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 .
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 05/13/2026 has been entered.
Detailed Action
This is a final Office Action for application 18/595,701, in response to arguments and amendments filed on 05/13/2026. Claims 1, 8 and 15 are currently amended. Claims 7, 14 and 20 are cancelled. Claims 1-6, 8-13, 15-19 and 21-23 are pending and examined below.
Response to Arguments
Applicant's arguments filed 05/13/2026 have been fully considered but they are not persuasive.
Applicant argues that previously cited reference Guan doesn’t teach claim 1 language
wherein the at least one transformation of the plurality of transformations produces the output data element from a second data element representing an input data element to the at least one transformation
because Guan teaches that the extracted elements from the graph are what’s related, not the input data elements themselves. Applicant argues that Guam doesn’t teach any graphing of input or output elements, only the extracted elements of those input elements and their semantic similarities. Ultimately, applicant contends, “even though Guan teaches audio data, the audio data is not the same as the ‘second data element representing an input data element to the at least one transformation’ as recited by amended claim 1.”
However, the “second data element representing an input data element to the at least one transformation” must be read under the broadest reasonable interpretation (BRI) standard, which requires the broadest reasonable reading of the claim language. Under the BRI standard, the transformed audio data is a second data element given that the audio file was the first. Therefore, Guan discloses the “second data element representing an input data element to the at least one transformation” as required by claim 1 language and argument is unpersuasive.
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, 4-6, 8, 11-13, 15, 18-19 and 21-23 is/are rejected under 35 U.S.C. 103 as being unpatentable over Guan et al. (US Pat. 11,811,626) in view of Kozina et al. (US Pub. 2014/0114907).
Regarding claim 1, Guan teaches
A computer-implemented method comprising: retrieving, by at least one processor, an output data record comprising a plurality of first data elements each first data element representing an output data element output from at least one transformation of a plurality of transformations wherein the at least one transformation of the plurality of transformations produces the output data element from a second data element representing an input data element to the at least one transformation; (Figs. 6, 9; Col. 10 [Lines 54-63], Col. 11 [Lines 42-47], Col. 11 [Line 60] - Col. 12 [Line 28], Col. 14 [Lines 1-67] ; a first graph (Fig. 6) is generated (i.e. output data element from at least one transformation) and can come from one or more types of media data (i.e. a related second data element representing an input element), including audio data that is converted to text before the text is then extracted in the multi-step (i.e. a plurality) process of transforming the input to produce the output)
wherein the plurality of data transformations produces output data from input data based at least in part on a data transformation process from a data source to a data target; (Col. 11 [Lines 42-47]) audio data is converted to text before the text is then extracted in the multi-step (i.e. a plurality) process of transforming the input to produce the output)
obtaining, by the at least one processor, at least one input data record comprising a plurality of second data elements, each second data element representing input data input into the at least one transformation; (Figs. 6, 9; Col. 10 [Lines 54-63], Col. 11 [Line 60] - Col. 12 [Line 28], Col. 14 [Lines 1-67] ; a first graph (Fig. 6) is generated (i.e. output data element from at least one transformation) and can come from (i.e. obtained) one or more types of media data (i.e. a related second data element representing an input element))
generating, by the at least one processor, a plurality of input-output pairs, each input-output pair representing a candidate pairing of inputs and outputs of the at least one transformation, the candidate pairing comprising a first data element of the plurality of first data elements with a second data element of the plurality of second data elements; (Fig. 4; Col. 10 [Lines 13-21]; the graph is generated (including pairs) and refined based on more than one type of media data (i.e. first, second, third, etc. data elements), where a node represents a data element and an edge represents a correlation between data elements)
utilizing, by at least one processor, an element lineage mapping module, to determine a correlation, indicative of element-wise data lineage through the plurality of transformations, in a graph database based at least in part on the first data element and the second data element of each input-output pair of the plurality of input-output pairs; (Fig. 4; Col. 10 [Lines 13-21]; the graph is generated (e.g. by an element mapping module) and refined based on user input for the more than one type of media data, where a node represents a data element and an edge represents a correlation between data elements; examiner notes that the lineage module is taught by Kozina as shown below, but is left here for referential clarity)
