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 .
This non-final action is responsive to the application filed on 1/26/24
Claims 1-20 are pending.
Obviousness-type, Nonstatutory Double Patenting
Claim 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 (e.g., logos).
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 § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
The claimed invention (claims 1-20) is directed to an abstract without significantly more. This judicial exception is not integrated into a practical application. The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Exemplary Claim 1 is ineligible
Under the broadest reasonable interpretation, the terms of the claim are presumed to have their plain meaning consistent with the specification as it would be interpreted by one of ordinary skill in the art. See MPEP 2111.
Based on the plain meaning of the words in the claim, the broadest reasonable interpretation of claim 1 involves a system (generically applying a machine learning model as a result/output) using the mental processes of observation, evaluation, choice, and judgment to determine features to use with text in populating a template (i.e., judgment) based on observations of language and evaluated sentiment and, further, make a judgment or choice of the populated templated using the determined features and text. These limitations encompass mental judgments, observations, choices and/or evaluations.
Step 1: This part of the eligibility analysis evaluates whether the claim falls within any statutory category. See MPEP 2106.03. The claims recite at least one step or act as described above. Thus, the claim is to, e.g., a process, which is one of the statutory categories of invention. (Step 1: YES).
Step 2A, Prong One: This part of the eligibility analysis evaluates whether the claim recites a judicial exception. As explained in MPEP 2106.04, subsection II, a claim “recites” a judicial exception when the judicial exception is “set forth” or “described” in the claim. As discussed above, the broadest reasonable interpretation of the limitations is that those steps fall within the mental process groupings of abstract ideas because they cover concepts performed in the human mind, including observation, evaluation, judgment, and opinion. See MPEP 2106.04(a)(2), subsection III.
Specifically, “receive…a recommendation indicating one or more features to use with the text in populating the template…uses natural language processing (NLP) and sentiment detection to parse the text” and “populate the template using the one or more features and the text” are nothing more than mental operations to examine and evaluate data using observations and make evaluations, judgments, and/or choices of a populated, mentally constructed template based on the determined features and text.
Step 2A, Prong Two: This part of the eligibility analysis evaluates whether the claim as a whole integrates the recited judicial exception into a practical application of the exception or whether the claim is “directed to” the judicial exception. This evaluation is performed by (1) identifying whether there are any additional elements recited in the claim beyond the judicial exception, and (2) evaluating those additional elements individually and in combination to determine whether the claim as a whole integrates the exception into a practical application. See MPEP 2106.04(d).
The recited “category of content for a template, and a sentiment associated with the template” is nothing more than data gathering involving a particular source or type of data (e.g., limiting a database to XML tags, Intellectual Ventures I LLC or collecting information in an online environment, Electric Power Group and, even further, updating an activity log, Ultramercial).
Further, the training based on “a set of observations associated with an organization associated with the text, the set of observations including a feature associated with sentiment detection and a feature associated with a content category” are insignificant extra-solution activity of a data source or type.
See MPEP 2106.05(g) (“whether the limitation is significant”). In addition, all uses of the recited judicial exceptions require such data gathering and/or output, and, as such, these limitations do not impose any meaningful limits on the claim. These limitations amount to necessary data gathering and/or outputting. See MPEP 2106.05.
Further, the claim recites a “machine learning model” and NLP which are nothing more than a generic attempt to apply the abstract idea to a generic computer and/or technological field (machine learning, natural language processing) and amounts to no more than a result and/or outcome of the machine learning model with nothing recited in the claim about the actual performance of the machine learning model. Further, the recites a “processor” which provides nothing more than mere instructions to implement an abstract idea on a generic computer.
See MPEP 2106.05(f). MPEP 2106.05(f) provides the following considerations for determining whether a claim simply recites a judicial exception with the words “apply it” (or an equivalent), such as mere instructions to implement an abstract idea on a computer: (1) whether the claim recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished; (2) whether the claim invokes computers or other machinery merely as a tool to perform an existing process; and (3) the particularity or generality of the application of the judicial exception.
Even when viewed in combination, these additional elements do not integrate the recited judicial exception into a practical application, and the claim is directed to the judicial exception.
