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 .
The instant application having Application No. 19/307,924 filed on 8/22/2025 is presented for examination by the Examiner. Claims 1-20 are currently pending in the present application.
Priority
As required by M.P.E.P. 201.14(c), acknowledgement is made of Applicant's claim for priorities as: a CIP of Non-Provisional Application 18/736,407 filed on 6/6/2024, and a CON of Non-Provisional Application 19/050,084 filed on 2/10/2025.
Drawings
The Applicant's drawings filed on 8/22/2025 are acceptable for examination purpose.
Information Disclosure Statement
As required by M.P.E.P. 609, the Applicant's submission of the Information Disclosure Statement dated 9/26/2025 is acknowledged by the Examiner and the cited references have been considered in the examination of the claims now pending.
Claim Objections
Claim 1 is objected to because of the following informalities:
As per claim 1, the limitation of “determining a compliance indicator of the dataset based on a degree of satisfaction between the one or more observed association rules and an expected association rule set” should be written or amended as “determining a compliance indicator of the dataset based on a degree of satisfaction between the one or more observed association rules and [[an]]the expected association rule set”.
Appropriate correction is respectfully required.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 1, 8 and 15 of the instant application are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1, 7, 8, 14, 15 and 20 of the US Patent Number 12,430,308 B1. Although the claims at issue are not identical, they are not patentably distinct from each other because the claims 1, 7, 8, 14, 15 and 20 of the US Patent Number 12,430,308 B1 contain every element of claim 1, 8 and 15 of the instant application respectively and as such anticipate(s) claims 1, 8 and 15 of the instant application (see i.e., table below).
Initially, it should be noted that the instant application and the U.S. Patent number 12,430,308 B1 have the same inventive entities. The inventor and/or assignee for the US Patent and the instant application are James Myers, Yael Man, John E. Ortega, Alberto Cetoli, Minjeong Cho, Jason Ryan Engelbrecht, and Ines Teixeira as the inventors; and Citibank, N.A. as the assignee.
Instant Application
US Patent 12,430,308 B1
Claim 1:
A computer-implemented method for improving data quality using an artificial intelligence (AI) model, the method comprising:
obtaining a dataset representing an observed value set for each variable in a variable set;
identifying, using a first AI model set, an anomaly set from the observed value set in the dataset by comparing an observed pattern set from the observed value set against one or more reference patterns that correspond to an expected value set for the variable set;
using a second AI model set to evaluate the identified anomaly set by: generating an observed association rule set configured to cause generation of the observed value set in the dataset, and comparing the observed association rule set with an expected association rule set to determine one or more observed association rules of the observed association rule set that correspond to the anomaly set;
determining a compliance indicator of the dataset based on a degree of satisfaction between the one or more observed association rules and an expected association rule set;
transmitting a representation of the compliance indicator of the dataset, wherein the representation indicates (a) the anomaly set and (b) a metadata set linked to the anomaly set; and
responsive to obtaining a subsequent input, updating the representation by modifying at least one of: (a) the anomaly set, (b) the metadata set, (c) the dataset, or (d) the one or more observed association rules.
Claim 1:
A computer-implemented method for improving data quality using an artificial intelligence (AI) model, the method comprising:
receiving a structured dataset including an observed set of values for each variable in a variable set;
identifying, using a first AI model set, an anomaly set from the observed set of values in the structured dataset by: determining multiple reference patterns that correspond to an expected set of values for the variable set, and comparing an observed pattern set from the observed set of values against the multiple reference patterns;
using a second AI model set to evaluate the identified anomaly set by: dynamically generating an observed association rule set configured to cause the second AI model set to generate the received observed set of values in the structured dataset, and comparing the observed association rule set with an expected association rule set to determine one or more observed association rules corresponding to the anomaly set;
using a third AI model set, generating a reconfiguration command set configured to modify the one or more observed association rules corresponding to the anomaly set into alignment with one or more expected association rules by:
identifying a portion of the observed set of values that corresponds to the one or more observed association rules of the observed association rule set that corresponds to the anomaly set, and mapping the portion of the observed set of values to one or more expected association rules of the expected association rule set, the one or more expected association rules being configured to adjust the portion of the observed set of values to a corresponding expected set of values; and
displaying a compliance report indicating (i) the identified anomaly set, (ii) the set of actions, and (iii) the increased degree of satisfaction of the unstructured
dataset against the predefined criterion set (see e.g., claim 7).
automatically executing the reconfiguration command set on the structured dataset to modify the one or more observed association rules corresponding to the anomaly set into alignment with the one or more expected association rules.
