DETAILED ACTION
This action is in response to the election to the restriction requirement dated 19 March 2026. No claims are amended. Claims 19-20 are withdrawn. No claims have been added. Claims 1-18 remain pending and have been considered below.
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 .
Election/Restrictions
Applicant’s election without traverse of Group I, claims 1-18, in the reply filed on 19 March 2026 is acknowledged.
Claims 19-20 withdrawn from further consideration pursuant to 37 CFR 1.142(b) as being drawn to a nonelected invention Group II, there being no allowable generic or linking claim. Election was made without traverse in the reply filed on 19 March 2026.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 23 April 2024 has been received, entered into the record, and considered. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
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.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual 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 www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 1, 2, 10-13 and 18 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-3, 6, 12, 13, 16, 19 and 20 of U.S. Patent No. 12,008,308. Although the claims at issue are not identical, they are not patentably distinct from each other because the features of the claims listed above of the instant application are anticipated by or are obvious variants of the identified claims of the reference patent (e.g. one of ordinary skill in the art would recognize that processing an intermediate input and processing a language instruction are equivalent).
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-6, 9-15, 17 and 18 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Peleg et al. (US 2023/0153546 A1).
As for independent claim 1, Peleg discloses a method comprising:
providing, to an endpoint device, an input interface configured for receiving natural language content [(e.g. see Peleg paragraphs 0061, 0067, 0091 and Figs. 2a and 4a) ”system 100 may include a plurality of client devices 110 operated by users 120 … FIGS. 2a-2p show a user interface that may be included with exemplary embodiments of the disclosed writing assistant system. FIGS. 2a-2p show an exemplary GUI 200 that may be associated with certain disclosed embodiments. In the example shown starting at FIG. 2a, GUI 200 may be associated with an email application and may include an email editor GUI 205, which in turn, may include a workspace 210 … FIGS. 4a-4g illustrate another example of possible interaction between the writing assistant and a user during generation of text for a document. Again, an email editor 405 is shown as the environment in which the writing assistant is employed, but any other text-related computer application may also be used”].
receiving, from the endpoint device, an initial input of natural language content associated with the document, wherein the initial input is based on user interface with the input interface [(e.g. see Peleg paragraphs 0069, 0091) ”FIG. 2c shows an example user input field 220 that may be presented on the GUI in response to initiation of the writing assistant by the user. For example, a user can summon field 220 in the writing assistant, where field 220 is configured to receive text input from the user in the form of characters, words, sentence fragments, phrases, sentences, paragraphs, punctuation, etc. As shown in FIG. 2d, a user can type input 225 into the field 220 (such as “and I understand from her”) … In the example, of FIG. 4a, the user can summon a field 420 in a workspace 410 using any suitable technique, such as those described above. In some cases, workspace 410 may include preexisting text 415 already entered by the user (or which may already appear as part of a preexisting document, such as a Word file, etc.). As show in FIG. 4b, the user can enter text input 425 (“Thanks for the meeting with Michael”) into user input field 420”].
