Detailed Action
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 .
Acknowledgments
The submission filed on 04/02/26 is acknowledged.
Status of Claims
Claims 1-3, 5-10, 12-17 and 19-23 are pending.
In the Amendment filed on 04/02/26, claims 1-3, 5-10, 12-17, 19 and 20 were amended, claims 4, 11 and 18 were cancelled, and claims 21-23 were added.
Claims 1-3, 5-10, 12-17 and 19-23 are rejected.
Information Disclosure Statements
Applicant filed two Information Disclosure Statement (IDSs) on 04/02/26.
One IDS lists PCT/US25/50744, a copending international patent application. Note the document listed on the IDS is the unpublished international application. Applicant does not provide any publication number of any published application corresponding to this unpublished application; indeed, Applicant does not indicate whether this application has been published. Pursuant to 37 CFR § 1.98(a)(2)(iv), the IDS must include a legible copy of this unpublished international application. However, no such copy was included with the IDS. Accordingly, this unpublished international application has not been considered.
The other IDS has been considered.
Response to Arguments
Regarding the rejection under 35 U.S.C. 101
Applicant’s arguments have been fully considered but are not persuasive.
The Office responds to Applicant’s arguments below. In the discussion below, page numbers refer to Applicant’s Response, unless otherwise indicated.
Regarding step 2A, prong 1, Applicant argues that the instant claims "are not directed to an abstract idea," based on alleged analogies with McRO, Enfish and SRI (pp. 11, 13).
As for the alleged analogy with McRO, Applicant argues:
Applicant submits that the identification of "a first subset . . . a second subset . . . and a third subset of the normalized dataset" based on a "comparison" of "the normalized dataset ... to verified attribute field data" "by using a trained machine learning model" of currently amended claims is analogous in certain respects to the application of a "set of rules that define output morph weight" of McRO. Much like the "set of rules" of McRO represents "a specific process for automatically animating characters using particular information and techniques" - the identification of "a first subset ... a second subset ... and a third subset of the normalized dataset" "using a trained machine learning model" and "modifying at least one character in at least one filed of the second subset" in Applicant's currently amended claims is likewise used to improve the correction of "at least one of the one or more errors" in the "normalized dataset," and therefore recite a specific technique for resolving errors in identity datasets. (p. 12; bold in original; underlining added)
In response:
The subject matter of Applicant's claims (e.g., detecting and correcting errors in a dataset) has nothing to do with the subject matter of McRO.
As the USPTO has explained:
In McRO, the Federal Circuit held the claimed methods of automatic lip synchronization and facial expression animation using computer-implemented rules patent eligible under 35 U.S.C. § 101, because they were not directed to an abstract idea (Step 2A of the USPTO's SME guidance). The basis for the McRO court's decision was that the claims were directed to an improvement in computer-related technology (allowing computers to produce "accurate and realistic lip synchronization and facial expressions in animated characters" that previously could only be produced by human animators), and thus did not recite a concept similar to previously identified abstract ideas.
…
An "improvement in computer-related technology" is not limited to improvements in the operation of a computer or a computer network per se, but may also be claimed as a set of "rules" (basically mathematical relationships) that improve computer-related technology by allowing computer performance of a function not previously performable by a computer.
An indication that a claim is directed to an improvement in computer-related technology may include --
(1) a teaching in the specification about how the claimed invention improves a computer or other technology (e.g., the McRO court relied on the specification's explanation of how the claimed rules enabled the automation of specific animation tasks that previously could not be automated when determining that the claims were directed to improvements in computer animation instead of an abstract idea). In contrast, the court in Affinity Labs ofTX v. DirecTVrelied on the specification's failure to provide details regarding the manner in which the invention accomplished the alleged improvement when holding the claimed methods of delivering broadcast content to cellphones directed to an abstract idea.1 (bold in original; underlining added)
As per the USPTO's explanation above, McRO's claims, pertaining to automatic lip synchronization and facial expression animation using computer-implemented rules, were not directed to an abstract idea but were found to be an improvement in technology, which was taught in McRO's specification.
