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 .
Response to Arguments
Applicant's arguments filed 1/27/2026 have been fully considered but they are not persuasive.
With respect to double patenting rejection, applicant maintains the previous double patenting rejection.
Applicant’s arguments/amendments, filed 1/27/2026, with respect to 1-14, 16-18 & 20 have been fully considered and are persuasive. The rejection of claims 1-14, 16-18 & 20 have been withdrawn.
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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Claim 1 of 18/625,861
A computing system for machine-learned model content generation, the system comprising: one or more processors; and one or more non-transitory computer-readable media that collectively store instructions that, when executed by the one or more processors, cause the computing system to perform operations, the operations comprising: obtaining input data, wherein the input data comprises source content that comprises a set of details associated with a particular topic, wherein the source content comprises a press release and one or more interviews associated with the particular topic; processing the input data with a generative model to generate a plurality of candidate model-generated news article drafts, wherein the plurality of candidate model-generated news article drafts are generated based on the source content, and wherein the generative model was tuned on a domain-specific training dataset comprising a plurality of news articles, wherein the plurality of news articles comprise a particular information structure and a particular set of publication type-specific stylistic characteristics, and wherein each of the plurality of candidate model-generated outputs comprises content associated with the particular topic; evaluating, based on a plurality of signals, the plurality of candidate model-generated news article drafts to generate a plurality of respective evaluation datasets, wherein each of the plurality of respective evaluation datasets is associated with a respective candidate model-generated news article draft of the plurality of candidate model-generated news article drafts; selecting a particular candidate model-generated news article draft of the plurality of candidate model-generated news article drafts based on the plurality of respective evaluation datasets; and providing the particular candidate model-generated news article draft as output.
Claim 7 of 18/625,861
The system of claim 1, wherein the operations further comprise: processing the particular candidate model-generated news article draft with the generative model to generate an outline of the particular candidate model-generated news article draft; and providing the outline of the particular candidate model-generated news article draft for display.
Claim 8 of 18/625,861
The system of claim 7, wherein the operations further comprise: obtaining an augmentation input associated with a request to augment the outline of the particular candidate model-generated news article draft; generating an augmented outline based on the augmentation input and the outline of the particular candidate model-generated news article draft; and providing the augmented outline for display.
Claim 11 of 18/625,571
A computer-implemented method, the method comprising: obtaining, by a computing system comprising one or more processors, source content, wherein the source content comprises details associated with a particular topic; processing, by the computing system, the source content with a domain-specific generative model to generate a model-generated content item, wherein the domain-specific generative model was tuned on a domain-specific training dataset to generate content items that comprise a particular information structure and a particular set of stylistic characteristics associated with news articles, and wherein the model-generated content item comprises a model-generated news article comprising one or more domain-specific attributes, wherein the one or more domain-specific attributes comprise the particular information structure and the particular set of stylistic characteristics; processing, by the computing system, the model-generated content item to generate an outline of the model-generated content item; providing, by the computing system, the outline of the model-generated content item for display; obtaining, by the computing system, an augmentation input, wherein the augmentation input is associated with augmenting the outline; and processing, by the computing system, the augmentation input and the outline with a domain-specific generative model to generate an updated model-generated content item, wherein the updated model-generated content item comprises an updated model-generated news article.
Claim 13 of 18/625,571
The method of claim 11, wherein the source content comprises a press release and one or more interview transcripts, and wherein the model-generated content item and the updated model-generated content item are associated with the particular topic of the press release and one or more interview transcripts.
Claim 17 of 18/625,861
One or more non-transitory computer-readable media that collectively store instructions that, when executed by one or more computing devices, cause the one or more computing devices to perform operations, the operations comprising: obtaining input data, wherein the input data comprises source content that comprises a set of details associated with a topic, wherein the source content comprises a press release and one or more interview transcripts; processing the input data with a generative model to generate a plurality of candidate model-generated outputs, wherein the plurality of candidate model-generated outputs are generated based on the source content, wherein the plurality of candidate model-generated outputs comprises a plurality of candidate model-generated news articles, and wherein the generative model was tuned on a domain-specific training dataset associated with a particular field of expertise, wherein the generative model comprises a pre-trained generative model that was tuned on the domain-specific training dataset after initial training; evaluating, based on a plurality of signals, the plurality of candidate model-generated outputs to generate a plurality of respective evaluation datasets, wherein each of the plurality of respective evaluation datasets is associated with a respective candidate model-generated output of the plurality of respective evaluation datasets; selecting a particular candidate model-generated output of the plurality of candidate model-generated outputs based on the plurality of respective evaluation datasets, wherein the particular candidate model-generated output comprises a particular model-generated news article of the plurality of candidate model-generated news articles; processing the particular candidate model-generated output with the generative model to generate a model-generated outline descriptive of a structure and content within the particular candidate model-generated output; and providing the model-generated outline as output.
Claim 17 of 18/625,571
One or more non-transitory computer-readable media that collectively store instructions that, when executed by one or more computing devices, cause the one or more computing devices to perform operations, the operations comprising: obtaining domain-specific training dataset, wherein the domain-specific training dataset comprises a plurality of press releases and a plurality of respective news articles, wherein the plurality of respective news articles comprise a journalistic style associated with a press style book and an inverted pyramid information structure, and wherein the plurality of respective news articles are associated with a plurality of news topics associated with the plurality of press releases; processing a particular press release of the plurality of press releases with a generative model to generate a model-generated article, wherein the model-generated article comprises a predicted article generated based on the particular press release; evaluating a loss function that evaluates a difference between the model-generated article and a particular news article of respective news articles and evaluates factual grounding of the model-generated article associated with details from the particular press release, and wherein the loss function evaluates a style and structure of the model-generated article based on a comparison with a ground truth style and structure of the particular news article; and adjusting one or more parameters of the generative model based at least in part on the loss function.
Claims 1, 7-8 & 17 of application number 18/625,861 (hereinafter referred to as ‘861) are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claim 11, 13, 17 of copending Application No. 18/625,571 (hereinafter referred to as ‘571).
Although the claims at issue are not identical, they are not patentably distinct from each other because ‘861 claims 1, 7-8 & 17 are broader recitations of ‘571 claims 11, 13 & 17. Therefore, the recitations of ‘571 claims 11, 13 & 17 are encompassed by the recitations of ‘861 claims 1, 7-8 & 17.
This is a provisional nonstatutory double patenting rejection because the patentably indistinct claims have not in fact been patented.
Claims 1-14, 16-18 & 20 would be allowable once double patenting rejection is overcome.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to GEORGE C MONIKANG whose telephone number is (571)270-1190. The examiner can normally be reached Mon. - Fri., 9AM-5PM, ALT. Fridays off.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Carolyn R Edwards can be reached at 571-270-7136. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/GEORGE C MONIKANG/Primary Examiner, Art Unit 2692 3/27/2026
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