DETAILED ACTION
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 .
Specification
The disclosure is objected to because of the following informalities:
“does” should be changed to plural form “do,” in view of the word “orders” in paragraph [0017] for the phrase, “However, for the orders which does not get processed automatically, the details may be processed manually…”
Appropriate correction is required.
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 therefore, subject to the conditions and requirements of this title.
Regarding claims 1 - 20, these claims are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The analysis below of the claims’ subject matter eligibility follows the 2019 Revised Patent Subject Matter Eligibility Guidance, 84 Fed. Reg. 50-57 (January 7, 2019) (“2019 PEG”) and the 2024 Guidance Update on Patent Subject Matter Eligibility, Including on Artificial Intelligence, 89 Fed. Reg. 58128-58138 (July 17, 2024) (“2024 AI SME Update”).
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, 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, 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.
Regarding independent claim 1, the claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: Claim 1 is directed to a method, corresponding to one of the four statutory categories - a process.
Step 2A Prong 1: The claim is directed to an abstract idea. In particular, the claim recites mental processes that are concepts performed in the human mind (including an observation, evaluation, judgment, opinion) based on mathematical concepts (i.e., mathematical relationships, mathematical formulas or equations, and mathematical calculations).
The claim recites the following limitations:
creating synthetic data by modeling real data;
augmenting the real data and the synthetic data;
generating a forecast based on the finalized synthetic data – as drafted, under their broadest reasonable interpretation (BRI), in view of the specification, cover concepts encompassing mental processes (evaluation, judgment, or opinion to generate a forecast based on observed/recorded real data and synthetic data).
applying loss function to minimize an error between the real data and the synthetic data; adding noise to the synthetic data to create finalized synthetic data. Under the BRI, in light of the specification, the above limitation encompasses a mathematical concept (mathematical calculations – to minimize error and add noise to data – see, e.g., paragraph 34 of applicant’s specification, “this process is attached to a Markov chain that gradually adds Gaussian noise to the data” and as shown in 200, figure 2 of drawings showing a mathematical noise addition block diagram depicting system components and their interactions to ensure synthetic data is mimicking real data).
MPEР 2106.04(a)(2)(II) provides "The mathematical concepts grouping is defined as mathematical relationships, mathematical formulas or equations, and mathematical calculations."
Therefore, the claims recite mental processes based on mathematical concepts. These steps and operations cover performance of the limitations in the mind (i.e., observing real data being collected, evaluating the minimization of real and synthetic data by a loss function, judging resource allocation performance via forecast and determining the best option through an opinion) based on a mathematical concept (mathematical calculations).
Accordingly claim 1 recites an abstract idea.
Step 2A Prong 2: The judicial exception is not integrated into a practical application. In particular, the claim recites these additional elements: A method, comprising collecting real data.
That is, the limitation: " A method, comprising collecting real data " is mere data gathering, which is an insignificant extra-solution activity that does not integrate the judicial exception into a practical application. See MPEP 2106.05(g).
Step 2B: The claim does not recite additional elements that are sufficient to amount to significantly more than the judicial exception.
Receiving and communicating data are insignificant extra-solution activities that are well-understood, routine, and conventional. See MPEP2106.05(d)(II) (“The courts have recognized the following computer functions as well‐understood, routine, and conventional functions… i. Receiving or transmitting data over a network…iv. Storing and retrieving information in memory”) (citing OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015)). Therefore, recitation of “collecting real data” is the well-understood, routine, conventional (WURC) activity of receiving or transmitting data over a network, as discussed in MPEP § 2106.05(d).
As an ordered whole, the claim is directed to a method of taking in data (WURC), altering, analyzing it (mental process/ mathematical concept - abstract) and optimizing the results from the generated forecast (mental process – abstract). Nothing in the claims provide significantly more than this. As such, the claim is not patent eligible.
Regarding claim 2, this claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
STEP 1: Claim 2 is directed to a method as depending from claim 1, thus the analysis for patent eligibility of claim 1 is incorporated herein.
STEP 2A Prong 1: The claim recites “wherein the real data comprises time series data that includes a seasonality component that is present in only a subset of the time series data.” The additional limitation added by this claim merely limits the invention to a narrower abstract idea by merely reciting what the real data includes (i.e., “time series data that includes a seasonality component that is present in only a subset of the time series data”). This limitation does nothing to alter the fundamental nature of the claim as a mental process combined with a mathematical concept. This is because the additional limitation merely limits the invention to a narrower abstract idea by further narrowing what the real data comprises and includes (i.e., time series data that includes a seasonality component).
Dependent claim 2, when analyzed as a whole, is not patent eligible under 35 U.S.C. 101 because the additional limitation fails to establish that the claim is not directed to an abstract idea.
Thus, this limitation does nothing to alter the analysis of claim 1.
Step 2A Prong 2: The judicial exceptions are not integrated into a practical application.
The recitation of “the real data comprises time series data that includes a seasonality component that is present in only a subset of the time series data” merely describes the type of data being collected and processed and thus can be considered as “generally linking the use of judicial exception to a particular technological environment or field of use”, which does not integrate the abstract idea into a practical application. See MPEP 2106.05(h).
The claim does not recite any additional elements that integrate the abstract idea into a practical application or provide significantly more than the abstract idea, and thus the claim is subject-matter ineligible.
Step 2B: The claim does not recite additional elements that are sufficient to amount to significantly more than the judicial exception. Viewing the additional element of this dependent claim as a combination does not add anything further than the individual elements.
This claim is not patent eligible.
Regarding claim 3, this claim is rejected under 35 U.S.C 101 because the claimed invention is directed to an abstract idea without significantly more.
STEP 1: Claim 3 is directed to a method as depending from claim 1, thus the analysis for patent eligibility of claim 1 is incorporated herein.
STEP 2A Prong 1: The claim recites “wherein the synthetic data is created with an ensemble that comprises a generative adversarial network and a diffused probabilistic model.” This limitation does nothing to alter the fundamental nature of the claim as a mental process combined with a mathematical concept. This is because the additional limitation merely limits the invention to a narrower abstract idea by further narrowing the ensemble into its constituent parts of a generative adversarial network (GAN) and diffused probabilistic model.
Dependent claim 3, when analyzed as a whole, is not patent eligible under 35 U.S.C. 101 because the additional limitation fails to establish that the claim is not directed to an abstract idea.
Thus, this limitation does nothing to alter the analysis of claim 1.
Step 2A Prong 2: The judicial exceptions are not integrated into a practical application.
The recitation of “the synthetic data is created with an ensemble that comprises a generative adversarial network and a diffused probabilistic model” merely describes the components/parts making up an ensemble used to create synthetic data and thus can be considered as “generally linking the use of judicial exception to a particular technological environment or field of use”, which does not integrate the abstract idea into a practical application. See MPEP 2106.05(h).
The claim does not recite any additional elements that integrate the abstract idea into a practical application or provide significantly more than the abstract idea, and thus the claim is subject-matter ineligible.
Step 2B: The claim does not recite additional elements that are sufficient to amount to significantly more than the judicial exception. Viewing the additional element of this dependent claim as a combination does not add anything further than the individual elements.
This claim is not patent eligible.
Regarding claim 4, this claim is rejected under 35 U.S.C 101 because the claimed invention is directed to an abstract idea without significantly more.
STEP 1: Claim 4 is directed to a method as depending from claim 1, thus the analysis for patent eligibility of claim 1 is incorporated herein.
STEP 2A Prong 1: The claim recites “wherein the loss function is a custom loss function that removes bias from the synthetic data.” The additional limitation added by this claim merely limits the invention to a narrower abstract idea by reciting that the loss function “is a custom loss function that removes bias from the synthetic data.” This limitation does nothing to alter the fundamental nature of the claim with a mental process combined with a mathematical concept. The above limitation covers concepts encompassing mental processes (evaluation, judgment, or opinion to remove bias from the synthetic data. This limitation also encompasses a mathematical concept (mathematical calculations – to perform a custom loss function). Such calculation/computation of the custom loss function, can be done using math as suggested by the discussion of this step, in paragraphs 36 of applicant’s specification.
Dependent claim 4, when analyzed as a whole, is not patent eligible under 35 U.S.C. 101 because the additional recited limitation fails to establish that the claim is not directed to an abstract idea. Thus, this limitation does nothing to alter the analysis of claim 1.
Step 2A Prong 2: The judicial exceptions are not integrated into a practical application.
The claim does not recite any additional elements that integrate the abstract idea into a practical application or provide significantly more than the abstract idea, and thus the claim is subject-matter ineligible.
Step 2B: The claim does not recite additional elements that are sufficient to amount to significantly more than the judicial exception.
