DETAILED ACTION
This is Final Office Action in response to amendment filed on May 29, 2026. Claims 1-20 are pending.
Response to Arguments
Applicants’ arguments regarding the claim rejections under 35 USC § 101, with respect to 1-20 have been fully considered and are persuasive. Accordingly, the rejection of the claims has been withdrawn.
Applicants’ arguments regarding the claim rejections under 35 USC § 103, with respect to 1-20 have been fully considered and are persuasive. Accordingly, the rejection of the claims has been withdrawn.
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, 3-4, 6-9, 11-17 and 19-20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-16 and 19 of U.S. Patent No. 12,353,432. Although the claims at issue are not identical, they are not patentably distinct from each other because instant application claims 1, 3-4, 6-9, 11-17 and 19-20 are anticipated by patent claims 1-16 and 19.
All limitations and elements in claim 1 of the instant application are found in claim 1 of Zhang et al., except for “convert the subset of continuous-type data values into a second subset of discrete-type data values based on a data binning operation.” However, “generating a second subset of discrete-type data values and a continuous data set based on the subset of continuous-type data values” inherently includes converting the subset of continuous-type data values into a second subset of discrete-type data values. Therefore, “generating a second subset of discrete-type data values and a continuous data set” is equivalent to “converting the subset of continuous-type data values into a second subset of discrete-type data values.” Although the claims at issue are not identical, they are not patentably distinct from one another because they are substantially similar in scope and they use the similar limitations as showed in the Claims Comparison Table below as the claims of the cited patent teach every claims of the instant application, and as such, anticipate the claims of the instant application.
Claims Comparison Table:
Instant application #19/221,871
Patent # 12,353,432
Claim 1. A computer system comprising: a processor set; one or more computer-readable storage media; and program instructions stored on the one or more computer-readable storage media to cause the processor set to perform operations comprising:
dividing a first data set into a subset of continuous-type data values and a first subset of discrete-type data values based on variable types in the first data set;
generating a second subset of discrete-type data values and a continuous data set based on the subset of continuous-type data values; and
executing a dimension reduction operation on the subset of continuous-type data values to generate a dimensionally-reduced subset;
executing a data binning operation on the dimensionally-reduced subset to generate a second subset of discrete-type data values;
generating a second data set based on combining a third subset of discrete-type data values from a conditional contingency table with the continuous data set, wherein the conditional contingency table is based on the first subset of discrete-type data values and the second subset of discrete-type variables.
3. The computer system of claim 2, wherein transforming the subset of continuous-type data values comprises: reducing, based on a principal component analysis (PCA) model, a number of dimensions within the subset of continuous-type data values; and generating the second subset of discrete-type data values based on execution of the data binning operation on the reduced number of dimensions.
4. The computer system of claim 1, wherein dividing the first data set comprises: splitting the first data set into a first subset of columns of the continuous-type data values and a second subset of columns of the discrete-type data values.
5. The computer system of claim 4, wherein the operations further comprise: generating the conditional contingency table that includes rows of data within the subset of columns of continuous-type data values that share a common value for one of the discrete-type data values.
6. The computer system of claim 1, wherein the operations further comprise: generating a plurality of conditional contingency tables that include different subsets of discrete-type values from the second subset of discrete-type data values; and generating the continuous data set after the plurality of conditional contingency tables is generated
7. The computer system of claim 6, wherein the operations further comprise: determining a probability of each of rows of data within the conditional contingency table being within a different conditional contingency table from among the plurality of conditional contingency tables; and adding the probability to each of the rows of data within the conditional contingency table.
8. The computer system of claim 1, wherein the operations further comprise: executing a machine learning model on the second data set; determining a predictive performance of the machine learning model; and displaying the predictive performance via a user interface.
9. A computer program product comprising: one or more computer-readable storage media; and program instructions stored on the one or more computer-readable storage media to perform operations comprising: dividing a first data set into a subset of continuous-type data values and a first subset of discrete-type data values based on variable types in the first data set; generating a second subset of discrete-type data values and a continuous data set based on the subset of continuous-type data values; and generating a second data set based on combining a third subset of discrete-type data values from a conditional contingency table with the continuous data set, wherein the conditional contingency table is based on the first subset of discrete-type data values and the second subset of discrete-type variables.
11. The computer program product of claim 10, wherein transforming the subset of continuous-type data values comprises: reducing, based on a principal component analysis (PCA) model, a number of dimensions within the subset of continuous-type data values; and generating the second subset of discrete-type data values based on execution of the data binning operation on the reduced number of dimensions.
12. The computer program product of claim 9, wherein dividing the first data set comprises: splitting the first data set into a first subset of columns of the continuous-type data values and a second subset of columns of the discrete-type data values.
13. The computer program product of claim 12, wherein the operations further comprise: generating the conditional contingency table that includes rows of data within the subset of columns of continuous-type data values that share a common value for one of the discrete-type data values.
14. The computer program product of claim 9, wherein the operations further comprise: generating a plurality of conditional contingency tables that include different subsets of discrete-type values from the second subset of discrete-type data values; and generating the continuous data set after the plurality of conditional contingency tables is generated.
15. The computer program product of claim 14, wherein the operations further comprise: determining a probability of each of rows of data within the conditional contingency table being within a different conditional contingency table from among the plurality of conditional contingency tables; and adding the probability to each of the rows of data within the conditional contingency table.
16. The computer program product of claim 9, wherein the operations further comprise: executing a machine learning model on the second data set; determining a predictive performance of the machine learning model; and displaying the predictive performance via a user interface.
