Notice of Pre-AIA or AIA Status
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
DETAILED ACTION
Continued Examination Under 37 CFR 1.114
2. A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 11/21/2025 has been entered. Claims 1, 8, and 15 have been amended. Claims 1-20 remain pending in the application.
Information Disclosure Statement
3. The information disclosure statement (IDS(s)) submitted on 11/21/2025, 04/24/2026 is/are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Response to Arguments
4. Applicant’s arguments with respect to claims have been considered but are moot in view of new ground of rejection. See rejections below for details.
Double Patenting
5. The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP §§ 706.02(l)(1) - 706.02(l)(3) for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
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Claims 1-20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-20 of U.S. Patent No. US 12,045,435 B2. Although the claims at issue are not identical, they are not patentably distinct from each other because of the following mapping below. Each corresponding limitation is either identical or does not have a patentable, nonobvious distinction unless otherwise noted.
Instant Application 18/741,352
Patent No.: US 12,045,435 B2
Claim 1
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Claim 9
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Claim Rejections – 35 USC § 103
6. 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 of this title, 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.
7. Claims 1-4, 7-11, and 14-18 are rejected under 35 U.S.C. 103 as being unpatentable over Sinha et al. (U.S. Patent Application Pub. No. US 20170288989 A1) in view of Huang (U.S. Patent Application Pub. No. US 20210407866 A1), and further in view of Harsha et al. (U.S. Patent Application Pub. No. US 20220207413 A1).
Claim 1: Sinha teaches a method comprising:
receiving data characterizing an impact function, a first plurality of inputs to the impact function (i.e. With the pairwise associations, hierarchical clusters, and visualizations computed by data correlation engine 134, an administrator is able to build predictive models to predict which observed users 110 (or how many observed users 110) are statistically likely to perform certain actions with online service 120. For example, embodiments described herein enable an administrator to build a predictive model to predict which observed users 110 are statistically likely to complete an order or otherwise generate revenue at online service 120; para. [0031, 0065, 0083]);
calculating a first coefficient characterizing an interaction between a first input and a second input of the plurality of inputs (i.e. the data correlation engine 134 determines an uncertainty coefficient U(x,y) that falls between 0 and 1. A value closer to 1 denotes stronger association between variables X and Y (e.g., a stronger correlation between geographic location of a observed user 110 and the web browser used by observed user 110). A value closer to 0 denotes a lesser association between variables X and Y; para. [0068]);
displaying, in a graphical user interface (GUI), a graphical object including a plurality of regions, wherein a first region and a second region of the plurality of regions are indicative of the first input and the second input, respectively (i.e. FIGS. 5 and 6 depict example chord diagrams in which an administrator has focused on certain data variables for clearer visual depiction of the focused data variables. The user input is received via any suitable user interface method (e.g., a touch input selecting one or more chords, a mouse input via hovering or clicking on one or more chords, etc.). In FIG. 5, chords 502 and 504 are highlighted; para. [0080]);
displaying, in the GUI, a second graphical object connecting the first region to the second region, wherein a first visual characteristic of the second graphical object is indicative of the first coefficient (i.e. figs. 4-9, Pairwise associations between the categorical data variables are shown as chords connecting the data variables. A thicker chord indicates a stronger correlation between two data variables than a thinner chord. Example chord 402 shows the high correlation between the web browser used and the operating system used by observed users 110; para. [0079]).
Sinha does not explicitly teach a plurality of weights associated with the first plurality of inputs, wherein the impact function determines the impact of an output of a predictive process for an analytical task and is indicative of the output of the predictive process achieving a target output of the analytical task when inputs of the analytical task are the first plurality of inputs; a first set of weights of the plurality of weights, wherein the first set of weights are indicative of interaction between the first input and the second input.
However, Huang teaches a plurality of weights associated with the first plurality of inputs (i.e. where a, b, c . . . z are weight coefficients; para. [0030, 0031]); a first set of weights of the plurality of weights, wherein the first set of weights are indicative of interaction between the first input and the second input (i.e. where a, b, c . . . z are weight coefficients, which may be generated or evaluated based on a model established by past abnormal data (for example, weight coefficients may be calculated based on correlation coefficients recorded in a database, and the correlation coefficients are obtained by analyzing the relation between CP and EQP, the relation between CP and LQC, and the relation between CP and RTM); para. [0030, 0031]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to modify the invention of Sinha to include the feature of Huang. One would have been motivated to make this modification because it improves the calculated relationship/interaction analysis by weighting input factors according to their relative significant, so that the predictive output better reflects which inputs or correlations are more important.
