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 . Claims 1-3, 5-10, 12, 14, and 16 are pending.
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 1-3, 5-10, 12, 14, and 16 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception without significantly more.
Step 1 (The Statutory Categories): Is the claim to a process, machine, manufacture, or composition of matter? MPEP 2106.03.
Per Step 1, claim 1 is to a non-transitory computer-readable storage medium (i.e., an article), claim 14 to a method (i.e., a process), and claim 16 to an apparatus (i.e., a machine). Thus, the claims are directed to statutory categories of invention. However, the claims are rejected under 35 U.S.C. 101 because they are directed to an abstract idea, a judicial exception, without reciting additional elements that integrate the judicial exception into a practical application.
The analysis proceeds to Step 2A Prong One.
Step 2A Prong One: Does the claim recite an abstract idea, law of nature, or natural phenomenon? MPEP 2106.04.
The abstract idea of claims 1, 14, and 16 is (claim 1 being representative):
receive post information including an image from a user;
determine, based on whether identification information indicating that the image has been generated by generative Al has been added to the image included in the received post information, whether the image included in the received post information is an image generated by generative Al;
in response to determining that the image included in the received post information is an image generated by generative Al, determine, based on generation source information included in the identification information, whether the image included in the received post information is an image generated by the generative Al using an input image as input or is an image generated by the generative Al using information other than the input image as input;
in response to determining that the image included in the received post information is an image generated by the generative Al using information other than the input image as input, perform modification processing for modifying the received post information; and
in response to determining that the image included in the received post information is an image generated by the generative Al using an input image as input, not perform the modification processing.
The abstract idea steps italicized above are those which could be performed mentally, including with pen and paper. The steps describe, at a high level, receiving a social media post containing an image; determining whether the image is AI-generated based on whether identification information saying so has been added to the image, e.g., metadata or a watermark; if the image is AI-generated, reading generation-source information inside the same identification information, which is used to decide whether the AI started from an input image or from something else, e.g., a text prompt; if the AI worked from something other than an input image, the post is modified, e.g., adding a label or removing the image; if the AI worked from an input image, the post is left unmodified. While applicant uses seemingly technical steps (e.g., “determining that the image included in the received post information is an image generated by generative Al”), the claims are broadly written and lack any technical focus. For example, an administrator could examine an image to determine whether or not it contains a watermark indicative of generative AI. An administrator could also examine a printout of an image’s metadata to determine the “generation source information.” If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind, including observations, evaluations, judgements, and/or opinions, then it falls within the Mental Processes – Concepts Performed in the Human Mind grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Additionally and alternatively, the abstract idea steps italicized above describe the rules or instructions pertaining to sharing social media content, which constitutes a process that, under its broadest reasonable interpretation, covers managing personal behavior relationships, interactions between people. This is further supported by [0001]-[0002] of applicant’s specification as filed. If a claim limitation, under its broadest reasonable interpretation, covers managing personal behavior relationships, interactions between people, including social activities, teaching, and/or following rules or instructions, then it falls within the Certain Methods of Organizing Human Activity – Managing Personal Behavior Relationships, Interactions Between People grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Step 2A Prong Two: Does the claim recite additional elements that integrate the judicial exception into a practical application? MPEP 2106.04.
This judicial exception is not integrated into a practical application because the additional elements are merely instructions to apply the abstract idea to a computer, as described in MPEP 2106.05(f).
The independent claims recite the following additional elements:
Claim 1: non-transitory computer-readable storage medium storing a program for managing content publication; a processor of an information processing apparatus including a display and a network interface.
Claim 14: executed by a processor of an information processing apparatus including a display and a network interface.
Claim 16: an information processing apparatus for managing content publication; at least one processor; a display; a network interface; a memory coupled to the processor and storing instructions.
These elements are merely instructions to apply the abstract idea to a computer, per MPEP 2106.05(f). Applicant has only described generic computing elements in their specification, as seen in [0030]-[0033] of applicant’s specification as filed, for example.
