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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 05/25/2022 and 01/23/2025 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Priority
Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d). The certified copy has been filed in parent Application No. DE10 2019 131 639.1, filed on12/22/2019.
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.
“receiving unit” in claim 15
“optimization unit” in claim 15
“providing unit” in claim 15
“AI unit” in claim 16
“client unit” in claim 17
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.
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 15-17 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 limitation “receiving unit, optimization unit, providing unit, AI unit and client unit” invokes 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. However, the written description fails to disclose the corresponding structure, material, or acts for performing the entire claimed function and to clearly link the structure, material, or acts to the function. Therefore, the claim is indefinite and is rejected under 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, second paragraph.
Applicant may:
(a) Amend the claim so that the claim limitation will no longer be interpreted as a limitation under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph;
(b) Amend the written description of the specification such that it expressly recites what structure, material, or acts perform the entire claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(c) Amend the written description of the specification such that it clearly links the structure, material, or acts disclosed therein to the function recited in the claim, without introducing any new matter (35 U.S.C. 132(a)).
If applicant is of the opinion that the written description of the specification already implicitly or inherently discloses the corresponding structure, material, or acts and clearly links them to the function so that one of ordinary skill in the art would recognize what structure, material, or acts perform the claimed function, applicant should clarify the record by either:
(a) Amending the written description of the specification such that it expressly recites the corresponding structure, material, or acts for performing the claimed function and clearly links or associates the structure, material, or acts to the claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(b) Stating on the record what the corresponding structure, material, or acts, which are implicitly or inherently set forth in the written description of the specification, perform the claimed function. For more information, see 37 CFR 1.75(d) and MPEP §§ 608.01(o) and 2181.
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 1-17 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea and does not integrate the judicial exception into a practical application or amount to significantly more than the judicial exception.
Regarding claim 1
Step 1: The claim recites a method; therefore, it falls into the statutory category of processes.
Step 2A Prong 1: The claim recites multiple mental processes, as explained below. The claim recites, inter alia:
“…wherein the Al module is adapted to compute an output dataset for the input dataset using at least one of a regression and a classification, wherein the user dataset comprises at least one target specification specifying a value of a data item in an output dataset of the Al module;”
This limitation is directed to the abstract idea of a mental process (concepts performed in the human mind, including observation and evaluation [see MPEP 2106.04(a)(2) III. C.]).
“loading at least one optimization task specifying a specific metric and/or a similarity metric; computing at least one solution of the at least one optimization task as an explanation dataset, based on the user dataset and the Al module, by at least applying at least one optimization method, wherein the Al module is adapted to compute for the explanation dataset an output dataset comprising the data item specified by the target specification; and providing the explanation dataset for the Al module.”
This limitation is directed to the abstract idea of a mental process (concepts performed in the human mind, including observation and evaluation [see MPEP 2106.04(a)(2) III. C.]).
Step 2A Prong 2: This judicial exception is not integrated into a practical. In particular, the claim only recites additional elements that are mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. See MPEP 2106.05(f). The additional element of “Al module”, as drafted, is reciting generic computer components. The generic computer components in these steps are recited at a high-level of generality (i.e., as a generic computer component performing a generic computer function) such that it amounts no more than mere instructions to apply the exception using a generic computer component. In addition, the claim limitation “A method for providing an explanation dataset for an Al module, the method comprising: receiving a user dataset specifying at least one input dataset of an Al module,;” as explained by the Supreme Court, the addition of insignificant extra-solution activity does not amount to an inventive concept, particularly when the activity is well-understood or conventional. See MPEP 2106.05(g). Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional element of using generic computer components to perform the abstract idea amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The only remaining limitation of the claim “A method for providing an explanation dataset for an Al module, the method comprising: receiving a user dataset specifying at least one input dataset of an Al module,” constitute storing and retrieving information in memory, which the courts have found to be well-understood, routine, and conventional. See MPEP 2106.05(d)(II); Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015). Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
Regarding claim 2
Step 1: The claim recites a method; therefore, it falls into the statutory category of processes.
