DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Amendment
The Amendment filed January 27, 2026 has been entered. Claims 1-13 and 15-20 remain pending in the application.
Regarding the objections to the specification, applicant’s amendments obviate the objections set forth in the Non-Final Office Action mailed November 5, 2025. Accordingly, the objections are withdrawn.
Regarding the claim rejections under 35 U.S.C. §103, the amendments have modified the scope of the claims and warrant new grounds for rejection as set forth below.
Response to Arguments
Applicant’s arguments with respect to claims 1-13 and 15-20 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Claim Objections
Claim 7 is objected to because of the following informality:
Claim 7 recites “wherein the generating the prompt… comprises uses the generative AI engine to create the prompt.” Examiner shall interpret the claim as reciting “comprises using…” Appropriate correction is required.
Claim Rejections - 35 USC § 103
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
Claims 1-6, 8-13, and 15-20 are rejected under 35 U.S.C. 103 as being unpatentable over US 20240320310 to Callegari et al. (hereinafter “Callegari”) in view of US 10097360 to Hachey (hereinafter "Hachey") and US 20170366564 to Ping et al. (hereinafter “Ping”).
Regarding claim 1, Callegari discloses:
A computer-implemented method, comprising:
generating a prompt (Callegari [0040]: “…developed to generate images from natural language descriptions (e.g., prompts) …”) for an object of the selected output type (Callegari [0040]: the output type is “image”);
generating a solution object (Callegari Fig. 2, items 204-208 are the images that correspond with the statement 202) of the selected output type based on the prompt using a generative artificial intelligence (AI) engine (Callegari [0040]: “…developed to generate images from natural language descriptions…”);
generating the candidate objects using the generative AI engine (Callegari Fig. 2, item 210) of the selected output type based on a modified prompt (Callegari [0040]: “…the fourth image 210 may be generated by using a second prompt that is different from the first…”);
presenting a challenge-response test comprising a question (Callegari Fig. 2, 202: “question” is the text that prompts the user to select the appropriate objects) based on the prompt, the solution object, and the candidate object to a user device (Callegari Fig. 3, 304); and
in response to receiving a response to the challenge-response test from the user device comprising an object selection (Callegari [0054]: “At operation 308, it is determined if the selection [by the user] is correct based on the description provided at operation 304…”),
performing a responsive action (Callegari [0055]: “…If the selection is not correct based on the provided description, flow branches “NO” to operation 310…”; [0057]: “If the selection is correct based on the provided description, flow branches “YES” to operation 312…”, where the responsive action is either 310 or 312).
Callegari does not disclose an explicit maximum number of candidate objects, or randomly generating a random value for the number of the candidate objects that is less than the maximum value.
However, Hachey discloses:
a maximum value is set for a number of candidate objects (Hachey 6:7-15: “In some embodiments, the number of images included in the set of selected images may vary. For example, in one embodiment a set of six images may be selected, while in other embodiments the number of images selected can vary between two and twelve. Any number of selected images are contemplated...”), and
randomly generating a random value for the number of the candidate objects that is less than the maximum value (Hachey 6:7-15: “…the number of images can vary between two and twelve. Any number of selected images are contemplated. Furthermore, in one embodiment, the number of images that are selected varies upon each invocation of the visual CAPTCHA. For example, upon the first invocation, the system may select a set of six images, while on a subsequent invocation, eight images are selected.”)
Callegari and Hachey are art analogous to the claimed invention because all are directed towards CAPTCHA technology. It would have been obvious to a person having ordinary skill in the art, prior to the effective filing date of the claimed invention, to randomly generate the number of objects presented, subject to a maximum value, as taught by Hachey because randomizing the number of images presented in a challenge increases the difficulty of the challenge for automated systems (Hachey 6:17-21: “By varying the number of images that are presented per invocation of the visual CAPTCHA, embodiments of the present invention may advantageously thwart automated systems that may attempt to use probabilistic analysis to defeat the visual CAPTCHA.”)
However, neither Callegari nor Hachey disclose randomly selecting the output type of the challenge from among audio, video, image, or text.
Ping discloses:
randomly selecting from an audio output type, a video output type, a text output type, and an image output type a randomly selected output type (Ping [0085]: “A CAPTCHA of a type is randomly selected from CAPTCHAs of different types… The CAPTCHA may be a puzzle CAPTCHA, or may be a conventional character CAPTCHA or the like…”; see also [0116]: “Various types of CAPTCHAs may be used. For example, the CAPTCHA may be a puzzle CAPTCHA, an image CAPTCHA or a character CAPTCHA.”)