Claim 8:
One or more non-transitory, computer-readable storage media comprising instructions thereon, wherein the instructions, when executed by at least one data processor of a system, cause the system to:
obtain a dataset representing an observed value set for each variable in a variable set;
identify, using a first artificial intelligence (Al) model set, an anomaly set from the observed value set in the dataset by comparing an observed pattern set from the observed value set against one or more reference patterns that correspond to an expected value set for the variable set;
use a second Al model set to evaluate the identified anomaly set by:
determining an observed rule set configured to cause generation of the
observed value set in the dataset, and
determining one or more observed rules of the observed rule set that
correspond to the anomaly set;
determine a compliance indicator of the dataset based on a degree of satisfaction between the one or more observed rules and an expected rule set;
transmit a representation of the compliance indicator of the dataset, wherein the representation indicates (a) the anomaly set and (b) a metadata set linked to the anomaly set; and
responsive to obtaining a subsequent input, update the representation by
modifying at least one of: (a) the anomaly set, (b) the metadata set, (c) the dataset, or (d) the one or more observed rules.
Claim 8:
One or more non-transitory, computer-readable storage media comprising instructions thereon, wherein the instructions, when executed by at least one data processor of a system, cause the system to:
receive a structured dataset including an observed set of values for each variable in a variable set;
identify, using a first AI model set, an anomaly set from the observed set of values in the structured dataset by comparing an observed pattern set from the observed set of values against multiple reference patterns;
use a second AI model set to evaluate the identified anomaly set by: dynamically generating an observed association rule set configured to cause the second AI model set to generate the received observed set of values in the structured dataset, and comparing the observed association rule set with an expected association rule set to determine one or more observed association rules corresponding to the anomaly set;
use a third AI model set to generate a reconfiguration command set configured to modify the one or more observed association rules corresponding to the anomaly set into alignment with one or more expected association rules by:
identifying a portion of the observed set of values that corresponds to the one or more observed association rules of the observed association rule set that corresponds to the anomaly set, and associating the portion of the observed set of values to one or more expected association rules of the expected association rule set, the one or more expected association rules being configured to adjust the portion of the observed set of values to a corresponding portion of the expected set of values; and
display a compliance report indicating (i) the identified anomaly set, (ii) the action
set, and (iii) the increased degree of satisfaction of the unstructured dataset
against the predefined criterion set (see e.g., claim 14).
execute the reconfiguration command set on the structured dataset to modify the one or more observed association rules corresponding to the anomaly set into alignment with the one or more expected association rules.
Claim 15:
A system comprising:
at least one hardware processor; and
at least one non-transitory memory storing instructions, which, when executed by the at least one hardware processor, cause the system to:
obtain a dataset representing an observed value set for each variable in a
variable set;
identify, using a first artificial intelligence (Al) model set, an anomaly set from the observed value set in the dataset by comparing an observed
pattern set from the observed value set against one or more reference patterns that correspond to an expected value set for the variable set;
use a second Al model set to evaluate the identified anomaly set by: determining an observed rule set configured to cause generation of the observed value set in the dataset, and
determining one or more observed rules of the observed rule set that correspond to the anomaly set;
determine a compliance indicator of the dataset based on a degree of
satisfaction between the one or more observed rules and an expected rule set;
transmit a representation of the compliance indicator of the dataset, wherein the representation indicates one or more of: (a) the anomaly set or (b) a metadata set linked to the anomaly set; and
responsive to obtaining a subsequent input, update the representation by modifying at least one of: (a) the anomaly set, (b) the metadata set, (c) the dataset, or (d) the one or more observed rules.