However, Harsha teaches wherein the impact function (i.e. a loss function L (of input X, target output Y, and model predicted output ƒ(X;β)); para. [0025, 0026]) determines the impact of an output of a predictive process (i.e. a loss function L (of input X, target output Y, and model predicted output ƒ(X;β)) is to be minimized; para. [0026]) for an analytical task (i.e. Deep Learning has enabled obtaining state-of-the-art results for a variety of predictive tasks and applications in many domains (e.g., image classification, text classification, language modeling, translation, game playing through reinforcement learning beating world champions, etc.,); para. [0002]) and is indicative of the output of the predictive process achieving a target output of the analytical task (i.e. a loss function L (of input X, target output Y, and model predicted output ƒ(X;β)) … The original loss function objective is modified by adding a penalty-based loss function related to constraint; para. [0026, 0028]) when inputs of the analytical task are the first plurality of inputs (i.e. a loss function L (of input X, target output Y, and model predicted output ƒ(X;β)) is to be minimized; para. [0017, 0026]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to modify the combination of Sinha and Huang to include the feature of Harsha. One would have been motivated to make this modification because this would allow the system to determine how closely a predictive output achieves a desired target output and to improve the predictive model’s accuracy and generalization.
Claim 2: Sinha, Huang, and Harsha teach the method of claim 1. Sinha further teaches wherein the graphical object depicts a circle, wherein the first region and the second region are a first portion of a circumference of the circle and a second portion of the circumference of the circle (fig. 4, Example chord 402 shows the high correlation between the web browser used and the operating system used by observed users 110. FIG. 4 also illustrates that the arc length (i.e. the percentage of the circle taken by a data variable) provided to each data variable is proportional to the sum of its association values (correlation strengths) with all other data variables. For example, the variable for operating system 410 (i.e. variable tracking the operating system used by observed users 110) has a longer arc than the variable for account 420 (i.e. variable tracking the duration of time when observed users 110 access an account information section of online service 120); para. [0079]).
Claim 3: Sinha, Huang, and Harsha teach the method of claim 2. Sinha further teaches wherein the second graphical object depicts a chord connecting the first portion of the circumference of the circle to the second portion of the circumference of the circle, wherein the first visual characteristic is a thickness of the chord (i.e. figs. 4-9, Pairwise associations between the categorical data variables are shown as chords connecting the data variables. A thicker chord indicates a stronger correlation between two data variables than a thinner chord. Example chord 402 shows the high correlation between the web browser used and the operating system used by observed users 110; para. [0079]).
Claim 4: Sinha, Huang, and Harsha teach the method of claim 1. Sinha further teaches wherein the impact function is a sum of a plurality of terms (i.e. the data correlation engine 134 determines an uncertainty coefficient U(x,y) that falls between 0 and 1. A value closer to 1 denotes stronger association between variables X and Y (e.g., a stronger correlation between geographic location of a observed user 110 and the web browser used by observed user 110). A value closer to 0 denotes a lesser association between variables X and Y; para. [0067, 0068]).
Sinha does not explicitly teach wherein each term of the plurality of terms is a product of a weight of the plurality of weights and at least one input of the first plurality of inputs.
However, Huang further teaches wherein the impact function is a sum of a plurality of terms, wherein each term of the plurality of terms is a product of a weight of the plurality of weights and at least one input of the first plurality of inputs (i.e. where a, b, c . . . z are weight coefficients, which may be generated or evaluated based on a model established by past abnormal data (for example, weight coefficients may be calculated based on correlation coefficients recorded in a database, and the correlation coefficients are obtained by analyzing the relation between CP and EQP, the relation between CP and LQC, and the relation between CP and RTM); para. [0030, 0031]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to modify the combination of Sinha and Harsha to include the feature of Huang. One would have been motivated to make this modification because it improves the calculated relationship/interaction analysis by weighting input factors according to their relative significant, so that the predictive output better reflects which inputs or correlations are more important.