This claim is not patent eligible.
Regarding claim 5, this claim is rejected under 35 U.S.C 101 because the claimed invention is directed to an abstract idea without significantly more.
STEP 1: Claim 5 is directed to a method as depending from claim 1, thus the analysis for patent eligibility of claim 1 is incorporated herein.
STEP 2A Prong 1: The claim recites “wherein the synthetic data preserves temporal dynamics that are present in the real data.” The additional limitation added by this claim merely limits the invention to a narrower abstract idea by reciting “the synthetic data preserves temporal dynamics that are present in the real data.” This limitation does nothing to alter the fundamental nature of the claim as a mental process combined with a mathematical concept. This is because the additional limitation merely limits the invention to a narrower abstract idea by further narrowing what the claimed synthetic data does, which is to “preserve temporal dynamics that are present in the real data.”
Dependent claim 5, when analyzed as a whole, is not patent eligible under 35 U.S.C. 101 because the additional recited limitation fails to establish that the claim is not directed to an abstract idea. Thus, this limitation does nothing to alter the analysis of claim 1.
Step 2A Prong 1: The judicial exceptions are not integrated into a practical application.
The claim does not recite any additional elements that integrate the abstract idea into a practical application or provide significantly more than the abstract idea, and thus the claim is subject-matter ineligible.
Step 2B: The claim does not recite additional elements that are sufficient to amount to significantly more than the judicial exception.
This claim is not patent eligible.
Regarding claim 6, this claim is rejected under 35 U.S.C 101 because the claimed invention is directed to an abstract idea without significantly more.
STEP 1: Claim 6 is directed to a method as depending from claim 1, thus the analysis for patent eligibility of claim 1 is incorporated herein.
STEP 2A Prong 1: The claim recites “The method as recited in claim 1 wherein the forecast is used as a basis for performing a resource allocation process, and the resource comprises computing resources and/or human resources.” The additional limitation added by this claim includes intended use language with no patentable weight. (e.g., “for performing a resource allocation process, and the resource comprises computing resources and/or human resources”). It does nothing to alter the fundamental nature of the claim with certain methods of organizing human activity. This is because the additional limitation merely limits the invention to a narrower abstract idea by further narrowing what the claimed forecast is used for, which is to perform a resource allocation process whereby, “the resource comprises computing resources and/or human resources.”
Dependent claim 6, when analyzed as a whole, is not patent eligible under 35 U.S.C. 101 because the additional recited limitation fails to establish that the claim is not directed to an abstract idea. Thus, this limitation does nothing to alter the analysis of claim 1.
Step 2A Prong 2: The judicial exceptions are not integrated into a practical application.
The claim does not recite any additional elements that integrate the abstract idea into a practical application or provide significantly more than the abstract idea, and thus the claim is subject-matter ineligible.
Step 2B: The claim does not recite additional elements that are sufficient to amount to significantly more than the judicial exception.
This claim is not patent eligible.
Regarding claim 7, this claim is rejected under 35 U.S.C 101 because the claimed invention is directed to an abstract idea without significantly more.
STEP 1: Claim 7 is directed to a method as depending from claim 1, thus the analysis for patent eligibility of claim 1 is incorporated herein.
STEP 2A Prong 1: The claim recites “wherein the augmenting comprises adding Gaussian noise.” This limitation covers a mathematical concept. That is, “the augmenting comprises adding Gaussian noise” is a mathematical concept (see, e.g., Applicant’s FIG. 2, element 200 and paragraph 34 of Applicant’s specification disclosing that “this process is attached to a Markov chain that gradually adds Gaussian noise to the data.”). This limitation does nothing to alter the fundamental nature of the claim as a mental process combined with a mathematical concept.
Dependent claim 7, when analyzed as a whole, is not patent eligible under 35 U.S.C 101 because the additional recited limitation fails to establish that the claim is not directed to an abstract idea.
Thus, this limitation does nothing to alter the analysis of claim 1.
Step 2A Prong 2: The judicial exceptions are not integrated into a practical application.
The claim does not recite any additional elements that integrate the abstract idea into a practical application or provide significantly more than the abstract idea, and thus the claim is subject-matter ineligible.
STEP 2B: The claim does not recite additional elements that are sufficient to amount to significantly more than the judicial exception.
This claim is not patent eligible.
Regarding claim 8, this claim is rejected under 35 U.S.C 101 because the claimed invention is directed to an abstract idea without significantly more.
STEP 1: Claim 8 is directed to a method as depending from claim 1, thus the analysis for patent eligibility of claim 1 is incorporated herein.
STEP 2A Prong 1: The claim recites “wherein adding noise to the synthetic data helps to ensure that the synthetic data mimics the real data, even when the real data lacks seasonality.” This limitation includes intended use language with no patentable weight (e.g., “to ensure that the synthetic data mimics the real data, even when the real data lacks seasonality”). The limitation does nothing to alter the fundamental nature of the claim as a mental process combined with a mathematical concept.
Dependent claim 8, when analyzed as a whole, is not patent eligible under 35 U.S.C 101 because the additional recited limitation fails to establish that the claim is not directed to an abstract idea.
Thus, this limitation does nothing to alter the analysis of claim 1.
STEP 2A Prong 2: The judicial exceptions are not integrated into a practical application.
The claim does not recite any additional elements that integrate the abstract idea into a practical application or provide significantly more than the abstract idea, and thus the claim is subject-matter ineligible.
STEP 2B: The claim does not recite additional elements that are sufficient to amount to significantly more than the judicial exception.
This claim is not patent eligible.
Regarding claim 9, this claim is rejected under 35 U.S.C 101 because the claimed invention is directed to an abstract idea without significantly more.
STEP 1: Claim 9 is directed to a method as depending from claim 1, thus the analysis for patent eligibility of claim 1 is incorporated herein.
STEP 2A Prong 1: The claim recites “wherein the noise is added to non-seasonal data of the synthetic data.” The additional limitation added by this claim merely limits the invention to a narrower abstract idea by reciting “noise added to synthetic data.” It does nothing to alter the fundamental nature of the claim as a mental process combined with a mathematical concept. This limitation covers a mathematical concept. This limitation covers a mathematical concept. In particular, the above-noted wherein clause adds noise to “non-seasonal data of the synthetic data.” Such addition of noise/perturbation is a mathematical concept of using a noise addition block/function to add noise in the non-seasonal data, as suggested by the discussion of this step, in figure 2 of applicant’s drawings and as mentioned in paragraph 19 of applicant’s specification.
Dependent claim 9, when analyzed as a whole, is not patent eligible under 35 U.S.C. 101 because the additional limitation fails to establish that the claim is not directed to an abstract idea.
Thus, this limitation does nothing to alter the analysis of claim 1.
Step 2A Prong 2: The judicial exceptions are not integrated into a practical application.
The claim does not recite any additional elements that integrate the abstract idea into a practical application or provide significantly more than the abstract idea, and thus the claim is subject-matter ineligible.
Step 2B: The claim does not recite additional elements that are sufficient to amount to significantly more than the judicial exception.
This claim is not patent eligible.
Regarding claim 10, this claim is rejected under 35 U.S.C 101 because the claimed invention is directed to an abstract idea without significantly more.
STEP 1: Claim 10 is directed to a method as depending from claim 1, thus the analysis for patent eligibility of claim 1 is incorporated herein.
STEP 2A Prong 1: The claim recites “wherein the synthetic data is created with a generative adversarial network and a diffused probabilistic model, and a contrastive learning process is applied that minimizes a loss between the generative adversarial network and the diffused probabilistic model to ensure that the synthetic data is bias-free.” These limitations do nothing to alter the fundamental nature of the claim as a mental process combined with a mathematical concept. This is because the additional limitation are mere instructions to apply an exception using generically-recited model, process and network. This alters the outcome rather than alter the fundamental nature of the claim. Also, this limitation includes intended use language with no patentable weight (e.g., “to ensure that the synthetic data mimics the real data, even when the real data lacks seasonality”).
Dependent claim 10, when analyzed as a whole, is not patent eligible under 35 U.S.C. 101 because the additional limitation fails to establish that the claim is not directed to an abstract idea.
Thus, this limitation does nothing to alter the analysis of claim 1.
Step 2A Prong 2: The judicial exceptions are not integrated into a practical application.
The claim does not recite any additional elements that integrate the abstract idea into a practical application or provide significantly more than the abstract idea, and thus the claim is subject-matter ineligible.
The claim recites the additional elements of “wherein the synthetic data is created with a generative adversarial network and a diffused probabilistic model, and a contrastive learning process is applied that minimizes a loss between the generative adversarial network and the diffused probabilistic model to ensure that the synthetic data is bias-free.”