Further, the combination of these elements is nothing more than a generic computing system applied to the tasks of the abstract idea. Because the additional elements are merely instructions to apply the abstract idea to a generic computing system, they do not integrate the abstract idea into a practical application, when viewed in combination. See MPEP 2106.05(f).
Therefore, per Step 2A Prong Two, the additional elements, alone and in combination, do not integrate the judicial exception into a practical application. The claim is directed to an abstract idea.
Step 2B (The Inventive Concept): Does the claim recite additional elements that amount to significantly more than the judicial exception? MPEP 2106.05.
Step 2B involves evaluating the additional elements to determine whether they amount to significantly more than the judicial exception itself.
The examination process involves carrying over identification of the additional element(s) in the claim from Step 2A Prong Two and carrying over conclusions from Step 2A Prong Two pertaining to MPEP 2106.05(f).
The additional elements and their analysis are therefore carried over: applicant has merely recited elements that facilitate the tasks of the abstract idea, as described in MPEP 2106.05(f).
Further, the combination of these elements is nothing more than a generic computing system applied to the tasks of the abstract idea. When the claim elements above are considered, alone and in combination, they do not amount to significantly more.
Therefore, per Step 2B, the additional elements, alone and in combination, are not significantly more. The claims are not patent eligible.
The analysis takes into consideration all dependent claims as well:
Dependent claims 2-3, 5-10, and 12 further narrow the abstract idea(s) above with additional abstract steps and/or information and would fall into the same groupings highlighted previously at Step 2A Prong One. This narrowing of the abstract idea doesn’t integrate it into practical application and/or add significantly more. Some of the dependent claims recite further additional elements:
Claim 2: perform display processing for displaying a screen for allowing a poster of the received post information to select whether or not to post the received post information on the display of the information processing apparatus.
Claim 3: perform display processing for displaying a screen for allowing a poster of the received post information to instruct cancellation of posting of the received post information on the display of the information processing apparatus.
Similar to above, these are generic computing elements, used in their ordinary capacity, to facilitate the tasks of the abstract idea. Whether viewed alone or in combination, this does not integrate the abstract idea into practical application and/or add significantly more. See MPEP 2106.05(f).
Accordingly, claims 1-3, 5-10, 12, 14, and 16 are rejected under 35 USC § 101 as being directed to non-statutory subject matter.
Response to Arguments
Applicant’s arguments filed 8/10/26 have been fully considered. Examiner’s response follows.
Claim Objections; Claim Interpretation Under 35 U.S.C. § 112(f); Claim Rejections Under 35 U.S.C. § 112(b)
In view of applicant’s amendments, the previous claim objections and rejections under 35 U.S.C. § 112(b) are withdrawn.
The cancellation of claims 16 and 17 has also obviated the interpretation under 35 U.S.C. § 112(f).
Claim Rejections Under 35 U.S.C. § 101
Regarding the rejections under 35 U.S.C. § 101, applicant offers:
The Office Action rejected claims 1-17 under 35 U.S.C. § 101 as being directed to a judicial exception without significantly more. (See Office Action, pages 6 to 11.)
Regarding subject-matter eligibility, MPEP 2106.04(II)(A) explains that "Step 2A is a two- prong inquiry, in which examiners determine in Prong One whether a claim recites a judicial exception, and if so, then determine in Prong Two if the recited judicial exception is integrated into a practical application of that exception."
With respect to Prong One, the Office Action states that independent claims 1, 14, and 16 recite a mental process or managing human activity. (See Office Action, pages 7 and 8.) Applicant respectfully disagrees and reserves the argument that amended independent claims 1, 14, and 16 are eligible at Prong One of Step 2A.
