Detailed Action
This action is in response to the application filed 05/31/2024, in which:
Claims 1, 11, and 17 are the independent claims.
Claims 1-20 are currently pending.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 12/02/2024 & 11/09/2025 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statements are being considered by the examiner.
Claim Objections
Claim 7 is objected to because of the following informalities: Claim 7 recites the term “casual” which appears to be a typo. For the purposes of examination, the examiner interprets this term as “causal”. Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 9 and 20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 9 recites “the projected technical effect” and appears to be typo as the term “projected technical effect” is not noted with any previous claims. The examiner is interpreting the term as “the predicted technical effect”. The phrase “projected technical effect “in Claim 9 is an unclear phrase which renders the claim indefinite. For the purpose of examination, “projected” term has been interpreted as “predicted”. Appropriate correction is required.
Claim 20 recites “the predicted causal action” which is an unclear phrase which renders the claim indefinite. There is insufficient antecedent basis for this limitation in the claim. Appropriate correction is required. For the purpose of examination, the claim will be interpreted as similar dependent claim 9 which is dependent on claim 8; thus, the phrase will be interpreted as “predicted technical effect”.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Regarding Claim 1:
Subject Matter Eligibility Analysis Step 1:
Claim 1 recites a computer-implemented method, thus a process, one of the four statutory categories of patentable subject matter.
Subject Matter Eligibility Analysis Step 2A Prong 1:
However, claim 1 further recites the method comprising of:
computing based on the first value and the second value … a predicted causal ordering of the first variable and the second variable (a human being can mentally apply evaluation and compute based on specific values a predicted causal ordering of the specific variables)
determining an action (a human being can mentally apply evaluation and make a judgement for determining an action; as the examiner interprets this as merely identifying a possible action which a human performs via evaluation)
generating … a predicted causal effect of the action (a human being can mentally apply evaluation and generate an estimate based on how much an outcome has changed due to a specific variable via observations)
based on the predicted causal effect, performing the action on a target system (a human being can mentally apply evaluation and make a judgement to perform an action on a specific system based on a specific estimate (predicted causal effect))
Claim 1 thus recites an abstract idea (that falls into the “mental processes” group of abstract ideas).
Subject Matter Eligibility Analysis Step 2A Prong 2:
This judicial exception is not integrated into a practical application because the additional elements consist of:
receiving as input a first value associated with a first variable and a second value associated with a second variable (which is insignificant extra-solution activity of data gathering, by MPEP 2106.05(g))
… using a trained causal ordering predictor … (to perform a mental process and the performance of an abstract idea on a computer is no more than instructions to “apply it” on a computer, by MPEP 2106.05(f))
training based on the first value, the second value and the predicted causal ordering a generative structural causal model (SCM), resulting in a trained generative SCM (the limitation attempts to cover any solution to an identified problem with no restriction on how the result is accomplished and no description of the mechanism for accomplishing the result, does not integrate a judicial exception into a practical application or provide significantly more because this type of recitation is equivalent to the words "apply it", by MPEP 2106.05(f))
… using the trained generative SCM … (to perform a mental process and the performance of an abstract idea on a computer is no more than instructions to “apply it” on a computer, by MPEP 2106.05(f))
Subject Matter Eligibility Analysis Step 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements recited, alone or in combination, do not provide significantly more than the abstract idea itself. Additional element a falls within MPEP 2106.05(d) as well-understood, routine and conventional activities of receiving or transmitting data over a network (MPEP 2106.05(d)(II): buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014)). Additional elements b-d are merely applying the abstract idea on a computer or recite only the idea of a solution or outcome (MPEP 2106.05(f)) which cannot provide significantly more. Thus, the claim is subject-matter ineligible.
Regarding Claim 2:
Subject Matter Eligibility Analysis Step 1:
Dependent Claim 2 recites the method of Claim 1. Claim 1 is a method, thus a process, one of the four statutory categories of patentable subject matter.
Subject Matter Eligibility Analysis Step 2A Prong 1:
However, claim 2 does not recite any additional abstract ideas and only inherits the abstract ideas from Claim 1. Claim 2 thus recites an abstract idea (that falls into the “mental processes” group of abstract ideas).
