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 action is in response to the application filed 09/18/2025.
Claims 1-14 and 16-21 are pending and have been examined.
Claims 1-14 and 16-21 are rejected.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 09/18/2025 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is partly being considered by the examiner.
References with strikeout all are pixelated and are not legible.
Response to Arguments
Applicant's arguments filed 09/18/2025 is considered.
Applicant argues that claims recite elements specifically related to computer-implemented data processing and corresponding database systems.
Examiner respectfully submits that the PTAB found the elements can be performed in the mind. There is no additional elements that removes the abstract idea.
Applicant argues that the recited claimed elements do not fall within the category of organizing human activity. However, PTAB found that the argued limitations squarely fall within the category of organizing human activity.
As far as the limitations of “processing new data of a data pipeline” to determine new data conformity is similar to determining new data step and receiving data steps that PTAB already stated falls within the abstract matter category. See PTAB decision dated 07/21/2025.
Applicant argues that the added limitations of, in part, “data pipeline” and “thereby maintaining the data integrity” is sufficient to find significantly more because it improves the functioning of a computer or another technology or technological field.
Consistent with PTAB’s finding, use of a data pipeline and a statement of improvement is not sufficient to find significantly more because the additional elements, alone or in combination, add limitation beyond the abstract ideas. The claims include additional elements in claims that simply append well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the claims’ abstract ideas. Please refer to the 101 rejection below.
Therefore, 101 rejection is maintained.
Claim Objections
Claim 8 is objected to because of the following informalities: Claim 8 includes improper clause stating “a computing device associated a data pipeline”. Appropriate correction is required.
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 1-14 and 16-21 are rejected under 35 U.S.C. 101 because claims contain abstract matter. See Alice analysis below:
Step 2A, Prong 1:
Each of independent claims 1, 8, and 14 contains at least one judicial exception. For example, claim 14 recites the following mental processes:
“generating a set of candidate patterns to distinguish between valid and invalid data, wherein each pattern of the set of candidate patterns is generated based at least in part on:
data of a first column of a data store; and
data of a second column of the data store of a computing system” (i.e., using your brain (as well as additionally pen/paper if necessary) to determine a candidate pattern set that can distinguish between valid and invalid data based on data found in a paper worksheet’s first and second columns and writing the candidate patterns down on paper);
“generating, using the set of candidate patterns, a first set of pattern scores associated with the first column of the data store” (i.e., using your brain (as well as additionally pen/paper if necessary) to determine a set of pattern scores associated with the paper worksheet’s first column by employing the candidate pattern set and writing the pattern scores down on paper);
“ranking the set of candidate patterns based on the first set of pattern scores” (i.e., using your brain (as well as additionally pen/paper if necessary) to rank the candidate patterns in the candidate pattern set based on the pattern scores and writing the ranked candidate patterns down on paper);
“processing new data of a data pipeline associated with the data store to determine, using a pattern of the ranked set of candidate patterns, that the new data conforms to the pattern of the ranked set of candidate patterns, thereby validating the new data, wherein the new data is associated with the fist column” and “[a] method of data validation using inferred pattern generation” (i.e., using your brain (as well as additionally pen/paper if necessary) to determine that new data associated with the worksheet’s first column conforms to a pattern of the ranked set of candidate patterns and to validate the new data by employing a pattern of the ranked set of candidate patterns); and
“based on validating the new data, storing the new data in the first column of the data store, thereby maintaining data integrity of the data store” (i.e., using your brain (as well as pen/paper if necessary) to validate the new data as discussed in the previous step and entering the new data in the paper worksheet’s first column) (emphases added).