wherein the element lineage mapping module is configured to: utilize at least one element-level lineage mapping model to determine a statistical relationship between the first data element and the second data element of each input-output pair comprising a plurality of interconnected nodes and edges in a graph database; (Fig. 4; Col. 10 [Lines 13-21]; the graph is generated (e.g. by an element mapping module) and refined based on user input for the more than one type of media data, where a node represents a data element and an edge represents a correlation (i.e. statistical relationship) between data elements; examiner notes that the lineage module is taught by Kozina as shown below, but is left here for referential clarity)
utilizing, by at least one processor, at least one element-level mapping model to determine a correlation between the first data element having interconnected nodes and edges in a graph database and a second element having interconnected nodes and edges in a graph database; (Fig. 4; Col. 10 [Lines 13-21]; the graph is generated and refined based on user input, where a node represents a data element and an edge represents a correlation between (e.g. first and second) data elements)
utilizing, based on at least one processor, a correlation measurement model to determine correlation between each of the nodes of the first data element and each of the nodes of the second element of the plurality of data elements based on the correlation measurement; (Fig. 4; Col. 10 [Lines 13-21]; the graph is generated and refined based on user input, where a node represents a data element and an edge represents a correlation (i.e. statistical relationship) between (e.g. first and second) data elements, and semantic correlation can be determined by attention-based semantic analysis models (i.e. correlation measurement models))
sorting, by the at least one processor, for each first data element of the plurality of first data elements, the plurality of second data elements based at least in part on the statistical relationship between the first data element and the second data element of each input-output pair; (Fig. 4; Col. 10 [Lines 13-21]; the graph is generated and refined based on user input, where a node represents a data element and an edge represents a correlation (i.e. sorted statistical relationship) between data elements)
determine, for each first data element, at least one correlated second data of the plurality of second data element based at least in part on the sorting, and upon identifying that a correlation measurement for the first data element and the second data element exceeds a threshold degree of statistical significance, to determine a statistically significant correlation indicative of an element-wise lineage between each first data element and the respective at least one correlated second data element; (Col. 14 [Line 60] – Col. 15 [Line 2] a criterion (e.g. for sorting) is designated by the user to determine which elements to connect based on the strength (i.e. statistical significance) of the correlation)
and mapping, by the at least one processor, by updating at least one knowledge graph comprising the plurality of interconnected the nodes and edges in the graph database to include, for each first data element, at least one edge to the at least one correlated second data element. (Fig. 4; Col. 10 [Lines 13-21]; the graph is generated (i.e. updated) and refined based on user input, where a node represents a data element and an edge represents a correlation between data elements)
Guan does not explicitly teach
an element lineage mapping module, to determine a correlation, indicative of element-wise data lineage through the plurality of transformations
and mapping, by the at least one processor, for the output data record, a data lineage comprising each input-output pair of each transformation for the output data record
However, from the same field, Kozina teaches
an element lineage mapping module, to determine a correlation, indicative of element-wise data lineage through the plurality of transformations (Fig.3; Par. [0036] the data lineage propagation module (i.e. data lineage model; #215) contains a record mapping module (#205) and column record mapping data (#210) data for maintaining data lineage information)
and mapping, by the at least one processor, for the output data record, a data lineage comprising each input-output pair of each transformation for the output data record (Fig.3; Par. [0036] the data lineage propagation module (#215) contains a record mapping module (#205) and column record mapping data (#210) data for maintaining data lineage information)
It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to combine the data lineage module of Kozina into the correlation calculations of Guan. The motivation for this combination would have been to make data lineage discrepancies visible as explained in Kozina (Par. [0029]).
Regarding claim 5, Guan and Kozina teach claim 1 as shown above, and Guan further teaches
The computer-implemented method according to claim 1, wherein sorting by the at least one processor of the nodes and edges determined by the correlation model is performed on nodes and edges comprising at least one transformation of the data elements. (Figs. 6-9; Col. 10 [Lines 54-63], Col. 11 [Line 60] - Col. 12 [Line 28]; a first graph (Fig. 6) is generated (i.e. output data element) and refined (i.e. transformed) based on user input)
Regarding claim 8, while worded slightly differently, is rejected under the same rationale as claim 1. Guan further teaches a non-transitory computer memory (Fig. 1 #106) and processor (Fig. 1 #104).
Regarding claim 12, while worded slightly differently, is rejected under the same rationale as claim 5.
Regarding claim 15, while worded slightly differently, is rejected under the same rationale as claim 8.
Regarding claim 19, while worded slightly differently, is rejected under the same rationale as claim 5.
