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 .
DETAILED ACTION
This responds to Applicant’s Arguments/Remarks filed 08/13/2025. Claims 1, 15, 17-20 have been amended. Claim 16 has been cancelled. Claims 1-15, 17-20 are now pending in this Application.
Continued Examination Under 37 CFR 1.114
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 8/13/2025 has been entered.
Response to Arguments
Applicant’s arguments with respect to claim(s) 1-15, 17-20 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
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) 1-15, 17-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Hudock et al (U.S. Pub No. 20230214586 A1), and in view of Prager (U.S. Patent No. 6,003,027 A1).
As per claim 1, Hudock discloses a system comprising:
at least one hardware processor; and at least one memory storing instructions that cause the at least one hardware processor to execute operations comprising (Par [0062, 0069-0070]):
accessing a table associated with features of a column (Fig 8);
retrieving a list of categories, each category in the list of categories being associated with a respective scoring model; for each category in the list of categories, applying a respective scoring model to the features of the column to generate a respective set of confidence values indicating a likelihood that the column belongs to a respective one of the categories (Par [0041-0044]);
processing the respective sets of confidence values to select a target category from the list of categories (Par [0049] and claim 2); and
associating the selected target category with the column (Par [0041, 0047-0049]).
Hudock does not explicitly disclose each respective scoring model selected from a plurality of scoring models, each scoring model of the plurality of scoring models associated with a different category of the list of categories, each scoring model of the plurality of scoring models generating a confidence value comprising a set of likelihoods that the column belongs to the category of the respective scoring models; for an individual category, identifying a first set of data entries of the column that are associated with a first confidence value of belonging to the individual category and a second set of data entries of the column that are associated with a second confidence value of belonging to the individual category; computing a first ratio of a first quantity in the first set of data entries relative to a total number of entries in the column and a second ratio of a second quantity in the second set of data entries relative to a total number of entries in the column; processing the respective sets of confidence values to select a target category from the list of categories based on the first ratio and the second ratio.
However, Prager discloses each respective scoring model selected from a plurality of scoring models, each scoring model of the plurality of scoring models associated with a different category of the list of categories, each scoring model of the plurality of scoring models generating a confidence value comprising a set of likelihoods that the column belongs to the category of the respective scoring models (Fig 4a-4b and Col 7 lines 47-67 through col 8 lines 1-67);
for an individual category, identifying a first set of data entries of the column that are associated with a first confidence value of belonging to the individual category and a second set of data entries of the column that are associated with a second confidence value of belonging to the individual category (col 8 lines 15-51);
computing a first ratio of a first quantity in the first set of data entries relative to a total number of entries in the column and a second ratio of a second quantity in the second set of data entries relative to a total number of entries in the column; processing the respective sets of confidence values to select a target category from the list of categories based on the first ratio and the second ratio (Col 7 lines 47-67 through col 8 lines 1-67).
It would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention was made to incorporate the features as disclosed in Prager into the teaching of Hudock in order to improve the system (col 8 lines 50-51).
As per claim 2, Hudock discloses the system of claim 1, wherein the features are derived from data entries of the column (par [0047-0049]).
As per claim 3, Hudock discloses the system of claim 2, wherein the features are derived from a column name (fig 8).
As per claim 4, Hudock discloses the system of claim 1, the operations further comprising: for a first category in the list of categories, applying a first scoring model to the features in the column to generate a first set of confidence values indicating a likelihood that the column belongs to the first category (Par [0049] and claim 2).
As per claim 5, Hudock discloses the system of claim 4, the operations further comprising: for a second category in the list of categories, applying a second scoring model to the features in the column to generate a second set of confidence values indicating a likelihood that the column belongs to the second category; and processing the first set of confidence values and the second set of confidence values to select the target category from the first and second categories (Par [0049-0051] and claim 2).
As per claim 6, Hudock discloses the system of claim 1, wherein each confidence value in the respective sets of confidence values is associated with a different score (Par [0049-0051]).