Some of the above bolded steps involve collecting and analyzing information (1.e., generating a candidate pattern set that distinguish between valid and invalid data based on data columns, generating pattern scores based on the candidate pattern set, ranking candidate patterns based on pattern scores, and determining that new data associated with a column conforms to a pattern of the ranked candidate pattern to validate the new data), which the courts consider recite mental processes. See CyberSource Corp. v. Retail Decisions, Inc., 654 F.3d 1366, 1372 (Fed. Cir. 2011) (indicating that “[a]ll of claim 3’s method steps” (obtaining information about other transactions related to an Internet address, constructing a map of credit card numbers, and determining if the credit card transaction is valid using the map) “can be performed in the human mind, or by a human using a pen and paper’’); Elec. Power Grp., LLC y. Alstom, S.A., 830 F.3d 1350, 1353-54 (Fed. Cir. 2016) (indicating that selecting information by content for collection and analysis “does nothing significant to differentiate a process from ordinary mental processes”); Mortg. Grader, Inc. v. First Choice Loan Servs. Inc., 811 F.3d 1314, 1324 (Fed. Cir. 2016) (indicating that calculating a credit grading and providing loan pricing information based on credit grading could be performed by humans); SAP Am., Inc. v. InvestPic, LLC, 898 F.3d 1161, 1167 (Fed. Cir. 2018) (noting “selecting certain information, analyzing it using mathematical techniques, and reporting . . . the results of the analysis” are mental processes); Trinity Info Media, LLC y. Covalent, Inc., 72 F.4th 1355, 1362 (Fed. Cir. 2023) (noting “{a| human mind could review people’s answers to questions and identify matches based on those answers’’).
Others recitations in the above bolded steps involve storing new data (i.e., that associated in a column), which the courts have determined recite a mental process. See Content Extraction & Transmission LLC y. Wells Fargo Bank, Nat. Ass’n, 776 F.3d 1343, 1347 (Fed. Cir. 2014) (noting that storing data is a function performed by humans); Credit Acceptance Corp. v. Westlake Servs., 859 F.3d 1044, 1056 (Fed. Cir. 2017) (quoting Content Extraction, 776 F.3d at 1347); Gust, Inc. v. Alphacap Ventures, 905 F.3d 1321, 1336 (Fed. Cir. 2018) (indicating that storing data is data organizing, which is a mental process).
Also, according to the disclosure, the following steps can be performed manually: selecting a pattern from ranked patterns for data validation (Spec. [{ 4, 40) and creating a set of constraints to describe “normal” and “expected” data attributes associated with a data column, which in turn, identifies deviations and validates data (id. {J 19, 21, 31). Moreover, at least the above “determining” step that “us[es] a pattern of the ranked set of candidate patterns” may involve mental processes. For example, the disclosure states validating data may involve manually selecting a candidate pattern. See also id. {{ 20, 40, 47, 48. The disclosure thus supports that various, recited operations (e.g., using the set of candidate patterns and validating data, which in part, involves determining whether data conforms to a pattern created using constraints) involve actions that humans can perform (i.e., mental processes). See also Voter Verified, Inc. v. Election Sys. & Software, LLC, 887 F.3d 1376, 1385 (Fed. Cir. 2018) (indicating that the Specification explains that the claimed steps of voting, verifying the vote, and submitting the vote for tabulation are “human cognitive actions” that humans have performed).
“*[M]ental processes[] and abstract intellectual concepts are not patentable, as they are the basic tools of scientific and technological work.’” Mayo, 566 U.S. at 71 (quoting Benson, 409 U.S. at 67). The courts consider a mental process (thinking) that “can be performed in the human mind, or by a human using a pen and paper” to be an abstract idea. CyberSource, 654 F.3d at 1372. Also, a claim that requires computer elements (e.g., the data store used to generate candidate patterns and to store the new data, such as in claim 14) can still recite mental processes. See Benson, 409 U.S. at 67 (determining that a mathematical algorithm converting binary coded decimal to pure binary within a computer's shift register is an abstract idea}; Morte. Grader, 811 F.3d at 1324 (using computer elements as tools to perform anonymous loan is a mental process); Voter Verified, 887 F.3d at 1385 (determining the steps of voting, verifying the vote, and submitting the vote for tabulation performed on a computer recite human cognitive actions that are abstract).
