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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 22 December 2025 has been entered.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a mental process without significantly more.
Representative claim 1 recites:
“A data lineage optimizing system comprising:
a processor; and
a memory operatively coupled with the processor, the processor to:
remove, via a simplification process, a redundant data operation from a data lineage graph of a dataset in a data lake;
identify, via an alteration process, an alternate transformation as a candidate to replace an original transformation in the data lineage graph,
calculate a first value for a quality metric associated with the alternate transformation, wherein the alternate transformation transforms an original dataset into a first resulting dataset; and
calculate a second value for the quality metric associated with the original transformation, wherein the original transformation transforms the original dataset into a second resulting dataset, and wherein the quality metric comprises one of a reliability, a consistency, or an accuracy of a transformation; and
in response to the first value of the quality metric associated with the alternate transformation exceeding the second value of the quality metric associated with the original transformation, replace the original transformation with the alternate transformation in the data lineage graph.”
Independent Claims 15 and 18 recite similar subject matter.
The claim is directed to a mental process because the “simplification process,” “alteration process,” calculation of quality metrics, and replacement of an original transformation with an alternate transformation appear to be data analysis and data judgment processes. It is noted that, while one transformation may be replaced by another transformation in a data lineage graph, there is no functional result to this. The data lineage graph is never claimed to actually transform data or be used in the data migration system.
A human being, equipped with pen and paper or a generic computer, is capable of performing data analyses and data judgment to modify a data lineage graph.
The additional elements in the claim include a processor and memory (claim 1), processor (claim 15), and computer-readable medium (claim 18).
This judicial exception is not integrated into a practical application because the claimed additional elements do not appear to improve the processing of a computer, require the use of a specific machine, effect a transformation or reduction of a particular article to a different state or thing, or provide a technological solution to a technological problem.
The processors, memory, and computer-readable medium are recited at a high level of generality. They appear to be generic computing elements or hardware elements. The recitation of generic hardware or generic computing elements is little more than using a computer to perform an abstract idea, see MPEP 2106.05(f)(2).
It is noted that none of the additional elements appear to improve the processing of a computer, require the use of a specific machine, effect a transformation or reduction of a particular article to a different state or thing, or provide a technological solution to a technological problem. As such, none of the additional elements appear to integrate the judicial exception into a practical application.
None of the additional elements are sufficient to amount to significantly more than the judicial exception, in part or in whole.
The recitation of generic hardware or computing elements of the processor, memory, and computer-readable medium are little more than using a computer to perform an abstract idea, see MPEP 2106.05(f)(2).
None of the additional elements, in part or in whole, appear to improve the processing of a computer, require the use of a particular machine, effect a transformation or reduction of a particular article to a different state or thing, or add a specific limitation other than what is well understood, routine, or conventional. As such, none of the additional elements appears to be, in part or in whole, significantly more than the judicial exception.
Dependent claims 2-14, 16-17, and 19-20 are merely directed towards additional limitations that further define data types or further describe analyses that will occur. It is noted that the claimed data definitions and data analysis and extraction steps do not appear to include additional elements that incorporate the claimed subject matter into a practical application. The dependent claims also do not include additional elements that, in part or in whole, appear to be significantly more than the abstract idea.
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.
Claims 1-2 and 15-18 are rejected under 35 U.S.C. 103 as being unpatentable over Dickie et al. (US Pre-Grant Publication 2025/0181319) in view of Fan et al. (US Pre-Grant Publication 2021/0303585).
As to claim 1, Dickie teaches a data lineage optimizing system comprising
a processor (see paragraph [0085]); and
a memory operatively coupled with the processor (see paragraph [0085]), the processor to:
remove, via a simplification process, a redundant data operation from a data lineage graph of a dataset in a data lake (see paragraph [0063]-[0064] for a definition of a dataflow graph describing operations, wherein components are nodes that accept input fields and produce output fields. Paragraph [0097]-[0098] discuss ways to transform a dataflow graph to be more efficient. Among the methods include removing redundant components, see paragraph [0098]. As noted in paragraph [0101], the storage in Dickie may comprise multiple databases in a data lake);
identify, via an alteration process, an alternate transformation as a candidate to replace an original transformation in the data lineage graph (see paragraphs [0097]-[0098] for identifying alternate data processing operations),
…
wherein the alternate transformation transforms an original dataset into a first resulting dataset… (see paragraphs [0097]-[0098] and [0100]. Both the original and optimized dataflow graph transform an original dataset into a resulting dataset)
wherein the original transformation transforms the original dataset into a second resulting dataset… (see paragraphs [0097]-[0098] and [0100]. Both the original and optimized dataflow graph transform an original dataset into a resulting dataset)
in response to … the alternate transformation … [optimizing] the original transformation, replace the original transformation with the alternate transformation in the data lineage graph (see paragraphs [0097]-[0098]. The original transformation undergoes an optimization process in which the alternate, replacement transformation is an optimized version of the original transformation).
