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 .
Claims 1 – 20 are pending in this Office Correspondence.
Priority
This application claims the benefit under 35 U.S.C. § 120 as a continuation of application 18/427,309, filed January 30, 2024, which claims the benefit under 35 U.S.C. § 119(e) of provisional patent application 63/483,357, filed February 6, 2023.
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., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Step 1: The claims 1 recites a “method for obtaining a candidate pair of a plurality of digitally stored documents. . ..; computing relative positional differences. . . comparing the relative positional differences with a similarity function . . . ; aggregating the components of the difference similarity vector . . . ; determining a document-level similarity metric . . . ; determining whether the document-level similarity metric is above a threshold value; and classifying the candidate pair based on determining . . . . “ the claim(s) recites a series of steps and, therefore, is a process
Step 2A Prong One:
"[automatically] computing relative positional differences. . . . " as drafted recites a mentally performable process as an evaluation or judgement. Please see Instant paragraphs [0047] where one can mentally evaluate to perform to compare the differences with a desirable similarity function.
“comparing the relative positional differences with a similarity function” as drafted recites a mentally performable process as an evaluation or judgement. Please see Instant paragraph [0047] where one can mentally evaluate to compare the differences with a desirable similarity function, resulting in a difference similarity vector having components corresponding to each positional difference.
“determining a document-level similarity metric” as drafted recites a mentally performable process as an evaluation or judgement. Please see Instant paragraphs [0039] where one can mentally evaluate such record pairs with dubious linkages are subject to manual clerical review to determine the final match status..
“determining whether the document-level similarity metric is above a threshold value“ as drafted recites a mentally performable process as an evaluation or judgement. Please see Instant paragraphs [0039] where one can mentally evaluate such record pairs with dubious linkages are subject to manual clerical review to determine the final match status.
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, other than reciting a "database" or "processor", nothing in the claim element precludes the step from practically being performed in a human mind or with the aid of pen and paper. For example, “computing”, “comparing” and “determining” in the context of this claim encompasses a user mentally, and with the aid of pen and paper, within the plurality of command sets, “computing relative positional differences . . . comparing the relative positional differences with a similarity function . . determining a document-level similarity metric . . . and determining whether the document-level similarity metric is above a threshold value (cut-off value).”
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Step 2A Prong Two: The judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements " obtaining a candidate pair", “aggregating the components of the difference similarity vector” and "classifying the candidate pair based on determining.” These limitations amount to a data gathering step and a mere generic transmission and presentation of collected and analyzed data which is considered to be insignificant extra solution activity (see MPEP 2106.05(g)). These limitations represent an extra-solution activity because it is a mere nominal or tangential addition to the claim, a mere generic transmission and presentation of collected and analyzed data. (See MPEP 2106.05(g)). Accordingly, 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 claim is directed to an abstract idea.
Step 2B: The limitations "obtaining”, “aggregating” and “classifying” 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)(iv) Storing and retrieving information in memory, Versata Dev. Group Inc....; Receiving or transmitting data over a network, e.g., using the Internet to gather data, buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network); (v) Presenting offers and gathering statistics, OIP Techs., 788 F.3d at 1362-63, 115 USPQ2d at 1092-93). Therefore, the claim is not patent eligible.
Accordingly, claim 11 is rejected for the same rational under 35 U.S.C. 101 as being directed to non-statutory subject matter as described hereinabove.
Therefore, claims 1 and 11 are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Further the limitations in the dependent claims 2 – 10 and 12 – 20, respectively, merely specify the type of the data gathered and analyzed without adding significantly more. Analysis of the dependent claims is shown below.
Claim 2 is dependent on claim 1 and includes all the limitations of claim 1. Therefore, claim 2 recites the same abstract idea of claim 1. The claim recites the additional limitation of “based on the classifying, removing duplicate transaction documents from the document database by any of deleting records, marking records, updating column attributes, or writing records to a different table”, which is equivalent to merely saying “apply it”, and amounts to no more than mere instructions to implement the abstract idea on a computer. Mere instructions to apply an exception using a generic computer does not amount to significantly more. Same rationale applies to claim 12, since they also recite limitations that further elaborate on the abstract idea.
