DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
This action is responsive to the original application filed on 11/10/2023. Acknowledgment is made with respect to a claim of priority to PCT Application PCT/JP2022/019713 filed on 5/9/2022 and Japanese Application JP2021-105786 filed on 6/25/2021.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier.
Such claim limitation(s) is/are: “a step of generating” and “a step of calculating” in claim 4 and its dependents and “a first step of receiving”, “a second step of calculating”, “a third step of calculating”., “a fourth step of calculating”, and “a fifth step of updating” in claim 5 and its dependent.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
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 1-6 are rejected under 35 U.S.C 101 because the claimed invention is directed to an abstract idea without significantly more. The analysis of the claims will follow the 2019 Revised Patent Subject Matter Eligibility Guidance, 84 Fed. Reg. 50 (“2019 PEG”).
When considering subject matter eligibility under 35 U.S.C. 101, it must be determined whether the claim is directed to one of the four statutory categories of invention, i.e., process, machine, manufacture, or composition of matter (Step 1). If the claim does fall within one of the statutory categories, the second step in the analysis is to determine whether the claim is directed to a judicial exception (Step 2A). The Step 2A analysis is broken into two prongs. In the first prong (Step 2A, Prong 1), it is determined whether or not the claims recite a judicial exception (e.g., mathematical concepts, mental processes, certain methods of organizing human activity). If it is determined in Step 2A, Prong 1 that the claims recite a judicial exception, the analysis proceeds to the second prong (Step 2A, Prong 2), where it is determined whether or not the claims integrate the judicial exception into a practical application. If it is determined at step 2A, Prong 2 that the claims do not integrate the judicial exception into a practical application, the analysis proceeds to determining whether the claim is a patent-eligible application of the exception (Step 2B). If an abstract idea is present in the claim, any element or combination of elements in the claim must be sufficient to ensure that the claim integrates the judicial exception into a practical application, or else amounts to significantly more than the abstract idea itself.
Claim 1
Step 1: The claim recites a computer system; therefore, it is directed to the statutory category of a machine.
Step 2A Prong 1: The claim recites, inter alia:
generate a feature by mapping a vector including values of a plurality of factors representing a state of the person to a feature space: Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of generating a feature by mapping a vector to a feature space, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper. For example, a human can practically and mentally map a vector to a feature space.
output, based on the feature, predicted values of the effects of the plurality of interventions on the person are managed: Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of outputting predicted values of effects of interventions, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper. For example, a human can practically and mentally predict the effects of an intervention.
the first model maps a plurality of pieces of training data used in the machine learning to the feature space such that a difference in distribution of the plurality of pieces of training data in the feature space is reduced: Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of mapping training data to a feature space, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper. For example, a human can practically and mentally map data to a feature space to reduce a distribution in the data.
generates the feature of the input data: Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of generating features of data, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper. For example, a human can practically and mentally generate important features of input data.
calculates the predicted values of the effects of the plurality of interventions: Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mathematical concept of calculating predicted values of the effects of interventions, which is performed through mathematical computation.
Step 2A Prong 2: The claim does not recite any additional limitations which integrate the abstract idea into a practical application. Specifically, the additional elements consist of “at least one computer including a processor and a storage device connected to the processor”, “a first model configured to”, “a second model configured to”, “the first model and the second model being generated by machine learning”, “the computer system”, “receives input data including the values of the plurality of factors”, “inputting the input data into the first model”, and “inputting the feature of the input data into the second model”.
The additional elements of “at least one computer including a processor and a storage device connected to the processor”, “a first model configured to”, “a second model configured to”, and “the computer system” amount to generic computer components or models used as a tool to perform an existing process. Thus, the additional elements amount to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
The additional elements “receives input data including the values of the plurality of factors”, “inputting the input data into the first model”, and “inputting the feature of the input data into the second model” are insignificant extra-solution activities required for any uses of the abstract ideas (see MPEP § 2106.05(g)).
The additional element of “the first model and the second model being generated by machine learning” amounts to no more than generally linking the use of a judicial exception to a particular technological environment or field of use (see MPEP § 2106.05(h).
Thus, even when viewed individually and as an ordered combination, these additional elements do not integrate the abstract idea into a practical application and the claim is thus directed to the abstract idea.
Step 2B: Finally, the claim taken as a whole does not contain an inventive concept which provides significantly more than the abstract idea.