As per claim 7, Hudock discloses the system of claim 1, wherein the scoring model generates a distribution of scores for the features of the column as belonging to a first category in the list of categories as a first set of confidence values, the distribution of scores comprising a first percentage of the features of the column having a first likelihood of belonging to the first category, a second percentage of the features of the column having a second likelihood of belonging to the first category, and a third percentage of the features of the column having a third likelihood of belonging to the first category (Par [0049-0051] and claim 2).
As per claim 8, Hudock discloses the system of claim 7, wherein the first likelihood is greater than the second likelihood; and wherein the second likelihood is greater than the third likelihood (Par [0008]).
As per claim 9, Hudock discloses the system of claim 8, the operations further comprising: determining that the first percentage is greater than a threshold value; and in response to determining that the first percentage is greater than the threshold value, generating an aggregate confidence value that the column belongs to the first category as a sum of a first portion of confidence values of the first set of confidence values associated with the first likelihood and a second portion of confidence values of the first set of confidence values associated with the second likelihood (par [0011] and claim 5-6).
As per claim 10, Hudock discloses the system of claim 9, the operations further comprising: determining that a first feature of the column of features comprises a column name; obtaining a confidence value from the first set of confidence value corresponding to the first feature comprising the column name; and selectively increasing the aggregate confidence value by a first amount or a second amount based on the confidence value of the first feature comprising the column name, the second amount being smaller than the first amount (Par [0053]).
As per claim 11, Hudock discloses the system of claim 10, the operations further comprising: determining that the confidence value corresponds to the first likelihood; and in response to determining that the confidence value of the first feature comprising the column name corresponds to the first likelihood, increasing the aggregate confidence value by the first amount (Par [0049-0051]).
As per claim 12, Hudock discloses the system of claim 10, the operations further comprising: determining that the confidence value corresponds to the second likelihood; and in response to determining that the confidence value of the first feature comprising the column name corresponds to the second likelihood, increasing the aggregate confidence value by the second amount (Par [0008]).
As per claim 13, Hudock discloses the system of claim 9, wherein the aggregate confidence value is a first aggregate confidence value, the operations further comprising: generating a second aggregate confidence value that the column belongs to a second category based on a second set of confidence values associated with the features of the column; comparing the first aggregate confidence value with the second aggregate confidence value; and selecting the target category from the first and second categories in response to comparing the first aggregate confidence value with the second aggregate confidence value (par [0043]).
As per claim 14, Prager discloses the system of claim 13, the operations further comprising: determining that the second aggregate confidence value is greater than the first aggregate confidence value in response to comparing the first aggregate confidence value with the second aggregate confidence value; selecting the second category as the target category in response to determining that the second aggregate confidence value is greater than the first aggregate confidence value (Colu,m 3 lines 59-67, col 8 lines 23-35).
As per claim 15, Hudock discloses the system of claim 1, wherein the plurality of scoring models comprise a plurality of machine learning models (par [0072]).
As per claim 17, Hudock discloses the system of claim 1, wherein the plurality of scoring models comprise a plurality of predefined lists of attributes (Par [0057]).
As per claim 18, Hudock discloses the system of claim 17, wherein the plurality of scorning models comprise one or more machine learning models and one or more predefined lists of attributes (par [0057]).
As per claim 19, Hudock discloses a method comprising:
accessing, by at least one hardware processor, a table associated with features of a column (Par [0069-0070] and fig 8);
retrieving a list of categories, each category in the list of categories being associated with a different scoring model; for each category in the list of categories, applying a respective scoring model to the features of the column to generate a respective set of confidence values indicating a likelihood that the column belongs to a respective one of the categories (Par [0041-0044]);
processing the respective sets of confidence values to select a target category from the list of categories (Par [0049] and claim 2); and
associating the selected target category with the column (Par [0041, 0047-0049]).
