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 .
Claim Objections
Claims 3, 4, 10, and 13 are objected to because of the following informalities: the claims recite the trademark “Metro®”. The trademark should be removed from the claims. Appropriate correction is required.
Claim 4 is also objected due to dependency on objected claim 3.
Claim Rejections - 35 USC § 112
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 1-20 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 applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claims 1, 11, and 20 recites the limitation “wherein the reformatting and mapping conform to the data formatting and mapping of the first data set". There is insufficient antecedent basis for this limitation in the claim. The claim limitation “conform to the data formatting and mapping of the first data set” appears to refer back to a formatting and mapping of a first data set that is not mentioned in previous limitations of the claim, and thus creates a bit of confusion. Examiner suggests that the claim limitation recite “wherein the reformatting and mapping conform to a data formatting and mapping of the first data set" to alleviate confusion. For examination purposes, “the data formatting and mapping of the first data set” is interpreted as being an already formatted and mapped first data set.
Note: the limitation at issue is also present in dependent claims 2 and 12.
Dependent claims 2-10 and 12-19 are also rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, due to their dependency on the rejected claims above.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e. an abstract idea) without significantly more.
Claims 1-10 recite a system (i.e. machine), claims 11-19 recite a method (i.e. process), and claim 20 recite non-transitory computer-readable storage medium (i.e. machine or article of manufacture). Therefore claims 1-20 fall within one of the four statutory categories of invention.
Claims 1, 11, and 20 recite the limitations: analyzing [a database object] comprising data values for conformance of the values to parameter protocols, and generating an error report for one or more identified inconsistencies in the data values; receive a first data set, of a first data set type, associated with [a database object], wherein the first data set originates from a first source external to [the computer]; wherein [the computer] processes the first data set by applying a plurality of error detection algorithms to the first data set, incorporating additional rules based on regulatory guidance, court settlements, and client feedback to verify conformance of the data values of the first data set to a first set of predefined parameter protocols, wherein the parameter protocols include rules based on interdependencies between two or more of the data values within the data set, for a specific month of data or across multiple months; identify and flag nonconformance of one or more of the data values of the first data set relative to the first set of predefined parameter protocols and generate a first report indicating nonconformance within the first data set; receive a second data set, of a second data set type, associated with [the object], wherein the second data set originates from a second source, external to the first source; reformat and map the data of the second data set associated with [the object], wherein the reformatting and mapping conform to the data formatting and mapping of the first data set; wherein [the computer] processes the second data set by applying a plurality of error detection algorithms to the second data set to verify conformance of the data values of the second data set to a second set of predefined parameter protocols based on interdependencies between two or more of the data values within the second data set; identify and flag nonconformance of one or more of the data values of the second data set relative to the second set of predefined parameter protocols and generate a second report indicating nonconformance within the second data set; receive a third data set, of a third data set type, associated with [the object], the third data set originating from the first source and received from the second source; reformat and map the data of the third data set associated with [the object], wherein the reformatting and mapping conform to the data formatting and mapping of the first data set; wherein [the computer] processes the third data set by applying a plurality of error detection algorithms to the third data set to verify conformance of the data values of the third data set to a third set of predefined parameter protocols based on interdependencies between two or more of the data values within the third data set; identify and flag nonconformance of one or more of the data values of the third data set relative to the second set of predefined parameter protocols and generate a third report indicating nonconformance within the third data set; reformat and map each data set from each respective data set type to conform to the data formatting and mapping of the first data set; identify and flag nonconformance of one or more of the data values for each data set for each respective data set type and generate a report indicating nonconformance for each data set; analyze the reports from available data sets across all data set types to identify inconsistent data values within one or more of the data set types; and generate an error report for one or more identified inconsistencies in the data values. The invention and claims are drawn towards database object analysis and non-compliance reporting and the claim limitations directly correspond to certain methods of organizing human activity (commercial interaction, business relations) as evidenced by limitations detailing analyzing data for conformance incorporating additional rules based in regulatory guidance, court settlements, client feedback, etc., to verify conformance, analyzing reports from available datasets to identify inconsistent data, and generating error reports for the identified inconsistencies. The claims also correspond to mental processes (observation, evaluation, judgment, opinion) since the claims heavily involve the observation and evaluation of various data sets, and making a decision (judgment/opinion) regarding inconsistencies, errors, etc. based on the observed and evaluated data. Further, the error detection algorithms amounts to mathematical concepts (mathematical equations). The claims recite an abstract idea.
