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 .
Drawings
The drawings are objected to as failing to comply with 37 CFR 1.84(p)(5) because they include the following reference character(s) not mentioned in the description:
Reference terms “260” and “270” in Fig. 2 are missing from the Specification.
Corrected drawing sheets in compliance with 37 CFR 1.121(d), or amendment to the specification to add the reference character(s) in the description in compliance with 37 CFR 1.121(b) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
Specification
The disclosure is objected to because of the following informalities:
Paragraph [0060], reference term “60” in Fig. 1 should be “160”.
Appropriate correction is required.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitations are: “an authentication system for verifying creation of digital content”, “a data collection module for collecting a variety of data points related to a content creation process”, “a machine learning classifier module for analyzing data points related to the content creation process”, “a programmatic rules-based analysis module for applying predefined criteria to the data points”, and “a human-viewable replay module for providing a visual representation of the content creation process” in claim 1. Similar limitations are also recited in claims 8 and 15.
Because these claim limitations are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, they are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have these limitations interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitations to avoid them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 112(b)
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim limitations “an authentication system for verifying creation of digital content”, “a data collection module for collecting a variety of data points related to a content creation process”, “a machine learning classifier module for analyzing data points related to the content creation process”, “a programmatic rules-based analysis module for applying predefined criteria to the data points”, and “a human-viewable replay module for providing a visual representation of the content creation process” in claim 1 invokes 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
However, the written description fails to disclose the corresponding structure, material, or acts for performing the entire claimed function and to clearly link the structure, material, or acts to the function. The Specification is devoid of adequate structure to perform the limitation of “verifying creation of digital content” to distinguish human created digital content as opposed to content created through an artificial intelligence, as described in the independent claims. Furthermore, the modules that work in tandem with the authentication system to perform the verification function are not described with sufficient structure to determine if the modules are integrated with the system either through hardware or software, either implicitly or explicitly. As would be recognized by a person of ordinary skill in the art, the function of verification to determine if digital content is created by either a human or an artificial intelligence is a process that use a machine learning model or equivalent that is trained on various human and AI-generated content to determine and differentiate between the two types of content, as described in paragraph [0051] of the Specification. The specification does not provide sufficient details such that one of ordinary skill in the art would understand which verification structure or structures that perform the claimed function. Therefore, the claim is indefinite and is rejected under 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, second paragraph.
Applicant may:
(a) Amend the claim so that the claim limitation will no longer be interpreted as a limitation under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph;
(b) Amend the written description of the specification such that it expressly recites what structure, material, or acts perform the entire claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(c) Amend the written description of the specification such that it clearly links the structure, material, or acts disclosed therein to the function recited in the claim, without introducing any new matter (35 U.S.C. 132(a)).
If applicant is of the opinion that the written description of the specification already implicitly or inherently discloses the corresponding structure, material, or acts and clearly links them to the function so that one of ordinary skill in the art would recognize what structure, material, or acts perform the claimed function, applicant should clarify the record by either:
(a) Amending the written description of the specification such that it expressly recites the corresponding structure, material, or acts for performing the claimed function and clearly links or associates the structure, material, or acts to the claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(b) Stating on the record what the corresponding structure, material, or acts, which are implicitly or inherently set forth in the written description of the specification, perform the claimed function. For more information, see 37 CFR 1.75(d) and MPEP §§ 608.01(o) and 2181.
Claims 2-7 are claims that depend upon independent claim 1, and as a result of claim 1 being rejected for failing to particularly point out and distinctly claim the subject matter above, claims 2-7 inherit the rejections of independent claim 1 above.
The term “adaptable and scalable to various types of digital content” in claim 6 is a relative term which renders the claim indefinite. The term “adaptable and scalable to various types of digital content” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. The claim is recited in paragraphs [0024] and [0044] of the Specification as the system being scalable to allow for various types of digital content and evolve alongside AI technology, but does not sufficiently explain how it is performed in the Specification, nor is there a specific method described as to how the Applicant intends to support various types of content, including new types of content that have yet to be invented or conceptualized.
The term “configured to evolve alongside advancements in AI technology” in claim 7 is a relative term which renders the claim indefinite. The term “configured to evolve alongside advancements in AI technology” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. As with the aforementioned aspect of “adaptable and scalable […]” limitation in claim 6 above, there is no specific method as to how the invention of the Applicant would evolve alongside AI technology in the future, such as through new features or modifying existing features for “updating analysis techniques, data points, and machine learning models”, such as through software or hardware updates, as paragraphs [0024] and [0044] of the Specification do not sufficiently describe how evolving the aspects of the invention is performed in sufficient detail.
Claims 8 and 15 recite similar limitations as described in independent claim 1 above, and as a result, are also rejected for similar rationale as claim 1 above. Dependent claims 9-14 and 16-20 inherit the rejections of independent claims 8 and 15, respectively.