Examiner notes that while Ping does not explicitly disclose audio and video types, these types of CAPTCHAs were known in the art and are motivated by Ping’s recitation that “[v]arious types of CAPTCHAs may be used.” One of ordinary skill in the art would recognize audio and video as being included among “various types of CAPTCHAs”. Ping’s teaching covers any grouping of CAPTCHA types that would have been reasonably considered by one of ordinary skill in the art prior at the time of consideration.
Ping is art analogous to the claimed invention because both are directed towards CAPTCHA technology. It would have been obvious to a person having ordinary skill in the art, prior to the effective filing date of the claimed invention, to randomly select the output type as taught by Ping because this adds another layer of randomness that could thwart automated systems attempting to bypass the security measures.
For brevity, “Callegari in view of Hachey further in view of Ping” shall be hereinafter referred to as “CHP”.
Regarding claim 2, CHP discloses:
The computer-implemented method of claim 1, wherein the generative AI engine is a text-to-image generative AI engine, a text-to-video generative AI engine, a text-to-audio generative AI engine, or a text-to-text generative AI engine (Callegari [0112]: It will be appreciated that input 902 and generative model output 906 may each include any of a variety of content types, including, but not limited to, text output, image output, audio output, video output…”) Examiner notes that Callegari provides Fig. 9 and its items 900-906 as “overviews of an example generative machine learning model that may be used according to aspects described [t]herein” (Callegari [0111]).
Regarding claim 3, CHP discloses:
The computer-implemented method of claim 1, wherein the responsive action comprises:
determining that the object selection of the response matches the solution object (Callegari [0054]: “At operation 308, it is determined if the selection [by the user] is correct based on the description provided at operation 304…”; [0057]: “If the selection is correct based on the provided description, flow branches “YES” to operation 312…”); and
granting the user device access to a protected resource (Callegari [0058]: “…the indication that the selection is correct may be the execution of a process, such as granting access to a system protected by the CAPTCHA generated via method 300.”)
Regarding claim 4, CHP discloses:
The computer-implemented method of claim 1, wherein the responsive action comprises:
determining that the object selection of the response does not match the solution object (Callegari [0055]: “…If the selection is not correct based on the provided description, flow branches “NO” to operation 310…”); and
performing a security action comprising preventing access to a protected resource for the user device (Callegari [0056]: “…the indication that the selection is incorrect may be the execution of a process, such as locking a user out of a system protected by the CAPTCHA generated via method 300…”) or presenting a second question, a second solution object, and a second candidate object to the user of the user device (Callegari [0056]: “…when the method 300 reaches operation 310 [incorrect selection], the method 300 may return to operation 302 and generate a second plurality of images using the generative model…”).
Regarding claim 5, CHP discloses:
The computer-implemented method of claim 1, wherein the operations further comprise generating the modified prompt by removing an entity from the prompt (Callegari [0050]: “To generate images… prompts may be created by fixing a variable from one or more categories and altering (e.g., randomizing) a variable for one or more other categories…”).
Regarding claim 6, CHP discloses:
wherein the operations further comprise: generating, by a text-to-text generative AI engine, the question based on the prompt (Callegari [0052]: “… the description may be generated based on one or more of the variables used to generate the plurality of images… the description may instruct a user to select images based on a similarity or difference…”).
Claim 8 recites:
A system comprising:
a memory (Callegari Fig. 11, 1162) having computer readable instructions (Callegari Fig. 11, 1166); and
one or more processors (Callegari Fig. 11, 1160-1161) for executing the computer readable instructions, the computer readable instructions controlling the one or more processors to perform operations comprising: the method of claim 1.
Therefore, claim 8 recites essentially the same material as claim 1, and is rejected for the same reasons.
Claim 9 recites essentially the same material as claim 2, and is rejected for the same reasons.
Claim 10 recites essentially the same material as claim 3, and is rejected for the same reasons.
Claim 11 recites essentially the same material as claim 4, and is rejected for the same reasons.
Claim 12 recites essentially the same material as claim 5, and is rejected for the same reasons.
Claim 13 recites essentially the same material as claim 6, and is rejected for the same reasons.