Step 2B: This part of the eligibility analysis evaluates whether the claim as a whole amounts to significantly more than the recited exception i.e., whether any additional element, or combination of additional elements, adds an inventive concept to the claim. See MPEP 2106.05. As explained with respect to Step 2A, Prong Two, there are additional elements which were shown to be merely insignificant extra-solution activity of data gathering (e.g., input/output, type of source data) and an attempt to apply the abstract ideas to a generic computer and/or technological field), which cannot provide an inventive concept. See MPEP 2106.05(f).
Even when considered in combination, these additional elements represent mere instructions to implement an abstract idea or other exception on a computer and insignificant extra-solution activity, which do not provide an inventive concept.
The other independent claims (e.g., 7 and 14) are rejected based on a similar rationale. Further, some of the other independent claims (e.g., claim 7) recite(s) generating the initial draft (a mental process) based on the mentally determined indication of features to use with the text in generating the initial draft. That is, the “machine learning model” is merely an attempt to apply the abstract idea and provides nothing more than a result/outcome involving the “recommendation.” Further, claim 13 recites the additional element of receiving the features from a database, which is merely insignificant extra-solution activity of a data source or type of data.
The dependent claims are rejected based on a similar rationale, all of which either merely recite further mental process, insignificant extra-solution activity of data gathering, or an attempt to apply the abstract idea using a generic computer and/or technological field.
Claim 2 recites insignificant extra-solution activity of data gathering specific to a “portion of the organization” (limiting a database index, Intellectual Ventures I LLC).
Claim 3 recites insignificant extra-solution activity of data gathering specific to “the portion of the organization” (Intellectual Ventures I LLC).
Claim 4 merely limits the input data to particular feature characteristics (selecting based on types of information, Electric Power Group).
Claim 5 recites insignificant extra-solution activity of a particular data source or type, wherein the “template is associated with the organization” (Intellectual Ventures I LLC) and, further, the mentally performed selection of the template is merely based on the observed organization association.
Claim 6 recites insignificant extra-solution activity of data gathering (receiving an indication of the category of content) and further refining the mental process of selecting the template based on the indication of the category of content.
Claim 8 recites “updating the machine learning model” which is merely learning (In re Brown) and updating an activity log (Ultramercial).
Claim 9 recites insignificant extra-solution activity of input using NLP and sentiment detection to parse the text, which is merely a data source or type (selecting information based on types of information, Electric Power Group).
Claim 10 recites mentally generating the recommendation using insignificant extra-solution activity of keywords, which is merely a data source or type performed in association with the mental process (limiting a database to XML tabs, Intellectual Ventures I LLC).
Claim 11 recites the mentally performed providing or judgment of a “category of content associated with the initial draft and a sentiment associated with the initial draft” which is easily performed in the human mand and further in association with “based on the category of content and the sentiment” which is insignificant extra-solution activity of data source or type (Intellectual Ventures).
Claim 12 recites insignificant extra-solution activity of data gathering involving merely receiving the data from a database (obtaining information over the Internet, CyberSource).
Claim 13 recites insignificant extra-solution activity of feature data including, e.g., visual components, which is merely data source or type (limiting a database index, Intellectual Ventures I LLC).
Claim 15 further limits the mental process to a further evaluation of a “constraint” and the further association with “compliance rule, or associated with the organization” is insignificant extra-solution activity of data source or type (selecting based on types/availability of information, Electric Power Group; see also Intellectual Ventures I).
Claim 16 recites further limiting the machine learning model to merely outcomes/results.
Claim 17 recites merely using NLP as a result/outcome and further insignificant extra-solution activity involving parsing the text (i.e., limited to sentiment detection) which is merely limiting the data to a particular source or type.
Claim 18 recites insignificant extra-solution activity of limiting the set of observations to a particular data source or type.
Claim 19 recites merely limits the generic machine learning model to the technological field of recurrent neural network.
Claim 20 recites insignificant extra-solution activity of data gathering (i.e., limiting the text to being received from a user).
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) 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).
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 associated with an organization as recited as follows: trained using a set of observations associated with an organization associated with the text.
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 udience 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 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 associated with 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 he 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 assocated 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 idons 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 (cpmuter 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 associated with 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 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 udience 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)).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JASON EDWARDS whose telephone number is (571) 272-5334. The examiner can normally be reached on Mon-Fri; 8am-5pm EST.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Cesar Paula can be reached on 571-272-4128. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/JASON T EDWARDS/ Examiner, Art Unit 2145
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