DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
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 1-9 are rejected under 35 U.S.C. 101 because the claimed invention are directed to abstract ideas without significantly more.
Regarding Claim 1:
Step 1 - Is the claim directed to a process, a machine, manufacture or composition of matter? - Yes, the claim is directed to a process.
Step 2A - Prong 1 - Does the claim recite an abstract idea, law of nature, or natural phenomenon? - Yes, the claim recites the abstract ideas:
training a machine learning system in such a way that the machine learning system ascertains the second variables assigned according to the assignment rule as a function of the first variables in each case: - This limitation recites the abstract idea of mathematical concepts, as when given the broadest reasonable interpretation in light of the specification, training the machine learning system can be done using the regression model which is an algorithm based on mathematical calculations, therefore, this limitation amounts to mathematical concepts;
ascertaining a cost function, the cost function characterizing distances between predictions of the machine learning system as a function of the first variables and the second variables that are assigned to the first variables according to the assignment rule: - This limitation recites the abstract idea of mathematical concepts, as when given the broadest reasonable interpretation in light of the specification, ascertaining the cost function can be done using a suitable distance measure such as L2 norm which is an algorithm based on mathematical calculations, therefore, this limitation amounts to mathematical concepts;
optimizing the assignment rule as a function of the cost function so that an assignment of the first variables to the second variables according to the assignment rule minimizes the cost function, wherein in the step of optimizing, a gradient of the cost function with regard to the assignment rule is ascertained and the gradient is projected onto a unit polytope, which includes a set of possible assignment rules, and the assignment rule is modified as a function of the projected gradient: - This limitation recites the abstract idea of mathematical concepts, as the process of optimizing the assignment rule as a function of the cost function can be done using a gradient descent method which is an algorithm based on mathematical calculations, therefore, this limitation amounts to mathematical concepts.
Step 2A - Prong 2 - Does the claim recite additional elements that integrate the judicial exception into a practical application? - No, there are no additional elements that integrate the judicial exception into a practical application. The additional elements:
initializing the assignment rule and providing the first and second set: - This limitation is directed to mere data gathering and outputting. The courts (as per Ultramercial, 772 F.3d at 715, 112 USPQ2d at 1754) have recognized mere data gathering and outputting as insignificant extra-solution activity (see MPEP 2106.05(g)(3)) and therefore fails to integrate the exception into a practical application;
repeatedly executing the following steps a)-c): - This limitation is simply manipulating data on iterative manner which does not impose a meaningful limit on the claim, being insignificant as extra solution activity.
Step 2B - Does the claim recite additional elements that amount to significantly more than the judicial exception? - No, there are no additional elements that amount to significantly more than the judicial exception. The additional elements:
initializing the assignment rule and providing the first and second set: - This limitation is directed to receiving or transmitting data over a network. The courts (as per Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362) have recognized receiving or transmitting data over a network as well-understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) (see MPEP 2106.05(d) II);
repeatedly executing the following steps a)-c): This limitation is directed to performing repetitive calculations. The courts have recognized performing repetitive calculations as well‐understood, routine, and conventional functions or as insignificant extra-solution activity when it’s claimed in a merely generic manner (see MPEP 2106.05(d)(II)(ii)).
Regarding Claim 2:
Step 1 - Is the claim directed to a process, a machine, manufacture or composition of matter? - Yes, the claim is directed to a process.
Step 2A - Prong 1 - Does the claim recite an abstract idea, law of nature, or natural phenomenon? - Yes, the claim is dependent on claim 1 which included an abstract idea (see rejection claim 1). Additionally, claim 2 recites the abstract ideas:
wherein the projecting of the gradient is carried out using a Boyle-Dykstra projection algorithm or a Sinkhorn algorithm: - This limitation recites the abstract idea of mathematical concepts, as the projecting of the gradient is done using a Boyle-Dykstra projection algorithm or a Sinkhorn algorithm, both are algorithms based on mathematical calculations, therefore, this limitation amounts to mathematical concepts.
Step 2A - Prong 2 - Does the claim recite additional elements that integrate the judicial exception into a practical application? - No, there are no additional elements that integrate the judicial exception into a practical application.
Step 2B - Does the claim recite additional elements that amount to significantly more than the judicial exception? - No, there are no additional elements that amount to significantly more than the judicial exception.
Regarding Claim 3:
Step 1 - Is the claim directed to a process, a machine, manufacture or composition of matter? - Yes, the claim is directed to a process.