Step 2A Prong 1:
“wherein the user dataset comprises at least one constraint for the at least one optimization task, wherein the optimization method computes the at least one optimization task based on the at least one constraint of the user dataset.”
This limitation is directed to the abstract idea of a mental process (concepts performed in the human mind, including observation and evaluation [see MPEP 2106.04(a)(2) III. C.]).
Thus, the judicial exception is not integrated into a practical application [see MPEP 2106.05(d) I.], failing Step 2A Prong 2. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception under step 2B.
Regarding claim 3
Step 1: The claim recites a method; therefore, it falls into the statutory category of processes.
Step 2A Prong 1:
“wherein the at least one constraint comprises an allowance specification, wherein the allowance specification specifies in which feature categories defined by the input dataset the explanation dataset differs from the input dataset.”
This limitation is directed to the abstract idea of a mental process (concepts performed in the human mind, including observation and evaluation [see MPEP 2106.04(a)(2) III. C.]).
Thus, the judicial exception is not integrated into a practical application [see MPEP 2106.05(d) I.], failing Step 2A Prong 2. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception under step 2B.
Regarding claim 4
Step 1: The claim recites a method; therefore, it falls into the statutory category of processes.
Step 2A Prong 1: The claim recites multiple mental processes, as explained below. The claim recites, inter alia:
“wherein the at least one constraint comprises at least one weight, wherein a weight specifies a preference for a change of a feature category of the input dataset in the explanation dataset.”
This limitation is directed to the abstract idea of a mental process (concepts performed in the human mind, including observation and evaluation [see MPEP 2106.04(a)(2) III. C.]).
Thus, the judicial exception is not integrated into a practical application [see MPEP 2106.05(d) I.], failing Step 2A Prong 2. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception under step 2B.
Regarding claim 5
Step 1: The claim recites a method; therefore, it falls into the statutory category of processes.
Step 2A Prong 1: The claim recites multiple mental processes, as explained below. The claim recites, inter alia:
“wherein the at least one constraint comprises at least one range specification, wherein the at least one range specification specifies a permitted value range of a feature category of the explanation dataset, wherein the permitted value range includes a maximum and/or minimum permitted deviation from a value in the input dataset.”
This limitation is directed to the abstract idea of a mental process (concepts performed in the human mind, including observation and evaluation [see MPEP 2106.04(a)(2) III. C.]).
Thus, the judicial exception is not integrated into a practical application [see MPEP 2106.05(d) I.], failing Step 2A Prong 2. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception under step 2B.
Regarding claim 6
Step 1: The claim recites a method; therefore, it falls into the statutory category of processes.
Step 2A Prong 1: The claim recites multiple mental processes, as explained below. The claim recites, inter alia:
“wherein the at least one constraint comprises at least one range specification, wherein the at least one range specification specifies a permitted value range of a feature category of the explanation dataset, wherein the permitted value range includes a maximum and/or minimum permitted deviation from a value in the input dataset.”
This limitation is directed to the abstract idea of a mental process (concepts performed in the human mind, including observation and evaluation [see MPEP 2106.04(a)(2) III. C.]).
Thus, the judicial exception is not integrated into a practical application [see MPEP 2106.05(d) I.], failing Step 2A Prong 2. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception under step 2B.
Regarding claim 7
Step 1: The claim recites a method; therefore, it falls into the statutory category of processes.
Step 2A Prong 1: The claim recites multiple mental processes, as explained below. The claim recites, inter alia:
“wherein the at least one constraint comprises at least one range specification, wherein the at least one range specification specifies a permitted value range of a feature category of the explanation dataset, wherein the permitted value range includes a maximum and/or minimum permitted deviation from a value in the input dataset.”
This limitation is directed to the abstract idea of a mental process (concepts performed in the human mind, including observation and evaluation [see MPEP 2106.04(a)(2) III. C.]).
Thus, the judicial exception is not integrated into a practical application [see MPEP 2106.05(d) I.], failing Step 2A Prong 2. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception under step 2B.
Regarding claim 8
Step 1: The claim recites a method; therefore, it falls into the statutory category of processes.