Furthermore, claims 13-14, and 20 are rejected for describing similar limitations as claims 6-7 above.
Claim Rejections - 35 USC § 112(a)
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1-20 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention. As described above, the disclosure does not provide adequate structure to perform the claimed function of verifying content to distinguish between human created and AI-generated digital content as described in claim 1 above, as well as with the use of a machine learning classifier module, a programmatic rules-based analysis module, and a human-viewable replay module. The specification does not demonstrate that applicant has made an invention that achieves the claimed function because the invention is not described with sufficient detail such that one of ordinary skill in the art can reasonably conclude that the inventor had possession of the claimed invention.
Claims 2-7 are claims that depend upon independent claim 1, and as a result of claim 1 being rejected for failing to particularly point out and distinctly claim the subject matter above, claims 2-7 inherit the rejections of independent claim 1 above.
Claim 6 recites the limitation of “wherein the authentication system is adaptable and scalable to various types of digital content including text, images, audio, and video” in paragraphs [0024] and [0044] of the Specification as the system being scalable to allow for various types of digital content and evolve alongside AI technology, but does not sufficiently explain how the authentication system supports new or future types of media in the invention, whether through software updates, hardware changes, or any other form of configuration to support the various types of media format that are supported in the future.
Claim 7 recites the limitation of “wherein the authentication system is configured to evolve alongside advancements in Al technology by updating analysis techniques, data points, and machine learning models” in paragraphs [0024] and [0044] of the Specification to evolve the technology when new discoveries in the field of artificial intelligence are implemented into the invention. As with the aforementioned aspect of “adaptable and scalable […]” limitation in claim 6 above, there is no specific method as to how the invention of the Applicant would evolve alongside AI technology in the future, such as through new features or modifying existing features for “updating analysis techniques, data points, and machine learning models”, such as through software updates or changes to the hardware configuration, and as the Specification does not provide a specific method to implement “advancements in AI technology by updating” aspects of the invention, the invention’s scope with regards to updates is insufficiently clear.
Claims 8 and 15 recite similar limitations as described in independent claim 1 above, and as a result, are also rejected for similar rationale as claim 1 above. Dependent claims 9-14 and 16-20 inherit the rejections of independent claims 8 and 15, respectively.
Furthermore, claims 13-14, and 20 are rejected for describing similar limitations as claims 6-7 above.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1, 5-8, 12-15, and 19-20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Mathews et al. (US 20210097382 A1), hereinafter Mathews.
Regarding claim 1, Mathews discloses “an authentication system for verifying creation of digital content, comprising: a data collection module for collecting a variety of data points related to a content creation process” ([0030]-[0032] As described in Fig. 2, there is an input media file that serves as input to a deepfake analyzer 110 to analyze the media file as real or deepfake based on either image properties or the individual frames of a video. Process of determination is performed by the deepfake classification model 204 in Fig. 2. Individual frames of a video or portions of images corresponds to data points of content creation process. This process is also described in Fig. 4, block 402, paragraph [0053], where media is obtained.);
“a machine learning classifier module for analyzing data points related to the content creation process” ([0054] Media is inputted into deepfake classification model in block 404, Fig. 4, where the model is also described in paragraph [0032], where it is a machine learning model based on properties of the media file, such as an image or video, analyzing portions of the image or video frames of a video, including "low-level" mesoscopic features of media files, as described in paragraph [0015].);
“a programmatic rules-based analysis module for applying predefined criteria to the data points” ([0055] Fig. 4, block 408, report generator 208 determines if a generated score corresponds to "real" or "deepfake" media based on a score from 0 to 1, inclusive. Depending on if the media is "real" or "deepfake", the process proceeds to block 410 if "real", or block 412 if "deepfake".);
“and a human-viewable replay module for providing a visual representation of the content creation process” ([0056] Fig. 4, block 414, explainability map is generated based on report of media being either "real" or "deepfake", with Fig. 7 of paragraph [0068] describing features of an image as a visual representation of the content creation process.);
“wherein the authentication system for determining whether the digital content was created by a human or generated by artificial intelligence based on outputs from the machine learning classifier, the programmatic rules-based analysis module, and the human-viewable replay module” ([0055] Fig. 4, block 408, report generator 208 determines if a generated score corresponds to "real" or "deepfake" media based on a score from 0 to 1, inclusive, as well as through the classification model through media that is input. Furthermore, as an explainability map is generated based on report of media being either "real" or "deepfake", the determination of media classification relies on the three aforementioned components of Mathews.);
Regarding claim 5, Mathews discloses the authentication system as described in claim 1 above. Mathews also discloses “wherein the authentication system reduces false positives in identifying AI-generated content by analyzing depth and complexity of the content creation process” ([0063] Fig. 6, training of known real and deepfake media is performed in process 600. Block 610 is performed after reports are generated during training in block 608, and when identified, reasoning is provided to the model for modifying said model. Process of determination is performed by the deepfake classification model 204 in Fig. 2. Individual frames of a video or portions of images corresponds to data points of content creation process, as described in paragraph [0032].).