Dickie does not teach to:
calculate a first value for a quality metric associated with the alternate transformation; and
calculate a second value for the quality metric associated with the original transformation, and wherein the quality metric comprises one of a reliability, a consistency, or an accuracy of a transformation; and
in response to the first value of the quality metric associated with the alternate transformation exceeding the second value of the quality metric associated with the original transformation, replace the original transformation with the alternate transformation in the data lineage graph.
Fan teaches:
calculate a first value for a quality metric associated with the alternate transformation, wherein the alternate transformation transforms an original dataset into a first resulting dataset (see Fan paragraph [0071]-[0073]. Fan teaches to analyze a data pipeline. Fan also teaches, as shown in [0073], to consider modifications to the data pipeline plan. Metrics between an original plan and a modified plan may be compared to determine whether a reduction or an increase in a metric occurs as a result of the modification); and
calculate a second value for the quality metric associated with the original transformation, wherein the original transformation transforms the original dataset into a second resulting dataset, and wherein the quality metric comprises one of a reliability, a consistency, or an accuracy of a transformation (see Fan paragraph [0073]. As noted above, metrics between an unmodified combination of datasets and a modified combination of datasets may be compared. The metrics include reliability of a transformation, notably, an increase in a delivery speed. An increased delivery speed is more reliable than a decreased or slower delivery speed. Also see paragraph [0017], which indicates that metrics may be gathered for a pipeline, including freshness, a measure of reliability and consistency, and quality); and
in response to the first value of the quality metric associated with the alternate transformation exceeding the second value of the quality metric associated with the original transformation, replace the original transformation with the alternate transformation in the data lineage graph (see Fan paragraph [0073]. A modification that improves upon a metric will be selected and implemented).
It would have been obvious to one of ordinary skill in the art before the earliest filing date of the invention to have modified Dickie by the teachings of Fan because both references are directed towards modifying and improving data analysis. Fan provides to Dickie additional monitoring steps to ensure that proposed updates to data analysis are more efficient and accurate than existing models, which will improve the ability of Dickie to manage and optimize multiple dataflows.
As to claim 2, Dickie as modified by Fan teaches the data lineage optimizing system of claim 1, the processor is further configured to:
execute the simplification process and the alteration process in parallel on additional datasets of the data lake (see Fan paragraph [0073]).
As to claims 15 and 18, see the rejection of claim 1.
As to claim 16, Dickie teaches the method of claim 15, wherein iteratively executing the simplification process and the alteration process further comprises:
serially executing by the processor, the simplification process, and the alteration process on additional datasets of the data lake (see Dickie paragraphs [0094]-[0095] and [0097]-[0098]. Dataflow graphs may be generated and processed in response to user commands).
As to claim 17, Dickie teaches method of claim 16, wherein serially executing the simplification process, and the alteration process further comprises:
replacing, by the processor, the original transformation with the alternate transformation in the data lineage graph of the first resulting dataset via the execution of the alteration process (see Dickie paragraphs [0097]-[0098]).
Claims 3-4 and 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over Dickie et al. (US Pre-Grant Publication 2025/0181319) in view of Fan et al. (US Pre-Grant Publication 2021/0303585), and further in view of Abdul-Jawad et al. (US Patent 10,775,976).
As to claim 3, Dickie as modified teaches the data lineage optimizing system of claim 1.
Dickie as modified does not teach wherein the processor is further configured to:
generate new abstract syntax trees (ASTs) from the simplification process and the alteration processes.
Abdul-Jawad teaches wherein the processor is further configured to:
generate new abstract syntax trees (ASTs) from the simplification process and the alteration processes (see Abdul-Jawad 144:25-50).