Subject Matter Eligibility Analysis Step 2A Prong 2:
This judicial exception is not integrated into a practical application because the sole additional element recited consists of wherein the first value and the second value have been obtained from the target system or another system representative of the target system (which is insignificant extra-solution activity of data gathering, by MPEP 2106.05(g)).
Subject Matter Eligibility Analysis Step 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the new sole additional element recited, alone or in combination, does not provide significantly more than the abstract idea itself. The additional element falls within MPEP 2106.05(d) as well-understood, routine and conventional activities of receiving or transmitting data over a network (MPEP 2106.05(d)(II): buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014)).
Regarding Claim 3:
Subject Matter Eligibility Analysis Step 1:
Dependent Claim 3 recites the method of Claim 1. Claim 1 is a method, thus a process, one of the four statutory categories of patentable subject matter.
Subject Matter Eligibility Analysis Step 2A Prong 1:
However, claim 3 does not recite any additional abstract ideas and only inherits the abstract ideas from Claim 1. Claim 3 thus recites an abstract idea (that falls into the “mental processes” group of abstract ideas).
Subject Matter Eligibility Analysis Step 2A Prong 2:
This judicial exception is not integrated into a practical application because the sole additional element recited consists of wherein the trained causal ordering predictor has an attention-based transformer architecture (which is restricting the abstract idea to a Particular Technological Environment, by MPEP 2106.05(h)).
Subject Matter Eligibility Analysis Step 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the new sole additional element recited, alone or in combination, does not provide significantly more than the abstract idea itself. The additional element is only restricting the abstract idea to a Particular Technological Environment (MPEP 2106.05(h)) which cannot provide significantly more. Thus, the claim is subject-matter ineligible.
Regarding Claim 4:
Subject Matter Eligibility Analysis Step 1:
Dependent Claim 4 recites the method of Claim 1. Claim 1 is a method, thus a process, one of the four statutory categories of patentable subject matter.
Subject Matter Eligibility Analysis Step 2A Prong 1:
However, claim 4 further recites:
generating a training sample based on the causal graph (a human being can mentally apply evaluation to generate a specific sample for training based on a specific type of graph)
determining based on the causal graph a ground truth causal ordering for the training sample (a human being can mentally apply evaluation to determine a ground truth causal ordering for a specific sample based on a specific graph)
Claim 4 thus recites an abstract idea (that falls into the “mental processes” group of abstract ideas).
Subject Matter Eligibility Analysis Step 2A Prong 2:
This judicial exception is not integrated into a practical application because the additional elements consist of:
receiving as input a causal graph (which is insignificant extra-solution activity of data gathering, by MPEP 2106.05(g))
training the causal ordering predictor based on the training sample and the ground truth causal ordering (which is restricting the abstract idea to a Particular Technological Environment, by MPEP 2106.05(h)
Subject Matter Eligibility Analysis Step 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements recited, alone or in combination, do not provide significantly more than the abstract idea itself. Additional element a falls within MPEP 2106.05(d) as well-understood, routine and conventional activities of receiving or transmitting data over a network (MPEP 2106.05(d)(II): buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014)). Additional element b is only restricting the abstract idea to a Particular Technological Environment (MPEP 2106.05(h)) which cannot provide significantly more. Thus, the claim is subject-matter ineligible.
Regarding Claim 5:
Subject Matter Eligibility Analysis Step 1:
Dependent Claim 5 recites the method of Claim 1. Claim 1 is a method, thus a process, one of the four statutory categories of patentable subject matter.
Subject Matter Eligibility Analysis Step 2A Prong 1:
However, claim 5 does not recite any additional abstract ideas and only inherits the abstract ideas from Claim 1. Claim 5 thus recites an abstract idea (that falls into the “mental processes” group of abstract ideas).
Subject Matter Eligibility Analysis Step 2A Prong 2:
This judicial exception is not integrated into a practical application because the sole additional element recited consists of wherein the target system is a machine or a software system (which is restricting the abstract idea to a Particular Technological Environment, by MPEP 2106.05(h)).
Subject Matter Eligibility Analysis Step 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the new sole additional element recited, alone or in combination, does not provide significantly more than the abstract idea itself. The additional element is only restricting the abstract idea to a Particular Technological Environment (MPEP 2106.05(h)) which cannot provide significantly more. Thus, the claim is subject-matter ineligible.