Regarding claim 21, Guan and Kozina teach claim 1 as shown above, and Guan further teaches
The method of claim 1, further comprising querying, by the at least one processor, the at least one knowledge graph by traversing the plurality of interconnected the nodes and edges in the graph database in response to an input query for at least one searched data element. (Col. 17 [Lines 51-67] nodes with similar semantic correlations are searched (i.e. queried) and provided to the user)
Regarding claim 22, while worded slightly differently, is rejected under the same rationale as claim 21.
Regarding claim 23, while worded slightly differently, is rejected under the same rationale as claim 21.
Claim(s) 2-3, 9-10 and 16-17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Guan et al. (US Pat. 11,811,626) in view of Kozina et al. (US Pub. 2014/0114907), and further in view of Goodsitt et al. (US Pat. 11,030,526).
Regarding claim(s) 2, Guan and Kozina do not explicitly teach
The computer-implemented method according to claim 1, wherein the statistical relationship determined by the element-level mapping module is at least in part based on a Pearson correlation.
However, from the same field, Goodsitt teaches
The computer-implemented method according to claim 1, wherein the statistical relationship determined by the element-level mapping module is at least in part based on a Pearson correlation. (Col. 6 [Lines 33-43] the threshold of intercorrelation of datasets is based on a Pearson correlation of 0.6)
It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to combine the intercorrelation of Goodsitt into the correlation calculations of Guan. The motivation for this combination would have been to improve the intercorrelations between synthetic datasets as explained in (Col. 9 [Lines 15-16]).
Regarding claim(s) 3, Guan and Kozina do not explicitly teach
The computer-implemented method according to claim 1, further comprising utilizing a correlation measurement model is based on a Pearson correlation.
However, from the same field, Goodsitt teaches
The computer-implemented method according to claim 1, wherein the correlation measurement model is at least in part based on a Pearson correlation. (Col. 6 [Lines 33-43] the threshold of intercorrelation of datasets is based on a Pearson correlation of 0.6)
It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to combine the intercorrelation of Goodsitt into the correlation calculations of Guan. The motivation for this combination would have been to improve the intercorrelations between synthetic datasets as explained in (Col. 9 [Lines 15-16]).
Regarding claim 9, while worded slightly differently, is rejected under the same rationale as claim 2.
Regarding claim 10, while worded slightly differently, is rejected under the same rationale as claim 3.
Regarding claim 16, while worded slightly differently, is rejected under the same rationale as claim 2.
Regarding claim 17, while worded slightly differently, is rejected under the same rationale as claim 3.
Claim(s) 4, 6, 11, 13 and 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Guan et al. (US Pat. 11,811,626) in view of Kozina et al. (US Pub. 2014/0114907) and further in view of Leach et al. (US Pat. 11,526,261).
Regarding claim(s) 4, Guan and Kozina do not explicitly teach
The computer-implemented method according to claim 1, wherein confidence intervals of a correlation measurement model determines at least in part the correlation measurement model.
However, from the same field, Leach teaches
The computer-implemented method according to claim 1, wherein confidence intervals of a correlation measurement model determines at least in part the correlation measurement model. (Col. 34 [Lines 2-26] the statistical processing techniques include confidence intervals)
It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to combine the statistical processing techniques of Leach into the correlation calculations of Guan. The motivation for this combination would have been to improve the interpretability or appearance of graphs as explained in Leach (Col. 14 [Lines 63-67]).
Regarding claim(s) 6, Guan and Kozina do not explicitly teach
The computer-implemented method according to claim 1, wherein sorting by the at least one processor of the nodes and edges determined by the correlation model is based on timestamp information of the data elements.
However, from the same field, Leach teaches
The computer-implemented method according to claim 1, wherein sorting by the at least one processor of the nodes and edges determined by the correlation model is based on timestamp information of the data elements. (Col. 4 Lines [25-35] a user defined date range (i.e. nodes and edges determined by timestamp) is used as a data filter on the input data)
It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to combine the statistical processing techniques of Leach into the correlation calculations of Guan. The motivation for this combination would have been to improve the interpretability or appearance of graphs as explained in Leach (Col. 14 [Lines 63-67]).
Regarding claim 11, while worded slightly differently, is rejected under the same rationale as claim 4.
Regarding claim 13, while worded slightly differently, is rejected under the same rationale as claim 6.
Regarding claim 18, while worded slightly differently, is rejected under the same rationale as claim 4.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to J MITCHELL CURRAN whose telephone number is (469)295-9081. The examiner can normally be reached M-F 8:00am - 5:00pm.
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/J MITCHELL CURRAN/Examiner, Art Unit 2161
/SHERIEF BADAWI/Supervisory Patent Examiner, Art Unit 2169
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