generating, based on the input, an intermediate input based on a document corpus that comprises a plurality of example documents, wherein the intermediate output comprises an example document from the document corpus [(e.g. see Peleg paragraphs 0072, 0073, 0093, 0098, 0169, 0371, 0374) ”the writing assistant may learn the personal style of the user or the style of a particular organization, in different contexts (e.g., based on internal business documents, external business email, personal email, etc.) … One aspect of the writing assistant may include the generation of natural language that may be controlled or influenced by multiple pieces of text that should be naturally and smoothly incorporated into a refined text passage or text output option. There may be various techniques for assembling a writing assistant application consistent with the presently disclosed examples and embodiments. In some cases, the disclosed writing assistant may be assembled and/or configured using machine learning techniques and/or by incorporating one or more trained models. In order to provide the described functionality, the disclosed writing assistant and model(s) on which the writing assistant is based may be trained, for example, to predict text within a document from a large corpus, conditioned upon text appearing before and/or after textual elements. For example, in order to train the model(s), one or more large text corpus documents (such as one or more of several publicly available corpus documents) … Embodiments of the disclosed writing assistant tool may also be configured to automatically re-purpose electronic documents. Such re-purposing may include revising one or more formatting, stylistic, grammatical, tone, length characteristics, etc., of an existing electronic document drafted for one platform or audience to adhere to standards associated with another platform or audience … Various techniques may be used for identifying source text segments for use by the writing assistant tool in generating text output re-purposing the identified text segments within a new document. For example, documents including source text segments may be selected from an interface window that lists files in a directory. Files shown in a directory may be dragged and dropped into a project window in order to identify to the writing assistant documents for re-purposing. In addition to loading full documents, one or more text segments within any number of electronic documents may be identified to the writing assistant tool for re-purposing … the writing assistant can generate multiple output options that each differ from one another. Despite the differences, however, all convey the idea associated with the user input (e.g., that Jessica Abrahams conveyed information to the user, Andres Lopez). Additionally, the text output options all agree with one or more contextual aspects of the preexisting text (a partial sentence) in workspace 210 … the text output options are not static, but rather, can be updated as a user continues to provide input to field 220, for example. In FIG. 2f, the user types updated input 235 that adds the phrase “you want to hear more on what we do” to the originally entered user input, “and I understand from her.” In response to receiving the updated user input, as shown in FIG. 2g, the writing assistant will generate a set of updated text output options 240a-240c, which may or may not include the originally generated text output options … as shown in FIG. 4c, the user can select any of the icons 435 to initiate one or more functions associated with the selected icon. In the example shown, a user may select icon 435a (denoted by gray highlighting over icon 435a) that corresponds with a particular text output option 430a. In response to selection of icon 435a, and as shown in FIG. 4d, the writing assistant GUI 400 can display another window (e.g., a style parameter control window) that identifies style parameters 440 (e.g., parameters 440a-d) for which values may be selected by the user. The values for the predetermined style parameters (which, in some cases, can be built into the writing assistant or which may be user-selectable) may specify a level of formality, conciseness, emotion, politeness, or a level associated with any other parameter type that may be relevant to the document”].
generating, using a machine-learned content generation model to process the intermediate input, a suggested portion of natural language content that completes a portion of the document [(e.g. see Peleg paragraphs 0073, 0075, 0094, 0173 and Figs. 2g and 4d) ”the writing assistant will generate a set of updated text output options 240a-240c … all three options reference the detail that the activities are occurring in Greece, despite there being no reference to Greece in either the user input in field 220 or in the preexisting text 215. For example, the writing assistant, as evidenced by the text output options, was able to determine that ITG Group is a real estate group operating in Greece. The writing assistant is able to pull contextual information not only from the words of the user input and/or the words of the preexisting text, but also from other available sources of information (e.g., Internet-accessible databases, among others) … the user can edit the level of the style parameters using the displayed toggles 480 (or any other suitable GUI control elements) or by manually typing entering the adjustment herself via the modifier windows 442. For example, as shown in FIG. 4e, the user has adjusted the level of formality 440b down to “−1” (e.g., to a lower level of formality using toggles 480 or modifier window 442). This change may cause the writing assistant to automatically update the text associated with selected text output option 430a according to the change in parameter value. For example, as shown in FIGS. 4d and 4e, the reduction in level of formality may cause the writing assistant to change the selected text output option (“I wanted to thank you for arranging the meeting with Michael”) to the adjusted text 485 (“Thanks for putting together the meeting with Michael … training for the model(s) associated with the disclosed writing assistant may be directed to enabling the model(s) to determine a desired position of generated text within a predetermined text (e.g., such that the generated text is incorporated naturally and smoothly within the preexisting text). Such capabilities may be provided by training a model to predict text within a document from a large corpus conditioned upon the preceding text and additional information regarding the position of the missing text”].
providing to the endpoint device, the suggested portion [(e.g. see Peleg paragraphs 0073, 0094 and Figs. 2g and 4f) ” In response to receiving the updated user input, as shown in FIG. 2g, the writing assistant will generate a set of updated text output options 240a-240c … the reduction in level of formality may cause the writing assistant to change the selected text output option (“I wanted to thank you for arranging the meeting with Michael”) to the adjusted text 485 (“Thanks for putting together the meeting with Michael”)”].