Regarding Claim 6:
Subject Matter Eligibility Analysis Step 1:
Dependent Claim 6 recites the method of Claim 1. Claim 1 is a method, thus a process, one of the four statutory categories of patentable subject matter.
Subject Matter Eligibility Analysis Step 2A Prong 1:
However, claim 6 does not recite any additional abstract ideas and only inherits the abstract ideas from Claim 1. Claim 6 thus recites an abstract idea (that falls into the “mental processes” group of abstract ideas).
Subject Matter Eligibility Analysis Step 2A Prong 2:
This judicial exception is not integrated into a practical application because the sole additional element recited consists of wherein the target system is a living being (which is restricting the abstract idea to a Particular Technological Environment, by MPEP 2106.05(h)).
Subject Matter Eligibility Analysis Step 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the new sole additional element recited, alone or in combination, does not provide significantly more than the abstract idea itself. The additional element is only restricting the abstract idea to a Particular Technological Environment (MPEP 2106.05(h)) which cannot provide significantly more. Thus, the claim is subject-matter ineligible.
Regarding Claim 7:
Subject Matter Eligibility Analysis Step 1:
Dependent Claim 7 recites the method of Claim 1. Claim 1 is a method, thus a process, one of the four statutory categories of patentable subject matter.
Subject Matter Eligibility Analysis Step 2A Prong 1:
However, claim 7 further recites the action is selected from the multiple actions … (a human being can mentally apply evaluation and make a judgement to select the action from a plurality of actions). Claim 7 thus recites an abstract idea (that falls into the “mental processes” group of abstract ideas).
Subject Matter Eligibility Analysis Step 2A Prong 2:
This judicial exception is not integrated into a practical application because the additional elements consist of:
wherein the action is one of multiple actions (which is restricting the abstract idea to a Particular Technological Environment, by MPEP 2106.05(h)
wherein respective predicted casual effects of the multiple actions are generated … (which is restricting the abstract idea to a Particular Technological Environment, by MPEP 2106.05(h)
Subject Matter Eligibility Analysis Step 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements recited, alone or in combination, do not provide significantly more than the abstract idea itself. Additional elements a-b are only restricting the abstract idea to a Particular Technological Environment (MPEP 2106.05(h)) which cannot provide significantly more. Thus, the claim is subject-matter ineligible.
Regarding Claim 8:
Subject Matter Eligibility Analysis Step 1:
Dependent Claim 8 recites the method of Claim 1. Claim 1 is a method, thus a process, one of the four statutory categories of patentable subject matter.
Subject Matter Eligibility Analysis Step 2A Prong 1:
However, claim 8 does not recite any additional abstract ideas and only inherits the abstract ideas from Claim 1. Claim 8 thus recites an abstract idea (that falls into the “mental processes” group of abstract ideas).
Subject Matter Eligibility Analysis Step 2A Prong 2:
This judicial exception is not integrated into a practical application because the sole additional element recited consists of wherein the predicted causal effect is a predicted technical effect controlled by the action (which is restricting the abstract idea to a Particular Technological Environment, by MPEP 2106.05(h)).
Subject Matter Eligibility Analysis Step 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the new sole additional element recited, alone or in combination, does not provide significantly more than the abstract idea itself. The additional element is only restricting the abstract idea to a Particular Technological Environment (MPEP 2106.05(h)) which cannot provide significantly more. Thus, the claim is subject-matter ineligible.
Regarding Claim 9:
Subject Matter Eligibility Analysis Step 1:
Dependent Claim 9 recites the method of Claim 8. Claim 8 is a method, thus a process, one of the four statutory categories of patentable subject matter.
Subject Matter Eligibility Analysis Step 2A Prong 1:
However, claim 9 does not recite any additional abstract ideas and only inherits the abstract ideas from Claim 8. Claim 9 thus recites an abstract idea (that falls into the “mental processes” group of abstract ideas).
Subject Matter Eligibility Analysis Step 2A Prong 2:
This judicial exception is not integrated into a practical application because the sole additional element recited consists of wherein the project technical effect is a predicted machine efficiency or predicted machine performance (which is restricting the abstract idea to a Particular Technological Environment, by MPEP 2106.05(h)).
