DETAILED ACTION
This action is responsive to the Application/amendment filed on 11/27/2023. Claims 1-20 are pending in the case. Claims 1, 13, and 18 are independent claims.
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 .
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.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 06/30/2025 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Rejections - 35 USC § 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 6, 16 and 20 recites the limitation output of the machine-learned mode. There is insufficient antecedent basis for this limitation in the claim. Examiner will interpret the limitations as output of the machine-learned model.
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.
Claim 1-20 rejected under 35 U.S.C. 101 because the claims are directed towards judicial exception without significantly more.
Regarding Claim 1:
Subject Matter Eligibility Analysis Step 2A Prong 1:
The claim recites predict values based on the plurality of datasets which, under the broadest reasonable interpretation, covers performance of the limitation in the mind. The limitations encompass using judgement to evaluate or choose values based on a set of data. See 2106.04.(a)(2).III.C.
The claim recites executing…a second plurality of inputs to produce a second output which, under the broadest reasonable interpretation, covers performance of the limitation in the mind. The limitations encompass using judgement to evaluate or choose a value based on another value. See 2106.04.(a)(2).III.C.
The claim recites a set of operations evaluated with the first plurality of inputs and producing the plurality of outputs which is an abstract idea (Mathematical Calculations (see MPEP 2106.04(a)(2)(I)(C))).
The claim recites producing values for the first plurality of inputs based on the first plurality of inputs and the plurality of outputs which, under the broadest reasonable interpretation, covers performance of the limitation in the mind. The limitations encompass using judgement to evaluate or choose values based on two sets of values. See 2106.04.(a)(2).III.C.
The claim recites produces values for the second plurality of inputs based on the second plurality of inputs and the second output which, under the broadest reasonable interpretation, covers performance of the limitation in the mind. The limitations encompass using judgement to evaluate or choose values based on two sets of values. See 2106.04.(a)(2).III.C.
Subject Matter Eligibility Analysis Step 2A Prong 2:
Accessing…a plurality of datasets recites insignificant extra-solution activity of data gathering (see MPEP 2106.05(g)) recites insignificant extra-solution activity of storing and retrieving information in memory(see MPEP 2106.05(g))
by a computing device recites a generic computer on which to perform the abstract idea, e.g. "apply it on a computer" (see MPEP 2106.05(f))
each dataset comprising an input of a first plurality of inputs to a computer program, a first output of a plurality of outputs of the computer program, and a value identifying an importance of the input to the first output and produced by an explanation service specifies a data gathering step that is limited to a particular data source or a particular type of data, i.e. a field of use (see MPEP 2106.05(h)
explanation service recites a generic computer on which to perform the abstract idea, e.g. "apply it on a computer" (see MPEP 2106.05(f)))
the computer program recites a generic computer on which to perform the abstract idea, e.g. "apply it on a computer" (see MPEP 2106.05(f))
training… machine-learned model recites a generic computer on which to perform the abstract idea, e.g. "apply it on a computer" (see MPEP 2106.05(f))
inserting…the second plurality of inputs and the second output into the machine-learned model recites insignificant extra-solution activity of data gathering (see MPEP 2106.05(g))
the machine-learned model recites a generic computer on which to perform the abstract idea, e.g. "apply it on a computer" (see MPEP 2106.05(f)))
during execution of the computer program specifies a particular technological environment in which the abstract idea is to take place, i.e. a field of use (see MPEP 2106.05(h))
Subject Matter Eligibility Analysis Step 2B:
Additional element (a) is well understood, routine, and conventional activity of “retrieving information in memory " (see MPEP 2106.05(d)(II)(iv), Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015))
Additional elements (b) (d) (e) (f) and (h) do not integrate the abstract idea into a practical application nor do the additional limitation provide significantly more than the abstract idea because the limitation amount to no more than mere instructions to apply the exception using a generic computer component. Please see MPEP §2106.05(f).
Additional elements (c) do not integrate the abstract idea into a practical application nor do the additional limitation provide significantly more than the abstract idea because the limitation merely specifies a data gathering step that is limited to a particular data source or a particular type of data, i.e. a field of use (see MPEP 2106.05(h)).
Additional element (g) obtaining a network input is well understood, routine, and conventional activity of “transmitting or receiving data over a network" (see MPEP 2106.05(d)(II)(i) using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 )
Additional elements (i) do not integrate the abstract idea into a practical application nor do the additional limitation provide significantly more than the abstract idea because the limitation merely specifies a field of use in which the abstract idea is to take place, i.e. a field of use (see MPEP 2106.05(h)).
The additional element(s) (a) (b) (c) (d) (e) (f) (g) (h) and (i) in the claim do/does not include any additional elements , when considered separately and in combination, that amount to an integration of the judicial exception into a practical application, nor significantly more than the judicial exception for the reasons set forth in step 2A prong 2 analysis above. The claim is not patent eligible.
Regarding Claim 2:
The rejection of claim 1 is incorporated and further claim recites further additional
elements/limitations:
Subject Matter Eligibility Analysis Step 2A Prong 1:
The claim recites determining, based on an execution of the explanation service with the first plurality of inputs and the plurality of outputs, a value for each input of the first plurality of inputs which, under the broadest reasonable interpretation, covers performance of the limitation in the mind. The limitations encompass using judgement to perform an evaluation and choosing a value for each item in a set. See 2106.04.(a)(2).III.C.
Subject Matter Eligibility Analysis Step 2A Prong 2:
storing, in a storage device, the first plurality of inputs, the plurality of outputs, and each value for each input of the first plurality of inputs recites insignificant extra-solution activity of storing and retrieving information in memory(see MPEP 2106.05(g))
prior to accessing the plurality of datasets, executing the computer program with the first plurality of inputs to produce the plurality of outputs recites a generic computer on which to perform the abstract idea, e.g. "apply it on a computer" (see MPEP 2106.05(f))
Subject Matter Eligibility Analysis Step 2B:
Additional element (a) is well understood, routine, and conventional activity of “storing information in memory " (see MPEP 2106.05(d)(II)(iv), Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015))
Additional elements (b) do not integrate the abstract idea into a practical application nor do the additional limitation provide significantly more than the abstract idea because the limitation amount to no more than mere instructions to apply the exception using a generic computer component. Please see MPEP §2106.05(f).
The additional element(s) (a) and (b) in the claim do/does not include any additional elements , when considered separately and in combination, that amount to an integration of the judicial exception into a practical application, nor significantly more than the judicial exception for the reasons set forth in step 2A prong 2 analysis above. The claim is not patent eligible.
Regarding Claim 3:
The rejection of claim 2 is incorporated and further claim recites further additional
elements/limitations:
Subject Matter Eligibility Analysis Step 2A Prong 1:
The claim does not contain elements that would warrant a Step 2A Prong 1 analysis.
Subject Matter Eligibility Analysis Step 2A Prong 2:
wherein accessing the plurality of datasets comprises obtaining…from the storage device recites insignificant extra-solution activity of storing and retrieving information in memory(see MPEP 2106.05(g))
the first plurality of inputs, the plurality of outputs, and each value for each input of the first plurality of inputs …wherein each dataset of the plurality of datasets comprises an input of the first plurality of inputs, an output of the plurality of outputs, and a value identifying an importance of the input to the output specifies a data gathering step that is limited to a particular data source or a particular type of data, i.e. a field of use (see MPEP 2106.05(h))
Subject Matter Eligibility Analysis Step 2B:
Additional element (a) is well understood, routine, and conventional activity of “retrieving information in memory " (see MPEP 2106.05(d)(II)(iv), Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015))
Additional elements (b) do not integrate the abstract idea into a practical application nor do the additional limitation provide significantly more than the abstract idea because the limitation merely specifies a data gathering step that is limited to a particular data source or a particular type of data, i.e. a field of use (see MPEP 2106.05(h)).
The additional element(s) (a) and (b) in the claim do/does not include any additional elements , when considered separately and in combination, that amount to an integration of the judicial exception into a practical application, nor significantly more than the judicial exception for the reasons set forth in step 2A prong 2 analysis above. The claim is not patent eligible.
Regarding Claim 4:
The rejection of claim 1 is incorporated and further claim recites further additional
elements/limitations:
Subject Matter Eligibility Analysis Step 2A Prong 1:
The claim recites prior to training the machine-learned model to predict the values based on the plurality of datasets, determining that the plurality of datasets comprises a predetermined amount of datasets which, under the broadest reasonable interpretation, covers performance of the limitation in the mind. The limitations encompass choosing a number of data to use in a dataset. See 2106.04.(a)(2).III.C.
Subject Matter Eligibility Analysis Step 2A Prong 2:
The claim does not contain elements that would warrant a Step 2A Prong 2 analysis.
Subject Matter Eligibility Analysis Step 2B:
The claim does not include any additional element, when considered separately and in combination, that amount to an integration of the judicial exception into a practical application, nor to significantly more than the judicial exception. The claim is not patent eligible.
Regarding Claim 5:
The rejection of claim 1 is incorporated and further claim recites further additional
elements/limitations:
Subject Matter Eligibility Analysis Step 2A Prong 1:
The claim recites generating a combined input comprising the first plurality of inputs and the plurality of outputs, wherein the first plurality of inputs comprises a first plurality of vectors and the plurality of outputs comprises a second plurality of vectors which, under the broadest reasonable interpretation, covers performance of the limitation in the mind. The limitations encompass choosing values from two vectors and forming another, third, vector. See 2106.04.(a)(2).III.C. Alternatively, the claim recites an abstract idea (Mathematical Calculations (see MPEP 2106.04(a)(2)(I)(C))).