17. A method, comprising: dividing a first data set into a subset of continuous-type data values and a first subset of discrete-type data values based on variable types in the first data set; generating a second subset of discrete-type data values and a continuous data set based on the subset of continuous-type data values; and generating a second data set based on combining a third subset of discrete-type data values from a conditional contingency table with the continuous data set, wherein the conditional contingency table is based on the first subset of discrete-type data values and the second subset of discrete-type variables.
19. The method of claim 18, wherein transforming the subset of continuous-type data values comprises: reducing, based on a principal component analysis (PCA) model, a number of dimensions within the subset of continuous-type data values; and generating the second subset of discrete-type data values based on execution of the data binning operation on the reduced number of dimensions.
20. The method of claim 17, wherein dividing the first data set comprises: splitting the first data set into a first subset of columns of the continuous-type data values and a second subset of columns of the discrete-type data values.
Claim 1. An apparatus comprising: a memory configured to store an original data set; and a processor configured to:
split the original data set into a subset of continuous-type data values and a subset of discrete-type data values based on variable types in the original data set,
convert the subset of continuous-type data values into a second subset of discrete-type data values based on a data binning operation,
generate a new subset of continuous-type data values based on the subset of continuous-type data values in the original data set, and combine a subset of discrete-type data values from a conditional contingency table within the new subset of continuous-type data values to generate a new data set.
5. The apparatus of claim 1, wherein the processor is configured to: execute a principal analysis component (PCA) model on the subset of continuous-type data values to reduce a number of dimensions within the subset of continuous-type data values, and execute the data binning operation on the reduced number of dimensions to generate the second subset of discrete-type data values.
2. The apparatus of claim 1, wherein the original data set comprises a table, and the processor is configured to: split the table into a subset of columns of the continuous-type data values and a second subset of columns of the discrete-type data values within the table.
3. The apparatus of claim 2, wherein the processor is configured to: generate a conditional contingency table that includes rows of data within the subset of columns of continuous-type data values that share a common value for one of the discrete-type data values.
6. The apparatus of claim 1, wherein the processor is configured to: generate a plurality of conditional contingency tables that include different subsets of discrete-type values from the second subset of discrete-type data values, and generate the new subset of continuous-type data values after the plurality of conditional contingency tables is generated.
4. The apparatus of claim 3, wherein the processor is configured to: determine a probability of each of the rows of data within the conditional contingency table being within a different conditional contingency table from among a plurality of conditional contingency tables, and add the probability to each of the rows of data within the conditional contingency table.
7. The apparatus of claim 1, wherein the processor is configured to: execute a machine learning model on the new data set, determine a predictive performance of the machine learning model, and display the predictive performance via a user interface.
8. A method comprising: storing an original data set in memory; splitting the original data set into a subset of continuous-type data values and a subset of discrete-type data values based on variable types in the original data set; converting the subset of continuous-type data values into a second subset of discrete-type data values based on a data binning operation; generating a new subset of continuous-type data values based on the subset of continuous-type data values in the original data set; and combining a subset of discrete-type data values from a conditional contingency table within the new subset of continuous-type data values to generate a new data set.
12. The method of claim 8, wherein the method further comprises: executing a principal analysis component (PCA) model on the subset of continuous-type data values to reduce a number of dimensions within the subset of continuous-type data values; and executing the data binning operation on the reduced number of dimensions to generate the second subset of discrete-type data values.
9. The method of claim 8, wherein the original data set comprises a table, and the splitting comprises splitting the table into a subset of columns of the continuous-type data values and a second subset of columns of the discrete-type data values within the table.
10. The method of claim 9, wherein the method further comprises: generating a conditional contingency table that includes rows of data within the subset of columns of continuous-type data values that share a common value for one of the discrete-type data values.
13. The method of claim 8, wherein the method further comprises: generating a plurality of conditional contingency tables that include different subsets of discrete-type values from the second subset of discrete-type data values; and generating the new subset of continuous-type data values after the plurality of conditional contingency tables is generated.
11. The method of claim 10, wherein the method further comprises: determining a probability of each of the rows of data within the conditional contingency table being within a different conditional contingency table from among a plurality of conditional contingency tables; and adding the probability to each of the rows of data within the conditional contingency table.
14. The method of claim 8, wherein the method further comprises: executing a machine learning model on the new data set; determining a predictive performance of the machine learning mode; and displaying the predictive performance via a user interface.
15. A computer-readable storage medium comprising instructions that, when executed by a processor, cause the processor to perform: storing an original data set in memory; splitting the original data set into a subset of continuous-type data values and a subset of discrete-type data values based on variable types in the original data set; converting the subset of continuous-type data values into a second subset of discrete-type data values based on a data binning operation; generating a new subset of continuous-type data values based on the subset of continuous-type data values in the original data set; and combining a subset of discrete-type data values from a conditional contingency table within the new subset of continuous-type data values to generate a new data set.
19. The computer-readable storage medium of claim 15, wherein the instructions further cause the processor to perform: executing a principal analysis component (PCA) model on the subset of continuous-type data values to reduce a number of dimensions within the subset of continuous-type data values; and executing the data binning operation on the reduced number of dimensions to generate the second subset of discrete-type data values.
16. The computer-readable storage medium of claim 15, wherein the original data set comprises a table, and the splitting further comprises: splitting the table into a subset of columns of the continuous-type data values and a second subset of columns of the discrete-type data values within the table.
Therefore, claims 1-16 and 19 of the above patent are in essence a “species” of the generic invention of claims 1, 3-4, 6-9, 11-17 and 19-20 of the instant application. It has been held that a generic invention is anticipated by a “species” within the scope of the generic invention. See In re Goodman, 29 USPQ2d 2010 (Fed. Cir. 1993).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Coleman et al. (US 20210200735 A1) disclose statistical fingerprinting of large structure datasets.
Applicants’ amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/HANH B THAI/Primary Examiner, Art Unit 2163
July 28, 2026
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