Accordingly, claim 14 recites the above-bolded steps, which involve the acts of collecting, evaluating, analyzing, and storing information, can be practically performed in the human mind. Thus, claim 14 contains an abstract idea in the “mental process” grouping.
Additionally, claim 14 is drawn to an abstract idea of validating data by following rules or instructions, including comparing new data to a ranked pattern selected from candidate patterns to detect matches, which is a method of organizing human activity, such as managing personal behavior or relationships (e.g., a social activity and/or following instructions). See Intellectual Ventures I LLC v. Symantec Corp., 838 F.3d 1307, 1313-14 (Fed. Cir. 2016) (indicating filtering data that have unwanted content is an abstract idea); Glasswall Solutions Ltd. v. Clearswift Ltd., 754 F. App’x 996, 998 (Fed. Cir. 2018) (similarly indicating that filtering files to regenerate a file without non-conforming data is an abstract idea). The disclosure supports this determination, discussing validating data using inferred patterns to distinguish between valid and invalid data (e.g., data which conforms or does not conform to the pattern). Spec. paras. 1, 3, 4, 20, 33. More specifically, the disclosure addresses validating data by creating declarative constraints that describe attributes of “normal” or “expected” data (id. paras. 19, 21, 26, 31) and scoring and ranking the patterns accordingly, such as by using an impurity score (percentage of values that match the pattern) or a coverage score (an impurity score below a certain threshold)) to determine which pattern best distinguishes data. See id. paras. 4, 20, 21, 29, 31.
Independent claim 1 recites the same steps as claim 14. However, claim 1 recites the steps as operations executed by a processor and stored in memory.
ranking the set of candidate patterns based on the first set of pattern scores; determining, using a pattern of the ranked set of candidate patterns, that new data associated with the first column conforms to the pattern of the ranked set of candidate patterns, thereby validating the new data; and based on validating the new data, storing the new data in the first column. Appeal Br. 22 (Claims App.) (emphases added).
To illustrate, claim 1 is reproduced below, bolding the mental processes:
A system comprising: at least one processor; and memory storing instructions that, when executed by the at least one processor, causes the system to perform a set of operations, the set of operations comprising: generating a set of candidate patterns to distinguish between valid and invalid data, wherein each pattern of the set of candidate patterns is generated based at least in part on: data of a first column of a data store; and data of a second column of the data store; generating, using the set of candidate patterns, a first set of pattern scores associated with the first column of the data store; ranking the set of candidate patterns based on the first set of pattern scores; processing new data of a data pipeline associated with the data store to determine, using a pattern of the ranked set of candidate patterns, that new data associated with the first column conforms to the pattern of the ranked set of candidate patterns, thereby validating the new data, wherein the new data is associated with the first column; and based on validating the new data, storing the new data in the first column of the data store, thereby maintaining data integrity of the data store.
For the reasons discussed above when addressing claim 14, the emphasized portions of claim 1 are directed to mental processes and a certain method of human activity.
As for independent claim 8, this claim differs in some aspects from claim 1, but still recites mental processes (e.g., collecting, analyzing, and storing information) and a method of organizing human activity (e.g., validating data by following rules or instructions). To illustrate, claim 8 is reproduced below bolding its mental processes and method of organizing human activity:
A system comprising:
at least one processor; and
memory storing instructions that, when executed by the at least one processor, causes system to perform a set of operations, the set of operations comprising: receiving, from a computing device associated a data pipeline, a request for a set of candidate patterns for a column of a data store [(i.e., using your brain (as well as additionally pen/paper if necessary) to receive a request for a set of candidate patterns related to a paper worksheet’s column or passing along the request in writing using paper)]; determining, based on the column, a set of pattern scores associated with a combined pattern set of the data store, wherein each pattern of the combined pattern set distinguishes between valid and invalid data for the column and is usable to determine whether new data from the data pipeline and for the column conforms to the pattern, such that the new data is stored in the column when it is determined that the new data conforms to the pattern [ (i.e., using your brain (as well as additionally pen/paper if necessary) to determine a set of pattern scores associated with a combined pattern set, where each pattern distinguishes between valid and invalid data for the paper worksheet’s column and can be used to determine whether new data for the column conforms to the pattern, and when the new data conforms to the pattern, the new data is stored in the paper worksheet’s column by writing the new data down in the column)]; ranking a plurality of patterns in the combined pattern set based on the set of pattern scores [(i.e., using your brain (as well as additionally pen/paper if necessary) to rank the set of the combined pattern set based on the set’s pattern scores and writing the ranked patterns down on paper)]; and
providing, to the computing device in response to the request, at least a part of the ranked plurality of patterns, thereby maintaining data integrity of the data store [(i.e., using your brain (as well as additionally pen/paper if necessary) to communicate or provide at least part of the ranked plurality of patterns to another in response to the request)].