The claim recites predicting the values as an output…wherein the output…comprises a third plurality of vectors which, under the broadest reasonable interpretation, covers performance of the limitation in the mind. The limitations encompass using judgement and estimating values into an ordered set. See 2106.04.(a)(2).III.C.
Subject Matter Eligibility Analysis Step 2A Prong 2:
inserting the combined input into the machine-learned model recites insignificant extra-solution activity of data gathering (see MPEP 2106.05(g))
of the machine-learned model recites a generic computer on which to perform the abstract idea, e.g. "apply it on a computer" (see MPEP 2106.05(f))
Subject Matter Eligibility Analysis Step 2B:
Additional element (a) obtaining a network input is well understood, routine, and conventional activity of “transmitting or receiving data over a network" (see MPEP 2106.05(d)(II)(i) using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 ).
Additional elements (b) do not integrate the abstract idea into a practical application nor do the additional limitation provide significantly more than the abstract idea because the limitation amount to no more than mere instructions to apply the exception using a generic computer component. Please see MPEP §2106.05(f).
The additional element(s) (a) and (b) in the claim do/does not include any additional elements , when considered separately and in combination, that amount to an integration of the judicial exception into a practical application, nor significantly more than the judicial exception for the reasons set forth in step 2A prong 2 analysis above. The claim is not patent eligible.
Regarding Claim 6:
The rejection of claim 1 is incorporated and further claim recites further additional
elements/limitations:
Subject Matter Eligibility Analysis Step 2A Prong 1:
The claim recites generating a first subset of datasets and a second subset of datasets from the plurality of datasets which, under the broadest reasonable interpretation, covers performance of the limitation in the mind. The limitations encompass using judgement to select a subset of values from a set. See 2106.04.(a)(2).III.C.
The claim recites predicting values based on the second subset of the plurality of datasets as an output which, under the broadest reasonable interpretation, covers performance of the limitation in the mind. The limitations encompass using judgement and estimating values. See 2106.04.(a)(2).III.C.
The claim recites performing a comparison of the values based on the second subset of the plurality of datasets and the values produced by the explanation service which, under the broadest reasonable interpretation, covers performance of the limitation in the mind. The limitations encompass using judgement to perform an evaluation and form an opinion on two sets of data. See 2106.04.(a)(2).III.C.
The claim recites determining, based on the comparison, that the machine-learned model is trained to predict the values based on the plurality of datasets which, under the broadest reasonable interpretation, covers performance of the limitation in the mind. The limitations encompass using judgement to perform an evaluation based on a comparison and forming an decision on whether the model can predict certain values. See 2106.04.(a)(2).III.C.
Subject Matter Eligibility Analysis Step 2A Prong 2:
training the machine-learned model on the first subset of datasets recites a generic computer on which to perform the abstract idea, e.g. "apply it on a computer" (see MPEP 2106.05(f))
of the machine-learned mode recites a generic computer on which to perform the abstract idea, e.g. "apply it on a computer" (see MPEP 2106.05(f))
inserting the second subset of datasets into the machine-learned model recites insignificant extra-solution activity of data gathering (see MPEP 2106.05(g))
Subject Matter Eligibility Analysis Step 2B:
Additional elements (a) and (b) do not integrate the abstract idea into a practical application nor do the additional limitation provide significantly more than the abstract idea because the limitation amount to no more than mere instructions to apply the exception using a generic computer component. Please see MPEP §2106.05(f).
Additional element (c) obtaining a network input is well understood, routine, and conventional activity of “transmitting or receiving data over a network" (see MPEP 2106.05(d)(II)(i) using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 ).
The additional element(s) (a) (b) and (c) in the claim do/does not include any additional elements , when considered separately and in combination, that amount to an integration of the judicial exception into a practical application, nor significantly more than the judicial exception for the reasons set forth in step 2A prong 2 analysis above. The claim is not patent eligible.
Regarding Claim 7:
The rejection of claim 1 is incorporated and further claim recites further additional
elements/limitations:
Subject Matter Eligibility Analysis Step 2A Prong 1:
The claim does not contain elements that would warrant a Step 2A Prong 1 analysis.
Subject Matter Eligibility Analysis Step 2A Prong 1:
storing, in a storage device, the second plurality of inputs, the second output, and each value for each input of the second plurality of inputs recites insignificant extra-solution activity of storing and storing information in memory(see MPEP 2106.05(g))
Subject Matter Eligibility Analysis Step 2B:
Additional element (a) is well understood, routine, and conventional activity of “storing information in memory " (see MPEP 2106.05(d)(II)(iv), Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015))
The additional element(s) (a) in the claim do/does not include any additional elements , when considered separately and in combination, that amount to an integration of the judicial exception into a practical application, nor significantly more than the judicial exception for the reasons set forth in step 2A prong 2 analysis above. The claim is not patent eligible.
Regarding Claim 8:
The rejection of claim 7 is incorporated and further claim recites further additional
elements/limitations:
Subject Matter Eligibility Analysis Step 2A Prong 1:
The claim recites generating a report based on the second plurality of inputs, the second output, and each value for each input of the second plurality of inputs which, under the broadest reasonable interpretation, covers performance of the limitation in the mind with the use of physical aid. The limitations encompass using judgement decide the relevance of elements and forming an opinion of conclusions. See 2106.04.(a)(2).III.C.
Subject Matter Eligibility Analysis Step 2A Prong 2:
obtaining the second plurality of inputs, the second output, and each value for each input of the second plurality of inputs from the storage device recites insignificant extra-solution activity of storing and retrieving information in memory(see MPEP 2106.05(g))
Subject Matter Eligibility Analysis Step 2B:
Additional element (a) is well understood, routine, and conventional activity of “retrieving information in memory " (see MPEP 2106.05(d)(II)(iv), Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015))
The additional element(s) (a) in the claim do/does not include any additional elements , when considered separately and in combination, that amount to an integration of the judicial exception into a practical application, nor significantly more than the judicial exception for the reasons set forth in step 2A prong 2 analysis above. The claim is not patent eligible.
Regarding Claim 9:
The rejection of claim 1 is incorporated and further claim recites further additional
elements/limitations:
Subject Matter Eligibility Analysis Step 2A Prong 1:
The claim recites produce a subset of values for the subset of the second plurality of inputs based on the subset of the second plurality of inputs and the second output which, under the broadest reasonable interpretation, covers performance of the limitation in the mind. The limitations encompass using judgement to select a subset of values from a set. See 2106.04.(a)(2).III.C.
Subject Matter Eligibility Analysis Step 2A Prong 2:
inserting the second output and a subset of the second plurality of inputs into the explanation service recites insignificant extra-solution activity of data gathering (see MPEP 2106.05(g))
the explanation service recites a generic computer on which to perform the abstract idea, e.g. "apply it on a computer" (see MPEP 2106.05(f))
Subject Matter Eligibility Analysis Step 2B:
Additional element (a) obtaining a network input is well understood, routine, and conventional activity of “transmitting or receiving data over a network" (see MPEP 2106.05(d)(II)(i) using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 ).
Additional elements (b) do not integrate the abstract idea into a practical application nor do the additional limitation provide significantly more than the abstract idea because the limitation amount to no more than mere instructions to apply the exception using a generic computer component. Please see MPEP §2106.05(f).
The additional element(s) (a) and (b) in the claim do/does not include any additional elements , when considered separately and in combination, that amount to an integration of the judicial exception into a practical application, nor significantly more than the judicial exception for the reasons set forth in step 2A prong 2 analysis above. The claim is not patent eligible.
Regarding Claim 10:
The rejection of claim 9 is incorporated and further claim recites further additional
elements/limitations:
Subject Matter Eligibility Analysis Step 2A Prong 1:
The claim recites performing a comparison of the values for the second plurality of inputs to the subset of values for the subset of the second plurality of inputs which, under the broadest reasonable interpretation, covers performance of the limitation in the mind. The limitations encompass using judgement to perform an evaluation and form an opinion on two sets of data. See 2106.04.(a)(2).III.C.
The claim recites determining, based on the comparison, that the machine-learned model is accurately predicting the values for the second plurality of inputs based on the second plurality of inputs and the second output which, under the broadest reasonable interpretation, covers performance of the limitation in the mind. The limitations encompass using judgement to perform an evaluation based on a comparison and forming an opinion on the accuracy. See 2106.04.(a)(2).III.C.
Subject Matter Eligibility Analysis Step 2A Prong 2:
The claim does not contain elements that would warrant a Step 2A Prong 2 analysis.
Subject Matter Eligibility Analysis Step 2B:
The claim does not include any additional element, when considered separately and in combination, that amount to an integration of the judicial exception into a practical application, nor to significantly more than the judicial exception. The claim is not patent eligible.
Regarding Claim 11:
The rejection of claim 1 is incorporated and further claim recites further additional
elements/limitations:
Subject Matter Eligibility Analysis Step 2A Prong 1:
The claim does not contain elements that would warrant a Step 2A Prong 1 analysis. Subject Matter Eligibility Analysis Step 2A Prong 2:
computer program comprises a machine-learned model as linking the use of a judicial exception to a particular technological environment or field of use(see MPEP 2106.05(h))
Subject Matter Eligibility Analysis Step 2B:
Additional elements (a) do not integrate the abstract idea into a practical application nor do the additional limitation provide significantly more than the abstract idea because the limitation merely links the use of a judicial exception to a particular technological environment or field of use(see MPEP 2106.05(h)).