Step 2A Prong 1: The claim recites multiple mental processes, as explained below. The claim recites, inter alia:
“wherein the at least one constraint of the provider dataset specifies an output count, and wherein the output count specifies how many variations of the input dataset are computed and comprised by the explanation dataset.”
This limitation is directed to the abstract idea of a mental process (concepts performed in the human mind, including observation and evaluation [see MPEP 2106.04(a)(2) III. C.]).
Thus, the judicial exception is not integrated into a practical application [see MPEP 2106.05(d) I.], failing Step 2A Prong 2. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception under step 2B.
Regarding claim 9
Step 1: The claim recites a method; therefore, it falls into the statutory category of processes.
Step 2A Prong 1: The claim recites multiple mental processes, as explained below. The claim recites, inter alia:
“wherein the at least one optimization method comprises a gradient method and/or a Newton method.”
This limitation is directed to the abstract idea of a mental process (concepts performed in the human mind, including observation and evaluation [see MPEP 2106.04(a)(2) III. C.]).
Thus, the judicial exception is not integrated into a practical application [see MPEP 2106.05(d) I.], failing Step 2A Prong 2. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception under step 2B.
Regarding claim 10
Step 1: The claim recites a method; therefore, it falls into the statutory category of processes.
Step 2A Prong 1: The claim recites multiple mental processes, as explained below. The claim recites, inter alia:
“wherein the at least one optimization method comprises a gradient method and/or a Newton method.”
This limitation is directed to the abstract idea of a mental process (concepts performed in the human mind, including observation and evaluation [see MPEP 2106.04(a)(2) III. C.]).
Thus, the judicial exception is not integrated into a practical application [see MPEP 2106.05(d) I.], failing Step 2A Prong 2. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception under step 2B.
Regarding claim 11
Step 1: The claim recites a method; therefore, it falls into the statutory category of processes.
Step 2A Prong 1: The claim recites multiple mental processes, as explained below. The claim recites, inter alia:
“wherein the similarity metric is adapted as an Lpnorm including an LO, L1 and/or L2 metric.”
This limitation is directed to the abstract idea of a mental process (concepts performed in the human mind, including observation and evaluation [see MPEP 2106.04(a)(2) III. C.]).
Thus, the judicial exception is not integrated into a practical application [see MPEP 2106.05(d) I.], failing Step 2A Prong 2. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception under step 2B.
Regarding claim 12
Step 1: The claim recites a method; therefore, it falls into the statutory category of processes.
Step 2A Prong 1: The claim recites multiple mental processes, as explained below. The claim recites, inter alia:
“whereinthe optimization task is given by the formula minMsp(d) + Mim(S), wherein Msp specifies the specific metric [[(14)]] and Mim specifies the similarity metric, and d is selected from a set of the allowable changes of the input dataset.”
This limitation is directed to the abstract idea of a mathematical concept.
Thus, the judicial exception is not integrated into a practical application [see MPEP 2106.05(d) I.], failing Step 2A Prong 2. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception under step 2B.
Regarding claim 13
Step 1: The claim recites a method; therefore, it falls into the statutory category of processes.
Step 2A Prong 1: The claim recites multiple mental processes, as explained below. The claim recites, inter alia:
“further comprising-computing an output dataset using the Al module, wherein the explanation dataset is used as an input dataset of the Al module.”
This limitation is directed to the abstract idea of a mental process (concepts performed in the human mind, including observation and evaluation [see MPEP 2106.04(a)(2) III. C.]).
Step 2A Prong 2: This judicial exception is not integrated into a practical. In particular, the claim only recites additional elements that are mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. See MPEP 2106.05(f). The additional element of “Al module”, as drafted, is reciting generic computer components. The generic computer components in these steps are recited at a high-level of generality (i.e., as a generic computer component performing a generic computer function) such that it amounts no more than mere instructions to apply the exception using a generic computer component. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into practical application, the additional element of using generic computer components to perform the abstract idea amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. Thus, the claim is not patent eligible.
Regarding claim 14 and 16
Claims 14 and 16 recites analogous limitations to claims 1-2 and therefore is rejected on the same ground as claims 1-2.