The “generative adversarial network and a diffused probabilistic model, and a contrastive learning process” are recited at a high level of generality, and no details of the neural network, model or process are recited. Therefore, the neural network, model and process are being interpreted as performing a mental process (i.e., creating synthetic data as discussed above with reference to claim 1) on a generic computer. See MPEP 2106.04(a)(2) § III.C which states that “a concept that is performed in the human mind and applicant is merely claiming that concept performed 1) on a generic computer, or 2) in a computer environment, or 3) is merely using a computer as a tool to perform the concept” still recite a mental process.
The above-noted additional elements in the claim amount to recitation of the words "apply it" (or an equivalent) or are mere instructions to implement an abstract idea or other exception on a computer, which does not integrate a judicial exception into a practical application. See MPEP 2106.05(f).
Merely asserting that a judicial exception is to be carried out on a generic computer (i.e., with the generically-recited “generative adversarial network and a diffused probabilistic model, and a contrastive learning process”) cannot meaningfully integrate the judicial exception into a practical application. See MPEP § 2106.05(f).
Also, the limitation “the synthetic data is created with a generative adversarial network and a diffused probabilistic model, and a contrastive learning process is applied that minimizes a loss between the generative adversarial network and the diffused probabilistic model” can be considered as “generally linking the use of judicial exception to a particular technological environment or field of use”. See MPEP 2106.05(h).
Thus, these limitations do nothing to alter the analysis of claim 1.
Step 2B: The claim does not recite additional elements that are sufficient to amount to significantly more than the judicial exception.
This claim is not patent eligible.
Regarding independent claim 11, the claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: Claim 11 is directed to a non-transitory storage medium, corresponding to an article of manufacture, which is one of the statutory categories.
Step 2A Prong 1: The claim is directed to an abstract idea. In particular, the claim recites mental processes that are concepts performed in the human mind (including an observation, evaluation, judgment, opinion) based on mathematical concepts (i.e., mathematical relationships, mathematical formulas or equations, and mathematical calculations).
The claim recites the following limitations:
creating synthetic data by modeling real data;
augmenting the real data and the synthetic data;
generating a forecast based on the finalized synthetic data – as drafted, under their broadest reasonable interpretation (BRI), in view of the specification, cover concepts encompassing mental processes (evaluation, judgment, or opinion to generate a forecast based on observed/recorded real data and synthetic data).
applying loss function to minimize an error between the real data and the synthetic data; adding noise to the synthetic data to create finalized synthetic data. Under the BRI, in light of the specification, the above limitation encompasses a mathematical concept (mathematical calculations – to minimize error and add noise to data – see, e.g., paragraph 34 of applicant’s specification, “this process is attached to a Markov chain that gradually adds Gaussian noise to the data” and as shown in 200, figure 2 of drawings showing a mathematical noise addition block diagram depicting system components and their interactions to ensure synthetic data is mimicking real data).
MPEР 2106.04(a)(2)(II) provides "The mathematical concepts grouping is defined as mathematical relationships, mathematical formulas or equations, and mathematical calculations."
Therefore, the claims recite mental processes based on mathematical concepts. These steps and operations cover performance of the limitations in the mind (i.e., observing real data being collected, evaluating the minimization of real and synthetic data by a loss function, judging resource allocation performance via a forecast and determining the best option through an opinion) based on a mathematical concept (mathematical calculations).
Accordingly claim 11 recites an abstract idea.
Step 2A Prong 2: The judicial exception is not integrated into a practical application. In particular, the claim recites these additional elements: A non-transitory storage medium having stored therein instructions that are executable by one or more hardware processors to perform operations comprising: collecting real data.
That is, the limitation: "collecting real data" is mere data gathering, which is an insignificant extra-solution activity that does not integrate the judicial exception into a practical application. See MPEP 2106.05(g).
Claim 11 also recites the additional elements: “A non-transitory storage medium having stored therein instructions that are executable by one or more hardware processors to perform operations”, which amounts to the recitation of the words “apply it” (or an equivalent) or amount to no more than mere instructions to implement an abstract idea or other exception on a computer or merely use a computer as a tool to perform an abstract idea (i.e., as generic computer components performing generic computer functions). See MPEP 2106.05(f).
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. The claim is directed to an abstract idea.
Step 2B: The claim does not recite additional elements that are sufficient to amount to significantly more than the judicial exception.
Receiving and communicating data are insignificant extra-solution activities that are well-understood, routine, and conventional. See MPEP2106.05(d)(II) (“The courts have recognized the following computer functions as well‐understood, routine, and conventional functions… i. Receiving or transmitting data over a network…iv. Storing and retrieving information in memory”) (citing OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015)). Therefore, recitation of “collecting real data” is the well-understood, routine, conventional (WURC) activity of receiving or transmitting data over a network, as discussed in MPEP § 2106.05(d).
As an ordered whole, the claim is directed to a method of taking in data (WURC), altering, analyzing it (mental process/ mathematical concept - abstract) and optimizing the results from the generated forecast (mental process – abstract). Nothing in the claims provide significantly more than this. As such, the claim is not patent eligible.
Regarding claim 12, this claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
STEP 1: Claim 12 is directed to a non-transitory storage medium as depending from claim 11, thus the analysis for patent eligibility of claim 11 is incorporated herein
STEP 2A Prong 1: The claim recites “wherein the real data comprises time series data that includes a seasonality component that is present in only a subset of the time series data.” The additional limitation added by this claim merely limits the invention to a narrower abstract idea by merely reciting what the real data includes (i.e., “time series data that includes a seasonality component that is present in only a subset of the time series data”). This limitation does nothing to alter the fundamental nature of the claim as a mental process combined with a mathematical concept. This is because the additional limitation merely limits the invention to a narrower abstract idea by further narrowing what the real data comprises and includes (i.e., time series data that includes a seasonality component).
Dependent claim 12, when analyzed as a whole, is not patent eligible under 35 U.S.C. 101 because the additional limitation fails to establish that the claim is not directed to an abstract idea.
Thus, this limitation does nothing to alter the analysis of claim 11.
Step 2A Prong 2: The judicial exceptions are not integrated into a practical application.
The recitation of “the real data comprises time series data that includes a seasonality component that is present in only a subset of the time series data” merely describes the type of data being collected and processed and thus can be considered as “generally linking the use of judicial exception to a particular technological environment or field of use”, which does not integrate the abstract idea into a practical application. See MPEP 2106.05(h).
The claim does not recite any additional elements that integrate the abstract idea into a practical application or provide significantly more than the abstract idea, and thus the claim is subject-matter ineligible.
Step 2B: The claim does not recite additional elements that are sufficient to amount to significantly more than the judicial exception. Viewing the additional element of this dependent claim as a combination does not add anything further than the individual elements.
This claim is not patent eligible.
Regarding claim 13, this claim is rejected under 35 U.S.C 101 because the claimed invention is directed to an abstract idea without significantly more.
STEP 1: Claim 13 is directed to a non-transitory storage medium as depending from claim 11, thus the analysis for patent eligibility of claim 11 is incorporated herein.
STEP 2A Prong 1: The claim recites “wherein the synthetic data is created with an ensemble that comprises a generative adversarial network and a diffused probabilistic model.” This limitation does nothing to alter the fundamental nature of the claim as a mental process combined with a mathematical concept. This is because the additional limitation merely limits the invention to a narrower abstract idea by further narrowing the ensemble into its constituent parts of a generative adversarial network (GAN) and diffused probabilistic model.
Dependent claim 13, when analyzed as a whole, is not patent eligible under 35 U.S.C. 101 because the additional limitation fails to establish that the claim is not directed to an abstract idea.
Thus, this limitation does nothing to alter the analysis of claim 11.
Step 2A Prong 2: The judicial exceptions are not integrated into a practical application.
The recitation of “the synthetic data is created with an ensemble that comprises a generative adversarial network and a diffused probabilistic model” merely describes the components/parts making up an ensemble used to create synthetic data and thus can be considered as “generally linking the use of judicial exception to a particular technological environment or field of use”, which does not integrate the abstract idea into a practical application. See MPEP 2106.05(h).
The claim does not recite any additional elements that integrate the abstract idea into a practical application or provide significantly more than the abstract idea, and thus the claim is subject-matter ineligible.
Step 2B: The claim does not recite additional elements that are sufficient to amount to significantly more than the judicial exception. Viewing the additional element of this dependent claim as a combination does not add anything further than the individual elements.
This claim is not patent eligible.
Regarding claim 14, this claim is rejected under 35 U.S.C 101 because the claimed invention is directed to an abstract idea without significantly more.