It would have been obvious to one of ordinary skill in the art before the earliest filing date of the invention to have modified Dickie by the teachings of Abdul-Jawad because both references are directed towards improving dataflows. Abdul-Jawad provides to Dickie an additional way of representing data flows to understand operations that occur.
As to claim 4, Dickie as modified by Abdul-Jawad teaches the data lineage optimizing system of claim 3, wherein the processor is further configured to:
translate the new abstract syntax trees (ASTs) into executable code that modifies one or more of Extract, Transform, Load (ETL) or Extract, Load, Transform (ELT) processes for the data lake (see Abdul-Jawad 146:31-61. Abdul-Jawad teaches to send the ASTs to the intake system, which performs data processing. See Abdul-Jawad 8:11-18 for a data intake system that extracts data).
As to claim 19, Dickie as modified teaches the non-transitory computer-readable medium of claim 18, wherein the instructions further cause the processor to:
Serially execute the simplification process and the alteration process on additional datasets of a data lake (see Dickie paragraphs [0094]-[0095] and [0097]-[0098] and the rejection of claim 16); and
Dickie does not teach to generate new abstract syntax trees (ASTs) from the serially executed simplification process and alteration processes.
.Abdul-Jawad teaches to generate new abstract syntax trees (ASTs) from the serially executed simplification process and alteration processes (see Abdul-Jawad 144:25-50).
It would have been obvious to one of ordinary skill in the art before the earliest filing date of the invention to have modified Dickie by the teachings of Abdul-Jawad because both references are directed towards improving dataflows. Abdul-Jawad provides to Dickie an additional way of representing data flows to understand operations that occur.
As to claim 20, Dickie teaches the non-transitory computer-readable medium of claim 19, wherein the instructions further cause the processor to:
translate the new abstract syntax trees (ASTs) into executable code that modifies one or more of Extract, Transform, Load (ETL) or Extract, Load, Transform (ELT) processes of the data lake (see Abdul-Jawad 146:31-61. Abdul-Jawad teaches to send the ASTs to the intake system, which performs data processing. See Abdul-Jawad 8:11-18 for a data intake system that extracts data).
Claim 5 is rejected under 35 U.S.C. 103 as being unpatentable over Dickie et al. (US Pre-Grant Publication 2025/0181319) in view of Fan et al. (US Pre-Grant Publication 2021/0303585), and further in view of Venkataramani et al. (US Patent 10,114,917)
As to claim 5, Dickie as modified teaches the data lineage optimizing system of claim 1.
Dickie does not clearly teach wherein the processor is further configured to:
calculate a transform complexity of a transformation used to generate at least one column of the dataset; and
extract dependencies of the at least one column of the dataset from an abstract syntax tree (AST) of the transformation.
Venkataramani teaches:
calculate a transform complexity of a transformation used to generate at least one column of the dataset (see 31:41-65. A data model, including a data flow, may be analyzed. Characteristics of each block of the data model may be analyzed, including with a view towards data complexity. It is noted that Dickie, cited above, teaches wherein data fields represent columns, see paragraph [0005], and a data flow being a data flow graph, [0097]-[0098]); and
extract dependencies of the at least one column of the dataset from an abstract syntax tree (AST) of the transformation (see 21:62-22:6 for creating an abstract data flow for an intermediate representation of a data model. It is noted that Dickie, cited above, teaches wherein data fields represent columns, see paragraph [0005]).
It would have been obvious to one of ordinary skill in the art before the earliest filing date of the invention to have modified Dickie by the teachings of Venkataramani because both references are directed towards improving dataflows. Venkataramani provides to users Dickie an additional way of analyzing data flows to better understand a data lineage and operations that occur.
Claims 6-7 are rejected under 35 U.S.C. 103 as being unpatentable over Dickie et al. (US Pre-Grant Publication 2025/0181319) in view of Fan et al. (US Pre-Grant Publication 2021/0303585), in view of Venkataramani et al. (US Patent 10,114,917), and further in view of Dickie et al. (US Pre-Grant Publication 2019/0370407, hereinafter “Dickie ‘407”).
As to claim 6, Dickie as modified teaches the data lineage optimizing system of claim 5.
