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.
Claims 1-8, 10-14, 20-22, 24 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Below is a claim-by-claim analysis.
Claim 1, 11, 20
Step 1: Recites a system (claim 1, claim 11), a method (claim 20). Therefore, they are directed to the statutory categories of invention.
Step 2A Prong 1: The claim recites:
Analyze a set of data points to generate a subset of data points that violate a trend rule
Analysis of data points to determine whether one follows a generic trend rule is a mental process that can be done in one’s head
Average the subset of data points to establish a pooled data point value
Simple arithmetic over a set of data points is a mental process that can be done in one’s head, (with aid of paper and pencil, MPEP 2106.04(a)(2), III)
Adjust the pooled data point value to satisfy an upper boundary and a lower boundary corresponding to respective subset data point interval bound information to generate a minimizer
The adjustment of data value to fit within a set boundary of values is, in its broadest interpretation, simple arithmetic that can be done in one’s head, (with aid of paper and pencil, MPEP 2106.04(a)(2), III)
Step 2B Prong 2: The judicial exception is not integrated into a practical application. There are no further
limitations in the claim.
Step 2B: The claim does not contain significantly more than the judicial exception. The analysis mirrors the analysis of step 2A prong 2.
Claim 2, 12, 24
Step 1: Recites a system (claim 2, claim 12), a method (claim 24). Therefore, they are directed to the statutory categories of invention.
Step 2A Prong 1: The claim recites the abstract idea it inherits from the claim it depends on.
Step 2B Prong 2: The judicial exception is not integrated into a practical application. The remaining limitation of the claim is directed to additional element of the creation of the subset of data (“…the subset of data points is complete if a non-violator data point is met”), which isn’t enough to render it more than the judicial exception it recited.
Step 2B: The claim does not contain significantly more than the judicial exception. The analysis mirrors the analysis of step 2A prong 2.
Claim 3, 13
Step 1: Recites a system (claim 3, claim 13). Therefore, they are directed to the statutory categories of invention.
Step 2A Prong 1: The claim recites the abstract idea it inherits from the claim it depends on.
Step 2B Prong 2: The judicial exception is not integrated into a practical application. The remaining limitation of the claim is directed to additional element of the data trend to be analyzed (“…the trend rule is the set of data point values that are non-decreasing”), which isn’t enough to render it more than the judicial exception it recited.
Step 2B: The claim does not contain significantly more than the judicial exception. The analysis mirrors the analysis of step 2A prong 2.
Claim 4, 14
Step 1: Recites a system (claim 4, claim 14). Therefore, they are directed to the statutory categories of invention.
Step 2A Prong 1: The claim recites the abstract idea it inherits from the claim it depends on.
Step 2B Prong 2: The judicial exception is not integrated into a practical application. The remaining limitation of the claim is directed to additional element of the data trend to be analyzed (“…the trend rule is the set of data point values that are non-increasing”), which isn’t enough to render it more than the judicial exception it recited.
Step 2B: The claim does not contain significantly more than the judicial exception. The analysis mirrors the analysis of step 2A prong 2.
Claim 5
Step 1: Recites a system (claim 5). Therefore, it is directed to the statutory categories of invention.
Step 2A Prong 1: The claim recites the abstract idea it inherits from the claim it depends on.
Step 2B Prong 2: The judicial exception is not integrated into a practical application. The remaining limitation of the claim is directed to merely a hardware application of the recited judicial exception (“…wherein the programmable circuitry is to generate the minimizer for the subset of data points”), which isn’t enough to render it more than the judicial exception it recited.
Step 2B: The claim does not contain significantly more than the judicial exception. The analysis mirrors the analysis of step 2A prong 2.
Claim 6
Step 1: Recites a system (claim 6). Therefore, it is directed to the statutory categories of invention.
Step 2A Prong 1: The claim recites the abstract idea it inherits from the claim it depends on.
Step 2B Prong 2: The judicial exception is not integrated into a practical application. The remaining limitation of the claim is directed to merely a hardware application of the recited judicial exception (“…wherein the programmable circuitry is to generate the minimizer for each data point within the subset of data points”), which isn’t enough to render it more than the judicial exception it recited.
Step 2B: The claim does not contain significantly more than the judicial exception. The analysis mirrors the analysis of step 2A prong 2.
Claim 7, 21
Step 1: Recites a system (claim 7) and a method (claim 21). Therefore, they are directed to the statutory categories of invention.
