated dataset of past real data items; and retraining the second machine learning model based on the distribution.”
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 analysis of the parent claim is incorporated.
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 “the second machine learning model” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer. See MPEP §§ 2106.04(d), 2106.05(f)(1).
No, the limitation “learning a distribution of an updated dataset of past real data items” is an additional element that amounts to adding insignificant extra-solution activity to the judicial exception. See MPEP §§ 2106.04(d), 2106.05(g).
No, the limitation “retraining the second machine learning model based on the distribution” is an additional element that amounts to adding insignificant extra-solution activity to the judicial exception. See MPEP §§ 2106.04(d), 2106.05(g).
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 “the second machine learning model” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer. See MPEP § 2106.05(f)(1). As mentioned on the parent claim, implementation of a machine learning model in this claim adds no significance, hence the instructions to implement the abstract idea on a computer are redundant.
No, the limitation “learning a distribution of an updated dataset of past real data items” is an additional element that amounts to adding insignificant extra-solution activity to the judicial exception. See MPEP § 2106.05(g). Under its broadest reasonable interpretation, the element is interpreted as processing incoming updated data, this element is insignificant as models have to process incoming data and does not amount to any significance; hence this element does not amount to any significance and is an extra-solution activity.
No, the limitation “retraining the second machine learning model based on the distribution” is an additional element that amounts to adding insignificant extra-solution activity to the judicial exception. See MPEP § 2106.05(g). This element of the claim is insignificant as models are constantly being updated based on incoming data so for an updated dataset, it is redundant to emphasize the element and regarded as well-understood, routine, and conventional for machine learning models; hence this element does not explain how the model is being trained and does not amount to any significance outside of adding extra-solution activity.
As to claim 6:
Claim 6 recites “The method of claim 1, wherein the first machine learning model is optimized using a stochastic gradient descent (SGD) algorithm and a logarithmic loss function.”
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 “optimized using a stochastic gradient descent (SGD) algorithm and a logarithmic loss function” (mathematical calculation – the model being optimized by an algorithm and function, See MPEP § 2106.04(a)(2)(I)(C))
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 “the first machine learning model” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer. See MPEP §§ 2106.04(d), 2106.05(f)(1).
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 “the first machine learning model” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer. See MPEP § 2106.05(f)(1). There is no significance or structure present in the claim to make the model an improvement, hence the instructions to implement the abstract idea on a computer are redundant.
As to claim 7:
Claim 7 recites “The method of claim 2, wherein the second machine learning model comprises: a generative adversarial network, and a variational autoencoder.”
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 analysis of the parent claim is incorporated.
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 “the second machine learning model” and “a generative adversarial network, and a variational autoencoder” are additional elements that 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. See MPEP §§ 2106.04(d), 2106.05(f)(1).
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 “the second machine learning model” and “a generative adversarial network, and a variational autoencoder” are additional elements that 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. See MPEP § 2106.05(f)(1). As mentioned in the parent claim, machine learning model does not add any significance, similar to that element, adding a generative adversarial network and a variational autoencoder lacks structure in the claim language causes the element to be interpreted as mere instructions to implement an abstract idea on a computer.
As to claim 8:
Claim 8 recites “The method of claim 1, comprising: prior to the generating of a prediction for each data item in the augmented batch, training the first machine learning model until a minimum required performance level is achieved.”
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).
generating of a prediction for each data item in the augmented batch (mental process of judgement – creating a prediction based on selected ideas);
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 “the first machine learning model” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer. See MPEP §§ 2106.04(d), 2106.05(f)(1).
No, the limitation “training the first machine learning model until a minimum required performance level is achieved” 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)(2).
No, the limitation “training the first machine learning model until a minimum required performance level is achieved” is an additional element that amounts to adding insignificant extra-solution activity to the judicial exception. See MPEP §§ 2106.04(d), 2106.05(g).
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 “the first machine learning model” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer. See MPEP § 2106.05(f)(1). There is no significance or structure present in the claim to make the model an improvement, hence the instructions to implement the abstract idea on a computer are redundant.
No, the limitation “training the first machine learning model until a minimum required performance level is achieved” 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 § 2106.05(f)(2). There is no significance in the model being trained outside of amounting to applying the process as training is the ordinary process for the model; hence making the element insignificant.
