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 .
Status of Claims
The following is a non-final office action.
Claims [1-6, 8-18, and 20-24] are currently pending and have been examined on their merits.
Claims 1, 13-18, and 20-24 are currently amended see REMARKS August 01, 2025.
Claims 7, 19, and 25 are newly cancelled see REMARKS August 01, 2024.
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.
Claims 1-6, 8-18, and 20-24 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception that is an abstract idea without a practical application or significantly more.
Step 1: Claims 1-8 recite a method (i.e. a process such as an act or series of steps), claims 9-16 recite a non-transitory computer readable medium, and claims 17-20 recite a system, and therefore each claim falls within one of the four statutory categories.
Step 2A prong 1 (Is a judicial exception recited?):
The representative claims 1 and 13 recite: A method comprising: associating content with a user; generate additional content based on the content associated with the user; and generating and revise captions both batch and on-demand workflows.
The claims recite a certain method of organizing human activity. The claims recite a certain method of organizing human activity as the disclosure is directed to managing personal behavior or relationships or interactions between people. The claims merely recite a method for associating content with a user such, generating additional content based on the content associated with the user, and generating captions. Merely generating content for things such as a post based on a user’s previous content as well as performing actions such as generating captions or other content are methods of managing personal behavior or the actions of a user.
Alternatively, the claims recite a mental process. The claims merely recite a method for generating content based on previous content associated with a user. Therefore, the examiner finds the claims to be similar to examples the courts have identified as reciting a mental process including observations, evaluations, judgements, and opinions. As a person is capable of mentally, or with simple tool such as pen and paper, of generating content for a social media post such as writing out a thought or constructing the idea of a post.
Therefore, the examiner finds the claims to be directed to an abstract idea.
Step 2A Prong 2 (Is the exception integrated into a practical application?): The claims additionally recite;
Claim 1: A system comprising: a post content generator; utilizing artificial intelligence functionalities; automatically communicating, to one or more social media platforms, the generated additional content via one or more channels for distribution to one or more other users, and use of artificial intelligence credits.
Claim 13: utilizing artificial intelligence functionalities; automatically communicating, to one or more social media platforms, the generated additional content via one or more channels for distribution to one or more other users, and use of artificial intelligence credits.
The additional element of using generic computer elements to perform the abstract idea are directed to merely applying a computer to execute the method in the recited claim limitations. Therefore, the limitations merely amount to adding the words “apply it” (or an equivalent) to the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea, as discussed in MPEP 2106.05(f). Accordingly, the additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. As the claims are merely directed to utilizing a generate artificial intelligence to perform the abstract idea of generating content for a user based on previous content and using generic computer elements to perform a basic function of communicating information to a social media platform. Merely using an AI model to generate an output based on an input and using generic computer elements to communicate information to a social media platform are not an improvement to a technology or technical field but applying basic functions of a computer to perform the abstract idea.
Step 2B (Does the claim recite additional elements that amount to significantly more that the judicial exception?): As discussed above, the additional imitations amount to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea, as discussed in MPEP 2106.05(f). The claims merely recite using generic Ai and computer elements to perform the abstract idea of generating and distributing content. Therefore, the additional elements do not amount to significantly more as they do not recite any improvements to a technology or technical field.
Dependent claims 2-6, 8-12, 14-18, and 20-24 further narrow the abstract idea of generating content for a user based on information associated with a user.
The dependent claims recite the following additional elements:
Claims 5 and 17: an artificial intelligence engine.
Claims 6 and 18: an image library and an image analysis technique.
Claims 8 and 20: a hashtag generation model.
However, the additional elements are directed to merely “apply it” or applying generic computer elements to perform the abstract idea.
Therefore, claims 1-6, 8-18, and 20-24 are rejected under 35 U.S.C. 101.
Claim Rejections - 35 USC § 103
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 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.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-6, 8-18, and 20-24 is/are rejected under 35 U.S.C. 103 as being unpatentable over Sami (US 2025/0078175) in view of Koehler (US 2024/0354634) further in view of Kannan (US 2018/0189260).