Even if, arguendo, Applicant's independent claims could be characterized as reciting an abstract idea, Applicant submits that they are patent-eligible under Prong Two of Step 2A by integrating any such abstract idea into a practical application. MPEP 2106.04(d)(I) sets forth "[l]imitations the courts have found indicative that an additional element (or combination of elements) may have integrated the exception into a practical application." An example which applies to Applicant's independent claims include "improvement in the functioning of a computer, or an improvement to other technology or technical field." (See MPEP 2106.04(d)(I).) In particular, Applicant's amended independent claims improve the technical field of automated moderation of online postings. As online postings further grow in volume, automated moderation of online postings improves the operation of such platforms. In particular, amended independent claim 1 recites determining, based on whether identification information indicating that the image has been generated by generative AI has been added to the image included in the received post information, whether the image included in the received post information is an image generated by generative AI, determining, based on generation source information included in that identification information, whether the image was generated using an input image or using other information, modification processing if the image was generated using other information, and no modification processing if the image was generated using an input image. Claims 14 and 16 include similar recitations. Such operations improve the technical field of automated moderation of online postings. Accordingly, Applicant submits that amended independent claims 1, 14, and 16 integrate an abstract idea (if any) into a practical application and are eligible at Prong Two of Step 2A.
With regard to Step 2B and whether a claim involves significantly more than an abstract idea, "most of these considerations overlap (i.e., they are evaluated in both Step 2A Prong Two and Step 2B)." (See MPEP 2106.04(d)(I).) Accordingly, for the same reasons discussed above, Applicant submits that independent claims 1, 14, and 16, and their dependent claims, are patent- eligible for involving significantly more than an abstract idea.
Examiner first notes that applicant has appeared to conflate the abstract idea with the additional elements. Steps 2A Prong Two and 2B consider the additional elements, both alone and in combination, when determining whether the claimed invention represents an improvement to technology.
In this instance, the additional elements – e.g., non-transitory computer-readable storage medium storing a program for managing content publication; a processor; an information processing apparatus including a display and a network interface (claim 1 being representative) – are generic computing elements used in their ordinary capacity to facilitate the tasks of the abstract idea.
Applicant has only described generic computing elements in their specification, as seen in [0030]-[0033] of applicant’s specification as filed, for example.
Further, the combination of these elements is nothing more than a generic computing system applied to the tasks of the abstract idea.
Because the additional elements are merely instructions to apply the abstract idea to a generic computing system, they do not integrate the abstract idea into a practical application, when viewed alone or in combination. See MPEP 2106.05(f).
The conclusion from Step 2A Prong Two applies at Step 2B as well – i.e., the elements, alone and in combination, are nothing more than generic computing components applied to the tasks of the abstract idea. This does not add significantly more, based on MPEP 2106.05(f).
Accordingly, examiner maintains the rejections under rejections under 35 U.S.C. § 101.
Claim Rejections Under 35 U.S.C. § 102, 35 U.S.C. § 103
Applicant’s amendments and clarifying remarks regarding the prior art rejections are persuasive. The previous prior rejections under 35 U.S.C. § 102, 35 U.S.C. § 103 are withdrawn.
In particular, examiner has been unable to find the following features in combination (claim 1 being representative, similar features found in claims 14 and 16):
receive post information including an image from a user;
determine, based on whether identification information indicating that the image has been generated by generative Al has been added to the image included in the received post information, whether the image included in the received post information is an image generated by generative Al;
in response to determining that the image included in the received post information is an image generated by generative Al, determine, based on generation source information included in the identification information, whether the image included in the received post information is an image generated by the generative Al using an input image as input or is an image generated by the generative Al using information other than the input image as input;
in response to determining that the image included in the received post information is an image generated by the generative Al using information other than the input image as input, perform modification processing for modifying the received post information; and
in response to determining that the image included in the received post information is an image generated by the generative Al using an input image as input, not perform the modification processing.
The claim recites multiple interrelated features: receiving a social media post containing an image; determining whether the image is AI-generated based on whether identification information saying so has been added to the image, e.g., metadata or a watermark; if the image is AI-generated, reading generation-source information inside the same identification information, which is used to decide whether the AI started from an input image or from something else, e.g., a text prompt; if the AI worked from something other than an input image, the post is modified, e.g., adding a label or removing the image; if the AI worked from an input image, the post is left unmodified.