Step 2A - Prong 1 - Does the claim recite an abstract idea, law of nature, or natural phenomenon? - Yes, the claim is dependent on claim 1 which included an abstract idea (see rejection claim 1). Additionally, claim 3 recites the abstract ideas:
wherein the assignment rule is a doubly stochastic matrix, and the unit polytope is a Birkhoff polytope: - This limitation recites the abstract idea of mathematical concepts, as the assignment rule is a doubly stochastic matrix, and the unit polytope is a Birkhoff polytope, both doubly stochastic matrix and Birkhoff polytope are algorithms based on mathematical calculations, therefore, this limitation amounts to mathematical concepts.
Step 2A - Prong 2 - Does the claim recite additional elements that integrate the judicial exception into a practical application? - No, there are no additional elements that integrate the judicial exception into a practical application.
Step 2B - Does the claim recite additional elements that amount to significantly more than the judicial exception? - No, there are no additional elements that amount to significantly more than the judicial exception.
Regarding Claim 4:
Step 1 - Is the claim directed to a process, a machine, manufacture or composition of matter? - Yes, the claim is directed to a process.
Step 2A - Prong 1 - Does the claim recite an abstract idea, law of nature, or natural phenomenon? - Yes, the claim is dependent on claim 3 which included an abstract idea (see rejection claim 3). Additionally, claim 4 recites the abstract ideas:
wherein an optimized assignment rule is mapped to a true permutation matrix at a conclusion of the repetitions of the steps a) to c): - This limitation recites the abstract idea of mathematical concepts, as the optimized assignment rule is mapped to the true permutation matrix which is mathematical calculation, therefore, this limitation amounts to mathematical concepts.
Step 2A - Prong 2 - Does the claim recite additional elements that integrate the judicial exception into a practical application? - No, there are no additional elements that integrate the judicial exception into a practical application.
Step 2B - Does the claim recite additional elements that amount to significantly more than the judicial exception? - No, there are no additional elements that amount to significantly more than the judicial exception.
Regarding Claim 5:
Step 1 - Is the claim directed to a process, a machine, manufacture or composition of matter? - Yes, the claim is directed to a process.
Step 2A - Prong 1 - Does the claim recite an abstract idea, law of nature, or natural phenomenon? - Yes, the claim is dependent on claim 4 which included an abstract idea (see rejection claim 4). Additionally, claim 5 recites the abstract ideas:
wherein for the optimized assignment rule, a direction in the Birkhoff polytope is ascertained in which the cost function does not change, the assignment rule being mapped along the ascertained direction to a facet of the Birkhoff polytope, and the ascertaining of the direction and the mapping are repeated multiple times until a vertex of the Birkhoff polytope that corresponds to a permutation matrix is reached, the permutation matrix of the vertex being output as an assignment rule: - This limitation recites the abstract idea of mathematical concepts, as the optimized assignment rule is obtained using the Birkhoff polytope which is mathematical calculation, therefore, this limitation amounts to mathematical concepts.
Step 2A - Prong 2 - Does the claim recite additional elements that integrate the judicial exception into a practical application? - No, there are no additional elements that integrate the judicial exception into a practical application.
Step 2B - Does the claim recite additional elements that amount to significantly more than the judicial exception? - No, there are no additional elements that amount to significantly more than the judicial exception.
Regarding Claim 6:
Step 1 - Is the claim directed to a process, a machine, manufacture or composition of matter? - Yes, the claim is directed to a process.
Step 2A - Prong 1 - Does the claim recite an abstract idea, law of nature, or natural phenomenon? - Yes, the claim is dependent on claim 1 which included an abstract idea (see rejection claim 1).
Step 2A - Prong 2 - Does the claim recite additional elements that integrate the judicial exception into a practical application? - No, there are no additional elements that integrate the judicial exception into a practical application. The additional elements:
wherein the first and second variables characterize products during their production following different production process steps, and the assignment rule characterizes which of the variables of the first and second set characterize the same product: - This limitation recites the first and second variables characterizing products during their production following different production process steps, and the assignment rule characterizing which of the variables of the first and second set characterize the same product, therefore, given the broadest reasonable interpretation in light of the specification, this limitation merely indicates a field of use or technological environment such as reconstructing the positions of semiconductor devices on a wafer. Therefore, it fails to integrate the judicial exception into a practical application (see MPEP 2106.05(h)).