Claim 15:
A system comprising: at least one hardware processor; and at least one non-transitory memory storing instructions, which, when executed by the at least one hardware processor, cause the system to:
receive a structured dataset including an observed set of values for each variable in a variable set;
identify, using a first AI model set, an anomaly set from the observed set of values in the structured dataset by comparing an observed pattern set from the observed set of values against multiple reference patterns that correspond to an expected set of values for the variable set;
use a second AI model set to evaluate the identified anomaly set by: generating an observed association rule set configured to cause the second AI model set to generate the received observed set of values in the structured dataset, and comparing the observed association rule set with an expected association rule set to determine one or more observed association rules of the observed association rule set that correspond to the anomaly set,
use a third AI model set to generate a reconfiguration command set configured to modify the one or more observed association rules corresponding to the anomaly set into alignment with one or more expected association rules by:
identifying a portion of the observed set of values that corresponds to the one or more observed association rules that correspond to the anomaly set, and associating the portion of the observed set of values to one or more expected association rules of the expected association rule set, the one or more expected association rules being configured to adjust the portion of the observed set of values to a corresponding expected set of values; and
display a compliance report indicating (i) the identified anomaly set, (ii) the set of
actions, and (iii) the increased degree of satisfaction of the unstructured
dataset against the predefined criterion set (see e.g., claim 20).
execute the reconfiguration command set on the structured dataset to modify the one or more observed association rules corresponding to the anomaly set into alignment with the one or more expected association rules.
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 an abstract idea without significantly more.
As per claim 1, the claim recites “A computer-implemented method for improving data quality using an artificial intelligence (AI) model, the method comprising:
obtaining a dataset representing an observed value set for each variable in a variable set;
identifying, using a first AI model set, an anomaly set from the observed value set in the dataset by comparing an observed pattern set from the observed value set against one or more reference patterns that correspond to an expected value set for the variable set;
using a second AI model set to evaluate the identified anomaly set by:
generating an observed association rule set configured to cause generation of the observed value set in the dataset, and
comparing the observed association rule set with an expected association rule set to determine one or more observed association rules of the observed association rule set that correspond to the anomaly set;
determining a compliance indicator of the dataset based on a degree of satisfaction between the one or more observed association rules and an expected association rule set;
transmitting a representation of the compliance indicator of the dataset, wherein the representation indicates (a) the anomaly set and (b) a metadata set linked to the anomaly set; and
responsive to obtaining a subsequent input, updating the representation by modifying at least one of: (a) the anomaly set, (b) the metadata set, (c) the dataset, or (d) the one or more observed association rules”.
Step 1: Statutory Category
Claim 1 discloses a method which is a process within the meaning of the section.
Step 2A - Prong One: Judicial Exception Recited
The claim recites the limitations “identifying”, “comparing”, “evaluating” and “determining” which specifically recite “identifying… by comparing…”, “evaluating…”, “comparing… to identifying…” and “determining…”. These limitations are processes that, under their broadest reasonable interpretation, cover performance of the limitation in the mind, but for the recitation of generic computer components. That is, nothing in the claim element precludes the steps from practically being performed in a human mind or with the aid of pen or paper. For example, “identifying… by comparing…” or “comparing… to identifying…”, “evaluating…” and “determining…” in the context of this claim encompass a user mentally, and with the aid of pen and paper looking at information and/or characteristics of data and examining to identify or determine the desired or relevant data.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Step 2A - Prong Two: Integrated into a Practical Application
The claim recites the additional elements “obtaining a dataset…”, “generating an observed association rule set…”, “transmitting a representation…” and “responsive to obtaining a subsequent input, updating the representation by modifying…”. The judicial exception is not integrated into a practical application. In particular, the additional steps: the “obtaining”, “generating” and “transmitting…” steps mount to data gathering and a mere generic transmission and presentation of collected and/or analyzed data which are considered to be insignificant extra-solution activity (see MPEP 2106.05(g)), and the “updating the representation by modifying…” step is considered as a mere instruction to apply an exception to perform an existing process on a generic computer and/or no more than an idea of a solution or outcome on a generic computer (see MPEP 2106.05(f)). Accordingly, the 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, thus fail to integrate the abstract idea into a practical application. See MPEP 2106.05(g).
Step 2B: Claim provides an Inventive Concept
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The insignificant extra-solution activities identified above, which include the data-gathering and the steps of “transmitting a representation…” and “responsive to obtaining a subsequent input, updating the representation by modifying…” are recognized by the courts as well-understood, routine, and conventional activities when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity (see MPEP 2106.05(d)(II)). For these reasons, there is no inventive concept in the claim, and thus it is ineligible.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional “transmitting a representation…” and “responsive to obtaining a subsequent input, updating the representation by modifying…” steps amount to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept.
