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 .
1. This action is in response to the communication filed on June 29, 2023. Claims 1-20 were originally received for consideration. No preliminary amendments for the claims have been received.
2. Claims 1-20 are currently pending consideration.
Information Disclosure Statement
3. Initialed and dated copies of Applicant’s IDS (form 1449), received on 6/29/23, 8/2/24, 9/9/24, 9/12/24, 10/2/24, 10/11/24, 12/6/24, 1/13/25, 2/4/25, 3/22/25, 4/8/25, 4/21/25, 5/21/25, 6/20/25, 8/12/25, 9/17/25, and 10/13/25, are attached to this Office Action.,
Allowable Subject Matter
4. Claims 2-5, 12-15, and 17-20 are 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.
Double Patenting
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 § 2146 et seq. 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).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
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Claims 1-20 are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-20 of copending Application No. 18/343,955 (reference application). Although the claims at issue are not identical, they are not patentably distinct from each other because the reference application ‘955 discloses the substantially same subject matter of the claims of the present application and the claims anticipate the independent claims of the present application. Claim 1 of the reference application discloses obtaining data comprising a populated field and unpopulated field, the unpopulated field lacking information, making a determination whether a first inference model can predict the unpopulated field with a sufficient degree of reliability, and in an instance of the determination in which the first inference model can predict at least the unpopulated field, generating an inference using the first inference model, and populating the unpopulated field using the inference to obtain the supplemental data and providing the supplemental data to a downstream consumer. The only difference in the claim is the reference patent application discloses a “sufficient degree of reliability” and the present application discloses a “reliability range.” These statements are analogous as a reliability range would compass a degree of reliability. Therefore the independent claims are see as anticipated by the reference application. The dependent claims disclose substantially similar subject matter (see claim 2 of both applications disclosing a second inference model).
This is a provisional nonstatutory double patenting rejection because the patentably indistinct claims have not in fact been patented.
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.
Claim(s) 1, 6-11, and 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Chu et al. (U.S. Patent Pub. No. US 2013/0226838) in view of Achin et al. (U.S. Patent Pub. No. US 2018/0046926).
Regarding claim 1, Chu discloses:
A method of managing operation of a data pipeline, the method comprising:
A method of managing operation of a data pipeline, the method comprising:
obtaining data comprising a populated field and an unpopulated field, the unpopulated field lacking information due to limitations on information collection (paragraph 0041: the missing value imputation system 110 uses only the target variable to impute missing values in predictor variables. Thus only univariate and bivariate statistics between the target variable and a predictor variable with missing values are used to build imputation models, regardless of their measurement levels, and those statistics can be computed for all predictor variables within each Mapper or data source independently);
making a determination regarding whether a first inference model can predict at least the unpopulated field within a reliability range, the reliability range ensuring that inferences generated by the first inference model comply with the limitations (paragraphs 0034-0035: In block 304, the missing value imputation system 110, for each of the one or more predictor variables, evaluates the imputation models based on the global validation sample and selecting a top number (i.e., a top K) of the imputation models to form an ensemble model. Block 304 represents a second map reduce job. In block 304, a global validation sample is scored by each Mapper to evaluate the accuracy of an imputation model. The Reducer selects the top K imputation models out of N possible imputation models based on some accuracy measures as the final ensemble model for each of the one or more predictor variables with missing values);
in an instance of the determination in which the first inference model can predict the at least the unpopulated field (paragraphs 0074-0076: missing value will be imputed):
generating an inference using the first inference model (paragraphs 0035, 0068-0077: missing value imputation system imputes the missing value for each of the predictor variables using data from the multiple data sources, one or more formed ensemble models, and a selected imputation strategy);
populating the unpopulated field using the inference to obtain supplemented data (paragraphs 0031, 0035-0036, 0083: The missing value imputation system 110 uses one or more imputation models to generate one set of values for inputs with missing values);
populating the unpopulated field using the inference to obtain supplemented data; and providing the supplemented data to a downstream consumer (paragraphs 0031, 0035-0036, 0083: The missing value imputation system 110 uses one or more imputation models to generate one set of values for inputs with missing values); and
Chu does not explicitly disclose providing the supplemented data to a downstream consumer. Chu does generally disclose providing the predictive models to end users (paragraphs 0035, 0041, 0118-0119: completed data set is provided to consumers to build predictive models and workload layers including the predictive models with missing value imputation). In an analogous art, Achin discloses communicating the output of the predictive modeling module to the client (paragraph 0283). It would have been obvious to one of ordinary skill in the art to provide the data to a consumer so they can have access to the modeling techniques and oversee the execution (Achin: paragraph 0283).
Claim 6 is rejected as applied above in rejecting claim 1. Furthermore, Achin discloses:
The method of claim 1, wherein making the determination comprises:
identifying a type of the unpopulated field (paragraph 0015-0017: values of targets to be predicted); and
identifying that the type of the unpopulated field is one of the types of the unpopulated fields for which the inferences are generated by the first inference model (paragraphs 0015-0017: values of targets to be predicted).
Claim 7 is rejected as applied above in rejecting claim 6. Furthermore, Achin discloses:
The method of claim 6, wherein the first inference model is based on qualified training data, the qualified training data comprising a subset of all available training data, the subset of the all available training data being selected based on a second inference model (Figure 11A and associated text: perform a second-order predictive modeling procedure on the first-order model).
Claim 8 is rejected as applied above in rejecting claim 7. Furthermore, Achin discloses:
The method of claim 7, wherein the subset of the all available training data is also selected based on the reliability range (Figure 10 and associated test: determining accuracy scores for each fitted predictive model).