For reasons similar to those previously discussed when addressing claims 1 and 14, claim 8 recites steps as operations executed by a processor and stored in memory (1.e., receiving a request for a candidate pattern set for a column, based on the column, determining pattern scores associated with a combined pattern set, wherein each pattern of the combined pattern set distinguishes between valid and invalid data for the column and is usable to determine whether new data for the column conforms to the pattern, and the new data is stored in the column when it is determined that the new data conforms to the pattern, ranking patterns in the combined pattern set based on the pattern scores, and in response to the request, providing at least a part of the ranked patterns) which as previously explained, absent the processor and memory, are mental processes (1.e., obtaining, evaluating, and storing information) that can be performed by the human mind or with the aid of pen and paper. Additionally, for reasons similar to those previously explained when addressing claims 1 and 14, claim 8 recites a certain method of organizing human activity (e.g., validating data by following instructions to determine whether data conforms to a pattern).
Having determined that claims 1, 8, and 14 recite mental processes and a certain method of organizing human activity, both of which are judicial exceptions, we proceed to Step 2A, Prong 2.
Step 2A, Prong 2
In this prong, determining whether the above-identified judicial exceptions are integrated into a practical application, namely whether claims 1, 8, and 14 apply, rely on, or use the judicial exceptions in a manner that impose a meaningful limit on the exceptions, such that the claims are more than a drafting effort designed to monopolize the abstract idea. See MPEP § 2106.04(a), (d). This involves (1) identifying whether there are any additional recited elements in claims 1, 8, and 14 beyond the judicial exceptions, and (2) evaluate those elements both individually and collectively to determine whether they integrate the exceptions into a practical application.
The additional elements of “at least one processor,” “memory,” “computing device”, “computing system” and “a data store” found in claims 1, 8, and 14 amount to no more than a recitation of the words “apply it” (or an equivalent) or are no more than mere instructions to implement an abstract idea on a computing device. For example, the recited “at least one processor” and “memory” (claims 1 and 8) are merely used as tools to implement an existing process—the recited “instructions that ... cause the system to perform a set of operations” that are abstract ideas as previously explained. But, simply appending a computer functionality (e.g., a processor or memory) to the judicial exceptions does not make the exceptions patentable. See Mayo, 566 U.S. at 82-83. That is, “transformation into a patent-eligible application “requires ‘more than simply stating the abstract idea while adding the words ‘apply it.” Alice, 573 U.S. at 221 (quoting Mayo, 566 U.S. at 72); Trinity Info Media, 72 F.4th at 1365 (quoting Alice, 573 U.S. at 221).