Step 2B - Does the claim recite additional elements that amount to significantly more than the judicial exception? - No, there are no additional elements that amount to significantly more than the judicial exception. The additional elements:
wherein the first and second variables characterize products during their production following different production process steps, and the assignment rule characterizes which of the variables of the first and second set characterize the same product: - This limitation recites the first and second variables characterizing products during their production following different production process steps, and the assignment rule characterizing which of the variables of the first and second set characterize the same product, therefore, given the broadest reasonable interpretation in light of the specification, this limitation merely indicates a field of use or technological environment such as reconstructing the positions of semiconductor devices on a wafer. Therefore, it fails to amount to significantly more than the judicial exception (see MPEP 2106.05(h)).
Regarding Claim 7:
Step 1 - Is the claim directed to a process, a machine, manufacture or composition of matter? - Yes, the claim is directed to a process.
Step 2A - Prong 1 - Does the claim recite an abstract idea, law of nature, or natural phenomenon? - Yes, the claim is dependent on claim 1 which included an abstract idea (see rejection claim 1).
Step 2A - Prong 2 - Does the claim recite additional elements that integrate the judicial exception into a practical application? - No, there are no additional elements that integrate the judicial exception into a practical application. The additional elements:
wherein the first variables are first test results from semiconductor device elements on a wafer, and the second variables are second test results of the semiconductor device elements after they have been cut out of the wafer, and the assignment rule characterizes which first and second test results originate from the same semiconductor device element: - This limitation recites the first and second variables characterizing products during their production following different production process steps, and the assignment rule characterizing which of the variables of the first and second set characterize the same product, therefore, given the broadest reasonable interpretation in light of the specification, this limitation merely indicates a field of use or technological environment such as reconstructing the positions of semiconductor devices on a wafer. Therefore, it fails to integrate the judicial exception into a practical application (see MPEP 2106.05(h)).
Step 2B - Does the claim recite additional elements that amount to significantly more than the judicial exception? - No, there are no additional elements that amount to significantly more than the judicial exception. The additional elements:
wherein the first variables are first test results from semiconductor device elements on a wafer, and the second variables are second test results of the semiconductor device elements after they have been cut out of the wafer, and the assignment rule characterizes which first and second test results originate from the same semiconductor device element: - This limitation recites the first and second variables characterizing products during their production following different production process steps, and the assignment rule characterizing which of the variables of the first and second set characterize the same product, therefore, given the broadest reasonable interpretation in light of the specification, this limitation merely indicates a field of use or technological environment such as reconstructing the positions of semiconductor devices on a wafer. Therefore, it fails to amount to significantly more than the judicial exception (see MPEP 2106.05(h)).
Regarding independent Claim 8, this claim is directed to a device and is rejected on the same basis as independent claim 1 since they are analogous claims.
Regarding independent Claim 9, this claim is directed to a non-transitory machine-readable memory medium and is rejected on the same basis as independent claim 1 since they are analogous claims.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-2 are rejected under 35 U.S.C. 103 as being unpatentable over Cao et al (US-11847538-B2 - hereinafter Cao) in view of Oswald et al (US-11675937-B2 - hereinafter Oswald).