STEP 1: Claim 14 is directed to a non-transitory storage medium as depending from claim 11, thus the analysis for patent eligibility of claim 11 is incorporated herein.
STEP 2A Prong 1: The claim recites “wherein the loss function is a custom loss function that removes bias from the synthetic data.” The additional limitation added by this claim merely limits the invention to a narrower abstract idea by reciting that the loss function “is a custom loss function that removes bias from the synthetic data.” This limitation does nothing to alter the fundamental nature of the claim with a mental process combined with a mathematical concept. The above limitation covers concepts encompassing mental processes (evaluation, judgment, or opinion to remove bias from the synthetic data. This limitation also encompasses a mathematical concept (mathematical calculations – to perform a custom loss function). Such calculation/computation of the custom loss function, can be done using math as suggested by the discussion of this step, in paragraphs 36 of applicant’s specification.
Dependent claim 14, when analyzed as a whole, is not patent eligible under 35 U.S.C. 101 because the additional recited limitation fails to establish that the claim is not directed to an abstract idea. Thus, this limitation does nothing to alter the analysis of claim 11.
Step 2A Prong 2: The judicial exceptions are not integrated into a practical application.
The claim does not recite any additional elements that integrate the abstract idea into a practical application or provide significantly more than the abstract idea, and thus the claim is subject-matter ineligible.
Step 2B: The claim does not recite additional elements that are sufficient to amount to significantly more than the judicial exception.
This claim is not patent eligible.
Regarding claim 15, this claim is rejected under 35 U.S.C 101 because the claimed invention is directed to an abstract idea without significantly more.
STEP 1: Claim 15 is directed to a non-transitory storage medium as depending from claim 11, thus the analysis for patent eligibility of claim 11 is incorporated herein.
STEP 2A Prong 1: The claim recites “wherein the synthetic data preserves temporal dynamics that are present in the real data.” The additional limitation added by this claim merely limits the invention to a narrower abstract idea by reciting “the synthetic data preserves temporal dynamics that are present in the real data.” This limitation does nothing to alter the fundamental nature of the claim as a mental process combined with a mathematical concept. This is because the additional limitation merely limits the invention to a narrower abstract idea by further narrowing what the claimed synthetic data does, which is to “preserve temporal dynamics that are present in the real data.”
Dependent claim 15, when analyzed as a whole, is not patent eligible under 35 U.S.C. 101 because the additional recited limitation fails to establish that the claim is not directed to an abstract idea. Thus, this limitation does nothing to alter the analysis of claim 11.
Step 2A Prong 1: The judicial exceptions are not integrated into a practical application.
The claim does not recite any additional elements that integrate the abstract idea into a practical application or provide significantly more than the abstract idea, and thus the claim is subject-matter ineligible.
Step 2B: The claim does not recite additional elements that are sufficient to amount to significantly more than the judicial exception.
This claim is not patent eligible.
Regarding claim 16, this claim is rejected under 35 U.S.C 101 because the claimed invention is directed to an abstract idea without significantly more.
STEP 1: Claim 16 is directed a non-transitory storage medium as depending from claim 11, thus the analysis for patent eligibility of claim 11 is incorporated herein.
STEP 2A Prong 1: The claim recites “The method as recited in claim 11 wherein the forecast is used as a basis for performing a resource allocation process, and the resource comprises computing resources and/or human resources.” The additional limitation added by this claim includes intended use language with no patentable weight. (e.g., “for performing a resource allocation process, and the resource comprises computing resources and/or human resources”). It does nothing to alter the fundamental nature of the claim with certain methods of organizing human activity. This is because the additional limitation merely limits the invention to a narrower abstract idea by further narrowing what the claimed forecast is used for, which is to perform a resource allocation process whereby, “the resource comprises computing resources and/or human resources.”
Dependent claim 16, when analyzed as a whole, is not patent eligible under 35 U.S.C. 101 because the additional recited limitation fails to establish that the claim is not directed to an abstract idea. Thus, this limitation does nothing to alter the analysis of claim 11.
Step 2A Prong 2: The judicial exceptions are not integrated into a practical application.
The claim does not recite any additional elements that integrate the abstract idea into a practical application or provide significantly more than the abstract idea, and thus the claim is subject-matter ineligible.
Step 2B: The claim does not recite additional elements that are sufficient to amount to significantly more than the judicial exception.
This claim is not patent eligible.
Regarding claim 17, this claim is rejected under 35 U.S.C 101 because the claimed invention is directed to an abstract idea without significantly more.
STEP 1: Claim 17 is directed to a non-transitory storage medium as depending from claim 11, thus the analysis for patent eligibility of claim 11 is incorporated herein.
STEP 2A Prong 1: The claim recites “wherein the augmenting comprises adding Gaussian noise.” This limitation covers a mathematical concept. That is, “the augmenting comprises adding Gaussian noise” is a mathematical concept (see, e.g., Applicant’s FIG. 2, element 200 and paragraph 34 of Applicant’s specification disclosing that “this process is attached to a Markov chain that gradually adds Gaussian noise to the data.”). This limitation does nothing to alter the fundamental nature of the claim as a mental process combined with a mathematical concept.
Dependent claim 17, when analyzed as a whole, is not patent eligible under 35 U.S.C 101 because the additional recited limitation fails to establish that the claim is not directed to an abstract idea.
Thus, this limitation does nothing to alter the analysis of claim 11.
Step 2A Prong 2: The judicial exceptions are not integrated into a practical application.
The claim does not recite any additional elements that integrate the abstract idea into a practical application or provide significantly more than the abstract idea, and thus the claim is subject-matter ineligible.
STEP 2B: The claim does not recite additional elements that are sufficient to amount to significantly more than the judicial exception.
This claim is not patent eligible.
Regarding claim 18, this claim is rejected under 35 U.S.C 101 because the claimed invention is directed to an abstract idea without significantly more.
STEP 1: Claim 18 is directed to a non-transitory storage medium as depending from claim 11, thus the analysis for patent eligibility of claim 11 is incorporated herein.
STEP 2A Prong 1: The claim recites “wherein adding noise to the synthetic data helps to ensure that the synthetic data mimics the real data, even when the real data lacks seasonality.” This limitation includes intended use language with no patentable weight (e.g., “to ensure that the synthetic data mimics the real data, even when the real data lacks seasonality”). The limitation does nothing to alter the fundamental nature of the claim as a mental process combined with a mathematical concept.
Dependent claim 18, when analyzed as a whole, is not patent eligible under 35 U.S.C 101 because the additional recited limitation fails to establish that the claim is not directed to an abstract idea.
Thus, this limitation does nothing to alter the analysis of claim 11.
STEP 2A Prong 2: The judicial exceptions are not integrated into a practical application.
The claim does not recite any additional elements that integrate the abstract idea into a practical application or provide significantly more than the abstract idea, and thus the claim is subject-matter ineligible.
STEP 2B: The claim does not recite additional elements that are sufficient to amount to significantly more than the judicial exception.
This claim is not patent eligible.
Regarding claim 19, this claim is rejected under 35 U.S.C 101 because the claimed invention is directed to an abstract idea without significantly more.
STEP 1: Claim 19 is directed to a non-transitory storage medium as depending from claim 11, thus the analysis for patent eligibility of claim 11 is incorporated herein.
STEP 2A Prong 1: The claim recites “wherein the noise is added to non-seasonal data of the synthetic data.” The additional limitation added by this claim merely limits the invention to a narrower abstract idea by reciting “noise added to synthetic data.” It does nothing to alter the fundamental nature of the claim as a mental process combined with a mathematical concept. This limitation covers a mathematical concept. This limitation covers a mathematical concept. In particular, the above-noted wherein clause adds noise to “non-seasonal data of the synthetic data.” Such addition of noise/perturbation is a mathematical concept of using a noise addition block/function to add noise in the non-seasonal data, as suggested by the discussion of this step, in figure 2 of applicant’s drawings and as mentioned in paragraph 19 of applicant’s specification.
Dependent claim 19, when analyzed as a whole, is not patent eligible under 35 U.S.C. 101 because the additional limitation fails to establish that the claim is not directed to an abstract idea.
Thus, this limitation does nothing to alter the analysis of claim 11.
Step 2A Prong 2: The judicial exceptions are not integrated into a practical application.
The claim does not recite any additional elements that integrate the abstract idea into a practical application or provide significantly more than the abstract idea, and thus the claim is subject-matter ineligible.
Step 2B: The claim does not recite additional elements that are sufficient to amount to significantly more than the judicial exception.
This claim is not patent eligible.
Regarding claim 20, this claim is rejected under 35 U.S.C 101 because the claimed invention is directed to an abstract idea without significantly more.