In contrast, Applicant's claims recite steps such as receiving data, normalizing data, identifying errors in the data, determining a score based on the errors, and correcting the errors, where, as per specification (0002), the error resolution is in the service of "customer services (e.g., call center services, customer support services, etc.), data processing services, and any business, organization, or entity that relies on accurate data to provide a good or service." These steps, which constitute the bulk of claim 1, constitute an abstract idea.
This abstract idea (detecting and correcting errors in data, in support of customer service or provision of a good/service) pre-dates computers.
Applicant's specification sets forth the problems it aims to solve at 0002 and 0017-0019 and the solution at 0020. As seen there, the problems include the complexity and size of the data, the different formats used, and the absence of dynamic updating, and the solution includes normalizing the data, alerting users, determining a data quality score, automating the process of identifying and correcting data, using AI models to identify and correct errors, and active monitoring.
As seen from the above, Applicant's claims and specification are not analogous to McRO. First, Applicant's claims do recite a concept similar to previously identified abstract ideas. Second, Applicant's claims and specification merely recite and describe generic computer elements (e.g., processor, machine learning model) used off the shelf as a tool in their ordinary capacities to implement (apply) the abstract idea of error detection and correction; they do not improve computer-related technology by allowing computer performance of a function not previously performable by a computer. The putative improvement provided by Applicant appears to be automation, i.e., the application of generic computer elements, to an abstract idea.
As for the alleged analogy with Enfish, Applicant argues:
Further, "processing the normalized dataset to identify . . . a second subset of the normalized dataset as likely including one or more errors," "modifying at least one character in at least one field of the second subset . .. to correct at least one of the one or more errors," and "updating at least one weight of the trained machine learning model" "based on the at least one of the one or more errors being corrected" of currently amended claims is analogous in certain respect to the application of a "self-referential table" of Enfish. Much like the "self-referential table" of Enfish is "designed to improve the way a computer stores and retrieves data in memory" - the "normalized dataset" in Applicant's currently amended claims is likewise used to improve computer functionality by resolving errors and enhancing data reliability in identity datasets, specifically by "processing the normalized dataset ... to identify a first subset ... as likely to be valid, a second subset . . . as likely including one or more errors, and a third subset . . . as likely to be fake," "modifying at least one character in at least one field of the second subset . .. to correct at least one of the one or more errors," "updating the data quality score based on . . .the one or more errors being corrected" and "updating at least one weight .. .based on the updated data quality score .. .the at least one weight having contributed to identification ... by the trained machine learning model." (pp. 12-13; bold in original; underlining added)
In response:
Applicant's claimed invention has been described above. As seen, the subject matter of Applicant's claimed invention has nothing to do with that of Enfish. As seen, Applicant's claimed invention merely uses generic computer elements (e.g., processor, memory, machine learning model) used off the shelf as a tool in their ordinary capacities to implement (apply) the abstract idea of error detection and correction. Applicant's claims do not improve computer-related or other technology in any way. The putative resolving of errors, enhancing data reliability, processing the normalized dataset to identify subsets, modifying at least one character to correct at least one of the one or more errors, and updating the data quality score based on the one or more errors being corrected are all merely data analysis/processing and as such do not improve computer functioning/technology. The updating of weights in the machine learning model is merely a generic computer element (generic machine learning) used off the shelf as a tool in its ordinary capacity to implement (apply) the abstract idea of error detection and correction.
As for the alleged analogy with SRI, Applicant argues:
The claims are also patent-eligible for similar reasons as the claims at issue in SRI International, Inc. v. Cisco Systems, Inc., 930 F.3d 1295 (Fed. Cir. 2019) ("SRI"). The claims at issue in SRI concern "detecting [...] suspicious network activity based on analysis of network traffic data." SRI, 1301. The Federal Circuit found the claims not abstract because they were directed to "using a specific technique . . . to solve a technological problem arising in computer networks," because they recite a "technology [that] 'overrides the routine and conventional sequence of events' by detecting suspicious network activity." Id., 1304. Applicant submits that "processing the normalized dataset by using a trained machine learning model to identify . .. a second subset of the normalized dataset as likely including one or more errors, and a third subset of the normalized dataset as likely to be fake" and "updating at least one weight . . . having contributed to identification" in currently amended claims is analogous to "detecting [...] suspicious network activity based on analysis of network traffic data" in SRI, because the claims recite a specific technique for improving detection and resolution of errors in identity datasets. (p. 13)
In response:
Applicant's claimed invention has been described above. As seen, the subject matter of Applicant's claimed invention has nothing to do with that of SRI. As seen, unlike SRI, Applicant's problem is not technological, nor do Applicant's claims provide any non-routine or unconventional technology. Insofar as the claims be deemed specific, such specificity is merely of the abstract idea or of the generic computer elements. Even assuming hypothetically for the sake of argument that the claims improve anything, the improvement would be of the abstract idea.