Referring to Claim 1, Cao teaches:
repeatedly executing the following steps a)-c) (see Cao at Cl. 6 Ln. 40-56: “As described below in connection with FIGS. 3 and 4, in an embodiment, training generative model 204 includes an optimal transport generative leaning method with backpropagation using gradient descent to minimize variants of a distance between elements of the private dataset 202 (e.g., images, data objects, customer profiles, etc. stored in the private dataset 202) and data generated by the generative model 204. In one example, the distance between the real data (e.g., the private data included in the private dataset 202) and the generated data (e.g., the training dataset 206 or other data generated by the generative model 204) is defined by the Wasserstein distance. In an embodiment, the distance between the two distributions (e.g., the private dataset 202 and the data generated by the generative model 204) is measured as the expectation of a point-wise cost function between pairs of samples as distributed according to the optimal transport plan”. Examiner interprets the utilization of backpropagation by using gradient descent to minimize variants of the distance to be equivalent as the claimed “repeatedly executing”);
b) ascertaining a cost function, the cost function characterizing distances between predictions of the machine learning system as a function of the first variables and the second variables that are assigned to the first variables according to the assignment rule (see Cao at Cl. 6 Ln. 40-56: “As described below in connection with FIGS. 3 and 4, in an embodiment, training generative model 204 includes an optimal transport generative leaning method with backpropagation using gradient descent to minimize variants of a distance between elements of the private dataset 202 (e.g., images, data objects, customer profiles, etc. stored in the private dataset 202) and data generated by the generative model 204. In one example, the distance between the real data (e.g., the private data included in the private dataset 202) and the generated data (e.g., the training dataset 206 or other data generated by the generative model 204) is defined by the Wasserstein distance. In an embodiment, the distance between the two distributions (e.g., the private dataset 202 and the data generated by the generative model 204) is measured as the expectation of a point-wise cost function between pairs of samples as distributed according to the optimal transport plan”. Examiner interprets the distance between the private dataset (interpreted as first variables) and the data generated (interpreted as predictions) by the generative model being measured as the expectation of a point-wise cost function between pairs of samples as distributed according to the optimal transport plan (interpreted as assignment rule) to be equivalent as the claimed ”ascertaining a cost function, the cost function characterizing distances between predictions of the machine learning system as a function of the first variables and the second variables that are assigned to the first variables according to the assignment rule”);
c) optimizing the assignment rule as a function of the cost function so that an assignment of the first variables to the second variables according to the assignment rule minimizes the cost function,
wherein in the step of optimizing, a gradient of the cost function with regard to the assignment rule is ascertained and the gradient is projected onto a unit polytope, which includes a set of possible assignment rules, and the assignment rule is modified as a function of the projected gradient (see Cao at Cl. 6 Ln. 40-56: “As described below in connection with FIGS. 3 and 4, in an embodiment, training generative model 204 includes an optimal transport generative leaning method with backpropagation using gradient descent to minimize variants of a distance between elements of the private dataset 202 (e.g., images, data objects, customer profiles, etc. stored in the private dataset 202) and data generated by the generative model 204. In one example, the distance between the real data (e.g., the private data included in the private dataset 202) and the generated data (e.g., the training dataset 206 or other data generated by the generative model 204) is defined by the Wasserstein distance. In an embodiment, the distance between the two distributions (e.g., the private dataset 202 and the data generated by the generative model 204) is measured as the expectation of a point-wise cost function between pairs of samples as distributed according to the optimal transport plan”. Examiner interprets the optimal transport generative leaning method using gradient descent to minimize variants of the distance between elements of the private dataset (interpreted as first variables) and data generated (second variables) by the generative model, and the distance being measured as the expectation of a point-wise cost function between the two distributions (sum of probability of the distributions being interpreted as equal to one) according to the optimal transport plan ( (interpreted as polytope) to be equivalent as the claimed ” optimizing the assignment rule as a function of the cost function so that an assignment of the first variables to the second variables according to the assignment rule minimizes the cost function, wherein in the step of optimizing, a gradient of the cost function with regard to the assignment rule is ascertained and the gradient is projected onto a unit polytope, which includes a set of possible assignment rules, and the assignment rule is modified as a function of the projected gradient”).
However, Cao fails to teach:
initializing the assignment rule and providing the first and second set.
Oswald teaches, in analogous system,
initializing the assignment rule and providing the first and second set (see Oswald at Cl. 3 Ln. 57-67 and Cl. 4 Ln. 1-3: “A model in the sense of the invention is a depiction of reality, in particular simplified. A model according to the invention may also denote an analogous model for a device's operation. Preferably, it can in turn comprise partial models and/or sub-models modeling individual components of the device. Such a model may thereby comprise a characteristic map-based model and/or a function-based model, particularly as partial models. In simulation with a characteristic map-based model, a map assigning values of an input variable to values of an output variable is stored. In simulation by means of a function-based model, a function is stored with function parameters or respectively coefficients and variables which assign values of input variables to values of output variables.”. Examiner interprets storing function with function parameters or respectively coefficients and variables which assign values of input variables to values of output variables to be equivalent as the claimed “initializing the assignment rule and providing the first and second set”);