The additional element(s) (a) in the claim do/does not include any additional elements , when considered separately and in combination, that amount to an integration of the judicial exception into a practical application, nor significantly more than the judicial exception for the reasons set forth in step 2A prong 2 analysis above. The claim is not patent eligible.
Regarding Claim 12:
The rejection of claim 1 is incorporated and further claim recites further additional
elements/limitations:
Subject Matter Eligibility Analysis Step 2A Prong 1:
The claim recites that identify an importance of an input to the computer program to an output of the computer program which, under the broadest reasonable interpretation, covers performance of the limitation in the mind. The limitations encompass performing an evaluation and choosing the importance of inputs. See 2106.04.(a)(2).III.C.
Subject Matter Eligibility Analysis Step 2A Prong 2:
wherein the explanation service comprises a saliency algorithm and the values comprise saliency values recites linking the use of a judicial exception to a particular technological environment or field of use(see MPEP 2106.05(h))
Subject Matter Eligibility Analysis Step 2B:
Additional elements (a) do not integrate the abstract idea into a practical application nor do the additional limitation provide significantly more than the abstract idea because the limitation merely links the use of a judicial exception to a particular technological environment or field of use(see MPEP 2106.05(h)).
The additional element(s) (a) in the claim do/does not include any additional elements , when considered separately and in combination, that amount to an integration of the judicial exception into a practical application, nor significantly more than the judicial exception for the reasons set forth in step 2A prong 2 analysis above. The claim is not patent eligible.
Regarding Claim 13:
Subject Matter Eligibility Analysis Step 2A Prong 1:
The claim recites a set of operations evaluated with the first plurality of inputs and producing the plurality of outputs which is an abstract idea (Mathematical Calculations (see MPEP 2106.04(a)(2)(I)(C))).
The claim recites producing values for the first plurality of inputs based on the first plurality of inputs and the plurality of outputs which, under the broadest reasonable interpretation, covers performance of the limitation in the mind. The limitations encompass using judgement to evaluate or choose values based on two sets of values. See 2106.04.(a)(2).III.C.
The claim recites predict values based on the plurality of datasets which, under the broadest reasonable interpretation, covers performance of the limitation in the mind. The limitations encompass using judgement to evaluate or choose values based on a set of data. See 2106.04.(a)(2).III.C.
The claim recites execute…with a second plurality of inputs to produce a second output which, under the broadest reasonable interpretation, covers performance of the limitation in the mind. The limitations encompass using judgement to evaluate or choose a value based on another value. See 2106.04.(a)(2).III.C.
The claim recites produces values for the second plurality of inputs based on the second plurality of inputs and the second output which, under the broadest reasonable interpretation, covers performance of the limitation in the mind. The limitations encompass using judgement to evaluate or choose values based on two sets of values. See 2106.04.(a)(2).III.C.
Subject Matter Eligibility Analysis Step 2A Prong 2:
access a plurality of datasets recites insignificant extra-solution activity of data gathering (see MPEP 2106.05(g)) recites insignificant extra-solution activity of storing and retrieving information in memory(see MPEP 2106.05(g)
a memory and a processor device coupled to the memory recites a generic computer on which to perform the abstract idea, e.g. "apply it on a computer" (see MPEP 2106.05(f))
each dataset comprising an input of a first plurality of inputs to a computer program, a first output of a plurality of outputs of the computer program, and a value identifying an importance of the input to the first output and produced by an explanation service specifies a data gathering step that is limited to a particular data source or a particular type of data, i.e. a field of use (see MPEP 2106.05(h)
the computer program recites a generic computer on which to perform the abstract idea, e.g. "apply it on a computer" (see MPEP 2106.05(f))
the explanation service recites a generic computer on which to perform the abstract idea, e.g. "apply it on a computer" (see MPEP 2106.05(f))
train a machine-learned model recites a generic computer on which to perform the abstract idea, e.g. "apply it on a computer" (see MPEP 2106.05(f))
insert the second plurality of inputs and the second output into the machine-learned model recites insignificant extra-solution activity of data gathering (see MPEP 2106.05(g))
the machine-learned model recites a generic computer on which to perform the abstract idea, e.g. "apply it on a computer" (see MPEP 2106.05(f))
during execution of the computer program specifies a particular technological environment in which the abstract idea is to take place, i.e. a field of use (see MPEP 2106.05(h))
Subject Matter Eligibility Analysis Step 2B:
Additional element (a) is well understood, routine, and conventional activity of “retrieving information in memory " (see MPEP 2106.05(d)(II)(iv), Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015))
Additional elements (b) (d) (e) (f) and (h) do not integrate the abstract idea into a practical application nor do the additional limitation provide significantly more than the abstract idea because the limitation amount to no more than mere instructions to apply the exception using a generic computer component. Please see MPEP §2106.05(f).
Additional elements (c) do not integrate the abstract idea into a practical application nor do the additional limitation provide significantly more than the abstract idea because the limitation merely specifies a data gathering step that is limited to a particular data source or a particular type of data, i.e. a field of use (see MPEP 2106.05(h)).
Additional element (g) obtaining a network input is well understood, routine, and conventional activity of “transmitting or receiving data over a network" (see MPEP 2106.05(d)(II)(i) using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 )
Additional elements (i) do not integrate the abstract idea into a practical application nor do the additional limitation provide significantly more than the abstract idea because the limitation merely specifies a field of use in which the abstract idea is to take place, i.e. a field of use (see MPEP 2106.05(h)).
The additional element(s) (a) (b) (c) (d) (e) (f) (g) (h) and (i) in the claim do/does not include any additional elements , when considered separately and in combination, that amount to an integration of the judicial exception into a practical application, nor significantly more than the judicial exception for the reasons set forth in step 2A prong 2 analysis above. The claim is not patent eligible.
Regarding Claim 14:
The rejection of claim 13 is incorporated and further claim recites further additional
elements/limitations:
Subject Matter Eligibility Analysis Step 2A Prong 1:
The claim recites determine, based on an execution of the explanation service with the first plurality of inputs and the plurality of outputs, a value for each input of the first plurality of inputs which, under the broadest reasonable interpretation, covers performance of the limitation in the mind. The limitations encompass using judgement to perform an evaluation and choosing a value for each item in a set. See 2106.04.(a)(2).III.C.
Subject Matter Eligibility Analysis Step 2A Prong 2:
store, in a storage device, the first plurality of inputs, the plurality of outputs, and each value for each input of the first plurality of inputs recites insignificant extra-solution activity of storing and retrieving information in memory(see MPEP 2106.05(g))
prior to accessing the plurality of datasets, execute the computer program with the first plurality of inputs to produce the plurality of outputs recites a generic computer on which to perform the abstract idea, e.g. "apply it on a computer" (see MPEP 2106.05(f))
wherein to access the plurality of datasets, the processor device is further to obtain… from the storage device recites insignificant extra-solution activity of storing and retrieving information in memory(see MPEP 2106.05(g))
the first plurality of inputs, the plurality of outputs, and each value for each input of the first plurality of inputs…wherein each dataset of the plurality of datasets comprises an input of the first plurality of inputs, an output of the plurality of outputs, and a value identifying an importance of the input to the output specifies a data gathering step that is limited to a particular data source or a particular type of data, i.e. a field of use (see MPEP 2106.05(h))
Subject Matter Eligibility Analysis Step 2B:
Additional element (a) (c) is well understood, routine, and conventional activity of “storing and retrieving information in memory" (see MPEP 2106.05(d)(II)(iv), Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015))
Additional elements (b) do not integrate the abstract idea into a practical application nor do the additional limitation provide significantly more than the abstract idea because the limitation amount to no more than mere instructions to apply the exception using a generic computer component. Please see MPEP §2106.05(f).
Additional elements (c) do not integrate the abstract idea into a practical application nor do the additional limitation provide significantly more than the abstract idea because the limitation merely specifies a data gathering step that is limited to a particular data source or a particular type of data, i.e. a field of use (see MPEP 2106.05(h)).
The additional element(s) (a) (b) and (c) in the claim do/does not include any additional elements , when considered separately and in combination, that amount to an integration of the judicial exception into a practical application, nor significantly more than the judicial exception for the reasons set forth in step 2A prong 2 analysis above. The claim is not patent eligible
Regarding Claim 15:
The rejection of claim 13 is incorporated and further claim recites further additional
elements/limitations:
Subject Matter Eligibility Analysis Step 2A Prong 1:
The claim recites generate a combined input comprising the first plurality of inputs and the plurality of outputs, wherein the first plurality of inputs comprises a first plurality of vectors and the plurality of outputs comprises a second plurality of vectors which, under the broadest reasonable interpretation, covers performance of the limitation in the mind. The limitations encompass choosing values from two vectors and forming another, third, vector. See 2106.04.(a)(2).III.C. Alternatively, the claim recites an abstract idea (Mathematical Calculations (see MPEP 2106.04(a)(2)(I)(C))).
The claim recites predict the values as an output…wherein the output…comprises a third plurality of vectors which, under the broadest reasonable interpretation, covers performance of the limitation in the mind. The limitations encompass using judgement and estimating values into an ordered set. See 2106.04.(a)(2).III.C.