Claims 1 and 13: Sami discloses (Claim 1) A system comprising: a post content generator configured to: (Claim 13) A method comprising: (Paragraph [0017]; [0019]; [0021-0022]; [0040-0041]; Fig. 8, AI uses machine learning models to make predictions, recommendations, and classifications. In general, machine learning models use algorithms to parse data, learn from the parsed data, and make informed decisions. Some such Ai tools are used to generate content for social media platforms as an important part of digital and e-marketing strategies. Embodiments disclosed herein facilitate automatically generating, scheduling, posing, and recycling social media posts using AI without intervention by a human operator. A social planner tool as disclosed herein may generate high-quality social media posts tailored to the user’s business needs ensuring visual identity across multiple social media platforms. In various embodiments, the social planner tool’s user may provide basic information about their business. In various embodiments, frontend may receive inputs from a human user regarding various marketing needs, business niche, target audience, geographic locations, desired social media platforms, and other such data. The input may be saved as configuration settings);
utilize artificial intelligence functionalities to generate additional content based on the content associated with the user (Paragraph [0019]; [0021-0022]; [0040-0041]; [0043]; Fig. 8, some such Ai tools are used to generate content for social media platforms as an important part of digital and e-marketing strategies. Embodiments disclosed herein facilitate automatically generating, scheduling, posing, and recycling social media posts using AI without intervention by a human operator. A social planner tool as disclosed herein may generate high-quality social media posts tailored to the user’s business needs ensuring visual identity across multiple social media platforms. In various embodiments, the social planner tool’s user may provide basic information about their business. In various embodiments, frontend may receive inputs from a human user regarding various marketing needs, business niche, target audience, geographic locations, desired social media platforms, and other such data. The input may be saved as configuration settings. The input received at the frontend may form the basis for seed data to AI engine. In various embodiments, the social media content generate may request AI engine for a plurality of prompts to create a social media post using a portion of the received input as seed data);
automatically communicate, to one or more social media platforms, the generated additional content via one or more channels for distribution to one or more other users (Paragraph [0040-0042]; [0048]; in various embodiments, frontend may receive inputs from a human user regarding various marketing needs, business niche, target audience, geographic locations, desired social media platforms, and other such data. Social media platforms may be associated with separate social media platform rules for posting social media posts using the relevant attributes. For example. Social media platforms may include rules for generating profiles, groups, etc., and posting stories, sharing stories, etc. Social media platform may include other rules for generating reels, posting photos, sharing photos, etc. The input received at the frontend may form the basis for seed data to AI engine. In various embodiments, the social media content generate may request AI engine for a plurality of prompts to create a social media post using a portion of the received input as seed data. The scheduling tool may generate a schedule for each one of the plurality of social media posts. Social media manager may thereafter automatically post each one of the plurality of social media posts on the particular social media platform according to the generate schedule).
Sami discloses a system of using an AI engine to automatically generate and post social media content based on user preferences and desired goals. However, Sami does not specifically disclose the following claim limitations: associate content with a user; and generate and revise captions by use of artificial intelligence credits for both batch and on-demand workflows.
In the same field of endeavor of using machine learning models to enhance social media content Koehler teaches associate content with a user (Paragraph [0004-0005]; [0009]; [0035]; Fig. 4, systems and methods are disclosed for generating representations of content items. The method includes obtaining a plurality of content items of a content creator and associated content items metrics. The method further includes, identifying, based on the plurality of content items and associated item metrics, an output of a generative machine learning model that is trained on a subset of content items. The output of the generative machine learning model provides a representation of an additional content item. The additional content item, when created based on the representation, is predicted to have one or more content item metrics that satisfy the one or more scoring criteria).
Before the effective filing date it would have been obvious to one of ordinary skill in the art to modify the system of generating content for a user based on a user’s inputs by using a machine learning model as disclosed by Sami (Sami [0017]) with the system of associate content with a user as taught by Koehler (Koehler [0004]). With the motivation of helping to optimize social media content (Koehler [0002]).
In the same field of endeavor of automatically generating social media content using a machine learning model Kannan teaches and generate and revise captions by use of artificial intelligence credits for both batch and on-demand workflows (Paragraph [0003-0005]; Fig. 1, various embodiments include systems, methods, and non-transitory computer readable media configured to train a sequence model to output respective captions, or portions of captions, for content items. A determination can be made that a user of the social networking system is posting a content item for publication through a social networking system. A set of captions can be determined for the content item being posted based on the sequence model. The set of captions can be provided as suggestions to the user for use in a caption describing the content item being posted).
