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 .
Claims 21-40 are pending in this application.
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).
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Claims 21-40 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-20 of U.S. Patent No. 11,397,716. Although the claims at issue are not identical, they are not patentably distinct from each other.
Current Application
U.S. 11,397,716
21. A computer-implemented method for automatically tagging data, the method comprising:
automatically generating a statistical summary of a first set of data, wherein the statistical summary includes:
a data-tagging pattern representing a value in the first set of data, and a first degree of generalizing the data-tagging pattern;
interactively receiving, based on a user selection through a graphical user interface, a second set of data;
generating, based on the data-tagging pattern in the statistical summary and the second set of data, a candidate data-tagging pattern;
selecting, based on a false negative rate associated with the candidate data-tagging pattern upon the data in the second set of data and a second degree of generalizing the candidate data-tagging pattern, the candidate data-tagging pattern as a data-tagging pattern;
automatically tagging, based on the selected data-tagging pattern, a data tag to a subset of the first set of data; and
presenting, through the graphical user interface, at least a part of the automatically tagged data tag to the subset of the first set of data.
1. A computer-implemented method for automatically tagging data, the method comprising:
receiving a first set of data;
automatically generating, based at least on a part of the first set of data, a statistical summary of the first set of data, wherein the statistical summary includes:
a plurality of data-tagging patterns for values of the part of the first set of data, and a degree of generalizing data patterns for each of the plurality of data-tagging patterns based on the part of the first set of data;
interactively receiving a second set of data with a selection of a subset of the second set of data, wherein the second set of data is distinct from the first set of data;
generating, based on the plurality of data-tagging patterns in the statistical summary and data in the selected subset of the second set of data, a set of candidate data-tagging patterns, wherein the set of candidate data-tagging patterns includes a candidate data-tagging pattern that matches one or more data in the subset of the second set of data;
selecting, based on a combination of a false negative rate associated with the candidate data-tagging pattern upon the one or more data in the subset of the second set of data and a degree of generalizing data pattern associated with the candidate data-tagging pattern upon the first set of data, the candidate data-tagging pattern as a data-tagging pattern;
automatically tagging, based on the selected data-tagging pattern, one or more subsets of the first set of data; and
providing the automatically tagged one or more subsets of the first set of data.
Claims 21-40 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-20 of U.S. Patent No. 12,380,082. Although the claims at issue are not identical, they are not patentably distinct from each other. The claims are similarly rejected as cited above.
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.
Claim(s) 21-40 is/are rejected under 35 U.S.C. 103 as being unpatentable over Whitney et al., US 2002/0159641 (hereinafter Whitney) in view of Kirshenbaum et al., US 2006/0248054 (hereinafter Kirshenbaum).
For claims 21, 28, 35, Whitney teaches a computer-implemented method for automatically tagging data, the method comprising:
automatically generating a statistical summary of a first set of data (see Whitney, [0052] – [0053], “selects and extracts feature vectors from the data objects 102 based upon a feature set,” [0062] – [0064], “performance measure for the candidate classifiers trained by the training process 108” and measuring “classifier performance...data analysis...summaries...that convey an analysis of classifier performance,” [0074], [0079] – [0080], [0093], where performance measure of classifier implemented for first set of data represents statistical summary), wherein the statistical summary includes:
a data-tagging pattern representing a value in the first set of data (see Whitney, [0050] – [0060], “pattern recognition process 100” and “analyzes the feature vectors extracted by the feature process 104 to select and train an appropriate classifier or classifiers,” [0074], [0080], “classifying” the data objects represent data-tagging pattern), and
a first degree of generalizing the data-tagging pattern (see Whitney, [0062] – [0064], [0123], “rule 360 establishes the relationship to the objects in the group (similar/dissimilar, etc.)...strongly similar or loosely similar,” [0126], “The rule establishes that, for the selected group or subset of data, the objects are similar, dissimilar, or other broad generalization across the group” and “By modifying the rules, a user may narrow or further distinguish a subset of data, broaden a subset of data,” [0131], “weight value 416 associated with the selected data objects 404...degree of attraction between the data objects contained in the rule. The farther to the left the slider is moved, the greater the degree of repulsion or dissimilarity between the data objects contained in the rule” where rules establish “broader” degree of attraction between classificaitons represent statistical summary detailing first degree of generalization for the classification);
interactively receiving, based on a user selection through a graphical user interface, a second set of data (see Whitney, [0066], “refine the candidate classifiers...where additional feature vectors may be extracted from the data set 102. This may require obtaining additional data objects, or obtaining feature vectors from alternative data sets for example where obtaining additional data objects represents receiving a second set of data);
generating, based on the data-tagging pattern in the statistical summary and the second set of data, a candidate data-tagging pattern (see Whitney, [0065] – [0073], [0079], “establish an initial candidate feature set as well as an initial candidate classifier or candidate classifier set. The testing data set 102B is presented to the pattern recognition construction process” where generation of candidate classifier set represents set of candidate data-tagging patterns;).