Subject Matter Eligibility Analysis Step 2A Prong 2:
insert the combined input into the machine-learned model recites insignificant extra-solution activity of data gathering (see MPEP 2106.05(g))
of the machine-learned model recites a generic computer on which to perform the abstract idea, e.g. "apply it on a computer" (see MPEP 2106.05(f))
Subject Matter Eligibility Analysis Step 2B:
Additional element (a) obtaining a network input is well understood, routine, and conventional activity of “transmitting or receiving data over a network" (see MPEP 2106.05(d)(II)(i) using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 ).
Additional elements (b) do not integrate the abstract idea into a practical application nor do the additional limitation provide significantly more than the abstract idea because the limitation amount to no more than mere instructions to apply the exception using a generic computer component. Please see MPEP §2106.05(f).
The additional element(s) (a) and (b) in the claim do/does not include any additional elements , when considered separately and in combination, that amount to an integration of the judicial exception into a practical application, nor significantly more than the judicial exception for the reasons set forth in step 2A prong 2 analysis above. The claim is not patent eligible.
Regarding Claim 16:
The rejection of claim 13 is incorporated and further claim recites further additional
elements/limitations:
Subject Matter Eligibility Analysis Step 2A Prong 1:
The claim recites generate a first subset of datasets and a second subset of datasets from the plurality of datasets which, under the broadest reasonable interpretation, covers performance of the limitation in the mind. The limitations encompass using judgement to select a subset of values from a set. See 2106.04.(a)(2).III.C.
The claim recites predict values based on the second subset of the plurality of datasets as an output which, under the broadest reasonable interpretation, covers performance of the limitation in the mind. The limitations encompass using judgement and estimating values. See 2106.04.(a)(2).III.C.
The claim recites perform a comparison of the values based on the second subset of the plurality of datasets and the values produced by the explanation service which, under the broadest reasonable interpretation, covers performance of the limitation in the mind. The limitations encompass using judgement to perform an evaluation and form an opinion on two sets of data. See 2106.04.(a)(2).III.C.
The claim recites determine, based on the comparison, that the machine-learned model is trained to predict the values based on the plurality of datasets which, under the broadest reasonable interpretation, covers performance of the limitation in the mind. The limitations encompass using judgement to perform an evaluation based on a comparison and forming an decision on whether the model can predict certain values. See 2106.04.(a)(2).III.C.
Subject Matter Eligibility Analysis Step 2A Prong 2:
train the machine-learned model on the first subset of datasets recites a generic computer on which to perform the abstract idea, e.g. "apply it on a computer" (see MPEP 2106.05(f))
of the machine-learned mode recites a generic computer on which to perform the abstract idea, e.g. "apply it on a computer" (see MPEP 2106.05(f))
insert the second subset of datasets into the machine-learned model recites insignificant extra-solution activity of data gathering (see MPEP 2106.05(g))
Subject Matter Eligibility Analysis Step 2B:
Additional elements (a) and (b) do not integrate the abstract idea into a practical application nor do the additional limitation provide significantly more than the abstract idea because the limitation amount to no more than mere instructions to apply the exception using a generic computer component. Please see MPEP §2106.05(f).
Additional element (c) obtaining a network input is well understood, routine, and conventional activity of “transmitting or receiving data over a network" (see MPEP 2106.05(d)(II)(i) using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 ).
The additional element(s) (a) (b) and (c) in the claim do/does not include any additional elements , when considered separately and in combination, that amount to an integration of the judicial exception into a practical application, nor significantly more than the judicial exception for the reasons set forth in step 2A prong 2 analysis above. The claim is not patent eligible.
Regarding Claim 17:
The rejection of claim 13 is incorporated and further claim recites further additional
elements/limitations:
Subject Matter Eligibility Analysis Step 2A Prong 1:
The claim recites produce a subset of values for the subset of the second plurality of inputs based on the subset of the second plurality of inputs and the second output which, under the broadest reasonable interpretation, covers performance of the limitation in the mind. The limitations encompass using judgement to select a subset of values from a set. See 2106.04.(a)(2).III.C.
The claim recites perform a comparison of the values for the second plurality of inputs to the subset of values for the subset of the second plurality of inputs which, under the broadest reasonable interpretation, covers performance of the limitation in the mind. The limitations encompass using judgement to perform an evaluation and form an opinion on two sets of data. See 2106.04.(a)(2).III.C.
The claim recites determine, based on the comparison, that the machine-learned model is accurately predicting the values for the second plurality of inputs based on the second plurality of inputs and the second output which, under the broadest reasonable interpretation, covers performance of the limitation in the mind. The limitations encompass using judgement to perform an evaluation based on a comparison and forming an opinion on the accuracy. See 2106.04.(a)(2).III.C.
Subject Matter Eligibility Analysis Step 2A Prong 2:
insert the second output and a subset of the second plurality of inputs into the explanation service recites insignificant extra-solution activity of data gathering (see MPEP 2106.05(g))
the explanation service recites a generic computer on which to perform the abstract idea, e.g. "apply it on a computer" (see MPEP 2106.05(f))
Subject Matter Eligibility Analysis Step 2B:
Additional element (a) obtaining a network input is well understood, routine, and conventional activity of “transmitting or receiving data over a network" (see MPEP 2106.05(d)(II)(i) using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 ).
Additional elements (b) do not integrate the abstract idea into a practical application nor do the additional limitation provide significantly more than the abstract idea because the limitation amount to no more than mere instructions to apply the exception using a generic computer component. Please see MPEP §2106.05(f).
The additional element(s) (a) and (b) in the claim do/does not include any additional elements , when considered separately and in combination, that amount to an integration of the judicial exception into a practical application, nor significantly more than the judicial exception for the reasons set forth in step 2A prong 2 analysis above. The claim is not patent eligible.
Regarding Claim 18:
Subject Matter Eligibility Analysis Step 2A Prong 1:
The claim recites a set of operations evaluated with the first plurality of inputs and producing the plurality of outputs which is an abstract idea (Mathematical Calculations (see MPEP 2106.04(a)(2)(I)(C))).
The claim recites producing values for the first plurality of inputs based on the first plurality of inputs and the plurality of outputs which, under the broadest reasonable interpretation, covers performance of the limitation in the mind. The limitations encompass using judgement to evaluate or choose values based on two sets of values. See 2106.04.(a)(2).III.C.
The claim recites predict values based on the plurality of datasets which, under the broadest reasonable interpretation, covers performance of the limitation in the mind. The limitations encompass using judgement to evaluate or choose values based on a set of data. See 2106.04.(a)(2).III.C.
The claim recites execute…with a second plurality of inputs to produce a second output which, under the broadest reasonable interpretation, covers performance of the limitation in the mind. The limitations encompass using judgement to evaluate or choose a value based on another value. See 2106.04.(a)(2).III.C.
The claim recites produces values for the second plurality of inputs based on the second plurality of inputs and the second output which, under the broadest reasonable interpretation, covers performance of the limitation in the mind. The limitations encompass using judgement to evaluate or choose values based on two sets of values. See 2106.04.(a)(2).III.C.
Subject Matter Eligibility Analysis Step 2A Prong 2:
A non-transitory computer-readable storage medium that includes computer-executable instructions recites a generic computer on which to perform the abstract idea, e.g. "apply it on a computer" (see MPEP 2106.05(f))
access a plurality of datasets recites insignificant extra-solution activity of data gathering (see MPEP 2106.05(g)) recites insignificant extra-solution activity of storing and retrieving information in memory(see MPEP 2106.05(g))
each dataset comprising an input of a first plurality of inputs to a computer program, a first output of a plurality of outputs of the computer program, and a value identifying an importance of the input to the first output and produced by an explanation service specifies a data gathering step that is limited to a particular data source or a particular type of data, i.e. a field of use (see MPEP 2106.05(h)
the explanation service recites a generic computer on which to perform the abstract idea, e.g. "apply it on a computer" (see MPEP 2106.05(f))
the computer program recites a generic computer on which to perform the abstract idea, e.g. "apply it on a computer" (see MPEP 2106.05(f))
train a machine-learned model recites a generic computer on which to perform the abstract idea, e.g. "apply it on a computer" (see MPEP 2106.05(f))
insert the second plurality of inputs and the second output into the machine-learned model recites insignificant extra-solution activity of data gathering (see MPEP 2106.05(g))
during execution of the computer program recites a generic computer on which to perform the abstract idea, e.g. "apply it on a computer" (see MPEP 2106.05(f))
execute the computer program to specifies a particular technological environment in which the abstract idea is to take place, i.e. a field of use (see MPEP 2106.05(h))
Subject Matter Eligibility Analysis Step 2B:
Additional elements (a) (d) (e) (f) and (h) do not integrate the abstract idea into a practical application nor do the additional limitation provide significantly more than the abstract idea because the limitation amount to no more than mere instructions to apply the exception using a generic computer component. Please see MPEP §2106.05(f).
Additional element (b) is well understood, routine, and conventional activity of “retrieving information in memory " (see MPEP 2106.05(d)(II)(iv), Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015))
Additional elements (c) do not integrate the abstract idea into a practical application nor do the additional limitation provide significantly more than the abstract idea because the limitation merely specifies a data gathering step that is limited to a particular data source or a particular type of data, i.e. a field of use (see MPEP 2106.05(h)).
Additional element (g) obtaining a network input is well understood, routine, and conventional activity of “transmitting or receiving data over a network" (see MPEP 2106.05(d)(II)(i) using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 )
Additional elements (i) do not integrate the abstract idea into a practical application nor do the additional limitation provide significantly more than the abstract idea because the limitation merely specifies a field of use in which the abstract idea is to take place, i.e. a field of use (see MPEP 2106.05(h)).