Examiner had previously identified Karpman (US 12321831), which also teaches receiving and reviewing a social media post {Col. 5, lines 30-45: For example, a social media or content platform can configure its backend infrastructure to automatically submit classification requests on newly posted content to computing system 100 (e.g., by programmatically sending an API call to communication interface 112). Then, based on confidence scores returned (e.g., via an API response from communication interface 112) by the set of AI-generated content classifiers 104—such as in response to one or more these confidence scores exceeding a threshold probability that the query content is AI-generated—the platform can automatically remove the corresponding post, ban the posting user, or flag the post for moderator review in accordance with its content policies.}.
Examiner also considered the following references:
“Labeling AI-Generated Images on Facebook, Instagram and Threads” by Meta (NPL attached; newly cited) teaches determining whether a post contains AI content, based on its metadata {Page 3: “When photorealistic images are created using our Meta AI feature, we do several things to make sure people know AI is involved, including putting visible markers that you can see on the images, and both invisible watermarks and metadata embedded within image files. Using both invisible watermarking and metadata in this way improves both the robustness of these invisible markers and helps other platforms identify them. This is an important part of the responsible approach we’re taking to building generative AI features.}.
Epstein (US 20250111565; previously cited) teaches classifying images to distinguish between “real” and synthetic images, where the training set includes images generated via text-to-image {[0092] According to some embodiments of the present disclosure, the machine learning model progressively trains a binary classifier with a cross-entropy loss to distinguish between naturally sourced “real” images and images generated by AI (e.g., synthetic images). [0110] Additionally, for generative machine learning models including GLIDE, LDM, RDM, Firefly (all datasets), and Stable Diffusion (including train dataset and ground-truth dataset), prompts from DiffusionDB are used as inputs to generate the set of synthetic images. The prompts may include, text prompt, color prompt, and image prompt. For the Stable Diffusion dataset, prompts from various web sources are used. Unique prompts are sampled so that the training dataset, ground-truth dataset, and test datasets are not overlapped.}.
Cha (US 20250005901; newly cited) also teaches text-to-image generation {[0036] In a first step 212, the AI engine can then create images based on the prompt (e.g., using available text-to-image creation tools such as but not limited to, for example, DALL-E and stable diffusion, among others). In a second step 214, the outputted generated images are processed by an instance segmentation module of the system, which will be discussed in further detail with reference to FIG. 3, to identify objects in the outputted images, resulting in the generation of labels and masks for each object. The segmented image is then used by the system to perform a main object selection at a third step 216 (e.g., see FIG. 4).}.
Cheruvu (US 20210390447; newly cited) teaches multi-modal content generation, i.e., text and images {[0030] In one implementation, content generation platform 110 may provide a content generator 112 that generates multi-modal content, such as text, an image, a video, audio, or any other form of content. The content generator 112 may be implemented via hardware, software, and/or firmware of the content generation platform 110. The content generator 112 may utilize an ML model 116 to generate the content. For example, content generator 112 may utilize an inference stage of ML model 116 to generate text for publication to a document, such as a web page published to the Internet. In another example, content generator 112 may utilize an inference stage of ML model 116 to identify subject matter in a video for entry into a monitoring system, such as identification of an event recorded by a surveillance device. Other implementations of content generation for various use cases are envisioned in embodiments of the disclosure.}.
At best, Karpman in view of Meta and Epstein and/or Cha and/or Cheruvu teaches:
receive post information including an image from a user {Karpman};
determine, based on whether identification information indicating that the image has been generated by generative Al has been added to the image included in the received post information, whether the image included in the received post information is an image generated by generative Al {Meta};
an image generated by the generative Al using an input image as input or is an image generated by the generative Al using information other than the input image as input {Epstein and/or Cha and/or Cheruvu}.