STEP 1: Claim 20 is directed to a non-transitory storage medium as depending from claim 11, thus the analysis for patent eligibility of claim 11 is incorporated herein.
STEP 2A Prong 1: The claim recites “wherein the synthetic data is created with a generative adversarial network and a diffused probabilistic model, and a contrastive learning process is applied that minimizes a loss between the generative adversarial network and the diffused probabilistic model to ensure that the synthetic data is bias-free.” These limitations do nothing to alter the fundamental nature of the claim as a mental process combined with a mathematical concept. This is because the additional limitation are mere instructions to apply an exception using generically-recited model, process and network. This alters the outcome rather than alter the fundamental nature of the claim. Also, this limitation includes intended use language with no patentable weight (e.g., “to ensure that the synthetic data mimics the real data, even when the real data lacks seasonality”).
Dependent claim 20, when analyzed as a whole, is not patent eligible under 35 U.S.C. 101 because the additional limitation fails to establish that the claim is not directed to an abstract idea.
Thus, this limitation does nothing to alter the analysis of claim 11.
Step 2A Prong 2: The judicial exceptions are not integrated into a practical application.
The claim does not recite any additional elements that integrate the abstract idea into a practical application or provide significantly more than the abstract idea, and thus the claim is subject-matter ineligible.
The claim recites the additional elements of “wherein the synthetic data is created with a generative adversarial network and a diffused probabilistic model, and a contrastive learning process is applied that minimizes a loss between the generative adversarial network and the diffused probabilistic model to ensure that the synthetic data is bias-free.”
The “generative adversarial network and a diffused probabilistic model, and a contrastive learning process” are recited at a high level of generality, and no details of the neural network, model or process are recited. Therefore, the neural network, model and process are being interpreted as performing a mental process (i.e., creating synthetic data as discussed above with reference to claim 1) on a generic computer. See MPEP 2106.04(a)(2) § III.C which states that “a concept that is performed in the human mind and applicant is merely claiming that concept performed 1) on a generic computer, or 2) in a computer environment, or 3) is merely using a computer as a tool to perform the concept” still recite a mental process.
The above-noted additional elements in the claim amount to recitation of the words "apply it" (or an equivalent) or are mere instructions to implement an abstract idea or other exception on a computer, which does not integrate a judicial exception into a practical application. See MPEP 2106.05(f).
Merely asserting that a judicial exception is to be carried out on a generic computer (i.e., with the generically-recited “generative adversarial network and a diffused probabilistic model, and a contrastive learning process”) cannot meaningfully integrate the judicial exception into a practical application. See MPEP § 2106.05(f).
Also, the limitation “the synthetic data is created with a generative adversarial network and a diffused probabilistic model, and a contrastive learning process is applied that minimizes a loss between the generative adversarial network and the diffused probabilistic model” can be considered as “generally linking the use of judicial exception to a particular technological environment or field of use”. See MPEP 2106.05(h).
Thus, these limitations do nothing to alter the analysis of claim 11.
Step 2B: The claim does not recite additional elements that are sufficient to amount to significantly more than the judicial exception.
This claim is not patent eligible.
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 11-13 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Soni, Ravi, et al. (U.S. Publication No. 2020/0342362, hereinafter “Soni”).
Regarding independent claim 1, Soni discloses the invention as claimed including a method, comprising: collecting real data (see, paragraphs 9, “a computer-implemented method to generate synthetic time series data” and 63, “data 210-214 from sources 220-224 can be collected offline to form sets of real data.” [i.e., method including collecting real data]);
creating synthetic data by modeling the real data (see, FIG. 2D - depicting real data (260) first being preprocessed before entering a modeling phase in the Generative Model (270) before becoming Synthetic Data (297) and paragraph 62, “An output of the generative model 270 (e.g., … including machine learning model(s) 250 to form a real data set 260 to be provided to the generative model 270. An output of the generative model 270 (e.g., the real data 260 supplemented by synthetic data generated by the generative model…” [i.e., Real data is fed to a generative model for modeling purposes and in return, synthetic data is created]);
augmenting the real data and the synthetic data (see, paragraph 59, “Latent variables inferred from the data by the VAE model can be assumed to have generated the data set and can then be used to generate additional data such as to enlarge the data set, impute missing data from a time series, etc.” [i.e., The data set corresponds to a training data set of real data, and paragraph 28, “In certain examples disclosed herein, data-driven generation of such events helps to lower healthcare costs and improve quality of care by using synthetic data/events for data augmentation, imputation of missing data, etc.” [i.e., the expansion of both real and fake/synthetic data]);
applying a loss function to minimize an error between the real data and the synthetic data (see, paragraphs 70, “if the discriminator 320 sees a difference (e.g., fake or synthetic) between the real time series data sample 342 and the synthetic time series data sample 332, then a loss 360 in synthetic data quality through the generator 310 can be determined. Such loss or error 360 (e.g., in the form of a gradient or other adaptive loss function, etc.) can be used to generate additional synthetic data, adjust operation of the generator 310” and 88, “In the training mode, the generator 810 is trained using the output 870 (and associated loss function, etc.) from the discriminator 820 to produce realistic synthetic data 830 usable for AI model training, for example.” [i.e., loss function applied/utilized to minimize loss/error between real and synthetic/fake data by adjusting synthetic data for training]);
adding noise to the synthetic data to create finalized synthetic data (see, paragraph 94, “For example, synthetic time series data can be generated in the form of a sine wave. Noise can be added to the sine wave, and the amplitude can be adjusted.” [i.e., finalized synthetic data generation/creation includes adding noise to the sine wave]);
generating a forecast based on the finalized synthetic data (see, paragraph 76, “The generated synthetic events function as annotations that can be used further for training event prediction, classification, and/or detection models.” [i.e., Here, event prediction is a type of forecast based on synthetic data/event data points]).
With respect to independent claim 11, Soni discloses the invention as claimed including A non-transitory storage medium having stored therein instructions that are executable by one or more hardware processors to perform operations comprising: collecting real data (see, paragraphs 8, “one tangible computer-readable storage medium including instructions” and 63, “data 210-214 from sources 220-224 can be collected offline to form sets of real data.” [i.e., a storage medium including instructions to perform operations including collecting real data]);
creating synthetic data by modeling the real data (see, FIG. 2D - depicting real data (260) first being preprocessed before entering a modeling phase in the Generative Model (270) before becoming Synthetic Data (297) and paragraph 62, “An output of the generative model 270 (e.g., … including machine learning model(s) 250 to form a real data set 260 to be provided to the generative model 270. An output of the generative model 270 (e.g., the real data 260 supplemented by synthetic data generated by the generative model…” [i.e., Real data is fed to a generative model for modeling purposes and in return, synthetic data is created]);
augmenting the real data and the synthetic data (see, paragraph 59, “Latent variables inferred from the data by the VAE model can be assumed to have generated the data set and can then be used to generate additional data such as to enlarge the data set, impute missing data from a time series, etc.” [i.e., The data set corresponds to a training data set of real data, and paragraph 28, “In certain examples disclosed herein, data-driven generation of such events helps to lower healthcare costs and improve quality of care by using synthetic data/events for data augmentation, imputation of missing data, etc.” [i.e., the expansion of both real and fake/synthetic data]);
applying a loss function to minimize an error between the real data and the synthetic data (see, paragraphs 70, “if the discriminator 320 sees a difference (e.g., fake or synthetic) between the real time series data sample 342 and the synthetic time series data sample 332, then a loss 360 in synthetic data quality through the generator 310 can be determined. Such loss or error 360 (e.g., in the form of a gradient or other adaptive loss function, etc.) can be used to generate additional synthetic data, adjust operation of the generator 310” and 88, “In the training mode, the generator 810 is trained using the output 870 (and associated loss function, etc.) from the discriminator 820 to produce realistic synthetic data 830 usable for AI model training, for example.” [i.e., loss function applied/utilized to minimize loss/error between real and synthetic/fake data by adjusting synthetic data for training]);
adding noise to the synthetic data to create finalized synthetic data (see, paragraph 94, “For example, synthetic time series data can be generated in the form of a sine wave. Noise can be added to the sine wave, and the amplitude can be adjusted.” [i.e., finalized synthetic data generation/creation includes adding noise to the sine wave]);
generating a forecast based on the finalized synthetic data (see, paragraph 76, “The generated synthetic events function as annotations that can be used further for training event prediction, classification, and/or detection models.” [i.e., Here, event prediction is a type of forecast based on synthetic data/event data points]).
Regarding claim 2, as discussed above, Soni discloses the method of claim 1.
