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 .
1. This communication is responsive to the Amendment filed 12/30/2025.
2. Claims 1-20 are pending in this application. Claims 1, 7 and 14 are independent claims. In the instant Amendment, claims 1, 7 and 14 were amended. This action is made Final.
Double Patenting
3. Claims 1-20 of this application is patentably indistinct from the claims of US 11,887,226. 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 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.
Claim 1 to 20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-20 of U.S. Patent No. 11,887,226. Although the claims at issue are not identical, they are not patentably distinct from each other.
Claim 1 is similar to claim 1 of the reference in that both train and use a machine learning model to receive a recommendation for a template using, e.g., NLP and sentiment to parse text and select output; however, the reference further recites limitations related to the content, such as images, colors, and logos are retrieved from a database (features similar to claims 2 to 4 of the instant application).
Claim 2 recites machine learning model refining which is similar to the model was refined using input specific to a portion of the organization of claim 1 of the reference. The system of claim 1,
Claim 3 recites generate the template based on the input specific to the portion of the organization which is similar to claim 4 of the reference.
Claim 4 recites features include one or more images, one or more colors, or one or more logos, which is similarly recited in claim 1 of the reference.
Claim 5 recites select the template based on output from the machine learning model, wherein the template is associated with the organization, which is similarly recited in claim 1 of the reference (and further in claim 6 of the reference which recites input specific to the organization).
Claim 6 recites receive an indication of the category of content; and select the template based on the indication of the category of content, which is similar to the indication of category of content and selection of template based on category as recited in claim 5 of the reference.
Claim 7 recites similar limitations as claims 1 and 7 of the reference, additionally related to the generating an initial draft and further retrieving, e.g., logos from a database.
Claim 8 recites similar limitations as claim 8 of the reference related to receiving feedback and updating the model based the initial draft and feedback regarding the initial draft.
Claim 9 recites similar limitations as claim 1 involving using NLP and sentiment detection to parse the text.
Claim 10 recites generates the recommendation using one or more keywords associated with the one or more features which is similar to the recited keywords to generate the recommendation in claim 10 of the reference.
Claim 11 recites providing, to the machine learning model, a category of content associated with the initial draft and a sentiment associated with the initial draft, wherein the recommendation is based on the category of content and the sentiment which is similar to claims 1 and 5 of the reference involving generating the template based on the category and sentiment.
Claim 12 recites receiving the one or more features from a database associated with the organization which is similar to claim 1 of the reference (e.g., retrieved from database).
Claim 13 recites wherein the one or more features include one or more visual components to use with the text which is similar to claim 1 (e.g., logos) of the reference.
Claim 14 is rejected based on a similar rationale as claims 1 and 7. Claim 15 recites the machine learning model uses at least one constraint associated with equal representation, associated with a compliance rule, or associated with the organization which is similar to claim 17 of the reference.
Claim 16 recites the at least one constraint is based, at least in part, on previous outputs from the machine learning model which is similar to claim 15 of the reference.
Claim 17 the machine learning model uses natural language processing (NLP) and sentiment detection to parse the text which is similar to claim 1 (ML uses at least natural language processing) of the reference.
Claim 18 recites the set of observations comprises content that pairs text with images, colors, logos which is similar to claim 1.
Claim 19 the machine learning model is a recurrent neural network which is similar to claim 16 of the reference reciting the ML model is a recurrent neural network.
Claim 20 recites the text is received from a user which is similar to claim 1 of the reference reciting the text received from a user.
Claim Rejections - 35 USC § 103
4. In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
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.
5. Claim(s) 1 to 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Co et al. (20210182468, Herein "Co") in view of Duke (US 20190392487) and further in view of Saunders et al (“Saunders” US 2012/0239506).