No, the limitation “training the first machine learning model until a minimum required performance level is achieved” is an additional element that amounts to adding insignificant extra-solution activity to the judicial exception. See MPEP § 2106.05(g). This element is redundant as under its broadest reasonable interpretation, the element is training the model till the model is sufficient, it is well understood, routine, and conventional for machine learning models to be trained until satisfaction. Hence, this element does not explain how the model is being trained and amounts to adding insignificant extra-solution activity.
As to claim 9:
Claim 9 recites “The method of claim 2, wherein the generating of at least one of the predictions by the first machine learning model and the generating of at least one of the synthetic data items by the second machine learning model are performed concurrently.”
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).
generating of at least one of the predictions by the first machine learning model (mental process of judgement – generating a prediction based off of previous knowledge);
generating of at least one of the synthetic data items by the second machine learning model are performed concurrently (mental process of judgement – generating AI generated data);
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 “the first machine learning model” and “the second machine learning model” are additional elements that 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. See MPEP §§ 2106.04(d), 2106.05(f)(1).
No, the limitation “the second machine learning model are performed concurrently” is an additional element that amounts to adding insignificant extra-solution activity to the judicial exception. See MPEP §§ 2106.04(d), 2106.05(g).
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 “the first machine learning model” and “the second machine learning model” are additional elements that 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. See MPEP § 2106.05(f)(1). As mentioned on the parent claim, implementation of a machine learning model in this claim adds no significance, hence the instructions to implement the abstract idea on a computer are redundant.
No, the limitation “the second machine learning model are performed concurrently” is an additional element that amounts to adding insignificant extra-solution activity to the judicial exception. See MPEP § 2106.05(g). With the specification being silent on the significance of models working concurrently, computer running concurrent actions are tasks that the computer is expected to do so stating the usage of models being run concurrently is excessive and an extra-solution activity.
As to claim 10:
Claim 10 recites “The method of claim 1, wherein the prediction comprises a score, and wherein the method comprises: if the score exceeds a threshold, transmitting an alert to a remote computer system over a communication network.”
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).
Prediction comprises a score (mental process of judgement – creating a prediction based on previous knowledge);
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 “transmitting an alert to a remote computer system over a communication network” is an additional element that amounts to adding insignificant extra-solution activity to the judicial exception. See MPEP §§ 2106.04(d), 2106.05(g).
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 “transmitting an alert to a remote computer system over a communication network” is an additional element that amounts to adding insignificant extra-solution activity to the judicial exception. See MPEP §§ 2106.04(d), 2106.05(g). The element is interpreted under its broadest reasonable interpretation as send an alert ping. Furthermore, the additional element is directed to receiving or transmitting data over a network, 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).
Due to claims 1-10 having similar syntax to claims 11-20, claims 11-20 are also rejected for similar reasons in claims 1-10.
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)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1-5, 7-8, 11-15, 17-18 is/are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Soni (US 20200342362 A1). Soni was filed on November 11, 2020, and this date is before the effective filing date of this application, i.e., January 31,2024. Therefore, Soni constitutes prior art under 35 U.S.C. 102(a)(2).
With respect to Claim 1:
Soni teaches:
“A method for mitigating forgetting in a machine learning model, the method comprising:
augmenting an input batch of one or more real data items with one or more synthetic data items;
and generating, by a first machine learning model, a prediction for each data item in the augmented batch, the first machine learning model trained using a dataset of past synthetic data items.”
Soni mentions in Paragraph [0007] that the selection of real data and synthetic data (augmenting an input batch) to adjust “first artificial intelligence network model” and generate prediction based on synthetic data using a first machine learning model. Paragraph [0061] also mentions the first machine learning model being trained using a dataset of past synthetic data items generated by the first machine learning model.
With respect to Claim 2:
Soni teaches:
“The method of claim 1, comprising generating, by a second machine learning model, one or more of the synthetic data items based on the input batch, the second machine learning model trained using a dataset of past real data items.”
Soni mentions in Paragraph [0007] that generating comprises of analyzing synthetic data where the second machine learning model is “respect to a real data set” and then outputting synthetic data set. Paragraph [0067] also mentions the second machine learning model being trained using a dataset of past real data items.