Subject Matter Eligibility Analysis Step 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the new sole additional element recited, alone or in combination, does not provide significantly more than the abstract idea itself. The additional element is only restricting the abstract idea to a Particular Technological Environment (MPEP 2106.05(h)) which cannot provide significantly more. Thus, the claim is subject-matter ineligible.
Regarding Claim 10:
Subject Matter Eligibility Analysis Step 1:
Dependent Claim 10 recites the method of Claim 8. Claim 8 is a method, thus a process, one of the four statutory categories of patentable subject matter.
Subject Matter Eligibility Analysis Step 2A Prong 1:
However, claim 10 does not recite any additional abstract ideas and only inherits the abstract ideas from Claim 8. Claim 10 thus recites an abstract idea (that falls into the “mental processes” group of abstract ideas).
Subject Matter Eligibility Analysis Step 2A Prong 2:
This judicial exception is not integrated into a practical application because the sole additional element recited consists of wherein the technical effect is: predicted usage of memory or processing resources, predicted manufacturing or production efficiency, or predicted manufacturing or production quality (which is restricting the abstract idea to a Particular Technological Environment, by MPEP 2106.05(h).
Subject Matter Eligibility Analysis Step 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the new sole additional element recited, alone or in combination, does not provide significantly more than the abstract idea itself. The additional element is only restricting the abstract idea to a Particular Technological Environment (MPEP 2106.05(h)) which cannot provide significantly more. Thus, the claim is subject-matter ineligible.
Regarding Claims 11-16:
Claims 11-16 incorporate substantively all the limitations of Claims 1 and 3-7 in a system (thus, a machine) and further recites a memory configured to store computer-readable instructions; a processor coupled to the memory, and configured to execute the computer-readable instructions, which upon execution cause the processor to perform operations comprising (these claim limitations appear to perform a mental process and the performance of an abstract idea on a computer is no more than instructions to “apply it” on a computer, by MPEP 2106.05(f)) and does not appear to integrate the abstract idea into a particular application; thus, the claim is subject-matter ineligible as it does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements, alone or in combination, do not provide significantly more than the abstract idea itself); thus, Claims 11-16 are rejected for reasons set forth in the rejections of Claims 1 and 3-7, respectively.
Regarding Claims 17-20:
Claims 17-20 incorporate substantively all the limitations of Claims 1-3 and 9 in a non-transitory medium (thus, a manufacture) and further recites comprising computer-readable instructions; a processor coupled to the memory, and configured to execute the computer-readable instructions, which upon execution on a processor cause the processor to perform operations comprising (these claim limitations appear to perform a mental process and the performance of an abstract idea on a computer is no more than instructions to “apply it” on a computer, by MPEP 2106.05(f)) and does not appear to integrate the abstract idea into a particular application; thus, the claim is subject-matter ineligible as it does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements, alone or in combination, do not provide significantly more than the abstract idea itself); thus, Claims 17-20 are rejected for reasons set forth in the rejections of Claims 1-3 and 9, respectively.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1-2, 4-8, 11, and 13-18 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Geffner et al., “Deep End-to-end Causal Inference”.
Regarding Claim 1:
A computer-implemented method, comprising:
(Geffner, Page 7, Paragraph 3, “… Our code is in the supplement”. Geffner’s method named DECI is performed utilizing the code from GitHub links which are noted within the appendix; thus, interpreted by the examiner as a computer-implemented method)
receiving as input a first value associated with a first variable and a second value associated with a second variable;
(Geffner, Figure 1: “… user provides observational data (1) as input … ”. Figure 1 depicts the table of the user inputs that were received denoted as X1 and X2; thus, interpreted by the examiner as receiving a first and second value where each X value is associated with a respective variable based on observation).
computing based on the first value and the second value …
(Geffner, Figure 1; Page 5, Paragraph 6, “Estimating ATE. After training, we can use the model learnt by DECI to simulate new samples x from pθ(x|G). We sample a graph G ~ qΦ(G) and a set of exogenous noise variables z ~ pz. We then input this noise into the learnt DECI structural equation model to simulate x, by applying eq. (1) and eq. (8) on z in the topological order defined by G”. Figure 1 shows (5) which depicts the computations of ATE/CATE which are based off the first and second values and are shown in (1). DECI utilizes causal inference for estimation of ATE/CATE via G (a sampled causal graph with topological order) which are used to simulate x as noted by pθ(x|G) . Thus, the examiner interprets this as computing based on the first and the second value).