Note: the features or elements in brackets in the above Step 2A Prong One section are inserted for reading clarity, but are analyzed as “additional elements” under Step 2A Prong Two and Step 2B below.
The judicial exception is not integrated into a practical application simply because the claims recite the additional elements of: a database object, a computer, a server coupled with the computer, and a non-transitory computer-readable medium (claims 1 and 20). The additional elements are computer components recited at a high-level of generality performing the above-mentioned limitations and therefore amounts to no more than mere instructions to apply the exception using a generic computer. The combination of the additional elements are no more than mere instructions to apply the judicial exception using a generic computer. Accordingly, 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. The claims are directed to an abstract idea.
The claims do 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 elements amount to no more than mere instructions to apply the exception using a generic computer. Mere instructions to apply an exception using a generic computer cannot provide an inventive concept. Thus, when viewed as an ordered combination, nothing in the claims add significantly more (i.e. an inventive concept) to the abstract idea. The claims are not patent eligible.
Dependent claims 2-10 and 12-19 recite additional limitations that are further directed to the abstract idea analyzed in the rejected claims above. The claims also recite additional elements that have been analyzed in the rejected claims above. Thus, claims 2-10 and 12-19 are also rejected under 35 U.S.C. 101.
Allowable Subject Matter
Claims 1-20 would be allowable if rewritten or amended to overcome the rejection(s) under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), 2nd paragraph, and 35 U.S.C. 101, set forth in this Office action.
The closest patent or patent application prior art reference(s) found that is/are relevant to the applicant’s invention includes Wong (2016/0267082) which discloses a system for the management of data related to risk exposure. A system may be provided includes receiving data from a plurality of data sources; a rules engine for applying one or more logical rules that are triggered by one or more conditions associated with the integrity of the received data; and one or more utilities configured to apply the one or more logical rules to validate received data and automatically request updated data from a subset of the plurality of data sources where the integrity of the received data does not meet a predefined threshold. The reference does not appear to disclose the detailed limitations of the applicant’s claims. Another reference found that is relevant to the applicant’s invention is Rausch (20210158171) which discloses receiving a request for a data catalog; in response to the request specifying a structural feature, analyzing metadata of multiple data sets for an indication of including it, and to retrieve an indicated degree of certainty of detecting it for data sets including it; in response to the request specifying a contextual aspect, analyzing context data of the multiple data sets for an indication of being subject to it, and to retrieve an indicated degree of certainty concerning it for data sets subject to it; selectively include each data set in the data catalog based on the request specifying a structural feature and/or a contextual aspect, and whether each data set meets what is specified; for each data set in the data catalog, generate a score indicative of the likelihood of meeting what is specified; and transmit the data catalog to the requesting device. The reference does not appear to disclose the detailed limitations of the applicant’s claims. The claims overcome the prior art.
The closest non-patent literature reference found that is relevant to the applicant’s invention includes the publication “IEEE Recommended Practice for the Quality Management of Datasets for Medical Artificial Intelligence” (IEEE Engineering in Medicine and Biology Society; 2022) which generally discloses best practices for establishing a quality management system for data sets used for artificial intelligence in the area of medical devices. The publication covers data set management, including items, such as but not limited to, data collection, transfer, utilization, storage, maintenance, and updates. The recommended practice recommends a list of critical factors that impact the quality of data sets, such as but not limited to, data sources, data quality, annotation, privacy protection, personnel qualification/training/evaluation, tools, equipment, environment, process control, and documentation. The reference does not appear to disclose the detailed limitations of the applicant’s claims. The claims overcome the prior art.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DIONE N SIMPSON whose telephone number is (571)272-5513. The examiner can normally be reached M-F; 7:30 a.m.-4:30 p.m..
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DIONE N. SIMPSON
Primary Examiner
Art Unit 3628
/DIONE N. SIMPSON/Primary Examiner, Art Unit 3628