Regarding claim 15 and 17
Claims 15 and 17 recites analogous limitations to claims 1-2 and therefore is rejected on the same ground as claims 1-2.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 1-2, 4-9 and 13-16 is/are rejected under 35 U.S.C. 103 as being unpatentable over McGrath et al. (“Interpretable Credit Application Predictions With Counterfactual Explanations”) in view of Sokol et al. (“Glass-Box: Explaining AI Decisions With Counterfactual Statements Through Conversation With a Voice-enabled Virtual Assistant”).
Regarding claim 1
McGrath teaches a method for providing an explanation dataset for an Al module, (Abstract “Our contribution is two-fold: i) we propose positive counterfactuals, i.e. we adapt counterfactual explanations to also explain accepted loan applications, and ii) we propose two weighting strategies to generate more interpretable counterfactuals. Experiments on the HELOC loan applications dataset show that our contribution outperforms the baseline counterfactual generation strategy, by leading to smaller and hence more interpretable counterfactuals.”)
…
wherein the Al module is adapted to compute an output dataset for the input dataset using at least one of a regression (pg. 6 section 5.2 “Predictive Power As preliminary experiment, we assess the predictive power of four classifiers: logistic regression (LogReg), gradient boosting (GradBoost), support vector machine with linear kernel (SVC), and multi-layer perceptron (MLP). Logistic regression apart, the others fall within the black box category.”)
and a classification, (section 5 “We perform a a binary classification task on a credit application dataset. We train a range of black box models and we explain their predictions with counterfactuals, the goal being explaining the classifier decision to reject or accept a loan application.”)
…
loading at least one optimization task specifying a specific metric and/or a similarity metric; computing at least one solution of the at least one optimization task as an explanation dataset, based on the user dataset and the Al module, by at least applying at least one optimization method, (pg. 4 “To generate counterfactuals we adopt the iterative approach described in Algorithm 1. We optimize L with the Nelder-Mead algorithm, as suggested in Molnar [2018]. We constrain the optimisation with a tolerance …Once an acceptable value for λ is obtained for the given x and y′ a set of counterfactuals can be obtained by repeating steps 1 and 2 with the calculated λ. Note that we constrain the features manually, since the heuristic in Algorithm 1 and the adopted optimization algorithm are designed for unconstrained optimization.”)
wherein the Al module is adapted to compute for the explanation dataset an output dataset comprising the data item specified by the target specification; and providing the explanation dataset for the Al module. (Section 6 “We explain credit application predictions obtained with black box models with counterfactuals. In case of positive prediction, we show how counterfactuals can be interpreted as a safety margin from the decision boundary. We propose two weighted strategies to generate counterfactuals: one derives weights from features importance, the other relies on nearest neighbors. Experiments on the HELOC loan applications dataset show that weights generated from feature importance lead to more compact counterfactuals, therefore offering more compact and intelligible explanations for end users.”)
McGrath does not teach the method comprising: receiving a user dataset specifying at least one input dataset of an Al module,
…wherein the user dataset comprises at least one target specification specifying a value of a data item in an output dataset of the Al module.
Sokol teaches the method comprising: receiving a user dataset specifying at least one input dataset of an Al module, (section 4 “The demo system first receives a data point to be classified by scanning a QR code or by asking questions to collect the necessary features. After that, it classifies the data point using the underlying Machine Learning model and outputs its decision. Then, the user can challenge the decision and request:”)
wherein the user dataset comprises at least one target specification specifying a value of a data item in an output dataset of the Al module. (Section 3 “Our prototype is based on Google’s DIY AI Voice Kit1, which provides a customisable hardware and software platform for development of voice interface-enabled systems. To improve the user interaction we have added a QR code scanner used as an alternative to voice input to enable quick loading of a data point to be classified. The QR codes encode data point features in JSON format and are printed on profile cards, which also display the feature values in a human readable format. The profile cards are used during the system demonstration to improve user experience, as described in the introduction.”)
McGrath and Sokol are analogous art because they are both directed to machine learning.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combined machine learning based prediction for counterfactual explanation of McGrath with AI decision with counterfactual statements of Sokol.