… using a trained causal ordering predictor …
(Geffner, Figure 1; Page 8, Paragraph 4, “… For DECI, we can use a trained model to immediately estimate (C)ATE”. The already trained model can be used to estimate treatment effects (ATE/CATE); thus, interpreted as a trained causal ordering predictor as the model predicts/estimates the causal ordering for the causal graphs).
… a predicted causal ordering of the first variable and the second variable;
(Geffner, Figure 1; Page 19, Paragraph 2, “Khemakhem et al. [30] introduced Carefl, a method that uses autoregressive flows [22, 33] to learn causal-aware models, using the variables’ causal ordering to define the autoregressive transformations … Our method extends Khemakhem et al. [30] to learn the causal graph among multiple variables and perform end-to-end causal inference”; Page 4, Equation 8:
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. Figure 1 shows the process of learning the causal relationships of the first and second values (Figure 1:(2)). Equation 8 (section: Likelihood of Structural Equation Model) shows the extension of the method taught by Khemakhem that uses causal ordering; as DECI uses G as the learned adjacency matrix which indicates the presence of the edge for source and target nodes. Thus, the topological order of G (sampled causal Grah) is used for the predicted causal ordering of the first, second, …, and nth variable).
training based on the first value, the second value and the predicted causal ordering a generative structural causal model (SCM), resulting in a trained generative SCM;
(Geffner, Page 3, Paragraph 6, “DECI takes a Bayesian approach to causal discovery [15]. We model the causal graph G jointly with the observations x1, … , xN … Once this model is fit, the posterior … characterizes our beliefs about the causal structure … where we weight the DAG penalty by … These are gradually increased during training … ensuring only DAGs remain at convergence.”. Section 3.1 shows the causal discovery via training based on the first and second values (x) and the predicted causal ordering (G: sampled causal graph) to generate the structural equation model for the casual model; thus, interpreted by the examiner as an SCM as the model is for a structural causal model (DAGs)).
determining an action;
(Geffner, Figure 1. Figure 1: (4) shows determining an action).
generating using the trained generative SCM a predicted causal effect of the action; and
(Geffner, Figure 1; Page 8, Paragraph 4, “… For DECI, we can use a trained model to immediately estimate (C)ATE”. Figure 1: (5) shows estimating causal quantities which are computations of predicted causal effect of the action on selected intervention/target variables (4) via estimations such as ATE/CATE).
based on the predicted causal effect, performing the action on a target system.
(Geffner, Figure 1; Page 3, Paragraphs 3-4, “
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”. Figure 1: shows performing the action (6) based on the causal estimations (5); thus, performing the action (treatments) on a target system (x) as noted within the Average Treatment Effects section).
Regarding Claim 2:
Geffner teaches the method of claim 1 and further teaches:
wherein the first value and the second value have been obtained from the target system …
(Geffner, Figure 1; Page 9, Paragraph 5, “Motivated by a real-world application where our knowledge of causal relationships is incomplete, DECI combines ideas from causal discovery and inference to go directly from observations to causal predictions.”. Figure 1 depicts the table of the user inputs denoted as X1 and X2 which are based off observations where (4) shows the selection of target variables and intervention variables. Thus, the system is interpreted by the examiner as receiving a first and second value where each X value is associated with a respective variable based on observation from a user who has observed the corresponding data within a target system; thus, the user is interpreted as being the person to obtain the values from the target system).
Regarding Claim 4:
Geffner teaches the method of claim 1 and further comprises:
receiving as input a causal graph;
(Geffner, Figure 1: “In causal inference, the user needs to provide both the data (1) and the causal graph (2) as input …”).
generating a training sample based on the causal graph;
(Geffner, Page 3, Paragraph 5, “Section 3.3 shows how the generative model learnt by DECI can be used to simulate samples from intervened distributions, allowing for treatment effect estimation”; Page 5, Paragraph 6, “Estimating ATE. After training, we can use the model learnt by DECI to simulate new samples x from pθ(x|G). We sample a graph G ~ qΦ(G) and a set of exogenous noise variables z ~ pz. We then input this noise into the learnt DECI structural equation model to simulate x, by applying eq. (1) and eq. (8) on z in the topological order defined by G.” Section 3.3 highlights DECI being trained on samples based on causal graphs G (DAG)).
determining based on the causal graph a ground truth causal ordering for the training sample; and
(Geffner, Abstract, “… We provide a theoretical guarantee that DECI can recover the ground truth causal graph under standard causal discovery assumptions …”)
training the causal ordering predictor based on the training sample and the ground truth causal ordering.