The additional element(s) (a) (b) (c) (d) (e) (f) (g) (h) and (i) in the claim do/does not include any additional elements , when considered separately and in combination, that amount to an integration of the judicial exception into a practical application, nor significantly more than the judicial exception for the reasons set forth in step 2A prong 2 analysis above. The claim is not patent eligible.
Regarding Claim 19:
The rejection of claim 18 is incorporated and further claim recites further additional
elements/limitations:
Subject Matter Eligibility Analysis Step 2A Prong 1:
The claim recites generate a combined input comprising the first plurality of inputs and the plurality of outputs, wherein the first plurality of inputs comprises a first plurality of vectors and the plurality of outputs comprises a second plurality of vectors which, under the broadest reasonable interpretation, covers performance of the limitation in the mind. The limitations encompass choosing values from two vectors and forming another, third, vector. See 2106.04.(a)(2).III.C. Alternatively, the claim recites an abstract idea (Mathematical Calculations (see MPEP 2106.04(a)(2)(I)(C))).
The claim recites predict the values as an output…wherein the output of the machine-learned model comprises a third plurality of vectors which, under the broadest reasonable interpretation, covers performance of the limitation in the mind. The limitations encompass using judgement and estimating values into an ordered set. See 2106.04.(a)(2).III.C.
Subject Matter Eligibility Analysis Step 2A Prong 2:
insert the combined input into the machine-learned model recites insignificant extra-solution activity of data gathering (see MPEP 2106.05(g))
of the machine-learned model recites a generic computer on which to perform the abstract idea, e.g. "apply it on a computer" (see MPEP 2106.05(f))
Subject Matter Eligibility Analysis Step 2B:
Additional element (a) obtaining a network input is well understood, routine, and conventional activity of “transmitting or receiving data over a network" (see MPEP 2106.05(d)(II)(i) using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 ).
Additional elements (b) do not integrate the abstract idea into a practical application nor do the additional limitation provide significantly more than the abstract idea because the limitation amount to no more than mere instructions to apply the exception using a generic computer component. Please see MPEP §2106.05(f).
The additional element(s) (a) and (b) in the claim do/does not include any additional elements , when considered separately and in combination, that amount to an integration of the judicial exception into a practical application, nor significantly more than the judicial exception for the reasons set forth in step 2A prong 2 analysis above. The claim is not patent eligible.
Regarding Claim 20:
The rejection of claim 18 is incorporated and further claim recites further additional
elements/limitations:
Subject Matter Eligibility Analysis Step 2A Prong 1:
The claim recites generate a first subset of datasets and a second subset of datasets from the plurality of datasets which, under the broadest reasonable interpretation, covers performance of the limitation in the mind. The limitations encompass using judgement to select a subset of values from a set. See 2106.04.(a)(2).III.C.
The claim recites predict values based on the second subset of the plurality of datasets as an output which, under the broadest reasonable interpretation, covers performance of the limitation in the mind. The limitations encompass using judgement and estimating values. See 2106.04.(a)(2).III.C.
The claim recites perform a comparison of the values based on the second subset of the plurality of datasets and the values produced by the explanation service which, under the broadest reasonable interpretation, covers performance of the limitation in the mind. The limitations encompass using judgement to perform an evaluation and form an opinion on two sets of data. See 2106.04.(a)(2).III.C.
The claim recites determine, based on the comparison, that the machine-learned model is trained to predict the values based on the plurality of datasets which, under the broadest reasonable interpretation, covers performance of the limitation in the mind. The limitations encompass using judgement to perform an evaluation based on a comparison and forming an decision on whether the model can predict certain values. See 2106.04.(a)(2).III.C.
Subject Matter Eligibility Analysis Step 2A Prong 2:
train the machine-learned model on the first subset of datasets recites a generic computer on which to perform the abstract idea, e.g. "apply it on a computer" (see MPEP 2106.05(f))
of the machine-learned mode recites a generic computer on which to perform the abstract idea, e.g. "apply it on a computer" (see MPEP 2106.05(f))
insert the second subset of datasets into the machine-learned model recites insignificant extra-solution activity of data gathering (see MPEP 2106.05(g))
Subject Matter Eligibility Analysis Step 2B:
Additional elements (a) and (b) do not integrate the abstract idea into a practical application nor do the additional limitation provide significantly more than the abstract idea because the limitation amount to no more than mere instructions to apply the exception using a generic computer component. Please see MPEP §2106.05(f).
Additional element (c) obtaining a network input is well understood, routine, and conventional activity of “transmitting or receiving data over a network" (see MPEP 2106.05(d)(II)(i) using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 ).
The additional element(s) (a) (b) and (c) in the claim do/does not include any additional elements , when considered separately and in combination, that amount to an integration of the judicial exception into a practical application, nor significantly more than the judicial exception for the reasons set forth in step 2A prong 2 analysis above. The claim is not patent eligible.
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.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1-7 and 9-20 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Situ et al.(“Learning to Explain: Generating Stable Explanations Fast”, henceforth known as Situ)
Regarding claim 1:
Situ discloses accessing, by a computing device, a plurality of datasets, each dataset comprising an input of a first plurality of inputs to a computer program, a first output of a plurality of outputs of the computer program, and a value identifying an importance of the input to the first output(Situ, Page 5342, Col. 2, Paragraph 2, “Our approach to train the explanation model gϕ is summarized in Algorithm 1. First, the algorithm generates training data in the form of triplets (x, y, w)” where generating the training dataset Z made of (x, y, w) from training set D corresponds to accessing… a plurality of datasets(for each input x from training set of documents D, algorithm 1 adds a triplet (x, y, w) to dataset Z), each dataset comprising an input of a first plurality of inputs to a computer program(where x is the input), a first output of a plurality of outputs of the computer program(where y is a first output as it is the output of the black-box model with x input), and a value identifying an importance of the input to the first output(where w is a weight that identifies the important) and produced by an explanation service(Situ, Page 5342, Col. 2, Algorithm 1, where Algorithm 1 is an explanation service that uses an explanation algorithm A(See also, Situ, Page 5342, Col. 1, Paragraph 5, “an explanation algorithm A(x, ˆy, fθ) → w, which generates explanation w))
Situ discloses the computer program comprising a set of operations evaluated with the first plurality of inputs and producing the plurality of outputs during execution of the computer program, and the explanation service producing values for the first plurality of inputs based on the first plurality of inputs and the plurality of outputs(Situ, Page 5342, Col. 1, Paragraph 5, “Our setup requires two inputs: (i) a black-box text classification model y = fθ(x), which as signs document x to a label y ∈ Y, where Y is the label set; and (ii) an explanation algorithm A(x, ˆy, fθ) → w, which generates explanation w ∈ R|x| for the class of document x obtained by the black-box fθ(x)”)
Situ discloses training, by the computing device, a machine-learned model to predict values based on the plurality of datasets(Situ, Page 5342, Col. 1, Paragraph 6, “The main idea of L2E is to train a separate explanation model gφ(x) to predict the explanation generated by A(.) for fθ(.)” and Algorithm 1, lines 14-18)
Situ discloses executing, by the computing device, the computer program with a second plurality of inputs to produce a second output and inserting, by the computing device, the second plurality of inputs and the second output into the machine-learned model(Situ, Page 5343, Figure 2, where x is a plurality of second inputs that is processed through fθ and A whose output correspond to second output and whose correspond outputs of fθ and A are inserted into the model gϕ), wherein the machine-learned model produces values for the second plurality of inputs based on the second plurality of inputs and the second output(Situ, Page 5343, Figure 2, where the output of the machine learned model gϕ corresponds to producing values for the second plurality of inputs based on the second plurality of inputs and the second output )
Regarding claim 2:
The rejection of claim 1 with prior art Situ is incorporated and further:
Situ discloses prior to accessing the plurality of datasets, executing the computer program with the first plurality of inputs to produce the plurality of outputs(Situ, Page 5342, Col. 2, Algorithm 1, Lines 6-10, where accessing input x from dataset D to produce output y corresponds executing the computer program with the first plurality of inputs to produce the plurality of outputs)
Situ discloses determining, based on an execution of the explanation service with the first plurality of inputs and the plurality of outputs, a value for each input of the first plurality of inputs(Situ, Page 5342, Col. 2, Algorithm 1, Line 9, where w is corresponds to a value that is determined by an explanation service with the first plurality of inputs x and plurality of outputs y)