a) training a machine learning system in such a way that the machine learning system ascertains the second variables assigned according to the assignment rule as a function of the first variables in each case (see Oswald at Cl. 16 Ln. 19-27: “The model M makes use of a partial model to simulate the operation of the internal combustion engine 1. This partial model is function-based; i.e. it is specified by a function which comprises function parameters, in particular coefficients, and variables, in particular manipulated variables such as e.g. the engine speed and accelerator pedal position. The function thereby reflects an assignment rule which continuously assigns a value of one or more simulated variables to a set of manipulated variable values”. Examiner interprets the function thereby reflects an assignment rule which continuously assigns a value of one or more simulated variables (interpreted as the second variables assigned) to a set of manipulated variable (interpreted as the first variables) values to be equivalent as the claimed “training a machine learning system in such a way that the machine learning system ascertains the second variables assigned according to the assignment rule as a function of the first variables in each case”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Cao with the above teachings of Oswald by iteratively ascertaining a cost function that characterizes distances and optimizing the assignment rule as a function of the cost function so that an assignment of the first variables to the second variables according to the assignment rule minimizes the cost function, as taught by Cao, and initializing the assignment rule and providing the first and second set as well as training the machine learning system in such a way that the machine learning system ascertains the second variables assigned according to the assignment rule as a function of the first variables in each case, as taught by Oswald. The modification would have been obvious because one of ordinary skill in the art would be motivated to simulate the operation of the internal combustion engine (as suggested by Oswald at Cl. 16 Ln. 19-27: “The model M makes use of a partial model to simulate the operation of the internal combustion engine 1. This partial model is function-based; i.e. it is specified by a function which comprises function parameters, in particular coefficients, and variables, in particular manipulated variables such as e.g. the engine speed and accelerator pedal position. The function thereby reflects an assignment rule which continuously assigns a value of one or more simulated variables to a set of manipulated variable values”).
Referring to Claim 2, Cao teaches the method of claim 1:
wherein the projecting of the gradient is carried out using a Boyle-Dykstra projection algorithm or a Sinkhorn algorithm (see Cao at Cl. 12 Ln. 50-64: “FIG. 4 illustrates an example of a backwards pass of the Sinkhorn algorithm in accordance with an embodiment. The method 400 illustrated in FIG. 4, in an embodiment, backpropagates a gradient descent for a generated dataset 408 in order to adjust the parameters of a generative model 404. In various embodiments, the generative model 404 is trained based at least in part on Differentially Private Stochastic Gradient Descent. The generative model 404, in an embodiment, includes any generative model as described above, such as the generative model 304. In various embodiments, the gradient for the generated data 408 is calculated based at least in part on the Sinkhorn loss 416”. Examiner interprets the gradient for the generated data being calculated based at least in part on the Sinkhorn loss to be equivalent as the claimed “the projecting of the gradient is carried out using a Boyle-Dykstra projection algorithm or a Sinkhorn algorithm”).
Claims 3-4 are rejected under 35 U.S.C. 103 as being unpatentable over Cao et al (US-11847538-B2 - hereinafter Cao) in view of Oswald et al (US-11675937-B2 - hereinafter Oswald) in further view of Mydlarz et al (US-20100161581-A1 – hereinafter Mydlarz).
Referring to Claim 3, Cao - Oswald teaches the method of claim 1:
However, Cao - Oswald fails to teach:
wherein the assignment rule is a doubly stochastic matrix, and the unit polytope is a Birkhoff polytope.
Mydlarz teaches, in analogous system,
wherein the assignment rule is a doubly stochastic matrix, and the unit polytope is a Birkhoff polytope (see Mydlarz at Pg. 93-96: “In order to model this restriction, let m be the number of groups. We may assign m groups instead of n bidders to the slots. Thus, we may set n to be m. Each of the incompatibility groups may receive, for each particular k, an aggregated probability of exactly yk of being assigned some position. Using these notations, the restriction may be established as follows:
∀G∀kΣi∈GΣj=1 mxi,j,k=yk (4′″) where G ranges over the bidder groups. A bidder group may be defined as a group of ads. Specifically, it may be assumed that each bidder has one ad (even though a bidder may actually place a bid for more than one ad). By incorporating this new restriction into the polyhedron, we can identify a point in the polyhedron using one or more objective functions. For instance, an objective function may be used to identify a set of good “group allocations.” From this set of group allocations, a single allocation may be selected. For each k such that yk>0, we can construct a matrix Z(k) that will be used to choose group permutations instead of bidder permutations and then, for each allocated group, choose a bidder to use the slot. Z(k) may be a matrix of m x m such that for 1≦l, j≦m, Zl,j (k)=Σi∈G t xi,j,k/yk. Each row and column of Z(k) sums up to 1 and therefore it is a doubly stochastic matrix. Therefore, we can apply the Birkhoff-von Neumann theorem as set forth above with respect to FIG. 3”. Examiner interprets each row and column of Z(k) sums up to 1 and therefore Z(k) being a doubly stochastic matrix as well as Birkhoff theorem being applied, to be equivalent as the claimed “the assignment rule is a doubly stochastic matrix, and the unit polytope is a Birkhoff polytope”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Cao and Oswald with the above teachings of Mydlarz by initializing the assignment rule and providing the first and second set as well as iteratively ascertaining a cost function that characterizes distances and optimizing the assignment rule as a function of the cost function so that an assignment of the first variables to the second variables according to the assignment rule minimizes the cost function, as taught by Cao and Oswald, and the assignment rule being a doubly stochastic matrix, and the unit polytope being a Birkhoff polytope., as taught by Mydlarz. The modification would have been obvious because one of ordinary skill in the art would be motivated to identify a set of good group allocations (as suggested by Mydlarz at Pg. 95: “By incorporating this new restriction into the polyhedron, we can identify a point in the polyhedron using one or more objective functions. For instance, an objective function may be used to identify a set of good “group allocations.” From this set of group allocations, a single allocation may be selected”).