One of ordinary skill in the art would have been motivated to make this modification in order to “demonstrate the capabilities of the device by allowing users to impersonate a loan applicant who can question the system to understand the automated decision that he received” as disclosed by (Sokol abstract “Our system explains algorithmic predictions with class-contrastive counterfactual statements (e.g., “Had a number of conditions been different:...the prediction would change...”), which show a difference in a particular scenario that causes an algorithm to “change its mind”. Such explanations do not require any prior technical knowledge to understand, hence are suitable for a lay audience, who interact with the system in a natural way– through an interactive dialogue. We demonstrate the capabilities of the device by allowing users to impersonate a loan applicant who can question the system to understand the automated decision that he received”).
Regarding claims 14 and 15
Claims 14 and 15 recite analogous limitations to independent claim 1 and therefore is rejected on the same ground as independent claim 1.
Regarding claim 2
McGrath in view of Sokol teaches the method of claim 1.
McGrath further teaches wherein the user dataset comprises at least one constraint for the at least one optimization task, wherein the optimization method computes the at least one optimization task based on the at least one constraint of the user dataset. (pg. 4 “To generate counterfactuals we adopt the iterative approach described in Algorithm 1. We optimize L with the Nelder-Mead algorithm, as suggested in Molnar [2018]. We constrain the optimisation with a tolerance …Once an acceptable value for λ is obtained for the given x and y′ a set of counterfactuals can be obtained by repeating steps 1 and 2 with the calculated λ. Note that we constrain the features manually, since the heuristic in Algorithm 1 and the adopted optimization algorithm are designed for unconstrained optimization.”)
Regarding claim 4
McGrath in view of Sokol teaches the method of claim 2.
McGrath further teaches wherein the at least one constraint comprises at least one weight, wherein a weight specifies a preference for a change of a feature category of the input dataset in the explanation dataset. (Pg. 4 “where x is the actual input vector, x′ is counterfactual vector, y′ is the desired output state, ˆ f(...) is the trained model, λ is the balance weight. λ balances the counterfactual between obtaining the exact desired output and making the smallest possible changes to the input vector x.”)
Regarding claim 5
McGrath in view of Sokol teaches the method of claim 2.
McGrath further teaches wherein the at least one constraint comprises at least one range specification, wherein the at least one range specification specifies a permitted value range of a feature category of the explanation dataset, wherein the permitted value range includes a maximum and/or minimum permitted deviation from a value in the input dataset. (Pg. 4 “We constrain the optimisation with a tolerance ε s.t | ˆ f(x′) − y′| ≤ ε. The value for ε depends on the problem space and is determined by the range and scale of y. Step 3 iterates over λ until the ε constraint is satisfied. A check is performed for a value greater than ε as increasing λ will place more weight on obtaining an ˆf(x′) closer to the given desired output y′. Once an acceptable value for λ is obtained for the given x and y′ a set of counterfactuals can be obtained by repeating steps 1 and 2 with the calculated λ. Note that we constrain the features manually, since the heuristic in Algorithm 1 and the adopted optimization algorithm are designed for unconstrained optimization”)
Regarding claim 6
McGrath in view of Sokol teaches the method of claim 2.
McGrath further teaches wherein the explanation dataset comprises a plurality of variations of the input dataset, each satisfying the at least one constraint. (pg. 4 “To generate counterfactuals we adopt the iterative approach described in Algorithm 1. We optimize L with the Nelder-Mead algorithm, as suggested in Molnar [2018]. We constrain the optimisation with a tolerance …Once an acceptable value for λ is obtained for the given x and y′ a set of counterfactuals can be obtained by repeating steps 1 and 2 with the calculated λ. Note that we constrain the features manually, since the heuristic in Algorithm 1 and the adopted optimization algorithm are designed for unconstrained optimization.”)
Regarding claim 7 (Currently Amended)
McGrath in view of Sokol teaches the method of claim 1.