(Geffner, Figure 1; Page 8, Paragraphs 3-4, “Datasets. We generate ground-truth treatment effects to compare against for the ER and SF synthetic graphs that were described … To thoroughly evaluate end-to-end inference, we consider different ways of combining discovery and inference algorithms. For DECI, we can use a trained model to immediately estimate (C)ATE … We also pair other discovery methods with DECI … treatment effect estimation, namely the PC algorithm as a baseline and the ground truth graph (when available) as a check”).
Regarding Claim 5:
Geffner teaches the method of claim 1 and further teaches:
wherein the target system is a machine or a software system.
(Geffner, Page 8, Paragraph 2, “Datasets … For more detailed analysis, we hand-craft a suite of synthetic SEMs, which we name CSuite. CSuite datasets elucidate particular features of the model, such as identifiability of the causal graph, correct specification of the SEM, exogenous noise distributions, and size of the optimal adjustment set. We draw conditional samples from CSuite SEMs with HMC, allowing us to evaluate CATE”. The datasets for CSuite are for different SEMs that were synthetically created as it is a package of multiple features of causal models to be able to and the correct specs for the SEMs to be able to evaluate CATE; thus, the target system is being interpreted as a software system).
Regarding Claim 6:
Geffner teaches the method of claim 1 and further teaches:
wherein the target system is a living being.
(Geffner, Page 8, Figure 5; Page 1, Paragraph 1, “For example, in healthcare, caregivers may wish to understand the effectiveness of different treatments given only historical data”; Page 8, Paragraph 2, “Datasets. … Finally, we include two semi-synthetic causal inference benchmark datasets for ATE evaluation: Twins (twin birth datasets in the US) [1] and IHDP (Infant Health and Development Program data) [16]”. Figure 5 depicts box-plots of infants/twins datasets; thus, interpreted as the target system is a living being as infants/twins are living beings).
Regarding Claim 7:
Geffner teaches the method of claim 1 and further teaches:
wherein the action is one of multiple actions, wherein respective predicted casual effects of the multiple actions are generated using the trained generative SCM, and the action is selected from the multiple actions for performing on the target system based on the respective predicted causal effects.
(Geffner, Page 3, Paragraphs 3-4, “
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”. Geffner teaches utilizing SEMs and ATE/CATE for predicted causal effects wherein the action is one of the treatments, and the estimate is the impact of the respective action(treatment); thus, interpreted by the examiner as the action is one of multiple actions where the action is for performing on the respective target system).
Regarding Claim 8:
Geffner teaches the method of claim 1 and further teaches:
wherein the predicted causal effect is a predicted technical effect controlled by the action.
(Geffner, Page 2, Figure 5. Figure 5 depicts (5) to show the estimation for ATE and CATE which has the equation the y axis with x (target system) on the x axis. The equation E[X5jdo(X2 = x)] is representing estimate of effect in changing X2 to x. The intervention action of applying a treatment is interpreted by the examiner as a predicted technical effect of treatment which is controlled by the action of intervening)).
Regarding Claims 11 and 13-16:
Claims 11 and 13-16 incorporate substantively all the limitations of Claims 1 and 4-7 in a system (thus, a machine) and further recites a memory configured to store computer-readable instructions; a processor coupled to the memory, and configured to execute the computer-readable instructions, which upon execution cause the processor to perform operations comprising (Geffner, Page 7, Paragraph 3, “… Our code is in the supplement”. Geffner’s method named DECI is performed utilizing the code from GitHub links which are noted within the appendix; thus, interpreted by the examiner as a system where a memory/processor/and computer-readable instructions are inherent to operate the code); thus, Claims 11 and 13-16 are rejected for reasons set forth in the rejections of Claims 1 and 4-7, respectively.