Referring to Claim 4, Cao - Oswald teaches the method of claim 1:
However, Cao - Oswald fails to teach:
wherein an optimized assignment rule is mapped to a true permutation matrix at a conclusion of the repetitions of the steps a) to c).
Mydlarz teaches, in analogous system,
wherein an optimized assignment rule is mapped to a true permutation matrix at a conclusion of the repetitions of the steps a) to c) (see Mydlarz at Pg. 97: “FIG. 4 is a process flow diagram illustrating an example method of identifying one of the set of the plurality of allocations as shown at 206 of FIG. 2 among incompatibility groups. First, k with probability yk may be, chosen at 402. Z(k) may be constructed at 404. Permutation matrices P1 and positive numbers λ1 may be found such that Σk λ1=1 and Z(k)=Σk λ1P1 at 406. A permutation matrix P1 with probability λ1 may be chosen at 408 (e.g., via the Birkhoff-von Neumann decomposition algorithm or variations of that algorithm). For j=1 to k, let g be such that P1[g,j]=1. Ad i of group g may be chosen with probability xi,j,k/∑i∈gxi,j,k”. Examiner interprets finding the permutation matrices P1 (interpreted as true permutation) and positive numbers λ1, such that Σk λ1=1 and Z(k)=Σk λ1P1 (interpreted as optimized assignment rule) to be equivalent as the claimed “an optimized assignment rule is mapped to a true permutation matrix”, chosen Ad i of group g when P1[g,j]=1 for j=1 to k is interpreted to be equivalent as “conclusion of the repetitions of the steps a) to c)”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Cao and Oswald with the above teachings of Mydlarz by initializing the assignment rule and providing the first and second set as well as iteratively ascertaining a cost function that characterizes distances and optimizing the assignment rule as a function of the cost function so that an assignment of the first variables to the second variables according to the assignment rule minimizes the cost function, as taught by Cao and Oswald, and optimizing the assignment rule that maps to the true permutation matrix, as taught by Mydlarz. The modification would have been obvious because one of ordinary skill in the art would be motivated to identify one of the set of the plurality of allocations among incompatibility groups (as suggested by Mydlarz at Pg. 97: “FIG. 4 is a process flow diagram illustrating an example method of identifying one of the set of the plurality of allocations as shown at 206 of FIG. 2 among incompatibility groups. First, k with probability yk may be, chosen at 402. Z(k) may be constructed at 404. Permutation matrices P1 and positive numbers λ1 may be found such that Σk λ1=1 and Z(k)=Σk λ1P1 at 406. A permutation matrix P1 with probability λ1 may be chosen at 408 (e.g., via the Birkhoff-von Neumann decomposition algorithm or variations of that algorithm). For j=1 to k, let g be such that P1[g,j]=1. Ad i of group g may be chosen with probability xi,j,k/∑i∈gxi,j,k”).
Claims 6-7 are rejected under 35 U.S.C. 103 as being unpatentable over Cao et al (US-11847538-B2 - hereinafter Cao) in view of Oswald et al (US-11675937-B2 - hereinafter Oswald) in further view of Stewart et al (US-6890773-B1– hereinafter Stewart).
Referring to Claim 6, Cao - Oswald teaches the method of claim 1:
However, Cao - Oswald fails to teach:
wherein the first and second variables characterize products during their production following different production process steps, and the assignment rule characterizes which of the variables of the first and second set characterize the same product.