receiving, from the endpoint device, a response descriptive of selection, via the input interface, for including the suggested portion in the document and updating the state of the document based on the response [(e.g. see Peleg paragraphs 0076, 0097 and Figs. 2i and 4g) ”Once the text output options provide the user with suitable text, the user can select one of the text options. For example, a user may select text output 240c, as shown in FIG. 2h. In response, as shown in FIG. 2i, the writing assistant can insert the user-selected text output option 240c into the workspace 210 with the initial text 215, creating a coherent and context fitting paragraph (e.g., inserted text 245) … once the user is satisfied with the adjusted text 485, the user can select the adjusted/refined text output by selecting the user acceptance icon 445. As shown in FIG. 4g, the writing assistant can automatically insert the adjusted/refined text into the document or email workspace 410 as inserted text 450”].
As for dependent claim 2, Peleg discloses the method as described in claim 1 and Peleg further discloses:
wherein the example document provides a positive example of at least one content string [(e.g. see Peleg paragraphs 0073, 0076, 0098) ”as shown in FIG. 2g, the writing assistant will generate a set of updated text output options 240a-240c … Once the text output options provide the user with suitable text, the user can select one of the text options. For example, a user may select text output 240c, as shown in FIG. 2h. In response, as shown in FIG. 2i, the writing assistant can insert the user-selected text output option 240c into the workspace 210 with the initial text 215, creating a coherent and context fitting paragraph (e.g., inserted text 245) … the writing assistant may learn the personal style of the user”]. Examiner notes that, based on applicant’s specification, a positive example is an option that is selected by the user.
As for dependent claim 3, Peleg discloses the method as described in claim 2 and Peleg further discloses:
wherein the example document is a user-selected document that is associated with a user engagement [(e.g. see Peleg paragraphs 0371, 0372, 0374) ”the disclosed writing assistant tool may also be configured to automatically re-purpose electronic documents. Such re-purposing may include revising one or more formatting, stylistic, grammatical, tone, length characteristics, etc., of an existing electronic document drafted for one platform or audience to adhere to standards associated with another platform or audience … the writing assistant tool can allow the user to select text (e.g., from an existing document) … documents including source text segments may be selected from an interface window that lists files in a directory”].
As for dependent claim 4, Peleg discloses the method as described in claim 3 and Peleg further discloses:
wherein the user-selected document was generated using the machine-learned document completion system [(e.g. see Peleg paragraph 0100) ”the writing assistant may enable users to select any piece of text, e.g., in the document being written or in another source, and choose to copy that text's style. For example, the writing assistant may detect at least one style attribute (politeness, emotion, formality, etc.) associated with the selected text and then may use or apply such a style attribute in modifying other text in the document. For example, a user may select any piece of text in the document and choose to ‘paste’ the copied style attribute. The assistant will then automatically rephrase the target text such that its style resembles that of the source text or the assistant may offer one or more text output options that rephrase one or more segments of the target text in the style of the source text”]. Examiner notes that the current document being written can contain portions generated from the writing model.
As for dependent claim 5, Peleg discloses the method as described in claim 3 and Peleg further discloses:
wherein the user engagement comprises at least one of the following: marking the user-selected document as a favorite, saving the user-selected document or selecting the user-selected document [(e.g. see Peleg paragraphs 0371, 0372, 0374) ”the disclosed writing assistant tool may also be configured to automatically re-purpose electronic documents. Such re-purposing may include revising one or more formatting, stylistic, grammatical, tone, length characteristics, etc., of an existing electronic document drafted for one platform or audience to adhere to standards associated with another platform or audience … the writing assistant tool can allow the user to select text (e.g., from an existing document) … documents including source text segments may be selected from an interface window that lists files in a directory”].