Regarding Claims 17 and 18:
Claims 17 and 18 incorporate substantively all the limitations of Claims 1 and 2 in a non-transitory medium (thus, a manufacture) and further recites comprising computer-readable instructions; a processor coupled to the memory, and configured to execute the computer-readable instructions, which upon execution on a processor cause the processor to perform operations comprising (Geffner, Page 7, Paragraph 3, “… Our code is in the supplement”. Geffner’s method named DECI is performed utilizing the code from GitHub links which are noted within the appendix; thus, interpreted by the examiner as a non-transitory medium where a memory/processor/and computer-readable instructions are inherent to operate the code); thus, Claims 17 and 18 are rejected for reasons set forth in the rejections of Claims 1 and 2, respectively.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 3, 12, and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Geffner et al., “Deep End-to-end Causal Inference”, in Ke et al., “Learning to Induce Causal Structure”.
Regarding Claim 3:
Geffner teaches the method of claim 1 which includes a trained causal ordering predictor but does not explicitly teach the predictor to contain an attention-based transformer:
However, Ke teaches:
wherein the trained causal ordering predictor has an attention-based transformer architecture.
(Ke, Page 1, Paragraph 2, “… We propose a model that is first trained on synthetic data generated using different CBNs to learn a mapping from data to graph structures and then used to induce the structures underlying datasets of interest (see Fig. 1(b)). The model is a novel variant of a transformer neural network that receives as input a dataset consisting of observational and interventional samples corresponding to the same CBN and outputs a prediction of the CBN graph structure. The mapping from the dataset to the underlying structure is achieved through an attention mechanism …”.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use the method of Geffner’s performing actions based on predicted causal effects which includes a trained causal ordering predictor with Ke’s explicit teaching of the trained causal ordering predictor having an attention-based transformer architecture as this leads to greater flexibility, utilize structure induction, meta-learning, detailed analysis of structures, and learn mappings between structures (Ke, Page 2, Figure 1; Page 1, Paragraph 2, “… In this work, we extend the supervised learning paradigm to also use interventional data, enabling greater flexibility. We propose a model that is first trained on synthetic data generated using different CBNs to learn a mapping from data to graph structures and then used to induce the structures underlying datasets of interest (see Fig. 1(b)). The model is a novel variant of a transformer neural network that receives as input a dataset consisting of observational and interventional samples corresponding to the same CBN and outputs a prediction of the CBN graph structure. The mapping from the dataset to the underlying structure is achieved through an attention mechanism which alternates between attending to different variables in the graph and to different samples from a variable. The output is produced by a decoder mechanism that operates as an autoregressive generative model on the inferred structure. Our approach can be viewed as a form of meta-learning, as the model learns about the relationship between datasets and structures underlying them.”).
Regarding Claim 12:
Claim 12 incorporates substantively all the limitations of Claims 3 in a system (thus, a machine) and further recites a memory configured to store computer-readable instructions; a processor coupled to the memory, and configured to execute the computer-readable instructions, which upon execution cause the processor to perform operations comprising (Geffner, Page 7, Paragraph 3, “… Our code is in the supplement”. Geffner’s method named DECI is performed utilizing the code from GitHub links which are noted within the appendix; thus, interpreted by the examiner as a computer-implemented method where a memory/processor/and computer-readable instructions are inherent to operate the code); thus, claim 12 is rejected for reasons set forth in the rejection of Claim 3.
Regarding Claim 19:
Claim 19 incorporates substantively all the limitations of Claims 3 in a system (thus, a machine) and further recites comprising computer-readable instructions; a processor coupled to the memory, and configured to execute the computer-readable instructions, which upon execution on a processor cause the processor to perform operations comprising (Geffner, Page 7, Paragraph 3, “… Our code is in the supplement”. Geffner’s method named DECI is performed utilizing the code from GitHub links which are noted within the appendix; thus, interpreted by the examiner as a computer-implemented method where a memory/processor/and computer-readable instructions are inherent to operate the code); thus, claim 19 is rejected for reasons set forth in the rejection of Claim 3.
Claims 9-10 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Geffner et al., “Deep End-to-end Causal Inference”, in Iqbal et al., “CADET: Debugging and Fixing Misconfigurations using Counterfactual Reasoning”.