Stewart teaches, in analogous system,
wherein the first and second variables characterize products during their production following different production process steps, and the assignment rule characterizes which of the variables of the first and second set characterize the same product (see Stewart at Cl. 9 Ln. 22-44: “Turning now to FIG. 6, a flow chart depiction of the methods associated with embodiments of the present invention is illustrated. The system 300 processes a plurality of semiconductor wafers 105 (block 610). Upon processing of semiconductor wafers 105, the system 300 may acquire metrology data associated with the processed semiconductor wafers 105 (block 620). The system 300 may also acquire manufacturing environment sensor data relating to the processing tool 510 during the processing of the semiconductor wafers 105 (block 630). The manufacturing environment sensor data may include temperature data, humidity data, gas flow rate data, pressure data, and the like. The system 300 may then correlate the metrology data with the particular manufacturing environment sensor data to provide the data for fault detection analysis (block 640). The system 300 then performs an error-trend sorting function, which is used to sort actual errors on the processed semiconductor wafers 105 and separate them from trends that reflect drifts or miss-calibration of manufacturing components associated with the system 300 (block 650). A more detailed flowchart illustration of the steps relating to the errors/trend sorting function indicated in block 650 of FIG. 6, is provided in FIG. 7 and accompanying description below”. Examiner interprets the system 300 performing correlation (interpreted as assignment rule) between the metrology data (interpreted as first variables) associated with the processed semiconductor wafers (interpreted as products), with the particular manufacturing environment sensor data (interpreted as second variables) relating to the processing tool 510 during the processing of the semiconductor wafers (interpreted as products) to be equivalent as the claimed “first and second variables characterize products during their production following different production process steps, and the assignment rule characterizes which of the variables of the first and second set characterize the same product”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Cao and Oswald with the above teachings of Stewart by initializing the assignment rule and providing the first and second set as well as iteratively ascertaining a cost function that characterizes distances and optimizing the assignment rule as a function of the cost function so that an assignment of the first variables to the second variables according to the assignment rule minimizes the cost function, as taught by Cao and Oswald, and the first and second variables characterizing products during their production following different production process steps, and the assignment rule characterizing which of the variables of the first and second set characterize the same product, as taught by Stewart. The modification would have been obvious because one of ordinary skill in the art would be motivated to sort actual errors on the processed semiconductor wafers and separate them from trends that reflect drifts or miss-calibration of manufacturing components (as suggested by Stewart at Cl. 9 Ln. 22-44: “Turning now to FIG. 6, a flow chart depiction of the methods associated with embodiments of the present invention is illustrated. The system 300 processes a plurality of semiconductor wafers 105 (block 610). Upon processing of semiconductor wafers 105, the system 300 may acquire metrology data associated with the processed semiconductor wafers 105 (block 620). The system 300 may also acquire manufacturing environment sensor data relating to the processing tool 510 during the processing of the semiconductor wafers 105 (block 630). The manufacturing environment sensor data may include temperature data, humidity data, gas flow rate data, pressure data, and the like. The system 300 may then correlate the metrology data with the particular manufacturing environment sensor data to provide the data for fault detection analysis (block 640). The system 300 then performs an error-trend sorting function, which is used to sort actual errors on the processed semiconductor wafers 105 and separate them from trends that reflect drifts or miss-calibration of manufacturing components associated with the system 300 (block 650). A more detailed flowchart illustration of the steps relating to the errors/trend sorting function indicated in block 650 of FIG. 6, is provided in FIG. 7 and accompanying description below”).
Referring to Claim 7, Cao - Oswald teaches the method of claim 1:
However, Cao - Oswald fails to teach:
wherein the first variables are first test results from semiconductor device elements on a wafer, and the second variables are second test results of the semiconductor device elements after they have been cut out of the wafer, and the assignment rule characterizes which first and second test results originate from the same semiconductor device element.