Claim 3 is dependent on claim 1 and includes all the limitations of claim 1. Therefore, claim 3 recites the same abstract idea of claim 1. The claim recites the additional limitation of “identifying text elements from each digitally stored document in the candidate pair; and storing the text elements as document extraction attributes”, which is equivalent to merely saying “apply it”, and amounts to no more than mere instructions to implement the abstract idea on a computer. Mere instructions to apply an exception using a generic computer does not amount to significantly more. Same rationale applies to claims 13, since they also recite limitations that further elaborate on the abstract idea.
Claim 4 is dependent on claim 3 and includes all the limitations of claim 3. Therefore, claim 4 recites the same abstract idea of claim 3. The claim recites the additional limitation of “determining whether the final score is above a cutoff value; and in response to determining that the final score for the candidate pair is above the cutoff value, comparing the document extraction attributes with the final score”, which further elaborates on the abstract idea, since analyzing of information is a mental process, and therefore, does not meaningfully limits the claim. Same rationale applies to claim 14, since they also recite limitations that further elaborate on the abstract idea.
Claim 5 is dependent on claim 1 and includes all the limitations of claim 1. Therefore, claim 5 recites the same abstract idea of claim 1. The claim recites the additional limitation of “computing a document similarity score based on the relative positional differences; and aggregating document similarity scores of each difference in the candidate pair to determine the document-level similarity metric from the final score”, which further elaborates on the abstract idea, since analyzing of information is a mental process, and therefore, does not meaningfully limit the claim. Same rationale applies to claim 15, since they also recite limitations that further elaborate on the abstract idea.
Claim 6 is dependent on claim 1 and includes all the limitations of claim 1. Therefore, claim 6 recites the same abstract idea of claim 1. The claim recites the additional limitation of “computing weights of the plurality of digitally stored documents in the candidate pair and computing a weighted average from the weights using a weighting function to determine the document-level similarity metric”, which further elaborates on the abstract idea, since analyzing of information is a mental process, and therefore, does not meaningfully limit the claim. Same rationale applies to claim 16, since they also recite limitations that further elaborate on the abstract idea.
Claim 7 is dependent on claim 1 and includes all the limitations of claim 1. Therefore, claim 7 recites the same abstract idea of claim 1. The claim recites the additional limitation of “generating a pair of images of the candidate pair of the plurality of digitally stored documents based on a common template, wherein the pair of images comprises static data values of the candidate pair based on the common template having common elements between the candidate pair, and the relative positional differences comprises dynamic data values of the candidate pair”, which further elaborates on the abstract idea, since analyzing of information is a mental process, and therefore, does not meaningfully limits the claim. Same rationale applies to claim 17, since they also recite limitations that further elaborate on the abstract idea.
Claim 8 is dependent on claim 7 and includes all the limitations of claim 7. Therefore, claim 8 recites the same abstract idea of claim 7. The claim recites the additional limitation of “preprocessing the plurality of digitally stored documents by linearizing into a sequence of strings with no line breaks; performing a pair of linearized document extractions based on the plurality of digitally stored documents of the candidate pair; identifying the common template by determining longest common sequence (LCS) of the pair of linearized document extractions; and computing the relative positional differences by isolating content between two consecutive sub-components of the common template”, which further elaborates on the abstract idea, since analyzing of information is a mental process, and therefore, does not meaningfully limits the claim. Same rationale applies to claim 18, since they also recite limitations that further elaborate on the abstract idea.
Claim 9 is dependent on claim 1 and includes all the limitations of claim 1. Therefore, claim 9 recites the same abstract idea of claim 1. The claim recites the additional limitation of “the candidate pair is classified using a targeted fuzzy matched-based classifier”, which further elaborates on the abstract idea, since analyzing of information is a mental process, and therefore, does not meaningfully limit the claim. Same rationale applies to claim 19, since they also recite limitations that further elaborate on the abstract idea.
Claim 10 is dependent on claim 1 and includes all the limitations of claim 1. Therefore, claim 10 recites the same abstract idea of claim 1. The claim recites the additional limitation of “computing the final score using a targeted fuzzy matching score based on the relative positional differences”, which further elaborates on the abstract idea, since analyzing of information is a mental process, and therefore, does not meaningfully limit the claim. Same rationale applies to claim 20, since they also recite limitations that further elaborate on the abstract idea.
Therefore, claims 1 – 20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception without significantly more than the abstract idea.