Before the effective filing date it would have been obvious to one of ordinary skill in the art to modify the system of generating content for a user based on a user’s inputs by using a machine learning model as disclosed by Sami (Sami [0017]) with the system of generate and revise captions by use of artificial intelligence credits for both batch and on-demand workflows as taught by Kannan (Kannan [0003]). With the motivation of being a simple substitution of the type of content a machine learning model generates. As Sami discloses a system for generating content using a machine learning model to optimize a social media post include text and image content (Sami [0018]) which could be substituted for content such as captions for a post as taught by Kannan (Kannan [0003]). As well as with the motivation of helping to create and optimize social media posts (Kannan [0002]).
Claims 2 and 14: Modified Sami discloses the system as per claim 1 and the method as per claim 13. However, Sami does not disclose wherein the content associated with the user is content previously posted by the user.
In the same field of endeavor of using machine learning models to enhance social media content Koehler teaches wherein the content associated with the user is content previously posted by the user (Paragraph [0004-0005]; [0009]; [0033-0035]; Fig. 4, systems and methods are disclosed for generating representations of content items. The method includes obtaining a plurality of content items of a content creator and associated content items metrics. The method further includes, identifying, based on the plurality of content items and associated item metrics, an output of a generative machine learning model that is trained on a subset of content items. The output of the generative machine learning model provides a representation of an additional content item. The additional content item, when created based on the representation, is predicted to have one or more content item metrics that satisfy the one or more scoring criteria. Content item repository and/or content item metrics may reside in one or more database systems).
Before the effective filing date it would have been obvious to one of ordinary skill in the art to modify the system of generating content for a user based on a user’s inputs by using a machine learning model as disclosed by Sami (Sami [0017]) with the system of wherein the content associated with the user is content previously posted by the user as taught by Koehler (Koehler [0004]). With the motivation of helping to optimize social media content (Koehler [0002]).
Claims 3 and 15: Modified Sami discloses the system as per claim 1 and the method as per claim 13. However, Sami does not disclose wherein the content associated with the user includes one of a text post, an image, a video, a carousel, a story, a short, audio, an interactive element, and a reel.
In the same field of endeavor of using machine learning models to enhance social media content Koehler teaches wherein the content associated with the user includes one of a text post, an image, a video, a carousel, a story, a short, audio, an interactive element, and a reel (Paragraph [0004-0005]; [0033-0035]; [0041]; Fig. 4, systems and methods are disclosed for generating representations of content items. The method includes obtaining a plurality of content items of a content creator and associated content items metrics. The method further includes, identifying, based on the plurality of content items and associated item metrics, an output of a generative machine learning model that is trained on a subset of content items. The output of the generative machine learning model provides a representation of an additional content item. Content item repository and/or content item metrics may reside in one or more database systems. Content item representation generator may receive as input content items of a first modality (such as image, video text, audio, etc.) and may output a representation of an additional content item in the same or a different modality).
Before the effective filing date it would have been obvious to one of ordinary skill in the art to modify the system of generating content for a user based on a user’s inputs by using a machine learning model as disclosed by Sami (Sami [0017]) with the system of wherein the content associated with the user includes one of a text post, an image, a video, a carousel, a story, a short, audio, an interactive element, and a reel as taught by Koehler (Koehler [0004]). With the motivation of helping to optimize social media content (Koehler [0002]).
Claims 4 and 16: Modified Sami discloses the system as per claim 1 and the method as per claim 13. Sami further discloses wherein the additional content generated by the artificial intelligence functions includes one of a text post, an image, a video, a carousel, a story, a short, audio, an interactive element, and a reel (Paragraph [0019]; [0021-0022]; [0040-0041]; [0043]; Fig. 8, some such Ai tools are used to generate content for social media platforms as an important part of digital and e-marketing strategies. Embodiments disclosed herein facilitate automatically generating, scheduling, posing, and recycling social media posts using AI without intervention by a human operator. A social planner tool as disclosed herein may generate high-quality social media posts tailored to the user’s business needs ensuring visual identity across multiple social media platforms. In various embodiments, the social planner tool’s user may provide basic information about their business. In various embodiments, frontend may receive inputs from a human user regarding various marketing needs, business niche, target audience, geographic locations, desired social media platforms, and other such data. The input may be saved as configuration settings. The input received at the frontend may form the basis for seed data to AI engine. In various embodiments, the social media content generate may request AI engine for a plurality of prompts to create a social media post using a portion of the received input as seed data).