As claimed, these additional elements also do not transform an article of the claims into a different state or thing, such that the abstract ideas in the claims are integrated into a practical application. See MPEP § 2106.05(c). Rather, the recited “at least one processor,” “memory,” “computing device”, “computing system” and “a data store” in claims 1, 8, and 14 are recited as generic computing devices and thus, do not recite significantly more than a judicial exception by applying the exception to a particular machine for example. See Alice, 573 U.S. at 223-24 (noting “the mere recitation of a generic computer cannot transform a patent- ineligible abstract idea into a patent-eligible invention” and “wholly generic computer implementation is not generally the sort of ‘additional featur[e]’ that provides any ‘practical assurance that the process is more than a drafting effort designed to monopolize the [abstract idea] itself.’”’) (quoting Mayo, 566 U.S. at 77)); In re TLI Commc’ns LLC Pat. Litig., 823 F.3d 607, 614 (Fed. Cir. 2016) (noting these steps in the disputed claims “fall squarely within our precedent finding generic computer components insufficient to add an inventive concept to an otherwise abstract idea.”). Merely adding generic computer components to perform abstract ideas does not integrate those ideas into a practical application. See MPEP § 2106.04(a), (d) (citing 2019 Revised Guidance, 84 Fed. Reg. at 55 (identifying “merely includ[ing] instructions to implement an abstract idea on a computer” as an example of when an abstract idea has not been integrated into a practical application)).
Moreover, the Specification describes the recited “at least one processor,” “memory,” “computing device”, “computing system” and “a data store” at a high level of generality. To illustrate, the disclosure describes “a circuit utilizing a microprocessor, or on a single chip containing electronic elements or microprocessors” (Spec. para. 91) and “processor 560” (id. para. 100, Fig. 5B) without any further discussion. As another example, “system memory 404 may comprise, but is not limited to, volatile storage (e.g., random access memory), non-volatile storage (e.g., read-only memory), flash memory, or any combination of such memories.” id. para. 89; see also id. para.93, Fig. 4. In both instances, the disclosure describes the processor and memory in generalities without any further details related to its structure or how it functions.
Regarding the recited “data store,” the Specification describes this feature to include “a data lake” (id. para.20; see also id. para.34, Fig. 1), which “may store data or data assets of the enterprise in a variety of data structures, in a form of columns and rows, for example” (id. para.18). Once again, the disclosure describes the data store in generalities without many details related to its structure or how it functions. As claimed and described in the disclosure, the “at least one processor,” “memory” and “data store” at best perform their intended computer functions (e.g., storing instructions/data and executing instructions). E.g., Spec. [J 18, 89, 93.
For the above-stated reasons, the “at least one processor,” “memory,” “computing device”, “computing system” and “a data store” in claims 1, 8, and 14 merely automate the previously described manual processes (i.e., the mental processes or method of organizing human activity of claims 1, 8, and 14 found to be abstract ideas) by using generic computing components and do not involve any specific processor, memory, or data store that improves on the way the system or process executes instructions or stores data. See Credit Acceptance Corp., 859 F.3d at 1055 (stating “[o]ur prior cases have made clear that mere automation of manual processes using generic computers does not constitute a patentable improvement in computer technology’’) (citing Elec. Power, 830 F.3d at 1354; TLI Commc’ns LLC, 823 F.3d at 612). Nor do claims 1, 8, and 14 recite an improvement in the functioning of the “at least one processor,” “memory,” and “data store,” or an improvement to a technology or technical field. See MPEP §§ 2106.04(d)(1), 2106.05(a), (h).
Also, for the above-stated reasons, the “at least one processor,” “memory,” “computing device”, “computing system” and “a data store” of claims 1, 8, and 14 do not implement the claims’ abstract ideas with a particular machine that is integral to the claim, such that the abstract ideas are integrated into a practical application. See CyberSource, 654 F.3d at 1375 (“[P]rogramming a general purpose computer to perform an algorithm ‘creates a new machine, because a general purpose computer in effect becomes a special purpose computer once it is programmed to perform particular functions pursuant to instructions from program software.’ But we have never suggested that simply reciting the use of a computer to execute an algorithm that can be performed entirely in the human mind falls within the Alappat rule.”) (quoting In re Alappat, 33 F.3d 1526, 1545 (Fed. Cir. 1994)); see also MPEP § 2106.05(b). As explained above, the recited computing elements (e.g., the “at least one processor’) simply executes a set of instruction (e.g., an algorithm) that can be performed entirely in the human mind and thus, does not recite a particular machine that integrates claims 1’s and 8’s abstract ideas into a practical application.