McGrath further teaches the method further comprising receiving at least one provider dataset, wherein the provider dataset comprises at least one constraint for the at least one optimization task, and wherein the optimization method computes the at least one optimization task based on the at least one constraint of the provider dataset. (Pg. 4 “To generate counterfactuals we adopt the iterative approach described in Algorithm 1. We optimize L with the Nelder-Mead algorithm, as suggested in Molnar [2018]. We constrain the optimisation with a tolerance …Once an acceptable value for λ is obtained for the given x and y′ a set of counterfactuals can be obtained by repeating steps 1 and 2 with the calculated λ. Note that we constrain the features manually, since the heuristic in Algorithm 1 and the adopted optimization algorithm are designed for unconstrained optimization.”)
Regarding claim 8 (Previously Presented)
McGrath in view of Sokol teaches the method of claim 7.
McGrath further teaches wherein the at least one constraint of the provider dataset specifies an output count, and wherein the output count specifies how many variations of the input dataset are computed and comprised by the explanation dataset. (Pg. 4 “The value for ε depends on the problem space and is determined by the range and scale of y. Step 3 iterates over λ until the ε constraint is satisfied. A check is performed for a value greater than ε as increasing λ will place more weight on obtaining an ˆf(x′) closer to the given desired output y′. Once an acceptable value for λ is obtained for the given x and y′ a set of counterfactuals can be obtained by repeating steps 1 and 2 with the calculated λ. Note that we constrain the features manually, since the heuristic in Algorithm 1 and the adopted optimization algorithm are designed for unconstrained optimization.”)
Regarding claim 9 (Previously Presented)
McGrath in view of Sokol teaches the method of claim 1.
McGrath further teaches wherein the at least one optimization method comprises a gradient method and/or a Newton method. (Section 5.2 “Predictive Power As preliminary experiment, we assess the predictive power of four classifiers: logistic regression (LogReg), gradient boosting (GradBoost), support vector machine with linear kernel (SVC), and multi-layer perceptron (MLP). Logistic regression apart, the others fall within the black box category. We perform 3-fold, cross-validated grid search model selection over a number of hyperparameters. We adopt balanced class weights for logistic regression, exponential loss for gradient boosting, for each dataset.”)
Regarding claim 13 (Previously Presented)
McGrath in view of Sokol teaches the method of claim 1.
McGrath further teaches further comprising-computing an output dataset using the Al module, wherein the explanation dataset is used as an input dataset of the Al module. (Section 5 “We perform a a binary classification task on a credit application dataset. We train a range of black box models and we explain their predictions with counterfactuals, the goal being explaining the classifier decision to reject or accept a loan application”… section 5.1 “Implementation Details. Our machine learning pipeline is written in Python 3.6. This includes preprocessing, training, counterfactuals generation, and performance evaluation.”)
Regarding claim 16 (Previously Presented)
McGrath in view of Sokol teaches the method of claim 14.
McGrath further teaches the device further comprising: an Al unit adapted to compute an output dataset, wherein the explanation dataset is used as an input dataset of the Al module. (Pg. 4 “where x is the actual input vector, x′ is counterfactual vector, y′ is the desired output state, ˆ f(...) is the trained model, λ is the balance weight. λ balances the counterfactual between obtaining the exact desired output and making the smallest possible changes to the input vector x.”)
Claim(s) 3 and 11 is/are rejected under 35 U.S.C. 103 as being unpatentable over McGrath et al. (“Interpretable Credit Application Predictions With Counterfactual Explanations”) in view of Sokol et al. (“Glass-Box: Explaining AI Decisions With Counterfactual Statements Through Conversation With a Voice-enabled Virtual Assistant”) and further in view of Wachter et al. “COUNTERFACTUAL EXPLANATIONS WITHOUT OPENING THE BLACK BOX: AUTOMATED DECISIONS AND THE GDPR”).
Regarding claim 3
McGrath in view of Sokol teaches the method of claim 2.
McGrath in view of Sokol does not teach wherein the at least one constraint comprises an allowance specification, wherein the allowance specification specifies in which feature categories defined by the input dataset the explanation dataset differs from the input dataset.