As for dependent claim 6, Peleg discloses the method as described in claim 5 and Peleg further discloses:
wherein the document is associated with a user, and wherein the user engagement is associated with the user [(e.g. see Peleg paragraph 0098, 0358, 0372) ”the writing assistant tool can present to the user (e.g., an author of the document … the disclosed embodiments of the writing assistant may also be configured to apply a default style that is predetermined or learned based on usage. For example, the writing assistant may learn the personal style of the user or the style of a particular organization, in different contexts (e.g., based on internal business documents, external business email, personal email, etc.). In this way, the writing assistant may generate suggested text output options in a style that resembles that personal or organizational style in the specific context of the document … the writing assistant tool can allow the user to select text (e.g., from an existing document)”].
As for dependent claim 9, Peleg discloses the method as described in claim 2 and Peleg further discloses:
wherein the example document is a user-submitted document that was received by the machine-learned document completion system from a user upload [(e.g. see Peleg paragraph 0374) ”Various techniques may be used for identifying source text segments for use by the writing assistant tool in generating text output re-purposing the identified text segments within a new document. For example, documents including source text segments may be selected from an interface window that lists files in a directory. Files shown in a directory may be dragged and dropped into a project window in order to identify to the writing assistant documents for re-purposing”].
As for dependent claim 10, Peleg discloses the method as described in claim 1 and Peleg further discloses:
wherein the example document provides a negative example of at least one content string [(e.g. see Peleg paragraphs 0078, 0098, 0132-0134) ”Offering multiple text output options may enable the user to select the generated text output option that most closely conveys an intended idea or that most closely fits with the context of the document … The writing assistant has the ability to iteratively interact with a user in order to refine or navigate through proposed text output options generated and displayed by the writing assistant … the user can further interact with the writing assistant to refine any of the generated text output options (e.g., by selecting virtual button 730 corresponding to text output option 725b). As shown in FIG. 7d, the writing assistant may use the selected text output 725b to generate one or more refined text output options … this process may be iterative, and a user may continue request for refined text output options until he is satisfied with one of the options. For example, the user may select button 730 to prompt the writing assistant to generate further refined text output options and so on … the writing assistant may learn the personal style of the user”]. Examiner notes that, based on applicant’s specification, a negative example is an option that is not initially selected by the user.
As for dependent claim 11, Peleg discloses the method as described in claim 10 and Peleg further discloses:
wherein the example document is a user-rejected document that was generated using the machine-learned document completion system and is not associated with a user selection [(e.g. see Peleg paragraphs 0078, 0098, 0132-0134) ”Offering multiple text output options may enable the user to select the generated text output option that most closely conveys an intended idea or that most closely fits with the context of the document … The writing assistant has the ability to iteratively interact with a user in order to refine or navigate through proposed text output options generated and displayed by the writing assistant … the user can further interact with the writing assistant to refine any of the generated text output options (e.g., by selecting virtual button 730 corresponding to text output option 725b). As shown in FIG. 7d, the writing assistant may use the selected text output 725b to generate one or more refined text output options … this process may be iterative, and a user may continue request for refined text output options until he is satisfied with one of the options. For example, the user may select button 730 to prompt the writing assistant to generate further refined text output options and so on … the writing assistant may learn the personal style of the user”].
As for independent claim 12, Peleg discloses a system. Claim 12 discloses substantially the same limitations as claim 1. Therefore, it is rejected with the same rational as claim 1.
As for dependent claim 13, Peleg discloses the system as described in claim 12; further, claim 13 discloses substantially the same limitations as claim 3. Therefore, it is rejected with the same rational as claim 3.
As for dependent claim 14, Peleg discloses the system as described in claim 13; further, claim 14 discloses substantially the same limitations as claim 4. Therefore, it is rejected with the same rational as claim 4.
As for dependent claim 15, Peleg discloses the system as described in claim 13; further, claim 15 discloses substantially the same limitations as claims 5 and 6. Therefore, it is rejected with the same rational as claims 5 and 6.
As for dependent claim 17, Peleg discloses the system as described in claim 12; further, claim 17 discloses substantially the same limitations as claim 9. Therefore, it is rejected with the same rational as claim 9.
As for dependent claim 18, Peleg discloses the system as described in claim 12; further, claim 18 discloses substantially the same limitations as claim 11. Therefore, it is rejected with the same rational as claim 11.