Regarding claim 1, Co teaches A system, comprising: one or more memories; and one or more processors, coupled to the one or more memories, configured (fig. 1) to:
provide, to a machine learning model (machine learning training [0029]): text (text [0030];
receive text to output in a page (fig. 5)), a category of content for a template (sentiment for input to machine learning model [0040]; fig. 1 showing input including text, tone/sentiment, linguistics, etc corresponding with received input text and classify the text [0012]; as such, graphical element type that should be outputted for a given classification of text [0030]), and a sentiment associated with the template (sentiment [0030]; text and tone/sentiment [0012]), wherein the machine learning model is trained using a set of observations associated with an organization associated with the text (in association with an organization (e.g., based on design expertise [0031]), such as a design choice associated with a particular sentiment [0031] and training a type of imagery style to be selected for different concepts and entities determined from the text to render [0032]; training (figs. 2 to 4) such that the machine learning model is trained according to an organization encompassing a confidence level [0030]), the set of observations including a feature associated with sentiment detection (e.g., classification and corresponding sentiment classification data (figs. 3 and 4)) and a feature associated with a content category (e.g., confidence level (fig. 3));
receive, from the machine learning model, a recommendation indicating one or more features to use with the text in populating the template (receive graphical element suggestions (figs. 5)), wherein the machine learning model uses natural language processing (NLP) and sentiment detection to parse the text (natural language processing [0012] and sentiment analysis (figs. 5)); and
populate the template using the one or more features and the text (generate output page (figs. 5) in addition to text with determined typeface/font [0028]).
However, Co fails to specifically teach specific to an organization as recited as follows: trained using a set of observations specific to an organization associated with the text; nor, insert, into the template, the one or more features and the text according to location indicators of the template.
Yet, in a related art, Duke discloses training associated with, e.g., a particular client or of other clients in the same industry or in a similar field [0079]. It would have been obvious to one of ordinary skill in the art prior to the invention's effective filing date to combine the organizational association of Duke with the feature or user association of Co to have associated with an organization. The combination would allow for, according to the motivation of Duke, performing the training specific to a particular organization so that results are more relevant to a particular audience who is a recipient of the results of the trained system [0079], thus providing more relevant content based on historical data such as based on the performance of advertisements that were historically shown by the same advertiser to a particular end-user [0005].
Moreover, Saunders discloses insert, into the template, the one or more features and the text according to location indicators of the template (see figs 5-7 and paragraphs [0050]-[0052]; e.g., insert logo into template’s placeholder). It would have been obvious to one or ordinary skill in the art prior to the invention’s effective filing date to combine Saunders’ teachings of inserting content into specific locations of a template with Co’s teachings in an effort to provide content which are of interest to the viewer which can be selected in an automatic or semiautomatic manner.
Regarding claim 2, Co in view of Duke teaches the limitations of claim 1, as above. Furthermore, Co teaches The system of claim 1, wherein the machine learning model is refined using input specific to a portion of the organization (machine learning using input specific to, e.g., categorization or outcome [0026]; further, retraining specific to a portion of classifications 110, 114, 118 and 119 such that when a similar portion of content is received as input, a more accurate representation of the output will be rendered based on the refining [0041]). Further, Duke discloses a feedback loop providing updates [0085] for optimizing the content generation such as from the programmatic team or from the client's marketing stack for updating specific to a customer/user [0088]; the system adapts using iterative content determination for the same product or service or later for a different product or service, in an autonomous self-learning manner [0005]; iterative training for the same product or for a different product or service of the client [0049].
Regarding claim 3, Co in view of Duke teach the limitations of claims 1 and 2, as above. Furthermore, Duke teaches The system of claim 2, wherein the one or more processors are further configured to: generate the template based on the input specific to the portion of the organization (feedback loop providing updates [0085] for optimizing the content generation such as from the programmatic team or from the client's marketing stack for updating specific to a customer/user [0088]; the system adapts using iterative content determination for the same product or service or later for a different product or service, in an autonomous self-learning manner [0005];
iterative training for the same product or for a different product or service of the client [0049]).
Further, Co even discloses selected template specific to the user and even further specific to classification and confidence levels (fig. 5).
Regarding claim 4, Co in view of Duke teaches the limitations of claim 1, as above. Furthermore, Co teaches The system of claim 1, wherein the one or more features include one or more images, one or more colors, or one or more logos (colors and images [0011]; figs. 2 to 5). Further, Duke even discloses logo (abstract, [0005], [0016]).
Regarding claim 5, Co in view of Duke teaches the limitations of claim 1, as above. Furthermore, Co teaches The system of claim 1, wherein the one or more processors are further configured to:
select the template based on output from the machine learning model, wherein the template is associated with the organization (selected template and even further graphical elements (fig. 5A) corresponding with the requesting user (i.e., organization)).
Duke makes clear selecting associated with the organization as follows: selected template [0077] for use by the particular client or even of other clients in the same industry or in the same field [0079].