Regarding step 2A, prong 2, Applicant does not present significant additional substantive argument, but rather refers to the previous discussion of McRO, Enfish and SRI. Specifically, Applicant argues:
MPEP § 2106.04(d)(III) indicates that the Federal Circuit's analysis in Finjan Inc. v. Blue Coat Systems, Inc., 879 F.3d 1299 (Fed. Cir. 2018) ("Finjan") "is equivalent to the Office's analysis of determining that the additional elements integrate the judicial exception into a practical application at Step 2A Prong Two" in ultimately determining that "the claims were not directed to the judicial exception." See MPEP § 2106.04(d)(III). Accordingly, even assuming arguendo that the claims recite a judicial exception, the Office must determine whether the entirety of the claims integrate the alleged exception into a practical application. Here, for at least the reasons discussed above with respect to McRO, Enfish, and SRI, the currently amended claims recite specific additional elements that improve computer functionality by resolving errors and enhancing data reliability in identity datasets, and therefore integrate any alleged judicial exception into a practical application under Step 2A, Prong Two. (p. 14; underlining added)
In response:
Initially, it is not clear what "specific additional elements" Applicant is referring to, as Applicant did not point out any elements as additional elements in the preceding discussion, and most of the claimed subject matter argued previously was content of the abstract idea. In any event, as discussed above, and as set forth in the rejection in the body of the Office Action hereinbelow, the additional elements are merely generic computer elements (e.g., processor, memory, machine learning model) that reflect no improvement whatsoever in computer functioning/technology but rather are merely used off the shelf as a tool in their ordinary capacities to implement (apply) the abstract idea of error detection and correction. Again, the resolving of errors and enhancing of data reliability constitute part of the abstract idea and do not improve computer functioning/ technology. Accordingly, no integration into a practical application is seen.
Regarding step 2B, Applicant argues:
Applicant notes that MPEP § 2106.05(f) clarifies that "[w]hen determining whether a claim simply recites a judicial exception with the words 'apply it' (or an equivalent), ... examiners may consider [...] [w]hether the claim ... fails to recite details of how a solution to a problem is accomplished" (emphasis added). Applicant submits that the amended independent claims recite details of how the systems and methods accomplish "processing the normalized dataset . . . to identify a first subset . . . a second subset . . . and a third subset," "correct[ing] at least one of the one or more errors" of the "second subset of the normalized dataset," and "updating .. the trained machine learning model based on the updated data quality score and the at least one of the one or more errors" among other details of how a solution to a problem is accomplished. Applicant further adds that additional elements, considered together, represent an inventive concept ("something more") at least because they represent "[i]mprovements to the functioning of a computer" under MPEP § 2106.05(I)(A)(i)-(iii) for at least the reasons discussed above with respect to the considerations for Step 2A (prong 2). (pp. 14-15; bold in original)
In response:
Insofar as Applicant's claims recite how a solution is accomplished, such details amount merely to details of the abstract idea or of the generic computer elements. Such details do not amount to 'significantly more' under step 2B. The alleged additional elements also do not represent an improvement to the functioning of a computer or other technology, as discussed above. As they merely apply the abstract idea, the additional elements do not amount to 'significantly more' under step 2B.
Regarding the rejections under 35 U.S.C. 103
In view of the instant claim amendments, the rejections are withdrawn. See "Subject Matter Distinguishable From Prior Art" below.
Subject Matter Distinguishable From Prior Art
The cited prior art of record, either alone or in combination, fails to expressly teach or suggest the features found in independent claim 1, 8 and 15.