Double Patenting
Claims 1 – 20 of this application is patentably indistinct from claim 1 – 20 of Application No. 18/427,309, now U.S. Patent 12,287,767. Pursuant to 37 CFR 1.78(f), when two or more applications filed by the same applicant or assignee contain patentably indistinct claims, elimination of such claims from all but one application may be required in the absence of good and sufficient reason for their retention during pendency in more than one application. Applicant is required to either cancel the patentably indistinct claims from all but one application or maintain a clear line of demarcation between the applications. See MPEP § 822.
The non-statutory 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 non-statutory 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 §§ 706.02(l)(1) - 706.02(l)(3) 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 USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The 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 http://www.uspto.gov/patents/process/file/efs/guidance/eTD-info-I.jsp.
The subject matter claimed in the instant application is fully disclosed in the co-pending application and is covered by the co-pending application since the co-pending application and the application are claiming common subject matter, as follows:
Instant application 19/093,471
Co-pending Application 18/427,309
USP 12,287,767
1. A computer-implemented method comprising:
obtaining a candidate pair of a plurality of digitally stored documents from a document database;
automatically computing relative positional differences between each digitally stored document of the candidate pair;
comparing the relative positional differences with a similarity function to form a difference similarity vector for the candidate pair, wherein the difference similarity vector comprises components corresponding to each relative positional difference;
aggregating the components of the difference similarity vector to determine a final score for the candidate pair;
determining a document-level similarity metric from the final score;
determining whether the document-level similarity metric is above a threshold value; and
classifying the candidate pair based on determining that the document-level similarity metric is above the threshold value to de-duplicate the plurality of digitally stored documents in the candidate pair.
2. The computer-implemented method of Claim 1, further comprising, based on the classifying, removing duplicate transaction documents from the document database by any of deleting records, marking records, updating column attributes, or writing records to a different table.
3. The computer-implemented method of Claim 1, further comprising: identifying text elements from each digitally stored document in the candidate pair; and storing the text elements as document extraction attributes.
4. The computer-implemented method of Claim 3, further comprising: determining whether the final score is above a cutoff value; and in response to determining that the final score for the candidate pair is above the cutoff value, comparing the document extraction attributes with the final score.
5. The computer-implemented method of Claim 1, further comprising: computing a document similarity score based on the relative positional differences; and aggregating document similarity scores of each difference in the candidate pair to determine the document-level similarity metric from the final score.
6. The computer-implemented method of Claim 1, further comprising computing weights of the plurality of digitally stored documents in the candidate pair and computing a weighted average from the weights using a weighting function to determine the document-level similarity metric.
7. The computer-implemented method of Claim 1, further comprising generating a pair of images of the candidate pair of the plurality of digitally stored documents based on a common template, wherein the pair of images comprises static data values of the candidate pair based on the common template having common elements between the candidate pair, and the relative positional differences comprises dynamic data values of the candidate pair.
8. The computer-implemented method of Claim 7, further comprising: preprocessing the plurality of digitally stored documents by linearizing into a sequence of strings with no line breaks;
performing a pair of linearized document extractions based on the plurality of digitally stored documents of the candidate pair;
identifying the common template by determining longest common sequence (LCS) of the pair of linearized document extractions; and
computing the relative positional differences by isolating content between two consecutive sub-components of the common template.
9. The computer-implemented method of Claim 1, wherein the candidate pair is classified using a targeted fuzzy matched-based classifier.
10. The computer-implemented method of Claim 1, further comprising computing the final score using a targeted fuzzy matching score based on the relative positional differences.
11. One or more non-transitory computer-readable storage media, storing instructions which, when executed, cause one or more processors to execute:
obtaining a candidate pair of a plurality of digitally stored documents from a document database;
automatically computing relative positional differences between each digitally stored document of the candidate pair;
comparing the relative positional differences with a similarity function to form a difference similarity vector for the candidate pair, wherein the difference similarity vector comprises components corresponding to each relative positional difference;
aggregating the components of the difference similarity vector to determine a final score for the candidate pair;
determining a document-level similarity metric from the final score;
determining whether the document-level similarity metric is above a threshold value; and
classifying the candidate pair based on determining that the document-level similarity metric is above the threshold value to de-duplicate the plurality of digitally stored documents in the candidate pair.
12. The one or more non-transitory computer-readable storage media of Claim 11, storing instructions which, when executed, cause the one or more processors to execute, further comprising, based on the classifying, removing duplicate transaction documents from the document database by any of deleting records, marking records, updating column attributes, or writing records to a different table.