The claim as a whole, does not amount to significantly more than the abstract idea itself. This is because the claim does not affect an improvement to the functioning of a computer itself; and the claim does not move beyond a general link of the use of an abstract idea to a particular technological environment.
Accordingly, claim 1 is directed to an abstract idea.
As per claim 2, the claim recites “The computer-implemented method of claim 1, wherein at least one model of the first AI model set or at least one model of the second AI model set are the same”. The judicial exception is not integrated into a practical application. In particular, this additional limitation amounts to no more than mere instructions to apply an exception to perform an existing process on a generic computer (MPEP 2106.05(f)). Mere instructions to apply an exception using a generic computer do not amount to significantly more.
As per claim 3, the claim recites “The computer-implemented method of claim 1, wherein updating the representation further comprises: using a third AI model set to generate a reconfiguration command set configured to modify the one or more observed association rules corresponding to the anomaly set into alignment with one or more expected association rules”. The judicial exception is not integrated into a practical application. In particular, the additional limitation of “generate” mounts to data gathering which is considered to be insignificant extra-solution activity (see MPEP 2106.05(g)) and does not amount to significantly more than the above-identified judicial exception, and the additional limitation of “modify” amounts to no more than mere instructions to apply an exception to perform an existing process on a generic computer (MPEP 2106.05(f)). Mere instructions to apply an exception using a generic computer do not amount to significantly more.
As per claim 4, the claim recites “The computer-implemented method of claim 3, further comprising: executing the reconfiguration command set on the dataset to modify the one or more observed association rules corresponding to the anomaly set into alignment with the one or more expected association rules”. The judicial exception is not integrated into a practical application. In particular, these additional limitations amount to no more than mere instructions to apply an exception to perform an existing process on a generic computer (MPEP 2106.05(f)). Mere instructions to apply an exception using a generic computer do not amount to significantly more.
As per claim 5, the claim recites “ The computer-implemented method of claim 3, wherein the third AI model set is configured to generate the reconfiguration command set by:”, the judicial exception is not integrated into a practical application.
“identifying a portion of the observed value set that corresponds to the one or more observed association rules that correspond to the anomaly set, and”, this additional limitation has been discussed above with respect to the abstract idea (i.e., “Mental Processes”) and does not amount to significantly more than the above-identified judicial exception.
“associating the portion of the observed value set to one or more expected association rules of the expected association rule set, the one or more expected association rules being configured to adjust the portion of the observed value set to a corresponding expected value set”, these additional limitations amount to no more than mere instructions to apply an exception to perform an existing process on a generic computer (MPEP 2106.05(f)). Mere instructions to apply an exception using a generic computer do not amount to significantly more.
As per claim 6, the claim recites “The computer-implemented method of claim 3, further comprising: selecting the third AI model set from multiple AI models using a respective set of performance metric values of each of the multiple AI models”. The judicial exception is not integrated into a practical application. In particular, this additional limitation has been discussed above with respect to the abstract idea (i.e., “Mental Processes”) and does not amount to significantly more than the above-identified judicial exception.
As per claim 7, the claim recites “The computer-implemented method of claim 1, wherein the metadata set is associated with one or more of: (a) a data lineage of the anomaly set or (b) a version of the anomaly set”. The judicial exception is not integrated into a practical application. In particular, this additional limitation amounts to no more than mere instructions to apply an exception to perform an existing process on a generic computer (MPEP 2106.05(f)). Mere instructions to apply an exception using a generic computer do not amount to significantly more.
As per claim 8, the claim recites “One or more non-transitory, computer-readable storage media comprising instructions thereon, wherein the instructions, when executed by at least one data processor of a system, cause the system to:
obtain a dataset representing an observed value set for each variable in a variable set;
identify, using a first artificial intelligence (AI) model set, an anomaly set from the observed value set in the dataset by comparing an observed pattern set from the observed value set against one or more reference patterns that correspond to an expected value set for the variable set;
use a second AI model set to evaluate the identified anomaly set by:
determining an observed rule set configured to cause generation of the observed value set in the dataset, and
determining one or more observed rules of the observed rule set that correspond to the anomaly set;
determine a compliance indicator of the dataset based on a degree of satisfaction between the one or more observed rules and an expected rule set;
transmit a representation of the compliance indicator of the dataset, wherein the representation indicates (a) the anomaly set and (b) a metadata set linked to the anomaly set; and
responsive to obtaining a subsequent input, update the representation by modifying at least one of: (a) the anomaly set, (b) the metadata set, (c) the dataset, or (d) the one or more observed rules”.