Schleith (US-20230195715-A1) teaches systems and methods for detection and correction of anomalies, including obtaining a dataset, converting the dataset into a standardized format, identifying clusters corresponding to portions of the dataset comprising anomalies, eliminating/correcting the anomalies, and obtaining the resultant corrected data, wherein the clustering may be performed by comparing data to historical data for reference or by using variance analysis, wherein the anomalies include a variety of types of anomalies and different clustering algorithms may be used to identify different types of anomalies, further including the use of trained machine learning.
Richardson (US-20230214369-A1) teaches systems and methods for detecting and repairing data errors using AI, where the errors may be errors in specified data fields, and including inter alia determining /assigning a data quality risk score for a dataset or data record.
Humphreys (US-20240386100-A1) teaches inter alia using machine learning specifically for generating a data quality score.
WHEELER (US-20250016128-A1) teaches normalizing data in a dataset and identifying and correcting errors in the data, using machine learning, and generating a data quality score, see Fig. 2 as an example;
TREANOR (WO-2025146571-A1) teaches normalizing an identify dataset and verifying it based on matching data of the dataset against data in a database;
Antonini (US-20250124502-A1) teaches, in respect of a mortgage, normalizing data and identifying and correcting discrepancies, including using machine learning, see, e.g., Figs. 6-9, 20-21;
Balakrishnan (US-20210124919-A1) teaches systems and methods for authenticating documents, including normalizing data and identifying errors in an identity document, and verifying the document, including generating a score reflecting confidence in the accuracy of the data, wherein the errors may be identified in specific fields of the document;
Reimer (US-20170228821-A1) teaches normalizing and validating financial information, including identifying and correcting anomalies and generating a score reflecting trustworthiness of the data;
Lombard (US-20240362735-A1) teaches receiving a user profile, generating a verified user profile based on the user profile, and evaluating the verified user profile;
Kim (US-20240127306-A1) teaches generating a data quality score based on errors within a normalized dataset, using machine learning;
Kelsey (US-20180260472-A1) teaches an automated tool for question generation including inter alia performing matching by comparing nodes, attributes, and relationships between a given text fragment and a given pattern to determine a matching score, where a component matching score can be determined for each individual node, attribute, or relationship, and an overall matching score can be determined by combining the various component matching scores, and if the matching score is at least equal to a matching threshold, then a match can be determined;
Dardia (US-20220414125-A1) teaches systems and methods for computer modeling using incomplete data including inter alia determining a total score for a plurality of datasets by aggregating component scores of the respective datasets, the total score indicating accuracy and reliability of a model corresponding to the plurality of datasets.
In particular, however, the cited prior art of record, either alone or in combination, fails to expressly teach or suggest all of the features in independent claims 1, 8 and 15 and more specifically the limitations of: processing the normalized dataset by using a trained machine learning model to identify a first subset of the normalized dataset as likely to be valid, a second subset of the normalized dataset as likely including one or more errors, and a third subset of the normalized dataset as likely to be fake based on a comparison to verified attribute field data; generating a data quality score for the normalized dataset based on the first subset, the second subset, and the third subset; modifying at least one character in at least one field of the second subset of the normalized dataset to correct at least one of the one or more errors; and updating at least one weight of the trained machine learning model based on the updated data quality score and the at least one of the one or more errors, the at least one weight having contributed to identification of the first subset, the second subset, and the third subset by the trained machine learning model, in combination with the other claim limitations.
Claim Objections
Claims 6, 13 and 20 are objected to because of the following informalities:
Claims 6, 13 and 20 recite:
dynamically updating the normalized dataset as data in the identity datasets continue to be monitored over time;
dynamically identifying at least one additional error in the second subset of the normalized dataset as the data in the identity datasets continue to be monitored over time; and
dynamically updating the data quality score for the normalized dataset as the data in the identity datasets continue to be monitored over time.
The language "identity datasets" has not previously been recited and accordingly lacks antecedent basis. Instead, base claims 1, 8 and 15 recite "identity data." However, in view of the instant amendments, the language "identity datasets" is understood to encompass a clerical error. In view of base claims 1, 8 and 15 and for consistency therewith, it is understood that Applicant intended instead of the above-referenced claim content to recite one of the following options:
Option 1
dynamically updating the normalized dataset as the identity data continue to be monitored over time;
dynamically identifying at least one additional error in the second subset of the normalized dataset as the identity data continue to be monitored over time; and
dynamically updating the data quality score for the normalized dataset as the identity data continue to be monitored over time.