Rather, the recited additional elements in claims 1, 8, and 14 do no more than generally link the use of the noted judicial exceptions to a particular technological environment or field of use (e.g., processing, data storage, and memory). But, limiting the judicial exceptions to a particular technical field does not make the claims non-abstract or patent eligible. See Diehr, 450 U.S. at 191-92 (noting abstract ideas “cannot be circumvented by attempting to limit the use of the formula to a particular technological environment”); Bilski, 561 U.S. at 611 (noting that “limiting an abstract idea to one field of use . . . did not make the concept patentable”’); Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 1366 (Fed. Cir. 2015) (“An abstract idea does not become nonabstract by limiting the invention to a particular field of use or technological environment, such as the Internet [or] a computer”). See also MPEP § 2106.05(f).
Lastly, to the extent that the previously discussed steps or operations in claims 1, 8, and 14 are not considered abstract ideas, we further determine that they are insignificant extra-solution activities, such as data gathering steps, a step of selecting data to be manipulated for collection and analysis, and a step of establishing inputs for instructions. See MPEP 2106.05(g). Insignificant extra-solution activities found in these claims include: ranking the set of candidate patterns based on the first set of pattern scores (claims 1, 8, and 14), selecting a pattern from candidate patterns to determine whether new data for the column conforms to the pattern (claims 1, 8, and 14), storing the new data in the first column (claims 1 and 14), receiving a request for a set of candidate patterns for a column of a data store (claim 8), and providing at least a part of the ranked plurality of patterns (claim 8). See Diehr, 450 U.S. at 191-92 (noting “insignificant post-solution activity will not transform an unpatentable principle into a patentable process”); Ultramercial, Inc. v. Hulu, LLC, 772 F.3d 709, 715-16 (Fed. Cir. 2014) (indicating that “the steps of consulting and updating an activity log represent insignificant ‘data-gathering steps’”) (quoting CyberSource, 654 F.3d at 1370); CyberSource, 654 F.3d at 1372 (discussing a step of “obtaining information” and noting “data-gathering steps cannot alone confer patentability”) (quoting Jn re Grams, 888 F.2d 835, 840 (Fed. Cir. 1989)); Elec. Power, 830 F.3d at 1355 (noting “merely selecting information, by content or source, for collection, analysis, and display does nothing significant to differentiate a process from ordinary mental processes’’).
For the above reasons, the claims are directed to an abstract idea because the present claims recite an abstract idea that are not integrated into a practical application.
Step 2B
Because the claims are directed to an abstract idea, we next analyze the claims under step two of Alice (1.e., step 2B of the Office Guidance) to determine if there are additional limitations that individually, or as an ordered combination, ensure the claims amount to “significantly more” than the abstract idea. Alice, 573 U.S. at 217-18 (citing Mayo, 566 U.S. at 77— 79). This involves determining whether or not the additional elements in claims 1, 8, and 14, alone or in combination, add a limitation beyond the abstract ideas in the claims that is not well-understood, routine, conventional activity in the field. See MPEP § 2106.05(d).
Upon review, the additional elements in claims simply append well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the claims’ abstract ideas.
The courts have recognized that collecting or retrieving, recognizing, sorting, storing, and updating data are well-known activities. See Alice, 573 U.S. at 225 (indicating that obtaining data is well-understood, routine, and conventional computer activities previously known in the industry); Content Extraction, 776 F.3d at 1347 (noting that data collection, recognition, and storage are “undisputedly well-known” activities); Versata Dev. Grp., Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334 (Fed. Cir. 2015) (noting storing, retrieving, sorting, and eliminating information are well-known, routine, and conventional activities known in the industry); Ultramercial, Inc. v. Hulu, LLC, 772 F.3d 709, 716 (Fed. Cir. 2014) (noting that consulting and updating an activity log are data-gathering steps); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355 (Fed. Cir. 2014) (noting that computer receiving and sending information over a network “is not even arguably inventive”). Similarly, at least one court has recognized that issuing automated instructions is a well-understood, routine, and conventional computer activities previously known in the industry. See Alice, 573 U.S. at 225.