Situ discloses and storing, in a storage device(Situ, Page 5343., Figure 2, and Situ, 5346, Footnote7, “Intel Xeon E5-2680 v3, NVIDIA Tesla K80, 32GBRAM” where the pipeline of L2E shows the training and inference being performed on the same system and the physical system disclosed corresponds to storing in a storage device), the first plurality of inputs, the plurality of outputs, and each value for each input of the first plurality of inputs(Situ, Page 5342, Col. 2, Algorithm 1, Line 10, where the dataset Z being stored to use corresponds storing the first plurality of inputs, the plurality of outputs, and each value for each input of the first plurality of inputs),
Regarding claim 3:
The rejection of claim 2 with prior art Situ is incorporated and further:
Situ discloses wherein accessing the plurality of datasets comprises obtaining the first plurality of inputs, the plurality of outputs, and each value for each input of the first plurality of inputs from the storage device, wherein each dataset of the plurality of datasets comprises an input of the first plurality of inputs, an output of the plurality of outputs, and a value identifying an importance of the input to the output(Situ, Page 5342, Col. 2, Algorithm 1, Line 10, where the dataset Z being stored to use and Line 15 accessing dataset Z corresponds to storing and accessing a first plurality of inputs, outputs and each value for each input and wherein each dataset of the plurality of datasets comprises an input of the first plurality of inputs, an output of the plurality of outputs, and a value identifying an importance of the input to the output as x is the input, y is the output and w are weights that correspond to the importance of the input to the output)
Regarding claim 4:
The rejection of claim 1 with prior art Situ is incorporated and further:
Situ discloses prior to training the machine-learned model to predict the values based on the plurality of datasets, determining that the plurality of datasets comprises a predetermined amount of datasets(Situ, Page 5352, Col. 2, Table 6, where the separation into specific number of training datasets, development/validation datasets and testing datasets corresponds to determining a predetermined amount of plurality of datasets prior to training)
Regarding claim 5:
The rejection of claim 1 with prior art Situ is incorporated and further:
Situ discloses generating a combined input comprising the first plurality of inputs and the plurality of outputs, wherein the first plurality of inputs comprises a first plurality of vectors and the plurality of outputs comprises a second plurality of vectors(Situ, Page 5342, Col. 2, Algorithm 1, Line 10, where algorithm 1 building Z = {(x1, y1, w1)…(xn,yn,wn)} and Z containing the 3 aligned collections X = [x1…xn], Y = [y1…yn] and W = [w1…wn] corresponds to generating a combined input that comprises a vector of inputs and a vector of output)
Situ discloses inserting the combined input into the machine-learned model(Situ, Page 5342, Col. 2, Algorithm 1, Line 15-16, where data set Z being accessed and used to generate a loss corresponds to inserting the combined input into a machine learned model)
Situ discloses and predicting the values as an output of the machine-learned model, wherein the output of the machine-learned model comprises a third plurality of vectors(Situ, Page 5342, Col. 2, Algorithm 1, Line 16, where the output vector prediction by model g corresponds to predicting the values as an output of the machine-learned model, wherein the output of the machine-learned model comprises a third plurality of vectors (See also Situ, Page 5343, Col 1, Paragraph 2, “component of the importance vector v = gϕ(x, y) predicted by the explanation model”))
Regarding claim 6:
The rejection of claim 1 with prior art Situ is incorporated and further:
Situ discloses generating a first subset of datasets(Situ, Page 5342, Col. 2, Algorithm 1, Line 7, where the subset of x from dataset D corresponds to generating a first subset of datasets from the plurality of datasets) and a second subset of datasets from the plurality of datasets(Situ, Page 5342, Col. 2, Algorithm 1, Line 15, where the subset (xt, yt, wt) from dataset Z corresponds to generating a second subset of datasets from the plurality of datasets)
Situ discloses training the machine-learned model on the first subset of datasets(Situ, Page 5342, Col. 2, Algorithm 1, Lines 7-17, where using x to generate training datasets used to train the model corresponds to training a machine-learned model on the first subset of datasets)
Situ discloses inserting the second subset of datasets into the machine-learned model(Situ, Page 5342, Col. 2, Algorithm 1, Line 116, where L(gϕ(xt, yt),wt) corresponds to inserting the second subset into the machine-learned model)
Situ discloses predicting values based on the second subset of the plurality of datasets as an output of the machine-learned model(Situ, Page 5342, Col. 2, Algorithm 1, Line 16 and Situ, Page 5342, Col. 2, Paragraph 3, “A crucial component in training the explanation model under supervised learning is the loss function L(gϕ(xt, y),w). It penalizes a deviation of the predicted explanation gϕ(xt, y) from the ground truth explanation w” where gϕ(xt, yt ) is the predicted explanation and corresponds to a predicted values based on the second subset of plurality of datasets as an output of the machine learned model)
Situ discloses performing a comparison(Situ, Page 5342, Col. 2, Paragraph 3, “A crucial component in training the explanation model under supervised learning is the loss function L(gϕ(xt, y),w)” where gϕ(xt, y),w) is a comparison of (x, y) and w) of the values based on the second subset of the plurality of datasets and the values produced by the explanation service(Situ, Page 5342, Col. 2, Algorithm 1, Lines 9-10 and 15-16, where (x, y) is subset of Z and w is value produced by an explanation model)
Situ discloses and determining, based on the comparison, that the machine-learned model is trained to predict the values based on the plurality of datasets(Situ, Page 5342, Col. 2, Algorithm 1, Line 16, ϕ - ηt∇ϕL(gϕ(xt, yt),wt), where L(gϕ(xt, y),wt) measures the error and ϕ - ∇ϕL is modifying the model parameters to reduce the error where measuring the error corresponds to determining, based on the comparison, that the machine-learned model is trained to predict values based on the plurality of datasets as the model is trained on the datasets)
Regarding claim 7:
The rejection of claim 1 with prior art Situ is incorporated and further:
Situ discloses storing, in a storage device(Situ, Page 5343., Figure 2, and Situ, 5346, Footnote7, “Intel Xeon E5-2680 v3, NVIDIA Tesla K80, 32GBRAM” where the pipeline of L2E shows the training and inference being performed on the same system and the physical system disclosed corresponds to storing in a storage device), the second plurality of inputs, the second output, and each value for each input of the second plurality of inputs(Situ, Page 5342, Col. 2, Algorithm 1, Line 16, ϕ ← ϕ - ηt∇ϕL(gϕ(xt, yt),wt), where replacing the old parameters(ϕ←) after calculating the Loss of the model g using xt, yt and wt corresponds to storing the second plurality of inputs, the second output, and each value for each input of the second plurality of inputs as xt is a second input, yt is value for each input of the second plurality of inputs and wt is a the second output for the second inputs)
Regarding claim 9:
The rejection of claim 1 with prior art Situ is incorporated and further:
Situ discloses inserting the second output and a subset of the second plurality of inputs into the explanation service(Situ, Page 5343, Figure 2, where the output of fθ and corresponding x being input explanation algorithm A corresponds to a plurality of second inputs and second outputs input into an explanation service) to produce a subset of values for the subset of the second plurality of inputs based on the subset of the second plurality of inputs and the second output(Situ, Page 5343, Figure 2, where weights w corresponds to a subset of values and weights w being produced by corresponding input values input into A corresponds producing a subset of values for the subset of the second plurality of inputs based on the subset of the second plurality of inputs and the second output)
Regarding claim 10:
The rejection of claim 9 with prior art Situ is incorporated and further:
Situ discloses performing a comparison of the values for the second plurality of inputs to the subset of values for the subset of the second plurality of inputs(Situ, Page 5342, Col. 2, Paragraph 3, “A crucial component in training the explanation model under supervised learning is the loss function L(gϕ(xt, y),w)” where L(gϕ(xt, y),w) is a comparison of inputs x and the subset of inputs w as it compares the predicted weights against w) and determining, based on the comparison, that the machine-learned model is accurately predicting the values for the second plurality of inputs based on the second plurality of inputs and the second output(Situ, Page 5342, Col. 2, Algorithm 1, Line 16, ϕ - ηt∇ϕL(gϕ(xt, y),wt), where L(gϕ(xt, y),wt) measures the error and ϕ - ∇ϕL is modifying the model parameters to reduce the error where measuring the error corresponds based on the comparison, determining that the machine-learned model is accurately predicting the values for the second plurality of inputs based on the second plurality of inputs and the second output as the measured error is used to measure accuracy)
Regarding claim 11:
The rejection of claim 1 with prior art Situ is incorporated and further:
Situ discloses wherein the computer program comprises a machine-learned model(Situ, Page 5346, Col. 1, Paragraph 3, “In our experiments, the black-box is a transformer-based model”(See also Situ, Page 5342, Col. 1, Paragraph 5, “Our setup requires two inputs: (i) a black-box text classification model y = fθ(x), which as signs document x to a label y ∈ Y, where Y is the label set; and (ii) an explanation algorithm A(x, ˆy, fθ) → w, which generates explanation w ∈ R|x| for the class of document x obtained by the black-box fθ(x)”))
Regarding claim 12:
The rejection of claim 1 with prior art Situ is incorporated and further:
Situ discloses wherein the explanation service comprises a saliency algorithm and the values comprise saliency values that identify an importance of an input to the computer program to an output of the computer program(Situ, Page 5343, Figure 2, “indicates that gθ considers ‘great’ to be more important than other words in the prediction ˆy.”)