Dickie does not teach wherein the processor is further configured to:
continue calculating the transform complexity and extracting of dependencies for a plurality of columns including the at least one column of the dataset until a sum of the transform complexities of the plurality of columns exceeds a transform complexity threshold.
Dickie ‘407 teaches wherein the processor is further configured to:
continue calculating the transform complexity and extracting of dependencies for a plurality of columns including the at least one column of the dataset until a sum of the transform complexities of the plurality of columns exceeds a transform complexity threshold (see Dickie ‘407 paragraph [0093]. A series of nodes in a data flow may be judged for “complexity,” such as whether there is a series of nodes that could be combined. There is minimum threshold length).
It would have been obvious to one of ordinary skill in the art before the earliest filing date of the invention to have modified Dickie by the teachings of Dickie ‘407 because both references are directed towards improving dataflows. Dickie ‘407 provides to Dickie an additional way of improving data flows by combining elements making a data flow more efficient.
As to claim 7, Dickie as modified by Dickie ‘407 teaches the data lineage optimizing system of claim 6, the processor is further configured to:
identify one of a plurality of datasets of the data lake processed in a step immediately preceding the sum of the transform complexities exceeding the transform complexity threshold as a source dataset directly receiving a dependency of the dataset (see Dickie ‘407 paragraph [0093]).
Claims 8-9 are rejected under 35 U.S.C. 103 as being unpatentable over Dickie et al. (US Pre-Grant Publication 2025/0181319) in view of Fan et al. (US Pre-Grant Publication 2021/0303585), in view of Polleri et al. (US Pre-Grant Publication 2024/0320303).
As to claim 8, Dickie teaches the data lineage optimizing system of claim 1.
Dickie does not teach wherein to execute the alteration process that identifies the alternate transformation, the processor is configured to:
extract quality metrics of data generated by the original transformation and data generated by the alternate transformation.
Polleri teaches wherein to execute the alteration process that identifies the alternate transformation, the processor is configured to:
extract quality metrics of data generated by the original transformation and data generated by the alternate transformation (see paragraph [0033]. Polleri monitors pipelines or data workflows for compliance with quality of service dimensions, see abstracts. Note that this may include a comparison of multiple workflows. Also see paragraphs [0067]-[0068] for a selection of multiple pipelines to compare and order).
It would have been obvious to one of ordinary skill in the art before the earliest filing date of the invention to have modified Dickie by the teachings of Polleri because both references are directed towards managing data flows. Polleri simply adds to Dickie the ability to compare two data flows and sort them based on how well they match a quality requirement. This will result in better data flows being identified.
As to claim 9, Dickie as modified by Polleri teaches the data lineage optimizing system of claim 8, wherein the quality metric further comprises at least one of: quality checks performed, a service level agreement guaranteed, or a frequency of fulfillment of the service level agreement (see Polleri paragraphs [0067]-[0070]).
Claims 10-11 and 13-14 are rejected under 35 U.S.C. 103 as being unpatentable over Dickie et al. (US Pre-Grant Publication 2025/0181319) in view of Fan et al. (US Pre-Grant Publication 2021/0303585), in view of Polleri et al. (US Pre-Grant Publication 2024/0320303), and further in view of Shapur et al. (US Pre-Grant Publication 2020/0081899).
As to claim 10, Dickie as modified by Polleri teaches the data lineage optimizing system of claim 8.
Dickie does not teach wherein to identify the alternate transformation, the processor is configured to:
generate statistical column metrics vectors for the first resulting dataset, and other datasets including the original dataset of the data lake; and
calculate similarities between the statistical column metrics vector of the first resulting dataset and the statistical column metrics vectors of other datasets including the original dataset of the data lake.
Shapur teaches:
wherein to identify the alternate transformation, the processor is configured to:
generate statistical column metrics vectors for the first resulting dataset, and other datasets including the original dataset of the data lake (see paragraphs [0072]-[0075]. Shapur generates vectors for source and target columns and compares them); and
calculate similarities between the statistical column metrics vector of the first resulting dataset and the statistical column metrics vectors of other datasets including the original dataset of the data lake (see paragraphs [0072]-[0075]. Shapur determines similarities from the source and target vectors).