Step 2A Prong 1: The claim recites the abstract idea it inherits from the claim it depends on.
Step 2B Prong 2: The judicial exception is not integrated into a practical application. The remaining limitation of the claim is directed to further information about the data points (“…wherein the set of data points includes interval bound information including upper bound values and lower bound values”), which isn’t enough to render it more than the judicial exception it recited.
Step 2B: The claim does not contain significantly more than the judicial exception. The analysis mirrors the analysis of step 2A prong 2.
Claim 8, 22
Step 1: Recites a system (claim 8) and a method (claim 22). Therefore, they are directed to the statutory categories of invention.
Step 2A Prong 1: The claim recites the abstract idea it inherits from the claim it depends on.
Additionally, it recites:
the programmable circuitry to determine the set of data points values satisfy the lower boundary and the upper boundary
Determining if a data point is between two values is a mental process that can be done in one’s head
Step 2B Prong 2: The judicial exception is not integrated into a practical application. The remaining limitation of the claim is directed to further information about what the boundary values are based on (“…wherein the lower boundary and the upper boundary are based on the interval bound information”), which isn’t enough to render it more than the judicial exception it recited.
Step 2B: The claim does not contain significantly more than the judicial exception. The analysis mirrors the analysis of step 2A prong 2.
Claim 10
Step 1: Recites a system (claim 19). Therefore, it is directed to the statutory categories of invention.
Step 2A Prong 1: The claim recites the abstract idea it inherits from the claim it depends on.
Step 2B Prong 2: The judicial exception is not integrated into a practical application. The remaining limitation of the claim is directed to directed to additional element of the application of the adjustment of the pooled data point (“…wherein the set of data points is revised with minimizers corresponding to respective ones of the pooled data points within the subset of data points”), which isn’t enough to render it more than the judicial exception it recited.
Step 2B: The claim does not contain significantly more than the judicial exception. The analysis mirrors the analysis of step 2A prong 2.
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.
Claims 1, 3-4, 6-8, 10-11, 13-14, 20-22 are rejected under 35 U.S.C. 103 as being unpatentable over Ahuja et al. (“A Fast Scaling Algorithm for Minimizing Separable Convex Functions Subject to Chain Constraints”, 2000) in view of Tibshirani et al. (“Nearly-Isotonic Regression”, 2010) and in further view of Winston et al. (“US 10762166”, 2019).
Regarding claim 1, Ahuja teaches an…1 to improve isotonic regression (Page 785, Column 1, Paragraph 3, “In this note, we focus on developing a faster algorithm to obtain an integer optimal solution of the generalized isotonic regression problem”), …to:
analyze a set of data points to generate a subset of data points that violate a trend rule (Page 786, Column 2, Figure 12);
adjust the pooled data point value (Page 786, Column 2, Figure 13) to satisfy an upper boundary and a lower boundary corresponding to respective subset data point interval bound information to generate a minimizer (Page 785, Column 2, Paragraph 2, “Let θj denote the value of xj at which C j (xj) attains the minimum value; if θj is nonunique, we choose the minimum among such values. Let θmin = min{θj : j ∈ N} and θmax = max{θj : j ∈ N}. It is easy to observe that there exists an optimal solution x∗ of (1) where θmin <= x∗j <= θmax for all j ∈ N . Therefore, we can set l = θmin and u = θmax, and require that l <= x∗j <= u for all j ∈ N”)
Ahuja fails to teach an apparatus comprising: interface circuitry; machine readable instructions; and programmable circuitry, and the averaging of the subset of data points to establish a pooled data point value.
However:
Tibshirani teaches the averaging of the subset of data points to establish a pooled data point value (Page 1, Paragraph 4, “Roughly speaking, PAVA works as follows. We start with y1 on the left. We move to the right until we encounter the first violation yi > yi+1. Then we replace this pair by their average, and back-average to the left as needed”), and
Winston teaches an apparatus for improving a form of statistical analysis (Abstract, “... systems, can computer program products for optimizing statistical method of computing output yields”) comprising interface circuitry (Figure 34), machine readable instructions (Column 14, Lines 10-13, “Computer readable program instructions for carrying out operations of the present invention may be assembler instructions…”) and programmable circuitry to at least one of instantiate or execute the machine readable instructions to (Column 14, Lines 30-35, “In some embodiments, electronic circuitry including, for example, programmable logic circuitry, field-programmable gate arrays (FPGA), or programmable logic arrays (PLA) may execute the computer readable program instruction”)
Ahuja, Tibshirani, and Winston are considered analogous to the invention because all are directed towards improving methods of statistical analysis. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to have modified Ahuja to incorporate the teachings of Tibshirani and Winston and average violators to create a pooled data point and then incorporate said improvement of isotonic regression within an apparatus. Doing so helps to reduce the distance between the points and the regression plot that follows.