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.
Claims 7, 8 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Peleg et al. (US 2023/0153546 A1) in view of Bar-on et al. (US 2022/0200937 A1).
As for dependent claim 7, Peleg teaches the method as described in claim 5, but does not specifically teach wherein the document is associated with a user, and wherein the user engagement is associated with one or more other users. However, in the same field of invention, Bar-on teaches:
wherein the document is associated with a user, and wherein the user engagement is associated with one or more other users [(e.g. see Bar-on paragraphs 0029, 0042) ”intelligent content suggestions to users of a collaborative content management and communication system … a user who is viewing or editing a document may receive a suggestion of another individual who may be associated with that document or the subject matter of that document … The content interaction histories 116 include historical records of content interactions between users and content items in the content and communication system 102. For example, the content interaction histories 116 may include records of which users interacted with which content items, as well as properties of those interactions. Content interactions that are stored by the content interaction histories 116 may include, without limitation, creating content items; editing content items; commenting on content items; viewing content items; “liking” or endorsing content items; sharing content items (e.g., emailing); linking to or referring to content items in other content items, chats, emails, communications, etc.; and durations interactions with content items. As described herein, content interaction histories 116 may be used to generate a multi-dimensional association graph that associates users to content items, as well as to generate identity vectors and/or access vectors for use in determining or estimating the relevancy of a particular content item to a particular user (and/or identifying content items, from the content store 118, that may be relevant to a particular user)”].
Therefore, considering the teachings of Peleg and Bar-on, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to add wherein the document is associated with a user, and wherein the user engagement is associated with one or more other users, as taught by Bar-on, to the teachings of Peleg because it allows the system to quickly and efficiently identify the most relevant content items (e.g. see Bar-on paragraph 0082).
As for dependent claim 8, Peleg and Bar-on teach the method as described in claim 7, but Peleg does not specifically teach the following limitation. However, Bar-on teaches:
wherein the user engagement comprises at least one of the following: aggregate interest in the user-selected document; aggregate shares of the user-selected document; or aggregate downloads of the user-selected document [(e.g. see Bar-on paragraphs 0029, 0042) ”intelligent content suggestions to users of a collaborative content management and communication system … a user who is viewing or editing a document may receive a suggestion of another individual who may be associated with that document or the subject matter of that document … The content interaction histories 116 include historical records of content interactions between users and content items in the content and communication system 102. For example, the content interaction histories 116 may include records of which users interacted with which content items, as well as properties of those interactions. Content interactions that are stored by the content interaction histories 116 may include, without limitation, creating content items; editing content items; commenting on content items; viewing content items; “liking” or endorsing content items; sharing content items (e.g., emailing); linking to or referring to content items in other content items, chats, emails, communications, etc.; and durations interactions with content items. As described herein, content interaction histories 116 may be used to generate a multi-dimensional association graph that associates users to content items, as well as to generate identity vectors and/or access vectors for use in determining or estimating the relevancy of a particular content item to a particular user (and/or identifying content items, from the content store 118, that may be relevant to a particular user)].
The motivation to combine is the same as that used for claim 7.
As for dependent claim 16, Peleg teaches the system as described in claim 13; further, claim 16 discloses substantially the same limitations as claims 7 and 8. Therefore, it is rejected with the same rational as claim 7 and 8.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
U.S. Patent 11,790,165 B2 issued to Wilde et al. on 17 October 2023. The subject matter disclosed therein is pertinent to that of claims 1-18 (e.g. content authoring with similar document and content suggestions).
U.S. PGPub 2024/0303247 A1 issued to Sokolov et al. on 12 September 2024. The subject matter disclosed therein is pertinent to that of claims 1-18 (e.g. AI document writing feedback).
Contact Information
Any inquiry concerning this communication or earlier communications from the examiner should be directed to CHRISTOPHER J FIBBI whose telephone number is (571)-270-3358. The examiner can normally be reached Monday - Thursday (8am-6pm).
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, William Bashore can be reached at (571)-272-4088. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/CHRISTOPHER J FIBBI/Primary Examiner, Art Unit 2174
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