With respect to Claim 3:
Soni teaches:
“The method of claim 1, wherein the augmenting comprises, for each synthetic data item:
if the synthetic data item corresponds to a plurality of statistical properties, adding the synthetic data item to the augmented batch, wherein the statistical properties describe the input batch.” Soni mentions in Paragraph [0029] that when synthetic data has plurality of statistical properties, adds the synthetic data to augmented batch, statistical property is silent in the specification so, under its broadest reasonable interpretation, statistical property can be interpreted as any metric information. Hence, Paragraph [0029] mentions synthetic medical series data (statistical property) is present, will further train and adds data to input batch to generate prediction for missing events.
With respect to Claim 4:
Soni teaches:
“The method of claim 2, comprising, for each synthetic data item:
mapping a correspondence between the synthetic data item and one or more of the real data items in the input batch;
and adjusting the augmenting of the input batch based on the mapping and based on at least one of: the dataset of past synthetic data items, and the dataset of past real data items.”
Soni mentions in Paragraph [0007] mentions the analysis (mapping) between the synthetic and real data in the input batch and adjusting the selected input based on feedback from the input of dataset of model trained on synthetic and real data set.
With respect to Claim 5:
Soni teaches:
“The method of claim 2, comprising:
learning a distribution of an updated dataset of past real data items;
and retraining the second machine learning model based on the distribution.”
Soni mentions in Paragraph [0068] the learning of a distribution of an updated dataset of past real data items and based on that distribution, the retraining of “the generative model.” (second machine learning model)
With respect to Claim 7:
Soni teaches:
“The method of claim 2, wherein the second machine learning model comprises: a generative adversarial network, and a variational autoencoder.”
Soni mentions in Paragraph [0059] of applications of the model (second machine learning model) with generative adversarial network and a variational autoencoder.
With respect to Claim 8:
Soni teaches:
“The method of claim 1, comprising: prior to the generating of a prediction for each data item in the augmented batch, training the first machine learning model until a minimum required performance level is achieved.”
Soni mentions in Paragraph [0029] before generating prediction for each data item in augmented batch, model is trained until minimum required performance is satisfied.
With respect to Claim 11:
Soni teaches:
“A computerized system for mitigating forgetting in a machine learning model, the system comprising:
a memory, and a computer processor configured to:
augment an input batch of one or more real data items with one or more synthetic data items;
and generate, by a first machine learning model, a prediction for each data item in the augmented batch, the first machine learning model trained using a dataset of past synthetic data items.”
Soni mentions in Paragraph [0007] that the selection of real data and synthetic data (input batch) to adjust “first artificial intelligence network model” and generate prediction based on synthetic data using a first machine learning model. Under its broadest reasonable interpretation, “analyze the synthetic data set with respect to a real data set” is selecting the synthetic data based on existing selected real data to determine the prediction. Paragraph [0103] also mentions the first machine learning model being trained using a dataset of past synthetic data items.
With respect to Claim 12:
Soni teaches:
“The computerized system of claim 11, wherein the processor is to generate, by a second machine learning model, one or more of the synthetic data items based on the input batch, the second machine learning model trained using a dataset of past real data items.”
Soni mentions in Paragraph [0007] that generating comprises of analyzing synthetic data where the second machine learning model is “respect to a real data set” and then outputting synthetic data set. Paragraph [0068] also mentions the second machine learning model being trained using a dataset of past synthetic data items.
With respect to Claim 13:
Soni teaches:
“The computerized system of claim 11, wherein the augmenting comprises, for each synthetic data item:
if the synthetic data item corresponds to a plurality of statistical properties, adding the synthetic data item to the augmented batch, wherein the statistical properties describe the input batch.”
Soni mentions in Paragraph [0029] that when synthetic data has plurality of statistical properties, adds the synthetic data to input batch, statistical property is silent in the specification so, under its broadest reasonable interpretation, statistical property can be interpreted as any metric information. Hence, Paragraph [0029] mentions synthetic medical series data (statistical property) is present, will further train and adds data to input batch to generate prediction for missing events.
With respect to Claim 14:
Soni teaches:
“The computerized system of claim 12, wherein the processor is to: for each synthetic data item:
map a correspondence between the synthetic data item and one or more of the real data items in the input batch;
and adjust the augmenting of the input batch based on the mapping and based on at least one of: the dataset of past synthetic data items, and the dataset of past real data items.”