Regarding claim 3, Ahuja teaches wherein the trend rule is the set of data point values that are non-decreasing (Page 784, Column 1, Equation (1b), “x1 <= x2 … <= xn”, Paragraph 2, “where Cj (xj) is a convex function of xj …”)
Regarding claim 4, Ahuja fails to teach wherein the trend rule is the set of data point values that are non-increasing. However, Tibshirani teaches wherein the trend rule is the set of data point values that are non-increasing (Page 1, Paragraph 1-2, “how can we best summarize this by a monotone sequence… Here and throughout we assume that “monotone” means monotone nondecreasing: an analogous problem statement exists for monotone nonincreasing.”).
Ahuja and Tibshirani are considered analogous to the invention because all are directed towards methods of isotonic regression. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to have modified Ahuja to incorporate the teachings of Tibshirani and also allow the data point trend to be non-increasing. Doing so expands the possible use cases of the isotonic regression.
Regarding claim 6, Ahuja teaches generating the minimizer for each data point within the subset of data points (see claim 1 analysis5). Ahuja fails to teach the use of programmable circuitry. However, Winston teaches the use of programmable circuitry (see claim 1 analysis).
Ahuja and Winston are considered analogous to the invention because all are directed towards improving methods of statistical analysis. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to have modified Ahuja to incorporate the teachings of Winston and applied the improvement of isotonic regression within an apparatus. Doing so expands the use of the algorithm from the theoretical to the practical.
Regarding claim 7, Ahuja teaches wherein the set of data points includes interval bound information including upper bound values and lower bound values (see claim 1 analysis6).
Regarding claim 8, Ahuja teaches wherein the lower boundary and upper boundary are based on the interval bound information (see claim 7 analysis) and determining the set of data point values that satisfy the lower boundary and the upper boundary (see claim 1 analysis). Ahuja fails to teach the use of programmable circuitry. However, Winston teaches the use of programmable circuitry (see claim 1 analysis).
Ahuja and Winston are considered analogous to the invention because all are directed towards improving methods of statistical analysis. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to have modified Ahuja to incorporate the teachings of Winston and applied the improvement of isotonic regression within an apparatus. Doing so expands the use of the algorithm from the theoretical to the practical.
Regarding claim 10, Ahuja teaches wherein the set of data points is revised with minimizers corresponding to respective ones of the pooled data points within the subset of data points (Page 786, Column 2, Figure 17).
Regarding claim 11, Regarding claim 11, Ahuja teaches an…8 to improve isotonic regression (Page 785, Column 1, Paragraph 3, “In this note, we focus on developing a faster algorithm to obtain an integer optimal solution of the generalized isotonic regression problem”), …to:
analyze a set of data points to generate a subset of data points that violate a trend rule (Page 786, Column 2, Figure 19);
adjust the pooled data point value (Page 786, Column 2, Figure 110) to satisfy an upper boundary and a lower boundary corresponding to respective subset data point interval bound information to generate a minimizer (Page 785, Column 2, Paragraph 2, “Let θj denote the value of xj at which C j (xj) attains the minimum value; if θj is nonunique, we choose the minimum among such values. Let θmin = min{θj : j ∈ N} and θmax = max{θj : j ∈ N}. It is easy to observe that there exists an optimal solution x∗ of (1) where θmin <= x∗j <= θmax for all j ∈ N . Therefore, we can set l = θmin and u = θmax, and require that l <= x∗j <= u for all j ∈ N”)
Ahuja fails to teach an apparatus comprising: interface circuitry to retrieve data; computer readable instructions; and programmable circuitry to instantiate, interim analysis circuitry, minimizer circuitry and the averaging of the subset of data points to establish a pooled data point value.