Hudock does not explicitly disclose each respective scoring model selected from a plurality of scoring models, each scoring model of the plurality of scoring models associated with a different category of the list of categories, each scoring model of the plurality of scoring models generating a confidence value comprising a set of likelihoods that the column belongs to the category of the respective scoring models; for an individual category, identifying a first set of data entries of the column that are associated with a first confidence value of belonging to the individual category and a second set of data entries of the column that are associated with a second confidence value of belonging to the individual category; computing a first ratio of a first quantity in the first set of data entries relative to a total number of entries in the column and a second ratio of a second quantity in the second set of data entries relative to a total number of entries in the column; processing the respective sets of confidence values to select a target category from the list of categories based on the first ratio and the second ratio.
However, Prager discloses each respective scoring model selected from a plurality of scoring models, each scoring model of the plurality of scoring models associated with a different category of the list of categories, each scoring model of the plurality of scoring models generating a confidence value comprising a set of likelihoods that the column belongs to the category of the respective scoring models (Fig 4a-4b and Col 7 lines 47-67 through col 8 lines 1-67);
for an individual category, identifying a first set of data entries of the column that are associated with a first confidence value of belonging to the individual category and a second set of data entries of the column that are associated with a second confidence value of belonging to the individual category (col 8 lines 15-51);
computing a first ratio of a first quantity in the first set of data entries relative to a total number of entries in the column and a second ratio of a second quantity in the second set of data entries relative to a total number of entries in the column; processing the respective sets of confidence values to select a target category from the list of categories based on the first ratio and the second ratio (Col 7 lines 47-67 through col 8 lines 1-67).
It would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention was made to incorporate the features as disclosed in Prager into the teaching of Hudock in order to improve the system (col 8 lines 50-51).
As per claim 20, Hudock discloses a computer-storage medium comprising instructions that, when executed by a processor of a machine, configure the machine to perform operations comprising:
accessing a table associated with features of a column (Fig 8);
retrieving a list of categories, each category in the list of categories being associated with a different scoring model; for each category in the list of categories, applying a respective scoring model to the features of the column to generate a respective set of confidence values indicating a likelihood that the column belongs to a respective one of the categories (Par [0041-0044]);
processing the respective sets of confidence values to select a target category from the list of categories (Par [0049] and claim 2); and
associating the selected target category with the column (Par [0041, 0047-0049]).
Hudock does not explicitly disclose each respective scoring model selected from a plurality of scoring models, each scoring model of the plurality of scoring models associated with a different category of the list of categories, each scoring model of the plurality of scoring models generating a confidence value comprising a set of likelihoods that the column belongs to the category of the respective scoring models; for an individual category, identifying a first set of data entries of the column that are associated with a first confidence value of belonging to the individual category and a second set of data entries of the column that are associated with a second confidence value of belonging to the individual category; computing a first ratio of a first quantity in the first set of data entries relative to a total number of entries in the column and a second ratio of a second quantity in the second set of data entries relative to a total number of entries in the column; processing the respective sets of confidence values to select a target category from the list of categories based on the first ratio and the second ratio.
However, Prager discloses each respective scoring model selected from a plurality of scoring models, each scoring model of the plurality of scoring models associated with a different category of the list of categories, each scoring model of the plurality of scoring models generating a confidence value comprising a set of likelihoods that the column belongs to the category of the respective scoring models (Fig 4a-4b and Col 7 lines 47-67 through col 8 lines 1-67);
for an individual category, identifying a first set of data entries of the column that are associated with a first confidence value of belonging to the individual category and a second set of data entries of the column that are associated with a second confidence value of belonging to the individual category (col 8 lines 15-51);
computing a first ratio of a first quantity in the first set of data entries relative to a total number of entries in the column and a second ratio of a second quantity in the second set of data entries relative to a total number of entries in the column; processing the respective sets of confidence values to select a target category from the list of categories based on the first ratio and the second ratio (Col 7 lines 47-67 through col 8 lines 1-67).
It would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention was made to incorporate the features as disclosed in Prager into the teaching of Hudock in order to improve the system (col 8 lines 50-51).
Conclusion
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September 16, 2025
/THU N NGUYEN/Examiner, Art Unit 2154