Claim 7: Sinha, Huang, and Harsha teach the method of claim 4. Sinha further teaches comprising: receiving data characterizing an analytical task and a target output of the analytical task; calculating, by the analytical task; and calculating an impact score by the impact function (i.e. the data correlation engine 134 determines an uncertainty coefficient U(x,y) that falls between 0 and 1. A value closer to 1 denotes stronger association between variables X and Y (e.g., a stronger correlation between geographic location of a observed user 110 and the web browser used by observed user 110). A value closer to 0 denotes a lesser association between variables X and Y; para. [0068]), wherein the impact score is indicative of a prediction of achieving the target output when inputs of the analytical task are the first plurality of inputs (i.e. With the pairwise associations, hierarchical clusters, and visualizations computed by data correlation engine 134, an administrator is able to build predictive models to predict which observed users 110 (or how many observed users 110) are statistically likely to perform certain actions with online service 120. For example, embodiments described herein enable an administrator to build a predictive model to predict which observed users 110 are statistically likely to complete an order or otherwise generate revenue at online service 120; para. [0083]).
Sinha does not explicitly teach the first set of weights based on the first plurality of inputs.
However, Huang further teaches calculating, by the analytical task, the first set of weights based on the first plurality of inputs; and calculating an impact score by the impact function, wherein the impact score is indicative of a prediction of achieving the target output when inputs of the analytical task are the first plurality of inputs (i.e. where a, b, c . . . z are weight coefficients, which may be generated or evaluated based on a model established by past abnormal data (for example, weight coefficients may be calculated based on correlation coefficients recorded in a database, and the correlation coefficients are obtained by analyzing the relation between CP and EQP, the relation between CP and LQC, and the relation between CP and RTM); para. [0030, 0031]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to modify the combination of Sinha and Harsha to include the feature of Huang. One would have been motivated to make this modification because it improves the calculated relationship/interaction analysis by weighting input factors according to their relative significant, so that the predictive output better reflects which inputs or correlations are more important.
Claim 8 is similar in scope to Claim 1 and is rejected under a similar rationale.
Sinha teaches a system comprising: at least one data processor (i.e. processor; para. [0086]); and at least one memory storing instructions which, when executed by the at least one data processor, result in operations comprising (i.e. The processor 1202 is configured to execute the stored instructions and comprises, for example, a logical processing unit, a microprocessor, a digital signal processor, and other processors. Execution of the program code in memory 1204 by the processor 1202 provides the functionality described above; para. [0086]).
Claims 9-11 and 14 are similar in scope to Claims 2-4, 7 and are rejected under a similar rationale.
Claim 15 is similar in scope to Claim 1 and is rejected under a similar rationale.
Sinha teaches a non-transitory computer readable medium (i.e. non-transitory computer readable mediums; para. [0086]) storing executable instructions that, when executed by at least one processor forming part of at least one computing system, cause the at least one processor to perform operations comprising (i.e. The processor 1202 is configured to execute the stored instructions and comprises, for example, a logical processing unit, a microprocessor, a digital signal processor, and other processors. Execution of the program code in memory 1204 by the processor 1202 provides the functionality described above; para. [0086]).
Claims 16-18 are similar in scope to Claims 2-4 and are rejected under a similar rationale.
8. Claims 5-6, 12-13, and 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over Sinha in view of Huang, Harsha, and further in view of Xi (U.S. Patent Pub. No. US 7111783 B2).
Claim 5: Sinha, Huang, and Harsha teach the method of claim 4. Sinha further teaches wherein calculating the first coefficient is based on a first set of terms of the plurality of terms, wherein each term of the first set of terms is a product of the first input, the second input (i.e. the data correlation engine 134 determines an uncertainty coefficient U(x,y) that falls between 0 and 1. A value closer to 1 denotes stronger association between variables X and Y (e.g., a stronger correlation between geographic location of a observed user 110 and the web browser used by observed user 110). A value closer to 0 denotes a lesser association between variables X and Y; para. [0067, 0068]).
Sinha does not explicitly teach a weight of the first set of weights.
However, Huang further teaches wherein calculating the first coefficient is based on a first set of terms of the plurality of terms, wherein each term of the first set of terms is a product of the first input, and a weight of the first set of weights (i.e. the probability that given equipment is not the root cause of the abnormality; ax.sub.1+bx.sub.2+cx.sub.3+dx.sub.4 . . . zx.sub.n in equation (2) is logistic regression, where a, b, c . . . z are weight coefficients, which may be generated or evaluated based on a model established by past abnormal data (for example, weight coefficients may be calculated based on correlation coefficients recorded in a database, and the correlation coefficients are obtained by analyzing the relation between CP and EQP; para. [0030, 0031]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to modify the combination of Sinha and Harsha to include the feature of Huang. One would have been motivated to make this modification because it improves the calculated relationship/interaction analysis by weighting input factors according to their relative significant, so that the predictive output better reflects which inputs or correlations are more important.