Regarding claim 6, Co in view of Duke teaches the limitations of claim 1, as above. Furthermore, Co teaches The system of claim 1, wherein the one or more processors are further configured to:
receive an indication of the category of content (e.g., sentiments, concepts, and entities corresponding with the text (fig. 5)); and
select the template based on the indication of the category of content (select template elements such as graphical element types (fig. 5) such that the user interface is rendered according to determined data store assets matching the metadata and, even further, select a template to provide the content to the user (fig. 5A)).
Regarding claim 7, Co teaches A method, comprising:
providing text (input text [0037]), associated with an organization (e.g., a given user requesting content (figs. 1 to 5)), to a machine learning model (input to machine learning [0037]), wherein the machine learning model is trained using a set of observations specific to the organization (figs. 2 to 4 showing training in association with a user request and specific to determined confidence levels), the set of observations including at least a feature associated with sentiment detection and a feature associated with a content category (classifications including, e.g., sentiment,
tones, emotions, categories, etc. [0030]);
receiving, from the machine learning model, a recommendation indicating one or more features to use with the text in generating an initial draft (e.g., graphical elements to use with the text based on rendering the text and the determined instances of the graphical elements further based on graphical element types [0038] and [0039]); and
generating the initial draft based on the text and the one or more features (the determined assets rendered to the user in a format for selection in association with the analyzed text [0038]). Furthermore, Duke makes clear associated with an organization, as explained above with respect to claim 1.
Regarding claim 8, Co in view of Duke teaches the limitations of claim 7, as above. Furthermore, Co teaches The method of claim 7, further comprising: updating the machine learning model based on the initial draft and feedback regarding the initial draft (retraining [0041]).
Regarding claim 9, Co in view of Duke teaches the limitations of claim 7, as above. Furthermore, Co teaches The method of claim 7, wherein the machine learning model uses natural language processing (NLP) and sentiment detection to parse the text (sentiment analysis and natural language processing [0012] and [0017]; e.g., input text to machine learning module including tone/sentiment classifier and natural language processing/classification [0037]). Further, Duke even discloses NLP to process input that is provided by the client or another entity of the organization such as brand-owner certain text that governs the layout and/or content and/or characteristics of the item to be generated [0040].
Regarding claim 10, Co in view of Duke teaches the limitations of claim 7, as above. Furthermore, Co teaches The method of claim 7, wherein the machine learning model generates the recommendation using one or more keywords associated with the one or more features (generating recommendations based on input such as keywords in additions to sentiments [0030] such as the machine learning module using tone/sentiment, natural language, and text comprising, e.g., keywords to output a template(s) [0037]).
Regarding claim 11, Co in view of Duke teaches the limitations of claim 7, as above. Furthermore, Co teaches The method of claim 7, further comprising: providing, to the machine learning model, a category of content associated with the initial draft (e.g., concepts and entities of the text (fig. 5)) and a sentiment associated with the initial draft (sentiment (fig. 5)), wherein the recommendation is based on the category of content and the sentiment (determine a predetermined number of outputted instances for each graphical element (fig. 5, 506) and, further, select a template 514 corresponding with graphical elements rendered in the user interface for user selection and refinement (figs. 5); in terms of initial and refined outputs, see, especially, 526 (fig. 5) showing user refinement corresponding with a modified template layout).
Regarding claim 12, Co in view of Duke teaches the limitations of claim 7, as above. Furthermore, Co teaches The method of claim 7, further comprising:
receiving the one or more features from a database associated with the organization (receive features associated with the text, such as classifications and confidence levels (fig. 5)). Furthermore, Duke discloses stored features and relevant classification features by applying rules and selections associated with a particular advertiser [0016]; even further, a database containing previously-prepared advertisements and also the associated briefs along with previous ads provided by a client or associated elsewhere from the organization such as external data providers and may be obtained from Internet searches or from websites of the clients [0018]; additional database storing relevant features [0023]; further, databases according to how well previous advertisements in the original database performed [0025].
Regarding claim 13, Co in view of Duke teaches the limitations of claim 7, as above. Furthermore, Co teaches The method of claim 7, wherein the one or more features include one or more visual components to use with the text (graphical elements such as icons and pictures for use with the text (fig. 5)). Furthermore, Duke even discloses generating rules [0040] for use in a model trained using features [0024] and [0025].