However:
Winston teaches an apparatus for improving a form of statistical analysis (Abstract, “... systems, can computer program products for optimizing statistical method of computing output yields”) comprising interface circuitry (Figure 311), computer readable instructions (Column 14, Lines 10-13, “Computer readable program instructions for carrying out operations of the present invention may be assembler instructions…”) and programmable circuitry to instantiate interim analysis circuitry (Column 14, Lines 30-35, “In some embodiments, electronic circuitry including, for example, programmable logic circuitry, field-programmable gate arrays (FPGA), or programmable logic arrays (PLA) may execute the computer readable program instruction”)12, …and minimizer circuitry13…
Ahuja, Tibshirani, and Winston are considered analogous to the invention because all are directed towards improving methods of statistical analysis. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to have modified Ahuja to incorporate the teachings of Tibshirani and Winston and average violators to create a pooled data point and then incorporate said improvement of isotonic regression within an apparatus. Doing so helps to reduce the distance between the points and the regression plot that follows.
Claim 13 is an apparatus claim dependent on independent claim 11 whose specific limitation corresponds to apparatus claim 3 and is rejected for the same reasons as given in the rejection of that claim.
Claim 14 is an apparatus claim dependent on independent claim 11 whose specific limitation corresponds to apparatus claim 4 and is rejected for the same reasons as given in the rejection of that claim.
Claim 20 is a method claim corresponding to apparatus claim 1 and is rejected for the same reasons as given in the rejection of that claim.
Claim 21 is a method claim corresponding to apparatus claim 7 and is rejected for the same reasons as given in the rejection of that claim.
Claim 22 is a method claim corresponding to apparatus claim 8 and is rejected for the same reasons as given in the rejection of that claim.
Claims 2, 12, 24 are rejected under 35 U.S.C. 103 as being unpatentable over Ahuja et al. (“A Fast Scaling Algorithm for Minimizing Separable Convex Functions Subject to Chain Constraints”, 2000) in view of Tibshirani et al. (“Nearly-Isotonic Regression”, 2023) and in further view of Winston et al. (“US 10762166”, 2019) and Kim et al. (“Global estimation of the 3′ untranslated region landscape using RNA sequencing”, 2015).
Regarding claim 2, Ahuja fails to teach wherein the subset of data points includes adjacent violators of the trend rule, and the subset of data points is complete if a non-violator data point of the trend rule is met. However, Kim teaches wherein the subset of data points includes adjacent violators of the trend rule, and the subset of data points is complete if a non-violator data point of the trend rule is met (Page 113, Figure 214).
Ahuja and Kim are considered analogous to the invention because all are directed isotonic regression implementation. Therefore, it would have been obvious to one of ordinary skill in the
art before the effective filing date of the invention to have modified Ahuja to incorporate the teachings of Kim and incorporate a means for subset creation that stops when a non-violating data point is met. Averaging the points after the entire subset is created, rather than a pair at a time is more computationally efficient.
Claim 12 is an apparatus claim dependent on independent claim 11 whose specific limitation corresponds to apparatus claim 2 and is rejected for the same reasons as given in the rejection of that claim.
Claim 24 is a method claim corresponding to apparatus claim 2 and is rejected for the same reasons as given in the rejection of that claim.
Claim 5 is rejected under 35 U.S.C. 103 as being unpatentable over Ahuja et al. (“A Fast Scaling Algorithm for Minimizing Separable Convex Functions Subject to Chain Constraints”, 2000) in view of Tibshirani et al. (“Nearly-Isotonic Regression”, 2023) and in further view of Winston et al. (“US 10762166”, 2019) and Canard et al. (“US 20190050599”).
Regarding claim 5, Ahuja teaches the generation and use of a minimizer (see claim 1 analysis). Ahuja fails to teach the use of programmable circuitry, and the generation of the minimizer for the subset of data.
However:
Winston teaches the use of programmable circuitry (see claim 1 analysis), and
Canard teaches the application of a type of minimizer to a selected subset of data points (Paragraph 15-19, “a partitioning step for partitioning the sensitive data set into a plurality of subsets as a function of a sensitivity level of the sensitive data… adding noise to the sensitive data of that subset with a noise level that depends on the sensitivity level determined for the subset”)15
Ahuja, Winston and Canard are considered analogous to the invention because all are directed methods of statistical analysis. Therefore, it would have been obvious to one of ordinary skill in the
art before the effective filing date of the invention to have modified Ahuja to incorporate the teachings of Canard and apply the minimizer for the subset of data points. Applying the minimizer to the whole subset, instead of each individual data point reduces computation.
Claims 9, 19, 23 are rejected under 35 U.S.C. 103 as being unpatentable over Ahuja et al. (“A Fast Scaling Algorithm for Minimizing Separable Convex Functions Subject to Chain Constraints”, 2000) in view of Tibshirani et al. (“Nearly-Isotonic Regression”, 2023) and in further view of Winston et al. (“US 10762166”, 2019) and Tang et al. (“US 12299033”, 2021).