As previously discussed, the recited “at least one processor” (claims 1 and 8), “memory” (claims 1 and 8), computing device (claim 8), computing system (claim 14), and “data store” (claims 1, 8, and 14) are recited and described at a high level of generality, do not recite a particular machine, do not transform an article into different state, and do not improve the functioning of any computer element or technical field. We refer above for a more detailed discussion.
Given the above case law, the following activities in claims 1, 8, and 14 are considered well-understood, routine, conventional activities in industry: the recited “memory” that “stor[es] instructions” that “perform a set of operations” (claims 1 and 8) (e.g., storing information), the recited “at least one processor” that “execut[e]s” the “stor[ed] instructions” (claims 1 and 8) (e.g., issuing automated instructions), the recited “data” of a “column of a data store” (claims 1 and 14) or “column of a data store” (claim 8) (e.g., storing data), “ranking the set of candidate patterns” (claims 1 and 14) or “ranking a plurality of patterns” (claim 8) (e.g., sorting information), the recited “combined pattern set of the data store” (claim 8) (e.g., storing information), the recited “data store” that “stor[es] new data” (claims 1 and 14) or “the new data is stored in the column” of a data store (claim 8) (e.g., storing or updating information), “receiving a request for a set of candidate patterns for a column of a data store” (claim 8) (e.g., retrieving information over a communication line), “processing new data of a pipeline” (claims 1 and 14), and “providing, in response to the request, at least a part of the ranked plurality of patterns” (claim 8) (e.g., sending information over a communication line) are no more than well-understood, routine, and conventional activities in industry.
Additionally, the Specification discusses using “well-known data/information . . . storage means.” Spec. { 102. This bolsters that the “storing” operations found in claims 1, 8, and 14 include activities previously known in the industry or activities that are well-understood and conventional computing activities in the field.
Because the recited additional elements in claims 1, 8, and 14 are used to perform well-understood, routine, and conventional activities, we determine the additional limitations do not add a limitation beyond the abstract ideas in the claims that is not well-understood, routine, conventional activity in the field. We thus determine the additional elements in the claims do not individually, or as an ordered combination, ensure the claims amount to significantly more than their recited abstract ideas.
Accordingly, we conclude that claims 1, 8, and 14, considered as a whole, are directed to a patent-ineligible abstract idea that is not integrated into a practical application and does not include an inventive concept.
All dependent claims are rejected under 35 U.S.C. § 101.
Below Abstract Idea is highlighted based on mental process and organizing human activity.
3. (Original) The system of claim 1, wherein the set of operations further comprises: automatically selecting the pattern of the ranked set of candidate patterns based on determining the pattern is a highest-ranked pattern of the ranked set of candidate patterns.
4. (Previously Presented) The system of claim 1, wherein the set of operations further comprises: determining at least a part of the new data does not conform to the pattern; and based on determining that at least a part of the new data does not conform to the pattern, generating a validation failure indication associated with the part of the new data.
5. (Original) The system of claim 1, wherein the first set of pattern scores comprises at least one of: an impurity score for the pattern that indicates a percentage of rows of the first column that do not conform to the pattern; or a coverage score for the pattern associated with a number of columns of the data store that conform to the pattern.
6. (Previously Presented) The system of claim 1, wherein: the first set of pattern scores comprises a first tolerance parameter for the pattern; and determining that the new data conforms to the pattern further comprises: generating a second tolerance parameter for the new data based on the pattern; and evaluating the first tolerance parameter and the second tolerance parameter to determine whether a difference exceeds a predetermined threshold.