Soni further discloses wherein the real data comprises time series data that includes a seasonality component that is present in only a subset of the time series data. (see, paragraph 29, “The data can be organized in an ordered arrangement per patient to generate synthetic data samples and corresponding synthetic events and/or to generate missing data for time-series real data imputation, for example.” [e.g., real data comprises time series data]). Additionally, (see, paragraph 57, “data 212, 214 can be captured from one or more medical devices 220, mobile digital health monitors 222, one or more diagnostic cardiology (DCAR) devices 224, etc., is provided in a data stream 230, 235 (e.g., continuous streaming, live streaming, periodic streaming, etc.) to a preprocessor 240, 245 to pre-process the data and apply one or more machine learning models 250, 255 (e.g., AI models, such as a DL model, a hybrid RL model, a DL+hybrid RL model, etc.) to detect events (e.g., heart attack, stroke, high blood pressure, accelerated heart rate, etc.) in a set of real data 260, 265 formed from the data stream 230, 235, etc., for example.” [e.g., For a set of real data which includes time series data, additionally includes periodic data streaming/a seasonality component]).,
Regarding claim 12, as discussed above, Soni discloses the non-transitory storage medium of claim 11.
Soni further discloses wherein the real data comprises time series data that includes a seasonality component that is present in only a subset of the time series data. (see, paragraph 29, “The data can be organized in an ordered arrangement per patient to generate synthetic data samples and corresponding synthetic events and/or to generate missing data for time-series real data imputation, for example.” [e.g., real data comprises time series data]). Additionally, (see, paragraph 57, “data 212, 214 can be captured from one or more medical devices 220, mobile digital health monitors 222, one or more diagnostic cardiology (DCAR) devices 224, etc., is provided in a data stream 230, 235 (e.g., continuous streaming, live streaming, periodic streaming, etc.) to a preprocessor 240, 245 to pre-process the data and apply one or more machine learning models 250, 255 (e.g., AI models, such as a DL model, a hybrid RL model, a DL+hybrid RL model, etc.) to detect events (e.g., heart attack, stroke, high blood pressure, accelerated heart rate, etc.) in a set of real data 260, 265 formed from the data stream 230, 235, etc., for example.” [e.g., For a set of real data which includes time series data, additionally includes periodic data streaming/a seasonality component]).,
Regarding claim 3, as discussed above, Soni discloses the method of claim 1.
Soni further discloses wherein the synthetic data is created with an ensemble that comprises a generative adversarial network and a diffused probabilistic model (see, paragraph 28, “Certain examples provide deep generative models to generate synthetic data and corresponding events.” [i.e., both generative adversarial networks and diffusion probabilistic models1 are generative models, and as synthetic data is created by generative models, both generative adversarial networks and diffusion probability models, that together form an ensemble, and generate fake/synthetic data]).
Regarding claim 13, as discussed above, Soni discloses the non-transitory storage medium of claim 11.
Soni further discloses wherein the synthetic data is created with an ensemble that comprises a generative adversarial network and a diffused probabilistic model (see, paragraph 28, “Certain examples provide deep generative models to generate synthetic data and corresponding events.” [i.e., both generative adversarial networks and diffusion probabilistic models2 are generative models, and as synthetic data is created by generative models, both generative adversarial networks and diffusion probability models, that together form an ensemble, and generate fake/synthetic data]).
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 non-obviousness.
Claims 4 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Soni as applied to claim 1 and 11 above and further in view of Casserini, Matteo et al. (U.S. Publication No. 20230024884, hereinafter “Casserini”).
Regarding claim 4, as discussed above, Soni discloses the method of claim 1.
Although Soni substantially discloses the claimed invention, Soni does not explicitly disclose wherein the loss function is a custom loss function is a custom loss function that removes bias from the synthetic data.
In the same field, analogous art, Casserini teaches wherein the loss function is a custom loss function is a custom loss function that removes bias form the synthetic data (see, paragraph 21, “Such use of importances removes bias from imbalanced data by weighting a loss function toward infrequent values of each feature.” [i.e., imbalanced data is a subset of data as is synthetic data, bias is removed from data that encompasses synthetic data via a loss function]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Soni to incorporate the teachings of Casserini so that “Such use of importances removes bias from imbalanced data by weighting a loss function toward infrequent values of each feature.” (see, e.g., Casserini, paragraph 21). Doing so would have allowed Soni to use Casserini’s weighting of a loss function toward infrequent values of features in order to remove bias from imbalanced data, as suggested by Casserini (see, e.g., paragraph 21).
Regarding claim 14, as discussed above, Soni discloses the non-transitory storage medium of claim 11.
Although Soni substantially discloses the claimed invention, Soni does not explicitly disclose wherein the loss function is a custom loss function is a custom loss function that removes bias form the synthetic data.
In the same field, analogous art Casserini teaches wherein the loss function is a custom loss function is a custom loss function that removes bias from the synthetic data (see, paragraph 21, “Such use of importances removes bias from imbalanced data by weighting a loss function toward infrequent values of each feature.” [i.e., imbalanced data is a subset of data as is synthetic data, and bias is removed from data that encompasses synthetic data via a loss function]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Soni to incorporate the teachings of Casserini so that “Such use of importances removes bias from imbalanced data by weighting a loss function toward infrequent values of each feature.” (see, e.g., Casserini, paragraph 21). Doing so would have allowed Soni to use Casserini’s weighting of a loss function toward infrequent values of features in order to remove bias from imbalanced data, as suggested by Casserini (see, e.g., paragraph 21).
Claims 5 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Soni as applied to claims 1 and 11 above and further in view of non-patent literature Zha, MengYue et al. (“Time Series Generation with Masked Autoencoder” (05/2022), hereinafter “Zha”).
Regarding claim 5, as discussed above, Soni discloses the method of claim1.
Although Soni substantially discloses the claimed invention, Soni does not explicitly disclose wherein the synthetic data preserves temporal dynamics that are present in the real data.
However, in the same field, analogous art Zha teaches wherein the synthetic data preserves temporal dynamics that are present in the real data (see, 2 Approach, Statistical Interpretation, “(1) fidelity: the produced synthetic data should preserve the temporal dynamics of the original data.” [e.g., original data is real data before becoming synthetic data, which preserves temporal dynamics in the original/real data]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Soni to incorporate the teachings of Zha in order to “take explicit control of the fidelity of synthetic data.” (see, e.g., Zha, page 4, paragraph 3).” Doing so would have allowed Soni to use Zha’s preserves temporal dynamics that are present in the real data/original data, with predictable result of controlling the fidelity of synthetic data, as suggested by Zha (see e.g., page 4, paragraph 2 and 3).
Regarding claim 15, as discussed above, Soni discloses the non-transitory storage medium of claim 11.
Although Soni substantially discloses the claimed invention, Soni does not explicitly disclose wherein the synthetic data preserves temporal dynamics that are present in the real data.
However, in the same field, analogous art Zha teaches wherein the synthetic data preserves temporal dynamics that are present in the real data (see, 2 Approach, Statistical Interpretation, “(1) fidelity: the produced synthetic data should preserve the temporal dynamics of the original data.” [e.g., original data is real data before becoming synthetic data, which preserves temporal dynamics in the original/real data]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Soni to incorporate the teachings of Zha to “take explicit control of the fidelity of synthetic data.” (see, e.g., Zha, page 4, paragraph 3).” Doing so would have allowed Soni to use Zha’s preserves temporal dynamics that are present in the real data/original data, with predictable result of controlling the fidelity of synthetic data.
Claims 6 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Soni as applied to claims 1 and 11 above, and further in view of Singhee, Amith (U.S. Publication No. 20170278116, hereinafter “Singhee”).
Regarding claim 6, as discussed above, Soni discloses the method of s claim 1.
Although Soni substantially discloses the claimed invention, Soni does not explicitly disclose wherein the forecast is used as a basis for performing a resource allocation process, and the resource comprises computing resources and/or human resources.
However, in the same field, analogous art Singhee teaches wherein the forecast is used as a basis for performing a resource allocation process, and the resource comprises computing resources and/or human resources (see, paragraph 5, “a system to calibrate a forecast model for resource allocation … wherein the calibrated forecast model is used to deploy equipment and personnel, schedule maintenance, order retail inventory, allocate compute resource, or prioritize activities.” [i.e., forecast is used to perform resource allocation such as compute/computing resource allocation]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Soni to incorporate the teachings of Singhee in which “forecasting models are used in planning in a variety of areas. As non-limiting examples, the forecast may be of damage to plan for repair tasks or of demand to plan for inventory or personnel availability. To aid in planning, the forecast must provide a quantified prediction.” where “planning refers to a variety of resource allocation tasks.” (see, e.g., Singhee, paragraphs 11-12). Additionally, Doing so would have allowed Soni to use Singhee’s forecast model for resource allocation in order to provide a forecast for a quantified prediction, as suggested by Singhee (see e.g., paragraph 5 and 11).