Kirshenbaum teaches generating, based on the data-tagging pattern in the statistical summary and the second set of data, a candidate data-tagging pattern (see Kirshenbaum, [0002], “category” refers to a “label” and represents data-tagging pattern, [0029] – [0030], [0049], received “new project” data represents received second set of data, [0051], where determining “possible categories” represents generating at least one candidate data-tagging pattern); selecting, based on a false negative rate associated with the candidate data-tagging pattern upon the data in the second set of data and a second degree of generalizing the candidate data-tagging pattern, the candidate data-tagging pattern as a data-tagging pattern (see Kirshenbaum, [0025], [0039] – [0040], [0049], “categorizer” predicts categories/tags based on “hierarchy of categories” where “subcategory” of hierarchy represents second degree of generalizing, and where “categorizer also predicts categories/tags based on “false negative rate”). It would have been obvious to one skilled in the art at the time of the invention to modify the teachings of Whitney with the teachings of Kirshenbaum because both relate to automatically determining accurate labels/classifiers for a given data set (see Kirshenbaum, [0047] – [0048]; see Whitney, [0101]).
The combination further teaches
automatically tagging, based on the selected data-tagging pattern, a data tag to a subset of the first set of data (see Kirshenbaum, [0039] – [0040], [0051], [0054], “automated” labeling of “subcategories” within a category represents tagging subset of first set of data; see Whitney, [0072] – [0075], [0085], “The final feature set and classifier 114 are used to assign an unknown data object 116 to its predicted class,” where implementation of final classifier responds in automatic tagging of data); and
presenting, through the graphical user interface, at least a part of the automatically tagged data tag to the subset of the first set of data (see Kirshenbaum, [0051], where “possible categories” of tags are “reviewed by a user or expert” to determine whether “appropriate”; see Whitney, see [0073] – [0074], [0079] – [0080], [0085], where “classified data set” is provided).
For claims 22, 29, 36, the combination teaches further comprising:
updating, by removing one or more under-generalizing candidate data-tagging patterns, a plurality of data-tagging patterns, wherein the one or more under-generalizing candidate data-tagging patterns include at least one false negative match of data in a selected column of the second set of data (see Whitney, [0065] – [0071], “Based upon the performance measure...one or more candidate classifiers may be removed from the candidate classifier set”; see Kirshenbaum [0025], [0040], determining “false negative rate” for data-tagging patterns in associated classifier represents false negative match); and
selecting, from the updated plurality of data-tagging patterns, the data-tagging pattern, wherein the data-tagging pattern includes the least number of columns among the updated plurality of data-tagging patterns where the data-tagging pattern is applicable at least in the first set of data (see Whitney, [0065] – [0071], “Based upon the performance measure, a completely different candidate classifier algorithm may be selected, new candidate classifiers or classifier algorithms may be added,” [0119], “columns” associate with “similarity and difference,” [0214]; see Kirshenbaum [0025], [0040]).
For claims 23, 30, the combination teaches further comprising:
interactively receiving the second set of data with a selection of the subset of the second set of data, wherein the second set of data represents an exemplar data file, and wherein the subset of the second set of data represents a column in the exemplar data file (see Whitney, [0038], receiving “A Data Object is any type of distinguishable data or information” represents received exemplar data file).
For claims 24, 31, 37, the combination teaches wherein the first set of data represents at least a data lake, and wherein the first set of data at least in part represents data in one or more rows across a plurality of columns in the data lake (see Whitney, [0038], where “A Data Object is any type of distinguishable data or information” represents non-functional descriptive material of data within data lake).
For claims 25, 32, 39, the combination teaches further comprising:
generating a false negative rate of the candidate data-tagging pattern for matching data in the subset of the second set of data (see Kirshenbaum [0025], [0040], determining “false negative rate” for data-tagging patterns in associated classifier represents false negative match);
when the candidate data-tagging pattern includes non-zero false negative rate, update a set of candidate data-tagging patterns by removing the candidate data-tagging pattern from the set of candidate data-tagging patterns (see Kirshenbaum, [0040], [0066], [0079] – [0081], measures “desirability” of each associated item to be in classification based on false negative rate, and remove patterns that do not meet threshold criteria); and
determining, based on the updated set of candidate data-tagging patterns, the data-tagging pattern, wherein the data-tagging pattern includes the least degree of generalizing data for each of a plurality of data-tagging patterns in the statistical summary (see Kirshenbaum, [0040], [0066], [0079] – [0081], determining classifier based on performance measure analysis).
For claims 26, 33, 40, the combination teaches wherein the second degree of generalizing the data pattern relates to a false negative rate of the data-tagging pattern matching data in the subset of the second set of data, and wherein the statistical summary further includes one or more data-tagging signatures, the one or more data-tagging signatures including column names and column headers (see Whitney, [0046], “Signature refers to the range of values that make up a particular class,” [0104], [0110]; see Kirshenbaum [0025], [0040], determining “false negative rate” for selection of data to be in classification).
For claims 27, 34, 38, the combination teaches wherein the first set of data includes columns of a data lake (see Whitney [0038], where “A Data Object is any type of distinguishable data or information” represents non-functional descriptive material of data within data lake).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Patil et al., US 2019/0347327. [0044].
Jeh et al., US 8,145,636. Fig. 2
Hagen et al., US 2018/0032917. [0028].
Carus, US 2010/0293451. [0092], [0150], [0210].
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/JENSEN HU/Primary Examiner, Art Unit 2169
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