Wachter teaches wherein the at least one constraint comprises an allowance specification, wherein the allowance specification specifies in which feature categories defined by the input dataset the explanation dataset differs from the input dataset. (Pg. 30 “The is further supported as the guidelines state that “meaningful information about the logic involved“ means that “Instead of providing a complex mathematical explanation about how algorithms or machine-learning work, the controller should consider using clear and comprehensive ways to deliver the information to the data subject, for example: the categories of data that have been or will be used in the profiling or decision-making process; why these categories are considered pertinent; how any profile used in the automated decision-making process is built, including any statistics used in the analysis; why this profile is relevant to the automated decision-making process; and how it is used for a decision concerning the data subject.”)
McGrath, Sokol and Wachter are analogous art because they are all directed to machine learning.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combined machine learning based prediction for counterfactual explanation of McGrath in view of Sokol with counterfactual explanation with automated decisions of Wachter.
One of ordinary skill in the art would have been motivated to make this modification in order to “provide insight into the internal logic of black box algorithms, counterfactual explanations do not attempt to clarify how decisions are made internally as disclosed by (Wachter pg. 43 “We have proposed a novel lightweight form of explanation that we refer to as counterfactual explanations. Unlike existing approaches that try to provide insight into the internal logic of black box algorithms, counterfactual explanations do not attempt to clarify how decisions are made internally. Instead, they provide insight into which external facts could be different in order to arrive at a desired outcome.203 Importantly, counterfactual explanations are efficiently computable for many standard classifiers, particularly neural networks. As our new form of explanation significantly differs from existing works, we have justified its nature as an explanation with reference to previous works in the philosophical literature and early A.I.”).
Regarding claim 11 (Previously Presented)
McGrath in view of Sokol teaches the method of claim 1.
McGrath in view of Sokol does not teach wherein the similarity metric is adapted as an Lpnorm including an LO, L1 and/or L2 metric.
Wachter teaches wherein the similarity metric is adapted as an Lpnorm including an LO, L1 and/or L2 metric. (Pg. 17 “The use of median absolute difference rather than the more usual standard deviation also makes this metric more robust to outliers. Of equal importance are the sparsity-inducing properties of the L1 norm. The L1 norm is widely recognised in mathematical and machine learning circles for its tendency to induce sparse solutions in which most entries are zero when paired with an appropriate cost function.63”)
McGrath, Sokol and Wachter are analogous art because they are all directed to machine learning.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combined machine learning based prediction for counterfactual explanation of McGrath in view of Sokol with counterfactual explanation with automated decisions of Wachter.
One of ordinary skill in the art would have been motivated to make this modification in order to “provide insight into the internal logic of black box algorithms, counterfactual explanations do not attempt to clarify how decisions are made internally as disclosed by (Wachter pg. 43 “We have proposed a novel lightweight form of explanation that we refer to as counterfactual explanations. Unlike existing approaches that try to provide insight into the internal logic of black box algorithms, counterfactual explanations do not attempt to clarify how decisions are made internally. Instead, they provide insight into which external facts could be different in order to arrive at a desired outcome.203 Importantly, counterfactual explanations are efficiently computable for many standard classifiers, particularly neural networks. As our new form of explanation significantly differs from existing works, we have justified its nature as an explanation with reference to previous works in the philosophical literature and early A.I.”).
Claim(s) 10 is/are rejected under 35 U.S.C. 103 as being unpatentable over McGrath et al. (“Interpretable Credit Application Predictions With Counterfactual Explanations”) in view of Sokol et al. (“Glass-Box: Explaining AI Decisions With Counterfactual Statements Through Conversation With a Voice-enabled Virtual Assistant”) and further in view of Garg et al. “Counterfactual Fairness in Text Classification through Robustness”).
Regarding claim 10 (Previously Presented)
McGrath in view of Sokol teaches the method of claim 1.
McGrath in view of Sokol does not teach wherein the specific metric is minimal if the target specification matches the data item of the output dataset of the Al module, and wherein the specific metric is formed as cross entropy and/or as mean square deviation.