It would have been obvious to one of ordinary skill in the art before the earliest filing date of the invention to have modified Dickie by the teachings of Shapur because both references are directed towards transforming data. Shapur simply adds to Dickie the ability to compare identify additional data metrics that will help to determine how well a source column maps to a target column. This will result in better data flows being identified.
As to claim 11, Dickie as modified by Shapur teaches the data lineage optimizing system of claim 10, wherein to calculate the similarities, the processor is configured to:
calculate the similarities with one of Euclidean distance or cosine similarity measures (see Shapur paragraph [0074]).
As to claim 13, Dickie as modified by Polleri teaches the data lineage optimizing system of claim 10, wherein to identify the alternate transformation, the processor is configured to:
select a transformation that maximizes the similarities and improves on the quality metrics of the original transformation as the alternate transformation (see Polleri paragraphs [0067]-[0070]).
As to claim 14, Dickie as modified by Fan teaches the data lineage optimizing system of claim 13, wherein the alternate transformation removes an existing dataset from a data lineage of an original dataset and further adds one or more of a new dataset and a new operation to a data lineage of the original dataset (see Fan paragraph [0073] for adding a dataset and operation. Also see Dickie paragraph [0097]-[0098] for removing a dataset).
Claim 12 is rejected under 35 U.S.C. 103 as being unpatentable over Dickie et al. (US Pre-Grant Publication 2025/0181319) in view of Fan et al. (US Pre-Grant Publication 2021/0303585), in view of Polleri et al. (US Pre-Grant Publication 2024/0320303), in view of Shapur et al. (US Pre-Grant Publication 2020/0081899), and further in view of Venkataramani et al. (US Patent 10,114,917).
As to claim 12, Dickie as modified teaches the data lineage optimizing system of claim 10.
Dickie does not teach wherein to identify the alternate transformation, the processor is configured to:
identify based at least on abstract syntax trees (ASTs) of the original transformation, column dependencies of the first resulting dataset.
Venkataramani teaches wherein to identify the alternate transformation, the processor is configured to:
identify based at least on abstract syntax trees (ASTs) of the original transformation, column dependencies of the first resulting dataset (see 21:62-22:6 for creating an abstract data flow for an intermediate representation of a data model, including dependencies. It is noted that Dickie, cited above, teaches wherein data fields represent columns, see paragraph [0005]).
It would have been obvious to one of ordinary skill in the art before the earliest filing date of the invention to have modified Dickie by the teachings of Venkataramani because both references are directed towards improving dataflows. Venkataramani provides to users Dickie an additional way of analyzing data flows to better understand a data lineage and operations that occur.
Response to Arguments
Applicant's arguments filed 22 December 2025 have been fully considered but they are not persuasive.
Applicant argues that “Applicant submits that the amended claims are not directed to a mental process or other abstract idea. The claims recite specific computer-based operations rooted in the technical management of a data processing system, rather than in human judgment or mental evaluation.”
Applicant argues that “As recited, the system operates on a data lineage graph maintained by the computer system. The processor calculates quantitative quality metrics associated with competing data transformations, where each transformation produces a resulting dataset within a data lake. Applicant submits that these calculations are not performed for advisory purposes or for human decision-making, but are used by the system to determine whether to modify the data lineage graph itself.”
Applicant argues that “Applicant submits that a data lineage graph is not a mental construct, but rather a computer- implemented data structure that governs how transformations are executed within a data processing environment. The automated calculation of transformation-level reliability, consistency, or accuracy, followed by modification of that graph, is not reflective of human judgment, but of automated control over a computer-maintained data structure governing transformation execution and does not reflect a mental process as contemplated by the eligibility guidance.”
Applicant concludes by arguing that “Accordingly, Applicant submits that the claims are directed to a specific improvement in computer-based data lineage technology and do not recite an abstract idea under Step 2A, Prong One.”
