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 .
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 21-40 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-20 of U.S. Patent No. 12,008,322. Although the claims at issue are not identical, they are not patentably distinct from each other because the claims in the present application define an invention that is merely an obvious variation of the invention claimed in the patent for the following reasons. Comparing the claims of the two documents, as shown below for claim 21, all the elements of the application claims are to be found in claim 1 of the patent. The difference is claim 1 of the patent comprises many more elements and much more specific than claim 21. For example, claim 1 of the patent comprises the additional steps of “parsing the content data to identify a plurality of content segmentation units based on one or more of semantically complete unit of text data associated with the content data, semantically incomplete unit of text data associated with the content data, or a content hierarchy associated with the content data” and “wherein the action item extraction machine learning model comprises: (i) a part-of-speech tagger model that is configured to generate a part-of-speech tag sequence for the content segmentation unit, and (ii) a sequence processing model that is configured to generate the action item set based on the part-of-speech tag sequence”, that are not included in claim 21 of the present application, therefore claim 1 of the patent represents a species of the generic invention of the application claim. Since it has been held that the generic invention is anticipated by the species, claims 21-40 of the present application are anticipated by the parent claims 1-20.
Claims 22-24 correspond to claim 2-4 of the patent.
Claim 25 comprises limitations found in claim 1.
Claims 26-27 correspond to claims 6-7 of the patent.
Claim 28 correspond to claim 3 of the patent.
Claims 29-40, citing the program and method having steps similar to the claims above are analogous, therefore anticipated by the patent claims for the reason set forth above.
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21. An apparatus for generating an action item log user interface element for a webpage that displays content data associated with a document data object, the apparatus comprising at least one processor and at least one memory including program code, the at least one memory and the program code configured to, with the at least one processor, cause the apparatus to at least: parse the content data to identify a plurality of content segmentation units;
apply an action item classification machine learning model to each content segmentation unit of the plurality of content segmentation units to determine an action item presence prediction for each content segmentation unit, wherein the action item classification machine learning model comprises a Bidirectional Encoder Representations from Transformers (BERT) model; determine, based on each action item presence prediction for each respective content segmentation unit, a candidate action item subset of the plurality of content segmentation units; apply an action item extraction machine learning model to the candidate action item subset to generate an action item set; and generate one or more action item log user interface elements configured for rendering to a computing device display based on the action item set.
1. An apparatus for generating action item log user interface data for a webpage that displays content data associated with a document data object, the apparatus comprising at least one processor and at least one memory including program code, the at least one memory and the program code configured to, with the processor, cause the apparatus to at least: parse the content data to identify a plurality of content segmentation units ( based on one or more of semantically complete unit of text data associated with the content data, semantically incomplete unit of text data associated with the content data, or a content hierarchy associated with the content data); for each content segmentation unit, process the content segmentation unit using/applying an action item classification machine learning model to determine an action item presence prediction for the content segmentation unit, (wherein the action item presence prediction for each content segmentation unit comprises one of an affirmative action item presence detection indicating that the content segmentation unit comprises one or more action items or a negative action item presence detection indicating that the content segmentation unit fails to comprise an action item; determine, based on each action item presence prediction, a candidate action item subset of the plurality of content segmentation units; for each content segmentation unit in the candidate action item subset, process the content segmentation unit using an action item extraction machine learning model to generate an action item set for the content segmentation unit, (wherein the action item extraction machine learning model comprises: (i) a part-of-speech tagger model that is configured to generate a part-of-speech tag sequence for the content segmentation unit, and (ii) a sequence processing model that is configured to generate the action item set based on the part-of-speech tag sequence); and generate an action item log based on each action item set for the candidate action item subset, wherein the action item log is configured to be used to generate the action item log user interface data for an action item log user interface element, and wherein the action item log user interface element is configured to be displayed to an end user of a computing device.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 21-40 are rejected under 35 U.S.C. 103 as being unpatentable over Hilleli et al. (US 2021/0097502).
As to claims 21, 24 and 26, Hilleli teaches (Figs.2-6) an apparatus 260 for generating an action item log user interface element for a webpage that displays content data associated with a document data object, the apparatus comprising at least one processor and at least one memory including program code, the at least one memory and the program code configured to, with the at least one processor, cause the apparatus to at least: parse (at parser 314) the content data 402 to identify a plurality of content segmentation units 406 (Fig.4; Pars.73-74, 100-102); apply an action item classification machine learning model 260 to each content segmentation unit of the plurality of content segmentation units to determine/detect an action item presence prediction 602 for each content segmentation unit, wherein the action item classification machine learning model comprises models such as deep learning classification neural network (e.g., a Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), or Transformers (Pars.56-57); determine/identify (via candidate identifier 262/321), based on each action item presence prediction (602) for each respective content segmentation unit, a candidate action item subset (604) of the plurality of content segmentation units (Pars.54-55, 97,112); apply an action item extraction machine learning model 264/330 to the candidate action item subset to generate 608 an action item set (Pars. 113,116-117, 88, 95-98); and generate 610 one or more action item log user interface elements (list of action items) configured for rendering to a computing device display 320 based on the action item set (Pars.62-65)
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It is noted that, while Hilleli teaches wherein the action item classifiers and sequence processor 312/321/330 use machine learning model, such as a deep learning classification neural network (e.g., a Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), or Transformers) and suggests that such model can use any suitable model or set of models such as random forest models, deep neural networks, Bayesian networks, or any other suitable machine learning model, he doesn’t explicitly teach where the models comprise BERT model. Official Notice is taken that Bidirectional Encoder Representations from Transformers (BERT) model is common and well known in the art of natural language processing involving machine learning models and it would be obvious to one of ordinary skill in the art before the time of applicant’s invention to apply it in the system of Hilleli, as an alternative or in addition to the cited models, for the purpose of accurately predicting and determining the action items. For such teachings see US 2025/0371317, US 11997138, US 2022/0284362.
As to claim 22, Hilleli teaches wherein the plurality of content segmentation units comprise one or more sentences of the document data object (Fig.4).
As to claim 23, Hilleli teaches wherein the document data object is a structured document data object that is associated with a structural scheme, and wherein the plurality of content segmentation units comprise one or more predefined structural elements of the document data object that are determined based on the structural scheme (Pars.75-77, 104).
As to claim 25, Hilleli teaches wherein the action item extraction machine learning model comprises a part-of-speech tagger model 314 that is configured to generate a part-of-speech tag sequence for each content segmentation unit, and (ii) a sequence processing model that is configured to generate the action item set based on the part-of-speech tag sequence (Pars.53-60, 73-75)
As to claim 27, Hilleli teaches wherein the sequence processing model is characterized by one or more action item detection regular expression rules (Pars.26, 60, 72-78).
As to claim 28, Hilleli teaches wherein the action item classification machine learning model is trained based on a plurality of training document data objects stored to a storage subsystem 225, wherein each training document data object of the plurality of training document data objects is a structured document data object that is associated with a structural scheme (Figs.2-3; Pars.72-75,104-105).
Regarding claims 29-40, the corresponding program and method, comprising the steps similar to the steps cited in claims 21-28, are analogous therefore rejected as being unpatentable over Hilleli et al. for the foregoing reasons.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DANIEL DEMELASH ABEBE whose telephone number is (571)272-7615. The examiner can normally be reached monday-friday 7-4.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Daniel Washburn can be reached at 571-272-5551. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/DANIEL ABEBE/Primary Examiner, Art Unit 2657
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