The additional elements of “at least one computer including a processor and a storage device connected to the processor”, “a first model configured to”, “a second model configured to”, and “the computer system” amount to generic computer components or models used as a tool to perform an existing process. Thus, the additional elements amount to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
The additional elements “receives input data including the values of the plurality of factors”, “inputting the input data into the first model”, and “inputting the feature of the input data into the second model” are insignificant extra-solution activities required for any uses of the abstract ideas (see MPEP § 2106.05(g)), and are well-understood, routine, conventional activities (see MPEP § 2106.05(d)(II)(i); “Receiving or transmitting data over a network”).
The additional element of “the first model and the second model being generated by machine learning” amounts to no more than generally linking the use of a judicial exception to a particular technological environment or field of use (see MPEP § 2106.05(h).
Taken alone or in combination, the additional elements of the claim do not provide an inventive concept and thus the claim is subject-matter ineligible.
Claim 2
Step 1: A machine, as above.
Step 2A Prong 1: The claim recites, inter alia:
identify, based on the feature, a type of an intervention received by the person is managed: Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of identifying a type of intervention, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper.
processing of calculating the feature of the training data: Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mathematical concept of calculating features of training data, which is performed through mathematical computation.
processing of calculating the predicted values of the effects of the plurality of interventions: Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mathematical concept of calculating predicted values of the effects of interventions, which is performed through mathematical computation.
processing of calculating a loss function based on the type of the intervention obtained by: Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mathematical concept of calculating a loss function, which is performed through mathematical computation as evidenced by formulas 1 and 3 of the originally filed specification.
processing of updating the first model, the second model, and the third model by using the loss function: Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mathematical concept of updating models using a loss function, which is performed through mathematical computation as evidenced by formula 4 of the originally filed specification.
Step 2A Prong 2, Step 2B: The additional elements of “a third model configured to” and “the machine learning is executed” amount to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)). The additional elements “processing of receiving training data including identification information on the person, the values of the plurality of factors of the person, a type of an intervention received by the person, and an effect value of the intervention”, “inputting the training data into the first model”, “inputting the feature of the training data into the second model”, and “inputting the feature of the training data into the third model, the type of the intervention included in the training data, the predicted values of the effects of the plurality of interventions, and the effect value included in the training data” are insignificant extra-solution activities required for any uses of the abstract ideas (see MPEP § 2106.05(g)), and are well-understood, routine, conventional activities (see MPEP § 2106.05(d)(II)(i); “Receiving or transmitting data over a network”). Taken alone or in combination, the additional elements of the claim do not provide an inventive concept, integrate the abstract ideas into a practical application, or provide significantly more than the abstract ideas of the claim and thus the claim is subject-matter ineligible.
Claim 3
Step 1: A machine, as above.
Step 2A Prong 1: The claim recites, inter alia:
processing of calculating a weight based on the feature of the training data: Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mathematical concept of calculating a weight, which is performed through mathematical computation as evidenced by formula 2 of the originally filed specification.
processing of calculating the loss function based on the type of the intervention obtained: Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mathematical concept of calculating a loss function, which is performed through mathematical computation as evidenced by formulas 1, 3, and 4 of the originally filed specification.
Step 2A Prong 2, Step 2B: The additional elements “inputting the feature of the training data into the third model, the type of the intervention included in the training data, the predicted values of the effects of the plurality of interventions, the effect value included in the training data, and the weight” are insignificant extra-solution activities required for any uses of the abstract ideas (see MPEP § 2106.05(g)), and are well-understood, routine, conventional activities (see MPEP § 2106.05(d)(II)(i); “Receiving or transmitting data over a network”). Taken alone or in combination, the additional elements of the claim do not provide an inventive concept, integrate the abstract ideas into a practical application, or provide significantly more than the abstract ideas of the claim and thus the claim is subject-matter ineligible.
Claims 4-6
Claims 4-6 recite a method (step 1: a process) to perform the steps of claims 1-3, respectively, without any additional elements that integrate the abstract ideas into a practical application or provide significantly more than the abstract idea by itself, and are thus rejected for the same reasons set forth in the rejection of claims 1-3, respectively.
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 and 4 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Johansson et al. (Johansson et al., “Learning Representations for Counterfactual Inference”, Jun. 6, 2018, arXiv:1605.03661v3, pp. 1-11, hereinafter “Johansson”).