Soni mentions in Paragraph [0007] mentions the analysis (mapping) between the synthetic and real data in the input batch and adjusting the selected input based on feedback from the input of dataset of model trained on synthetic and real data set.
With respect to Claim 15:
Soni teaches:
“The computerized system of claim 12, wherein the processor is to:
learn a distribution of an updated dataset of past real data items;
and retrain the second machine learning model based on the distribution.”
Soni mentions in Paragraph [0068] the learning of a distribution of an updated dataset of past real data items and based on that distribution, the retraining of “the generative model.” (second machine learning model)
With respect to Claim 17:
Soni teaches:
“The computerized system of claim 12, wherein the second machine learning model comprises: a generative adversarial network, and a variational autoencoder.”
Soni mentions in Paragraph [0059] of applications of the model (second machine learning model) with generative adversarial network and a variational autoencoder.
With respect to Claim 18:
Soni teaches:
“The computerized system of claim 11, wherein the processor is to: prior to the generating of a prediction for each data item in the augmented batch, train the first machine learning model until a minimum required performance level is achieved.”
Soni mentions in Paragraph [0029] before generating prediction for each data item in augmented batch, model is trained until minimum required performance is satisfied.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 6 and 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Soni (US 20200342362 A1, filed on November 20,2019) in view of Butsch (US 20240046393 A1, filed on April 11, 2023).
With respect to Claim 6:
Soni does not appear to explicitly disclose:
“The method of claim 1, wherein the first machine learning model is optimized using a stochastic gradient descent (SGD) algorithm and a logarithmic loss function.”
However, Butsch teaches:
“The method of claim 1, wherein the first machine learning model is optimized using a stochastic gradient descent (SGD) algorithm and a logarithmic loss function.” Butsch mentions in Paragraph [0118] a machine learning model being optimized using SGD algorithm and logarithmic loss function.
Soni and Butsch are analogous art and in the same field of invention because both references pertain to usage of various gradient descent and loss function to optimize machine learning models. While Soni mentions in Paragraph [0070] that various gradient descent algorithms and loss functions can improve with generating predictions, Butsch mentions that the usage of SGD algorithm and logarithmic loss function can improve generation of prediction with “large amount of information.” It would have been obvious to a person having ordinary skill in the art (PHOSITA) to make this combination to improve generation of prediction with large, augmented input batches as mentioned by Butsch in Paragraph [0120].
With respect to Claim 16:
Soni does not appear to explicitly disclose:
“The computerized system of claim 11, wherein the first machine learning model is optimized using a stochastic gradient descent (SGD) algorithm and a logarithmic loss function.”
However, Butsch teaches:
“The computerized system of claim 11, wherein the first machine learning model is optimized using a stochastic gradient descent (SGD) algorithm and a logarithmic loss function.” Butsch mentions in Paragraph [0118] a machine learning model being optimized using SGD algorithm and logarithmic loss function.
Soni and Butsch are analogous art and in the same field of invention because both references pertain to usage of various gradient descent and loss function to optimize machine learning models. While Soni mentions in Paragraph [0070] that various gradient descent algorithms and loss functions can improve with generating predictions, Butsch mentions that the usage of SGD algorithm and logarithmic loss function can improve generation of prediction with “large amount of information.” It would have been obvious to a person having ordinary skill in the art (PHOSITA) to make this combination to improve generation of prediction with large, augmented input batches as mentioned by Butsch in Paragraph [0120].
Claim(s) 9 and 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Soni (US 20200342362 A1, filed on November 20,2019) in view of Rama (US 20230334290 A1, filed on April 13, 2022).
With respect to Claim 9:
Soni does not appear to explicitly disclose:
“The method of claim 2, wherein the generating of at least one of the predictions by the first machine learning model and the generating of at least one of the synthetic data items by the second machine learning model are performed concurrently.”