Regarding claim 16, as discussed above, Soni discloses the non-transitory storage medium of claim 11.
Although Soni substantially discloses the claimed invention, Soni does not explicitly disclose wherein the forecast is used as a basis for performing a resource allocation process, and the resource comprises computing resources and/or human resources.
However, in the same field, analogous art, Singhee teaches wherein the forecast is used as a basis for performing a resource allocation process, and the resource comprises computing resources and/or human resources (see, paragraph 5, “According to another embodiment, a system to calibrate a forecast model for resource allocation… wherein the calibrated forecast model is used to deploy equipment and personnel, schedule maintenance, order retail inventory, allocate compute resource, or prioritize activities.” [i.e., forecast is used to perform resource allocation such as compute resource allocation]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Soni to incorporate the teachings of Singhee in which “forecasting models are used in planning in a variety of areas. As non-limiting examples, the forecast may be of damage to plan for repair tasks or of demand to plan for inventory or personnel availability. To aid in planning, the forecast must provide a quantified prediction.” where “planning refers to a variety of resource allocation tasks.” (see, e.g., Singhee, paragraphs 11-12). Additionally, Doing so would have allowed Soni to use Singhee’s forecast model for resource allocation in order to provide a forecast for a quantified prediction, as suggested by Singhee (see e.g., paragraph 5 and 11).
Claims 7 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Soni as applied to claims 1 and 11 above, and further in view of Wei, Haiqi (U.S. Publication No. 20200265273, hereinafter “Wei”).
Regarding claim 7, as discussed above, Soni discloses the method of claim 1.
Although Soni substantially discloses the claimed invention, Soni does not explicitly disclose wherein the augmenting comprises adding Gaussian noise.
However, in the same field, analogous art, Wei teaches wherein the augmenting comprises adding Gaussian noise (see, paragraph 351, “Data can be augmented in various situations, for example, by adding Gaussian noise, or conducting transformations, such as masking, pitch shifting, among others” [e.g., data can be expanded via the addition of Gaussian noise]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Soni to incorporate the teachings of Wei so that “data augmentation can be used where data requires upsampling” (see, e.g., Wei, paragraph 351). Doing so would have allowed Soni to use Wei’s Data augmentation technique in various situations by adding Gaussian noise with as required for upsampling, as suggested by Wei (see, e.g., paragraph 351).
Regarding claim 17, as discussed above, Soni discloses the non-transitory storage medium of claim 11.
Although Soni substantially discloses the claimed invention, Soni does not explicitly disclose wherein the augmenting comprises adding Gaussian noise.
However, in the same field, analogous art, Wei teaches wherein the augmenting comprises adding Gaussian noise (see, paragraph 351, “Data can be augmented in various situations, for example, by adding Gaussian noise, or conducting transformations, such as masking, pitch shifting, among others” [i.e., data can be expanded via the addition of Gaussian noise]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Soni to incorporate the teachings of Wei so that “data augmentation can be used where data requires upsampling” (see, e.g., Wei, paragraph 351). Doing so would have allowed Soni to use Wei’s Data augmentation technique in various situations by adding Gaussian noise with as required for upsampling, as suggested by Wei (see, e.g., paragraph 351).
Claims 8 and 18 are rejected under 35 U.S.C. 103 as being unpatentable as applied to claims 1 and 11 above and further in view of Raymond, Thierry (U.S. Publication No. 20070260410, hereinafter “Raymond”).
Regarding claim 8, as discussed above, Soni discloses the method of claim 1.
Soni further discloses wherein adding noise to the synthetic data helps to ensure that the synthetic data mimics the real data, see, paragraph 84 and 86, “The latent vectors 840-844 represent random noise and/or other data distribution of values (e.g., a probability distribution of signal data variation, etc.) to be used by the circuits 812-816 to form synthetic data values” and “initially, the synthetic data series 830 may resemble random noise (e.g., based on the latent input vector 840-844, etc.), but, over time and based on feedback, the synthetic data series 830 comes to resemble the content and pattern of real data 835 such that the synthetic data 830 can be used in addition to and/or in place of real data 835 to train, test, and/or otherwise development AI models for deployment to process patient 1D time series waveform data, for example.” [i.e., noise is used to create synthetic data that over time mimics the properties of real data]).
Although Soni substantially discloses the claimed invention, Soni does not explicitly disclose even when the real data lacks seasonality.
However, in the same field, analogous art, Raymond teaches even when the real data lacks seasonality (see, paragraph 114, “More particularly, when the Gap value is higher than the sampling period, a sample is received too late, and one or several data collection cycles will be missed creating a hole in data collection” [i.e., sometimes real data is not collected when data collection cycles/seasons of seasonality are missed]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Soni to incorporate the teachings of Raymond so that “the real data value is kept and added into the data storage, instead of putting a Not Available (NA) value indicating a missing value. The intermediate missed samples are not written into the data storage as NA values: there will be a hole into the collect and DCQV will only be affected by this hole, and not by explicit missing data values.” (see, e.g., Raymond, paragraphs 115 and 116). Doing so would have allowed Soni to use Raymond’s one or several data collection cycles to be missed in order to create a hole in data collection instead of Not Available (NA) so that a “Data Collection Quality Value (DCQV) will only be affected by the hole and not explicit data values” as suggested by Raymond (see, e.g., paragraphs 115 and 116).
Regarding claim 18, as discussed above, Soni discloses the non-transitory storage medium of claim 11.
Soni further discloses wherein adding noise to the synthetic data helps to ensure that the synthetic data mimics the real data, see, paragraph 84 and 86, “The latent vectors 840-844 represent random noise and/or other data distribution of values (e.g., a probability distribution of signal data variation, etc.) to be used by the circuits 812-816 to form synthetic data values” and “initially, the synthetic data series 830 may resemble random noise (e.g., based on the latent input vector 840-844, etc.), but, over time and based on feedback, the synthetic data series 830 comes to resemble the content and pattern of real data 835 such that the synthetic data 830 can be used in addition to and/or in place of real data 835 to train, test, and/or otherwise development AI models for deployment to process patient 1D time series waveform data, for example.” [i.e., noise is used to create synthetic data that over time mimics the properties of real data]).
Although Soni substantially discloses the claimed invention, Soni does not explicitly disclose even when the real data lacks seasonality.
However, in the same field, analogous art, Raymond teaches even when the real data lacks seasonality (see, paragraph 114, “More particularly, when the Gap value is higher than the sampling period, a sample is received too late, and one or several data collection cycles will be missed creating a hole in data collection” [i.e., sometimes real data is not collected when data collection cycles/seasons of seasonality are missed]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Soni to incorporate the teachings of Raymond so that “the real data value is kept and added into the data storage, instead of putting a Not Available (NA) value indicating a missing value. The intermediate missed samples are not written into the data storage as NA values: there will be a hole into the collect and DCQV will only be affected by this hole, and not by explicit missing data values.” (see, e.g., Raymond, paragraphs 115 and 116). Doing so would have allowed Soni to use Raymond’s one or several data collection cycles to be missed in order to create a hole in data collection instead of Not Available (NA) so that a “Data Collection Quality Value (DCQV) will only be affected by the hole and not explicit data values” as suggested by Raymond (see, e.g., paragraphs 115 and 116).
Claims 9 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Soni as applied to claims 1 and 11 above, and further in view of Brauer, Robert (U.S. Publication No. 20230108422, hereinafter “Brauer”).
Regarding claim 9, as discussed above, Soni discloses the method of claim 1.
Although Soni substantially discloses the claimed invention, Soni does not explicitly disclose wherein the noise is added to non-seasonal data of the synthetic data.
However, in the same field, analogous art Brauer teaches wherein the noise is added to non-seasonal data of the synthetic data (see, paragraph 52, “a chain of steps to slowly add random noise to image data” [i.e., data can be synthetic or non-seasonal types of data - even image data and noise is added to synthetic image data]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Soni to incorporate the teachings of Brauer so that “once the diffusion model is trained on image data, it may be used to generate a defined spatial resolution image from a starting image, for example (and without limitation), in accordance with the operations provided in Table 5.” (see, e.g., Brauer, paragraph 60). Doing so would have allowed Soni to use Brauer’s chain of steps to slowly add random noise to image data where image data includes synthetic image data and/or real image data to enable generating a defined spatial resolution image (i.e., determining the size of the smallest distinct object or feature that can be clearly identified and distinguished from its surroundings) as suggested by Brauer (see, e.g., paragraphs 52 and 60).