Regarding claim 1, Johansson discloses [a] computer system for predicting effects of a plurality of interventions on a person, the computer system comprising: at least one computer including a processor and a storage device connected to the processor, wherein (Abstract; “We consider the task of answering counterfactual questions such as, “Would this patient have lower blood sugar had she received a different medication?”. We propose a new algorithmic framework for counterfactual inference which brings together ideas from domain adaptation and representation learning”, which discloses a system for predicting or estimating treatment effects or interventions on a person; §2; “Let T be the set of potential interventions or actions we wish to consider, X the set of contexts, and Y the set of possible outcomes. For example, for a patient x ∈ X the set T of interventions of interest might be two different treatments, and the set of outcomes might be Y = [0,200] indicating blood sugar levels in mg/dL”; and §6; the experiments section is inherently performed using a computer system with a processor and storage device)
a first model configured to generate a feature by mapping a vector including values of a plurality of factors representing a state of the person to a feature space, (Page 3, §3; “Our method, see Figure 1, learns a representation Φ : X → Rd, (either using a deep neural network, or by feature re weighting and selection), and a function h : Rd × T → R,”, which discloses a machine-learned representation network Φ or first model that maps the input context vector x (comprising a person’s covariates) to a latent feature space Rd; and Figure 1 Caption; “Contexts x are represented by Φ(x), which are used, with group indicator t, to predict the response y while minimizing the imbalance in distributions measured by disc(ΦC,ΦT)”; and Page 4, §3.2; “The first dr hidden layers are used to learn a representation Φ(x) of the input x”, which discloses a first model or machine learned encoder Φ that maps an input vector or factors representing the person’s state into a feature space Rd)
and a second model configured to output, based on the feature, predicted values of the effects of the plurality of interventions on the person are managed, the first model and the second model being generated by machine learning, (Page 4, §3.2; “The do layers following the first dr layers take as additional input the treatment assignment ti and generate a prediction h([Φ(xi), ti]) of the outcome”, which discloses a prediction function h that constitutes a second model, and the output h([Φ(xi), ti]) is computed for all t, which results in predicted outcome values for each of the plurality of interventions; and Page 2, Equation 1; the equation discloses a computation of h or the second model across all t values to result in the predicted values of the effects of the interventions on the person)
the first model maps a plurality of pieces of training data used in the machine learning to the feature space such that a difference in distribution of the plurality of pieces of training data in the feature space is reduced, and (Page 3, Equation 2; the equation discloses a middle term “αdiscH” that is the distribution discrepancy penalty between treated groups and control groups in the feature space; and Page 3, §3; “. Finally, we accomplish the third objective by minimizing the so-called discrepancy distance, introduced by Mansour et al. (2009), which is a hypothesis class dependent distance measure tailored for domain adaptation. For hypothesis space H, we denote the discrepancy distance by discH. See Section 4 for the formal definition and motivation. Other discrepancy measures such as Maximum Mean Discrepancy (Gretton et al., 2012) could also be used for this purpose. Intuitively, representations that reduce the discrepancy between the treated and control populations prevent the learner from using “unreliable” aspects of the data when trying to generalize from the factual to counterfactual domains”, which discloses mapping training data to a feature space so that a difference in distribution is reduced; and Page 1, §1; “We then introduce a form of regularization by enforcing similarity between the distributions of representations learned for populations with different interventions. For example, the representations for patients who received medication A versus those who received medication B. This reduces the variance from fitting a model on one distribution and applying it to another”)
the computer system receives input data including the values of the plurality of factors, generates the feature of the input data by inputting the input data into the first model, and calculates the predicted values of the effects of the plurality of interventions by inputting the feature of the input data into the second model (Page 3, Algorithm 1; Algorithm 1 receives the input data X,T,Y F, computes Φ∗,g∗ by minimizing B, and then fits h* on the factual data using Φ∗. At inference, a new input x is passed through Φ∗ to obtain the feature Φ∗(x), which is then input into h* or the second model to compute h*( Φ∗(x), t) which is the predicted value for each intervention; and §3.2; “The first dr hidden layers are used to learn a representation Φ(x) of the input x. The output of the dr:th layer is used to calculate the discrepancy discH( ˆ PF Φ, ˆ PCF Φ ). The do layers following the first dr layers take as additional input the treatment assignment ti and generate a prediction h([Φ(xi), ti]) of the outcome”).
Regarding claim 4, it is a method claim corresponding to the steps of claim 1, and is rejected for the same reasons as claim 1.
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.
Claims 2, 3, 5, and 6 are rejected under 35 USC § 103 as being obvious over Johansson and further in view of Bica et al. (Bica et al., “ESTIMATING COUNTERFACTUAL TREATMENT OUTCOMES OVER TIME THROUGH ADVERSARIALLY BALANCED REPRESENTATIONS”, Feb. 10, 2020, arXiv:2002.04083v1, pp. 1-28, hereinafter “Bica”).