Option 2
dynamically updating the normalized dataset as data in the identity data continue to be monitored over time;
dynamically identifying at least one additional error in the second subset of the normalized dataset as the data in the identity data continue to be monitored over time; and
dynamically updating the data quality score for the normalized dataset as the data in the identity data continue to be monitored over time.
Appropriate correction is required.
Claim Rejections - 35 USC § 112
35 USC § 112(a)
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1-3, 5-10, 12-17 and 19-23 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for pre-AIA the inventor(s), at the time the application was filed, had possession of the claimed invention.
Lack of Written Description/Not in Specification
Claims 1, 8 and 15 recite:
processing the normalized dataset by using a trained machine learning model to identify a first subset of the normalized dataset as likely to be valid, a second subset of the normalized dataset as likely including one or more errors, and a third subset of the normalized dataset as likely to be fake based on a comparison to verified attribute field data;
generating a data quality score for the normalized dataset based on the first subset, the second subset, and the third subset;
modifying at least one character in at least one field of the second subset of the normalized dataset to correct at least one of the one or more errors;
…
updating at least one weight of the trained machine learning model based on the updated data quality score and the at least one of the one or more errors, the at least one weight having contributed to identification of the first subset, the second subset, and the third subset by the trained machine learning model.
Support in the disclosure is not found for the above-indicated recitations.
As best understood, the subject matter in the originally filed disclosure most relevant to the above limitations is in the specification, paragraphs 0032-0043 and 0061-0063. As closest content to the above processing step, these portions teach:
"each attribute field will be noted as being valid and validated or as invalid" (0032)
"the validator 312 will leverage established databases to determine whether the captured and normalized attribute fields of the preprocessed dataset 122 are valid as consistent with the established databases or are inconsistent and thus either invalid or contain an error. … If the plausible name fields match the information from the established databases, then the first name field or last name field (or related fields) are likely authentic. However, if the plausible name fields do not match the information from the established databases, then the name fields are marked as likely fake and marked as null, and the error resolution system 100 will use other user records and PII from other databases to fill the attribute fields." (0034)
"determin[ing] which attribute fields contain errors 132 for correction and which are likely fake or invalid" (0040)
As per above, the specification teaches determining whether attribute fields are valid or are invalid/fake (0032, 0034), and determining which attribute fields contain errors for correction and which are likely fake/invalid (0040). However, the specification does not teach "identify a first subset of the normalized dataset as likely to be valid, a second subset of the normalized dataset as likely including one or more errors, and a third subset of the normalized dataset as likely to be fake based on a comparison to verified attribute field data." For example, "identifying a first subset of the normalized dataset as likely to be valid" involves not merely determining if attribute fields are valid or invalid/fake but identifying a group (subset) of fields likely to be valid qua such group. The specification provides no such teaching of identifying such groups (recited first, second, and third subsets). It is noted that the word "subset" appears only once in the specification (0078), and this occurrence is in a context unrelated to the recitations here at issue. No other term similar in meaning to "subset" appears in the portions of the specification related to the recitations at issue.
Inasmuch as the specification does not teach the recited subsets or identifying the subsets, it also does not teach the further recitations/uses of these subsets quoted above.
Accordingly, support in the disclosure is not found for the above-indicated recitations of claims 1, 8 and 15.
Claims 3, 6, 7, 10, 13, 14, 17, 20 and 21 recite various instance of the "second subset" or the "first subset" of claims 1, 8 or 15.
Inasmuch as the specification does not teach the recited subsets or identifying the subsets in claims 1, 8 and 15, it also does not teach the further recitations/uses of these subsets in the dependent claims, as listed above.
Accordingly, support in the disclosure is not found for the above-indicated recitations of claims 3, 6, 7, 10, 13, 14, 17, 20 and 21.