Claims 5 and 17: Modified Sami discloses the system as per claim 1 and the method as per claim 13. However, Sami does not disclose wherein the post content generator is configured to associate the content with the user via an artificial intelligence engine that generates a unique identifier by use of business profile inputs.
In the same field of endeavor of using machine learning models to enhance social media content Koehler teaches wherein the post content generator is configured to associate the content with the user via an artificial intelligence engine that generates a unique identifier by use of business profile inputs (Paragraph [0004-0005]; [0033-0035]; [0040-0041]; Fig. 4, systems and methods are disclosed for generating representations of content items. The method includes obtaining a plurality of content items of a content creator and associated content items metrics. The method further includes, identifying, based on the plurality of content items and associated item metrics, an output of a generative machine learning model that is trained on a subset of content items. The output of the generative machine learning model provides a representation of an additional content item. Content item repository and/or content item metrics may reside in one or more database systems. The metadata characteristics may include one or more tags associated with the content item. Content item representation generator may receive as input content items of a first modality (such as image, video text, audio, etc.) and may output a representation of an additional content item in the same or a different modality).
Before the effective filing date it would have been obvious to one of ordinary skill in the art to modify the system of generating content for a user based on a user’s inputs by using a machine learning model as disclosed by Sami (Sami [0017]) with the system of wherein the post content generator is configured to associate the content with the user via an artificial intelligence engine that generates a unique identifier by use of business profile inputs as taught by Koehler (Koehler [0004]). With the motivation of helping to optimize social media content (Koehler [0002]).
Claims 6 and 18: Modified Sami discloses the system as per claim 1 and the method as per claim 13. However, Sami does not disclose wherein the content associated with the user includes images imported into an image library, and wherein the post content generator is configured to generate metadata tags for the imported images via an image analysis technique.
In the same field of endeavor of using machine learning models to enhance social media content Koehler teaches wherein the content associated with the user includes images imported into an image library, and wherein the post content generator is configured to generate metadata tags for the imported images via an image analysis technique (Paragraph [0004-0005]; [0033-0035]; [0040-0041]; Fig. 4, systems and methods are disclosed for generating representations of content items. The method includes obtaining a plurality of content items of a content creator and associated content items metrics. The method further includes, identifying, based on the plurality of content items and associated item metrics, an output of a generative machine learning model that is trained on a subset of content items. The output of the generative machine learning model provides a representation of an additional content item. Content item repository and/or content item metrics may reside in one or more database systems. The metadata characteristics may include one or more tags associated with the content item. Content item representation generator may receive as input content items of a first modality (such as image, video text, audio, etc.) and may output a representation of an additional content item in the same or a different modality).
Before the effective filing date it would have been obvious to one of ordinary skill in the art to modify the system of generating content for a user based on a user’s inputs by using a machine learning model as disclosed by Sami (Sami [0017]) with the system of wherein the content associated with the user includes images imported into an image library, and wherein the post content generator is configured to generate metadata tags for the imported images via an image analysis technique as taught by Koehler (Koehler [0004]). With the motivation of helping to optimize social media content (Koehler [0002]).
Claims 8 and 20: Modified Sami discloses the system as per claim 1 and the method as per claim 13. Sami further discloses wherein the post content generator is configured to generate hashtags for the content using a hashtag generation model (Paragraph [0019]; [0021-0022]; [0040-0041]; [0043]; [0122]; Fig. 8, some such Ai tools are used to generate content for social media platforms as an important part of digital and e-marketing strategies. Embodiments disclosed herein facilitate automatically generating, scheduling, posing, and recycling social media posts using AI without intervention by a human operator. In various embodiments, the social media content generate may request AI engine for a plurality of prompts to create a social media post using a portion of the received input as seed data. Textual content includes at least one of: bult text, hashtags, and keywords).
Claims 9 and 21: Modified Sami discloses the system as per claim 1 and the method as per claim 13. Sami further discloses wherein the post content generator is configured to schedule posts based on user-specific engagement history and industry best-practice posting times (Paragraph [0019]; [0021-0022]; [0040-0041]; [0043]; [0128]; Fig. 8, some such Ai tools are used to generate content for social media platforms as an important part of digital and e-marketing strategies. Embodiments disclosed herein facilitate automatically generating, scheduling, posing, and recycling social media posts using AI without intervention by a human operator. A social planner tool as disclosed herein may generate high-quality social media posts tailored to the user’s business needs ensuring visual identity across multiple social media platforms. In various embodiments, the social planner tool’s user may provide basic information about their business. In various embodiments, frontend may receive inputs from a human user regarding various marketing needs, business niche, target audience, geographic locations, desired social media platforms, and other such data. The input may be saved as configuration settings. The input received at the frontend may form the basis for seed data to AI engine. In various embodiments, the social media content generate may request AI engine for a plurality of prompts to create a social media post using a portion of the received input as seed data. The seed data includes customer engagement rates and business needs).