Step 1: Statutory Category
Claim 8 discloses one or more non-transitory, computer-readable storage media which is a manufacture within the meaning of the section.
Step 2A - Prong One: Judicial Exception Recited
The claim recites the limitations “identify”, “comparing”, “evaluate” and “determining” which specifically recite “identify… by comparing…”, “evaluate…” and “determining…”. These limitations are processes that, under their broadest reasonable interpretation, cover performance of the limitation in the mind, but for the recitation of generic computer components. That is, nothing in the claim element precludes the steps from practically being performed in a human mind or with the aid of pen or paper. For example, “identify… by comparing…”, “evaluate…” and “determining…” in the context of this claim encompass a user mentally, and with the aid of pen and paper looking at information and/or characteristics of data and examining to identify or determine the desired or relevant data.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Step 2A - Prong Two: Integrated into a Practical Application
The claim recites the additional elements “obtain a dataset…”, “transmit a representation…” and “responsive to obtaining a subsequent input, update the representation by modifying…”. The judicial exception is not integrated into a practical application. In particular, the additional steps: the “obtain” and “transmit…” steps mount to data gathering and a mere generic transmission and presentation of collected and/or analyzed data which are considered to be insignificant extra-solution activity (see MPEP 2106.05(g)), and the “update the representation by modifying…” step is considered as a mere instruction to apply an exception to perform an existing process on a generic computer and/or no more than an idea of a solution or outcome on a generic computer (see MPEP 2106.05(f)). Accordingly, the 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, thus fail to integrate the abstract idea into a practical application. See MPEP 2106.05(g).
Step 2B: Claim provides an Inventive Concept
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The insignificant extra-solution activities identified above, which include the data-gathering and the steps of “transmit a representation…” and “responsive to obtaining a subsequent input, update the representation by modifying…” are recognized by the courts as well-understood, routine, and conventional activities when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity (see MPEP 2106.05(d)(II)). For these reasons, there is no inventive concept in the claim, and thus it is ineligible.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional “transmit a representation…” and “responsive to obtaining a subsequent input, update the representation by modifying…” steps amount to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept.
The claim as a whole, does not amount to significantly more than the abstract idea itself. This is because the claim does not affect an improvement to the functioning of a computer itself; and the claim does not move beyond a general link of the use of an abstract idea to a particular technological environment.
Accordingly, claim 8 is directed to an abstract idea.
As per claim 9, the claim recites “The one or more non-transitory, computer-readable storage media of claim 8, wherein the instructions further cause the system to: cause execution of a set of workflows for a first type of anomaly, or cause trigger of one or more alerts for a second type of anomaly”. The judicial exception is not integrated into a practical application. In particular, these additional limitations amount to no more than mere instructions to apply an exception to perform an existing process on a generic computer (MPEP 2106.05(f)). Mere instructions to apply an exception using a generic computer do not amount to significantly more.
As per claim 10, the claim recites “The one or more non-transitory, computer-readable storage media of claim 8, wherein the representation indicates a compliance status of the dataset with a predefined guideline set associated with the expected rule set. The judicial exception is not integrated into a practical application. In particular, this additional limitation amounts to no more than mere instructions to apply an exception to perform an existing process on a generic computer (MPEP 2106.05(f)). Mere instructions to apply an exception using a generic computer do not amount to significantly more.
As per claim 11, the claim recites “The one or more non-transitory, computer-readable storage media of claim 8, wherein the expected rule set is configured to dynamically adjust based on a time period associated with the observed rule set”. The judicial exception is not integrated into a practical application. In particular, this additional limitation amounts to no more than mere instructions to apply an exception to perform an existing process on a generic computer (MPEP 2106.05(f)). Mere instructions to apply an exception using a generic computer do not amount to significantly more.