Regarding claim 13:
Situ discloses a memory and a processor device coupled to the memory(Situ, Page 5343., Figure 2, and Situ, 5346, Footnote7, “Intel Xeon E5-2680 v3, NVIDIA Tesla K80, 32GBRAM” where the pipeline of L2E shows the training and inference being performed on the same system and the physical system disclosed corresponds a computing device with memory and processor)
Situ discloses access a plurality of datasets, each dataset comprising an input of a first plurality of inputs to a computer program, a first output of a plurality of outputs of the computer program, and a value identifying an importance of the input to the first output(Situ, Page 5342, Col. 2, Algorithm 1 and Situ, Page 5342, Col. 2, Paragraph 2, “Our approach to train the explanation model gϕ is summarized in Algorithm 1. First, the algorithm generates training data in the form of triplets (x, y, w)” where generating the training dataset Z made of (x, y, w) from training set D corresponds to accessing… a plurality of datasets(for each input x from training set of documents D, algorithm 1 adds a triplet (x, y, w) to dataset Z), each dataset comprising an input of a first plurality of inputs to a computer program(where x is the input), a first output of a plurality of outputs of the computer program(where y is a first output as it is the output of the black-box model with x input), and a value identifying an importance of the input to the first output(where w is a weight that identifies the important) and produced by an explanation service(Situ, Page 5342, Col. 2, Algorithm 1, where Algorithm 1 is an explanation service that uses an explanation algorithm A(See also, Situ, Page 5342, Col. 1, Paragraph 5, “an explanation algorithm A(x, ˆy, fθ) → w, which generates explanation w))
Situ discloses the computer program comprising a set of operations evaluated with the first plurality of inputs and producing the plurality of outputs during execution of the computer program, and the explanation service producing values for the first plurality of inputs based on the first plurality of inputs and the plurality of outputs(Situ, Page 5342, Col. 1, Paragraph 5, “Our setup requires two inputs: (i) a black-box text classification model y = fθ(x), which as signs document x to a label y ∈ Y, where Y is the label set; and (ii) an explanation algorithm A(x, ˆy, fθ) → w, which generates explanation w ∈ R|x| for the class of document x obtained by the black-box fθ(x)”)
Situ discloses train a machine-learned model to predict values based on the plurality of datasets(Situ, Page 5342, Col. 1, Paragraph 6, “The main idea of L2E is to train a separate explanation model gφ(x) to predict the explanation generated by A(.) for fθ(.)” and Algorithm 1, lines 14-18)
Situ discloses execute the computer program with a second plurality of inputs to produce a second output and insert the second plurality of inputs and the second output into the machine-learned model(Situ, Page 5343, Figure 2, where x is a plurality of second inputs that is processed through fθ and A whose output correspond to second output and whose correspond outputs of fθ and A are inserted into the model gϕ), wherein the machine-learned model produces values for the second plurality of inputs based on the second plurality of inputs and the second output(Situ, Page 5343, Figure 2, where the output of the machine learned model gϕ corresponds to producing values for the second plurality of inputs based on the second plurality of inputs and the second output )
Regarding claim 14:
The rejection of claim 13 with prior art Situ is incorporated and further:
Situ discloses prior to accessing the plurality of datasets, execute the computer program with the first plurality of inputs to produce the plurality of outputs(Situ, Page 5342, Col. 2, Algorithm 1, Lines 6-10, where accessing input x from dataset D to produce output y corresponds executing the computer program with the first plurality of inputs to produce the plurality of outputs)
Situ discloses determine, based on an execution of the explanation service with the first plurality of inputs and the plurality of outputs, a value for each input of the first plurality of inputs(Situ, Page 5342, Col. 2, Algorithm 1, Line 9, where w is corresponds to a value that is determined by an explanation service with the first plurality of inputs x and plurality of outputs y)
Situ discloses and store, in a storage device(Situ, Page 5343., Figure 2, and Situ, 5346, Footnote7, “Intel Xeon E5-2680 v3, NVIDIA Tesla K80, 32GBRAM” where the pipeline of L2E shows the training and inference being performed on the same system and the physical system disclosed corresponds to storing in a storage device), the first plurality of inputs, the plurality of outputs, and each value for each input of the first plurality of inputs(Situ, Page 5342, Col. 2, Algorithm 1, Line 10, where the dataset Z being stored to use corresponds storing the first plurality of inputs, the plurality of outputs, and each value for each input of the first plurality of inputs), wherein to access the plurality of datasets, the processor device is further to obtain the first plurality of inputs, the plurality of outputs, and each value for each input of the first plurality of inputs from the storage device, wherein each dataset of the plurality of datasets comprises an input of the first plurality of inputs, an output of the plurality of outputs, and a value identifying an importance of the input to the output(Situ, Page 5342, Col. 2, Algorithm 1, Line 10, where the dataset Z being stored to use and Line 15 accessing dataset Z corresponds to storing and accessing a first plurality of inputs, outputs and each value for each input and wherein each dataset of the plurality of datasets comprises an input of the first plurality of inputs, an output of the plurality of outputs, and a value identifying an importance of the input to the output as x is the input, y is the output and w are weights that correspond to the importance of the input to the output)
Regarding claim 15:
The rejection of claim 13 with prior art Situ is incorporated and further:
Situ discloses generate a combined input comprising the first plurality of inputs and the plurality of outputs, wherein the first plurality of inputs comprises a first plurality of vectors and the plurality of outputs comprises a second plurality of vectors(Situ, Page 5342, Col. 2, Algorithm 1, Line 10, where algorithm 1 building Z = {(x1, y1, w1)…(xn,yn,wn)} and Z containing the 3 aligned collections X = [x1…xn], Y = [y1…yn] and W = [w1…wn] corresponds to generating a combined input that comprises a vector of inputs and a vector of output)
Situ discloses insert the combined input into the machine-learned model(Situ, Page 5342, Col. 2, Algorithm 1, Line 15-16, where data set Z being accessed and used to generate a loss corresponds to inserting the combined input into a machine learned model) and predict the values as an output of the machine-learned model, wherein the output of the machine-learned model comprises a third plurality of vectors(Situ, Page 5342, Col. 2, Algorithm 1, Line 16, where the output vector prediction by model g corresponds to predicting the values as an output of the machine-learned model, wherein the output of the machine-learned model comprises a third plurality of vectors (See also Situ, Page 5343, Col 1, Paragraph 2, “component of the importance vector v = gϕ(x, y) predicted by the explanation model”))
Regarding claim 16:
The rejection of claim 13 with prior art Situ is incorporated and further:
Situ discloses generate a first subset of datasets(Situ, Page 5342, Col. 2, Algorithm 1, Line 7, where the subset of x from dataset D corresponds to generating a first subset of datasets from the plurality of datasets) and a second subset of datasets from the plurality of datasets(Situ, Page 5342, Col. 2, Algorithm 1, Line 15, where the subset (xt, yt, wt) from dataset Z corresponds to generating a second subset of datasets from the plurality of datasets)
Situ discloses train the machine-learned model on the first subset of datasets(Situ, Page 5342, Col. 2, Algorithm 1, Lines 7-17, where using x to generate training datasets used to train the model corresponds to training a machine-learned model on the first subset of datasets)
Situ discloses insert the second subset of datasets into the machine-learned model(Situ, Page 5342, Col. 2, Algorithm 1, Line 116, where L(gϕ(xt, yt),wt) corresponds to inserting the second subset into the machine-learned model)
Situ discloses predict values based on the second subset of the plurality of datasets as an output of the machine-learned model(Situ, Page 5342, Col. 2, Algorithm 1, Line 16 and Situ, Page 5342, Col. 2, Paragraph 3, “A crucial component in training the explanation model under supervised learning is the loss function L(gϕ(xt, y),w). It penalizes a deviation of the predicted explanation gϕ(xt, y) from the ground truth explanation w” where gϕ(xt, yt ) is the predicted explanation and corresponds to a predicted values based on the second subset of plurality of datasets as an output of the machine learned model)
Situ discloses perform a comparison(Situ, Page 5342, Col. 2, Paragraph 3, “A crucial component in training the explanation model under supervised learning is the loss function L(gϕ(xt, y),w)” where gϕ(xt, y),w) is a comparison of (x, y) and w) of the values based on the second subset of the plurality of datasets and the values produced by the explanation service(Situ, Page 5342, Col. 2, Algorithm 1, Lines 9-10 and 15-16, where (x, y) is subset of Z and w is value produced by an explanation model)
Situ discloses and determine, based on the comparison, that the machine-learned model is trained to predict the values based on the plurality of datasets(Situ, Page 5342, Col. 2, Algorithm 1, Line 16, ϕ - ηt∇ϕL(gϕ(xt, yt),wt), where L(gϕ(xt, y),wt) measures the error and ϕ - ∇ϕL is modifying the model parameters to reduce the error where measuring the error corresponds to determining, based on the comparison, that the machine-learned model is trained to predict values based on the plurality of datasets as the model is trained on the datasets)
Regarding claim 17:
The rejection of claim 13 with prior art Situ is incorporated and further:
Situ discloses insert the second output and a subset of the second plurality of inputs into the explanation service(Situ, Page 5343, Figure 2, where the output of fθ and corresponding x being input explanation algorithm A corresponds to a plurality of second inputs and second outputs input into an explanation service) to produce a subset of values for the subset of the second plurality of inputs based on the subset of the second plurality of inputs and the second output(Situ, Page 5343, Figure 2, where weights w corresponds to a subset of values and weights w being produced by corresponding input values input into A corresponds producing a subset of values for the subset of the second plurality of inputs based on the subset of the second plurality of inputs and the second output)