13. The one or more non-transitory computer-readable storage media of Claim 11, storing instructions which, when executed, cause the one or more processors to execute, further comprising: identifying text elements from each digitally stored document in the candidate pair; and storing the text elements as document extraction attributes.
14. The one or more non-transitory computer-readable storage media of Claim 13, storing instructions which, when executed, cause the one or more processors to execute, further comprising: determining whether the final score is above a cutoff value; and in response to determining that the final score for the candidate pair is above the cutoff value, comparing the document extraction attributes with the final score.
15. The one or more non-transitory computer-readable storage media of Claim 11, storing instructions which, when executed, cause the one or more processors to execute, further comprising: computing a document similarity score based on the relative positional differences; and aggregating document similarity scores of each difference in the candidate pair to determine the document-level similarity metric from the final score.
16. The one or more non-transitory computer-readable storage media of Claim 11, storing instructions which, when executed, cause the one or more processors to execute, further comprising computing weights of the plurality of digitally stored documents in the candidate pair and computing a weighted average from the weights using a weighting function to determine the document-level similarity metric.
17. The one or more non-transitory computer-readable storage media of Claim 11, storing instructions which, when executed, cause the one or more processors to execute, further comprising generating a pair of images of the candidate pair of the plurality of digitally stored documents based on a common template, wherein the pair of images comprises static data values of the candidate pair based on the common template having common elements between the candidate pair, and the relative positional differences comprises dynamic data values of the candidate pair.
18. The one or more non-transitory computer-readable storage media of Claim 17, storing instructions which, when executed, cause the one or more processors to execute, further comprising: preprocessing the plurality of digitally stored documents by linearizing into a sequence of strings with no line breaks; performing a pair of linearized document extractions based on the plurality of digitally stored documents of the candidate pair; identifying the common template by determining longest common sequence (LCS) of the pair of linearized document extractions; and computing the relative positional differences by isolating content between two consecutive sub-components of the common template.
19. The one or more non-transitory computer-readable storage media of Claim 11, wherein the candidate pair is classified using a targeted fuzzy matched-based classifier.
20. The one or more non-transitory computer-readable storage media of Claim 11, storing instructions which, when executed, cause the one or more processors to execute, further comprising computing the final score using a targeted fuzzy matching score based on the relative positional differences.
1. A computer-implemented method comprising:
obtaining, by a de-duplication server, a candidate pair of a plurality of digitally stored documents from a document database,
identifying text elements from each digitally stored document in the candidate pair in response, and storing the text elements as document extraction attributes;
automatically computing and storing, by the de-duplication server, relative positional differences of the text elements between each digitally stored document of the candidate pair
and a document similarity score based on the relative positional differences;
comparing, by the de-duplication server, the relative positional differences with a similarity function to form a difference similarity vector for the candidate pair, wherein the difference similarity vector comprises components corresponding to each relative positional difference;
aggregating the components of the difference similarity vector to determine a final score for the candidate pair;
determining a document-level similarity metric from the final score;
determining, by the de-duplication server, whether the final score is above a cutoff value,
and in response to determining that the final score for the candidate pair is above the cutoff value, comparing the document extraction attributes with the final score;
determining whether the document-level similarity metric is above a threshold value by the de-duplication server;
classifying the candidate pair based on determining that the document-level similarity metric is above the threshold value to de-duplicate the plurality of digitally stored documents in the candidate pair;
based on classifying, removing duplicate transaction documents from the document database by any of deleting records, marking records, updating column attributes, or writing records to a different table.
2. The computer-implemented method of claim 1, further comprising computing weights of the plurality of digitally stored documents in the candidate pair and computing a weighted average from the weights using a weighting function to determine the document-level similarity metric.
3. The computer-implemented method of claim 1, further comprising generating a pair of images of the candidate pair of the plurality of digitally stored documents based on a common template, wherein the pair of images comprises static data values of the candidate pair based on the common template having common elements between the candidate pair, and the relative positional differences comprises dynamic data values of the candidate pair.
4. The computer-implemented method of claim 3, further comprising: preprocessing the plurality of digitally stored documents by linearizing into a sequence of strings with no line breaks;
performing a pair of linearized document extractions based on the plurality of digitally stored documents of the candidate pair;
identifying the common template by determining longest common sequence (LCS) of the pair of linearized document extractions;
computing the relative positional differences by isolating content between two consecutive sub-components of the common template.
5. The computer-implemented method of claim 1, further comprising determining a document-level similarity metric from the final score by aggregating document similarity scores of each difference in the candidate pair.