Regarding claim 6, Mathews discloses the authentication system as described in claim 1 above. Mathews also discloses “wherein the authentication system is adaptable and scalable to various types of digital content including text, images, audio, and video” (Deepfakes and media are described as "fake emergency alerts, fake videos to destroy someone's reputation, or fake video and/or audio of politicians during an election" in paragraph [0013], and in paragraph [0002], media to be classified is described as "(e.g., an image, video, and/or audio)". where alerts correspond to text.).
Regarding claim 7, Mathews discloses the authentication system as described in claim 1 above. Mathews also discloses “wherein the authentication system is configured to evolve alongside advancements in Al technology by updating analysis techniques, data points, and machine learning models” (Fig. 1 can tune the deepfake classification model before deploying to the processing device 108, as described in paragraphs [0027]-[0028], and the deepfake analyzer 110 is deployed to the processing device as part of a software update. As described in paragraphs [0014]-[0015], as deepfakes becoming more convincing, neural networks are utilized to classify media as "real" or "deepfake", and based on advancements and feedback from the user, the model can be trained and updated.).
Regarding claim 8, Mathews discloses the method that recites similar limitations to the authentication system as described in claim 1 above.
Regarding claim 12, Mathews discloses the method as described in claim 8 above. Mathews also discloses the limitations also described in claim 5 above.
Regarding claim 13, Mathews discloses the method as described in claim 8 above. Mathews also discloses the limitations also described in claim 6 above.
Regarding claim 14, Mathews discloses the method as described in claim 8 above. Mathews also discloses the limitations also described in claim 7 above.
Regarding claim 15, Mathews discloses the method that recites similar limitations to the authentication system as described in claim 1 above. Mathews also discloses “a non-transitory computer-readable storage medium storing instructions that, when executed by a processor, cause the processor to perform operations for verifying creation of digital content” ();
Regarding claim 19, Mathews discloses the non-transitory computer-readable storage medium as described in claim 15 above. Mathews also discloses the limitations also described in claim 5 above.
Regarding claim 20, Mathews discloses the non-transitory computer-readable storage medium as described in claim 15 above. Mathews also discloses the limitations also described in claims 6-7 above.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 2, 9, and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Mathews in view of Seth et al. (US 20220374105 A1), and Ziolkowski et al. (US 20240248711 A1).
Regarding claim 2, Mathews discloses the authentication system as described in claim 1 above. Mathews also discloses “wherein the data points include one or more of voice and audio analysis, eye tracking and gaze patterns, and biometric data” ([0068] Fig. 7 displays example images 700/704 and explainability maps 702/706, where hair, eyes, mouth, and the background are analyzed to determine if an image is "real" or "deepfake", which is equivalent to biometric data as data points. A trained neural network can also be trained on audio or video media as well, which deals with audio analysis and eye tracking patterns.).
Mathews does not appear to disclose, but Seth teaches “content revision history, content creation timeline” ([0012] Prior representations of digital content is taken into account, where they represent technical instances of prior versions of the content that were rendered, along with the original versions. Fig. 2, blocks 206 and 208 detect portions of the content that are synthetic, and in combination with the previous statement of versions of the content, take into account revision history as well as a creation timeline of the content.).
Therefore, one of ordinary skill in the art would have been capable of applying this known method of "content revision history, content creation timeline" in an authentication system for verifying creation of digital content and the results would have been predictable to one of ordinary skill in the art. The one of ordinary skill in the art would have been motivated to increase effectiveness of synthetic media detection by comparing video frames through segmented digital content to prepare for evaluation by utilizing image processing and multiple trained AI models applied to the segmented media, with CNNs, or convolutional neural networks, being trained to automatically extract features from the segments of the media (Seth [0037]).
Mathews in view of Seth does not appear to teach, but Ziolkowski teaches “keystroke dynamics, syntax and style analysis, error patterns and corrections, behavioral data, gestures and touch interactions, brushstrokes and drawing patterns, physical interaction with devices” ([0155] Fig. 6 shows regions of the passages of text that were written either by a human author or an AI author. Paragraph [0061] describes Figs. 2A and 2B for operation 210 of determining authorship based on edits made to the text passages. Furthermore, operation 214 determines if an edit to a work is manual or artificial (done by AI), such as through keystrokes such as a single character at a time, writing patterns and styles including making edits in "bursty manner", or pausing to think after writing a segment, as well as correcting spelling mistakes, as well as other human tendencies when typing, as compared to an artificial intelligence. Paragraph [0188] further describes gesture controllers and touch screen as haptic input components that are to be analyzed in the invention to determine edits made to content. Finally, in paragraph [0119], it is stated that the same techniques described to identify creation of content as well as authorship tokens can also be applied to visual content creation, such as with 2D images, 3D objects, photographs, and video, and in the case of 2D images and 3D objects, drawing patterns in editing software can be determined through touch inputs.).