Stewart teaches, in analogous system,
wherein the first variables are first test results from semiconductor device elements on a wafer, and the second variables are second test results of the semiconductor device elements after they have been cut out of the wafer, and the assignment rule characterizes which first and second test results originate from the same semiconductor device element (see Stewart at Cl. 9 Ln. 22-44: “Turning now to FIG. 6, a flow chart depiction of the methods associated with embodiments of the present invention is illustrated. The system 300 processes a plurality of semiconductor wafers 105 (block 610). Upon processing of semiconductor wafers 105, the system 300 may acquire metrology data associated with the processed semiconductor wafers 105 (block 620). The system 300 may also acquire manufacturing environment sensor data relating to the processing tool 510 during the processing of the semiconductor wafers 105 (block 630). The manufacturing environment sensor data may include temperature data, humidity data, gas flow rate data, pressure data, and the like. The system 300 may then correlate the metrology data with the particular manufacturing environment sensor data to provide the data for fault detection analysis (block 640). The system 300 then performs an error-trend sorting function, which is used to sort actual errors on the processed semiconductor wafers 105 and separate them from trends that reflect drifts or miss-calibration of manufacturing components associated with the system 300 (block 650). A more detailed flowchart illustration of the steps relating to the errors/trend sorting function indicated in block 650 of FIG. 6, is provided in FIG. 7 and accompanying description below”. Examiner interprets the system 300 performing correlation (interpreted as assignment rule) between the metrology data (associated with the processed semiconductor wafers and interpreted as first variables) with the particular manufacturing environment sensor data (relating to the processing tool 510 during the processing of the semiconductor wafers and interpreted as second variables) to be equivalent as the claimed “the first variables are first test results from semiconductor device elements on a wafer, and the second variables are second test results of the semiconductor device elements after they have been cut out of the wafer, and the assignment rule characterizes which first and second test results originate from the same semiconductor device element”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Cao and Oswald with the above teachings of Stewart by initializing the assignment rule and providing the first and second set as well as iteratively ascertaining a cost function that characterizes distances and optimizing the assignment rule as a function of the cost function so that an assignment of the first variables to the second variables according to the assignment rule minimizes the cost function, as taught by Cao and Oswald, and the first variables being first test results from semiconductor device elements on a wafer, and the second variables being second test results of the semiconductor device elements after they have been cut out of the wafer, and the assignment rule characterizing which first and second test results originate from the same semiconductor device element, as taught by Stewart. The modification would have been obvious because one of ordinary skill in the art would be motivated to sort actual errors on the processed semiconductor wafers and separate them from trends that reflect drifts or miss-calibration of manufacturing components (as suggested by Stewart at Cl. 9 Ln. 22-44: “Turning now to FIG. 6, a flow chart depiction of the methods associated with embodiments of the present invention is illustrated. The system 300 processes a plurality of semiconductor wafers 105 (block 610). Upon processing of semiconductor wafers 105, the system 300 may acquire metrology data associated with the processed semiconductor wafers 105 (block 620). The system 300 may also acquire manufacturing environment sensor data relating to the processing tool 510 during the processing of the semiconductor wafers 105 (block 630). The manufacturing environment sensor data may include temperature data, humidity data, gas flow rate data, pressure data, and the like. The system 300 may then correlate the metrology data with the particular manufacturing environment sensor data to provide the data for fault detection analysis (block 640). The system 300 then performs an error-trend sorting function, which is used to sort actual errors on the processed semiconductor wafers 105 and separate them from trends that reflect drifts or miss-calibration of manufacturing components associated with the system 300 (block 650). A more detailed flowchart illustration of the steps relating to the errors/trend sorting function indicated in block 650 of FIG. 6, is provided in FIG. 7 and accompanying description below”).
Referring to independent Claims 8 and 9, these claims are rejected on the same basis as independent claim 1 since they are analogous claims.
Allowable Subject Matter
Claim 5 objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims. Examiner notes that the claim 5 must also be amended rejection under 35 U.S.C. 101 prior to allowance.
While Mydlarz et al (US-20100161581-A1) recites the assignment rule being a doubly stochastic matrix, and the unit polytope being the Birkhoff polytope, and the optimized assignment rule being mapped to a true permutation matrix and Cao et al (US-11847538-B2) recites optimizing the assignment rule as a function of the cost function, none explicitly recite a direction in the Birkhoff polytope is ascertained in which the cost function does not change, and the ascertaining of the direction and the mapping are repeated multiple times until a vertex of the Birkhoff polytope that corresponds to a permutation matrix is reached. Therefore, the explicit recitation of the aforementioned limitation in claim 5 constitutes a distinct feature of the claimed invention over the prior arts. When viewed individually or in as a combination with other prior arts of record, the limitation specified in claim 5 is distinct.
Conclusion
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/AWADAGBE G HOUNTON/Examiner, Art Unit 2126
/DAVID YI/Supervisory Patent Examiner, Art Unit 2126
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