6. The computer-implemented method of claim 1, further comprising classifying the candidate pair using a targeted fuzzy matched-based classifier.
7. The computer-implemented method of claim 1, further comprising computing the final score using a targeted fuzzy matching score based on the relative positional differences.
15. One or more non-transitory computer-readable storage media, storing instructions which, when executed, cause one or more processors to execute:
obtaining, by a de-duplication server, a candidate pair of a plurality of digitally stored documents from a document database,
identifying text elements from each digitally stored document in the candidate pair in response, and storing the text elements as document extraction attributes;
automatically computing and storing, by the de-duplication server, relative positional differences of the text elements between each digitally stored document of the candidate pair
and a document similarity score based on the relative positional differences;
comparing, by the de-duplication server, the relative positional differences with a similarity function to form a difference similarity vector for the candidate pair, wherein the difference similarity vector comprises components corresponding to each relative positional difference;
aggregating the components of the difference similarity vector to determine a final score for the candidate pair;
determining a document-level similarity metric from the final score;
determining, by the de-duplication server, whether the final score is above a cutoff value,
and in response to determining that the final score for the candidate pair is above the cutoff value, comparing the document extraction attributes with the final score;
determining whether the document-level similarity metric is above a threshold value by the de-duplication server;
classifying the candidate pair based on determining that the document-level similarity metric is above the threshold value to de-duplicate the plurality of digitally stored documents in the candidate pair;
based on classifying, removing duplicate transaction documents from the document database by any of deleting records, marking records, updating column attributes, or writing records to a different table.
16. The one or more non-transitory computer-readable storage media of claim 15, storing instructions which, when executed, cause the one or more processors to execute, further comprising: computing weights of the plurality of digitally stored documents in the candidate pair and computing a weighted average from the weights using a weighting function to determine the document-level similarity metric.
17. The one or more non-transitory computer-readable storage media of claim 15, storing instructions which, when executed, cause the one or more processors to execute, further comprising: generating a pair of images of the candidate pair of the plurality of digitally stored documents based on a common template, wherein the pair of images comprises static data values of the candidate pair based on the common template having common elements between the candidate pair, and the relative positional differences comprises dynamic data values of the candidate pair.
18. The one or more non-transitory computer-readable storage media of claim 17, storing instructions which, when executed, cause the one or more processors to execute, further comprising: preprocessing the plurality of digitally stored documents by linearizing into a sequence of strings with no line breaks; performing a pair of linearized document extractions based on the plurality of digitally stored documents of the candidate pair; identifying the common template by determining longest common sequence (LCS) of the pair of linearized document extractions; computing the relative positional differences by isolating content between two consecutive sub-components of the common template.
19. The one or more non-transitory computer-readable storage media of claim 15, storing instructions which, when executed, cause the one or more processors to execute, further comprising: determining a document-level similarity metric from the final score by aggregating document similarity scores of each difference in the candidate pair.
20. The one or more non-transitory computer-readable storage media of claim 15, storing instructions which, when executed, cause the one or more processors to execute, further comprising: classifying the candidate pair using a targeted fuzzy matched-based classifier
8. A computer system, comprising: one or more processors; one or more non-transitory computer-readable media coupled to the one or more processors and storing one or more sequences of stored program instructions which when executed using the one or more processors, cause the one or more processors to execute: obtaining, by a de-duplication server, a candidate pair of a plurality of digitally stored documents from a document database, identifying text elements from each digitally stored document in the candidate pair in response, and storing the text elements as document extraction attributes; automatically computing and storing, by the de-duplication server, relative positional differences of the text elements between each digitally stored document of the candidate pair and a document similarity score based on the relative positional differences; comparing, by the de-duplication server, the relative positional differences with a similarity function to form a difference similarity vector for the candidate pair, wherein the difference similarity vector comprises components corresponding to each relative positional difference; aggregating the components of the difference similarity vector to determine a final score for the candidate pair; determining a document-level similarity metric from the final score; determining, by the de-duplication server, whether the final score is above a cutoff value, and in response to determining that the final score for the candidate pair is above the cutoff value, comparing the document extraction attributes with the final score; determining whether the document-level similarity metric is above a threshold value by the de-duplication server; classifying the candidate pair based on determining that the document-level similarity metric is above the threshold value to de-duplicate the plurality of digitally stored documents in the candidate pair; based on classifying, removing duplicate transaction documents from the document database by any of deleting records, marking records, updating column attributes, or writing records to a different table.