7. (Previously Presented) The system of claim 1, wherein: the second column comprises a plurality of subdomains; and the generated set of candidate patterns comprises at least: a first subset of patterns associated with a first subdomain of the plurality of subdomains; and a second subset of patterns associated with a second subdomain of the plurality of subdomains.
9. (Currently Amended) The system of claim 8, wherein the set of operations further comprises: receiving an indication of a selection of a pattern of the ranked plurality of patterns, wherein the indication of the selection of the pattern further comprises an edited pattern; and generating an association between the column and the indicated pattern.
11. (Previously Presented) The system of claim 9, wherein the set of operations further comprises: validating new data associated with the column using the indicated pattern based on the association; and based on validating the new data, storing the new data in the column.
12. (Original) The system of claim 11, wherein the set of operations further comprises:
determining at least a part of the new data does not conform to the indicated pattern; and
based on determining that at least a part of the new data does not conform to the indicated pattern, generating a validation failure indication associated with the part of the new data.
13. (Original) The system of claim 8, wherein the set of pattern scores comprises at least one of:
an impurity score for the pattern that indicates a percentage of rows of the column that do not conform to the pattern; or
a coverage score for the pattern associated with a number of columns of the data store that conform to the pattern.
16. (Original) The method of claim 14, further comprising: automatically selecting the pattern of the ranked set of candidate patterns based on determining the pattern is a highest-ranked pattern of the ranked set of candidate patterns.
17. (Previously Presented) The method of claim 14, further comprising: determining at least a part of the new data does not conform to the pattern; and based on determining that at least a part of the new data does not conform to the pattern, generating a validation failure indication associated with the part of the new data.
18. (Original) The method of claim 14, wherein the first set of pattern scores comprises at least one of: an impurity score for the pattern that indicates a percentage of rows of the first column that do not conform to the pattern; or a coverage score for the pattern associated with a number of columns of the data store that conform to the pattern.
19. (Previously Presented) The method of claim 14, wherein: the first set of pattern scores comprises a first tolerance parameter for the pattern; and determining that the new data conforms to the pattern further comprises: generating a second tolerance parameter for the new data based on the pattern; and evaluating the first tolerance parameter and the second tolerance parameter to determine whether a difference is exceeds a predetermined threshold.
20. (Original) The method of claim 14, wherein: the second column comprises a plurality of subdomains; the generated set of candidate patterns comprises at least: a first subset of patterns associated with a first subdomain of the plurality of subdomains; and a second subset of patterns associated with a second subdomain of the plurality of subdomains.
21. (Previously Presented) The system of claim 1, wherein a pattern of the set of candidate patterns comprises a sequence of a plurality of tokens associated with at least one of the first column of the data store and the second column of the data store.
22. (New) The system of claim 21, wherein generating the set of candidate patterns comprises tokenizing, using a lexer, data in a row of the data store, thereby forming the sequence of the plurality of tokens.
23. (New) The system of claim 22, wherein generating the set of candidate patterns further comprises processing each token of the sequence of the plurality of tokens to generate a respective generalization for each token of the sequence of the plurality of tokens with which to validate the new data.
The above highlighted sections include abstract matter consistent with the analysis for claims 1, 8 and 14. Also, the recited additional elements above do no more than generally link the use of the noted judicial exceptions to a particular technological environment or field of use (e.g., processing, data storage, and memory) or include “apply it” steps. Refer to the rationale above for lack of finding practical application. Finally, there is no finding of significantly more. As previously discussed above with respect to the independent claims, the additional elements found in the dependent claims are described at a high level of generality, do not recite a particular machine, do not transform an article into different state, and do not improve the functioning of any computer element or technical field. We refer above for a more detailed discussion.
Therefore 101 rejection is maintained.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Middleton et al. (US 20100100577) – Teaches data ingestion using pattern score.
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Taelor Kim
Primary Patent Examiner
Art Unit 2156
/TAELOR KIM/
Primary Examiner, Art Unit 2156