Regarding claim 19, as discussed above, Soni discloses the non-transitory storage medium of claim 11.
Although Soni substantially discloses the claimed invention, Soni does not explicitly disclose wherein the noise is added to non-seasonal data of the synthetic data.
However, in the same field, analogous art Brauer teaches wherein the noise is added to non-seasonal data of the synthetic data (see, paragraph 52, “a chain of steps to slowly add random noise to image data” [i.e., data can be synthetic or non-seasonal types of data - even image data and noise is added to synthetic image data]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Soni to incorporate the teachings of Brauer so that “once the diffusion model is trained on image data, it may be used to generate a defined spatial resolution image from a starting image, for example (and without limitation), in accordance with the operations provided in Table 5.” (see, e.g., Brauer, paragraph 60). Doing so would have allowed Soni to use Brauer’s chain of steps to slowly add random noise to image data where image data includes synthetic image data and/or real image data to enable generating a defined spatial resolution image (i.e., determining the size of the smallest distinct object or feature that can be clearly identified and distinguished from its surroundings) as suggested by Brauer (see, e.g., paragraphs 52 and 60).
Claims 10 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Soni as applied to claims 1 and 11 above and further in view of Shachar, Amir (U.S. Publication No. 20230237494, hereinafter “Shachar”).
Regarding claim 10, as discussed above, Soni discloses the method of claim 1.
Soni further discloses wherein the synthetic data is created with a generative adversarial network and a diffused probabilistic model (see, paragraph 28, “Certain examples provide deep generative models to generate synthetic data and corresponding events.” [i.e., both generative adversarial networks and diffusion probabilistic models are generative models3, and as synthetic data is created by generative models, both generative adversarial networks and diffusion probability models, that together form an ensemble, generate fake/synthetic data as well]), and a contrastive learning process is applied that minimizes a loss between the generative adversarial network and the diffused probabilistic model (see, paragraph 70, “Based on the outcome 350 determined by the discriminator 320, a loss 360 can be determined.” [i.e., discriminator determining loss corresponds to a contrastive learning process]).
Although Soni substantially discloses claim 10, Soni is not relied on for explicitly disclosing to ensure that the synthetic data is bias-free.
In the same field, analogous art Shachar teaches to ensure that the synthetic data is bias-free (see, paragraph 35, “In this manner, bias with respect to each of the entities particular data can be substantially eliminated.” [e.g., generic data is synthetic data (see, Shachar e.g., paragraph 3) in which bias can be eliminated from – bias-free data]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Soni to incorporate the teachings of Shachar so that “prior to training the machine learned models with entity [e.g., financial entity] specific transaction data, one or more generic models can be used. However, the generic models can produce high rates of errors [bias]. Training the machine learned models with transaction data that is specific to a particular entity can take a long period of time (e.g., nine months). Therefore, it can be desirable to create fraud detection models that have accuracy without requiring a long period of training time.” and “In this manner, bias with respect to each of the entities particular data can be substantially eliminated.” (see e.g., Sanchar, paragraphs 3 and 35). Doing so would have allowed Soni to use Sanchar’s elimination of error/bias with respect to each of the entities particular data with predictable result of creating fraud detection models that have accuracy without requiring a long period of training time because it is “desirable to create fraud detection models that have accuracy without requiring a long period of training time”, as suggested by Sanchar (see e.g., paragraph 3).
Regarding claim 20, as discussed above, Soni discloses the non-transitory storage medium of claim 11.
Soni further discloses wherein the synthetic data is created with a generative adversarial network and a diffused probabilistic model (see, paragraphs 28, “Certain examples provide deep generative models to generate synthetic data and corresponding events” and [i.e., both generative adversarial networks and diffusion probabilistic models are generative models4, and as synthetic data is created by generative models, both generative adversarial networks and diffusion probability models, that together form an ensemble, generate fake/synthetic data as well]), and a contrastive learning process is applied that minimizes a loss between the generative adversarial network and the diffused probabilistic model (see, paragraph 70, “Based on the outcome 350 determined by the discriminator 320, a loss 360 can be determined.” [i.e., discriminator determining loss corresponds to a contrastive learning process]).
Although Soni substantially discloses claim 10, Soni is not relied on for explicitly disclosing to ensure that the synthetic data is bias-free.
In the same field, analogous art Shachar teaches to ensure that the synthetic data is bias-free (see, paragraph 35, “In this manner, bias with respect to each of the entities particular data can be substantially eliminated.” [e.g., generic data is synthetic data (see, Shachar e.g., paragraph 3) in which bias can be eliminated from – bias-free data]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Soni to incorporate the teachings of Shachar so that “prior to training the machine learned models with entity [e.g., financial entity] specific transaction data, one or more generic models can be used. However, the generic models can produce high rates of errors [bias]. Training the machine learned models with transaction data that is specific to a particular entity can take a long period of time (e.g., nine months). Therefore, it can be desirable to create fraud detection models that have accuracy without requiring a long period of training time.” and “In this manner, bias with respect to each of the entities particular data can be substantially eliminated.” (see e.g., Sanchar, paragraphs 3 and 35). Doing so would have allowed Soni to use Sanchar’s elimination of error/bias with respect to each of the entities particular data with predictable result of creating fraud detection models that have accuracy without requiring a long period of training time because it is “desirable to create fraud detection models that have accuracy without requiring a long period of training time”, as suggested by Sanchar (see e.g., paragraph 3).
Conclusion
The prior art made of record, listed on form PTO-892, and not relied upon, is considered pertinent to applicant’s disclosure.
The references listed on form PTO-892 are all generally related to techniques, methods and systems for generating modifying and selecting sets of data used in machine learning models and neural networks.
For example, Forgeat, Julien, et al. (U.S. Patent No.12,456,037 B2, hereinafter “Forgeat”) discloses that “The method includes collecting data including sensitive data and non-sensitive data, executing a first machine learning model to generate the synthetic data from the collected data” (see, e.g., Summary, col. 1, lines 60-63); and “The data collector can collect and add any other data to this collected data set and other third party data can also be used to augment the collected data (e.g., data related to local events, holidays, weather, advertisement, and similar information)” (see, e.g., col. 7, line 64-col. 8, line 1).
Also, for example, Flores, German, et al. (U.S. Patent No. 11,27,5597 B1, hereinafter “Flores”) discloses “to minimize the error with the neural network, a cost or loss function is employed in step 902. The computation entails the standard process used by neural networks when training the model to minimize the error in order to have good prediction rates. In step 904 the calculated loss data is updated, and in step 906 the neural network parameters including weight values are updated.” (see, e.g., col. 11, lines 36-42).
The examiner requests, in response to this office action, support be shown for language added to any original claims on amendment and any new claims. That is, indicate support for newly added claim language by specifically pointing to page(s) and line no(s) in the specification and/or drawing figure(s). This will assist the examiner in prosecuting the application.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to STEVEN PENG whose telephone number is (571)270-0897. The examiner can normally be reached Monday - Friday 8am-5pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Kamran Afshar can be reached at (571) 272-7796. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/STEVEN PENG/Examiner, Art Unit 2125
/KAMRAN AFSHAR/Supervisory Patent Examiner, Art Unit 2125
1 see, e.g., page 63 of Ju Sun (“Generative Models: GAN, VAE, Normalization Flow, Diffusion Models”, December 6, 2022, https://sunju.org/teach/DL-Fall-2022/dec-06.pdf), “Summary of generative models … GAN: Adversarial training … Diffusion models: Gradually add Gaussian noise and then reverse”.
2 see, e.g., page 63 of Ju Sun (“Generative Models: GAN, VAE, Normalization Flow, Diffusion Models”, December 6, 2022, https://sunju.org/teach/DL-Fall-2022/dec-06.pdf), “Summary of generative models … GAN: Adversarial training … Diffusion models: Gradually add Gaussian noise and then reverse”.
3 see, e.g., page 63 of Ju Sun (“Generative Models: GAN, VAE, Normalization Flow, Diffusion Models”, December 6, 2022, https://sunju.org/teach/DL-Fall-2022/dec-06.pdf), “Summary of generative models … GAN: Adversarial training … Diffusion models: Gradually add Gaussian noise and then reverse”.
4 see, e.g., page 63 of Ju Sun (“Generative Models: GAN, VAE, Normalization Flow, Diffusion Models”, December 6, 2022, https://sunju.org/teach/DL-Fall-2022/dec-06.pdf), “Summary of generative models … GAN: Adversarial training … Diffusion models: Gradually add Gaussian noise and then reverse”.
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