Regarding claim 14, Co teaches A non-transitory computer-readable medium storing a set of instructions, the set of instructions comprising:
one or more instructions that, when executed by one or more processors of a device, cause the device to (computer system (fig. 1)):
provide text, associated with an organization, to a machine learning model (input text to machine learning module (fig. 5)), wherein the machine learning model is trained using a set of observations specific to the organization (training with respect to, e.g., different types of classifications [0036] and, even further, based on design expertise and user specific selections [0011]), the set of observations including at least a feature associated with sentiment detection and a feature associated with a content category (training (fig. 2) corresponding with, e.g., classification, instance graphical element, confidence level (fig. 3) for training the machine learning module (fig. 4), classification corresponding with, e.g., sentiment [0030]);
receive, from the machine learning model, a recommendation indicating one or more features to use with the text in generating an initial draft (recommended graphical element types, text, and
template layout along with text and various graphics (fig. 5A); even further template provision (fig. 5A));
receive the one or more features from a database (selected template and received graphical elements, text, and template layout for rendering text along with graphics and selected template layout (fig. 5A)); and
generate the initial draft based on the text and the one or more features (initial draft such a layout available to be modified by the user (fig. 5B)).
However, Co fails to specifically teach associated with an organization, as recited as follows: provide text, associated with an organization, to a machine learning model.
Yet, in a related art, Duke discloses text input such as brand guidelines and a creative brief input to the machine learning model [0005] associated with a certain provider (e.g., advertiser) and user [0005]. It would have been obvious to one of ordinary skill in the art prior to the invention's effective filing date to combine the organizational association of Duke with the feature or user association of Co to have associated with an organization. The combination would allow for, according to the motivation of Duke, performing the training specific to a particular organization so that results are more relevant to a particular audience who is a recipient of the results of the trained system [0079], thus providing more relevant content based on historical data such as based on the performance of advertisements that were historically shown by the same advertiser to a particular end-user [0005].
Regarding claim 15, Co in view of Duke teaches the limitations of claim 14, as above. Furthermore, Co teaches The non-transitory computer-readable medium of claim 14, wherein the machine learning model uses at least one constraint associated with equal representation, associated with a compliance rule, or associated with the organization (compliance rule such as if the text is part of an adult article, then a certain output such as a photo may be more appropriate or of the text is part of a children's book, then clip art may be more appropriate [0032]; further, organization such as targeted users [0033]).
Regarding claim 16, Co in view of Duke teaches the limitations of claims 14 and 15, as above. Furthermore, Co teaches The non-transitory computer-readable medium of claim 15, wherein the at least one constraint is based, at least in part, on previous outputs from the machine learning model (machine learning feedback and retraining [0026] and [0041]).
Regarding claim 17, Co in view of Duke teaches the limitations of claim 14, as above. Furthermore, Co teaches The non-transitory computer-readable medium of claim 14, wherein the machine learning model uses natural language processing (NLP) and sentiment detection to parse the text (natural language processing to process the text [0012]).
Regarding claim 18, Co in view of Duke teaches the limitations of claim 14, as above. Furthermore, Co teaches The non-transitory computer-readable medium of claim 14, wherein the set of observations comprises content that pairs text with images, colors, logos, or a combination thereof (text determined to be paired with images, icons, etc. based on the training (figs. 2 to 5)).
Regarding claim 19, Co in view of Duke teaches the limitations of claim 14, as above. Furthermore, Co teaches The non-transitory computer-readable medium of claim 14, wherein the machine learning model is a recurrent neural network (neural network trained [0026]). And Duke makes clear ML modeling using recurrent neural networks [0025].
Regarding claim 20, Co in view of Duke teaches the limitations of claim 14, as above. Furthermore, Co teaches The non-transitory computer-readable medium of claim 14, wherein the text is received from a user (receiving text to output in a page, 500 (fig. 5)).
Response to Arguments
4. Applicant’s arguments with respect to the claim(s) have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Conclusion
5. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Kumar et al (US 10,963,636).
6. Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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.
7. Any inquiry concerning this communication or earlier communications from the examiner should be directed to RASHAWN N TILLERY whose telephone number is (571)272-6480. The examiner can normally be reached M-F 9:00a - 5:30p.
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, William L 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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/RASHAWN N TILLERY/ Primary Examiner, Art Unit 2174
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