Claim 3, 10 and 17 further recite:
generating a corrected dataset by incorporating a corrected second subset of the normalized dataset;
As best understood, the subject matter in the originally filed disclosure most relevant to the above limitation is in the specification, paragraphs 0043-0048. These portions teach "correct[ing] errors 132 to generate [a] corrected dataset" (0043), but do not teach any "incorporating" along the lines of or in the context of the above recitation. See also paragraph 14 above.
Accordingly, support in the disclosure is not found for the above-indicated recitation of claims 3, 10 and 17.
Claim 21 further recites:
adding the first subset of the normalized dataset to the verified attribute field data to generate updated verified attribute field data;
As best understood, the subject matter in the originally filed disclosure most relevant to the above limitation is in the specification, paragraph 0052. 0052 states in pertinent part:
Additionally, as the user records and PII of the corrected dataset 142 are corrected automatically or by the user, the error resolution system 100 will capture additional verified user records and PII, which the error resolution system 100 can put back into the system to improve the algorithmic models within the model engine 170, thereby improving the efficiency and accuracy of the data preprocessing module 120, error detection module 130, correction process 140, and data quality scoring process 150 as time progresses.
However, no teaching is seen whereby data identified as valid is added to the verified filed attribute data to generate updated verified attribute field data, such as recited. See also paragraph 14 above.
Accordingly, support in the disclosure is not found for the above-indicated recitation of claim 21.
Claims 2, 3, 5-7, 9, 10, 12-14, 16, 17 and 19-23 are (also) rejected by virtue of their dependency from a rejected claim.
35 USC § 112(b)
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 2, 3, 9, 10, 16 and 17 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor, or for pre-AIA the applicant regards as the invention.
Unclear Scope
Claims 2, 9 and 16 recite:
providing an alert to a device associated with the one or more entities based on the data quality score exceeding a predetermined threshold.
Note base claims 1, 8 and 15 recite that the data quality score is generated and then updated. It is not clear if "the data quality score" of dependent claims 2, 9 and 16 refers to the data quality score of claims 1, 8 and 15 before it is updated or after it is updated.
As best understood, Applicant intends "the data quality score" of dependent claims 2, 9 and 16 to refer to the data quality score of claims 1, 8 and 15 before it is updated. The language "the data quality score" of dependent claims 2, 9 and 16 is interpreted accordingly.
Claims 3, 10 and 17 recite:
generating a corrected dataset by incorporating a corrected second subset of the normalized dataset;
It is not clear what is meant by "incorporating" because the claim does not say into what the corrected second subset is being incorporated. The word "incorporate" as used here takes a prepositional object: 'A is incorporated into B'. To say "A is incorporated" without any specification of any prepositional object (i.e., without specifying the thing/entity into which A is incorporated) does not make sense absent clarifying context. No clarifying context is provided by the claim.
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 1-3, 5-10, 12-17 and 19-23 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Claims 1-3, 5-10, 12-17 and 19-23 are directed to a method, system, or non-transitory computer-readable storage medium, which are/is one of the statutory categories of invention. (Step 1: YES)
Claims 1, 8 and 15 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claims recite a method, computing apparatus, and non-transitory computer-readable storage medium for error resolution for a dataset (detecting and correcting errors, and evaluating quality of the data based on the errors) (as per specification 0002 the error resolution is in the service of "customer services (e.g., call center services, customer support services, etc.), data processing services, and any business, organization, or entity that relies on accurate data to provide a good or service").
For claims 1, 8 and 15 (claim 1 being deemed representative), the limitations (indicated below in bold) of:
receiving, through a network, identity data associated with one or more entities, wherein the identity data includes a plurality of attribute fields;
generating a normalized dataset by converting the plurality of attribute fields from a respective input format to a respective standardized format;
processing the normalized dataset by using a trained machine learning model to identify a first subset of the normalized dataset as likely to be valid, a second subset of the normalized dataset as likely including one or more errors, and a third subset of the normalized dataset as likely to be fake based on a comparison to verified attribute field data;
generating a data quality score for the normalized dataset based on the first subset, the second subset, and the third subset;
modifying at least one character in at least one field of the second subset of the normalized dataset to correct at least one of the one or more errors;
updating the data quality score based on the at least one of the one or more errors being corrected; and
updating at least one weight of the trained machine learning model based on the updated data quality score and the at least one of the one or more errors, the at least one weight having contributed to identification of the first subset, the second subset, and the third subset by the trained machine learning model.