9. The computer system of claim 8, further comprising sequences of stored program instructions which, when executed using the one or more processors, cause the one or more processors to execute: computing weights of the plurality of digitally stored documents in the candidate pair and computing a weighted average from the weights using a weighting function to determine the document-level similarity metric.
10. The computer system of claim 9, further comprising sequences of stored program instructions which, when executed using the one or more processors, cause the one or more processors to execute: generating a pair of images of the candidate pair of the plurality of digitally stored documents based on a common template, wherein the pair of images comprises static data values of the candidate pair based on the common template having common elements between the candidate pair, and the relative positional differences comprises dynamic data values of the candidate pair.
11. The computer system of claim 10, further comprising sequences of stored program instructions which, when executed using the one or more processors, cause the one or more processors to execute: preprocessing the plurality of digitally stored documents by linearizing into a sequence of strings with no line breaks; performing a pair of linearized document extractions based on the plurality of digitally stored documents of the candidate pair; identifying the common template by determining longest common sequence (LCS) of the pair of linearized document extractions; computing the relative positional differences by isolating content between two consecutive sub-components of the common template.
12. The computer system of claim 8, further comprising sequences of stored program instructions which, when executed using the one or more processors, cause the one or more processors to execute: determining a document-level similarity metric from the final score by aggregating document similarity scores of each difference in the candidate pair.
13. The computer system of claim 8, further comprising sequences of stored program instructions which, when executed using the one or more processors, cause the one or more processors to execute: classifying the candidate pair using a targeted fuzzy matched-based classifier.
14. The computer system of claim 8, further comprising sequences of stored program instructions which, when executed using the one or more processors, cause the one or more processors to execute: computing the final score using a targeted fuzzy matching score based on the relative positional differences.
.
Claims 1 – 20 are rejected under the judicially created doctrine of obviousness-type double patenting as being unpatentable over claims 1 – 20 of co-pending application 18/427,309, now U.S. Patent 12,287,767. Although the conflicting claims are not identical, they are not patentably distinct from each other because of corresponding language that recites virtually all of the same elements and functions claimed in the claim 1 of instant application and claim 1 of the co-pending invention, e.g., “determining a document-level similarity metric from the final score; determining whether the document-level similarity metric is above a threshold value; and classifying the candidate pair based on determining that the document-level similarity metric is above the threshold value to de-duplicate the plurality of digitally stored documents in the candidate pair.”
The claimed differences would be obvious to a programmer of ordinary skill in the art, because the instant claims are merely broader and/or alternate variations of the claims recited in the co-pending application.
Because the instant claims merely add/omit/modify the additional elements from the set of elements and functions claimed in the parent application, such modifications would be readily apparent to a programmer of ordinary skill.
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the invention was made to omit/add/modify the additional elements of claim 1 to arrive at the claim 1 of the instant application because the person would have realized that the remaining element would perform the same functions as before.
Therefore, it would have been obvious to modify the instant claims in order to
enables ensuring invaluable robust and effective deduplication process is avoided to improve data quality and integrity of a business spend management system. The method enables providing robust incorporation and exploitation of structural information residing within raw document extractions for purpose of de-duplication.
Examiner Notes
The examiner has considered the applicant's claims in light of the disclosure. However, the examiner respectfully reminds the applicant that during prosecution before the USPTO, claims are to be given their broadest reasonable interpretation, and the scope of a claim cannot be narrowed by reading disclosed limitations into the claim. See In re Morris, 127 F.3d 1048, 1054 (Fed. Cir. 1997). The Office must apply the broadest reasonable meaning to the claim language, taking into account any definitions presented in the specification. In re Am. Acad. of Sci. Tech Ctr., 367 F.3d 1359, 1364 (Fed. Cir. 2004) (citing In re Bass, 314 F.3d 575,577(Fed. Cir. 2002)); “[i]t is the claims that measure the invention.” SRIInt’l v. Matsushita Elec. Corp. of Am., 775 F.2d 1107, 1121 (Fed. Cir. 1985) (enbanc). Written description may not be read into a claim when the claim language is broader than the embodiment. SuperGuide Corp. v. DirecTV Enters, Inc., 358 F.3d 870, 875 (Fed. Cir. 2004) (citing Electro Med. Sys. S.A. v. Cooper Life Sci., Inc., 34 F.3d 1048, 1054 (Fed. Cir. 1994))
Note that “limitations appearing in the specification will not be read into the claims, and … interpreting what is meant by a word in a claim is not to be confused with adding an extraneous limitation appearing in the specification, which is improper.” Intervet Am., v. Kee-Vet Labs., 887 F.2d 1050, 1053, 12 USPQ2d 1474 1476 (fed. Cir. 1989).