In response to these arguments, it is noted that merely automating a decision making process does not necessarily result in patent eligible subject matter (see MPEP2106.05(a)(I), part (iii) under the list of “Examples that the courts have indicated may not be sufficient to show an improvement in computer-functionality,” which states:
iii. Mere automation of manual processes, such as using a generic computer to process an application for financing a purchase, Credit Acceptance Corp. v. Westlake Services, 859 F.3d 1044, 1055, 123 USPQ2d 1100, 1108-09 (Fed. Cir. 2017) or speeding up a loan-application process by enabling borrowers to avoid physically going to or calling each lender and filling out a loan application, LendingTree, LLC v. Zillow, Inc., 656 Fed. App'x 991, 996-97 (Fed. Cir. 2016) (non-precedential);
It is noted that the claimed steps remove an operation from a data lineage graph, identify an alternate transformation to replace a transformation in a data lineage graph, calculate a first quality metric, calculate a second quality metric, compare the two quality metrics, and replace an original transformation with the alternate transformation when the first quality metric exceeds the second quality metric. A human being monitoring quality metrics of a data graph on a generic computer is capable of performing these data analyses.
A ”data lineage graph” shows the flow and transformation of data from one system to another (see Applicant’s specification as filed, [0012]). This is merely a logical outline of the flow of data, or a “mental process” that a human equipped with pen and paper or a generic computer is capable of mapping. A logical outline of a data flow, improved through calculations and analysis is a mental process and patent ineligible.
Applicant argues that “The Office Action previously stated that the data lineage graph was merely created and not used, and that alleged improvements were not reflected in the claim language. (Office action, Pages 20-21.) Applicant submits that the amended claims expressly address this concern by reciting automated replacement of an original transformation with an alternate transformation in the data lineage graph when the alternate transformation exceeds the original according to a specified quality metric.”
It is noted that merely replacing a portion of a logical plan results in an improved logical plan. No improvement to a computer occurs until such a plan is executed.
It is noted that none of the data lineage graphs are executed to perform the data transformations that allegedly have improved data quality metrics. Because of this, no improvement to a computer or technological process occurs in view of the amended claims.
Applicant argues that “Applicant submits that this replacement constitutes a concrete operational change to how the computer system performs data processing. The calculated quality metrics are not merely generated or displayed, but are used as control inputs that cause the system to reconfigure the transformation structure governing the data lake. Applicant submits that replacing the transformation in the data lineage graph causes subsequent execution of data transformations in the data lake to proceed according to the modified data lineage graph.”
It is noted, as defined by Applicant in paragraph [0012], that a data lineage graph is merely a description of how data flows and transforms from one system to another. Without execution, it is nothing more than a diagram or outline of a system process.
Each of the steps of the claims is merely operating on a data lineage graph data structure by either analyzing it or replacing a transformation in it. The data lineage graph appears to be a representation of how data flows and transforms in a system. It does not appear to be self-executing and no step in the claim actually executes the transformations according to the data lineage graph. As such, any improvement to the “metrics” of the data transformations that occur when a data lineage graph is implemented are only realized when the replaced transformations are implemented. This does not appear present in the claims.
Applicant argues that “Applicant further submits that the claimed quality metrics are technological and operational in nature. Reliability, consistency, and accuracy reflect characteristics of transformation execution that directly affect data processing system behavior, output correctness, and downstream data integrity. By dynamically selecting and enforcing transformations that improve these characteristics, the claimed system provides a technological solution to a technological problem arising in large-scale data processing systems. Accordingly, Applicant submits that the claims integrate any alleged abstract idea into a practical application that improves computer functionality, satisfying Step 2A, Prong Two.”
Applicant continues, arguing that “For at least the foregoing reasons, Applicant submits that the amended claims are not directed to an abstract idea under Step 2A, Prong One. Even if a judicial exception were implicated, Applicant submits that the claims are integrated into a practical application that improves the functioning of a computer-based data lineage optimization system under Step 2A, Prong Two. Therefore, the claims recite patent-eligible subject matter. Accordingly, Applicant respectfully requests the rejection of claim 1-20 under 35 U.S.C. §101 be withdrawn.”
As noted above, the replaced transformations in the data lineage graph that are claimed to have improved metrics are not actually executed. Because the transformations are not executed, no improvement to a computer process occurs. As such, Applicant’s arguments are unpersuasive.
Applicant is reminded that unclaimed limitations from the specification receive no patentable weight until claimed.
Applicant’s remaining arguments with respect to the claims 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.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to CHARLES D ADAMS whose telephone number is (571)272-3938. The examiner can normally be reached M-F, 9-5:30 EST.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Neveen Abel-Jalil can be reached at 571-270-0474. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/CHARLES D ADAMS/ Primary Examiner, Art Unit 2152