as drafted, constitute a process that, under the broadest reasonable interpretation, covers "certain methods of organizing human activity," specifically, "fundamental economic practices or principles" and/or "commercial or legal interactions," but for recitation of generic computer components and generally linking the use of a judicial exception to a particular technological environment or field of use. The Examiner notes that "fundamental economic practices" or "fundamental economic principles" describe concepts relating to the economy and commerce, including hedging, insurance, and mitigating risks, and "commercial interactions" or "legal interactions" include agreements in the form of contracts, legal obligations, advertising, marketing or sales activities or behaviors, and business relations. MPEP 2106.04(a)(2)II.A.,B. If a claim limitation, under its broadest reasonable interpretation, covers "fundamental economic practices or principles" and/or "commercial or legal interactions," but for recitation of generic computer components and generally linking the use of a judicial exception to a particular technological environment or field of use, then it falls within the "certain methods of organizing human activity" grouping of abstract ideas. Accordingly, claims 1, 8 and 15 recite an abstract idea. (Step 2A - Prong 1: YES. The claims recite an abstract idea.)
This judicial exception is not integrated into a practical application. Claims 1, 8 and 15 recite the additional elements of a processor, and a memory storing instructions, wherein execution of the instructions by the processor causes the processor to: (perform actions) (the foregoing recited in claim 8); a non-transitory computer-readable storage medium, the non-transitory computer-readable storage medium including instructions, wherein execution of the instructions by a processor causes the processor to: (perform actions) (the foregoing recited in claim 15); and through a network; by using a trained machine learning model; and updating at least one weight of the trained machine learning model … the at least one weight having contributed to … by the trained machine learning model (the foregoing recited in claims 1, 8 and 15), that implement the abstract idea. These additional elements are not described by the applicant and they are recited at a high level of generality (i.e., one or more generic computer elements performing generic computer functions, or generally linking the use of a judicial exception to a particular technological environment or field of use), such that they amount to no more than mere instructions to apply the exception using generic computer elements (namely, all of the additional elements), or such that they amount to no more than generally linking the use of a judicial exception to a particular technological environment or field of use (namely, by using a trained machine learning model; and updating at least one weight of the trained machine learning model … the at least one weight having contributed to … by the trained machine learning model). Accordingly, even in combination these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. (Step 2A - prong 2: NO. The additional elements do not integrate the abstract idea into a practical application.)
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception itself. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of a processor, and a memory storing instructions, wherein execution of the instructions by the processor causes the processor to: (perform actions) (the foregoing recited in claim 8); a non-transitory computer-readable storage medium, the non-transitory computer-readable storage medium including instructions, wherein execution of the instructions by a processor causes the processor to: (perform actions) (the foregoing recited in claim 15); and through a network; by using a trained machine learning model; and updating at least one weight of the trained machine learning model … the at least one weight having contributed to … by the trained machine learning model (the foregoing recited in claims 1, 8 and 15), to perform the noted steps amount to no more than mere instructions to apply the exception using generic computer elements or generally linking the use of a judicial exception to a particular technological environment or field of use. Mere instructions to apply an exception using generic computer elements or generally linking the use of a judicial exception to a particular technological environment or field of use cannot provide an inventive concept ("significantly more"). Accordingly, even in combination, these additional elements do not provide significantly more. As such, claims 1, 8 and 15 are not patent eligible. (Step 2B: NO. The claims do not provide significantly more.)
Dependent claims 2, 3, 5-7, 9, 10, 12-14, 16, 17 and 19-23 are similarly rejected because they further define/narrow the abstract idea of independent claims 1, 8 and 15 as discussed above, and/or do not integrate the abstract idea into a practical application or provide an inventive concept such as would render the claims eligible, whether each is considered individually or as an ordered combination.
As for further defining/narrowing the abstract idea:
Dependent claims 2, 9 and 16 merely further describe providing an alert … associated with the one or more entities based on the data quality score exceeding a predetermined threshold.