Claims 10 and 22: Modified Sami discloses the system as per claim 1 and the method as per claim 13. However, Sami does not disclose wherein the post content generator is configured to compute a score for each post at a predetermined time subsequent to the respective post based on weighted channel metrics.
In the same field of endeavor of using machine learning models to enhance social media content Koehler teaches wherein the post content generator is configured to compute a score for each post at a predetermined time subsequent to the respective post based on weighted channel metrics (Paragraph [0004-0005]; [0056] Fig. 4, systems and methods are disclosed for generating representations of content items. The method includes obtaining a plurality of content items of a content creator and associated content items metrics. The method further includes, identifying, based on the plurality of content items and associated item metrics, an output of a generative machine learning model that is trained on a subset of content items. In some embodiments, a content item metric of the associated content item metrics includes at least one of: a number of items users watched the content item, a duration of time users watched the content item, a number of “likes” given to the content item, or an amount of revenue generated by the content item. Different weights can be used for different content items).
Before the effective filing date it would have been obvious to one of ordinary skill in the art to modify the system of generating content for a user based on a user’s inputs by using a machine learning model as disclosed by Sami (Sami [0017]) with the system of wherein the post content generator is configured to compute a score for each post at a predetermined time subsequent to the respective post based on weighted channel metrics as taught by Koehler (Koehler [0004]). With the motivation of helping to optimize social media content (Koehler [0002]).
Claims 11 and 23: Modified Sami discloses the system as per claim 1 and the method as per claim 13. Sami further discloses wherein the post content generator is configured to adapt the content to channel-specific formats and requirements (Paragraph [0019]; [0021-0022]; [0040-0041]; [0043]; Fig. 8, some such Ai tools are used to generate content for social media platforms as an important part of digital and e-marketing strategies. Embodiments disclosed herein facilitate automatically generating, scheduling, posing, and recycling social media posts using AI without intervention by a human operator. A social planner tool as disclosed herein may generate high-quality social media posts tailored to the user’s business needs ensuring visual identity across multiple social media platforms. In various embodiments, the social planner tool’s user may provide basic information about their business. In various embodiments, frontend may receive inputs from a human user regarding various marketing needs, business niche, target audience, geographic locations, desired social media platforms, and other such data. The input may be saved as configuration settings. The input received at the frontend may form the basis for seed data to AI engine. In various embodiments, the social media content generate may request AI engine for a plurality of prompts to create a social media post using a portion of the received input as seed data).
Claims 12 and 24: Modified Sami discloses the system as per claim 1 and the method as per claim 13. Sami further discloses wherein user approval of the generated additional content is required prior to communication (Paragraph [0023] the system may also send social media posts to the user for review, allowing the user to make any adjustments or provide feedback).
Therefore, claims 1-6, 8-18, and 20-24 are rejected under 35 U.S.C. 103.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure.
Herken (US 2025/0022023) Transactional platform utilizing artificial intelligence.
Rabines (US 2022/0180050) Systems and methods for facilitating the generation and publishing of personal social media.
Fernadez-Ruiz (US 2015/0205768) Method and system for identifying and delivering enriched content.
Jakbosson (US 2023/0075884) Systems and methods for token management in social media environment.
Marey (US 2022/0012296) Systems and methods to automatically categorize social media posts and recommend social media posts.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to COREY RUSS whose telephone number is (571)270-5902. The examiner can normally be reached on M-F 7:30-4:30.
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, Lynda Jasmin can be reached on 5712726782. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of an application may be obtained from the Patent Application Information Retrieval (PAIR) system. Status information for published applications may be obtained from either Private PAIR or Public PAIR. Status information for unpublished applications is available through Private PAIR only. For more information about the PAIR system, see https://ppair-my.uspto.gov/pair/PrivatePair. Should you have questions on access to the Private PAIR system, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative or access to the automated information system, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/COREY RUSS/Examiner, Art Unit 3629