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 .
Information Disclosure Statement
The information disclosure statement filed 02/27/2026 fails to comply with the provisions of 37 CFR 1.98(a)(4) because it lacks the appropriate size fee assertion. It has been placed in the application file, but the information referred to therein has not been considered as to the merits.
The information disclosure statement filed 02/27/2026 fails to comply with the provisions of 37 CFR 1.97(a) because it lacks the appropriate size fee set forth in 37 CFR 1.17(v). It has been placed in the application file, but the information referred to therein has not been considered as to the merits.
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.
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Claims 1, 2, 4, 5, 8, 9, 11, 12, 15, 16, 18, and rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1 and of U.S. Patent No. 12,169,692 and claims 2 and 7 of U.S. Patent No. 11,620,451. Although the claims at issue are not identical, they are not patentably distinct from each other because the claims are obvious variations of each other.
Regarding Claim 1 (drawn to a system):
Current Application
Claim 1:
A system comprising:
one or more hardware processors of a machine;
and at least one memory storing instructions that, when executed by the one or more hardware processors, cause the machine to perform operations comprising:
performing a plurality of iterations to train a Natural Language Processing (NLP) model, each iteration comprising:
defining a list of data points and assigning labels to each data point; preparing a query in natural language for each data point; processing, via a neural network, a real-world document to generate an extracted value for the query;
validating the extracted value, the validating comprising comparing the extracted value to data in the real-world document to identify a validated extracted value;
defining a trained NLP model as a fine-tuned model; and
evaluating, using the validated extracted value, a model quality of the NLP model; and
determining a quality score of the NLP model using the model quality;
configuring the trained NLP model to process a new real-world document to extract a data point.
‘692
Claim 1:
A system comprising:
one or more hardware processors of a machine;
and at least one memory storing instructions that, when executed by the one or more hardware processors, cause the machine to perform operations comprising:
performing a plurality of iterations to generate a Natural Language Processing (NLP) model, each iteration comprising:
receiving a plurality of real-world documents, the plurality of real-world documents including text data, layout data, and image data;
processing, by at least one or more hardware processors, the plurality of real-world documents to generate an initial prediction for data points within the plurality of real-world documents using a neural network;
validating the initial prediction by comparing extracted values corresponding with information present in the plurality of real-world documents
and correcting discrepancies found based on the comparing;
evaluating a quality of the validated initial prediction; and
determining that the quality of the validated initial prediction satisfies a quality constraint; and
configuring the NLP model to process a new document to extract data points without validation.
Regarding Claim 8 (drawn to a method):
Current Application
Claim 8:
A method comprising:
performing, by at least one hardware processor, a plurality of iterations to train a Natural Language Processing (NLP) model, each iteration comprising:
defining a list of data points and assigning labels to each data point; preparing a query in natural language for each data point; processing, via a neural network, a real-world document to generate an extracted value for the query;
validating the extracted value, the validating comprising comparing the extracted value to data in the real-world document to identify a validated extracted value;
defining a trained NLP model as a fine-tuned model; and
evaluating, using the validated extracted value, a model quality of the NLP model; and
determining a quality score of the NLP model using the model quality;
configuring the trained NLP model to process a new real-world document to extract a data point.
‘692
Claim 11:
A method comprising:
performing, by at least one hardware processor, a plurality of iterations to generate a Natural Language Processing (NLP) model, each iteration comprising:
receiving a plurality of real-world documents, the plurality of real-world documents including text data, layout data, and image data;
processing, by at least one or more hardware processors, the plurality of real-world documents to generate an initial prediction for data points within the plurality of real-world documents using a neural network;
validating the initial prediction by comparing extracted values corresponding with information present in the plurality of real-world documents
and correcting discrepancies found based on the comparing;
evaluating a quality of the validated initial prediction; and
determining that the quality of the validated initial prediction satisfies a quality constraint; and
configuring the NLP model to process a new document to extract data points without validation.
Regarding Claim 15 (drawn to a non-transitory CRM):
Current Application
Claim 15:
A non-transitory computer medium embodying instructions that, when executed by a machine, cause the computer medium to perform operations comprising:
performing, by at least one hardware processor, a plurality of iterations to train a Natural Language Processing (NLP) model, each iteration comprising:
defining a list of data points and assigning labels to each data point; preparing a query in natural language for each data point; processing, via a neural network, a real-world document to generate an extracted value for the query;
validating the extracted value, the validating comprising comparing the extracted value to data in the real-world document to identify a validated extracted value;
defining a trained NLP model as a fine-tuned model; and
evaluating, using the validated extracted value, a model quality of the NLP model; and
determining a quality score of the NLP model using the model quality;
configuring the trained NLP model to process a new real-world document to extract a data point.
‘692
Claim 21:
A non-transitory computer medium embodying instructions that, when executed by a machine, cause the computer medium to perform operations comprising:
performing, by at least one hardware processor, a plurality of iterations to generate a Natural Language Processing (NLP) model, each iteration comprising:
receiving a plurality of real-world documents, the plurality of real-world documents including text data, layout data, and image data;
processing, by at least one or more hardware processors, the plurality of real-world documents to generate an initial prediction for data points within the plurality of real-world documents using a neural network;
validating the initial prediction by comparing extracted values corresponding with information present in the plurality of real-world documents
and correcting discrepancies found based on the comparing;
evaluating a quality of the validated initial prediction; and
determining that the quality of the validated initial prediction satisfies a quality constraint; and
configuring the NLP model to process a new document to extract data points without validation.
As shown in the tables above, it is clear that all the elements of the application claims 1, 8, and 15 are to be found in patent claims 1, 11, and 21, as the application claims 1, 8, and 15 fully encompasses patent claims 1, 11, and 21. The difference between the application claims 1, 8, and 15 and the patent claims 1, 11, and 21 lies in the fact that the patent claims includes more elements and is thus more specific. Thus the invention of claims 1, 11, and 21 of the patent is in effect a “species” of the “generic” invention of the application claims 1, 8, and 15. It has been held that the generic invention is “anticipated” by the “species”. See In re Goodman, 29 USPQ2d 2010 (Fed. Cir. 1993).
Claim 2 of the current application corresponds to the corresponding portion of claim 1 of U.S. Patent No. 12,169,692.
Claim 4 of the current application corresponds to claim 2 of 11,620,451.
Claim 5 of the current application corresponds to claim 7 of 11,620,451.
Claim 9 of the current application corresponds to the corresponding portion of claim 11 of U.S. Patent No. 12,169,692.
Claim 11 of the current application corresponds to claim 14 of 11,620,451.
Claim 12 of the current application corresponds to claim 19 of 11,620,451.
Claim 16 of the current application corresponds to the corresponding portion of claim 21 of U.S. Patent No. 12,169,692.
Claim 18 of the current application corresponds to claim 26 of 11,620,451.
Claim 19 of the current application corresponds to claims 7 and 19 of 11,620,451.
Allowable Subject Matter
Claims 3, 6, 7, 10, 13, 14, 17, and 20 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Cited Art
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Huang et al. (US 2021/0081729) discloses image text recognition.
Bentley et al. (US 10,639,075) discloses determining and executing application functionality based on text analysis.
Lucas et al. (US 2020/0176098) discloses clinical concept identification, extraction and prediction.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SATWANT K SINGH whose telephone number is (571)272-7468. The examiner can normally be reached Monday thru Friday 9:00 AM to 6:00 PM EST.
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/SATWANT K SINGH/Primary Examiner, Art Unit 2653
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