As per claim 12, the claim recites “The one or more non-transitory, computer-readable storage media of claim 8, wherein the dataset includes one or more of: text documents, emails, chat logs, images, or voice recordings”. The judicial exception is not integrated into a practical application. In particular, this additional limitation amounts to no more than mere instructions to apply an exception to perform an existing process on a generic computer (MPEP 2106.05(f)). Mere instructions to apply an exception using a generic computer do not amount to significantly more.
As per claim 13, the claim recites “The one or more non-transitory, computer-readable storage media of claim 8, wherein the representation is displayed on a user interface of a computing device”. The judicial exception is not integrated into a practical application. In particular, this additional limitation amounts to no more than mere instructions to apply an exception to perform an existing process on a generic computer (MPEP 2106.05(f)). Mere instructions to apply an exception using a generic computer do not amount to significantly more.
As per claims 14 and 20, the claims are rejected under the same premise as claim 2.
As per claim 15, the claim recites “A system comprising: at least one hardware processor; and at least one non-transitory memory storing instructions, which, when executed by the at least one hardware processor, cause the system to:” to perform the limitations as same as claim 8.
Step 1: Statutory Category
Claim 15 discloses a system which is a machine within the meaning of the section.
Step 2A – Prong One: Judicial Exception Recited
The claim recites the limitations as same as claim 8, and therefore are interpreted as an abstract idea under the same premise as claim 8.
Step 2A – Prong Two: Integrated into a Practical Application
The claim recites additional elements as same as claim 8, and therefore are interpreted as an abstract idea under the same premise as claim 8.
Step 2B: Claim provides an Inventive Concept
The claim recites the limitation as same as claim 8, and therefore is considered under the same premise as claim 8 as no inventive concept in the claim, and thus it is ineligible.
As per claim 16, the claim recites “The system of claim 15, wherein determine the compliance indicator further comprises: comparing the degree of satisfaction against a predefined threshold, wherein the threshold is determined via a user input”. The judicial exception is not integrated into a practical application. In particular, this additional limitation has been discussed above with respect to the abstract idea (i.e., “Mental Processes”) and does not amount to significantly more than the above-identified judicial exception.
As per claims 17 and 18, the claims are rejected under the same premises as claims 2 and 3 respectively.
As per claim 19, the claim recites “The system of claim 15, wherein the metadata set is associated with one or more of: (a) a data lineage of the anomaly set or (b) a version of the anomaly set”. The judicial exception is not integrated into a practical application. In particular, this additional limitation amounts to no more than mere instructions to apply an exception to perform an existing process on a generic computer (MPEP 2106.05(f)). Mere instructions to apply an exception using a generic computer do not amount to significantly more.
Allowable Subject Matter
Claims 1-20 would be allowable if rewritten or amended to overcome the objection, the rejections, and a terminal disclaimer is filed to overcome the double patenting rejection as set forth in this Office action.
Reasons for Allowance
The following is an examiner’s statement of reasons for allowance:
After consideration of the prior arts in the filed IDS and conducting different searches in PE2E - SEARCH, Similarity Search, Google Scholar, and ACM Digital Library, it appears that none of prior arts discloses, teaches or fairly suggests the limitations as a whole in the independent claims 1, 8 and 15.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US 2022/0044133 A1 by Otto et al. teaches analyzing data collections to determine if they may be anomalous as compared with other data collections. For example, one or more values for data elements of a data collection may be unusually high or low, or may represent infrequently occurring values. Or, values of data elements in a data collection may not be anomalous when considered individually, but may be anomalous in combination. A machine learning model is trained with training data collections, where the training data collections include a plurality of data elements. An inference data collection, also having the data elements of the training data collections, is analyzed using the trained machine learning model to provide an anomaly score. The anomaly score can be based at least in part on feature anomaly scores, which indicate anomality of individual data elements of the inference data collection.
US 2022/0237501 A1 by Hamerla et al. teaches track interactions with a user application during use of the user application, generate, based on tracking the interactions, interaction data identifying multiple interaction events during the use, and perform a validity assessment of the interaction data. The hardware processor is further configured to execute the software code to identify, based on the validity assessment, one or more anomalies in the interaction data, and output, based on identifying the one or more anomalies in the interaction data, one or more of the interaction events corresponding respectively to the one or more anomalies.
Contact Information
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/BAI D VU/Primary Examiner, Art Unit 2163 2/6/2026