Situ discloses perform a comparison of the values for the second plurality of inputs to the subset of values for the subset of the second plurality of inputs(Situ, Page 5342, Col. 2, Paragraph 3, “A crucial component in training the explanation model under supervised learning is the loss function L(gϕ(xt, y),w)” where L(gϕ(xt, y),w) is a comparison of inputs x and the subset of inputs w as it compares the predicted weights against w) and determine, based on the comparison, that the machine-learned model is accurately predicting the values for the second plurality of inputs based on the second plurality of inputs and the second output(Situ, Page 5342, Col. 2, Algorithm 1, Line 16, ϕ - ηt∇ϕL(gϕ(xt, y),wt), where L(gϕ(xt, y),wt) measures the error and ϕ - ∇ϕL is modifying the model parameters to reduce the error where measuring the error corresponds based on the comparison, determining that the machine-learned model is accurately predicting the values for the second plurality of inputs based on the second plurality of inputs and the second output as the measured error is used to measure accuracy)
Regarding claim 18:
Situ discloses access a plurality of datasets, each dataset comprising an input of a first plurality of inputs to a computer program, a first output of a plurality of outputs of the computer program, and a value identifying an importance of the input to the first output(Situ, Page 5342, Col. 2, Algorithm 1 and Situ, Page 5342, Col. 2, Paragraph 2, “Our approach to train the explanation model gϕ is summarized in Algorithm 1. First, the algorithm generates training data in the form of triplets (x, y, w)” where generating the training dataset Z made of (x, y, w) from training set D corresponds to accessing… a plurality of datasets(for each input x from training set of documents D, algorithm 1 adds a triplet (x, y, w) to dataset Z), each dataset comprising an input of a first plurality of inputs to a computer program(where x is the input), a first output of a plurality of outputs of the computer program(where y is a first output as it is the output of the black-box model with x input), and a value identifying an importance of the input to the first output(where w is a weight that identifies the important) and produced by an explanation service(Situ, Page 5342, Col. 2, Algorithm 1, where Algorithm 1 is an explanation service that uses an explanation algorithm A(See also, Situ, Page 5342, Col. 1, Paragraph 5, “an explanation algorithm A(x, ˆy, fθ) → w, which generates explanation w))
Situ discloses the computer program comprising a set of operations evaluated with the first plurality of inputs and producing the plurality of outputs during execution of the computer program, and the explanation service producing values for the first plurality of inputs based on the first plurality of inputs and the plurality of outputs(Situ, Page 5342, Col. 1, Paragraph 5, “Our setup requires two inputs: (i) a black-box text classification model y = fθ(x), which as signs document x to a label y ∈ Y, where Y is the label set; and (ii) an explanation algorithm A(x, ˆy, fθ) → w, which generates explanation w ∈ R|x| for the class of document x obtained by the black-box fθ(x)”)
Situ discloses train a machine-learned model to predict values based on the plurality of datasets(Situ, Page 5342, Col. 1, Paragraph 6, “The main idea of L2E is to train a separate explanation model gφ(x) to predict the explanation generated by A(.) for fθ(.)” and Algorithm 1, lines 14-18)
Situ discloses execute the computer program with a second plurality of inputs to produce a second output and insert the second plurality of inputs and the second output into the machine-learned model(Situ, Page 5343, Figure 2, where x is a plurality of second inputs that is processed through fθ and A whose output correspond to second output and whose correspond outputs of fθ and A are inserted into the model gϕ), wherein the machine-learned model produces values for the second plurality of inputs based on the second plurality of inputs and the second output(Situ, Page 5343, Figure 2, where the output of the machine learned model gϕ corresponds to producing values for the second plurality of inputs based on the second plurality of inputs and the second output )
Regarding claim 19:
The rejection of claim 18 with prior art Situ is incorporated and further:
Situ discloses generate a combined input comprising the first plurality of inputs and the plurality of outputs, wherein the first plurality of inputs comprises a first plurality of vectors and the plurality of outputs comprises a second plurality of vectors(Situ, Page 5342, Col. 2, Algorithm 1, Line 10, where algorithm 1 building Z = {(x1, y1, w1)…(xn,yn,wn)} and Z containing the 3 aligned collections X = [x1…xn], Y = [y1…yn] and W = [w1…wn] corresponds to generating a combined input that comprises a vector of inputs and a vector of output)
Situ discloses insert the combined input into the machine-learned model(Situ, Page 5342, Col. 2, Algorithm 1, Line 15-16, where data set Z being accessed and used to generate a loss corresponds to inserting the combined input into a machine learned model) and predict the values as an output of the machine-learned model, wherein the output of the machine-learned model comprises a third plurality of vectors(Situ, Page 5342, Col. 2, Algorithm 1, Line 16, where the output vector prediction by model g corresponds to predicting the values as an output of the machine-learned model, wherein the output of the machine-learned model comprises a third plurality of vectors (See also Situ, Page 5343, Col 1, Paragraph 2, “component of the importance vector v = gϕ(x, y) predicted by the explanation model”))
Regarding claim 20:
The rejection of claim 18 with prior art Situ is incorporated and further:
Situ discloses generate a first subset of datasets(Situ, Page 5342, Col. 2, Algorithm 1, Line 7, where the subset of x from dataset D corresponds to generating a first subset of datasets from the plurality of datasets) and a second subset of datasets from the plurality of datasets(Situ, Page 5342, Col. 2, Algorithm 1, Line 15, where the subset (xt, yt, wt) from dataset Z corresponds to generating a second subset of datasets from the plurality of datasets)
Situ discloses train the machine-learned model on the first subset of datasets(Situ, Page 5342, Col. 2, Algorithm 1, Lines 7-17, where using x to generate training datasets used to train the model corresponds to training a machine-learned model on the first subset of datasets)
Situ discloses insert the second subset of datasets into the machine-learned model(Situ, Page 5342, Col. 2, Algorithm 1, Line 116, where L(gϕ(xt, yt),wt) corresponds to inserting the second subset into the machine-learned model)
Situ discloses predict values based on the second subset of the plurality of datasets as an output of the machine-learned model(Situ, Page 5342, Col. 2, Algorithm 1, Line 16 and Situ, Page 5342, Col. 2, Paragraph 3, “A crucial component in training the explanation model under supervised learning is the loss function L(gϕ(xt, y),w). It penalizes a deviation of the predicted explanation gϕ(xt, y) from the ground truth explanation w” where gϕ(xt, yt ) is the predicted explanation and corresponds to a predicted values based on the second subset of plurality of datasets as an output of the machine learned model)
Situ discloses perform a comparison(Situ, Page 5342, Col. 2, Paragraph 3, “A crucial component in training the explanation model under supervised learning is the loss function L(gϕ(xt, y),w)” where gϕ(xt, y),w) is a comparison of (x, y) and w) of the values based on the second subset of the plurality of datasets and the values produced by the explanation service(Situ, Page 5342, Col. 2, Algorithm 1, Lines 9-10 and 15-16, where (x, y) is subset of Z and w is value produced by an explanation model)
Situ discloses and determine, based on the comparison, that the machine-learned model is trained to predict the values based on the plurality of datasets(Situ, Page 5342, Col. 2, Algorithm 1, Line 16, ϕ - ηt∇ϕL(gϕ(xt, yt),wt), where L(gϕ(xt, y),wt) measures the error and ϕ - ∇ϕL is modifying the model parameters to reduce the error where measuring the error corresponds to determining, based on the comparison, that the machine-learned model is trained to predict values based on the plurality of datasets as the model is trained on the datasets)
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.
Claim(s) 8 is/are rejected under 35 U.S.C. 103 as being unpatentable over Situ et al.(“Learning to Explain: Generating Stable Explanations Fast”, henceforth known as Situ) in view of Sanjiv et. al (US20220171991A1, henceforth known as Sanjiv)
Regarding claim 8:
The rejection of claim 7 with prior art Situ is incorporated and further:
Situ does not discloses, however Sanjiv discloses obtaining the second plurality of inputs, the second output, and each value for each input of the second plurality of inputs from the storage device and generating a report based on the second plurality of inputs, the second output, and each value for each input of the second plurality of inputs(Sanjiv, [0065] “The bias metrics and feature attribution captured at various stages as part of machine learning pipeline 100 may be integrated into various techniques for analyzing, visualizing and monitoring, as discussed in detail below with regard to FIGS. 2-16. This data information be stored (e.g., at a common backend store, such as a storage service 230 in FIG. 2), for later use. Various machine learning and evaluation tools 102 may implement or rely upon the stored information to implement various features for bias analysis and reporting 192, bias mitigation 194, model explanation 196, and model performance 198, in various embodiments” where the storage of data for use in reporting corresponds to obtaining inputs, outputs and value from storage to generate a report based on the data retrieved)
References Situ and Sanjiv are analogous art because they are from the same field of endeavor of using machine learning explainability/interpretability to help explain why a model or computation system produces a result.
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Situ and Sanjiv before him or her, to modify the system of Situ to include the report generation of Sanjiv is advantageous because the reports store the data for later access and analyzed by users and used in implementation in machine learning serves. The suggestion/motivation for doing so would have been Sanjiv, [0104], “…Report generation 360 may be implemented to store various bias metrics for pre and post training, as well as feature attribution measurements as part of fairness report(s) 390 in storage service 230. In this way, other components, systems, or other nodes in machine learning service 210 can implement them”
Relevant Art
While not used the following relevant art was found to be relevant:
Patrick Schwab, “CXPlain: Causal Explanations for Model Interpretation under Uncertainty” as it trains a an explanation model to estimate feature-importance scores and computes importance targets
Giuseppe Casalicchio, “Visualizing the Feature Importance for Black Box Models” as it discloses a learned explainer model that estimates Shapley values for an input/class pair
FATAKDAWALA; MUSTAFA, US20230306308A1, “METHOD AND SYSTEM FOR INTERPRETING MACHINE LEARNING MODEL'S PREDICTION”, as it discloses explaining and interpreting the feature importance of inputs and reporting the results
Conclusion
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/C.J.J./ Examiner, Art Unit 2122
/KAKALI CHAKI/ Supervisory Patent Examiner, Art Unit 2122
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