However, Xi teaches wherein each term of the first set of terms is a product of the first input, the second input, and a weight of the first set of weights (i.e. function..times..times..function..
ltoreq..ltoreq..times..times..times..t- imes. ##EQU00004## where N is the order of the polynomial; .alpha..sub.ijk are coefficients; x, y and z coordinates. Equation (6) can be written as: f(x, y, z)=AX (8) with A=[.alpha..sub.000.alpha..sub.100.alpha..sub.010.alpha..sub.001.alpha..su- b.200.alpha..sub.110 . . . .alpha..sub.00N] X=[(1)(x)(y)(z)(x.sup.2)(xy)(yz)(xz)(y.sup.2) . . . (z.sup.N)].sup.T It is well-known that fourth order polynomial models can represent many useful three-dimensional surfaces; col. 7, lines 1-35).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to modify the combination of Sinha, Huang, and Harsha to include the feature of Xi. One would have been motivated to make this modification because implicit surface fitting with implicit polynomials avoids the error distance problem.
Claim 6: Sinha, Huang, Harsha, and Xi teach the method of claim 5. Sinha further teaches wherein the first interaction coefficient is the sum of the first set of terms (i.e. the data correlation engine 134 determines an uncertainty coefficient U(x,y) that falls between 0 and 1. A value closer to 1 denotes stronger association between variables X and Y (e.g., a stronger correlation between geographic location of a observed user 110 and the web browser used by observed user 110). A value closer to 0 denotes a lesser association between variables X and Y; para. [0067, 0068]).
However, Huang further teaches wherein the first interaction coefficient is the sum of the first set of terms (i.e. the probability that given equipment is not the root cause of the abnormality; ax.sub.1+bx.sub.2+cx.sub.3+dx.sub.4 . . . zx.sub.n in equation (2) is logistic regression, where a, b, c . . . z are weight coefficients, which may be generated or evaluated based on a model established by past abnormal data (for example, weight coefficients may be calculated based on correlation coefficients recorded in a database, and the correlation coefficients are obtained by analyzing the relation between CP and EQP; para. [0030, 0031]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to modify the combination of Sinha, Harsha, and Xi to include the feature of Huang. One would have been motivated to make this modification because it improves the calculated relationship/interaction analysis by weighting input factors according to their relative significant, so that the predictive output better reflects which inputs or correlations are more important.
However, Xi further teaches teaches wherein the first interaction coefficient is the sum of the first set of terms (i.e. function..times..times..function.. ltoreq..ltoreq..times..times..times..t- imes. ##EQU00004## where N is the order of the polynomial; .alpha..sub.ijk are coefficients; x, y and z coordinates. Equation (6) can be written as: f(x, y, z)=AX (8) with A=[.alpha..sub.000.alpha..sub.100.alpha..sub.010.alpha..sub.001.alpha..su- b.200.alpha..sub.110 . . . .alpha..sub.00N] X=[(1)(x)(y)(z)(x.sup.2)(xy)(yz)(xz)(y.sup.2) . . . (z.sup.N)].sup.T It is well-known that fourth order polynomial models can represent many useful three-dimensional surfaces; col. 7, lines 1-35).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to modify the combination of Sinha, Huang, and Harsha to include the feature of Xi. One would have been motivated to make this modification because implicit surface fitting with implicit polynomials avoids the error distance problem.
Claims 12, 13, 19, and 20 are similar in scope to Claims 5, 6 and are rejected under a similar rationale.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure.
Nixon et al. (Pub. No. US 20200104678 A1), The optimizer neural network is associated with an outer loss function that measures how well the optimizer neural network generates updated values of target parameters for the target neural network.
It is noted that any citation to specific pages, columns, lines, or figures in the prior art references and any interpretation of the references should not be considered to be limiting in any way. A reference is relevant for all it contains and may be relied upon for all that it would have reasonably suggested to one having ordinary skill in the art. In re Heck, 699 F.2d 1331, 1332-33, 216 U.S.P.Q. 1038, 1039 (Fed. Cir. 1983) (quoting In re Lemelson, 397 F.2d 1006, 1009, 158 U.S.P.Q. 275, 277 (C.C.P.A. 1968)).
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/TAN H TRAN/Primary Examiner, Art Unit 2141
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