Regarding Claim 9:
Note: There is a 112(b) rejection to this claim that makes claim interpretation unclear.
Geffner teaches the method of claim 8 and teaches predicted technical effects but does not teach a predicted technical effect is:
wherein the project technical effect is a predicted machine efficiency or predicted machine performance.
However, Iqbal teaches:
wherein the project technical effect is a predicted machine efficiency …
(Iqbal, Page 6, Column 2, Paragraph 1, “… To rank the paths, we measure the causal effect of changing the value of one node (say GPU memory growth or 𝑋) on its successor in the path (say swap memory or 𝑍). We express this with the do-calculus notation: E[𝑍 | do(𝑋 = 𝑥)]. This notation represents the expected value of 𝑍 (swap memory) if we set the value of the node 𝑋 (GPU memory growth) to 𝑥. To compute the average causal effect (ACE) of 𝑋 → 𝑍 (i.e., GPU memory growth swap memory), we find the average effect over all permissible values of 𝑋 (GPU memory growth), i.e.”. Thus, Iqbal teaches a predicted technical effect based on performing a change for machine efficiency by GPU memory growth).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use the method of Geffner’s performing actions based on predicted causal effects which includes a trained causal ordering predictor with Iqbal’s explicit teaching of predicted technical effects being for predicted machine efficiency as this leads to apply to more real-world scenarios explicitly, analyzing effects on machine efficiency and computer performance for fast fixing and optimization approaches (Iqbal, Abstract, “Compared to multi-objective optimization approaches, CADET can find fixes (at most) 8× faster with comparable or better performance gain. Our study of non-functional faults reported in NVIDIA’s forum shows that CADET can find 14% better repairs than the experts’ advice in less than 30 minutes”).
Regarding Claim 10:
Geffner teaches the method of claim 8 but does not teach:
wherein the technical effect is: predicted usage of memory …
However, Iqbal teaches:
wherein the technical effect is: predicted usage of memory …
(Iqbal, Page 1, Figure 1; Page 6, Column 2, Paragraph 1, “… To rank the paths, we measure the causal effect of changing the value of one node (say GPU memory growth or 𝑋) on its successor in the path (say swap memory or 𝑍). We express this with the do-calculus notation: E[𝑍 | do(𝑋 = 𝑥)]. This notation represents the expected value of 𝑍 (swap memory) if we set the value of the node 𝑋 (GPU memory growth) to 𝑥. To compute the average causal effect (ACE) of 𝑋 → 𝑍 (i.e., GPU memory growth swap memory), we find the average effect over all permissible values of 𝑋 (GPU memory growth), i.e.”. Thus, Iqbal teaches growth of GPU memory and growth with latency and resource pressure as noted by Figure 1; which is interpreted by the examiner as predicted usage of memory).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use the method of Geffner’s performing actions based on predicted causal effects which includes a trained causal ordering predictor with Iqbal’s explicit teaching of predicted technical effects being for predicted machine efficiency as this leads to apply to more real-world scenarios explicitly, analyzing effects on machine efficiency and computer performance for fast fixing and optimization approaches (Iqbal, Abstract, “Compared to multi-objective optimization approaches, CADET can find fixes (at most) 8× faster with comparable or better performance gain. Our study of non-functional faults reported in NVIDIA’s forum shows that CADET can find 14% better repairs than the experts’ advice in less than 30 minutes”).
Regarding Claim 20:
Claim 20 incorporates substantively all the limitations of Claim 9 in a system (thus, a machine) and further recites comprising computer-readable instructions; a processor coupled to the memory, and configured to execute the computer-readable instructions, which upon execution on a processor cause the processor to perform operations comprising (Geffner, Page 7, Paragraph 3, “… Our code is in the supplement”. Geffner’s method named DECI is performed utilizing the code from GitHub links which are noted within the appendix; thus, interpreted by the examiner as a computer-implemented method where a memory/processor/and computer-readable instructions are inherent to operate the code); thus, claim 20 is rejected for reasons set forth in the rejection of Claim 9.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to IBRAHIM RAHMAN whose telephone number is (703)756-1646. The examiner can normally be reached M-F 8am-5pm.
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/I.R./Examiner, Art Unit 2122
/MICHAEL H HOANG/PRIMARY EXAMINER, Art Unit 2122
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