Dependent claims 3, 10 and 17 merely further describe generating a corrected dataset by incorporating a corrected second subset of the normalized dataset, and adding the corrected dataset to the verified attribute field data to generate updated verified attribute field data.
Dependent claims 5, 12 and 19 merely further describe wherein generating the data quality score includes analyzing the normalized dataset and the one or more errors … to generate the data quality score based on the comparison of the normalized dataset to the verified attribute field data.
Dependent claims 6, 13 and 20 merely further describe dynamically updating the normalized dataset as data in the identity datasets continue to be monitored over time; dynamically identifying at least one additional error in the second subset of the normalized dataset as the data in the identity datasets continue to be monitored over time; and dynamically updating the data quality score for the normalized dataset as the data in the identity datasets continue to be monitored over time.
Dependent claims 7 and 14 merely further describe … to the one or more entities, … providing the data quality score and the one or more errors within the second subset of the normalized dataset.
Dependent claim 21 merely further describes adding the first subset of the normalized dataset to the verified attribute field data to generate updated verified attribute field data, and comparing a second normalized dataset to the updated verified attribute field data … to identify one or more additional errors in the second normalized dataset.
Dependent claim 22 merely further describes wherein generating the data quality score includes generating individual attribute scores for respective attribute fields and combining the individual attribute scores to generate the data quality score.
Dependent claim 23 merely further describes wherein correcting the one or more errors includes categorizing similar attribute fields of the normalized dataset.
As for additional elements:
Dependent claims 2, 9 and 16 recite "wherein the execution of the instructions causes the processor to:" (claims 9 and 16) and "to a device" (claims 2, 9 and 16). This recitation is at a high level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computer element. Even in combination these additional elements do not integrate the abstract idea into a practical application and do not amount to significantly more than the abstract idea itself.
Dependent claims 10, 13, 17 and 20 recite "wherein the execution of the instructions causes the processor to: …." This recitation is at a high level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computer element. Even in combination these additional elements do not integrate the abstract idea into a practical application and do not amount to significantly more than the abstract idea itself.
Dependent claims 5, 12 and 19 recite "the execution of the instruction causes the processor to:" (claims 12 and 19) and "using a second trained machine learning model" (claims 5, 12 and 19). This recitation is at a high level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computer element (namely, "the execution of the instruction causes the processor to:" and "using a second trained machine learning model") or such that it amounts to no more than generally linking the use of a judicial exception to a particular technological environment or field of use (namely, "using a second trained machine learning model") . Even in combination these additional elements do not integrate the abstract idea into a practical application and do not amount to significantly more than the abstract idea itself.
Dependent claims 7 and 14 recites "wherein the execution of the instructions causes the processor to:" (claim 14) and "providing a user interface …, the user interface" (claims 7 and 14). This recitation is at a high level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computer element. Even in combination these additional elements do not integrate the abstract idea into a practical application and do not amount to significantly more than the abstract idea itself.
Dependent claim 21 recites "using the trained machine learning model." This recitation is at a high level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computer element or such that it amounts to no more than generally linking the use of a judicial exception to a particular technological environment or field of use. Even in combination these additional elements do not integrate the abstract idea into a practical application and do not amount to significantly more than the abstract idea itself.
Dependent claims 3, 6, 22 and 23 do not recite any additional elements, and accordingly, for the reasons provided above with respect to the independent claims, are not patent eligible.
Therefore, dependent claims 2, 3, 5-7, 9, 10, 12-14, 16, 17 and 19-23 are not patent eligible.
Conclusion
The prior art made of record and not relied upon, as set forth in the accompanying Notice of References Cited (PTO-892), is considered pertinent to applicant's disclosure. Description of the cited prior art is provided above ("Subject Matter Distinguishable From Prior Art").
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DOUGLAS W PINSKY whose telephone number is (571)272-4131. The examiner can normally be reached on 8:30 am - 5:30 pm ET.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Jessica Lemieux can be reached on 571-270-3445. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/DOUGLAS W PINSKY/
Examiner, Art Unit 3626
/JESSICA LEMIEUX/Supervisory Patent Examiner, Art Unit 3626
1 USPTO Memorandum to Patent Examining Corps, "Recent Subject Matter Eligibility Decisions" November 2, 2016, p. 2
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