Regarding claims 2 and 5, the rejection of claims 1 and 4 are incorporated and Johansson further discloses the machine learning including: processing of receiving training data including identification information on the person, the values of the plurality of factors of the person, a type of an intervention received by the person, and an effect value of the intervention; (Page 2, §2; “common approach for estimating the ITE is by direct modelling: given n samples {(xi,ti,yF i )}n i=1, where yF i = ti·Y1(xi)+(1−ti)Y0(xi), learn a function h : X ×T → Y such that h(xi,ti) ≈ yF i “, where xi is the covariate vector or values of the plurality of factors, ti is the type of intervention received, and yiF is the observed effect value on the intervention)
processing of calculating the feature of the training data by inputting the training data into the first model; (Page 3, Algorithm 1, Step 2; and §3.2; “The first dr hidden layers are used to learn a representation Φ(x) of the input x”)
processing of calculating the predicted values of the effects of the plurality of interventions by inputting the feature of the training data into the second model; (§3.2; “The do layers following the first dr layers take as additional input the treatment assignment ti and generate a prediction h([Φ(xi), ti]) of the outcome”)
Johansson fails to explicitly disclose but Bica discloses a third model configured to identify, based on the feature, a type of an intervention received by the person is managed, and the machine learning is executed, (Figure 2 Caption; “Encoder builds representation Φ(¯ Ht) that maximizes loss of treatment classifier Ga and minimizes loss of outcome predictor Gy”, which discloses a treatment classifier network Ga or a third model that identifies, based on a feature, a type of intervention received by a person; and Abstract; “At each timestep, CRN constructs a treatment invariant representation which removes the association between patient history and treatment assignments and thus can be reliably used for making counterfactual predictions”) …
processing of calculating a loss function based on the type of the intervention obtained by inputting the feature of the training data into the third model, the type of the intervention included in the training data, the predicted values of the effects of the plurality of interventions, and the effect value included in the training data; and (Figure 2 and its Caption; “.Encoder builds representation Φ(¯ Ht) that maximizes loss of treatment classifier Ga and minimizes loss of outcome predictor Gy.Φ(¯ Ht)”; and §4)
processing of updating the first model, the second model, and the third model by using the loss function (§4; and Abstract; “To handle the bias from time-varying confounders, covariates affect ing the treatment assignment policy in the observational data, CRN uses domain adversarial training to build balancing representations of the patient history”).
Johansson and Bica are analogous art because both are concerned with predicting treatment outcomes using machine learning. Before the effective filing date of the claimed invention, it would have been obvious to one skilled in machine learning to combine the third model and loss functions of Bica with the prediction system of Johansson to yield to the predictable result of a third model configured to identify, based on the feature, a type of an intervention received by the person is managed, and the machine learning is executed… processing of calculating a loss function based on the type of the intervention obtained by inputting the feature of the training data into the third model, the type of the intervention included in the training data, the predicted values of the effects of the plurality of interventions, and the effect value included in the training data; and processing of updating the first model, the second model, and the third model by using the loss function. The motivation for doing so would be to use a CRN to leverage patient observational data to estimate treatment effects over time (Bica; Abstract).
Regarding claims 3 and 6, the rejection of claims 1, 2, 4, and 5 are incorporated and Johansson discloses processing of calculating a weight based on the feature of the training data; and (§3.1; “We implement the re-weighting as a diagonal matrix W, forming the representation Φ(x) = Wx, with diag(W) subject to a simplex constraint to achieve sparsity. Let N = {x → Wx : W =diag(w), wi ∈ [0,1], iwi = 1}de note the space of such representations. We can now apply Algorithm 1 with Hl the space of linear hypotheses. Be cause the hypotheses are linear, disc(Φ) is a function of the distance between the weighted population means, see n Section 4.1. With p = E[t],c = p − 1/2, nt = n i=1ti, µ1 = 1 nt i:ti=1 xi, and µ0 analogously defined, disc Hl (XW) = c+ c2 +W(pµ1−(1−p)µ0)] 2 2 To minimize the discrepancy, features k that differ a lot be tween treatment groups will receive a smaller weight wk.”)
processing of calculating the loss function based on the type of the intervention obtained by inputting the feature of the training data into the third model, the type of the intervention included in the training data, the predicted values of the effects of the plurality of interventions, the effect value included in the training data, and the weight (Page 3, Equation 2).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Ganin et al., “Domain-Adversarial Training of Neural Networks”, May 26, 2016, arXiv:1505.07818v4, pp. 1-35.
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/BRENT JOHNSTON HOOVER/Primary Examiner, Art Unit 2127
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