Regarding claim 9, Ahuja teaches the use of upper and lower bounds (see claim 1 analysis) and an evaluation of the two (Page 784, Abstract, Let U denote the difference between an upper bound on an optimal value of xn and a lower bound on an optimal value of x1)16. Ahuja fails to teach evaluating whether ones of the lower bound values are less than respective ones of the upper bound values, and one of (a) maintain model training when the ones of the lower bound values are less than respective ones of the upper bound values or (b) prevent model training if any lower bound value is greater than the respective upper bound value. However, Tang teaches evaluating whether ones of the [preset threshold] values are less than respective ones of the [loss] values (Column 7, Lines 34-35, “…determining whether the loss value is less than a preset threshold”) and one of (a) maintain model training when the ones of the [preset threshold] values are less than respective ones of the [loss] values or (b) prevent model training if any [preset threshold] value is greater than the respective [loss] value (Column 7, Lines 36-40, “…returning to the step of acquiring the image sample and continue the training in a case that the loss value is not less than the preset threshold; and determining that the training stop condition is reached in a case that the loss value is less than the preset threshold.”)17
Ahuja and Tang are considered analogous to the invention because all are directed towards forms of statistical analysis. Therefore, it would have been obvious to one of ordinary skill in the
art before the effective filing date of the invention to have modified Ahuja to incorporate the teachings of Tang and incorporate a means stopping model training if specific value criteria isn’t meant. Doing so reduces unnecessary calculations and improves computational efficiency.
Claim 19 is an apparatus claim corresponding to apparatus claim 9 and is rejected for the same reasons as given in the rejection of that claim.
Claim 23 is an apparatus claim corresponding to apparatus claim 9 and is rejected for the same reasons as given in the rejection of that claim.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MATTHEWOS MESFIN whose telephone number is (571)270-0782. The examiner can normally be reached Monday-Friday 8am-5pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Cesar Paula can be reached at (571) 272-4128. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/MATTHEWOS MESFIN/Examiner, Art Unit 2145
/CESAR B PAULA/Supervisory Patent Examiner, Art Unit 2145
1 Ahuja teaches an algorithm for improving isotonic regression. That, in combination with the apparatus taught in Winston covers the whole limitation.
2 Data points: “J = [[1,1],[2,2],…[n,n]];…”. Subset that violates trend rule: “…select a pair of out-of-order blocks”
3 “for each block [p, q] ∈ J do x*j = θpq...x*is an optimal solution of the generalized isotonic regression problem”. This takes place after the merging done earlier in the algorithm. Θ would thus be our minimizer.
4 I/O Adapter
5 Said reference shows the algorithm generate a minimizer (θ) for each point within the subset (“for all j ∈ [p,q]”)
6 The interval bound being l (lower bound) and u (upper bound) in the reference.
7 The algorithm shows the revising of data points (x*j = θpq) corresponding to the respective pooled data points within the subset of data point (“for all j ∈ [p, q]”). Using our minimizer (θ) we find x* (our revised value, i.e. the optimal solution).
8 Ahuja teaches an algorithm for improving isotonic regression. That, in combination with the apparatus taught in Winston covers the whole limitation.
9 Data points: “J = [[1,1],[2,2],…[n,n]];…”. Subset that violates trend rule: “…select a pair of out-of-order blocks”
10 “for each block [p, q] ∈ J do x*j = θpq...x*is an optimal solution of the generalized isotonic regression problem”. This takes place after the merging done earlier in the algorithm.
11 I/O Adapter
12 Winston doesn’t teach hardware
13 The various circuitry listed is covered by the above reference, and their use cases are covered by Ahuja and Tibshirani
14 The PAV algorithm shows that items are added to V while a trend is violated, and keeps going until it isn’t (when it breaks out of Sj < Si loop, the subset is averaged and then cleared)
15 Note that the type of minimization used here is different from what is claimed. Canard specifically teaches the application of a function on a subset (as opposed to single data points). In combination with what is taught In Ahuja (generation and use of a minimizer on data points) and Winston (a programmable circuitry) the whole limitation is covered.
16 A value between the difference of the two bounds implies an evaluation of sorts
17 Tang doesn’t teach the evaluation of lower and upper bounds, but in combination with Ahuja, the whole limitation is covered
Prosecution Insights