Garg teaches wherein the specific metric is minimal if the target specification matches the data item of the output dataset of the Al module, and wherein the specific metric is formed as cross entropy and/or as mean square deviation. (Section 5 “All of the models are CNNs trained with cross entropy loss against the binary toxicity label. All hyperparameters except for the fairness regularizer λ for CLP were fixed for all runs of all models. Models were trained for five epochs, and the best model on the dev set was taken. Each model was trained ten times, and the average of the runs is reported”)
McGrath, Sokol and Garg are analogous art because they are all directed to machine learning.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combined machine learning based prediction for counterfactual explanation of McGrath in view of Sokol with counterfactual fairness text classification through robustness of Garg.
One of ordinary skill in the art would have been motivated to make this modification in order to “optimizing counterfactual token fairness during training, bridging the robustness and fair ness literature” as disclosed by (Sokol abstract “We offer a metric, counterfactual token fairness (CTF), for measuring this particular form of fairness in text classifiers, and describe its relationship with group fairness. Further, we of fer three approaches, blindness, counterfactual augmentation, and counterfactual logit pairing (CLP), for optimizing counterfactual token fairness during training, bridging the robustness and fair ness literature. Empirically, we find that blindness and CLP address counterfactual token fairness. The methods do not harm classifier performance, and have varying tradeoffs with group fairness. These approaches, both for measurement and optimization, provide a new path forward for addressing fairness concerns in text classification.”).
Claim(s) 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over McGrath et al. (“Interpretable Credit Application Predictions With Counterfactual Explanations”) in view of Sokol et al. (“Glass-Box: Explaining AI Decisions With Counterfactual Statements Through Conversation With a Voice-enabled Virtual Assistant”) and further in view of Garg et al. “Counterfactual Fairness in Text Classification through Robustness”).
Regarding claim 17 (Previously Presented)
McGrath in view of Sokol teaches the method of claim 15.
McGrath further teaches a system, comprising: at least one server unit, comprising the device of claim 15 and a server communication interface; (section 5.1 All experiments were run under Ubuntu 16.04 on an Intel Xeon E5-2620 v4 2.10 GHz workstation with 32 GB of system memory.”)
…
wherein the … communication interface is adapted to provide an application programmable interface adapted to receive a user dataset and transmit an explanation dataset. (section 4 “The demo system first receives a data point to be classified by scanning a QR code or by asking questions to collect the necessary features. After that, it classifies the data point using the underlying Machine Learning model and outputs its decision. Then, the user can challenge the decision and request:”)
McGrath in view of Sokol does not teach and at least one client unit having a client communication interface, adapted to send a request to the server communication interface, via a communication network.
Po teaches and at least one client unit having a client communication interface, adapted to send a request to the server communication interface, via a communication network. (Para [0086] “FIG. 1 illustrates an example system environment in which various components of a machine learning service (MLS) may be implemented, according to at least some embodiments. In system 100, the MLS may implement a set of programmatic interfaces 161 (e.g., APIs, command-line tools, web pages, or standalone GUIs) that can be used by clients 164 (e.g., hardware or software entities owned by or assigned to customers of the MLS) to submit requests 111 for a variety of machine learning tasks or operations. The administrative or control plane portion of the MLS may include MLS request handler 180, which accepts the client requests 111 and inserts corresponding job objects into MLS job queue 142, as indicated by arrow 112. In general, the control plane of the MLS may comprise a plurality of components (including the request handler, workload distribution strategy selectors, one or more job schedulers, metrics collectors, and modules that act as interfaces with other services)”)
McGrath, Sokol and Po are analogous art because they are all directed to machine learning.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combined machine learning based prediction for counterfactual explanation of McGrath in view of Sokol with counterfactual fairness text classification through robustness of Po.
One of ordinary skill in the art would have been motivated to make this modification in order to “optimizing counterfactual token fairness during training, bridging the robustness and fair ness literature” as disclosed by (Po abstract “first data set corresponding to an evaluation run of a model is generated at a machine learning service for display via an interactive interface. The data set includes a prediction qual ity metric. A target value of an interpretation threshold asso ciated with the model is determined based on a detection of a particular clients interaction with the interface.”).
Allowable Subject Matter
Claim 12 is objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Conclusion
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/VAN C MANG/Primary Examiner, Art Unit 2126
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