However, the combination of new and previously cited references stops well short of the specificity of the claims, where the underlined are features not taught or suggested by the prior art:
receive post information including an image from a user;
determine, based on whether identification information indicating that the image has been generated by generative Al has been added to the image included in the received post information, whether the image included in the received post information is an image generated by generative Al;
in response to determining that the image included in the received post information is an image generated by generative Al, determine, based on generation source information included in the identification information, whether the image included in the received post information is an image generated by the generative Al using an input image as input or is an image generated by the generative Al using information other than the input image as input;
in response to determining that the image included in the received post information is an image generated by the generative Al using information other than the input image as input, perform modification processing for modifying the received post information; and
in response to determining that the image included in the received post information is an image generated by the generative Al using an input image as input, not perform the modification processing.
Accordingly, the previous prior art rejection of claim 1, its dependents is withdrawn. Similar reasoning applies to claims 14 and 16.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
“Labeling AI-Generated Images on Facebook, Instagram and Threads” by Meta (NPL attached; newly cited) teaches determining whether a post contains AI content, based on its metadata {Page 3: “When photorealistic images are created using our Meta AI feature, we do several things to make sure people know AI is involved, including putting visible markers that you can see on the images, and both invisible watermarks and metadata embedded within image files. Using both invisible watermarking and metadata in this way improves both the robustness of these invisible markers and helps other platforms identify them. This is an important part of the responsible approach we’re taking to building generative AI features.}.
Epstein (US 20250111565; previously cited) teaches classifying images to distinguish between “real” and synthetic images, where the training set includes images generated via text-to-image {[0092] According to some embodiments of the present disclosure, the machine learning model progressively trains a binary classifier with a cross-entropy loss to distinguish between naturally sourced “real” images and images generated by AI (e.g., synthetic images). [0110] Additionally, for generative machine learning models including GLIDE, LDM, RDM, Firefly (all datasets), and Stable Diffusion (including train dataset and ground-truth dataset), prompts from DiffusionDB are used as inputs to generate the set of synthetic images. The prompts may include, text prompt, color prompt, and image prompt. For the Stable Diffusion dataset, prompts from various web sources are used. Unique prompts are sampled so that the training dataset, ground-truth dataset, and test datasets are not overlapped.}.
Cha (US 20250005901; newly cited) also teaches text-to-image generation {[0036] In a first step 212, the AI engine can then create images based on the prompt (e.g., using available text-to-image creation tools such as but not limited to, for example, DALL-E and stable diffusion, among others). In a second step 214, the outputted generated images are processed by an instance segmentation module of the system, which will be discussed in further detail with reference to FIG. 3, to identify objects in the outputted images, resulting in the generation of labels and masks for each object. The segmented image is then used by the system to perform a main object selection at a third step 216 (e.g., see FIG. 4).}.
Cheruvu (US 20210390447; newly cited) teaches multi-modal content generation {[0030] In one implementation, content generation platform 110 may provide a content generator 112 that generates multi-modal content, such as text, an image, a video, audio, or any other form of content. The content generator 112 may be implemented via hardware, software, and/or firmware of the content generation platform 110. The content generator 112 may utilize an ML model 116 to generate the content. For example, content generator 112 may utilize an inference stage of ML model 116 to generate text for publication to a document, such as a web page published to the Internet. In another example, content generator 112 may utilize an inference stage of ML model 116 to identify subject matter in a video for entry into a monitoring system, such as identification of an event recorded by a surveillance device. Other implementations of content generation for various use cases are envisioned in embodiments of the disclosure.}.
THIS ACTION IS MADE FINAL. 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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JOHN SAMUEL WASAFF whose telephone number is (571)270-5091. The examiner can normally be reached Monday through Friday 8:00 am to 6:00 pm.
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, SARAH MONFELDT can be reached at (571) 270-1833. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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JOHN SAMUEL WASAFF
Primary Examiner
Art Unit 3629
/JOHN S. WASAFF/Primary Examiner, Art Unit 3629
Prosecution Insights