Claim 15 recites:
A computer program product comprising a computer readable storage medium (Callegari Fig. 11, 1168) having program instructions embodied therewith (Callegari Fig. 11, 1166), the program instructions executable by one or more processors (Callegari Fig. 11, 1160-1161) to cause the one or more processors to perform operations comprising: the method of claim 1.
Therefore, claim 15 recites essentially the same content as claims 1 and 8, and is rejected for the same reasons.
Claim 16 recites essentially the same content as claims 2 and 9, and is rejected for the same reasons.
Claim 17 recites essentially the same content as claims 3 and 10, and is rejected for the same reasons.
Claim 18 recites essentially the same content as claims 4 and 11, and is rejected for the same reasons.
Claim 19 recites essentially the same content as claims 5 and 12, and is rejected for the same reasons.
Claim 20 recites essentially the same content as claims 6 and 13, and is rejected for the same reasons.
Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over CHP as applied to claim 1 above, and further in view of "BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding" by J. Devlin et al., published May 2019 and retrieved from https://arxiv.org/abs/1810.04805 (hereinafter "D1").
Regarding claim 7, CHP discloses:
The computer-implemented method of claim 1, wherein the generating the prompt for the object of the randomly selected output type involves generating (Callegari [0043]: “To generate images according to aspects provided herein, prompts may be created by fixing a variable for one or more categories of the plurality of categories and altering (e.g., randomizing) a variable for one or more other categories of the plurality of categories”; Callegari [0042]: "In some examples, the prompts may be generated based on interests specific to a user (e.g., from a database of personal data that is collected with a user's permission)… [or] demographic features of a user… [or] the prompts may be generated based on geographic boundaries corresponding to where a user is located and/or cultural norms associated with the geographic boundaries…").
CHP is silent as to whether generating the prompt for the object of the randomly selected output type uses the generative AI engine to create the prompt.
However, D1 teaches Bidrectional Encoder Representations from Transformers (BERT), a language representation artificial intelligence model (D1 p. 1, Abstract) that is trained on a task that is essentially identical to prompt generation. BERT is trained, in part, on Masked Language Model (Masked LM) training task, where a token (conceptually, a word) is masked out. The model’s goal is then to successfully predict words that fill in the blanks left by the masked tokens. Adapting the example provided by D1 (p. 12, Appendix A, A.1 “Masked LM and the Masking Procedure”), the sentence “My dog is hairy” may be masked as “My dog is [MASK]”. BERT predict words that are likely to complete the sentence, (e.g., “hairy”, “friendly”) and avoids unlikely ones (e.g., “human”). This encompasses the prompt generation process of fixing some variables and randomizing others as taught by Callegari. The masked token is the variable being randomized, with the likelihood of various terms determined by BERT’s training and the model confidence for each of the terms. Examiner notes that one of ordinary skill in the art would recognize that while BERT’s technology is not especially unique, and that other similar generative models of varying architectures could accomplish the same task, its use of Masked LM provides additional motivation for use in the prompt generation task described by Callegari.
D1 is art analogous to the claimed invention because both are directed towards advances in and uses of generative AI. It would have been obvious to a person having ordinary skill in the art, prior to the effective filing date of the claimed invention, to use the model and technology of BERT, or a similar generative AI model, to generate prompts in a fashion similar to that described by Callegari [0043] to generate prompts faster and more consistently compared to manual prompt generation. Additionally, BERT or a similar model can be trained on “a database of personal data that is collected with a user's permission”, “which may make corresponding CATPCHAs relatively more effective for and/or enjoyable to a user” (Callegari [0042]).
Conclusion
The following prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
US 11636193 to Plenderleith et al. discloses randomly selecting the type of challenge a generative AI will create for a CAPTCHA.
US 8036902 to Strom et al. discloses audio-based challenge CAPTCHAs.
US 8925057 to Ansari et al. discloses video-based challenge CAPTCHAs.
US 8978121 to Shuster discloses image-based CAPTCHAs that make use of human vision to solve the challenge, teaching variants of image-based CAPTCHAs that are not based on classification or object recognition.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DANIEL HABASHI whose telephone number is (571)272-2245. The examiner can normally be reached M-F: 9 AM-6 PM.
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DH
Examiner
Art Unit 2407
/Catherine Thiaw/ Supervisory Patent Examiner, Art Unit 2407 3/18/2026