However, Rama teaches:
“The method of claim 2, wherein the generating of at least one of the predictions by the first machine learning model and the generating of at least one of the synthetic data items by the second machine learning model are performed concurrently.” Rama mentions in Paragraph [0014], the models are working in parallel (concurrent) to continuously learn and optimize from each model, specifically for synthetic data to resemble real data and "effectively distinguishes the synthetic data from the original…"
Soni and Rama are analogous art and in the same field of invention because both references pertain to the generation of prediction based on incoming augmented input batch. While Soni mentions in Paragraph [0029] the generation of both synthetic data and prediction, Rama mentions the generation of prediction and synthetic data in parallel. It would have been obvious to a person having ordinary skill in the art (PHOSITA) to make this combination to continuously improve and optimize the machine learning models in data as mentioned by Rama in Paragraph [0014].
With respect to Claim 19:
Soni does not appear to explicitly disclose:
“The computerized system of claim 12, wherein the generating of at least one of the predictions by the first machine learning model and the generating of at least one of the synthetic data items by the second machine learning model are performed concurrently.”
However, Rama teaches:
“The computerized system of claim 12, wherein the generating of at least one of the predictions by the first machine learning model and the generating of at least one of the synthetic data items by the second machine learning model are performed concurrently.” Rama mentions in Paragraph [0014], the models are working in parallel (concurrent) to continuously learn and optimize from each model, specifically for synthetic data to resemble real data and "effectively distinguishes the synthetic data from the original…"
Soni and Rama are analogous art and in the same field of invention because both references pertain to the generation of prediction based on incoming augmented input batch. While Soni mentions in Paragraph [0029] the generation of both synthetic data and prediction, Rama mentions the generation of prediction and synthetic data in parallel. It would have been obvious to a person having ordinary skill in the art (PHOSITA) to make this combination to continuously improve and optimize the machine learning models in data as mentioned by Rama in Paragraph [0014].
Claim(s) 10 and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Soni (US 20200342362 A1, filed on November 20,2019) in view of Narayanan (US 20240249158 A1, filed on March 03, 2023).
With respect to Claim 10:
Soni does not appear to explicitly disclose:
“The method of claim 1, wherein the prediction comprises a score, and wherein the method comprises: if the score exceeds a threshold, transmitting an alert to a remote computer system over a communication network.”
However, Narayanan teaches:
“The method of claim 1, wherein the prediction comprises a score, and wherein the method comprises: if the score exceeds a threshold, transmitting an alert to a remote computer system over a communication network.” Narayanan mentions in Paragraphs [0137] and [0138], they teach the prediction comprising a score and alert based on exceeding threshold of score specifically mentioning “evaluation output...such as an alert...may be indicative of a performance degradation” and “performance degradation... threshold decrease in a holistic evaluation score.”
Soni and Narayanan are analogous art and in the same field of invention because both references pertain to the generation of prediction based on augmented data. While Soni mentions generating prediction based on the augmented input batch, Narayanan also mentions this and adds on with an evaluation score as well as an alert based on passing score threshold. It would have been obvious to a person having ordinary skill in the art (PHOSITA) to make this combination to improve “machine learning evaluation, training, and monitoring techniques” as well as notice any “performance degradation” in the machine language model as mentioned by Narayanan in Paragraph [0003] and [0137].
With respect to Claim 20:
Soni does not appear to explicitly disclose:
“The computerized system of claim 11, wherein the prediction comprises a score, and wherein the processor is to: if the score exceeds a threshold, transmit an alert to a remote computer system over a communication network.”
However, Narayanan teaches:
“The computerized system of claim 11, wherein the prediction comprises a score, and wherein the processor is to: if the score exceeds a threshold, transmit an alert to a remote computer system over a communication network.” Narayanan mentions in Paragraphs [0137] and [0138], they teach the prediction comprising a score and alert based on exceeding threshold of score specifically mentioning “evaluation output...such as an alert...may be indicative of a performance degradation” and “performance degradation... threshold decrease in a holistic evaluation score.”
Soni and Narayanan are analogous art and in the same field of invention because both references pertain to the generation of prediction based on augmented data. While Soni mentions generating prediction based on the augmented input batch, Narayanan also mentions this and adds on with an evaluation score as well as an alert based on passing score threshold. It would have been obvious to a person having ordinary skill in the art (PHOSITA) to make this combination to improve “machine learning evaluation, training, and monitoring techniques” as well as notice any “performance degradation” in the machine language model as mentioned by Narayanan in Paragraph [0003] and [0137].
Conclusion
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/C.A./Examiner, Art Unit 2142
/HAIMEI JIANG/Primary Examiner, Art Unit 2142
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