Claim 9 is rejected as applied above in rejecting claim 8. Furthermore, Achin discloses:
The method of claim 8, wherein the subset of the all available training data comprises a set of features that prevents perfect prediction of labels of the training data by the first inference model (paragraph 0027: cross-validation of the predictive models includes generating second training data and fitting the predictive model to the second training data to obtain a second fitted model).
Claim 10 is rejected as applied above in rejecting claim 7. Furthermore, Achin discloses:
The method of claim 7, wherein the second inference model is a self-supervised learning inference model, and the first inference model being a supervised learning inference model (Figure 11A and associated text: perform a second-order predictive modeling procedure on the first-order model).
Regarding claim 11, Chu discloses:
A non-transitory machine-readable medium having instructions stored therein, which when executed by a processor, cause the processor to perform operations for managing operation of a data pipeline, the operations comprising:
obtaining data comprising a populated field and an unpopulated field, the unpopulated field lacking information due to limitations on information collection (paragraph 0041: the missing value imputation system 110 uses only the target variable to impute missing values in predictor variables. Thus only univariate and bivariate statistics between the target variable and a predictor variable with missing values are used to build imputation models, regardless of their measurement levels, and those statistics can be computed for all predictor variables within each Mapper or data source independently);
making a determination regarding whether a first inference model can predict at least the unpopulated field within a reliability range, the reliability range ensuring that inferences generated by the first inference model comply with the limitations (paragraphs 0034-0035: In block 304, the missing value imputation system 110, for each of the one or more predictor variables, evaluates the imputation models based on the global validation sample and selecting a top number (i.e., a top K) of the imputation models to form an ensemble model. Block 304 represents a second map reduce job. In block 304, a global validation sample is scored by each Mapper to evaluate the accuracy of an imputation model. The Reducer selects the top K imputation models out of N possible imputation models based on some accuracy measures as the final ensemble model for each of the one or more predictor variables with missing values);
in an instance of the determination in which the first inference model can predict the at least the unpopulated field (paragraphs 0074-0076: missing value will be imputed):
generating an inference using the first inference model (paragraphs 0035, 0068-0077: missing value imputation system imputes the missing value for each of the predictor variables using data from the multiple data sources, one or more formed ensemble models, and a selected imputation strategy);
populating the unpopulated field using the inference to obtain supplemented data; and providing the supplemented data to a downstream consumer (paragraphs 0031, 0035-0036, 0083: The missing value imputation system 110 uses one or more imputation models to generate one set of values for inputs with missing values); and
Chu does not explicitly disclose providing the supplemented data to a downstream consumer. Chu does generally disclose providing the predictive models to end users (paragraphs 0035, 0041, 0118-0119: completed data set is provided to consumers to build predictive models and workload layers including the predictive models with missing value imputation). In an analogous art, Achin discloses communicating the output of the predictive modeling module to the client (paragraph 0283). It would have been obvious to one of ordinary skill in the art to provide the data to a consumer so they can have access to the modeling techniques and oversee the execution (Achin: paragraph 0283).
Regarding claim 16, Chu discloses:
A data processing system, comprising:
a processor (paragraph 0107: processor); and
a memory coupled to the processor to store instructions, which when executed by the processor (paragraph 0107: memory), cause the processor to perform operations for managing operation of a data pipeline, the operations comprising:
obtaining data comprising a populated field and an unpopulated field, the unpopulated field lacking information due to limitations on information collection (paragraph 0041: the missing value imputation system 110 uses only the target variable to impute missing values in predictor variables. Thus only univariate and bivariate statistics between the target variable and a predictor variable with missing values are used to build imputation models, regardless of their measurement levels, and those statistics can be computed for all predictor variables within each Mapper or data source independently);
making a determination regarding whether a first inference model can predict at least the unpopulated field within a reliability range, the reliability range ensuring that inferences generated by the first inference model comply with the limitations (paragraphs 0034-0035: In block 304, the missing value imputation system 110, for each of the one or more predictor variables, evaluates the imputation models based on the global validation sample and selecting a top number (i.e., a top K) of the imputation models to form an ensemble model. Block 304 represents a second map reduce job. In block 304, a global validation sample is scored by each Mapper to evaluate the accuracy of an imputation model. The Reducer selects the top K imputation models out of N possible imputation models based on some accuracy measures as the final ensemble model for each of the one or more predictor variables with missing values);
in an instance of the determination in which the first inference model can predict the at least the unpopulated field (paragraphs 0074-0076: missing value will be imputed):
generating an inference using the first inference model (paragraphs 0035, 0068-0077: missing value imputation system imputes the missing value for each of the predictor variables using data from the multiple data sources, one or more formed ensemble models, and a selected imputation strategy);
populating the unpopulated field using the inference to obtain supplemented data; and providing the supplemented data to a downstream consumer (paragraphs 0031, 0035-0036, 0083: The missing value imputation system 110 uses one or more imputation models to generate one set of values for inputs with missing values); and
Chu does not explicitly disclose providing the supplemented data to a downstream consumer. Chu does generally disclose providing the predictive models to end users (paragraphs 0035, 0041, 0118-0119: completed data set is provided to consumers to build predictive models and workload layers including the predictive models with missing value imputation). In an analogous art, Achin discloses communicating the output of the predictive modeling module to the client (paragraph 0283). It would have been obvious to one of ordinary skill in the art to provide the data to a consumer so they can have access to the modeling techniques and oversee the execution (Achin: paragraph 0283).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to KAVEH ABRISHAMKAR whose telephone number is (571)272-3786. The examiner can normally be reached M-F 9-5:30.
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/KAVEH ABRISHAMKAR/
02/11/2026Primary Examiner, Art Unit 2494