Therefore, one of ordinary skill in the art would have been capable of applying this known method of "keystroke dynamics, syntax and style analysis, error patterns and corrections, behavioral data, gestures and touch interactions, brushstrokes and drawing patterns, physical interaction with devices" in an authentication system for verifying creation of digital content and the results would have been predictable to one of ordinary skill in the art. The one of ordinary skill in the art would have been motivated to allow for distinguishment between manual and artificial edits to content, such as the use of autocorrect, or spell check, as well as through human typing patterns being taken into account, even if spell check or another form of correcting spelling mistakes is utilized (Ziolkowski [0058] and [0060]).
Regarding claim 9, Mathews discloses the method as described in claim 8 above. Mathews in view of Seth and Ziolkowski also teaches the limitations also described in claim 2 above.
Regarding claim 16, Mathews discloses the non-transitory computer-readable storage medium as described in claim 15 above. Mathews in view of Seth and Ziolkowski also teaches the limitations also described in claim 2 above.
Claims 3-4, 10-11, and 17-18 are rejected under 35 U.S.C. 103 as being unpatentable over Mathews in view of Seth.
Regarding claim 3, Mathews discloses the authentication system as described in claim 1 above. Mathews also discloses “wherein the authentication system provides a confidence score indicating a likelihood that the digital content was created by a human” ([0055] Fig. 4, block 408, report generator 208 determines if a generated score corresponds to "real" or "deepfake" media based on a score from 0 to 1, inclusive. As also described in paragraph [0035] of Fig. 2's probability score, if the score is between 0 and 0.5, it is a "deepfake", and if the score is between 0.5 and 1, the media is determined to be "real".).
Mathews does not appear to disclose, but Seth teaches “the confidence score expressed as a percentage” ([0077] Fig. 3D, confidence that content was created by a human is shown in GUI notification 366, where the confidence level is represented as a percentage.).
Therefore, one of ordinary skill in the art would have been capable of applying this known method of "the confidence score expressed as a percentage" in an authentication system for verifying creation of digital content and the results would have been predictable to one of ordinary skill in the art. The one of ordinary skill in the art would have been motivated to use a confidence score for determining if media is considered “synthetic” or “real” based on a comparative analysis of data attributes in each of the chunks of the media relative to the area of interest based on data attributes (Seth [0059]).
Regarding claim 4, Mathews in view of Seth teaches the authentication system as described in claim 3 above. Mathews also discloses “wherein the confidence score is based on outputs from the machine learning classifier and the programmatic rules-based analysis module” ([0055] Fig. 4, block 408, report generator 208 determines if a generated score corresponds to "real" or "deepfake" media based on a score from 0 to 1, inclusive. The score is an output in block 406 is based on outputs from a deepfake classification model, and is used in block 408 to determine "real" or "deepfake" media.).
Regarding claim 10, Mathews discloses the method as described in claim 8 above. Mathews in view of Seth teaches the limitations also described in claim 3 above.
Regarding claim 11, Mathews in view of Seth teaches the method as described in claim 10 above. Mathews also discloses the limitations also described in claim 4 above.
Regarding claim 17, Mathews discloses the non-transitory computer-readable storage medium as described in claim 15 above. Mathews in view of Seth teaches the limitations also described in claim 3 above.
Regarding claim 18, Mathews in view of Seth teaches the non-transitory computer-readable storage medium as described in claim 17 above. Mathews also discloses the limitations also described in claim 4 above.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Tremblay et al. (US 20250371127 A1, "SYSTEMS AND METHODS FOR MANAGING USE OF GENERATIVE ARTIFICIAL INTELLIGENCE (AI)")
Behan et al. (US 20250077952 A1, "Pre-Publication Assessment Of Digital Content")
Henderson et al. (US 20180150752 A1, "IDENTIFYING ARTIFICIAL INTELLIGENCE CONTENT")
Wessling et al. (US 20110072117 A1, "Generating A Synthetic Table Of Contents For A Volume By Using Statistical Analysis")
Sun et al. (US 20200311901 A1, "CLASSIFYING PANORAMIC IMAGES")
Aburass et al. (NPL, “Authenticity in authorship: the Writer’s Integrity framework for verifying human‑generated text”, 2024)
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/T.M./ Examiner, Art Unit 2496
/JORGE L ORTIZ CRIADO/Supervisory Patent Examiner, Art Unit 2496
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