“The ordinary and customary meaning of a claim term is the meaning that the term would have to a person of ordinary skill in the art in question at the time of the invention, i.e., as of the effective filing date of the patent application.” Phillips v. AWH Corp,. 415 F.3d 1303, 1313, 75 USPQ2d 1321, 1326 (fed. Cir. 2005).
“One purpose for examining the specification is to determine if the patentee has limited the scope of the claims.’… For example, an inventor may choose to be his own lexicographer is he defines the specific terms used to describe the invention’ with reasonable clarity, deliberateness, and precision.” Such a definition may appear in the written description, … or in the prosecution history, …” Teleflex, Inc. v. Ficosa N. Am Corp., 299 F.3d 1313, 1325, 63 USPQ2d 1374, 1381 (Fed. Cir. 2002).
Prior art pertinent to the disclosed invention is also cited and Applicants are reminded that they must consider all cited art under Rule 111(c) when amending the claims to conform with 35 U.S.C. 112.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Listed prior art could be used as an obviousness type Office correspondence.
Yih (USP 9183173), which involves creating a similarity measure between objects. Each object represented by vectors, includes learning values of vector elements based upon a functional form of the element value with parameters to learn. A similarity function computes similarity score given the vectors and a loss function of computed similarity scores and labels of the vectors. The parameters in the similarity function are tuned (210) to decide the element values including by minimizing the loss function; Le Leannee (USP 11,595,685), which discloses for obtaining a first spatiotemporal motion vector prediction candidate based on two motion vectors being available among first or second spatial motion vectors associated with respective first and second spatial locations neighboring a current block. One of the first and second spatiotemporal motion vector prediction candidates are included in the ordered set of non-sub-block spatiotemporal motion vector prediction candidates. The processor encodes the video data to produce encoded video data based on the first and second spatiotemporal motion vector prediction candidates;
Ponte (USPGPUB 20120047172), which discloses extracting several matching features and scoring features from collection of documents in multiple languages. A forward index is generated based on scoring features, and includes scoring feature lists containing scoring feature extracted from documents in collection. An inverted index is generated based on matching features, and includes matching document lists. A corresponding matching document pairs are generated. A score is calculated based on information from forward index;
Avagyan (USPGPUB 2018/0246943), which involves generating transaction pair records from identified transaction pairs, where each transaction pair record comprises related transaction records. Selected fields corresponding to selected data categories are identified from each of the transaction pair records, where the selected fields are identified based on use of field identification technique that applies transaction record rules to determine selected fields in the transaction pair records, and the selected fields includes an entity field. Data is processed from the field identification technique applied to the identified transaction pairs to determine entity relationship scores relating the entities associated with the transaction pairs; and
Balasubramanian (USPGPUB 20230098926), which discloses receiving a data record with set of data fields. Subset of the data fields become fewer than data fields are selected from among the data fields. First rule is applied to select a first one of the data fields in the data record for inclusion in the subset of the data fields. Content of the subset of the data fields is used to generate stable identifier (stableID) for the data record. StableID is inserted into a primary key data field of the data record. Data quality parameters with null, duplication, and inconsistency conditions is determined for data fields. Report of the data quality parameters is generated for data fields.
Allowable Subject Matter
Claims could be allowable upon overcoming all formal requirements or specifically traverse each requirement not complied with and upon further search and examinations.
Contact Information
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SHAHID AL ALAM whose telephone number is (571)272-4030. The examiner can normally be reached on M-F 8:00 AM-5:00 PM.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Apu Mofiz can be reached on 571-272-4080. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of an application may be obtained from the Patent Application Information Retrieval (PAIR) system. Status information for published applications may be obtained from either Private PAIR or Public PAIR. Status information for unpublished applications is available through Private PAIR only. For more information about the PAIR system, see https://ppair-my.uspto.gov/pair/PrivatePair. Should you have questions on access to the Private PAIR system, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative or access to the automated information system, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
January 24, 2026
/SHAHID A ALAM/Primary Examiner, Art Unit 2161